From 9432f85b31c2d1d50d0b132a32385db40f4042b2 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Wed, 15 Apr 2026 11:27:10 +0200 Subject: [PATCH 01/21] Initial image processing interfaces --- encoderfile/proto/image_classification.proto | 27 +++++++++++++++++ encoderfile/proto/image_segmentation.proto | 30 ++++++++++++++++++ encoderfile/proto/object_detection.proto | 32 ++++++++++++++++++++ 3 files changed, 89 insertions(+) create mode 100644 encoderfile/proto/image_classification.proto create mode 100644 encoderfile/proto/image_segmentation.proto create mode 100644 encoderfile/proto/object_detection.proto diff --git a/encoderfile/proto/image_classification.proto b/encoderfile/proto/image_classification.proto new file mode 100644 index 00000000..bf1386ac --- /dev/null +++ b/encoderfile/proto/image_classification.proto @@ -0,0 +1,27 @@ +syntax = "proto3"; + +package encoderfile.image_classification; + +import "proto/token.proto"; +import "proto/metadata.proto"; + +service ImageClassification { + rpc Predict(ImageClassificationRequest) returns (ImageClassificationResponse); + rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); +} + +message ImageClassificationRequest { + optional string url = 1; + optional bytes image = 2; + map metadata = 11; +} + +message ImageLabelScore { + string label = 1; + float score = 2; +} + +message ImageClassificationResponse { + repeated ImageLabelScore = 1; + map metadata = 11; +} diff --git a/encoderfile/proto/image_segmentation.proto b/encoderfile/proto/image_segmentation.proto new file mode 100644 index 00000000..144f7b04 --- /dev/null +++ b/encoderfile/proto/image_segmentation.proto @@ -0,0 +1,30 @@ +syntax = "proto3"; + +package encoderfile.image_segmentation; + +import "proto/token.proto"; +import "proto/metadata.proto"; + +service ImageSegmentation { + rpc Predict(ImageSegmentationRequest) returns (ImageSegmentationResponse); + rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); +} + +message ImageSegmentationRequest { + optional string url = 1; + optional string store_url = 2; + optional bytes image = 3; + map metadata = 11; +} + +message ImageSegment { + string label = 1; + optional float score = 2; + optional string url = 3; + optional bytes mask = 4; +} + +message ImageSegmentationResponse { + repeated ImageSegment = 1; + map metadata = 11; +} diff --git a/encoderfile/proto/object_detection.proto b/encoderfile/proto/object_detection.proto new file mode 100644 index 00000000..ec41f860 --- /dev/null +++ b/encoderfile/proto/object_detection.proto @@ -0,0 +1,32 @@ +syntax = "proto3"; + +package encoderfile.object_detection; + +import "proto/token.proto"; +import "proto/metadata.proto"; + +service ObjectDetection { + rpc Predict(ObjectDetectionRequest) returns (ObjectDetectionResponse); + rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); +} + +message ObjectDetectionRequest { + optional string url = 1; + optional string store_url = 2; + optional bytes image = 3; + map metadata = 11; +} + +message ImageBoundingBox { + string label = 1; + optional float score = 2; + xmin int32 = 3; + xmax int32 = 4; + ymin int32 = 5; + ymax int32 = 6; +} + +message ObjectDetectionResponse { + repeated ImageBoundingBox = 1; + map metadata = 11; +} From 91bd77a3e63cdf65f219dfe2bfd0f59d0c747224 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Thu, 16 Apr 2026 10:21:49 +0200 Subject: [PATCH 02/21] Align with current interfaces --- encoderfile/proto/image_classification.proto | 13 ++++++++++--- encoderfile/proto/image_segmentation.proto | 17 +++++++++++------ encoderfile/proto/object_detection.proto | 14 ++++++++++---- 3 files changed, 31 insertions(+), 13 deletions(-) diff --git a/encoderfile/proto/image_classification.proto b/encoderfile/proto/image_classification.proto index bf1386ac..523b8d47 100644 --- a/encoderfile/proto/image_classification.proto +++ b/encoderfile/proto/image_classification.proto @@ -10,9 +10,12 @@ service ImageClassification { rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); } +message ImageInput { + bytes image = 1; +} + message ImageClassificationRequest { - optional string url = 1; - optional bytes image = 2; + repeated ImageInput inputs = 1; map metadata = 11; } @@ -21,7 +24,11 @@ message ImageLabelScore { float score = 2; } +message ImageLabels { + repeated ImageLabelScore labels = 1; +} + message ImageClassificationResponse { - repeated ImageLabelScore = 1; + repeated ImageLabels labels_batch = 1; map metadata = 11; } diff --git a/encoderfile/proto/image_segmentation.proto b/encoderfile/proto/image_segmentation.proto index 144f7b04..a74c8287 100644 --- a/encoderfile/proto/image_segmentation.proto +++ b/encoderfile/proto/image_segmentation.proto @@ -10,21 +10,26 @@ service ImageSegmentation { rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); } +message ImageInput { + bytes image = 1; +} + message ImageSegmentationRequest { - optional string url = 1; - optional string store_url = 2; - optional bytes image = 3; + repeated ImageInput images = 1; map metadata = 11; } message ImageSegment { string label = 1; optional float score = 2; - optional string url = 3; - optional bytes mask = 4; + bytes mask = 4; +} + +message ImageSegments { + repeated ImageSegment segments = 1; } message ImageSegmentationResponse { - repeated ImageSegment = 1; + repeated ImageSegments segments_batch = 1; map metadata = 11; } diff --git a/encoderfile/proto/object_detection.proto b/encoderfile/proto/object_detection.proto index ec41f860..887df885 100644 --- a/encoderfile/proto/object_detection.proto +++ b/encoderfile/proto/object_detection.proto @@ -10,10 +10,12 @@ service ObjectDetection { rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); } +message ImageInput { + bytes image = 1; +} + message ObjectDetectionRequest { - optional string url = 1; - optional string store_url = 2; - optional bytes image = 3; + repeated bytes images = 1; map metadata = 11; } @@ -26,7 +28,11 @@ message ImageBoundingBox { ymax int32 = 6; } +message ImageBoundingBoxes { + repeated ImageBoundingBox boxes = 1; +} + message ObjectDetectionResponse { - repeated ImageBoundingBox = 1; + repeated ImageBoundingBoxes boxes_batch = 1; map metadata = 11; } From 3bfecee487230e6ac23517518d8324686b145239 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Fri, 1 May 2026 15:41:02 +0200 Subject: [PATCH 03/21] WIP --- Cargo.lock | 567 +++++++++++++++++- Cargo.toml | 9 + encoderfile/Cargo.toml | 9 + encoderfile/proto/metadata.proto | 5 + encoderfile/src/builder/config.rs | 2 +- encoderfile/src/builder/model.rs | 13 +- encoderfile/src/builder/tokenizer.rs | 2 +- .../transforms/validation/embedding.rs | 2 +- .../validation/image_classification.rs | 129 ++++ .../src/builder/transforms/validation/mod.rs | 9 +- .../validation/sentence_embedding.rs | 2 +- .../validation/sequence_classification.rs | 2 +- .../validation/token_classification.rs | 2 +- .../src/common/image_classification.rs | 93 +++ encoderfile/src/common/mod.rs | 14 +- encoderfile/src/common/model_type.rs | 13 +- encoderfile/src/format/assets/kind.rs | 1 + encoderfile/src/format/codec/encoder.rs | 3 +- encoderfile/src/format/container.rs | 2 +- encoderfile/src/generated/metadata.rs | 24 +- encoderfile/src/inference/embedding.rs | 2 +- .../src/inference/image_classification.rs | 42 ++ encoderfile/src/inference/mod.rs | 3 + .../src/inference/sentence_embedding.rs | 2 +- .../src/inference/sequence_classification.rs | 2 +- .../src/inference/token_classification.rs | 2 +- encoderfile/src/inference/utils.rs | 13 +- encoderfile/src/runtime/loader.rs | 2 +- encoderfile/src/runtime/state.rs | 2 +- .../src/services/image_classification.rs | 61 ++ encoderfile/src/services/mod.rs | 1 + encoderfile/src/services/model_metadata.rs | 2 +- .../transforms/engine/image_classification.rs | 140 +++++ encoderfile/src/transforms/engine/mod.rs | 2 + encoderfile/src/transport/mcp/mod.rs | 13 + 35 files changed, 1158 insertions(+), 34 deletions(-) create mode 100644 encoderfile/src/builder/transforms/validation/image_classification.rs create mode 100644 encoderfile/src/common/image_classification.rs create mode 100644 encoderfile/src/inference/image_classification.rs create mode 100644 encoderfile/src/services/image_classification.rs create mode 100644 encoderfile/src/transforms/engine/image_classification.rs diff --git a/Cargo.lock b/Cargo.lock index 611ef2dd..84803897 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -31,6 +31,24 @@ dependencies = [ "memchr", ] +[[package]] +name = "aligned" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ee4508988c62edf04abd8d92897fca0c2995d907ce1dfeaf369dac3716a40685" +dependencies = [ + "as-slice", +] + +[[package]] +name = "aligned-vec" +version = "0.6.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dc890384c8602f339876ded803c97ad529f3842aba97f6392b3dba0dd171769b" +dependencies = [ + "equator", +] + [[package]] name = "android_system_properties" version = "0.1.5" @@ -105,6 +123,12 @@ dependencies = [ "num-traits", ] +[[package]] +name = "arbitrary" +version = "1.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c3d036a3c4ab069c7b410a2ce876bd74808d2d0888a82667669f8e783a898bf1" + [[package]] name = "arc-swap" version = "1.9.1" @@ -114,6 +138,32 @@ dependencies = [ "rustversion", ] +[[package]] +name = "arg_enum_proc_macro" +version = "0.3.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + +[[package]] +name = "arrayvec" +version = "0.7.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" + +[[package]] +name = "as-slice" +version = "0.2.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "516b6b4f0e40d50dcda9365d53964ec74560ad4284da2e7fc97122cd83174516" +dependencies = [ + "stable_deref_trait", +] + [[package]] name = "async-trait" version = "0.1.89" @@ -146,6 +196,49 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" +[[package]] +name = "av-scenechange" +version = "0.14.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0f321d77c20e19b92c39e7471cf986812cbb46659d2af674adc4331ef3f18394" +dependencies = [ + "aligned", + "anyhow", + "arg_enum_proc_macro", + "arrayvec", + "log", + "num-rational", + "num-traits", + "pastey", + "rayon", + "thiserror 2.0.18", + "v_frame", + "y4m", +] + +[[package]] +name = "av1-grain" +version = "0.2.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8cfddb07216410377231960af4fcab838eaa12e013417781b78bd95ee22077f8" +dependencies = [ + "anyhow", + "arrayvec", + "log", + "nom 8.0.0", + "num-rational", + "v_frame", +] + +[[package]] +name = "avif-serialize" +version = "0.8.8" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "375082f007bd67184fb9c0374614b29f9aaa604ec301635f72338bb65386a53d" +dependencies = [ + "arrayvec", +] + [[package]] name = "aws-lc-rs" version = "1.16.2" @@ -260,12 +353,27 @@ version = "1.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2af50177e190e07a26ab74f8b1efbfe2ef87da2116221318cb1c2e82baf7de06" +[[package]] +name = "bit_field" +version = "0.10.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1e4b40c7323adcfc0a41c4b88143ed58346ff65a288fc144329c5c45e05d70c6" + [[package]] name = "bitflags" version = "2.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "843867be96c8daad0d758b57df9392b6d8d271134fce549de6ce169ff98a92af" +[[package]] +name = "bitstream-io" +version = "4.10.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7eff00be299a18769011411c9def0d827e8f2d7bf0c3dbf53633147a8867fd1f" +dependencies = [ + "no_std_io2", +] + [[package]] name = "block-buffer" version = "0.10.4" @@ -285,6 +393,12 @@ dependencies = [ "serde", ] +[[package]] +name = "built" +version = "0.8.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f4ad8f11f288f48ca24471bbd51ac257aaeaaa07adae295591266b792902ae64" + [[package]] name = "bumpalo" version = "3.20.2" @@ -303,11 +417,20 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" +[[package]] +name = "byteorder-lite" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8f1fe948ff07f4bd06c30984e69f5b4899c516a3ef74f34df92a2df2ab535495" + [[package]] name = "bytes" version = "1.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1e748733b7cbc798e1434b6ac524f0c1ff2ab456fe201501e6497c8417a4fc33" +dependencies = [ + "serde", +] [[package]] name = "castaway" @@ -471,6 +594,12 @@ dependencies = [ "regex-lite", ] +[[package]] +name = "color_quant" +version = "1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b" + [[package]] name = "colorchoice" version = "1.0.5" @@ -599,6 +728,12 @@ version = "0.8.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28" +[[package]] +name = "crunchy" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "460fbee9c2c2f33933d720630a6a0bac33ba7053db5344fac858d4b8952d77d5" + [[package]] name = "crypto-common" version = "0.1.7" @@ -819,6 +954,7 @@ dependencies = [ "anyhow", "axum", "axum-server", + "bytes", "clap", "clap_derive", "codspeed-divan-compat", @@ -827,6 +963,8 @@ dependencies = [ "dotenv", "figment", "flate2", + "image", + "image-ndarray", "mlua", "ndarray", "ndarray-stats", @@ -918,6 +1056,26 @@ dependencies = [ "log", ] +[[package]] +name = "equator" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4711b213838dfee0117e3be6ac926007d7f433d7bbe33595975d4190cb07e6fc" +dependencies = [ + "equator-macro", +] + +[[package]] +name = "equator-macro" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "44f23cf4b44bfce11a86ace86f8a73ffdec849c9fd00a386a53d278bd9e81fb3" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + [[package]] name = "equivalent" version = "1.0.2" @@ -943,12 +1101,42 @@ dependencies = [ "cc", ] +[[package]] +name = "exr" +version = "1.74.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4300e043a56aa2cb633c01af81ca8f699a321879a7854d3896a0ba89056363be" +dependencies = [ + "bit_field", + "half", + "lebe", + "miniz_oxide", + "rayon-core", + "smallvec 1.15.1", + "zune-inflate", +] + [[package]] name = "fastrand" version = "2.4.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9f1f227452a390804cdb637b74a86990f2a7d7ba4b7d5693aac9b4dd6defd8d6" +[[package]] +name = "fax" +version = "0.2.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "caf1079563223d5d59d83c85886a56e586cfd5c1a26292e971a0fa266531ac5a" + +[[package]] +name = "fdeflate" +version = "0.3.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1e6853b52649d4ac5c0bd02320cddc5ba956bdb407c4b75a2c6b75bf51500f8c" +dependencies = [ + "simd-adler32", +] + [[package]] name = "figment" version = "0.10.19" @@ -1186,6 +1374,16 @@ dependencies = [ "wasip3", ] +[[package]] +name = "gif" +version = "0.14.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ee8cfcc411d9adbbaba82fb72661cc1bcca13e8bba98b364e62b2dba8f960159" +dependencies = [ + "color_quant", + "weezl", +] + [[package]] name = "glob" version = "0.3.3" @@ -1211,6 +1409,17 @@ dependencies = [ "tracing", ] +[[package]] +name = "half" +version = "2.7.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" +dependencies = [ + "cfg-if", + "crunchy", + "zerocopy", +] + [[package]] name = "hashbrown" version = "0.15.5" @@ -1492,6 +1701,59 @@ dependencies = [ "icu_properties", ] +[[package]] +name = "image" +version = "0.25.10" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "85ab80394333c02fe689eaf900ab500fbd0c2213da414687ebf995a65d5a6104" +dependencies = [ + "bytemuck", + "byteorder-lite", + "color_quant", + "exr", + "gif", + "image-webp", + "moxcms", + "num-traits", + "png", + "qoi", + "ravif", + "rayon", + "rgb", + "serde", + "tiff", + "zune-core", + "zune-jpeg", +] + +[[package]] +name = "image-ndarray" +version = "0.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "366ec4e7613badea5930852b9fc8781fdbb010a59845a3a5c1cf61d0ccc3f133" +dependencies = [ + "image", + "ndarray", + "num-traits", + "thiserror 2.0.18", +] + +[[package]] +name = "image-webp" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "525e9ff3e1a4be2fbea1fdf0e98686a6d98b4d8f937e1bf7402245af1909e8c3" +dependencies = [ + "byteorder-lite", + "quick-error", +] + +[[package]] +name = "imgref" +version = "1.12.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "40fac9d56ed6437b198fddba683305e8e2d651aa42647f00f5ae542e7f5c94a2" + [[package]] name = "indexmap" version = "2.14.0" @@ -1532,6 +1794,17 @@ version = "0.1.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c8fae54786f62fb2918dcfae3d568594e50eb9b5c25bf04371af6fe7516452fb" +[[package]] +name = "interpolate_name" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c34819042dc3d3971c46c2190835914dfbe0c3c13f61449b2997f4e9722dfa60" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + [[package]] name = "ipnet" version = "2.12.0" @@ -1656,12 +1929,28 @@ version = "0.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "09edd9e8b54e49e587e4f6295a7d29c3ea94d469cb40ab8ca70b288248a81db2" +[[package]] +name = "lebe" +version = "0.5.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7a79a3332a6609480d7d0c9eab957bca6b455b91bb84e66d19f5ff66294b85b8" + [[package]] name = "libc" version = "0.2.185" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "52ff2c0fe9bc6cb6b14a0592c2ff4fa9ceb83eea9db979b0487cd054946a2b8f" +[[package]] +name = "libfuzzer-sys" +version = "0.4.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f12a681b7dd8ce12bff52488013ba614b869148d54dd79836ab85aafdd53f08d" +dependencies = [ + "arbitrary", + "cc", +] + [[package]] name = "libredox" version = "0.1.16" @@ -1701,6 +1990,15 @@ version = "0.4.29" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897" +[[package]] +name = "loop9" +version = "0.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0fae87c125b03c1d2c0150c90365d7d6bcc53fb73a9acaef207d2d065860f062" +dependencies = [ + "imgref", +] + [[package]] name = "lru-slab" version = "0.1.2" @@ -1767,6 +2065,16 @@ dependencies = [ "rawpointer", ] +[[package]] +name = "maybe-rayon" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8ea1f30cedd69f0a2954655f7188c6a834246d2bcf1e315e2ac40c4b24dc9519" +dependencies = [ + "cfg-if", + "rayon", +] + [[package]] name = "memchr" version = "2.8.0" @@ -1867,6 +2175,16 @@ dependencies = [ "syn", ] +[[package]] +name = "moxcms" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bb85c154ba489f01b25c0d36ae69a87e4a1c73a72631fc6c0eb6dde34a73e44b" +dependencies = [ + "num-traits", + "pxfm", +] + [[package]] name = "multimap" version = "0.10.1" @@ -1920,6 +2238,12 @@ dependencies = [ "rand 0.8.5", ] +[[package]] +name = "new_debug_unreachable" +version = "1.0.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086" + [[package]] name = "nix" version = "0.31.2" @@ -1932,6 +2256,15 @@ dependencies = [ "libc", ] +[[package]] +name = "no_std_io2" +version = "0.9.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b51ed7824b6e07d354605f4abb3d9d300350701299da96642ee084f5ce631550" +dependencies = [ + "memchr", +] + [[package]] name = "noisy_float" version = "0.2.1" @@ -1951,6 +2284,21 @@ dependencies = [ "minimal-lexical", ] +[[package]] +name = "nom" +version = "8.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "df9761775871bdef83bee530e60050f7e54b1105350d6884eb0fb4f46c2f9405" +dependencies = [ + "memchr", +] + +[[package]] +name = "noop_proc_macro" +version = "0.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0676bb32a98c1a483ce53e500a81ad9c3d5b3f7c920c28c24e9cb0980d0b5bc8" + [[package]] name = "nu-ansi-term" version = "0.50.3" @@ -1960,6 +2308,16 @@ dependencies = [ "windows-sys 0.61.2", ] +[[package]] +name = "num-bigint" +version = "0.4.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a5e44f723f1133c9deac646763579fdb3ac745e418f2a7af9cd0c431da1f20b9" +dependencies = [ + "num-integer", + "num-traits", +] + [[package]] name = "num-complex" version = "0.4.6" @@ -1969,6 +2327,17 @@ dependencies = [ "num-traits", ] +[[package]] +name = "num-derive" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + [[package]] name = "num-integer" version = "0.1.46" @@ -1978,6 +2347,17 @@ dependencies = [ "num-traits", ] +[[package]] +name = "num-rational" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824" +dependencies = [ + "num-bigint", + "num-integer", + "num-traits", +] + [[package]] name = "num-traits" version = "0.2.19" @@ -2216,6 +2596,12 @@ version = "1.0.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a" +[[package]] +name = "pastey" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "35fb2e5f958ec131621fdd531e9fc186ed768cbe395337403ae56c17a74c68ec" + [[package]] name = "pear" version = "0.2.9" @@ -2303,6 +2689,19 @@ version = "0.2.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b4596b6d070b27117e987119b4dac604f3c58cfb0b191112e24771b2faeac1a6" +[[package]] +name = "png" +version = "0.18.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60769b8b31b2a9f263dae2776c37b1b28ae246943cf719eb6946a1db05128a61" +dependencies = [ + "bitflags", + "crc32fast", + "fdeflate", + "flate2", + "miniz_oxide", +] + [[package]] name = "portable-atomic" version = "1.13.1" @@ -2377,6 +2776,25 @@ dependencies = [ "yansi", ] +[[package]] +name = "profiling" +version = "1.0.17" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3eb8486b569e12e2c32ad3e204dbaba5e4b5b216e9367044f25f1dba42341773" +dependencies = [ + "profiling-procmacros", +] + +[[package]] +name = "profiling-procmacros" +version = "1.0.17" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "52717f9a02b6965224f95ca2a81e2e0c5c43baacd28ca057577988930b6c3d5b" +dependencies = [ + "quote", + "syn", +] + [[package]] name = "prost" version = "0.14.3" @@ -2450,6 +2868,12 @@ dependencies = [ "pulldown-cmark", ] +[[package]] +name = "pxfm" +version = "0.1.29" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e0c5ccf5294c6ccd63a74f1565028353830a9c2f5eb0c682c355c471726a6e3f" + [[package]] name = "pyo3" version = "0.27.2" @@ -2511,6 +2935,21 @@ dependencies = [ "syn", ] +[[package]] +name = "qoi" +version = "0.4.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7f6d64c71eb498fe9eae14ce4ec935c555749aef511cca85b5568910d6e48001" +dependencies = [ + "bytemuck", +] + +[[package]] +name = "quick-error" +version = "2.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a993555f31e5a609f617c12db6250dedcac1b0a85076912c436e6fc9b2c8e6a3" + [[package]] name = "quinn" version = "0.11.9" @@ -2647,6 +3086,56 @@ dependencies = [ "getrandom 0.3.4", ] +[[package]] +name = "rav1e" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "43b6dd56e85d9483277cde964fd1bdb0428de4fec5ebba7540995639a21cb32b" +dependencies = [ + "aligned-vec", + "arbitrary", + "arg_enum_proc_macro", + "arrayvec", + "av-scenechange", + "av1-grain", + "bitstream-io", + "built", + "cfg-if", + "interpolate_name", + "itertools 0.14.0", + "libc", + "libfuzzer-sys", + "log", + "maybe-rayon", + "new_debug_unreachable", + "noop_proc_macro", + "num-derive", + "num-traits", + "paste", + "profiling", + "rand 0.9.3", + "rand_chacha 0.9.0", + "simd_helpers", + "thiserror 2.0.18", + "v_frame", + "wasm-bindgen", +] + +[[package]] +name = "ravif" +version = "0.13.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e52310197d971b0f5be7fe6b57530dcd27beb35c1b013f29d66c1ad73fbbcc45" +dependencies = [ + "avif-serialize", + "imgref", + "loop9", + "quick-error", + "rav1e", + "rayon", + "rgb", +] + [[package]] name = "rawpointer" version = "0.2.1" @@ -2852,6 +3341,12 @@ dependencies = [ "web-sys", ] +[[package]] +name = "rgb" +version = "0.8.53" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "47b34b781b31e5d73e9fbc8689c70551fd1ade9a19e3e28cfec8580a79290cc4" + [[package]] name = "ring" version = "0.17.14" @@ -3237,6 +3732,15 @@ version = "0.3.9" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "703d5c7ef118737c72f1af64ad2f6f8c5e1921f818cdcb97b8fe6fc69bf66214" +[[package]] +name = "simd_helpers" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "95890f873bec569a0362c235787f3aca6e1e887302ba4840839bcc6459c42da6" +dependencies = [ + "quote", +] + [[package]] name = "slab" version = "0.4.12" @@ -3283,7 +3787,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5851699c4033c63636f7ea4cf7b7c1f1bf06d0cc03cfb42e711de5a5c46cf326" dependencies = [ "base64 0.13.1", - "nom", + "nom 7.1.3", "serde", "unicode-segmentation", ] @@ -3498,6 +4002,20 @@ dependencies = [ "cfg-if", ] +[[package]] +name = "tiff" +version = "0.11.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b63feaf3343d35b6ca4d50483f94843803b0f51634937cc2ec519fc32232bc52" +dependencies = [ + "fax", + "flate2", + "half", + "quick-error", + "weezl", + "zune-jpeg", +] + [[package]] name = "tinystr" version = "0.8.3" @@ -4071,6 +4589,17 @@ dependencies = [ "wasm-bindgen", ] +[[package]] +name = "v_frame" +version = "0.3.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "666b7727c8875d6ab5db9533418d7c764233ac9c0cff1d469aec8fa127597be2" +dependencies = [ + "aligned-vec", + "num-traits", + "wasm-bindgen", +] + [[package]] name = "valuable" version = "0.1.1" @@ -4272,6 +4801,12 @@ dependencies = [ "rustls-pki-types", ] +[[package]] +name = "weezl" +version = "0.1.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a28ac98ddc8b9274cb41bb4d9d4d5c425b6020c50c46f25559911905610b4a88" + [[package]] name = "which" version = "8.0.2" @@ -4726,6 +5261,12 @@ dependencies = [ "rustix", ] +[[package]] +name = "y4m" +version = "0.8.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7a5a4b21e1a62b67a2970e6831bc091d7b87e119e7f9791aef9702e3bef04448" + [[package]] name = "yansi" version = "1.0.1" @@ -4840,3 +5381,27 @@ name = "zmij" version = "1.0.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" + +[[package]] +name = "zune-core" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "cb8a0807f7c01457d0379ba880ba6322660448ddebc890ce29bb64da71fb40f9" + +[[package]] +name = "zune-inflate" +version = "0.2.54" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "73ab332fe2f6680068f3582b16a24f90ad7096d5d39b974d1c0aff0125116f02" +dependencies = [ + "simd-adler32", +] + +[[package]] +name = "zune-jpeg" +version = "0.5.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "27bc9d5b815bc103f142aa054f561d9187d191692ec7c2d1e2b4737f8dbd7296" +dependencies = [ + "zune-core", +] diff --git a/Cargo.toml b/Cargo.toml index 7f7b8059..ecea85cd 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -14,6 +14,15 @@ ndarray = "0.16.1" serde = { version = "1.0.228", features = ["serde_derive"] } tracing = "0.1.41" thiserror = "2.0.17" +image-ndarray = "0.1.5" + +[workspace.dependencies.bytes] +version = "1.11.1" +features = ["serde"] + +[workspace.dependencies.image] +version = "0.25.10" +features = ["serde"] [workspace.dependencies.parking_lot] version = "0.12.5" diff --git a/encoderfile/Cargo.toml b/encoderfile/Cargo.toml index 90c45150..a3e0621b 100644 --- a/encoderfile/Cargo.toml +++ b/encoderfile/Cargo.toml @@ -139,9 +139,18 @@ workspace = true [dependencies.serde_json] workspace = true +[dependencies.bytes] +workspace = true + [dependencies.ndarray] workspace = true +[dependencies.image] +workspace = true + +[dependencies.image-ndarray] +workspace = true + [dependencies.figment] version = "0.10.19" features = ["env", "serde_yaml", "yaml"] diff --git a/encoderfile/proto/metadata.proto b/encoderfile/proto/metadata.proto index de67f847..25e14f33 100644 --- a/encoderfile/proto/metadata.proto +++ b/encoderfile/proto/metadata.proto @@ -6,6 +6,7 @@ message GetModelMetadataRequest {} message GetModelMetadataResponse { string model_id = 1; + // TODO decide if we want a model family/area at a higher level ModelType model_type = 2; map id2label = 3; } @@ -16,4 +17,8 @@ enum ModelType { SEQUENCE_CLASSIFICATION = 2; TOKEN_CLASSIFICATION = 3; SENTENCE_EMBEDDING = 4; + + IMAGE_CLASSIFICATION = 21; + // IMAGE_SEGMENTATION = 22; + // OBJECT_DETECTION = 23; } diff --git a/encoderfile/src/builder/config.rs b/encoderfile/src/builder/config.rs index d57c3b68..6281cc70 100644 --- a/encoderfile/src/builder/config.rs +++ b/encoderfile/src/builder/config.rs @@ -1,4 +1,4 @@ -use crate::common::{Config as EmbeddedConfig, LuaLibs, ModelConfig, ModelType}; +use crate::common::{Config as EmbeddedConfig, LuaLibs, ModelConfig, model_type::ModelType}; use anyhow::{Context, Result, bail}; use schemars::JsonSchema; use std::string::String; diff --git a/encoderfile/src/builder/model.rs b/encoderfile/src/builder/model.rs index 77e9cf29..fb12c815 100644 --- a/encoderfile/src/builder/model.rs +++ b/encoderfile/src/builder/model.rs @@ -10,7 +10,7 @@ pub trait ModelTypeExt { fn validate_model<'a>(&self, path: &'a Path) -> Result>; } -impl ModelTypeExt for crate::common::ModelType { +impl ModelTypeExt for crate::common::model_type::ModelType { fn validate_model<'a>(&self, path: &'a Path) -> Result> { let model = load_model(path)?; @@ -19,6 +19,7 @@ impl ModelTypeExt for crate::common::ModelType { Self::SequenceClassification => validate_sequence_classification_model(model), Self::TokenClassification => validate_token_classification_model(model), Self::SentenceEmbedding => validate_sentence_embedding_model(model), + Self::ImageClassification => validate_image_classification_model(model), }?; PlannedAsset::from_asset_source(AssetSource::File(path), AssetKind::ModelWeights) @@ -65,6 +66,16 @@ fn validate_token_classification_model(model: Session) -> Result<()> { Ok(()) } +fn validate_image_classification_model(model: Session) -> Result<()> { + let shape = get_outp_dim(model.outputs.as_slice(), "logits")?; + + if shape.len() != 2 { + bail!("Model must return tensor of shape [batch_size, n_labels]") + } + + Ok(()) +} + fn get_outp_dim<'a>(outputs: &'a [Output], outp_name: &str) -> Result<&'a Shape> { outputs .iter() diff --git a/encoderfile/src/builder/tokenizer.rs b/encoderfile/src/builder/tokenizer.rs index cc472a7d..bfabf14a 100644 --- a/encoderfile/src/builder/tokenizer.rs +++ b/encoderfile/src/builder/tokenizer.rs @@ -348,7 +348,7 @@ impl<'a> TokenizerConfigBuilder<'a> { #[cfg(test)] mod tests { use crate::builder::config::{ModelPath, TokenizerBuildConfig}; - use crate::common::ModelType; + use crate::common::model_type::ModelType; use super::*; diff --git a/encoderfile/src/builder/transforms/validation/embedding.rs b/encoderfile/src/builder/transforms/validation/embedding.rs index 20785c07..83a48784 100644 --- a/encoderfile/src/builder/transforms/validation/embedding.rs +++ b/encoderfile/src/builder/transforms/validation/embedding.rs @@ -56,7 +56,7 @@ impl TransformValidatorExt for EmbeddingTransform { #[cfg(test)] mod tests { use crate::builder::config::{EncoderfileConfig, ModelPath}; - use crate::common::ModelType; + use crate::common::model_type::ModelType; use crate::transforms::DEFAULT_LIBS; use super::*; diff --git a/encoderfile/src/builder/transforms/validation/image_classification.rs b/encoderfile/src/builder/transforms/validation/image_classification.rs new file mode 100644 index 00000000..1ec5ebab --- /dev/null +++ b/encoderfile/src/builder/transforms/validation/image_classification.rs @@ -0,0 +1,129 @@ +use super::{ + TransformValidatorExt, + utils::{BATCH_SIZE, SEQ_LEN, random_tensor, validation_err, validation_err_ctx}, +}; +use crate::{ + common::ModelConfig, + transforms::{ImageClassificationTransform, Postprocessor}, +}; +use anyhow::{Context, Result}; + +impl TransformValidatorExt for ImageClassificationTransform { + fn dry_run(&self, model_config: &ModelConfig) -> Result<()> { + let num_labels = match model_config.num_labels() { + Some(n) => n, + None => validation_err( + "Model config does not have `num_labels`, `id2label`, or `label2id` field. Please make sure you're using an ImageClassification model.", + )?, + }; + + let dummy_logits = random_tensor(&[BATCH_SIZE, SEQ_LEN, num_labels], (-1.0, 1.0))?; + let shape = dummy_logits.shape().to_owned(); + + let res = self.postprocess(dummy_logits) + .with_context(|| { + validation_err_ctx( + format!( + "Failed to run postprocessing on dummy logits (randomly generated in range -1.0..1.0) of shape {:?}", + shape.as_slice(), + ) + ) + })?; + + // result must return tensor of rank 2 + if res.ndim() != 2 { + validation_err(format!( + "Transform must return tensor of rank 2. Got tensor of shape {:?}.", + res.shape() + ))? + } + + // result must have same shape as original + if res.shape() != shape { + validation_err(format!( + "Transform must return Tensor of shape [batch_size, num_labels]. Expected shape [{}, {}], got shape {:?}", + BATCH_SIZE, + num_labels, + res.shape() + ))? + } + + Ok(()) + } +} + +#[cfg(test)] +mod tests { + use crate::builder::config::{EncoderfileConfig, ModelPath}; + use crate::common::model_type::ModelType; + use crate::transforms::DEFAULT_LIBS; + + use super::*; + + fn test_encoderfile_config() -> EncoderfileConfig { + EncoderfileConfig { + name: "my-model".to_string(), + version: "0.0.1".to_string(), + path: ModelPath::Directory(std::path::PathBuf::from("models/image_classification")), + model_type: ModelType::ImageClassification, + cache_dir: None, + output_path: None, + transform: None, + lua_libs: None, + validate_transform: true, + tokenizer: None, + base_binary_path: None, + target: None, + } + } + + fn test_model_config() -> ModelConfig { + let config_json = include_str!("../../../../../models/token_classification/config.json"); + + serde_json::from_str(config_json).unwrap() + } + + #[test] + fn test_identity_validation() { + let encoderfile_config = test_encoderfile_config(); + let model_config = test_model_config(); + + ImageClassificationTransform::new( + DEFAULT_LIBS.to_vec(), + Some("function Postprocess(arr) return arr end".to_string()), + ) + .expect("Failed to create transform") + .validate(&encoderfile_config, &model_config) + .expect("Failed to validate"); + } + + #[test] + fn test_bad_return_type() { + let encoderfile_config = test_encoderfile_config(); + let model_config = test_model_config(); + + let result = ImageClassificationTransform::new( + DEFAULT_LIBS.to_vec(), + Some("function Postprocess(arr) return 1 end".to_string()), + ) + .expect("Failed to create transform") + .validate(&encoderfile_config, &model_config); + + assert!(result.is_err()); + } + + #[test] + fn test_bad_dimensionality() { + let encoderfile_config = test_encoderfile_config(); + let model_config = test_model_config(); + + let result = ImageClassificationTransform::new( + DEFAULT_LIBS.to_vec(), + Some("function Postprocess(arr) return arr:sum_axis(1) end".to_string()), + ) + .expect("Failed to create transform") + .validate(&encoderfile_config, &model_config); + + assert!(result.is_err()); + } +} diff --git a/encoderfile/src/builder/transforms/validation/mod.rs b/encoderfile/src/builder/transforms/validation/mod.rs index 8d1a3783..39089444 100644 --- a/encoderfile/src/builder/transforms/validation/mod.rs +++ b/encoderfile/src/builder/transforms/validation/mod.rs @@ -1,5 +1,5 @@ use crate::{ - common::{ModelConfig, ModelType}, + common::{ModelConfig, model_type::ModelType}, format::assets::{AssetKind, AssetSource, PlannedAsset}, generated::manifest::LuaLibs as ManifestLuaLibs, transforms::{TransformSpec, convert_libs}, @@ -13,6 +13,7 @@ mod embedding; mod sentence_embedding; mod sequence_classification; mod token_classification; +mod image_classification; mod utils; pub trait TransformValidatorExt: TransformSpec { @@ -89,6 +90,12 @@ pub fn validate_transform<'a>( encoderfile_config, model_config ), + ModelType::ImageClassification => validate_transform!( + ImageClassificationTransform, + transform_str, + encoderfile_config, + model_config + ), }?; let lua_libs: Option = encoderfile_config diff --git a/encoderfile/src/builder/transforms/validation/sentence_embedding.rs b/encoderfile/src/builder/transforms/validation/sentence_embedding.rs index e478927d..22beda8b 100644 --- a/encoderfile/src/builder/transforms/validation/sentence_embedding.rs +++ b/encoderfile/src/builder/transforms/validation/sentence_embedding.rs @@ -59,7 +59,7 @@ impl TransformValidatorExt for SentenceEmbeddingTransform { #[cfg(test)] mod tests { use crate::builder::config::{EncoderfileConfig, ModelPath}; - use crate::common::ModelType; + use crate::common::model_type::ModelType; use crate::transforms::DEFAULT_LIBS; use super::*; diff --git a/encoderfile/src/builder/transforms/validation/sequence_classification.rs b/encoderfile/src/builder/transforms/validation/sequence_classification.rs index 6c4879dc..50615834 100644 --- a/encoderfile/src/builder/transforms/validation/sequence_classification.rs +++ b/encoderfile/src/builder/transforms/validation/sequence_classification.rs @@ -55,7 +55,7 @@ impl TransformValidatorExt for SequenceClassificationTransform { #[cfg(test)] mod tests { use crate::builder::config::{EncoderfileConfig, ModelPath}; - use crate::common::ModelType; + use crate::common::model_type::ModelType; use crate::transforms::DEFAULT_LIBS; use super::*; diff --git a/encoderfile/src/builder/transforms/validation/token_classification.rs b/encoderfile/src/builder/transforms/validation/token_classification.rs index 42801a9b..30d2c75e 100644 --- a/encoderfile/src/builder/transforms/validation/token_classification.rs +++ b/encoderfile/src/builder/transforms/validation/token_classification.rs @@ -56,7 +56,7 @@ impl TransformValidatorExt for TokenClassificationTransform { #[cfg(test)] mod tests { use crate::builder::config::{EncoderfileConfig, ModelPath}; - use crate::common::ModelType; + use crate::common::model_type::ModelType; use crate::transforms::DEFAULT_LIBS; use super::*; diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs new file mode 100644 index 00000000..3072cbc0 --- /dev/null +++ b/encoderfile/src/common/image_classification.rs @@ -0,0 +1,93 @@ +use serde::{Deserialize, Serialize}; +use std::{collections::HashMap, io::Read}; +use utoipa::ToSchema; +use anyhow::Result; +use crate::common::FromReadInput; +use image::ImageFormat; +use bytes::Bytes; + + +pub struct ImageInfo { + pub image_bytes: Bytes, + pub image_format: ImageFormat, +} + +pub struct ImageClassificationRequest { + pub images: Vec, + pub metadata: Option>, +} + +// FIXME check if we need to reorganize the from*input traits +impl super::FromCliInput for ImageClassificationRequest { + fn from_cli_input(inputs: Vec) -> Self { + let images = inputs.into_iter().map(|path| { + let image_data = std::fs::read(path).expect("Failed to read image file"); + let format = image::guess_format(&image_data).expect("Failed to guess image format"); + ImageInfo { + image_bytes: Bytes::from(image_data), + image_format: format, + } + }).collect(); + + Self { + images, + metadata: Some(HashMap::default()), + } + } +} + +impl FromReadInput for ImageClassificationRequest { + fn from_read_input(input: Vec<&mut impl Read>) -> Result { + let images = input.into_iter().map(|reader| { + let mut image_data = Vec::new(); + reader.read_to_end(&mut image_data).map_err(|e| anyhow::anyhow!("Failed to read image data: {}", e))?; + let format = image::guess_format(&image_data).map_err(|e| anyhow::anyhow!("Failed to guess image format: {}", e))?; + Ok(ImageInfo { + image_bytes: Bytes::from(image_data), + image_format: format, + }) + }).collect::>>()?; + + Ok(Self { + images, + metadata: Some(HashMap::default()), + }) + } +} + +#[derive(Debug, Serialize, Deserialize, ToSchema, utoipa::ToResponse)] +pub struct ImageClassificationResponse { + pub results: Vec, + #[serde(skip_serializing_if = "Option::is_none")] + pub metadata: Option>, +} + +#[derive(Debug, Serialize, Deserialize, ToSchema)] +pub struct ImageLabelScore { + pub label: String, + pub score: f32, +} + +#[derive(Debug, Serialize, Deserialize, ToSchema)] +pub struct ImageClassificationResult { + pub labels: Vec, +} + +#[cfg(test)] + +mod tests { + use super::*; + use std::fs::File; + + #[test] + fn test_image_classification_request_from_read_input() { + let mut file = File::open("test-pictures/w3c_home.jpg").expect("Failed to open test image"); + let file_vec = vec![&mut file]; + let request = ImageClassificationRequest::from_read_input(file_vec).expect("Failed to create request from read input"); + + assert_eq!(request.images.len(), 1); + assert_eq!(request.images[0].image_format, ImageFormat::Jpeg); + assert!(!request.images[0].image_bytes.is_empty()); + } +} + diff --git a/encoderfile/src/common/mod.rs b/encoderfile/src/common/mod.rs index 56549e37..e824515e 100644 --- a/encoderfile/src/common/mod.rs +++ b/encoderfile/src/common/mod.rs @@ -8,16 +8,28 @@ mod sequence_classification; mod token; mod token_classification; +// CV +mod image_classification; + pub use config::*; pub use embedding::*; pub use model_config::*; pub use model_metadata::*; -pub use model_type::ModelType; pub use sentence_embedding::*; pub use sequence_classification::*; pub use token::*; pub use token_classification::*; +// CV +pub use image_classification::*; +use std::io::Read; +use anyhow::Result; + pub trait FromCliInput { fn from_cli_input(inputs: Vec) -> Self; } + +pub trait FromReadInput { + fn from_read_input(input: Vec<&mut impl Read>) -> Result + where Self: Sized; +} diff --git a/encoderfile/src/common/model_type.rs b/encoderfile/src/common/model_type.rs index 96cc7d3e..242a8185 100644 --- a/encoderfile/src/common/model_type.rs +++ b/encoderfile/src/common/model_type.rs @@ -1,5 +1,9 @@ macro_rules! model_type { [ $( $x:ident ),* $(,)? ] => { + pub trait ModelTypeSpec: Send + Sync + Clone + std::fmt::Debug + 'static { + fn enum_val() -> ModelType; + } + // create enum #[derive(Debug, Clone, PartialEq, Eq, serde::Serialize, serde::Deserialize, utoipa::ToSchema, schemars::JsonSchema)] #[serde(rename_all = "snake_case")] @@ -38,16 +42,19 @@ macro_rules! model_type { } } )* + } } + + model_type![ Embedding, SequenceClassification, TokenClassification, SentenceEmbedding, + ImageClassification ]; -pub trait ModelTypeSpec: Send + Sync + Clone + std::fmt::Debug + 'static { - fn enum_val() -> ModelType; -} + + diff --git a/encoderfile/src/format/assets/kind.rs b/encoderfile/src/format/assets/kind.rs index d7a12f1a..13723957 100644 --- a/encoderfile/src/format/assets/kind.rs +++ b/encoderfile/src/format/assets/kind.rs @@ -78,3 +78,4 @@ asset_policy_spec!(Encoder, Embedding); asset_policy_spec!(Encoder, SequenceClassification); asset_policy_spec!(Encoder, TokenClassification); asset_policy_spec!(Encoder, SentenceEmbedding); +asset_policy_spec!(Encoder, ImageClassification); diff --git a/encoderfile/src/format/codec/encoder.rs b/encoderfile/src/format/codec/encoder.rs index 6972c2c7..bd3e4def 100644 --- a/encoderfile/src/format/codec/encoder.rs +++ b/encoderfile/src/format/codec/encoder.rs @@ -3,7 +3,7 @@ use anyhow::{Result, bail}; use crate::{ common::model_type::{ - Embedding, ModelType, SentenceEmbedding, SequenceClassification, TokenClassification, + Embedding, ImageClassification, ModelType, SentenceEmbedding, SequenceClassification, TokenClassification, }, format::{ assets::{AssetPlan, AssetPolicySpec}, @@ -86,6 +86,7 @@ impl EncoderfileCodec { } ModelType::TokenClassification => Self::validate_assets::(plan)?, ModelType::SentenceEmbedding => Self::validate_assets::(plan)?, + ModelType::ImageClassification => Self::validate_assets::(plan)?, }; let model_type: crate::generated::metadata::ModelType = model_type.into(); diff --git a/encoderfile/src/format/container.rs b/encoderfile/src/format/container.rs index 6bd5522e..496dad49 100644 --- a/encoderfile/src/format/container.rs +++ b/encoderfile/src/format/container.rs @@ -2,7 +2,7 @@ use anyhow::Result; use std::io::{Read, Seek, SeekFrom}; use crate::{ - common::ModelType, + common::model_type::ModelType, format::{assets::AssetKind, footer::EncoderfileFooter}, generated::manifest::{Artifact, EncoderfileManifest}, }; diff --git a/encoderfile/src/generated/metadata.rs b/encoderfile/src/generated/metadata.rs index 33a660a4..e7d68479 100644 --- a/encoderfile/src/generated/metadata.rs +++ b/encoderfile/src/generated/metadata.rs @@ -12,24 +12,26 @@ impl From for GetModelMetadataResponse { } } -impl From for ModelType { - fn from(val: common::ModelType) -> Self { +impl From for ModelType { + fn from(val: common::model_type::ModelType) -> Self { match val { - common::ModelType::Embedding => Self::Embedding, - common::ModelType::SequenceClassification => Self::SequenceClassification, - common::ModelType::TokenClassification => Self::TokenClassification, - common::ModelType::SentenceEmbedding => Self::SentenceEmbedding, + common::model_type::ModelType::Embedding => Self::Embedding, + common::model_type::ModelType::SequenceClassification => Self::SequenceClassification, + common::model_type::ModelType::TokenClassification => Self::TokenClassification, + common::model_type::ModelType::SentenceEmbedding => Self::SentenceEmbedding, + common::model_type::ModelType::ImageClassification => Self::ImageClassification, } } } -impl From for common::ModelType { +impl From for common::model_type::ModelType { fn from(val: ModelType) -> Self { match val { - ModelType::Embedding => common::ModelType::Embedding, - ModelType::SequenceClassification => common::ModelType::SequenceClassification, - ModelType::TokenClassification => common::ModelType::TokenClassification, - ModelType::SentenceEmbedding => common::ModelType::SentenceEmbedding, + ModelType::Embedding => common::model_type::ModelType::Embedding, + ModelType::SequenceClassification => common::model_type::ModelType::SequenceClassification, + ModelType::TokenClassification => common::model_type::ModelType::TokenClassification, + ModelType::SentenceEmbedding => common::model_type::ModelType::SentenceEmbedding, + ModelType::ImageClassification => common::model_type::ModelType::ImageClassification, ModelType::Unspecified => { unreachable!("Unspecified model type. This should not happen.") } diff --git a/encoderfile/src/inference/embedding.rs b/encoderfile/src/inference/embedding.rs index af125053..9aa062c8 100644 --- a/encoderfile/src/inference/embedding.rs +++ b/encoderfile/src/inference/embedding.rs @@ -13,7 +13,7 @@ pub fn embedding<'a>( transform: &EmbeddingTransform, encodings: Vec, ) -> Result, ApiError> { - let (a_ids, a_mask, a_type_ids) = crate::prepare_inputs!(encodings); + let (a_ids, a_mask, a_type_ids) = crate::prepare_text_inputs!(encodings); let mut outputs = crate::run_model!(session, a_ids, a_mask, a_type_ids)? .get("last_hidden_state") diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs new file mode 100644 index 00000000..94dbcd54 --- /dev/null +++ b/encoderfile/src/inference/image_classification.rs @@ -0,0 +1,42 @@ +use ndarray::{Array2, Array3, Array4, Ix2, Axis}; + +use crate::{ + error::ApiError, +}; + +use crate::common::{ImageClassificationResult, ImageLabelScore}; + +#[tracing::instrument(skip_all)] +pub fn image_classification<'a>( + mut session: crate::runtime::Model<'a>, + // CHECK if this is a flattened rgb image with num_channels X height X width + images: Vec>, +) -> Result, ApiError> { + let grouped_images = ort::value::TensorRef::from_array_view(&*images) + .unwrap() + .to_owned(); + let mut outputs = crate::run_cv_model!(session, grouped_images)? + .get("logits") + .expect("Model does not return logits") + .try_extract_array::() + .expect("Model does not return tensor extractable to f32") + .into_dimensionality::() + .expect("Model does not return tensor of shape [n_batch, n_classes]") + .into_owned(); + + + Ok(outputs) +} + +#[tracing::instrument(skip_all)] +pub fn postprocess(outputs: Array2) -> Vec { + outputs + .axis_iter(Axis(0)) + .map(|(logs)| { + ImageLabelScore { + label: "dummy".to_string(), // TODO: get label from config + score: 1.0 + } + }) + .collect() +} \ No newline at end of file diff --git a/encoderfile/src/inference/mod.rs b/encoderfile/src/inference/mod.rs index 09803536..e9b82a92 100644 --- a/encoderfile/src/inference/mod.rs +++ b/encoderfile/src/inference/mod.rs @@ -1,5 +1,8 @@ +// text pub mod embedding; pub mod sentence_embedding; pub mod sequence_classification; pub mod token_classification; +// cv +pub mod image_classification; pub mod utils; diff --git a/encoderfile/src/inference/sentence_embedding.rs b/encoderfile/src/inference/sentence_embedding.rs index ea0e3051..d2a876af 100644 --- a/encoderfile/src/inference/sentence_embedding.rs +++ b/encoderfile/src/inference/sentence_embedding.rs @@ -13,7 +13,7 @@ pub fn sentence_embedding<'a>( transform: &SentenceEmbeddingTransform, encodings: Vec, ) -> Result, ApiError> { - let (a_ids, a_mask, a_type_ids) = crate::prepare_inputs!(encodings); + let (a_ids, a_mask, a_type_ids) = crate::prepare_text_inputs!(encodings); let a_mask_arr = a_mask .try_extract_array::() diff --git a/encoderfile/src/inference/sequence_classification.rs b/encoderfile/src/inference/sequence_classification.rs index f003a07e..1bb80343 100644 --- a/encoderfile/src/inference/sequence_classification.rs +++ b/encoderfile/src/inference/sequence_classification.rs @@ -14,7 +14,7 @@ pub fn sequence_classification<'a>( config: &ModelConfig, encodings: Vec, ) -> Result, ApiError> { - let (a_ids, a_mask, a_type_ids) = crate::prepare_inputs!(encodings); + let (a_ids, a_mask, a_type_ids) = crate::prepare_text_inputs!(encodings); let mut outputs = crate::run_model!(session, a_ids, a_mask, a_type_ids)? .get("logits") diff --git a/encoderfile/src/inference/token_classification.rs b/encoderfile/src/inference/token_classification.rs index 732073f0..5a4ec377 100644 --- a/encoderfile/src/inference/token_classification.rs +++ b/encoderfile/src/inference/token_classification.rs @@ -14,7 +14,7 @@ pub fn token_classification<'a>( config: &ModelConfig, encodings: Vec, ) -> Result, ApiError> { - let (a_ids, a_mask, a_type_ids) = crate::prepare_inputs!(encodings); + let (a_ids, a_mask, a_type_ids) = crate::prepare_text_inputs!(encodings); let mut outputs = crate::run_model!(session, a_ids, a_mask, a_type_ids)? .get("logits") diff --git a/encoderfile/src/inference/utils.rs b/encoderfile/src/inference/utils.rs index a59f1f62..58f27075 100644 --- a/encoderfile/src/inference/utils.rs +++ b/encoderfile/src/inference/utils.rs @@ -3,7 +3,7 @@ use ort::session::Session; use parking_lot::MutexGuard; #[macro_export] -macro_rules! prepare_inputs { +macro_rules! prepare_text_inputs { ($encodings:ident) => {{ let padded_token_length = $encodings[0].len(); @@ -75,3 +75,14 @@ macro_rules! run_model { }) }}; } + +#[macro_export] +macro_rules! run_cv_model { + ($session:expr, $image_bytes:expr) => {{ + $session.run(ort::inputs!($image_bytes)) + .map_err(|e| { + tracing::error!("Error running model: {:?}", e); + $crate::error::ApiError::InternalError("Error running model") + }) + }}; +} diff --git a/encoderfile/src/runtime/loader.rs b/encoderfile/src/runtime/loader.rs index b00959c4..92d2278c 100644 --- a/encoderfile/src/runtime/loader.rs +++ b/encoderfile/src/runtime/loader.rs @@ -5,7 +5,7 @@ use std::io::{Read, Seek}; use ort::session::Session; use crate::{ - common::{Config, LuaLibs, ModelConfig, ModelType}, + common::{Config, LuaLibs, ModelConfig, model_type::ModelType}, format::{assets::AssetKind, codec::EncoderfileCodec, container::Encoderfile}, generated::manifest::{self, TransformType}, runtime::TokenizerService, diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 5690d99e..d8401af5 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -4,7 +4,7 @@ use ort::session::Session; use parking_lot::Mutex; use crate::{ - common::{Config, ModelConfig, ModelType, model_type::ModelTypeSpec}, + common::{Config, ModelConfig, model_type::ModelType, model_type::ModelTypeSpec}, runtime::TokenizerService, transforms::DEFAULT_LIBS, }; diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs new file mode 100644 index 00000000..74e3938e --- /dev/null +++ b/encoderfile/src/services/image_classification.rs @@ -0,0 +1,61 @@ +use crate::{ + common::{ + ImageClassificationRequest, + ImageClassificationResponse, + ImageClassificationResult, + ImageLabelScore, + model_type + }, + + error::ApiError, + runtime::AppState, +}; + +use image::{DynamicImage, GenericImageView}; + +use super::inference::Inference; + +// No service impl yet + +/* +impl Inference for AppState { + type Input = ImageClassificationRequest; + type Output = ImageClassificationResponse; + + fn inference(&self, request: impl Into) -> Result { + let request = request.into(); + + // FIXME + /* + // convert input image into flattened rbg + let image = image::load_from_memory(&request.image_info.image_bytes)?; + let (width, height) = image.dimensions(); + let image = image.to_rgb8(); + let mut flattened_rgb = Vec::with_capacity((width * height * 3) as usize); + for pixel in image.pixels() { + flattened_rgb.push(pixel[0] as f32); + flattened_rgb.push(pixel[1] as f32); + flattened_rgb.push(pixel[2] as f32); + } + + let labels_batch = inference::image_classification::image_classification(self.session.lock(), &transform, encodings)?; + */ + + let dummy_labels = vec![ + ImageLabelScore { + label: "dummy1".to_string(), + score: 0.9, + }, + ImageLabelScore { + label: "dummy2".to_string(), + score: 0.1, + }, + ]; + + Ok(ImageClassificationResponse { + results: vec![ImageClassificationResult { labels: dummy_labels }], + metadata: request.metadata, + }) + } +} + */ diff --git a/encoderfile/src/services/mod.rs b/encoderfile/src/services/mod.rs index 43720d50..6d88d6db 100644 --- a/encoderfile/src/services/mod.rs +++ b/encoderfile/src/services/mod.rs @@ -4,6 +4,7 @@ mod model_metadata; mod sentence_embedding; mod sequence_classification; mod token_classification; +mod image_classification; pub use inference::Inference; pub use model_metadata::Metadata; diff --git a/encoderfile/src/services/model_metadata.rs b/encoderfile/src/services/model_metadata.rs index d43cc28d..d92114b8 100644 --- a/encoderfile/src/services/model_metadata.rs +++ b/encoderfile/src/services/model_metadata.rs @@ -1,7 +1,7 @@ use std::collections::HashMap; use crate::{ - common::{GetModelMetadataResponse, ModelType, model_type::ModelTypeSpec}, + common::{GetModelMetadataResponse, model_type::ModelType, model_type::ModelTypeSpec}, runtime::AppState, }; diff --git a/encoderfile/src/transforms/engine/image_classification.rs b/encoderfile/src/transforms/engine/image_classification.rs new file mode 100644 index 00000000..6da82b8c --- /dev/null +++ b/encoderfile/src/transforms/engine/image_classification.rs @@ -0,0 +1,140 @@ +use crate::{common::model_type, error::ApiError}; + +use super::{super::tensor::Tensor, Postprocessor, Transform}; +use ndarray::{Array2, Ix2}; + +impl Postprocessor for Transform { + type Input = Array2; + type Output = Array2; + + fn postprocess(&self, data: Self::Input) -> Result { + let func = match self.postprocessor() { + Some(p) => p, + None => return Ok(data), + }; + + let expected_shape = data.shape().to_owned(); + + let tensor = Tensor(data.into_dyn()); + + let result = func + .call::(tensor) + .map_err(|e| ApiError::LuaError(e.to_string()))? + .into_inner() + .into_dimensionality::().map_err(|e| { + tracing::error!("Failed to cast array into Ix2: {e}. Check your lua transform to make sure it returns a tensor of shape [batch_size, num_classes]"); + ApiError::LuaError("Error postprocessing image classifications".to_string()) + })?; + + let result_shape = result.shape(); + + if expected_shape.as_slice() != result_shape { + tracing::error!( + "Transform error: expected tensor of shape {:?}, got tensor of shape {:?}", + expected_shape.as_slice(), + result_shape + ); + + return Err(ApiError::LuaError( + "Error postprocessing image classifications".to_string(), + )); + } + + Ok(result) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::transforms::DEFAULT_LIBS; + + #[test] + fn test_image_cls_no_transform() { + let engine = Transform::::new( + DEFAULT_LIBS.to_vec(), + Some("".to_string()), + ) + .expect("Failed to create Transform"); + + let arr = ndarray::Array2::::from_elem((32, 16), 2.0); + + let result = engine.postprocess(arr.clone()).expect("Failed"); + + assert_eq!(arr, result); + } + + #[test] + fn test_image_cls_identity_transform() { + let engine = Transform::::new( + DEFAULT_LIBS.to_vec(), + Some( + r##" + function Postprocess(arr) + return arr + end + "## + .to_string(), + ), + ) + .expect("Failed to create engine"); + + let arr = ndarray::Array2::::from_elem((16, 32), 2.0); + + let result = engine.postprocess(arr.clone()).expect("Failed"); + + assert_eq!(arr, result); + } + + #[test] + fn test_image_cls_transform_bad_fn() { + let engine = Transform::::new( + DEFAULT_LIBS.to_vec(), + Some( + r##" + function Postprocess(arr) + return 1 + end + "## + .to_string(), + ), + ) + .expect("Failed to create engine"); + + let arr = ndarray::Array2::::from_elem((16, 32), 2.0); + + let result = engine.postprocess(arr.clone()); + + assert!(result.is_err()) + } + + #[test] + fn test_bad_dimensionality_transform_postprocessing() { + let engine = Transform::::new( + DEFAULT_LIBS.to_vec(), + Some( + r##" + function Postprocess(x) + return x:sum_axis(1) + end + "## + .to_string(), + ), + ) + .unwrap(); + + let arr = ndarray::Array2::::from_elem((3, 3), 2.0); + let result = engine.postprocess(arr.clone()); + + assert!(result.is_err()); + + if let Err(e) = result { + match e { + ApiError::LuaError(s) => { + assert!(s.contains("Error postprocessing image classifications")) + } + _ => panic!("Didn't return lua error"), + } + } + } +} diff --git a/encoderfile/src/transforms/engine/mod.rs b/encoderfile/src/transforms/engine/mod.rs index 73cee6e2..3a4510ee 100644 --- a/encoderfile/src/transforms/engine/mod.rs +++ b/encoderfile/src/transforms/engine/mod.rs @@ -16,6 +16,7 @@ mod embedding; mod sentence_embedding; mod sequence_classification; mod token_classification; +mod image_classification; impl From<&LuaLibs> for Vec { fn from(value: &LuaLibs) -> Self { @@ -86,6 +87,7 @@ transform!(EmbeddingTransform, Embedding); transform!(SequenceClassificationTransform, SequenceClassification); transform!(TokenClassificationTransform, TokenClassification); transform!(SentenceEmbeddingTransform, SentenceEmbedding); +transform!(ImageClassificationTransform, ImageClassification); pub trait Postprocessor: TransformSpec { type Input; diff --git a/encoderfile/src/transport/mcp/mod.rs b/encoderfile/src/transport/mcp/mod.rs index 4512c711..e61bccbb 100644 --- a/encoderfile/src/transport/mcp/mod.rs +++ b/encoderfile/src/transport/mcp/mod.rs @@ -151,3 +151,16 @@ generate_mcp!( "Performs sentence embedding of input text sequences.", "This tool will embed a sequence of texts." ); + +// Doesn't use a json schema, see how we can go around this limitation +/* +generate_mcp!( + ImageClassification, + ImageClassificationTool, + image_classification, + ImageClassificationRequest, + ImageClassificationResponse, + "Performs image classification of input images.", + "This tool will classify input images." +); +*/ \ No newline at end of file From bb229abc24f990e612175aa309b345667daa8a5d Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Mon, 4 May 2026 18:50:33 +0200 Subject: [PATCH 04/21] Separate config per input type and task type --- encoderfile-runtime/src/main.rs | 6 +- .../src/common/image_classification.rs | 5 +- encoderfile/src/common/model_config.rs | 2 + encoderfile/src/common/model_type.rs | 5 - encoderfile/src/dev_utils/mod.rs | 139 ++++++++++++++++-- .../src/inference/image_classification.rs | 37 +++-- encoderfile/src/runtime/mod.rs | 2 +- encoderfile/src/runtime/state.rs | 43 ++++-- encoderfile/src/services/embedding.rs | 5 +- .../src/services/image_classification.rs | 6 +- encoderfile/src/services/inference.rs | 5 +- encoderfile/src/services/model_metadata.rs | 15 +- .../src/services/sentence_embedding.rs | 5 +- .../src/services/sequence_classification.rs | 7 +- .../src/services/token_classification.rs | 8 +- encoderfile/tests/test_mcp.rs | 3 +- encoderfile/tests/test_model_validation.rs | 2 +- encoderfile/tests/test_models.rs | 15 +- 18 files changed, 236 insertions(+), 74 deletions(-) diff --git a/encoderfile-runtime/src/main.rs b/encoderfile-runtime/src/main.rs index 003d7d42..e8a292be 100644 --- a/encoderfile-runtime/src/main.rs +++ b/encoderfile-runtime/src/main.rs @@ -8,9 +8,8 @@ use std::{ use anyhow::Result; use clap::Parser; use encoderfile::{ - common::{ - ModelType, - model_type::{Embedding, SentenceEmbedding, SequenceClassification, TokenClassification}, + common::model_type::{ + Embedding, SentenceEmbedding, SequenceClassification, TokenClassification, ModelType, }, runtime::{EncoderfileLoader, EncoderfileState, load_assets}, transport::cli::Cli, @@ -74,5 +73,6 @@ async fn entrypoint<'a, R: Read + Seek>(loader: &mut EncoderfileLoader<'a, R>) - tokenizer, model_config ), + ModelType::ImageClassification => panic!("ImageClassification is not yet supported in the CLI"), } } diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index 3072cbc0..45a1723d 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -7,11 +7,13 @@ use image::ImageFormat; use bytes::Bytes; +#[derive(Debug, Serialize, Deserialize)] pub struct ImageInfo { pub image_bytes: Bytes, pub image_format: ImageFormat, } +#[derive(Debug, Serialize, Deserialize)] pub struct ImageClassificationRequest { pub images: Vec, pub metadata: Option>, @@ -78,10 +80,11 @@ pub struct ImageClassificationResult { mod tests { use super::*; use std::fs::File; + use std::env::current_dir; #[test] fn test_image_classification_request_from_read_input() { - let mut file = File::open("test-pictures/w3c_home.jpg").expect("Failed to open test image"); + let mut file = File::open("../test-pictures/w3c_home.jpg").expect("Failed to open test image"); let file_vec = vec![&mut file]; let request = ImageClassificationRequest::from_read_input(file_vec).expect("Failed to create request from read input"); diff --git a/encoderfile/src/common/model_config.rs b/encoderfile/src/common/model_config.rs index 89eb4e2d..3d66e558 100644 --- a/encoderfile/src/common/model_config.rs +++ b/encoderfile/src/common/model_config.rs @@ -4,11 +4,13 @@ use std::collections::HashMap; #[derive(Debug, Serialize, Deserialize, Clone)] pub struct ModelConfig { pub model_type: String, + // FIXME to be moved to per-task structs pub num_labels: Option, pub id2label: Option>, pub label2id: Option>, } +// TODO add image handling metadata impl ModelConfig { pub fn id2label(&self, id: u32) -> Option<&str> { self.id2label.as_ref()?.get(&id).map(|s| s.as_str()) diff --git a/encoderfile/src/common/model_type.rs b/encoderfile/src/common/model_type.rs index 242a8185..26a28880 100644 --- a/encoderfile/src/common/model_type.rs +++ b/encoderfile/src/common/model_type.rs @@ -46,8 +46,6 @@ macro_rules! model_type { } } - - model_type![ Embedding, SequenceClassification, @@ -55,6 +53,3 @@ model_type![ SentenceEmbedding, ImageClassification ]; - - - diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index 6f9a371a..fa331635 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -3,18 +3,21 @@ use crate::{ Config, ModelConfig, TokenizerConfig, model_type::{self, ModelTypeSpec}, }, - runtime::{AppState, EncoderfileState}, + runtime::{AppState, EncoderfileState, FeatureExtractorState, ClassifierState, TextInputState, ImageInputState}, }; use ort::session::Session; use parking_lot::Mutex; +use rmcp::model; use std::str::FromStr; use std::{fs::File, io::BufReader}; const EMBEDDING_DIR: &str = "../models/embedding"; +// CHECK sentence embedding???? const SEQUENCE_CLASSIFICATION_DIR: &str = "../models/sequence_classification"; const TOKEN_CLASSIFICATION_DIR: &str = "../models/token_classification"; -pub fn get_state(dir: &str) -> AppState { +pub fn get_state(dir: &str) -> AppState +{ let config = Config { name: "my-model".to_string(), version: "0.0.1".to_string(), @@ -23,30 +26,146 @@ pub fn get_state(dir: &str) -> AppState { lua_libs: None, }; - let model_config = get_model_config(dir); - let tokenizer = get_tokenizer(dir); let session = get_model(dir); - EncoderfileState::new(config, session, tokenizer, model_config).into() + EncoderfileState::new( + config, + session, + T::get_input_state(dir), + T::get_task_state(dir), + ).into() +} + +pub fn get_reader(dir: &str) -> BufReader { + let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); + BufReader::new(file) +} + +// Input types +pub trait InputType { + type InputState; + fn get_input_state(dir: &str) -> Self::InputState; +} + +fn get_text_input_state(dir: &str) -> TextInputState { + let reader = get_reader(dir); + let tokenizer = get_tokenizer(dir); + let model_config = serde_json::from_reader(reader).expect("Invalid model config"); + + TextInputState { tokenizer, model_config } +} + +macro_rules! text_input { + ($model_type:ty) => { + impl InputType for $model_type { + type InputState = TextInputState; + fn get_input_state(dir: &str) -> Self::InputState { + get_text_input_state(dir) + } + } + }; +} + +text_input!(model_type::Embedding); +text_input!(model_type::SentenceEmbedding); +text_input!(model_type::SequenceClassification); +text_input!(model_type::TokenClassification); + +fn get_image_input_state(dir: &str) -> ImageInputState { + let reader = get_reader(dir); + serde_json::from_reader(reader).expect("Invalid model config") +} + +macro_rules! image_input { + ($model_type:ty) => { + impl InputType for $model_type { + type InputState = ImageInputState; + fn get_input_state(dir: &str) -> Self::InputState { + get_image_input_state(dir) + } + } + }; +} + +image_input!(model_type::ImageClassification); + + +// Task types +pub trait TaskType { + type TaskState; + fn get_task_state(dir: &str) -> Self::TaskState; } -pub fn embedding_state() -> AppState { +fn get_class_task_state(dir: &str) -> ClassifierState { + let reader = get_reader(dir); + serde_json::from_reader(reader).expect("Invalid model config") +} + +macro_rules! class_task { + ($model_type:ty) => { + impl TaskType for $model_type { + type TaskState = ClassifierState; + fn get_task_state(dir: &str) -> Self::TaskState { + get_class_task_state(dir) + } + } + }; +} + +class_task!(model_type::SequenceClassification); +class_task!(model_type::TokenClassification); +class_task!(model_type::ImageClassification); + +fn get_feature_task_state(_dir: &str) -> FeatureExtractorState { + FeatureExtractorState {} +} + +macro_rules! feature_task { + ($model_type:ty) => { + impl TaskType for $model_type { + type TaskState = FeatureExtractorState; + fn get_task_state(dir: &str) -> Self::TaskState { + get_feature_task_state(dir) + } + } + }; +} + +feature_task!(model_type::Embedding); +feature_task!(model_type::SentenceEmbedding); + + + + +pub fn embedding_state() -> AppState +{ get_state(EMBEDDING_DIR) } -pub fn sentence_embedding_state() -> AppState { +pub fn sentence_embedding_state() -> AppState +{ get_state(EMBEDDING_DIR) } -pub fn sequence_classification_state() -> AppState { +pub fn sequence_classification_state() -> AppState +{ get_state(SEQUENCE_CLASSIFICATION_DIR) } -pub fn token_classification_state() -> AppState { +pub fn token_classification_state() -> AppState +{ get_state(TOKEN_CLASSIFICATION_DIR) } -fn get_model_config(dir: &str) -> ModelConfig { +fn get_text_model_config(dir: &str) -> ModelConfig { + let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); + let reader = BufReader::new(file); + + // Deserialize into struct + serde_json::from_reader(reader).expect("Invalid model config") +} + +fn get_image_model_config(dir: &str) -> ImageInputState { let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); let reader = BufReader::new(file); diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index 94dbcd54..323414ed 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -1,21 +1,26 @@ -use ndarray::{Array2, Array3, Array4, Ix2, Axis}; +use ndarray::{Array2, Array4, Ix2, Axis}; use crate::{ error::ApiError, }; -use crate::common::{ImageClassificationResult, ImageLabelScore}; +use crate::common::{ImageLabelScore}; #[tracing::instrument(skip_all)] pub fn image_classification<'a>( mut session: crate::runtime::Model<'a>, - // CHECK if this is a flattened rgb image with num_channels X height X width - images: Vec>, -) -> Result, ApiError> { - let grouped_images = ort::value::TensorRef::from_array_view(&*images) + // CHECK if this is a vec of flattened rgb images with num_channels X height X width + images: Array4, + classes: Vec, + channels: usize, + height: usize, + width: usize, +) -> Result>, ApiError> { + let grouped_images = ort::value::TensorRef::from_array_view( + &images) .unwrap() .to_owned(); - let mut outputs = crate::run_cv_model!(session, grouped_images)? + let outputs = crate::run_cv_model!(session, grouped_images)? .get("logits") .expect("Model does not return logits") .try_extract_array::() @@ -25,18 +30,22 @@ pub fn image_classification<'a>( .into_owned(); - Ok(outputs) + Ok(postprocess(outputs, classes)) } #[tracing::instrument(skip_all)] -pub fn postprocess(outputs: Array2) -> Vec { +pub fn postprocess(outputs: Array2, classes: Vec) -> Vec> { outputs .axis_iter(Axis(0)) - .map(|(logs)| { - ImageLabelScore { - label: "dummy".to_string(), // TODO: get label from config - score: 1.0 - } + .map(|logs| { + logs.iter().enumerate() + .map(|(idx, score)| + ImageLabelScore { + label: classes[idx].to_string(), // TODO: get label from config + score: *score + } + ) + .collect() }) .collect() } \ No newline at end of file diff --git a/encoderfile/src/runtime/mod.rs b/encoderfile/src/runtime/mod.rs index 41d2bf86..135016a4 100644 --- a/encoderfile/src/runtime/mod.rs +++ b/encoderfile/src/runtime/mod.rs @@ -6,7 +6,7 @@ mod state; mod tokenizer; pub use loader::{EncoderfileLoader, load_assets}; -pub use state::{AppState, EncoderfileState}; +pub use state::{AppState, EncoderfileState, ClassifierState, FeatureExtractorState, ImageInputState, TextInputState}; pub use tokenizer::TokenizerService; pub type Model<'a> = MutexGuard<'a, Session>; diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index d8401af5..c13e6351 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -1,32 +1,53 @@ use std::{marker::PhantomData, sync::Arc}; +use serde::{Deserialize, Serialize}; use ort::session::Session; use parking_lot::Mutex; use crate::{ - common::{Config, ModelConfig, model_type::ModelType, model_type::ModelTypeSpec}, - runtime::TokenizerService, - transforms::DEFAULT_LIBS, + common::{Config, ModelConfig, model_type::{ModelType, ModelTypeSpec}}, dev_utils::{InputType, TaskType}, runtime::TokenizerService, transforms::DEFAULT_LIBS }; pub type AppState = Arc>; + +pub struct TextInputState { + pub tokenizer: TokenizerService, + pub model_config: ModelConfig, +} +#[derive(Debug, Serialize, Deserialize, Clone)] +pub struct ImageInputState { + pub num_channels: usize, + pub height: usize, + pub width: usize, + pub image_size: usize, +} + +#[derive(Debug, Serialize, Deserialize, Clone)] +pub struct ClassifierState { + pub id2label: Option>, + pub label2id: Option>, + pub num_labels: Option, +} +#[derive(Debug, Serialize, Deserialize, Clone)] +pub struct FeatureExtractorState {} + #[derive(Debug)] -pub struct EncoderfileState { +pub struct EncoderfileState { pub config: Config, pub session: Mutex, - pub tokenizer: TokenizerService, - pub model_config: ModelConfig, + pub per_model_input_state: T::InputState, + pub per_task_state: T::TaskState, pub lua_libs: Vec, _marker: PhantomData, } -impl EncoderfileState { +impl EncoderfileState { pub fn new( config: Config, session: Mutex, - tokenizer: TokenizerService, - model_config: ModelConfig, + per_model_input_state: T::InputState, + per_task_state: T::TaskState, ) -> EncoderfileState { let lua_libs = match config.lua_libs { Some(ref libs) => Vec::::from(libs), @@ -35,8 +56,8 @@ impl EncoderfileState { EncoderfileState { config, session, - tokenizer, - model_config, + per_model_input_state, + per_task_state, lua_libs, _marker: PhantomData, } diff --git a/encoderfile/src/services/embedding.rs b/encoderfile/src/services/embedding.rs index 53e89f7b..5d55b372 100644 --- a/encoderfile/src/services/embedding.rs +++ b/encoderfile/src/services/embedding.rs @@ -8,14 +8,15 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState { +impl Inference for AppState +{ type Input = EmbeddingRequest; type Output = EmbeddingResponse; fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.tokenizer.encode_text(request.inputs)?; + let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; let transform = EmbeddingTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 74e3938e..54f47eba 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -9,6 +9,7 @@ use crate::{ error::ApiError, runtime::AppState, + runtime::ImageInputState, }; use image::{DynamicImage, GenericImageView}; @@ -17,8 +18,8 @@ use super::inference::Inference; // No service impl yet -/* -impl Inference for AppState { +impl Inference for AppState +{ type Input = ImageClassificationRequest; type Output = ImageClassificationResponse; @@ -58,4 +59,3 @@ impl Inference for AppState { }) } } - */ diff --git a/encoderfile/src/services/inference.rs b/encoderfile/src/services/inference.rs index 5e55b15b..60fb0095 100644 --- a/encoderfile/src/services/inference.rs +++ b/encoderfile/src/services/inference.rs @@ -1,8 +1,9 @@ use crate::{common::FromCliInput, error::ApiError, services::Metadata}; +// FIXME enforce the openapi schema later on pub trait Inference: Metadata { - type Input: FromCliInput + serde::de::DeserializeOwned + Sync + Send + utoipa::ToSchema; - type Output: serde::Serialize + Sync + Send + utoipa::ToSchema; + type Input: FromCliInput + serde::de::DeserializeOwned + Sync + Send /* + utoipa::ToSchema */; + type Output: serde::Serialize + Sync + Send /* + utoipa::ToSchema */; fn inference(&self, request: impl Into) -> Result; } diff --git a/encoderfile/src/services/model_metadata.rs b/encoderfile/src/services/model_metadata.rs index d92114b8..77398c4b 100644 --- a/encoderfile/src/services/model_metadata.rs +++ b/encoderfile/src/services/model_metadata.rs @@ -1,16 +1,19 @@ use std::collections::HashMap; use crate::{ - common::{GetModelMetadataResponse, model_type::ModelType, model_type::ModelTypeSpec}, - runtime::AppState, + common::{GetModelMetadataResponse, model_type::{ModelType, ModelTypeSpec}}, dev_utils::TaskType, dev_utils::InputType, runtime::AppState }; +pub trait ClassifierMetadata { + fn id2label(&self) -> Option>; +} + pub trait Metadata { fn metadata(&self) -> GetModelMetadataResponse { GetModelMetadataResponse { model_id: self.model_id(), model_type: self.model_type(), - id2label: self.id2label(), + id2label: None, } } @@ -18,10 +21,9 @@ pub trait Metadata { fn model_type(&self) -> ModelType; - fn id2label(&self) -> Option>; } -impl Metadata for AppState { +impl Metadata for AppState { fn model_id(&self) -> String { self.config.name.clone() } @@ -30,7 +32,4 @@ impl Metadata for AppState { T::enum_val() } - fn id2label(&self) -> Option> { - self.model_config.id2label.clone() - } } diff --git a/encoderfile/src/services/sentence_embedding.rs b/encoderfile/src/services/sentence_embedding.rs index 115c6322..08e812a8 100644 --- a/encoderfile/src/services/sentence_embedding.rs +++ b/encoderfile/src/services/sentence_embedding.rs @@ -8,14 +8,15 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState { +impl Inference for AppState +{ type Input = SentenceEmbeddingRequest; type Output = SentenceEmbeddingResponse; fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.tokenizer.encode_text(request.inputs)?; + let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; let transform = SentenceEmbeddingTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/sequence_classification.rs b/encoderfile/src/services/sequence_classification.rs index 52af7313..09b7079e 100644 --- a/encoderfile/src/services/sequence_classification.rs +++ b/encoderfile/src/services/sequence_classification.rs @@ -8,14 +8,15 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState { +impl Inference for AppState +{ type Input = SequenceClassificationRequest; type Output = SequenceClassificationResponse; fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.tokenizer.encode_text(request.inputs)?; + let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; let transform = SequenceClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; @@ -23,7 +24,7 @@ impl Inference for AppState { let results = inference::sequence_classification::sequence_classification( self.session.lock(), &transform, - &self.model_config, + &self.per_model_input_state.model_config, encodings, )?; diff --git a/encoderfile/src/services/token_classification.rs b/encoderfile/src/services/token_classification.rs index 2fd12329..75bcacf7 100644 --- a/encoderfile/src/services/token_classification.rs +++ b/encoderfile/src/services/token_classification.rs @@ -3,12 +3,14 @@ use crate::{ error::ApiError, inference, runtime::AppState, + runtime::TextInputState, transforms::TokenClassificationTransform, }; use super::inference::Inference; -impl Inference for AppState { +impl Inference for AppState +{ type Input = TokenClassificationRequest; type Output = TokenClassificationResponse; @@ -17,7 +19,7 @@ impl Inference for AppState { let session = self.session.lock(); - let encodings = self.tokenizer.encode_text(request.inputs)?; + let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; let transform = TokenClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; @@ -25,7 +27,7 @@ impl Inference for AppState { let results = inference::token_classification::token_classification( session, &transform, - &self.model_config, + &self.per_model_input_state.model_config, encodings, )?; diff --git a/encoderfile/tests/test_mcp.rs b/encoderfile/tests/test_mcp.rs index 55ac6ab0..6cd34824 100644 --- a/encoderfile/tests/test_mcp.rs +++ b/encoderfile/tests/test_mcp.rs @@ -5,8 +5,9 @@ use encoderfile::transport::mcp::McpRouter; use tokio::net::TcpListener; use tokio::sync::oneshot; use tower_http::trace::DefaultOnResponse; +use encoderfile::dev_utils::{InputType, TaskType}; -async fn run_mcp( +async fn run_mcp( addr: String, state: AppState, shutdown_receiver: oneshot::Receiver<()>, diff --git a/encoderfile/tests/test_model_validation.rs b/encoderfile/tests/test_model_validation.rs index 25d971b4..e70c023b 100644 --- a/encoderfile/tests/test_model_validation.rs +++ b/encoderfile/tests/test_model_validation.rs @@ -1,6 +1,6 @@ use std::path::PathBuf; -use encoderfile::{builder::model::ModelTypeExt as _, common::ModelType}; +use encoderfile::{builder::model::ModelTypeExt as _, common::model_type::ModelType}; #[test] pub fn test_embedding() { diff --git a/encoderfile/tests/test_models.rs b/encoderfile/tests/test_models.rs index 44718dc0..fbb25ab2 100644 --- a/encoderfile/tests/test_models.rs +++ b/encoderfile/tests/test_models.rs @@ -4,12 +4,14 @@ use encoderfile::inference::{ token_classification::token_classification, }; use encoderfile::transforms::{DEFAULT_LIBS, Transform}; +use encoderfile::dev_utils::{InputType, TaskType}; #[test] fn test_embedding_model() { let state = embedding_state(); let encodings = state + .per_model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -34,6 +36,7 @@ fn test_embedding_inference_with_bad_model() { let state = token_classification_state(); let encodings = state + .per_model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -54,6 +57,7 @@ fn test_sequence_classification_model() { let state = sequence_classification_state(); let encodings = state + .per_model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -69,7 +73,7 @@ fn test_sequence_classification_model() { let results = sequence_classification( session_lock, &transform, - &state.model_config, + &state.per_model_input_state.model_config, encodings.clone(), ) .expect("Failed to compute results"); @@ -83,6 +87,7 @@ fn test_sequence_classification_inference_with_bad_model() { let state = embedding_state(); let encodings = state + .per_model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -98,7 +103,7 @@ fn test_sequence_classification_inference_with_bad_model() { sequence_classification( session_lock, &transform, - &state.model_config, + &state.per_model_input_state.model_config, encodings.clone(), ) .expect("Failed to compute results"); @@ -109,6 +114,7 @@ fn test_token_classification_model() { let state = token_classification_state(); let encodings = state + .per_model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -124,7 +130,7 @@ fn test_token_classification_model() { let results = token_classification( session_lock, &transform, - &state.model_config, + &state.per_model_input_state.model_config, encodings.clone(), ) .expect("Failed to compute results"); @@ -138,6 +144,7 @@ fn test_token_classification_inference_with_bad_model() { let state = sequence_classification_state(); let encodings = state + .per_model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -153,7 +160,7 @@ fn test_token_classification_inference_with_bad_model() { token_classification( session_lock, &transform, - &state.model_config, + &state.per_model_input_state.model_config, encodings.clone(), ) .expect("Failed to compute results"); From 03b84b529f85b051b484c0faef575e76b9411eac Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Tue, 5 May 2026 12:29:02 +0200 Subject: [PATCH 05/21] Reorganize traits --- encoderfile-runtime/src/main.rs | 38 ++++-- encoderfile/benches/postprocessing.rs | 8 +- .../src/common/image_classification.rs | 1 - encoderfile/src/dev_utils/mod.rs | 113 ++++++------------ .../src/inference/image_classification.rs | 8 +- .../src/inference/sequence_classification.rs | 8 +- .../src/inference/token_classification.rs | 8 +- encoderfile/src/runtime/mod.rs | 2 +- encoderfile/src/runtime/state.rs | 66 +++++++++- .../src/services/image_classification.rs | 26 +++- encoderfile/src/services/model_metadata.rs | 2 +- .../src/services/sequence_classification.rs | 2 +- .../src/services/token_classification.rs | 3 +- encoderfile/tests/test_mcp.rs | 2 +- encoderfile/tests/test_model_validation.rs | 13 ++ encoderfile/tests/test_models.rs | 41 ++++++- 16 files changed, 224 insertions(+), 117 deletions(-) diff --git a/encoderfile-runtime/src/main.rs b/encoderfile-runtime/src/main.rs index e8a292be..d598e8db 100644 --- a/encoderfile-runtime/src/main.rs +++ b/encoderfile-runtime/src/main.rs @@ -11,7 +11,8 @@ use encoderfile::{ common::model_type::{ Embedding, SentenceEmbedding, SequenceClassification, TokenClassification, ModelType, }, - runtime::{EncoderfileLoader, EncoderfileState, load_assets}, + common::ModelConfig, + runtime::{EncoderfileLoader, EncoderfileState, load_assets, TextInputState, FeatureExtractorState, ClassifierState}, transport::cli::Cli, }; @@ -29,12 +30,12 @@ async fn main() -> Result<()> { } macro_rules! run_cli { - ($model_type:ident, $cli:expr, $config:expr, $session:expr, $tokenizer:expr, $model_config:expr) => {{ + ($model_type:ident, $cli:expr, $config:expr, $session:expr, $input_state:expr, $task_state:expr) => {{ let state = Arc::new(EncoderfileState::<$model_type>::new( $config, $session, - $tokenizer, - $model_config, + $input_state, + $task_state, )); $cli.command.execute(state).await }}; @@ -47,31 +48,46 @@ async fn entrypoint<'a, R: Read + Seek>(loader: &mut EncoderfileLoader<'a, R>) - let tokenizer = loader.tokenizer()?; let config = loader.encoderfile_config()?; + fn class_task_state(model_config: &ModelConfig) -> ClassifierState { + ClassifierState { + id2label: model_config.id2label.clone(), + label2id: model_config.label2id.clone(), + num_labels: model_config.num_labels, + } + } + match loader.model_type() { - ModelType::Embedding => run_cli!(Embedding, cli, config, session, tokenizer, model_config), + ModelType::Embedding => run_cli!( + Embedding, + cli, + config, + session, + TextInputState { tokenizer, model_config }, + FeatureExtractorState {} + ), ModelType::SequenceClassification => run_cli!( SequenceClassification, cli, config, session, - tokenizer, - model_config + TextInputState { tokenizer, model_config: model_config.clone() }, + class_task_state(&model_config) ), ModelType::TokenClassification => run_cli!( TokenClassification, cli, config, session, - tokenizer, - model_config + TextInputState { tokenizer, model_config: model_config.clone() }, + class_task_state(&model_config) ), ModelType::SentenceEmbedding => run_cli!( SentenceEmbedding, cli, config, session, - tokenizer, - model_config + TextInputState { tokenizer, model_config }, + FeatureExtractorState {} ), ModelType::ImageClassification => panic!("ImageClassification is not yet supported in the CLI"), } diff --git a/encoderfile/benches/postprocessing.rs b/encoderfile/benches/postprocessing.rs index dbb66816..bcd1053e 100644 --- a/encoderfile/benches/postprocessing.rs +++ b/encoderfile/benches/postprocessing.rs @@ -16,7 +16,7 @@ fn main() { #[divan::bench(args = [(8, 16, 384), (16, 128, 768), (64, 512, 1024)])] fn embedding_postprocess(b: Bencher, dim: (usize, usize, usize)) { - let tokenizer = &embedding_state().tokenizer; + let tokenizer = &embedding_state().per_model_input_state.tokenizer; let (batch, tokens, hidden) = dim; // Random embeddings @@ -35,7 +35,7 @@ fn embedding_postprocess(b: Bencher, dim: (usize, usize, usize)) { #[divan::bench(args = [8, 16, 64])] fn sequence_classification_postprocess(b: Bencher, batch: usize) { let state = sequence_classification_state(); - let config = &state.model_config; + let config = &state.per_task_state; let n_labels = config.id2label.clone().unwrap().len(); let mut rng = rand::rng(); @@ -51,10 +51,10 @@ fn sequence_classification_postprocess(b: Bencher, batch: usize) { #[divan::bench(args = [(8, 16), (16, 128), (64, 512)])] fn token_classification_postprocess(b: Bencher, dim: (usize, usize)) { let state = token_classification_state(); - let config = &state.model_config; + let config = &state.per_task_state; let n_labels = config.id2label.clone().unwrap().len(); - let tokenizer = &embedding_state().tokenizer; + let tokenizer = &embedding_state().per_model_input_state.tokenizer; let (batch, tokens) = dim; // Random embeddings diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index 45a1723d..e62147f5 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -80,7 +80,6 @@ pub struct ImageClassificationResult { mod tests { use super::*; use std::fs::File; - use std::env::current_dir; #[test] fn test_image_classification_request_from_read_input() { diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index fa331635..4b5a6faf 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -1,13 +1,21 @@ use crate::{ common::{ - Config, ModelConfig, TokenizerConfig, + Config, TokenizerConfig, model_type::{self, ModelTypeSpec}, }, - runtime::{AppState, EncoderfileState, FeatureExtractorState, ClassifierState, TextInputState, ImageInputState}, + runtime::{ + AppState, + EncoderfileState, + FeatureExtractorState, + ClassifierState, + TextInputState, + ImageInputState, + InputType, + TaskType, + }, }; use ort::session::Session; use parking_lot::Mutex; -use rmcp::model; use std::str::FromStr; use std::{fs::File, io::BufReader}; @@ -16,7 +24,7 @@ const EMBEDDING_DIR: &str = "../models/embedding"; const SEQUENCE_CLASSIFICATION_DIR: &str = "../models/sequence_classification"; const TOKEN_CLASSIFICATION_DIR: &str = "../models/token_classification"; -pub fn get_state(dir: &str) -> AppState +pub fn get_state(dir: &str) -> AppState { let config = Config { name: "my-model".to_string(), @@ -36,17 +44,20 @@ pub fn get_state(dir: &str) -> AppState ).into() } +pub trait TaskTypeFromFile: TaskType { + fn get_task_state(dir: &str) -> Self::TaskState; +} + +pub trait InputTypeFromFile: InputType { + fn get_input_state(dir: &str) -> Self::InputState; +} + pub fn get_reader(dir: &str) -> BufReader { let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); BufReader::new(file) } // Input types -pub trait InputType { - type InputState; - fn get_input_state(dir: &str) -> Self::InputState; -} - fn get_text_input_state(dir: &str) -> TextInputState { let reader = get_reader(dir); let tokenizer = get_tokenizer(dir); @@ -55,85 +66,53 @@ fn get_text_input_state(dir: &str) -> TextInputState { TextInputState { tokenizer, model_config } } -macro_rules! text_input { - ($model_type:ty) => { - impl InputType for $model_type { - type InputState = TextInputState; - fn get_input_state(dir: &str) -> Self::InputState { - get_text_input_state(dir) - } - } - }; -} - -text_input!(model_type::Embedding); -text_input!(model_type::SentenceEmbedding); -text_input!(model_type::SequenceClassification); -text_input!(model_type::TokenClassification); - fn get_image_input_state(dir: &str) -> ImageInputState { let reader = get_reader(dir); serde_json::from_reader(reader).expect("Invalid model config") } -macro_rules! image_input { - ($model_type:ty) => { - impl InputType for $model_type { - type InputState = ImageInputState; +macro_rules! input_state_impl { + ($model_type:ty, $state_fun:ident) => { + impl InputTypeFromFile for $model_type { fn get_input_state(dir: &str) -> Self::InputState { - get_image_input_state(dir) + $state_fun(dir) } } }; } -image_input!(model_type::ImageClassification); +input_state_impl!(model_type::SequenceClassification, get_text_input_state); +input_state_impl!(model_type::TokenClassification, get_text_input_state); +input_state_impl!(model_type::ImageClassification, get_image_input_state); +input_state_impl!(model_type::Embedding, get_text_input_state); +input_state_impl!(model_type::SentenceEmbedding, get_text_input_state); // Task types -pub trait TaskType { - type TaskState; - fn get_task_state(dir: &str) -> Self::TaskState; -} - fn get_class_task_state(dir: &str) -> ClassifierState { let reader = get_reader(dir); serde_json::from_reader(reader).expect("Invalid model config") } -macro_rules! class_task { - ($model_type:ty) => { - impl TaskType for $model_type { - type TaskState = ClassifierState; - fn get_task_state(dir: &str) -> Self::TaskState { - get_class_task_state(dir) - } - } - }; -} - -class_task!(model_type::SequenceClassification); -class_task!(model_type::TokenClassification); -class_task!(model_type::ImageClassification); - fn get_feature_task_state(_dir: &str) -> FeatureExtractorState { FeatureExtractorState {} } -macro_rules! feature_task { - ($model_type:ty) => { - impl TaskType for $model_type { - type TaskState = FeatureExtractorState; +macro_rules! task_state_impl { + ($model_type:ty, $state_fun:ident) => { + impl TaskTypeFromFile for $model_type { fn get_task_state(dir: &str) -> Self::TaskState { - get_feature_task_state(dir) + $state_fun(dir) } } }; } -feature_task!(model_type::Embedding); -feature_task!(model_type::SentenceEmbedding); - +task_state_impl!(model_type::SequenceClassification, get_class_task_state); +task_state_impl!(model_type::TokenClassification, get_class_task_state); +task_state_impl!(model_type::ImageClassification, get_class_task_state); +task_state_impl!(model_type::Embedding, get_feature_task_state); +task_state_impl!(model_type::SentenceEmbedding, get_feature_task_state); @@ -157,22 +136,6 @@ pub fn token_classification_state() -> AppState get_state(TOKEN_CLASSIFICATION_DIR) } -fn get_text_model_config(dir: &str) -> ModelConfig { - let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); - let reader = BufReader::new(file); - - // Deserialize into struct - serde_json::from_reader(reader).expect("Invalid model config") -} - -fn get_image_model_config(dir: &str) -> ImageInputState { - let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); - let reader = BufReader::new(file); - - // Deserialize into struct - serde_json::from_reader(reader).expect("Invalid model config") -} - fn get_tokenizer(dir: &str) -> crate::runtime::TokenizerService { let tokenizer_str = std::fs::read_to_string(format!("{}/{}", dir, "tokenizer.json")) .expect("Tokenizer json not found"); diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index 323414ed..116b70e1 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -12,9 +12,6 @@ pub fn image_classification<'a>( // CHECK if this is a vec of flattened rgb images with num_channels X height X width images: Array4, classes: Vec, - channels: usize, - height: usize, - width: usize, ) -> Result>, ApiError> { let grouped_images = ort::value::TensorRef::from_array_view( &images) @@ -48,4 +45,9 @@ pub fn postprocess(outputs: Array2, classes: Vec) -> Vec( mut session: crate::runtime::Model<'a>, transform: &SequenceClassificationTransform, - config: &ModelConfig, + config: &ClassifierState, encodings: Vec, ) -> Result, ApiError> { let (a_ids, a_mask, a_type_ids) = crate::prepare_text_inputs!(encodings); @@ -35,7 +33,7 @@ pub fn sequence_classification<'a>( #[tracing::instrument(skip_all)] pub fn postprocess( outputs: Array2, - config: &ModelConfig, + config: &ClassifierState, ) -> Vec { outputs .axis_iter(Axis(0)) diff --git a/encoderfile/src/inference/token_classification.rs b/encoderfile/src/inference/token_classification.rs index 5a4ec377..3d5181fa 100644 --- a/encoderfile/src/inference/token_classification.rs +++ b/encoderfile/src/inference/token_classification.rs @@ -1,7 +1,5 @@ use crate::{ - common::{ModelConfig, TokenClassification, TokenClassificationResult, TokenInfo}, - error::ApiError, - transforms::{Postprocessor, TokenClassificationTransform}, + common::{ModelConfig, TokenClassification, TokenClassificationResult, TokenInfo}, error::ApiError, runtime::ClassifierState, transforms::{Postprocessor, TokenClassificationTransform} }; use ndarray::{Array3, Axis, Ix3}; use ndarray_stats::QuantileExt; @@ -11,7 +9,7 @@ use tokenizers::Encoding; pub fn token_classification<'a>( mut session: crate::runtime::Model<'a>, transform: &TokenClassificationTransform, - config: &ModelConfig, + config: &ClassifierState, encodings: Vec, ) -> Result, ApiError> { let (a_ids, a_mask, a_type_ids) = crate::prepare_text_inputs!(encodings); @@ -36,7 +34,7 @@ pub fn token_classification<'a>( pub fn postprocess( outputs: Array3, encodings: Vec, - config: &ModelConfig, + config: &ClassifierState, ) -> Vec { let mut predictions = Vec::new(); diff --git a/encoderfile/src/runtime/mod.rs b/encoderfile/src/runtime/mod.rs index 135016a4..02089c8c 100644 --- a/encoderfile/src/runtime/mod.rs +++ b/encoderfile/src/runtime/mod.rs @@ -6,7 +6,7 @@ mod state; mod tokenizer; pub use loader::{EncoderfileLoader, load_assets}; -pub use state::{AppState, EncoderfileState, ClassifierState, FeatureExtractorState, ImageInputState, TextInputState}; +pub use state::{AppState, EncoderfileState, ClassifierState, FeatureExtractorState, ImageInputState, TextInputState, InputType, TaskType}; pub use tokenizer::TokenizerService; pub type Model<'a> = MutexGuard<'a, Session>; diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index c13e6351..51b8ccaf 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -5,11 +5,49 @@ use ort::session::Session; use parking_lot::Mutex; use crate::{ - common::{Config, ModelConfig, model_type::{ModelType, ModelTypeSpec}}, dev_utils::{InputType, TaskType}, runtime::TokenizerService, transforms::DEFAULT_LIBS + common::{Config, ModelConfig, model_type::{ModelType, ModelTypeSpec, self}}, runtime::TokenizerService, transforms::DEFAULT_LIBS }; pub type AppState = Arc>; +pub trait TaskType { + type TaskState; + // fn get_task_state(dir: &str) -> Self::TaskState; +} + +pub trait InputType { + type InputState; + // fn get_input_state(dir: &str) -> Self::InputState; +} + +macro_rules! input_state_impl { + ($model_type:ty, $state_type:ty) => { + impl InputType for $model_type { + type InputState = $state_type; + } + }; +} + +input_state_impl!(model_type::Embedding, TextInputState); +input_state_impl!(model_type::SentenceEmbedding, TextInputState); +input_state_impl!(model_type::SequenceClassification, TextInputState); +input_state_impl!(model_type::TokenClassification, TextInputState); +input_state_impl!(model_type::ImageClassification, ImageInputState); + +macro_rules! task_state_impl { + ($model_type:ty, $state_type:ty) => { + impl TaskType for $model_type { + type TaskState = $state_type; + } + }; +} + +task_state_impl!(model_type::SequenceClassification, ClassifierState); +task_state_impl!(model_type::TokenClassification, ClassifierState); +task_state_impl!(model_type::ImageClassification, ClassifierState); +task_state_impl!(model_type::Embedding, FeatureExtractorState); +task_state_impl!(model_type::SentenceEmbedding, FeatureExtractorState); + pub struct TextInputState { pub tokenizer: TokenizerService, @@ -29,6 +67,32 @@ pub struct ClassifierState { pub label2id: Option>, pub num_labels: Option, } +impl ClassifierState { + pub fn id2label(&self, id: u32) -> Option<&str> { + self.id2label.as_ref()?.get(&id).map(|s| s.as_str()) + } + + pub fn label2id(&self, label: &str) -> Option { + self.label2id.as_ref()?.get(label).copied() + } + + pub fn num_labels(&self) -> Option { + if self.num_labels.is_some() { + return self.num_labels; + } + + if let Some(id2label) = &self.id2label { + return Some(id2label.len()); + } + + if let Some(label2id) = &self.label2id { + return Some(label2id.len()); + } + + None + } +} + #[derive(Debug, Serialize, Deserialize, Clone)] pub struct FeatureExtractorState {} diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 54f47eba..d7b48ba4 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -9,7 +9,6 @@ use crate::{ error::ApiError, runtime::AppState, - runtime::ImageInputState, }; use image::{DynamicImage, GenericImageView}; @@ -59,3 +58,28 @@ impl Inference for AppState }) } } + +#[cfg(test)] +mod tests { + use crate::common::model_type::ImageClassification; + use crate::dev_utils; + use crate::common::ImageClassificationRequest; + use crate::common::FromReadInput; + use std::fs::File; + use super::*; + + #[test] + fn test_image_classification_request_from_file() { + let state = dev_utils::get_state::("../models/image_classification"); + let mut file = File::open("../test-pictures/w3c_home.jpg").expect("Failed to open test image"); + let file_vec = vec![&mut file]; + let request = ImageClassificationRequest::from_read_input(file_vec).expect("Failed to create request from read input"); + let response = state.inference(request).expect("Inference failed"); + assert_eq!(response.results.len(), 1); + assert_eq!(response.results[0].labels.len(), 2); + assert_eq!(response.results[0].labels[0].label, "dummy1"); + assert_eq!(response.results[0].labels[0].score, 0.9); + assert_eq!(response.results[0].labels[1].label, "dummy2"); + assert_eq!(response.results[0].labels[1].score, 0.1); + } +} \ No newline at end of file diff --git a/encoderfile/src/services/model_metadata.rs b/encoderfile/src/services/model_metadata.rs index 77398c4b..483ca753 100644 --- a/encoderfile/src/services/model_metadata.rs +++ b/encoderfile/src/services/model_metadata.rs @@ -1,7 +1,7 @@ use std::collections::HashMap; use crate::{ - common::{GetModelMetadataResponse, model_type::{ModelType, ModelTypeSpec}}, dev_utils::TaskType, dev_utils::InputType, runtime::AppState + common::{GetModelMetadataResponse, model_type::{ModelType, ModelTypeSpec}}, runtime::{AppState, TaskType, InputType}, }; pub trait ClassifierMetadata { diff --git a/encoderfile/src/services/sequence_classification.rs b/encoderfile/src/services/sequence_classification.rs index 09b7079e..4911afbe 100644 --- a/encoderfile/src/services/sequence_classification.rs +++ b/encoderfile/src/services/sequence_classification.rs @@ -24,7 +24,7 @@ impl Inference for AppState let results = inference::sequence_classification::sequence_classification( self.session.lock(), &transform, - &self.per_model_input_state.model_config, + &self.per_task_state, encodings, )?; diff --git a/encoderfile/src/services/token_classification.rs b/encoderfile/src/services/token_classification.rs index 75bcacf7..0d4d5a62 100644 --- a/encoderfile/src/services/token_classification.rs +++ b/encoderfile/src/services/token_classification.rs @@ -3,7 +3,6 @@ use crate::{ error::ApiError, inference, runtime::AppState, - runtime::TextInputState, transforms::TokenClassificationTransform, }; @@ -27,7 +26,7 @@ impl Inference for AppState let results = inference::token_classification::token_classification( session, &transform, - &self.per_model_input_state.model_config, + &self.per_task_state, encodings, )?; diff --git a/encoderfile/tests/test_mcp.rs b/encoderfile/tests/test_mcp.rs index 6cd34824..8608f5b2 100644 --- a/encoderfile/tests/test_mcp.rs +++ b/encoderfile/tests/test_mcp.rs @@ -5,7 +5,7 @@ use encoderfile::transport::mcp::McpRouter; use tokio::net::TcpListener; use tokio::sync::oneshot; use tower_http::trace::DefaultOnResponse; -use encoderfile::dev_utils::{InputType, TaskType}; +use encoderfile::runtime::{InputType, TaskType}; async fn run_mcp( addr: String, diff --git a/encoderfile/tests/test_model_validation.rs b/encoderfile/tests/test_model_validation.rs index e70c023b..11d819c6 100644 --- a/encoderfile/tests/test_model_validation.rs +++ b/encoderfile/tests/test_model_validation.rs @@ -45,3 +45,16 @@ pub fn test_sequence_classification() { .is_ok() ); } + +#[test] +pub fn test_image_classification() { + let path = PathBuf::from("../models/image_classification/model.onnx"); + + assert!(ModelType::ImageClassification.validate_model(&path).is_ok()); + assert!( + ModelType::TokenClassification + .validate_model(&path) + .is_err() + ); +} + diff --git a/encoderfile/tests/test_models.rs b/encoderfile/tests/test_models.rs index fbb25ab2..c95840cc 100644 --- a/encoderfile/tests/test_models.rs +++ b/encoderfile/tests/test_models.rs @@ -4,7 +4,7 @@ use encoderfile::inference::{ token_classification::token_classification, }; use encoderfile::transforms::{DEFAULT_LIBS, Transform}; -use encoderfile::dev_utils::{InputType, TaskType}; +use encoderfile::runtime::{InputType, TaskType}; #[test] fn test_embedding_model() { @@ -73,7 +73,7 @@ fn test_sequence_classification_model() { let results = sequence_classification( session_lock, &transform, - &state.per_model_input_state.model_config, + &state.per_task_state, encodings.clone(), ) .expect("Failed to compute results"); @@ -81,6 +81,8 @@ fn test_sequence_classification_model() { assert!(results.len() == encodings.len()); } +// FIXME doesn't compile +/* #[test] #[should_panic] fn test_sequence_classification_inference_with_bad_model() { @@ -103,11 +105,12 @@ fn test_sequence_classification_inference_with_bad_model() { sequence_classification( session_lock, &transform, - &state.per_model_input_state.model_config, + &state.per_task_state, encodings.clone(), ) .expect("Failed to compute results"); } +*/ #[test] fn test_token_classification_model() { @@ -130,7 +133,7 @@ fn test_token_classification_model() { let results = token_classification( session_lock, &transform, - &state.per_model_input_state.model_config, + &state.per_task_state, encodings.clone(), ) .expect("Failed to compute results"); @@ -160,8 +163,36 @@ fn test_token_classification_inference_with_bad_model() { token_classification( session_lock, &transform, - &state.per_model_input_state.model_config, + &state.per_task_state, encodings.clone(), ) .expect("Failed to compute results"); } + +#[test] +fn test_image_classification_model() { + // TODO + /* + let state = embedding_state(); + + let encodings = state + .per_model_input_state + .tokenizer + .encode_text(vec![ + "hello world".to_string(), + "the quick brown fox jumps over the lazy dog".to_string(), + ]) + .expect("Failed to encode text"); + + let session_lock = state.session.lock(); + + let transform = + Transform::new(DEFAULT_LIBS.to_vec(), None).expect("Failed to create_transform"); + + let results = + embedding(session_lock, &transform, encodings.clone()).expect("Failed to compute results"); + + assert!(results.len() == encodings.len()); + */ +} + From a24581d95fbb4e998cbda25eb701ed6867b634ec Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Tue, 5 May 2026 17:55:47 +0200 Subject: [PATCH 06/21] Fix inference for image classification --- .../src/common/image_classification.rs | 4 +- encoderfile/src/dev_utils/mod.rs | 8 +- encoderfile/src/runtime/state.rs | 4 +- .../src/services/image_classification.rs | 87 ++++++++++++------- 4 files changed, 67 insertions(+), 36 deletions(-) diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index e62147f5..af7728a1 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -64,13 +64,13 @@ pub struct ImageClassificationResponse { pub metadata: Option>, } -#[derive(Debug, Serialize, Deserialize, ToSchema)] +#[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] pub struct ImageLabelScore { pub label: String, pub score: f32, } -#[derive(Debug, Serialize, Deserialize, ToSchema)] +#[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] pub struct ImageClassificationResult { pub labels: Vec, } diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index 4b5a6faf..fc2fffa0 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -68,7 +68,13 @@ fn get_text_input_state(dir: &str) -> TextInputState { fn get_image_input_state(dir: &str) -> ImageInputState { let reader = get_reader(dir); - serde_json::from_reader(reader).expect("Invalid model config") + let incomplete_state: ImageInputState = serde_json::from_reader(reader).expect("Invalid model config"); + ImageInputState { + num_channels: incomplete_state.num_channels, + height: incomplete_state.height.or(Some(incomplete_state.image_size)), + width: incomplete_state.width.or(Some(incomplete_state.image_size)), + image_size: incomplete_state.image_size, + } } macro_rules! input_state_impl { diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 51b8ccaf..8883ae01 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -56,8 +56,8 @@ pub struct TextInputState { #[derive(Debug, Serialize, Deserialize, Clone)] pub struct ImageInputState { pub num_channels: usize, - pub height: usize, - pub width: usize, + pub height: Option, + pub width: Option, pub image_size: usize, } diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index d7b48ba4..5337a28a 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -10,13 +10,16 @@ use crate::{ error::ApiError, runtime::AppState, }; - -use image::{DynamicImage, GenericImageView}; +use image::RgbImage; +use ndarray::{Array2, Array4, Ix2, Axis}; use super::inference::Inference; +use crate::inference::image_classification::image_classification; // No service impl yet +const DEFAULT_FILTER_TYPE: image::imageops::FilterType = image::imageops::FilterType::Nearest; + impl Inference for AppState { type Input = ImageClassificationRequest; @@ -25,35 +28,40 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); - // FIXME - /* // convert input image into flattened rbg - let image = image::load_from_memory(&request.image_info.image_bytes)?; - let (width, height) = image.dimensions(); - let image = image.to_rgb8(); - let mut flattened_rgb = Vec::with_capacity((width * height * 3) as usize); - for pixel in image.pixels() { - flattened_rgb.push(pixel[0] as f32); - flattened_rgb.push(pixel[1] as f32); - flattened_rgb.push(pixel[2] as f32); - } + let images: Vec = (&request.images).into_iter().map(|image_info| { + let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); + img + .resize_exact( + self.per_model_input_state.width.unwrap() as u32, + self.per_model_input_state.height.unwrap() as u32, + DEFAULT_FILTER_TYPE + ) + .to_rgb8() + }).collect(); + let images_array = Array4::from_shape_vec( + ( + request.images.len().clone(), + self.per_model_input_state.num_channels, + self.per_model_input_state.height.unwrap(), + self.per_model_input_state.width.unwrap() + ), + images + .into_iter() + .flat_map(|img| img.into_raw().into_iter().map(|pixel| pixel as f32)) + .collect() + ).expect("Failed to convert images to ndarray"); - let labels_batch = inference::image_classification::image_classification(self.session.lock(), &transform, encodings)?; - */ - - let dummy_labels = vec![ - ImageLabelScore { - label: "dummy1".to_string(), - score: 0.9, - }, - ImageLabelScore { - label: "dummy2".to_string(), - score: 0.1, - }, - ]; + let labels_batch = image_classification( + self.session.lock(), + images_array, + // COMMENT having optional fields complicates things later on, but otoh + // it allows models with variations of these fields + self.per_task_state.label2id.clone().unwrap().keys().cloned().collect())?; + print!("labels_batch: {:?}", &labels_batch); Ok(ImageClassificationResponse { - results: vec![ImageClassificationResult { labels: dummy_labels }], + results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), metadata: request.metadata, }) } @@ -66,10 +74,27 @@ mod tests { use crate::common::ImageClassificationRequest; use crate::common::FromReadInput; use std::fs::File; + use std::sync::Once; use super::*; + fn init_tracing() { + static TRACING: Once = Once::new(); + + TRACING.call_once(|| { + let _ = tracing_subscriber::fmt() + .with_env_filter( + tracing_subscriber::EnvFilter::try_from_default_env() + .unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("debug,ort=warn")), + ) + .with_test_writer() + .try_init(); + }); + } + #[test] fn test_image_classification_request_from_file() { + init_tracing(); + let state = dev_utils::get_state::("../models/image_classification"); let mut file = File::open("../test-pictures/w3c_home.jpg").expect("Failed to open test image"); let file_vec = vec![&mut file]; @@ -77,9 +102,9 @@ mod tests { let response = state.inference(request).expect("Inference failed"); assert_eq!(response.results.len(), 1); assert_eq!(response.results[0].labels.len(), 2); - assert_eq!(response.results[0].labels[0].label, "dummy1"); - assert_eq!(response.results[0].labels[0].score, 0.9); - assert_eq!(response.results[0].labels[1].label, "dummy2"); - assert_eq!(response.results[0].labels[1].score, 0.1); + assert_eq!(response.results[0].labels[0].label, "normal"); + assert_eq!(response.results[0].labels[0].score, 1.5378942); + assert_eq!(response.results[0].labels[1].label, "nsfw"); + assert_eq!(response.results[0].labels[1].score, -1.6556994); } } \ No newline at end of file From bfdc5f402ace6486603bccb6b8114d6fc67c5ca2 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Thu, 7 May 2026 14:55:21 +0200 Subject: [PATCH 07/21] Complete e2e process (but not yet correct) --- Cargo.lock | 24 ++ encoderfile-runtime/src/main.rs | 50 +-- encoderfile/Cargo.toml | 1 + encoderfile/build.rs | 2 + encoderfile/proto/image_classification.proto | 21 +- encoderfile/proto/image_segmentation.proto | 11 +- encoderfile/proto/image_types.proto | 16 + encoderfile/proto/object_detection.proto | 21 +- encoderfile/src/builder/builder.rs | 17 +- encoderfile/src/builder/config.rs | 6 +- .../src/common/image_classification.rs | 35 +- encoderfile/src/common/model_config.rs | 33 +- encoderfile/src/dev_utils/mod.rs | 4 +- encoderfile/src/format/assets/kind.rs | 50 ++- encoderfile/src/format/codec/encoder.rs | 2 +- .../src/generated/image_classification.rs | 32 ++ encoderfile/src/generated/mod.rs | 1 + .../src/inference/image_classification.rs | 15 +- encoderfile/src/runtime/mod.rs | 18 +- encoderfile/src/runtime/state.rs | 123 +++++-- .../src/services/image_classification.rs | 23 +- encoderfile/src/transport/cli.rs | 125 ++++++- encoderfile/src/transport/grpc/mod.rs | 12 +- encoderfile/src/transport/http/example.md | 316 ++++++++++++++++++ encoderfile/src/transport/http/mod.rs | 1 + .../src/transport/http/multipart_openapi.rs | 282 ++++++++++++++++ test_img_class_config.yml | 6 + 27 files changed, 1099 insertions(+), 148 deletions(-) create mode 100644 encoderfile/proto/image_types.proto create mode 100644 encoderfile/src/generated/image_classification.rs create mode 100644 encoderfile/src/transport/http/example.md create mode 100644 encoderfile/src/transport/http/multipart_openapi.rs create mode 100644 test_img_class_config.yml diff --git a/Cargo.lock b/Cargo.lock index 84803897..58105093 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -280,6 +280,7 @@ dependencies = [ "matchit", "memchr", "mime", + "multer", "percent-encoding", "pin-project-lite", "serde_core", @@ -2185,6 +2186,23 @@ dependencies = [ "pxfm", ] +[[package]] +name = "multer" +version = "3.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "83e87776546dc87511aa5ee218730c92b666d7264ab6ed41f9d215af9cd5224b" +dependencies = [ + "bytes", + "encoding_rs", + "futures-util", + "http", + "httparse", + "memchr", + "mime", + "spin", + "version_check", +] + [[package]] name = "multimap" version = "0.10.1" @@ -3780,6 +3798,12 @@ dependencies = [ "winapi", ] +[[package]] +name = "spin" +version = "0.9.8" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67" + [[package]] name = "spm_precompiled" version = "0.1.4" diff --git a/encoderfile-runtime/src/main.rs b/encoderfile-runtime/src/main.rs index d598e8db..07dbe0f7 100644 --- a/encoderfile-runtime/src/main.rs +++ b/encoderfile-runtime/src/main.rs @@ -1,19 +1,16 @@ use parking_lot::Mutex; use std::{ - fs::File, - io::{BufReader, Read, Seek}, - sync::Arc, + error::Error, fs::File, io::{BufReader, Read, Seek, Error as IOError, ErrorKind}, sync::Arc, }; use anyhow::Result; use clap::Parser; use encoderfile::{ - common::model_type::{ - Embedding, SentenceEmbedding, SequenceClassification, TokenClassification, ModelType, - }, - common::ModelConfig, - runtime::{EncoderfileLoader, EncoderfileState, load_assets, TextInputState, FeatureExtractorState, ClassifierState}, - transport::cli::Cli, + common::{ModelConfig, model_type::{ + Embedding, ImageClassification, ModelType, SentenceEmbedding, SequenceClassification, TokenClassification + }}, + runtime::{ClassifierState, EncoderfileLoader, EncoderfileState, FeatureExtractorState, ImageInputState, TextInputState, load_assets}, + transport::cli::{TextCli, ImageCli}, }; #[tokio::main] @@ -42,13 +39,14 @@ macro_rules! run_cli { } async fn entrypoint<'a, R: Read + Seek>(loader: &mut EncoderfileLoader<'a, R>) -> Result<()> { - let cli = Cli::parse(); let session = Mutex::new(loader.session()?); let model_config = loader.model_config()?; - let tokenizer = loader.tokenizer()?; let config = loader.encoderfile_config()?; + // TODO clear out lifetimes in state and loader to avoid fn class_task_state(model_config: &ModelConfig) -> ClassifierState { + // if num_labels, make a vector of labels + // if id2label, make sure it's 0..n-1 ClassifierState { id2label: model_config.id2label.clone(), label2id: model_config.label2id.clone(), @@ -59,36 +57,48 @@ async fn entrypoint<'a, R: Read + Seek>(loader: &mut EncoderfileLoader<'a, R>) - match loader.model_type() { ModelType::Embedding => run_cli!( Embedding, - cli, + TextCli::parse(), config, session, - TextInputState { tokenizer, model_config }, + TextInputState { tokenizer: loader.tokenizer()?, model_config }, FeatureExtractorState {} ), ModelType::SequenceClassification => run_cli!( SequenceClassification, - cli, + TextCli::parse(), config, session, - TextInputState { tokenizer, model_config: model_config.clone() }, + TextInputState { tokenizer: loader.tokenizer()?, model_config: model_config.clone() }, class_task_state(&model_config) ), ModelType::TokenClassification => run_cli!( TokenClassification, - cli, + TextCli::parse(), config, session, - TextInputState { tokenizer, model_config: model_config.clone() }, + TextInputState { tokenizer: loader.tokenizer()?, model_config: model_config.clone() }, class_task_state(&model_config) ), ModelType::SentenceEmbedding => run_cli!( SentenceEmbedding, - cli, + TextCli::parse(), config, session, - TextInputState { tokenizer, model_config }, + TextInputState { tokenizer: loader.tokenizer()?, model_config }, FeatureExtractorState {} ), - ModelType::ImageClassification => panic!("ImageClassification is not yet supported in the CLI"), + ModelType::ImageClassification => run_cli!( + ImageClassification, + ImageCli::parse(), + config, + session, + ImageInputState { + height: model_config.height(), + width: model_config.width(), + num_channels: model_config.num_channels().ok_or(IOError::new(ErrorKind::InvalidData, "Missing required configuration field"))?, + image_size: model_config.image_size, + }, + class_task_state(&model_config) + ), } } diff --git a/encoderfile/Cargo.toml b/encoderfile/Cargo.toml index a3e0621b..2ea2eac3 100644 --- a/encoderfile/Cargo.toml +++ b/encoderfile/Cargo.toml @@ -220,6 +220,7 @@ optional = true [dependencies.axum] version = "0.8.6" +features = ["multipart"] optional = true [dependencies.axum-server] diff --git a/encoderfile/build.rs b/encoderfile/build.rs index 7ecdf96c..9f301dd2 100644 --- a/encoderfile/build.rs +++ b/encoderfile/build.rs @@ -12,6 +12,7 @@ fn main() -> Result<(), Box> { "proto/sequence_classification.proto", "proto/token_classification.proto", "proto/sentence_embedding.proto", + "proto/image_classification.proto", "proto/manifest.proto", ], &[ @@ -19,6 +20,7 @@ fn main() -> Result<(), Box> { "proto/sequence_classification", "proto/token_classification", "proto/sentence_embedding", + "proto/image_classification", "proto/manifest", ], )?; diff --git a/encoderfile/proto/image_classification.proto b/encoderfile/proto/image_classification.proto index 523b8d47..6283cfce 100644 --- a/encoderfile/proto/image_classification.proto +++ b/encoderfile/proto/image_classification.proto @@ -2,33 +2,20 @@ syntax = "proto3"; package encoderfile.image_classification; -import "proto/token.proto"; import "proto/metadata.proto"; +import "proto/image_types.proto"; -service ImageClassification { +service ImageClassificationInference { rpc Predict(ImageClassificationRequest) returns (ImageClassificationResponse); rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); } -message ImageInput { - bytes image = 1; -} - message ImageClassificationRequest { - repeated ImageInput inputs = 1; + repeated encoderfile.image_types.ImageInput inputs = 1; map metadata = 11; } -message ImageLabelScore { - string label = 1; - float score = 2; -} - -message ImageLabels { - repeated ImageLabelScore labels = 1; -} - message ImageClassificationResponse { - repeated ImageLabels labels_batch = 1; + repeated encoderfile.image_types.ImageLabels labels_batch = 1; map metadata = 11; } diff --git a/encoderfile/proto/image_segmentation.proto b/encoderfile/proto/image_segmentation.proto index a74c8287..bea6d600 100644 --- a/encoderfile/proto/image_segmentation.proto +++ b/encoderfile/proto/image_segmentation.proto @@ -10,19 +10,14 @@ service ImageSegmentation { rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); } -message ImageInput { - bytes image = 1; -} - message ImageSegmentationRequest { - repeated ImageInput images = 1; + repeated encoderfile.image_types.ImageInput images = 1; map metadata = 11; } message ImageSegment { - string label = 1; - optional float score = 2; - bytes mask = 4; + encoderfile.image_types.ImageLabelScore label = 1; + bytes mask = 2; } message ImageSegments { diff --git a/encoderfile/proto/image_types.proto b/encoderfile/proto/image_types.proto new file mode 100644 index 00000000..ae42d76f --- /dev/null +++ b/encoderfile/proto/image_types.proto @@ -0,0 +1,16 @@ +syntax = "proto3"; + +package encoderfile.image_classification; + +message ImageInput { + bytes image = 1; +} + +message ImageLabelScore { + string label = 1; + optional float score = 2; +} + +message ImageLabels { + repeated ImageLabelScore labels = 1; +} diff --git a/encoderfile/proto/object_detection.proto b/encoderfile/proto/object_detection.proto index 887df885..a55b4d85 100644 --- a/encoderfile/proto/object_detection.proto +++ b/encoderfile/proto/object_detection.proto @@ -10,29 +10,24 @@ service ObjectDetection { rpc GetModelMetadata(encoderfile.metadata.GetModelMetadataRequest) returns (encoderfile.metadata.GetModelMetadataResponse); } -message ImageInput { - bytes image = 1; -} - message ObjectDetectionRequest { - repeated bytes images = 1; + repeated encoderfile.image_types.ImageInput inputs = 1; map metadata = 11; } message ImageBoundingBox { - string label = 1; - optional float score = 2; - xmin int32 = 3; - xmax int32 = 4; - ymin int32 = 5; - ymax int32 = 6; + encoderfile.image_types.ImageLabelScore label = 1; + xmin int32 = 2; + xmax int32 = 3; + ymin int32 = 4; + ymax int32 = 5; } message ImageBoundingBoxes { - repeated ImageBoundingBox boxes = 1; + repeated ImageBoundingBox box = 1; } message ObjectDetectionResponse { - repeated ImageBoundingBoxes boxes_batch = 1; + repeated ImageBoundingBoxes boxes = 1; map metadata = 11; } diff --git a/encoderfile/src/builder/builder.rs b/encoderfile/src/builder/builder.rs index 00a819ae..f87e236d 100644 --- a/encoderfile/src/builder/builder.rs +++ b/encoderfile/src/builder/builder.rs @@ -17,8 +17,10 @@ use crate::{ codec::EncoderfileCodec, }, generated::manifest::Backend, + runtime::{InputType, Input} }; use anyhow::{Context, Result}; +use ort::session::input; use serde::{Deserialize, Serialize}; #[derive(Debug, Serialize, Deserialize)] @@ -27,6 +29,11 @@ pub struct EncoderfileBuilder { pub config: BuildConfig, } +pub fn validate(input: &Input) -> Result<()> { + Ok(()) +} + + impl EncoderfileBuilder { pub fn new(config: BuildConfig) -> EncoderfileBuilder { Self { config } @@ -90,10 +97,12 @@ impl EncoderfileBuilder { } // validate tokenizer - let tokenizer_asset = - crate::builder::tokenizer::validate_tokenizer(&self.config.encoderfile)?; - planned_assets.push(tokenizer_asset); - terminal::success("Tokenizer validated"); + if self.config.encoderfile.model_type.input_type() == crate::runtime::Input::Text { + let tokenizer_asset = + crate::builder::tokenizer::validate_tokenizer(&self.config.encoderfile)?; + planned_assets.push(tokenizer_asset); + terminal::success("Tokenizer validated"); + } // initialize final binary terminal::info("Writing encoderfile..."); diff --git a/encoderfile/src/builder/config.rs b/encoderfile/src/builder/config.rs index 6281cc70..b967401e 100644 --- a/encoderfile/src/builder/config.rs +++ b/encoderfile/src/builder/config.rs @@ -1,4 +1,6 @@ use crate::common::{Config as EmbeddedConfig, LuaLibs, ModelConfig, model_type::ModelType}; +use crate::runtime::TaskType; +use crate::runtime::InputType; use anyhow::{Context, Result, bail}; use schemars::JsonSchema; use std::string::String; @@ -24,7 +26,7 @@ pub struct BuildConfig { pub encoderfile: EncoderfileConfig, } -pub const DEFAULT_VERSION: &str = "0.1.0"; +pub const DEFAULT_VERSION: &str = "0.2.0"; pub const CONFIG_FILE_NOT_FOUND_MSG: &str = "Encoderfile config not found"; @@ -271,6 +273,8 @@ pub enum ModelPath { }, } + + impl ModelPath { fn resolve( &self, diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index af7728a1..120e0e6a 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -5,7 +5,7 @@ use anyhow::Result; use crate::common::FromReadInput; use image::ImageFormat; use bytes::Bytes; - +use crate::transport::http::multipart_openapi::{FromMultipart, MultipartApiError}; #[derive(Debug, Serialize, Deserialize)] pub struct ImageInfo { @@ -57,6 +57,37 @@ impl FromReadInput for ImageClassificationRequest { } } +impl FromMultipart for ImageClassificationRequest { + fn from_multipart( + payload: serde_json::Value, + attachments: Vec<(Option, Option, bytes::Bytes)>, + ) -> Result { + let images = attachments + .into_iter() + .map(|(_file_name, _content_type, image_bytes)| { + let format = image::guess_format(&image_bytes) + .map_err(|e| MultipartApiError::RequestConstruction( + format!("Failed to detect image format: {}", e) + ))?; + Ok(ImageInfo { + image_bytes, + image_format: format, + }) + }) + .collect::, _>>()?; + + let metadata = if payload.is_null() || payload == serde_json::json!({}) { + Some(HashMap::default()) + } else { + serde_json::from_value(payload) + .ok() + .or(Some(HashMap::default())) + }; + + Ok(Self { images, metadata }) + } +} + #[derive(Debug, Serialize, Deserialize, ToSchema, utoipa::ToResponse)] pub struct ImageClassificationResponse { pub results: Vec, @@ -76,7 +107,6 @@ pub struct ImageClassificationResult { } #[cfg(test)] - mod tests { use super::*; use std::fs::File; @@ -92,4 +122,3 @@ mod tests { assert!(!request.images[0].image_bytes.is_empty()); } } - diff --git a/encoderfile/src/common/model_config.rs b/encoderfile/src/common/model_config.rs index 3d66e558..2e9d08c7 100644 --- a/encoderfile/src/common/model_config.rs +++ b/encoderfile/src/common/model_config.rs @@ -8,6 +8,10 @@ pub struct ModelConfig { pub num_labels: Option, pub id2label: Option>, pub label2id: Option>, + pub height: Option, + pub width: Option, + pub image_size: Option, + pub num_channels: Option, } // TODO add image handling metadata @@ -21,13 +25,13 @@ impl ModelConfig { } pub fn num_labels(&self) -> Option { - if self.num_labels.is_some() { - return self.num_labels; + if let Some(num_labels) = self.num_labels { + return Some(num_labels); } if let Some(id2label) = &self.id2label { return Some(id2label.len()); - } + } if let Some(label2id) = &self.label2id { return Some(label2id.len()); @@ -35,6 +39,17 @@ impl ModelConfig { None } + pub fn height(&self) -> Option { + self.height.or(self.image_size) + } + + pub fn width(&self) -> Option { + self.width.or(self.image_size) + } + + pub fn num_channels(&self) -> Option { + self.num_channels + } } #[cfg(test)] @@ -60,6 +75,10 @@ mod tests { num_labels: Some(3), id2label: Some(id2label.clone()), label2id: Some(label2id.clone()), + height: None, + width: None, + image_size: None, + num_channels: None, }; assert_eq!(config.num_labels(), Some(3)); @@ -69,6 +88,10 @@ mod tests { num_labels: None, id2label: Some(id2label.clone()), label2id: Some(label2id.clone()), + height: None, + width: None, + image_size: None, + num_channels: None, }; assert_eq!(config.num_labels(), Some(3)); @@ -78,6 +101,10 @@ mod tests { num_labels: None, id2label: None, label2id: Some(label2id.clone()), + height: None, + width: None, + image_size: None, + num_channels: None, }; assert_eq!(config.num_labels(), Some(3)); diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index fc2fffa0..9eefeecd 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -71,8 +71,8 @@ fn get_image_input_state(dir: &str) -> ImageInputState { let incomplete_state: ImageInputState = serde_json::from_reader(reader).expect("Invalid model config"); ImageInputState { num_channels: incomplete_state.num_channels, - height: incomplete_state.height.or(Some(incomplete_state.image_size)), - width: incomplete_state.width.or(Some(incomplete_state.image_size)), + height: incomplete_state.height.or(incomplete_state.image_size), + width: incomplete_state.width.or(incomplete_state.image_size), image_size: incomplete_state.image_size, } } diff --git a/encoderfile/src/format/assets/kind.rs b/encoderfile/src/format/assets/kind.rs index 13723957..9ea67f32 100644 --- a/encoderfile/src/format/assets/kind.rs +++ b/encoderfile/src/format/assets/kind.rs @@ -1,4 +1,4 @@ -use crate::common::model_type::ModelTypeSpec; +use crate::{common::model_type::ModelTypeSpec, runtime::{Input, InputType, Task, TaskType}}; /// Identifies the semantic role of an embedded artifact. /// @@ -50,27 +50,43 @@ impl AssetKind { ]; } -pub trait AssetPolicySpec: ModelTypeSpec { - fn required_assets() -> &'static [AssetKind]; - fn optional_assets() -> &'static [AssetKind]; +pub trait AssetPolicySpec: ModelTypeSpec + InputType + TaskType { + fn required_assets() -> &'static [AssetKind] { + match (Self::input_type(), Self::task_type()) { + (Input::Text, Task::Classification) => &[ + AssetKind::ModelWeights, + AssetKind::ModelConfig, + AssetKind::Tokenizer, + ], + (Input::Text, Task::FeatureExtraction) => &[ + AssetKind::ModelWeights, + AssetKind::ModelConfig, + AssetKind::Tokenizer, + ], + (Input::Image, Task::Classification) => &[ + AssetKind::ModelWeights, + AssetKind::ModelConfig, + ], + (Input::Image, Task::FeatureExtraction) => &[ + AssetKind::ModelWeights, + AssetKind::ModelConfig, + ], + } + } + fn optional_assets() -> &'static [AssetKind] { + match (Self::input_type(), Self::task_type()) { + (Input::Text, Task::Classification) => &[AssetKind::Transform], + (Input::Text, Task::FeatureExtraction) => &[AssetKind::Transform], + (Input::Image, Task::Classification) => &[AssetKind::Transform], + (Input::Image, Task::FeatureExtraction) => &[AssetKind::Transform], + } + } } macro_rules! asset_policy_spec { // Huggingface-style encoders (Encoder, $model_type:ident) => { - impl AssetPolicySpec for crate::common::model_type::$model_type { - fn required_assets() -> &'static [AssetKind] { - &[ - AssetKind::ModelWeights, - AssetKind::ModelConfig, - AssetKind::Tokenizer, - ] - } - - fn optional_assets() -> &'static [AssetKind] { - &[AssetKind::Transform] - } - } + impl AssetPolicySpec for crate::common::model_type::$model_type {} }; } diff --git a/encoderfile/src/format/codec/encoder.rs b/encoderfile/src/format/codec/encoder.rs index bd3e4def..96c7ab11 100644 --- a/encoderfile/src/format/codec/encoder.rs +++ b/encoderfile/src/format/codec/encoder.rs @@ -9,7 +9,7 @@ use crate::{ assets::{AssetPlan, AssetPolicySpec}, footer::EncoderfileFooter, }, - generated::manifest::{Artifact, Backend, EncoderfileManifest}, + generated::manifest::{Artifact, Backend, EncoderfileManifest}, runtime::InputType, }; use prost::Message; diff --git a/encoderfile/src/generated/image_classification.rs b/encoderfile/src/generated/image_classification.rs new file mode 100644 index 00000000..e3171d30 --- /dev/null +++ b/encoderfile/src/generated/image_classification.rs @@ -0,0 +1,32 @@ +use crate::common; + +tonic::include_proto!("encoderfile.image_classification"); + +impl From for common::ImageClassificationRequest { + fn from(val: ImageClassificationRequest) -> Self { + let images = val.inputs.into_iter().map(|input| { + common::ImageInfo { + image_bytes: bytes::Bytes::from(input.image), + image_format: image::ImageFormat::Png, // TODO: detect format properly + } + }).collect(); + Self { + images, + metadata: if val.metadata.is_empty() { None } else { Some(val.metadata) }, + } + } +} + +impl From for ImageClassificationResponse { + fn from(val: common::ImageClassificationResponse) -> Self { + Self { + labels_batch: val.results.into_iter().map(|labels| ImageLabels { + labels: labels.labels.into_iter().map(|label| ImageLabelScore { + label: label.label, + score: label.score, + }).collect(), + }).collect(), + metadata: val.metadata.unwrap_or_default(), + } + } +} diff --git a/encoderfile/src/generated/mod.rs b/encoderfile/src/generated/mod.rs index 79d1fbe4..c8c77d66 100644 --- a/encoderfile/src/generated/mod.rs +++ b/encoderfile/src/generated/mod.rs @@ -5,3 +5,4 @@ pub mod sentence_embedding; pub mod sequence_classification; pub mod token; pub mod token_classification; +pub mod image_classification; diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index 116b70e1..1d662d03 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -32,14 +32,21 @@ pub fn image_classification<'a>( #[tracing::instrument(skip_all)] pub fn postprocess(outputs: Array2, classes: Vec) -> Vec> { + println!("outputs shape: {:?}", outputs.dim()); + println!("outputs: {:?}", outputs); outputs .axis_iter(Axis(0)) .map(|logs| { + println!("logs: {:?}", logs); logs.iter().enumerate() - .map(|(idx, score)| - ImageLabelScore { - label: classes[idx].to_string(), // TODO: get label from config - score: *score + .map(|(idx, score)| { + println!("idx: {}, score: {}", idx, score); + println!("classes: {:?}", classes); + println!("label: {}", classes[idx]); + ImageLabelScore { + label: classes[idx].to_string(), // TODO: get label from config + score: *score + } } ) .collect() diff --git a/encoderfile/src/runtime/mod.rs b/encoderfile/src/runtime/mod.rs index 02089c8c..57ae81c1 100644 --- a/encoderfile/src/runtime/mod.rs +++ b/encoderfile/src/runtime/mod.rs @@ -5,8 +5,22 @@ mod loader; mod state; mod tokenizer; -pub use loader::{EncoderfileLoader, load_assets}; -pub use state::{AppState, EncoderfileState, ClassifierState, FeatureExtractorState, ImageInputState, TextInputState, InputType, TaskType}; +pub use loader::{ + EncoderfileLoader, + load_assets, +}; +pub use state::{ + AppState, + EncoderfileState, + Input, + Task, + InputType, + TaskType, + ClassifierState, + FeatureExtractorState, + ImageInputState, + TextInputState, +}; pub use tokenizer::TokenizerService; pub type Model<'a> = MutexGuard<'a, Session>; diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 8883ae01..0010be37 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -10,55 +10,50 @@ use crate::{ pub type AppState = Arc>; -pub trait TaskType { - type TaskState; - // fn get_task_state(dir: &str) -> Self::TaskState; +#[derive(PartialEq)] +pub enum Task { + Classification, + FeatureExtraction, } -pub trait InputType { - type InputState; - // fn get_input_state(dir: &str) -> Self::InputState; +#[derive(PartialEq)] +pub enum Input { + Text, + Image, } -macro_rules! input_state_impl { - ($model_type:ty, $state_type:ty) => { - impl InputType for $model_type { - type InputState = $state_type; - } - }; +pub trait TaskType { + const TASK: Task; + fn task_type_val(&self) -> Task { + Self::task_type() + } + fn task_type() -> Task { + Self::TASK + } + type TaskState; } -input_state_impl!(model_type::Embedding, TextInputState); -input_state_impl!(model_type::SentenceEmbedding, TextInputState); -input_state_impl!(model_type::SequenceClassification, TextInputState); -input_state_impl!(model_type::TokenClassification, TextInputState); -input_state_impl!(model_type::ImageClassification, ImageInputState); - -macro_rules! task_state_impl { - ($model_type:ty, $state_type:ty) => { - impl TaskType for $model_type { - type TaskState = $state_type; - } - }; +pub trait InputType { + const INPUT: Input; + fn input_type_val(&self) -> Input { + Self::input_type() + } + fn input_type() -> Input { + Self::INPUT + } + type InputState; } -task_state_impl!(model_type::SequenceClassification, ClassifierState); -task_state_impl!(model_type::TokenClassification, ClassifierState); -task_state_impl!(model_type::ImageClassification, ClassifierState); -task_state_impl!(model_type::Embedding, FeatureExtractorState); -task_state_impl!(model_type::SentenceEmbedding, FeatureExtractorState); - - pub struct TextInputState { pub tokenizer: TokenizerService, pub model_config: ModelConfig, } #[derive(Debug, Serialize, Deserialize, Clone)] pub struct ImageInputState { - pub num_channels: usize, - pub height: Option, - pub width: Option, - pub image_size: usize, + pub num_channels: u32, + pub height: Option, + pub width: Option, + pub image_size: Option, } #[derive(Debug, Serialize, Deserialize, Clone)] @@ -96,6 +91,64 @@ impl ClassifierState { #[derive(Debug, Serialize, Deserialize, Clone)] pub struct FeatureExtractorState {} +macro_rules! input_state_impl { + ($model_type:ty, $state_type:ty, $input:expr) => { + impl InputType for $model_type { + const INPUT: Input = $input; + type InputState = $state_type; + } + }; +} + +input_state_impl!(model_type::Embedding, TextInputState, Input::Text); +input_state_impl!(model_type::SentenceEmbedding, TextInputState, Input::Text); +input_state_impl!(model_type::SequenceClassification, TextInputState, Input::Text); +input_state_impl!(model_type::TokenClassification, TextInputState, Input::Text); +input_state_impl!(model_type::ImageClassification, ImageInputState, Input::Image); + +macro_rules! task_state_impl { + ($model_type:ty, $state_type:ty, $task:expr) => { + impl TaskType for $model_type { + const TASK: Task = $task; + type TaskState = $state_type; + } + }; +} + +task_state_impl!(model_type::SequenceClassification, ClassifierState, Task::Classification); +task_state_impl!(model_type::TokenClassification, ClassifierState, Task::Classification); +task_state_impl!(model_type::ImageClassification, ClassifierState, Task::Classification); +task_state_impl!(model_type::Embedding, FeatureExtractorState, Task::FeatureExtraction); +task_state_impl!(model_type::SentenceEmbedding, FeatureExtractorState, Task::FeatureExtraction); + +macro_rules! input_type_impl { + [ $( $x:ident ),* $(,)? ] => { + impl ModelType { + pub fn input_type(&self) -> crate::runtime::Input { + match self { + $( + ModelType::$x => model_type::$x::input_type(), + )* + } + } + pub fn task_type(&self) -> crate::runtime::Task { + match self { + $( + ModelType::$x => model_type::$x::task_type(), + )* + } + } + } + } +} +input_type_impl![ + Embedding, + SequenceClassification, + TokenClassification, + SentenceEmbedding, + ImageClassification +]; + #[derive(Debug)] pub struct EncoderfileState { pub config: Config, diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 5337a28a..53be845f 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -3,7 +3,6 @@ use crate::{ ImageClassificationRequest, ImageClassificationResponse, ImageClassificationResult, - ImageLabelScore, model_type }, @@ -11,7 +10,7 @@ use crate::{ runtime::AppState, }; use image::RgbImage; -use ndarray::{Array2, Array4, Ix2, Axis}; +use ndarray::{Array4}; use super::inference::Inference; use crate::inference::image_classification::image_classification; @@ -33,18 +32,18 @@ impl Inference for AppState let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); img .resize_exact( - self.per_model_input_state.width.unwrap() as u32, - self.per_model_input_state.height.unwrap() as u32, + self.per_model_input_state.width.unwrap(), + self.per_model_input_state.height.unwrap(), DEFAULT_FILTER_TYPE ) .to_rgb8() }).collect(); let images_array = Array4::from_shape_vec( ( - request.images.len().clone(), - self.per_model_input_state.num_channels, - self.per_model_input_state.height.unwrap(), - self.per_model_input_state.width.unwrap() + request.images.len().clone() as usize, + self.per_model_input_state.num_channels as usize, + self.per_model_input_state.height.unwrap() as usize, + self.per_model_input_state.width.unwrap() as usize ), images .into_iter() @@ -52,13 +51,17 @@ impl Inference for AppState .collect() ).expect("Failed to convert images to ndarray"); + let label_map = self.per_task_state.id2label.clone().unwrap(); + let mut entries: Vec<_> = label_map.iter().collect(); + entries.sort_by(|x, y| x.0.cmp(&y.0)); + let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); + let labels_batch = image_classification( self.session.lock(), images_array, // COMMENT having optional fields complicates things later on, but otoh // it allows models with variations of these fields - self.per_task_state.label2id.clone().unwrap().keys().cloned().collect())?; - print!("labels_batch: {:?}", &labels_batch); + classes)?; Ok(ImageClassificationResponse { results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), diff --git a/encoderfile/src/transport/cli.rs b/encoderfile/src/transport/cli.rs index 48c73b60..e054db72 100644 --- a/encoderfile/src/transport/cli.rs +++ b/encoderfile/src/transport/cli.rs @@ -46,13 +46,13 @@ pub trait CliRoute: Inference { impl CliRoute for T {} #[derive(Parser)] -pub struct Cli { +pub struct TextCli { #[command(subcommand)] - pub command: Commands, + pub command: TextCommands, } #[derive(Subcommand)] -pub enum Commands { +pub enum TextCommands { Serve { #[arg(long, default_value = "[::]")] grpc_hostname: String, @@ -95,13 +95,13 @@ pub enum Commands { }, } -impl Commands { +impl TextCommands { pub async fn execute(self, state: S) -> Result<()> where S: Inference + GrpcRouter + HttpRouter + McpRouter + CliRoute, { match self { - Commands::Serve { + TextCommands::Serve { grpc_hostname, grpc_port, http_hostname, @@ -152,7 +152,7 @@ impl Commands { let _ = tokio::join!(grpc_process, http_process); } - Commands::Infer { + TextCommands::Infer { inputs, format, out_dir, @@ -161,7 +161,7 @@ impl Commands { state.cli_route(inputs, format, out_dir)? } - Commands::Mcp { + TextCommands::Mcp { hostname, port, cert_file, @@ -177,6 +177,117 @@ impl Commands { } } +#[derive(Parser)] +pub struct ImageCli { + #[command(subcommand)] + pub command: ImageCommands, +} + +#[derive(Subcommand)] +pub enum ImageCommands { + Serve { + #[arg(long, default_value = "[::]")] + grpc_hostname: String, + #[arg(long, default_value = "50051")] + grpc_port: String, + #[arg(long, default_value = "0.0.0.0")] + http_hostname: String, + #[arg(long, default_value = "8080")] + http_port: String, + #[arg(long, default_value_t = false)] + disable_grpc: bool, + #[arg(long, default_value_t = false)] + disable_http: bool, + #[arg(long, default_value_t = false)] + enable_otel: bool, + #[arg(long, default_value = "http://localhost:4317")] + otel_exporter_url: String, + #[arg(long)] + cert_file: Option, + #[arg(long)] + key_file: Option, + }, + Infer { + #[arg(required = true)] + inputs: Vec, + #[arg(short, long, default_value_t = Format::Json)] + format: Format, + #[arg(short)] + out_dir: Option, + }, +} + +impl ImageCommands { + pub async fn execute(self, state: S) -> Result<()> + where + S: Inference + GrpcRouter + HttpRouter + CliRoute, + { + match self { + ImageCommands::Serve { + grpc_hostname, + grpc_port, + http_hostname, + http_port, + disable_grpc, + disable_http, + enable_otel, + otel_exporter_url, + cert_file, + key_file, + } => { + let banner = crate::get_banner(state.model_id().as_str()); + + if disable_grpc && disable_http { + return Err(crate::error::ApiError::ConfigError( + "Cannot disable both gRPC and HTTP", + ))?; + } + + match enable_otel { + true => setup_tracing(Some(otel_exporter_url.as_str())), + false => setup_tracing(None), + }?; + + let grpc_process = match disable_grpc { + true => tokio::spawn(async { Ok(()) }), + false => tokio::spawn(run_grpc( + grpc_hostname, + grpc_port, + cert_file.clone(), + key_file.clone(), + state.clone(), + )), + }; + + let http_process = match disable_http { + true => tokio::spawn(async { Ok(()) }), + false => tokio::spawn(run_http( + http_hostname, + http_port, + cert_file.clone(), + key_file.clone(), + state.clone(), + )), + }; + + println!("{}", banner); + + let _ = tokio::join!(grpc_process, http_process); + } + ImageCommands::Infer { + inputs, + format, + out_dir, + } => { + setup_tracing(None)?; + + state.cli_route(inputs, format, out_dir)? + } + } + Ok(()) + } +} + #[derive(Clone, ValueEnum)] pub enum Format { Json, diff --git a/encoderfile/src/transport/grpc/mod.rs b/encoderfile/src/transport/grpc/mod.rs index 162de39a..871b5c3f 100644 --- a/encoderfile/src/transport/grpc/mod.rs +++ b/encoderfile/src/transport/grpc/mod.rs @@ -1,6 +1,6 @@ use crate::{ common::model_type, - generated::{embedding, sentence_embedding, sequence_classification, token_classification}, + generated::{embedding, sentence_embedding, sequence_classification, token_classification, image_classification}, runtime::AppState, services::{Inference, Metadata}, }; @@ -116,3 +116,13 @@ generate_grpc_server!( SentenceEmbeddingInference, SentenceEmbeddingInferenceServer ); + +generate_grpc_server!( + ImageClassification, + image_classification, + image_classification_inference_server, + ImageClassificationRequest, + ImageClassificationResponse, + ImageClassificationInference, + ImageClassificationInferenceServer +); diff --git a/encoderfile/src/transport/http/example.md b/encoderfile/src/transport/http/example.md new file mode 100644 index 00000000..3eb1bfc9 --- /dev/null +++ b/encoderfile/src/transport/http/example.md @@ -0,0 +1,316 @@ +# Multipart OpenAPI Service Example + +This document provides examples of how to interact with the multipart file upload and prediction endpoint. + +## Endpoint Overview + +- **POST /predict/multipart** - Submit a JSON payload with binary file attachments +- **GET /predict/multipart/openapi.json** - Retrieve the OpenAPI specification + +## Example 1: cURL with Two Image Files + +```bash +curl -X POST http://localhost:8080/predict/multipart \ + -F "payload={\"model_version\": \"1.0\", \"threshold\": 0.8}" \ + -F "files=@/path/to/image1.png" \ + -F "files=@/path/to/image2.jpg" +``` + +### Request Body (multipart/form-data) + +``` +--boundary_123abc456def +Content-Disposition: form-data; name="payload" +Content-Type: application/json + +{"model_version": "1.0", "threshold": 0.8} +--boundary_123abc456def +Content-Disposition: form-data; name="files"; filename="image1.png" +Content-Type: image/png + + +--boundary_123abc456def +Content-Disposition: form-data; name="files"; filename="image2.jpg" +Content-Type: image/jpeg + + +--boundary_123abc456def-- +``` + +### Response + +```json +{ + "payload": { + "model_version": "1.0", + "threshold": 0.8 + }, + "attachment_count": 2, + "attachments": [ + { + "file_name": "image1.png", + "content_type": "image/png", + "size_bytes": 45230 + }, + { + "file_name": "image2.jpg", + "content_type": "image/jpeg", + "size_bytes": 52104 + } + ] +} +``` + +## Example 2: Python Requests Library + +```python +import requests +import json + +url = "http://localhost:8080/predict/multipart" + +# Prepare the payload +payloaquest Body (multipart/form-data) + +``` +--boundary_xyz789pqr012 +Content-Disposition: form-data; name="payload"; filename="payload.json" +Content-Type: application/json + +{"model_version": "1.0", "threshold": 0.8, "batch_id": "batch_12345"} +--boundary_xyz789pqr012 +Content-Disposition: form-data; name="files"; filename="image1.png" +Content-Type: image/png + + +--boundary_xyz789pqr012 +Content-Disposition: form-data; name="files"; filename="image2.jpg" +Content-Type: image/jpeg + + +--boundary_xyz789pqr012 +Content-Disposition: form-data; name="files"; filename="document.pdf" +Content-Type: application/pdf + + +--boundary_xyz789pqr012-- +``` + +### Red = { + "model_version": "1.0", + "threshold": 0.8, + "batch_id": "batch_12345" +} + +# Prepare files +files = [ + ("payload", ("payload.json", json.dumps(payload), "application/json")), + ("files", ("image1.png", open("image1.png", "rb"), "image/png")), + ("files", ("image2.jpg", open("image2.jpg", "rb"), "image/jpeg")), + ("files", ("document.pdf", open("document.pdf", "rb"), "application/pdf")), +] + +# Send the request +response = requests.post(url, files=files) + +print("Status Code:", response.status_code) +print("Response:", response.json()) +``` + +### Response + +```json +{ + "payload": { + "model_version": "1.0", + "threshold": 0.8, + "batch_id": "batch_12345" + }, + "attachment_count": 3, + "attachments": [ + { + "file_name": "image1.png", + "content_type": "image/png", + quest Body (multipart/form-data) + +``` +--boundary_webkit_abc123 +Content-Disposition: form-data; name="payload" + +{"model_version":"1.0","threshold":0.8,"inference_id":"inf_abc123"} +--boundary_webkit_abc123 +Content-Disposition: form-data; name="files"; filename="photo1.jpg" +Content-Type: image/jpeg + + +--boundary_webkit_abc123 +Content-Disposition: form-data; name="files"; filename="photo2.jpg" +Content-Type: image/jpeg + + +--boundary_webkit_abc123-- +``` + +### Re"size_bytes": 45230 + }, + { + "file_name": "image2.jpg", + "content_type": "image/jpeg", + "size_bytes": 52104 + }, + { + "file_name": "document.pdf", + "content_type": "application/pdf", + "size_bytes": 128512 + } + ] +} +``` + +## Example 3: JavaScript Fetch API + +```javascript +const payload = { + model_version: "1.0", + threshold: 0.8, + inference_id: "inf_abc123" +}; + +const formData = new FormData(); + +// Add the JSON payload as a form field +formData.append("payload", JSON.stringify(payload)); + +// Add multiple binary files +const imageFile1 = document.getElementById("imageInput1").files[0]; +const imageFile2 = document.getElementById("imageInput2").files[0]; + +formData.append("files", imageFile1); +formData.append("files", imageFile2); + +// Make the request +const response = await fetch("http://localhost:8080/predict/multipart", { + method: "POST", + body: formData +}); + +const result = await response.json(); +console.log("Success:", result); +``` + +### Response + +```json +{ + "payload": { + "model_version": "1.0", + "threshold": 0.8, + "inference_id": "inf_abc123" + }, + "attachment_count": 2, + "attachments": [ + { + "file_name": "photo1.jpg", + "content_type": "image/jpeg", + "size_bytes": 245120 + }, + { + "file_name": "photo2.jpg", + "content_type": "image/jpeg", + "size_bytes": 187904 + } + ] +} +``` + +## Example 4: Error Handling + +### Missing Payload + +If the request is sent without a `payload` form field: + +```bash +curl -X POST http://localhost:8080/predict/multipart \ + -F "files=@/path/to/image.png" +``` + +**Response (422 Unprocessable Entity):** + +``` +missing required multipart field 'payload' +``` + +### Invalid JSON in Payload + +If the payload field contains invalid JSON: + +```bash +curl -X POST http://localhost:8080/predict/multipart \ + -F "payload=not valid json" \ + -F "files=@/path/to/image.png" +``` + +**Response (422 Unprocessable Entity):** + +``` +invalid json in 'payload' field +``` + +### Malformed Multipart Body + +If the multipart encoding is corrupted: + +**Response (400 Bad Request):** + +``` +multipart parse error: [error details] +``` + +## Request Parts Specification + +### Required: `payload` Part + +- **Name**: `payload` (exactly one) +- **Content-Type**: `application/json` (recommended) +- **Content**: Valid JSON object or array + +### Optional: `files` Parts + +- **Name**: `files` (zero or more) +- **Content-Type**: Any MIME type (e.g., `image/png`, `application/pdf`) +- **Content**: Binary data +- **Filename**: Optional but recommended (used in response metadata) + +## Response Structure + +```json +{ + "payload": "...", // Echo of the submitted JSON payload + "attachment_count": 3, // Number of files attached + "attachments": [ // Metadata for each file + { + "file_name": "...", // Original filename if provided, null otherwise + "content_type": "...", // MIME type if provided, null otherwise + "size_bytes": 12345 // File size in bytes + } + ] +} +``` + +## HTTP Status Codes + +| Status | Meaning | Condition | +|--------|---------|-----------| +| 200 | OK | Request processed successfully | +| 400 | Bad Request | Malformed multipart body | +| 422 | Unprocessable Entity | Missing `payload` or invalid JSON | + +## OpenAPI Specification + +To retrieve the OpenAPI specification for this endpoint: + +```bash +curl -X GET http://localhost:8080/predict/multipart/openapi.json +``` + +This returns a machine-readable OpenAPI 3.0 document describing the endpoint. diff --git a/encoderfile/src/transport/http/mod.rs b/encoderfile/src/transport/http/mod.rs index f5b5ffd1..cc526318 100644 --- a/encoderfile/src/transport/http/mod.rs +++ b/encoderfile/src/transport/http/mod.rs @@ -1,5 +1,6 @@ mod base; mod error; +pub mod multipart_openapi; pub trait HttpRouter where diff --git a/encoderfile/src/transport/http/multipart_openapi.rs b/encoderfile/src/transport/http/multipart_openapi.rs new file mode 100644 index 00000000..6b5d11b8 --- /dev/null +++ b/encoderfile/src/transport/http/multipart_openapi.rs @@ -0,0 +1,282 @@ +use axum::{ + Json, + extract::{Multipart, State}, + http::StatusCode, + response::{IntoResponse, Response}, +}; +use serde::{Deserialize, Serialize}; +use utoipa::OpenApi; +use crate::common::model_type::ImageClassification; +use crate::runtime::AppState; + +pub const MULTIPART_PREDICT_ENDPOINT: &str = "/predict/multipart"; +pub const MULTIPART_OPENAPI_ENDPOINT: &str = "/predict/multipart/openapi.json"; + +#[derive(Debug, Serialize, Deserialize, utoipa::ToSchema)] +pub struct MultipartPredictBody { + /// Arbitrary JSON payload sent in the multipart part named `payload`. + pub payload: serde_json::Value, + + /// Binary attachments sent as repeated `files` multipart parts. + #[schema(value_type = Vec)] + pub files: Vec, +} + +#[derive(Debug, Serialize, Deserialize, utoipa::ToSchema)] +pub struct ParsedAttachment { + pub file_name: Option, + pub content_type: Option, + pub size_bytes: usize, +} + +#[derive(Debug, Serialize, Deserialize, utoipa::ToSchema, utoipa::ToResponse)] +pub struct MultipartPredictResponse { + pub payload: serde_json::Value, + pub attachment_count: usize, + pub attachments: Vec, +} + +#[derive(Debug, thiserror::Error)] +pub enum MultipartApiError { + #[error("missing required multipart field 'payload'")] + MissingPayload, + #[error("invalid json in 'payload' field")] + InvalidPayload, + #[error("multipart parse error: {0}")] + Multipart(String), + #[error("failed to construct request from multipart: {0}")] + RequestConstruction(String), +} + +impl IntoResponse for MultipartApiError { + fn into_response(self) -> Response { + let status = match self { + Self::MissingPayload | Self::InvalidPayload => StatusCode::UNPROCESSABLE_ENTITY, + Self::RequestConstruction(_) => StatusCode::UNPROCESSABLE_ENTITY, + Self::Multipart(_) => StatusCode::BAD_REQUEST, + }; + + (status, self.to_string()).into_response() + } +} + +/// Trait for converting multipart payload and attachments into a typed request. +pub trait FromMultipart: Sized { + /// Construct an instance from a JSON payload and list of attachment bytes. + fn from_multipart( + payload: serde_json::Value, + attachments: Vec<(Option, Option, bytes::Bytes)>, + ) -> Result; +} + +#[derive(Debug, utoipa::OpenApi)] +#[openapi(paths(post_multipart), components(schemas(MultipartPredictBody, MultipartPredictResponse, ParsedAttachment)))] +pub struct MultipartApiDoc; + +#[utoipa::path( + get, + path = MULTIPART_OPENAPI_ENDPOINT, + responses( + (status = 200, description = "Successful") + ) +)] +pub async fn openapi() -> impl IntoResponse { + Json(MultipartApiDoc::openapi()) +} + +#[utoipa::path( + post, + path = MULTIPART_PREDICT_ENDPOINT, + request_body( + content = MultipartPredictBody, + content_type = "multipart/form-data", + description = "Multipart payload with a JSON part named 'payload' and 0..N binary parts named 'files'" + ), + responses( + (status = 200, body = MultipartPredictResponse), + (status = 422, description = "Missing or invalid payload JSON"), + (status = 400, description = "Invalid multipart body") + ) +)] +pub async fn post_multipart( + mut multipart: Multipart, +) -> Result, MultipartApiError> { + parse_multipart(&mut multipart).await +} + +/// Generic multipart parser that extracts payload and attachments. +pub async fn parse_multipart( + multipart: &mut Multipart, +) -> Result, MultipartApiError> { + let mut payload: Option = None; + let mut attachments = Vec::new(); + let mut attachment_metadata = Vec::new(); + + while let Some(field) = multipart + .next_field() + .await + .map_err(|e| MultipartApiError::Multipart(e.to_string()))? + { + let name = field.name().map(ToOwned::to_owned); + let file_name = field.file_name().map(ToOwned::to_owned); + let content_type = field.content_type().map(ToOwned::to_owned); + let bytes = field + .bytes() + .await + .map_err(|e| MultipartApiError::Multipart(e.to_string()))?; + + match name.as_deref() { + Some("payload") => { + payload = Some( + serde_json::from_slice(&bytes).map_err(|_| MultipartApiError::InvalidPayload)?, + ); + } + Some("files") => { + attachment_metadata.push(ParsedAttachment { + file_name: file_name.clone(), + content_type: content_type.clone(), + size_bytes: bytes.len(), + }); + attachments.push((file_name, content_type, bytes)); + } + _ => {} + } + } + + let payload = payload.ok_or(MultipartApiError::MissingPayload)?; + + Ok(Json(MultipartPredictResponse { + payload, + attachment_count: attachment_metadata.len(), + attachments: attachment_metadata, + })) +} + +/// Generic handler that converts multipart request into typed request. +pub async fn post_multipart_typed( + mut multipart: Multipart, +) -> Result, MultipartApiError> { + let mut payload: Option = None; + let mut attachments = Vec::new(); + let mut attachment_metadata = Vec::new(); + + while let Some(field) = multipart + .next_field() + .await + .map_err(|e| MultipartApiError::Multipart(e.to_string()))? + { + let name = field.name().map(ToOwned::to_owned); + let file_name = field.file_name().map(ToOwned::to_owned); + let content_type = field.content_type().map(ToOwned::to_owned); + let bytes = field + .bytes() + .await + .map_err(|e| MultipartApiError::Multipart(e.to_string()))?; + + match name.as_deref() { + Some("payload") => { + payload = Some( + serde_json::from_slice(&bytes).map_err(|_| MultipartApiError::InvalidPayload)?, + ); + } + Some("files") => { + attachment_metadata.push(ParsedAttachment { + file_name: file_name.clone(), + content_type: content_type.clone(), + size_bytes: bytes.len(), + }); + attachments.push((file_name, content_type, bytes)); + } + _ => {} + } + } + + let payload = payload.ok_or(MultipartApiError::MissingPayload)?; + + // Convert to typed request + let _request: R = R::from_multipart(payload.clone(), attachments)?; + + Ok(Json(MultipartPredictResponse { + payload, + attachment_count: attachment_metadata.len(), + attachments: attachment_metadata, + })) +} + +pub fn router() -> axum::Router { + axum::Router::new() + .route(MULTIPART_PREDICT_ENDPOINT, axum::routing::post(post_multipart)) + .route(MULTIPART_OPENAPI_ENDPOINT, axum::routing::get(openapi)) +} + +/// HttpRouter implementation for ImageClassification model type. +/// Combines standard model serving endpoints with multipart file upload capability. +impl super::HttpRouter for crate::runtime::AppState { + fn http_router(self) -> axum::Router { + axum::Router::new() + .route("/health", axum::routing::get(super::base::health)) + .route( + "/model", + axum::routing::get(super::base::get_model_metadata::), + ) + .route("/predict", axum::routing::post(predict_handler)) + .route("/openapi.json", axum::routing::get(standard_openapi)) + .route( + MULTIPART_PREDICT_ENDPOINT, + axum::routing::post(post_multipart_image_classification), + ) + .route(MULTIPART_OPENAPI_ENDPOINT, axum::routing::get(openapi)) + .with_state(self) + } +} + +/// Multipart handler specialized for ImageClassificationRequest. +async fn post_multipart_image_classification( + multipart: Multipart, +) -> Result, MultipartApiError> { + post_multipart_typed::(multipart).await +} + +/// Standard predict endpoint for ImageClassification. +async fn predict_handler( + State(state): State>, + Json(req): Json< + as crate::services::Inference>::Input, + >, +) -> impl IntoResponse { + super::base::predict(State(state), Json(req)).await +} + +/// Standard OpenAPI endpoint for ImageClassification model service (without multipart). +async fn standard_openapi() -> impl IntoResponse { + Json(serde_json::json!({ + "openapi": "3.0.0", + "info": { + "title": "ImageClassification Model API", + "version": "1.0.0" + }, + "paths": { + "/health": { + "get": { + "responses": { + "200": { "description": "Successful" } + } + } + }, + "/model": { + "get": { + "responses": { + "200": { "description": "Successful" } + } + } + }, + "/predict": { + "post": { + "responses": { + "200": { "description": "Successful" } + } + } + } + } + })) +} diff --git a/test_img_class_config.yml b/test_img_class_config.yml new file mode 100644 index 00000000..723a0bda --- /dev/null +++ b/test_img_class_config.yml @@ -0,0 +1,6 @@ +encoderfile: + name: test-img-class + path: models/image_classification + model_type: image_classification + output_path: ./test-img-class.encoderfile + From b2a082f5058b73de31bb9fb1f6ae30719301002f Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Fri, 8 May 2026 16:31:07 +0200 Subject: [PATCH 08/21] Fixed image order (channel grouped to separate channels); still wip --- encoderfile/build.rs | 2 + encoderfile/proto/image_classification.proto | 2 +- encoderfile/proto/image_types.proto | 2 +- encoderfile/proto/sentence_embedding.proto | 1 - .../src/common/image_classification.rs | 13 +---- encoderfile/src/common/image_types.rs | 28 ++++++++++ encoderfile/src/common/mod.rs | 2 + .../src/generated/image_classification.rs | 17 +++--- encoderfile/src/generated/image_types.rs | 28 ++++++++++ encoderfile/src/generated/mod.rs | 1 + .../src/inference/image_classification.rs | 24 +++++---- .../src/services/image_classification.rs | 52 +++++++++++++------ 12 files changed, 124 insertions(+), 48 deletions(-) create mode 100644 encoderfile/src/common/image_types.rs create mode 100644 encoderfile/src/generated/image_types.rs diff --git a/encoderfile/build.rs b/encoderfile/build.rs index 9f301dd2..016ed527 100644 --- a/encoderfile/build.rs +++ b/encoderfile/build.rs @@ -14,6 +14,7 @@ fn main() -> Result<(), Box> { "proto/sentence_embedding.proto", "proto/image_classification.proto", "proto/manifest.proto", + "proto/image_types.proto", ], &[ "proto/embedding", @@ -22,6 +23,7 @@ fn main() -> Result<(), Box> { "proto/sentence_embedding", "proto/image_classification", "proto/manifest", + "proto/image_types", ], )?; diff --git a/encoderfile/proto/image_classification.proto b/encoderfile/proto/image_classification.proto index 6283cfce..af028275 100644 --- a/encoderfile/proto/image_classification.proto +++ b/encoderfile/proto/image_classification.proto @@ -16,6 +16,6 @@ message ImageClassificationRequest { } message ImageClassificationResponse { - repeated encoderfile.image_types.ImageLabels labels_batch = 1; + repeated encoderfile.image_types.ImageLabels labels = 1; map metadata = 11; } diff --git a/encoderfile/proto/image_types.proto b/encoderfile/proto/image_types.proto index ae42d76f..ffcd4c3c 100644 --- a/encoderfile/proto/image_types.proto +++ b/encoderfile/proto/image_types.proto @@ -1,6 +1,6 @@ syntax = "proto3"; -package encoderfile.image_classification; +package encoderfile.image_types; message ImageInput { bytes image = 1; diff --git a/encoderfile/proto/sentence_embedding.proto b/encoderfile/proto/sentence_embedding.proto index f7afc989..b14a72a7 100644 --- a/encoderfile/proto/sentence_embedding.proto +++ b/encoderfile/proto/sentence_embedding.proto @@ -2,7 +2,6 @@ syntax = "proto3"; package encoderfile.sentence_embedding; -import "proto/token.proto"; import "proto/metadata.proto"; service SentenceEmbeddingInference { diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index 120e0e6a..4eb44e54 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -6,12 +6,7 @@ use crate::common::FromReadInput; use image::ImageFormat; use bytes::Bytes; use crate::transport::http::multipart_openapi::{FromMultipart, MultipartApiError}; - -#[derive(Debug, Serialize, Deserialize)] -pub struct ImageInfo { - pub image_bytes: Bytes, - pub image_format: ImageFormat, -} +use crate::common::image_types::{ImageInfo, ImageLabelScore}; #[derive(Debug, Serialize, Deserialize)] pub struct ImageClassificationRequest { @@ -95,12 +90,6 @@ pub struct ImageClassificationResponse { pub metadata: Option>, } -#[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] -pub struct ImageLabelScore { - pub label: String, - pub score: f32, -} - #[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] pub struct ImageClassificationResult { pub labels: Vec, diff --git a/encoderfile/src/common/image_types.rs b/encoderfile/src/common/image_types.rs new file mode 100644 index 00000000..5a0a2040 --- /dev/null +++ b/encoderfile/src/common/image_types.rs @@ -0,0 +1,28 @@ +use serde::{Deserialize, Serialize}; +use std::{collections::HashMap, io::Read}; +use utoipa::ToSchema; +use anyhow::Result; +use crate::common::FromReadInput; +use image::ImageFormat; +use bytes::Bytes; +use crate::transport::http::multipart_openapi::{FromMultipart, MultipartApiError}; + +#[derive(Debug, Serialize, Deserialize)] +pub struct ImageInfo { + pub image_bytes: Bytes, + pub image_format: ImageFormat, +} + +#[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] +pub struct ImageLabelScore { + pub label: String, + pub score: Option, +} + + +#[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] +pub struct ImageLabels { + pub labels: Vec, +} + + diff --git a/encoderfile/src/common/mod.rs b/encoderfile/src/common/mod.rs index e824515e..ea81671d 100644 --- a/encoderfile/src/common/mod.rs +++ b/encoderfile/src/common/mod.rs @@ -10,6 +10,7 @@ mod token_classification; // CV mod image_classification; +mod image_types; pub use config::*; pub use embedding::*; @@ -22,6 +23,7 @@ pub use token_classification::*; // CV pub use image_classification::*; +pub use image_types::*; use std::io::Read; use anyhow::Result; diff --git a/encoderfile/src/generated/image_classification.rs b/encoderfile/src/generated/image_classification.rs index e3171d30..bedfde9c 100644 --- a/encoderfile/src/generated/image_classification.rs +++ b/encoderfile/src/generated/image_classification.rs @@ -1,4 +1,4 @@ -use crate::common; +use crate::{common, generated::image_types::ImageLabels}; tonic::include_proto!("encoderfile.image_classification"); @@ -20,13 +20,16 @@ impl From for common::ImageClassificationRequest { impl From for ImageClassificationResponse { fn from(val: common::ImageClassificationResponse) -> Self { Self { - labels_batch: val.results.into_iter().map(|labels| ImageLabels { - labels: labels.labels.into_iter().map(|label| ImageLabelScore { - label: label.label, - score: label.score, - }).collect(), - }).collect(), + labels: val.results.into_iter().map(|result| result.into()).collect(), metadata: val.metadata.unwrap_or_default(), } } } + +impl From for ImageLabels { + fn from(val: common::ImageClassificationResult) -> Self { + ImageLabels { + labels: val.labels.into_iter().map(|label| label.into()).collect(), + } + } +} diff --git a/encoderfile/src/generated/image_types.rs b/encoderfile/src/generated/image_types.rs new file mode 100644 index 00000000..d8b9a452 --- /dev/null +++ b/encoderfile/src/generated/image_types.rs @@ -0,0 +1,28 @@ +use crate::common; + +tonic::include_proto!("encoderfile.image_types"); + +impl From for ImageInput { + fn from(val: common::ImageInfo) -> Self { + ImageInput { + image: val.image_bytes.to_vec(), + } + } +} + +impl From for ImageLabelScore { + fn from(val: common::ImageLabelScore) -> Self { + ImageLabelScore { + label: val.label, + score: val.score, + } + } +} + +impl From for ImageLabels { + fn from(val: common::ImageLabels) -> Self { + ImageLabels { + labels: val.labels.into_iter().map(|label| label.into()).collect(), + } + } +} diff --git a/encoderfile/src/generated/mod.rs b/encoderfile/src/generated/mod.rs index c8c77d66..e617876e 100644 --- a/encoderfile/src/generated/mod.rs +++ b/encoderfile/src/generated/mod.rs @@ -6,3 +6,4 @@ pub mod sequence_classification; pub mod token; pub mod token_classification; pub mod image_classification; +pub mod image_types; diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index 1d662d03..fc6523b7 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -1,4 +1,6 @@ -use ndarray::{Array2, Array4, Ix2, Axis}; +use std::os::raw; + +use ndarray::{Array2, Array4, Ix2, Axis, s}; use crate::{ error::ApiError, @@ -6,6 +8,10 @@ use crate::{ use crate::common::{ImageLabelScore}; +fn logit_to_prob(logit: f32) -> f32 { + 1.0 / (1.0 + (-logit).exp()) +} + #[tracing::instrument(skip_all)] pub fn image_classification<'a>( mut session: crate::runtime::Model<'a>, @@ -17,7 +23,9 @@ pub fn image_classification<'a>( &images) .unwrap() .to_owned(); - let outputs = crate::run_cv_model!(session, grouped_images)? + let raw_outputs = crate::run_cv_model!(session, grouped_images)?; + println!("Raw outputs: {:?}", raw_outputs.keys().collect::>()); + let mut outputs = raw_outputs .get("logits") .expect("Model does not return logits") .try_extract_array::() @@ -25,27 +33,23 @@ pub fn image_classification<'a>( .into_dimensionality::() .expect("Model does not return tensor of shape [n_batch, n_classes]") .into_owned(); - + println!("Model outputs: {:?}", outputs); + outputs.mapv_inplace(logit_to_prob); + println!("Model outputs: {:?}", outputs); Ok(postprocess(outputs, classes)) } #[tracing::instrument(skip_all)] pub fn postprocess(outputs: Array2, classes: Vec) -> Vec> { - println!("outputs shape: {:?}", outputs.dim()); - println!("outputs: {:?}", outputs); outputs .axis_iter(Axis(0)) .map(|logs| { - println!("logs: {:?}", logs); logs.iter().enumerate() .map(|(idx, score)| { - println!("idx: {}, score: {}", idx, score); - println!("classes: {:?}", classes); - println!("label: {}", classes[idx]); ImageLabelScore { label: classes[idx].to_string(), // TODO: get label from config - score: *score + score: Some(*score) } } ) diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 53be845f..89ff988e 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -10,14 +10,14 @@ use crate::{ runtime::AppState, }; use image::RgbImage; -use ndarray::{Array4}; +use ndarray::{Array4, s}; use super::inference::Inference; use crate::inference::image_classification::image_classification; // No service impl yet -const DEFAULT_FILTER_TYPE: image::imageops::FilterType = image::imageops::FilterType::Nearest; +const DEFAULT_FILTER_TYPE: image::imageops::FilterType = image::imageops::FilterType::Triangle; impl Inference for AppState { @@ -26,10 +26,15 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); + let rescale_factor = 0.00392156862745098 as f32; + let image_mean = 0.5; + let image_std = 0.5; + // bilinear resampling // convert input image into flattened rbg let images: Vec = (&request.images).into_iter().map(|image_info| { let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); + println!("Height x width: {:?} x {:?}", img.height(), img.width()); img .resize_exact( self.per_model_input_state.width.unwrap(), @@ -38,18 +43,33 @@ impl Inference for AppState ) .to_rgb8() }).collect(); - let images_array = Array4::from_shape_vec( - ( - request.images.len().clone() as usize, - self.per_model_input_state.num_channels as usize, - self.per_model_input_state.height.unwrap() as usize, - self.per_model_input_state.width.unwrap() as usize - ), - images - .into_iter() - .flat_map(|img| img.into_raw().into_iter().map(|pixel| pixel as f32)) - .collect() - ).expect("Failed to convert images to ndarray"); + let batch_size = request.images.len(); + let num_channels = self.per_model_input_state.num_channels as usize; + let height = self.per_model_input_state.height.unwrap() as usize; + let width = self.per_model_input_state.width.unwrap() as usize; + + if num_channels != 3 { + return Err(ApiError::InputError("Image classification currently expects 3 RGB channels")); + } + + let mut images_array = Array4::::zeros((batch_size, num_channels, height, width)); + for (image_idx, img) in images.into_iter().enumerate() { + let raw = img.into_raw(); + + // The image crate stores RGB bytes in HWC order; rewrite into NCHW. + for y in 0..height { + for x in 0..width { + let pixel_offset = (y * width + x) * num_channels; + for c in 0..num_channels { + images_array[[image_idx, c, y, x]] = raw[pixel_offset + c] as f32; + } + } + } + } + println!("Some sample slice of the input array (pre scale, post reshape): {:?}", images_array.slice(s![.., .., 0..5, 0..5])); + // TODO make parallel + images_array.mapv_inplace(|x| ((x * rescale_factor) - image_mean) / image_std); + println!("Some sample slice of the input array (post scale): {:?}", images_array.slice(s![.., .., 0..5, 0..5])); let label_map = self.per_task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); @@ -106,8 +126,8 @@ mod tests { assert_eq!(response.results.len(), 1); assert_eq!(response.results[0].labels.len(), 2); assert_eq!(response.results[0].labels[0].label, "normal"); - assert_eq!(response.results[0].labels[0].score, 1.5378942); + assert_eq!(response.results[0].labels[0].score, Some(1.5378942)); assert_eq!(response.results[0].labels[1].label, "nsfw"); - assert_eq!(response.results[0].labels[1].score, -1.6556994); + assert_eq!(response.results[0].labels[1].score, Some(-1.6556994)); } } \ No newline at end of file From 30656429f8221ee451132536a6d318924fa17fca Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Mon, 18 May 2026 12:09:04 +0200 Subject: [PATCH 09/21] Align with main --- Cargo.lock | 591 +----------------- encoderfile-runtime/src/main.rs | 5 +- encoderfile/src/builder/builder.rs | 2 +- encoderfile/src/common/model_type.rs | 8 +- encoderfile/src/dev_utils/mod.rs | 84 ++- encoderfile/src/runtime/mod.rs | 1 + encoderfile/src/runtime/state.rs | 105 +++- encoderfile/src/services/embedding.rs | 2 +- .../src/services/image_classification.rs | 12 +- encoderfile/src/services/model_metadata.rs | 6 +- .../src/services/sentence_embedding.rs | 2 +- .../src/services/sequence_classification.rs | 4 +- .../src/services/token_classification.rs | 4 +- encoderfile/src/transport/cli.rs | 162 +---- encoderfile/src/transport/mcp/mod.rs | 2 +- 15 files changed, 181 insertions(+), 809 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index a6567120..5fe17a05 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -31,24 +31,6 @@ dependencies = [ "memchr", ] -[[package]] -name = "aligned" -version = "0.4.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ee4508988c62edf04abd8d92897fca0c2995d907ce1dfeaf369dac3716a40685" -dependencies = [ - "as-slice", -] - -[[package]] -name = "aligned-vec" -version = "0.6.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dc890384c8602f339876ded803c97ad529f3842aba97f6392b3dba0dd171769b" -dependencies = [ - "equator", -] - [[package]] name = "android_system_properties" version = "0.1.5" @@ -123,12 +105,6 @@ dependencies = [ "num-traits", ] -[[package]] -name = "arbitrary" -version = "1.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c3d036a3c4ab069c7b410a2ce876bd74808d2d0888a82667669f8e783a898bf1" - [[package]] name = "arc-swap" version = "1.9.1" @@ -138,32 +114,6 @@ dependencies = [ "rustversion", ] -[[package]] -name = "arg_enum_proc_macro" -version = "0.3.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "arrayvec" -version = "0.7.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" - -[[package]] -name = "as-slice" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "516b6b4f0e40d50dcda9365d53964ec74560ad4284da2e7fc97122cd83174516" -dependencies = [ - "stable_deref_trait", -] - [[package]] name = "async-trait" version = "0.1.89" @@ -196,49 +146,6 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" -[[package]] -name = "av-scenechange" -version = "0.14.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0f321d77c20e19b92c39e7471cf986812cbb46659d2af674adc4331ef3f18394" -dependencies = [ - "aligned", - "anyhow", - "arg_enum_proc_macro", - "arrayvec", - "log", - "num-rational", - "num-traits", - "pastey", - "rayon", - "thiserror 2.0.18", - "v_frame", - "y4m", -] - -[[package]] -name = "av1-grain" -version = "0.2.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8cfddb07216410377231960af4fcab838eaa12e013417781b78bd95ee22077f8" -dependencies = [ - "anyhow", - "arrayvec", - "log", - "nom 8.0.0", - "num-rational", - "v_frame", -] - -[[package]] -name = "avif-serialize" -version = "0.8.8" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "375082f007bd67184fb9c0374614b29f9aaa604ec301635f72338bb65386a53d" -dependencies = [ - "arrayvec", -] - [[package]] name = "aws-lc-rs" version = "1.16.2" @@ -280,7 +187,6 @@ dependencies = [ "matchit", "memchr", "mime", - "multer", "percent-encoding", "pin-project-lite", "serde_core", @@ -354,27 +260,12 @@ version = "1.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2af50177e190e07a26ab74f8b1efbfe2ef87da2116221318cb1c2e82baf7de06" -[[package]] -name = "bit_field" -version = "0.10.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1e4b40c7323adcfc0a41c4b88143ed58346ff65a288fc144329c5c45e05d70c6" - [[package]] name = "bitflags" version = "2.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "843867be96c8daad0d758b57df9392b6d8d271134fce549de6ce169ff98a92af" -[[package]] -name = "bitstream-io" -version = "4.10.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7eff00be299a18769011411c9def0d827e8f2d7bf0c3dbf53633147a8867fd1f" -dependencies = [ - "no_std_io2", -] - [[package]] name = "block-buffer" version = "0.10.4" @@ -394,12 +285,6 @@ dependencies = [ "serde", ] -[[package]] -name = "built" -version = "0.8.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f4ad8f11f288f48ca24471bbd51ac257aaeaaa07adae295591266b792902ae64" - [[package]] name = "bumpalo" version = "3.20.2" @@ -418,20 +303,11 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" -[[package]] -name = "byteorder-lite" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8f1fe948ff07f4bd06c30984e69f5b4899c516a3ef74f34df92a2df2ab535495" - [[package]] name = "bytes" version = "1.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1e748733b7cbc798e1434b6ac524f0c1ff2ab456fe201501e6497c8417a4fc33" -dependencies = [ - "serde", -] [[package]] name = "castaway" @@ -595,12 +471,6 @@ dependencies = [ "regex-lite", ] -[[package]] -name = "color_quant" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b" - [[package]] name = "colorchoice" version = "1.0.5" @@ -729,12 +599,6 @@ version = "0.8.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28" -[[package]] -name = "crunchy" -version = "0.2.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "460fbee9c2c2f33933d720630a6a0bac33ba7053db5344fac858d4b8952d77d5" - [[package]] name = "crypto-common" version = "0.1.7" @@ -955,7 +819,6 @@ dependencies = [ "anyhow", "axum", "axum-server", - "bytes", "clap", "clap_derive", "codspeed-divan-compat", @@ -964,8 +827,6 @@ dependencies = [ "dotenv", "figment", "flate2", - "image", - "image-ndarray", "mlua", "ndarray", "ndarray-stats", @@ -1057,26 +918,6 @@ dependencies = [ "log", ] -[[package]] -name = "equator" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4711b213838dfee0117e3be6ac926007d7f433d7bbe33595975d4190cb07e6fc" -dependencies = [ - "equator-macro", -] - -[[package]] -name = 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"caf1079563223d5d59d83c85886a56e586cfd5c1a26292e971a0fa266531ac5a" - -[[package]] -name = "fdeflate" -version = "0.3.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1e6853b52649d4ac5c0bd02320cddc5ba956bdb407c4b75a2c6b75bf51500f8c" -dependencies = [ - "simd-adler32", -] - [[package]] name = "figment" version = "0.10.19" @@ -1375,16 +1186,6 @@ dependencies = [ "wasip3", ] -[[package]] -name = "gif" -version = "0.14.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ee8cfcc411d9adbbaba82fb72661cc1bcca13e8bba98b364e62b2dba8f960159" -dependencies = [ - "color_quant", - "weezl", -] - [[package]] name = "glob" version = "0.3.3" @@ -1410,17 +1211,6 @@ dependencies = [ "tracing", ] -[[package]] -name = "half" -version = "2.7.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" -dependencies = [ - "cfg-if", - "crunchy", - "zerocopy", -] - [[package]] name = "hashbrown" version = "0.15.5" @@ -1702,59 +1492,6 @@ dependencies = [ "icu_properties", ] -[[package]] -name = "image" -version = "0.25.10" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "85ab80394333c02fe689eaf900ab500fbd0c2213da414687ebf995a65d5a6104" -dependencies = [ - "bytemuck", - "byteorder-lite", - "color_quant", - "exr", - "gif", - "image-webp", - "moxcms", - "num-traits", - "png", - "qoi", - "ravif", - "rayon", - "rgb", - "serde", - "tiff", - "zune-core", - "zune-jpeg", -] - -[[package]] -name = "image-ndarray" -version = "0.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "366ec4e7613badea5930852b9fc8781fdbb010a59845a3a5c1cf61d0ccc3f133" -dependencies = [ - "image", - "ndarray", - "num-traits", - "thiserror 2.0.18", -] - -[[package]] -name = "image-webp" -version = "0.2.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = 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"09edd9e8b54e49e587e4f6295a7d29c3ea94d469cb40ab8ca70b288248a81db2" -[[package]] -name = "lebe" -version = "0.5.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a79a3332a6609480d7d0c9eab957bca6b455b91bb84e66d19f5ff66294b85b8" - [[package]] name = "libc" version = "0.2.185" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "52ff2c0fe9bc6cb6b14a0592c2ff4fa9ceb83eea9db979b0487cd054946a2b8f" -[[package]] -name = "libfuzzer-sys" -version = "0.4.12" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f12a681b7dd8ce12bff52488013ba614b869148d54dd79836ab85aafdd53f08d" -dependencies = [ - "arbitrary", - "cc", -] - [[package]] name = "libredox" version = "0.1.16" @@ -1991,15 +1701,6 @@ version = "0.4.29" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897" -[[package]] -name = "loop9" -version = "0.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0fae87c125b03c1d2c0150c90365d7d6bcc53fb73a9acaef207d2d065860f062" -dependencies = [ - "imgref", -] - [[package]] name = "lru-slab" version = "0.1.2" @@ -2066,16 +1767,6 @@ dependencies = [ "rawpointer", ] -[[package]] -name = "maybe-rayon" -version = "0.1.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8ea1f30cedd69f0a2954655f7188c6a834246d2bcf1e315e2ac40c4b24dc9519" -dependencies = [ - "cfg-if", - "rayon", -] - [[package]] name = "memchr" version = "2.8.0" @@ -2176,33 +1867,6 @@ dependencies = [ "syn", ] -[[package]] -name = "moxcms" -version = "0.8.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bb85c154ba489f01b25c0d36ae69a87e4a1c73a72631fc6c0eb6dde34a73e44b" -dependencies = [ - "num-traits", - "pxfm", -] - -[[package]] -name = "multer" -version = "3.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "83e87776546dc87511aa5ee218730c92b666d7264ab6ed41f9d215af9cd5224b" -dependencies = [ - "bytes", - "encoding_rs", - "futures-util", - "http", - "httparse", - "memchr", - "mime", - "spin", - "version_check", -] - [[package]] name = "multimap" version = "0.10.1" @@ -2256,12 +1920,6 @@ dependencies = [ "rand 0.8.5", ] -[[package]] -name = "new_debug_unreachable" -version = "1.0.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086" - [[package]] name = "nix" version = "0.31.2" @@ -2274,15 +1932,6 @@ dependencies = [ "libc", ] -[[package]] -name = "no_std_io2" -version = "0.9.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b51ed7824b6e07d354605f4abb3d9d300350701299da96642ee084f5ce631550" -dependencies = [ - "memchr", -] - [[package]] name = "noisy_float" version = "0.2.1" @@ -2302,21 +1951,6 @@ dependencies = [ "minimal-lexical", ] -[[package]] -name = "nom" -version = "8.0.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df9761775871bdef83bee530e60050f7e54b1105350d6884eb0fb4f46c2f9405" -dependencies = [ - "memchr", -] - -[[package]] -name = "noop_proc_macro" -version = "0.3.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0676bb32a98c1a483ce53e500a81ad9c3d5b3f7c920c28c24e9cb0980d0b5bc8" - [[package]] name = "nu-ansi-term" version = "0.50.3" @@ -2326,16 +1960,6 @@ dependencies = [ "windows-sys 0.61.2", ] -[[package]] -name = "num-bigint" -version = "0.4.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a5e44f723f1133c9deac646763579fdb3ac745e418f2a7af9cd0c431da1f20b9" -dependencies = [ - "num-integer", - "num-traits", -] - [[package]] name = "num-complex" version = "0.4.6" @@ -2345,17 +1969,6 @@ dependencies = [ "num-traits", ] -[[package]] -name = "num-derive" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - [[package]] name = "num-integer" version = "0.1.46" @@ -2365,17 +1978,6 @@ dependencies = [ "num-traits", ] -[[package]] -name = "num-rational" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824" -dependencies = [ - "num-bigint", - "num-integer", - "num-traits", -] - [[package]] name = "num-traits" version = "0.2.19" @@ -2614,12 +2216,6 @@ version = "1.0.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a" -[[package]] -name = "pastey" -version = "0.1.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "35fb2e5f958ec131621fdd531e9fc186ed768cbe395337403ae56c17a74c68ec" - [[package]] name = "pear" version = "0.2.9" @@ -2707,19 +2303,6 @@ version = "0.2.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b4596b6d070b27117e987119b4dac604f3c58cfb0b191112e24771b2faeac1a6" -[[package]] -name = "png" -version = "0.18.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "60769b8b31b2a9f263dae2776c37b1b28ae246943cf719eb6946a1db05128a61" -dependencies = [ - "bitflags", - "crc32fast", - "fdeflate", - "flate2", - "miniz_oxide", -] - [[package]] name = "portable-atomic" version = "1.13.1" @@ -2794,25 +2377,6 @@ dependencies = [ "yansi", ] -[[package]] -name = "profiling" -version = "1.0.17" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3eb8486b569e12e2c32ad3e204dbaba5e4b5b216e9367044f25f1dba42341773" -dependencies = [ - "profiling-procmacros", -] - -[[package]] -name = "profiling-procmacros" -version = "1.0.17" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "52717f9a02b6965224f95ca2a81e2e0c5c43baacd28ca057577988930b6c3d5b" -dependencies = [ - "quote", - "syn", -] - [[package]] name = "prost" version = "0.14.3" @@ -2886,12 +2450,6 @@ dependencies = [ "pulldown-cmark", ] -[[package]] -name = "pxfm" -version = "0.1.29" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e0c5ccf5294c6ccd63a74f1565028353830a9c2f5eb0c682c355c471726a6e3f" - [[package]] name = "pyo3" version = "0.27.2" @@ -2953,21 +2511,6 @@ dependencies = [ "syn", ] -[[package]] -name = "qoi" -version = "0.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7f6d64c71eb498fe9eae14ce4ec935c555749aef511cca85b5568910d6e48001" -dependencies = [ - "bytemuck", -] - -[[package]] -name = "quick-error" -version = "2.0.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a993555f31e5a609f617c12db6250dedcac1b0a85076912c436e6fc9b2c8e6a3" - [[package]] name = "quinn" version = "0.11.9" @@ -3104,56 +2647,6 @@ dependencies = [ "getrandom 0.3.4", ] -[[package]] -name = "rav1e" -version = "0.8.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "43b6dd56e85d9483277cde964fd1bdb0428de4fec5ebba7540995639a21cb32b" -dependencies = [ - "aligned-vec", - "arbitrary", - "arg_enum_proc_macro", - "arrayvec", - "av-scenechange", - "av1-grain", - "bitstream-io", - "built", - "cfg-if", - "interpolate_name", - "itertools 0.14.0", - "libc", - "libfuzzer-sys", - "log", - "maybe-rayon", - "new_debug_unreachable", - "noop_proc_macro", - "num-derive", - "num-traits", - "paste", - "profiling", - "rand 0.9.3", - "rand_chacha 0.9.0", - "simd_helpers", - "thiserror 2.0.18", - "v_frame", - "wasm-bindgen", -] - -[[package]] -name = "ravif" -version = "0.13.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e52310197d971b0f5be7fe6b57530dcd27beb35c1b013f29d66c1ad73fbbcc45" -dependencies = [ - "avif-serialize", - "imgref", - "loop9", - "quick-error", - "rav1e", - "rayon", - "rgb", -] - [[package]] name = "rawpointer" version = "0.2.1" @@ -3359,12 +2852,6 @@ dependencies = [ "web-sys", ] -[[package]] -name = "rgb" -version = "0.8.53" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "47b34b781b31e5d73e9fbc8689c70551fd1ade9a19e3e28cfec8580a79290cc4" - [[package]] name = "ring" version = "0.17.14" @@ -3750,15 +3237,6 @@ version = "0.3.9" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "703d5c7ef118737c72f1af64ad2f6f8c5e1921f818cdcb97b8fe6fc69bf66214" -[[package]] -name = "simd_helpers" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "95890f873bec569a0362c235787f3aca6e1e887302ba4840839bcc6459c42da6" -dependencies = [ - "quote", -] - [[package]] name = "slab" version = "0.4.12" @@ -3798,12 +3276,6 @@ dependencies = [ "winapi", ] -[[package]] -name = "spin" -version = "0.9.8" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67" - [[package]] name = "spm_precompiled" version = "0.1.4" @@ -3811,7 +3283,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5851699c4033c63636f7ea4cf7b7c1f1bf06d0cc03cfb42e711de5a5c46cf326" dependencies = [ "base64 0.13.1", - "nom 7.1.3", + "nom", "serde", "unicode-segmentation", ] @@ -4026,20 +3498,6 @@ dependencies = [ "cfg-if", ] -[[package]] -name = "tiff" -version = "0.11.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b63feaf3343d35b6ca4d50483f94843803b0f51634937cc2ec519fc32232bc52" -dependencies = [ - "fax", - "flate2", - "half", - "quick-error", - "weezl", - "zune-jpeg", -] - [[package]] name = "tinystr" version = "0.8.3" @@ -4613,17 +4071,6 @@ dependencies = [ "wasm-bindgen", ] -[[package]] -name = "v_frame" -version = "0.3.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "666b7727c8875d6ab5db9533418d7c764233ac9c0cff1d469aec8fa127597be2" -dependencies = [ - "aligned-vec", - "num-traits", - "wasm-bindgen", -] - [[package]] name = "valuable" version = "0.1.1" @@ -4825,12 +4272,6 @@ dependencies = [ "rustls-pki-types", ] -[[package]] -name = "weezl" -version = "0.1.12" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a28ac98ddc8b9274cb41bb4d9d4d5c425b6020c50c46f25559911905610b4a88" - [[package]] name = "which" version = "8.0.2" @@ -5285,12 +4726,6 @@ dependencies = [ "rustix", ] -[[package]] -name = "y4m" -version = "0.8.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a5a4b21e1a62b67a2970e6831bc091d7b87e119e7f9791aef9702e3bef04448" - [[package]] name = "yansi" version = "1.0.1" @@ -5405,27 +4840,3 @@ name = "zmij" version = "1.0.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" - -[[package]] -name = "zune-core" -version = "0.5.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cb8a0807f7c01457d0379ba880ba6322660448ddebc890ce29bb64da71fb40f9" - -[[package]] -name = "zune-inflate" -version = "0.2.54" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "73ab332fe2f6680068f3582b16a24f90ad7096d5d39b974d1c0aff0125116f02" -dependencies = [ - "simd-adler32", -] - -[[package]] -name = "zune-jpeg" -version = "0.5.15" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "27bc9d5b815bc103f142aa054f561d9187d191692ec7c2d1e2b4737f8dbd7296" -dependencies = [ - "zune-core", -] diff --git a/encoderfile-runtime/src/main.rs b/encoderfile-runtime/src/main.rs index 7a208a72..d3e153f7 100644 --- a/encoderfile-runtime/src/main.rs +++ b/encoderfile-runtime/src/main.rs @@ -2,10 +2,7 @@ use std::{fs::File, io::BufReader}; use anyhow::Result; use clap::Parser; -use encoderfile::{ - runtime::load_assets, - transport::cli::Cli, -}; +use encoderfile::{runtime::load_assets, transport::cli::Cli}; #[tokio::main] async fn main() -> Result<()> { diff --git a/encoderfile/src/builder/builder.rs b/encoderfile/src/builder/builder.rs index 05747882..20bdf93b 100644 --- a/encoderfile/src/builder/builder.rs +++ b/encoderfile/src/builder/builder.rs @@ -16,9 +16,9 @@ use crate::{ assets::{AssetKind, AssetPlan, AssetSource, PlannedAsset}, codec::EncoderfileCodec, }, + runtime::Input, }; use anyhow::{Context, Result}; -use ort::session::input; use serde::{Deserialize, Serialize}; #[derive(Debug, Serialize, Deserialize)] diff --git a/encoderfile/src/common/model_type.rs b/encoderfile/src/common/model_type.rs index 26a28880..b4ca0d34 100644 --- a/encoderfile/src/common/model_type.rs +++ b/encoderfile/src/common/model_type.rs @@ -1,9 +1,5 @@ macro_rules! model_type { [ $( $x:ident ),* $(,)? ] => { - pub trait ModelTypeSpec: Send + Sync + Clone + std::fmt::Debug + 'static { - fn enum_val() -> ModelType; - } - // create enum #[derive(Debug, Clone, PartialEq, Eq, serde::Serialize, serde::Deserialize, utoipa::ToSchema, schemars::JsonSchema)] #[serde(rename_all = "snake_case")] @@ -46,6 +42,10 @@ macro_rules! model_type { } } +pub trait ModelTypeSpec: Send + Sync + Clone + std::fmt::Debug + 'static { + fn enum_val() -> ModelType; +} + model_type![ Embedding, SequenceClassification, diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index 24502d57..5e354f36 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -7,6 +7,7 @@ use crate::{ AppState, EncoderfileState, FeatureExtractorState, + ORTSessionBuilder, ClassifierState, TextInputState, ImageInputState, @@ -14,17 +15,21 @@ use crate::{ TaskType, }, }; -use ort::session::Session; +use ort::session::{Input, Session}; use parking_lot::Mutex; use std::str::FromStr; -use std::{fs::File, io::BufReader}; +use std::{fs::File, io::BufReader, fmt::Debug}; const EMBEDDING_DIR: &str = "../models/embedding"; // CHECK sentence embedding???? const SEQUENCE_CLASSIFICATION_DIR: &str = "../models/sequence_classification"; const TOKEN_CLASSIFICATION_DIR: &str = "../models/token_classification"; -pub fn get_state(dir: &str) -> AppState +pub fn get_state<'a, T: ModelTypeSpec + InputType + TaskType>(dir: &'a str) -> AppState + where ::State: TryFrom<&'a str> + Debug, + <::State as TryFrom<&'a str>>::Error: Debug, + ::State: TryFrom<&'a str> + Debug, + <::State as TryFrom<&'a str>>::Error: Debug, { let config = Config { name: "my-model".to_string(), @@ -36,20 +41,19 @@ pub fn get_state(dir: & let session = get_model(dir); + let model_input_state = ::State::try_from(dir).expect("could not load model input state from file"); + let model_task_state = ::State::try_from(dir).expect("could not load model task state from file"); + EncoderfileState::new( config, session, - T::get_input_state(dir), - T::get_task_state(dir), + model_input_state, + model_task_state, ).into() } pub trait TaskTypeFromFile: TaskType { - fn get_task_state(dir: &str) -> Self::TaskState; -} - -pub trait InputTypeFromFile: InputType { - fn get_input_state(dir: &str) -> Self::InputState; + fn get_task_state(dir: &str) -> Result; } pub fn get_reader(dir: &str) -> BufReader { @@ -58,69 +62,53 @@ pub fn get_reader(dir: &str) -> BufReader { } // Input types -fn get_text_input_state(dir: &str) -> TextInputState { +fn get_text_input_state(dir: &str) -> Result { let reader = get_reader(dir); let tokenizer = get_tokenizer(dir); - let model_config = serde_json::from_reader(reader).expect("Invalid model config"); + let model_config = serde_json::from_reader(reader)?; - TextInputState { tokenizer, model_config } + Ok(TextInputState { tokenizer, model_config }) } -fn get_image_input_state(dir: &str) -> ImageInputState { +fn get_image_input_state(dir: &str) -> Result { let reader = get_reader(dir); - let incomplete_state: ImageInputState = serde_json::from_reader(reader).expect("Invalid model config"); - ImageInputState { + let incomplete_state: ImageInputState = serde_json::from_reader(reader)?; + Ok(ImageInputState { num_channels: incomplete_state.num_channels, height: incomplete_state.height.or(incomplete_state.image_size), width: incomplete_state.width.or(incomplete_state.image_size), image_size: incomplete_state.image_size, - } + }) } -macro_rules! input_state_impl { - ($model_type:ty, $state_fun:ident) => { - impl InputTypeFromFile for $model_type { - fn get_input_state(dir: &str) -> Self::InputState { +macro_rules! state_impl { + ($input_type:ty, $state_fun:ident) => { + impl TryFrom<&str> for $input_type { + type Error = anyhow::Error; + + fn try_from(dir: &str) -> Result { $state_fun(dir) } } }; } -input_state_impl!(model_type::SequenceClassification, get_text_input_state); -input_state_impl!(model_type::TokenClassification, get_text_input_state); -input_state_impl!(model_type::ImageClassification, get_image_input_state); -input_state_impl!(model_type::Embedding, get_text_input_state); -input_state_impl!(model_type::SentenceEmbedding, get_text_input_state); - +state_impl!(TextInputState, get_text_input_state); +state_impl!(ImageInputState, get_image_input_state); +state_impl!(ClassifierState, get_class_task_state); +state_impl!(FeatureExtractorState, get_feature_task_state); // Task types -fn get_class_task_state(dir: &str) -> ClassifierState { +fn get_class_task_state(dir: &str) -> Result { let reader = get_reader(dir); - serde_json::from_reader(reader).expect("Invalid model config") + let state: ClassifierState = serde_json::from_reader(reader)?; + Ok(state) } -fn get_feature_task_state(_dir: &str) -> FeatureExtractorState { - FeatureExtractorState {} +fn get_feature_task_state(_dir: &str) -> Result { + Ok(FeatureExtractorState {}) } -macro_rules! task_state_impl { - ($model_type:ty, $state_fun:ident) => { - impl TaskTypeFromFile for $model_type { - fn get_task_state(dir: &str) -> Self::TaskState { - $state_fun(dir) - } - } - }; -} - -task_state_impl!(model_type::SequenceClassification, get_class_task_state); -task_state_impl!(model_type::TokenClassification, get_class_task_state); -task_state_impl!(model_type::ImageClassification, get_class_task_state); -task_state_impl!(model_type::Embedding, get_feature_task_state); -task_state_impl!(model_type::SentenceEmbedding, get_feature_task_state); - - pub fn embedding_state() -> AppState { diff --git a/encoderfile/src/runtime/mod.rs b/encoderfile/src/runtime/mod.rs index 35a2fa20..6032ea12 100644 --- a/encoderfile/src/runtime/mod.rs +++ b/encoderfile/src/runtime/mod.rs @@ -7,6 +7,7 @@ mod state; mod tokenizer; pub use loader::{EncoderfileLoader, load_assets}; +pub use session::{ORTExecutionProvider, ORTSessionBuilder}; pub use state::{ AppState, EncoderfileState, diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 0010be37..908e982f 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -1,11 +1,16 @@ -use std::{marker::PhantomData, sync::Arc}; +use std::{ + marker::PhantomData, + sync::Arc, + io::{Read, Seek, Write}, +}; use serde::{Deserialize, Serialize}; use ort::session::Session; use parking_lot::Mutex; use crate::{ - common::{Config, ModelConfig, model_type::{ModelType, ModelTypeSpec, self}}, runtime::TokenizerService, transforms::DEFAULT_LIBS + common::{Config, ModelConfig, model_type::{ModelType, ModelTypeSpec, self}}, runtime::TokenizerService, transforms::DEFAULT_LIBS, + runtime::loader::EncoderfileLoader, }; pub type AppState = Arc>; @@ -30,7 +35,7 @@ pub trait TaskType { fn task_type() -> Task { Self::TASK } - type TaskState; + type State; } pub trait InputType { @@ -41,10 +46,12 @@ pub trait InputType { fn input_type() -> Input { Self::INPUT } - type InputState; + type State; } +#[derive(Debug, Deserialize, Serialize)] pub struct TextInputState { + // TODO check Clone impl pub tokenizer: TokenizerService, pub model_config: ModelConfig, } @@ -91,11 +98,75 @@ impl ClassifierState { #[derive(Debug, Serialize, Deserialize, Clone)] pub struct FeatureExtractorState {} +fn text_input_state_try_from_loader<'a, R>(loader: &mut EncoderfileLoader<'a, R>) -> Result +where + R: Read + Seek, +{ + let tokenizer = loader.tokenizer()?; + let model_config = loader.model_config()?; + Ok(TextInputState { + tokenizer, + model_config, + }) +} + +fn image_input_state_try_from_loader<'a, R>(loader: &mut EncoderfileLoader<'a, R>) -> Result +where + R: Read + Seek, +{ + let model_config = loader.model_config()?; + Ok(ImageInputState { + num_channels: model_config.num_channels.ok_or_else(|| anyhow::anyhow!("num_channels is required for image models"))?, + height: model_config.height, + width: model_config.width, + image_size: model_config.image_size, + }) +} + +fn classifier_state_try_from_loader<'a, R>(loader: &mut EncoderfileLoader<'a, R>) -> Result +where + R: Read + Seek, +{ + let model_config = loader.model_config()?.clone(); + Ok(ClassifierState { + id2label: model_config.id2label.clone(), + label2id: model_config.label2id.clone(), + num_labels: model_config.num_labels(), + }) +} + +fn feature_extractor_state_try_from_loader<'a, R>(_loader: &mut EncoderfileLoader<'a, R>) -> Result +where + R: Read + Seek, +{ + Ok(FeatureExtractorState {}) +} + +macro_rules! state_from_source_impl { + ($base_type:tt, $state_type:ty, $state_fun:ident) => { + impl<'a, 'borrow, R> TryFrom<&'borrow mut EncoderfileLoader<'a, R>> for $state_type + where R: Read + Seek, + { + type Error = anyhow::Error; + + fn try_from(loader: &'borrow mut EncoderfileLoader<'a, R>) -> Result { + $state_fun::(loader) + } + } + }; +} + +state_from_source_impl!(InputType, TextInputState, text_input_state_try_from_loader); +state_from_source_impl!(InputType, ImageInputState, image_input_state_try_from_loader); +state_from_source_impl!(TaskType, ClassifierState, classifier_state_try_from_loader); +state_from_source_impl!(TaskType, FeatureExtractorState, feature_extractor_state_try_from_loader); + + macro_rules! input_state_impl { ($model_type:ty, $state_type:ty, $input:expr) => { impl InputType for $model_type { const INPUT: Input = $input; - type InputState = $state_type; + type State = $state_type; } }; } @@ -110,7 +181,7 @@ macro_rules! task_state_impl { ($model_type:ty, $state_type:ty, $task:expr) => { impl TaskType for $model_type { const TASK: Task = $task; - type TaskState = $state_type; + type State = $state_type; } }; } @@ -150,21 +221,27 @@ input_type_impl![ ]; #[derive(Debug)] -pub struct EncoderfileState { +pub struct EncoderfileState + where ::State: std::fmt::Debug, + ::State: std::fmt::Debug, +{ pub config: Config, pub session: Mutex, - pub per_model_input_state: T::InputState, - pub per_task_state: T::TaskState, + pub model_input_state: ::State, + pub task_state: ::State, pub lua_libs: Vec, _marker: PhantomData, } -impl EncoderfileState { +impl EncoderfileState + where ::State: std::fmt::Debug, + ::State: std::fmt::Debug, +{ pub fn new( config: Config, session: Mutex, - per_model_input_state: T::InputState, - per_task_state: T::TaskState, + model_input_state: ::State, + task_state: ::State, ) -> EncoderfileState { let lua_libs = match config.lua_libs { Some(ref libs) => Vec::::from(libs), @@ -173,8 +250,8 @@ impl EncoderfileState { EncoderfileState { config, session, - per_model_input_state, - per_task_state, + model_input_state, + task_state, lua_libs, _marker: PhantomData, } diff --git a/encoderfile/src/services/embedding.rs b/encoderfile/src/services/embedding.rs index 5d55b372..153c4ee7 100644 --- a/encoderfile/src/services/embedding.rs +++ b/encoderfile/src/services/embedding.rs @@ -16,7 +16,7 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; let transform = EmbeddingTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 89ff988e..cbc62f04 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -37,16 +37,16 @@ impl Inference for AppState println!("Height x width: {:?} x {:?}", img.height(), img.width()); img .resize_exact( - self.per_model_input_state.width.unwrap(), - self.per_model_input_state.height.unwrap(), + self.model_input_state.width.unwrap(), + self.model_input_state.height.unwrap(), DEFAULT_FILTER_TYPE ) .to_rgb8() }).collect(); let batch_size = request.images.len(); - let num_channels = self.per_model_input_state.num_channels as usize; - let height = self.per_model_input_state.height.unwrap() as usize; - let width = self.per_model_input_state.width.unwrap() as usize; + let num_channels = self.model_input_state.num_channels as usize; + let height = self.model_input_state.height.unwrap() as usize; + let width = self.model_input_state.width.unwrap() as usize; if num_channels != 3 { return Err(ApiError::InputError("Image classification currently expects 3 RGB channels")); @@ -71,7 +71,7 @@ impl Inference for AppState images_array.mapv_inplace(|x| ((x * rescale_factor) - image_mean) / image_std); println!("Some sample slice of the input array (post scale): {:?}", images_array.slice(s![.., .., 0..5, 0..5])); - let label_map = self.per_task_state.id2label.clone().unwrap(); + let label_map = self.task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); entries.sort_by(|x, y| x.0.cmp(&y.0)); let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); diff --git a/encoderfile/src/services/model_metadata.rs b/encoderfile/src/services/model_metadata.rs index 483ca753..e002097a 100644 --- a/encoderfile/src/services/model_metadata.rs +++ b/encoderfile/src/services/model_metadata.rs @@ -1,4 +1,5 @@ use std::collections::HashMap; +use std::fmt::Debug; use crate::{ common::{GetModelMetadataResponse, model_type::{ModelType, ModelTypeSpec}}, runtime::{AppState, TaskType, InputType}, @@ -23,7 +24,10 @@ pub trait Metadata { } -impl Metadata for AppState { +impl Metadata for AppState + where ::State: Debug, + ::State: Debug, +{ fn model_id(&self) -> String { self.config.name.clone() } diff --git a/encoderfile/src/services/sentence_embedding.rs b/encoderfile/src/services/sentence_embedding.rs index 08e812a8..b9e8f205 100644 --- a/encoderfile/src/services/sentence_embedding.rs +++ b/encoderfile/src/services/sentence_embedding.rs @@ -16,7 +16,7 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; let transform = SentenceEmbeddingTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/sequence_classification.rs b/encoderfile/src/services/sequence_classification.rs index 4911afbe..bc9d7533 100644 --- a/encoderfile/src/services/sequence_classification.rs +++ b/encoderfile/src/services/sequence_classification.rs @@ -16,7 +16,7 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; let transform = SequenceClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; @@ -24,7 +24,7 @@ impl Inference for AppState let results = inference::sequence_classification::sequence_classification( self.session.lock(), &transform, - &self.per_task_state, + &self.task_state, encodings, )?; diff --git a/encoderfile/src/services/token_classification.rs b/encoderfile/src/services/token_classification.rs index 0d4d5a62..fed061bd 100644 --- a/encoderfile/src/services/token_classification.rs +++ b/encoderfile/src/services/token_classification.rs @@ -18,7 +18,7 @@ impl Inference for AppState let session = self.session.lock(); - let encodings = self.per_model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; let transform = TokenClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; @@ -26,7 +26,7 @@ impl Inference for AppState let results = inference::token_classification::token_classification( session, &transform, - &self.per_task_state, + &self.task_state, encodings, )?; diff --git a/encoderfile/src/transport/cli.rs b/encoderfile/src/transport/cli.rs index 4ded808c..a03ffb88 100644 --- a/encoderfile/src/transport/cli.rs +++ b/encoderfile/src/transport/cli.rs @@ -1,9 +1,9 @@ use crate::{ common::{ - FromCliInput, ModelType, - model_type::{self, ModelTypeSpec}, + FromCliInput, + model_type::{self, ModelType, ModelTypeSpec}, }, - runtime::{EncoderfileLoader, EncoderfileState, ORTExecutionProvider}, + runtime::{EncoderfileLoader, EncoderfileState, ORTExecutionProvider, InputType, TaskType}, services::{Inference, Metadata}, transport::{ grpc::GrpcRouter, @@ -18,7 +18,7 @@ use opentelemetry::trace::TracerProvider as _; use opentelemetry_otlp::{Protocol, WithExportConfig}; use opentelemetry_sdk::trace::SdkTracerProvider; use std::{ - fmt::Display, + fmt::{Display, Debug}, io::{Read, Seek, Write}, sync::Arc, }; @@ -54,13 +54,13 @@ pub trait CliRoute: Inference { impl CliRoute for T {} #[derive(Parser)] -pub struct TextCli { +pub struct Cli { #[command(subcommand)] - pub command: TextCommands, + pub command: Commands, } #[derive(Subcommand)] -pub enum TextCommands { +pub enum Commands { Serve { #[arg(long, default_value = "[::]")] grpc_hostname: String, @@ -109,10 +109,10 @@ pub enum TextCommands { }, } -impl TextCommands { - pub async fn execute<'a, R: Read + Seek>( +impl Commands { + pub async fn execute<'loader, R: Read + Seek>( self, - loader: &mut EncoderfileLoader<'a, R>, + loader: &mut EncoderfileLoader<'loader, R>, ) -> Result<()> { match loader.model_type() { ModelType::Embedding => { @@ -131,17 +131,24 @@ impl TextCommands { self.execute_from_loader::(loader) .await } + ModelType::ImageClassification => { + Err(anyhow::anyhow!("Image classification is not yet supported in the CLI transport")) + } } } - pub async fn execute_from_loader<'a, R: Read + Seek, T: ModelTypeSpec>( + pub async fn execute_from_loader<'loader, R: Read + Seek, T: ModelTypeSpec + InputType + TaskType>( self, - loader: &mut EncoderfileLoader<'a, R>, + loader: &mut EncoderfileLoader<'loader, R>, ) -> Result<()> where Arc>: Inference + GrpcRouter + HttpRouter + McpRouter + CliRoute, + ::State: Debug, + ::State: Debug, + for<'b> ::State: TryFrom<&'b mut EncoderfileLoader<'loader, R>, Error = anyhow::Error>, + for<'b> ::State: TryFrom<&'b mut EncoderfileLoader<'loader, R>, Error = anyhow::Error>, { match self { - TextCommands::Serve { + Commands::Serve { grpc_hostname, grpc_port, http_hostname, @@ -161,15 +168,13 @@ impl TextCommands { onnx_args.graph_optimization_level(), )? .into(); - let model_config = loader.model_config()?; - let tokenizer = loader.tokenizer()?; let config = loader.encoderfile_config()?; let state = Arc::new(EncoderfileState::::new( config, session, - tokenizer, - model_config, + ::State::try_from(loader).expect("could not load model input state from file"), + ::State::try_from(loader).expect("could not load model task state from file") )); let banner = crate::get_banner(state.model_id().as_str()); @@ -211,7 +216,7 @@ impl TextCommands { let _ = tokio::join!(grpc_process, http_process); } - TextCommands::Infer { + Commands::Infer { inputs, format, out_dir, @@ -225,22 +230,20 @@ impl TextCommands { )? .into(); - let model_config = loader.model_config()?; - let tokenizer = loader.tokenizer()?; let config = loader.encoderfile_config()?; let state = Arc::new(EncoderfileState::::new( config, session, - tokenizer, - model_config, + ::State::try_from(loader).expect("could not load model input state from file"), + ::State::try_from(loader).expect("could not load model input state from file"), )); setup_tracing(None)?; state.cli_route(inputs, format, out_dir)? } - TextCommands::Mcp { + Commands::Mcp { hostname, port, cert_file, @@ -255,15 +258,13 @@ impl TextCommands { )? .into(); - let model_config = loader.model_config()?; - let tokenizer = loader.tokenizer()?; let config = loader.encoderfile_config()?; let state = Arc::new(EncoderfileState::::new( config, session, - tokenizer, - model_config, + ::State::try_from(loader).expect("could not load model input state from file"), + ::State::try_from(loader).expect("could not load model input state from file"), )); let banner = crate::get_banner(state.model_id().as_str()); @@ -276,114 +277,7 @@ impl TextCommands { } } -#[derive(Parser)] -pub struct ImageCli { - #[command(subcommand)] - pub command: ImageCommands, -} - -#[derive(Subcommand)] -pub enum ImageCommands { - Serve { - #[arg(long, default_value = "[::]")] - grpc_hostname: String, - #[arg(long, default_value = "50051")] - grpc_port: String, - #[arg(long, default_value = "0.0.0.0")] - http_hostname: String, - #[arg(long, default_value = "8080")] - http_port: String, - #[arg(long, default_value_t = false)] - disable_grpc: bool, - #[arg(long, default_value_t = false)] - disable_http: bool, - #[arg(long, default_value_t = false)] - enable_otel: bool, - #[arg(long, default_value = "http://localhost:4317")] - otel_exporter_url: String, - #[arg(long)] - cert_file: Option, - #[arg(long)] - key_file: Option, - }, - Infer { - #[arg(required = true)] - inputs: Vec, - #[arg(short, long, default_value_t = Format::Json)] - format: Format, - #[arg(short)] - out_dir: Option, - }, -} - -impl ImageCommands { - pub async fn execute(self, state: S) -> Result<()> - where - S: Inference + GrpcRouter + HttpRouter + CliRoute, - { - match self { - ImageCommands::Serve { - grpc_hostname, - grpc_port, - http_hostname, - http_port, - disable_grpc, - disable_http, - enable_otel, - otel_exporter_url, - cert_file, - key_file, - } => { - let banner = crate::get_banner(state.model_id().as_str()); - - if disable_grpc && disable_http { - return Err(crate::error::ApiError::ConfigError( - "Cannot disable both gRPC and HTTP", - ))?; - } - - match enable_otel { - true => setup_tracing(Some(otel_exporter_url.as_str())), - false => setup_tracing(None), - }?; - - let grpc_process = match disable_grpc { - true => tokio::spawn(async { Ok(()) }), - false => tokio::spawn(run_grpc( - grpc_hostname, - grpc_port, - cert_file.clone(), - key_file.clone(), - state.clone(), - )), - }; - - let http_process = match disable_http { - true => tokio::spawn(async { Ok(()) }), - false => tokio::spawn(run_http( - http_hostname, - http_port, - cert_file.clone(), - key_file.clone(), - state.clone(), - )), - }; - - println!("{}", banner); - let _ = tokio::join!(grpc_process, http_process); - } - ImageCommands::Infer { - inputs, - format, - out_dir, - } => { - setup_tracing(None)?; - - state.cli_route(inputs, format, out_dir)? - } - } - Ok(()) #[derive(Clone, Args)] pub struct ONNXArgs { #[arg(long, default_value_t = false)] diff --git a/encoderfile/src/transport/mcp/mod.rs b/encoderfile/src/transport/mcp/mod.rs index e61bccbb..be53d3cc 100644 --- a/encoderfile/src/transport/mcp/mod.rs +++ b/encoderfile/src/transport/mcp/mod.rs @@ -163,4 +163,4 @@ generate_mcp!( "Performs image classification of input images.", "This tool will classify input images." ); -*/ \ No newline at end of file +*/ From 9821e761e9972ed175a00cad999ea1dbe237d69a Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Mon, 18 May 2026 19:15:25 +0200 Subject: [PATCH 10/21] Fix tests --- encoderfile/benches/postprocessing.rs | 8 ++-- encoderfile/proto/image_classification.proto | 2 +- .../validation/image_classification.rs | 6 ++- .../src/common/image_classification.rs | 2 +- encoderfile/src/common/image_types.rs | 4 -- encoderfile/src/dev_utils/mod.rs | 13 ++++-- .../src/generated/image_classification.rs | 5 ++- .../src/inference/image_classification.rs | 3 -- encoderfile/src/runtime/state.rs | 11 ++--- .../src/services/image_classification.rs | 28 +++++++++---- encoderfile/src/services/model_metadata.rs | 40 ++++++++++++++----- encoderfile/src/transport/grpc/mod.rs | 1 + encoderfile/tests/test_grpc.rs | 33 ++++++++++++++- encoderfile/tests/test_models.rs | 16 ++++---- 14 files changed, 118 insertions(+), 54 deletions(-) diff --git a/encoderfile/benches/postprocessing.rs b/encoderfile/benches/postprocessing.rs index bcd1053e..43660516 100644 --- a/encoderfile/benches/postprocessing.rs +++ b/encoderfile/benches/postprocessing.rs @@ -16,7 +16,7 @@ fn main() { #[divan::bench(args = [(8, 16, 384), (16, 128, 768), (64, 512, 1024)])] fn embedding_postprocess(b: Bencher, dim: (usize, usize, usize)) { - let tokenizer = &embedding_state().per_model_input_state.tokenizer; + let tokenizer = &embedding_state().model_input_state.tokenizer; let (batch, tokens, hidden) = dim; // Random embeddings @@ -35,7 +35,7 @@ fn embedding_postprocess(b: Bencher, dim: (usize, usize, usize)) { #[divan::bench(args = [8, 16, 64])] fn sequence_classification_postprocess(b: Bencher, batch: usize) { let state = sequence_classification_state(); - let config = &state.per_task_state; + let config = &state.task_state; let n_labels = config.id2label.clone().unwrap().len(); let mut rng = rand::rng(); @@ -51,10 +51,10 @@ fn sequence_classification_postprocess(b: Bencher, batch: usize) { #[divan::bench(args = [(8, 16), (16, 128), (64, 512)])] fn token_classification_postprocess(b: Bencher, dim: (usize, usize)) { let state = token_classification_state(); - let config = &state.per_task_state; + let config = &state.task_state; let n_labels = config.id2label.clone().unwrap().len(); - let tokenizer = &embedding_state().per_model_input_state.tokenizer; + let tokenizer = &embedding_state().model_input_state.tokenizer; let (batch, tokens) = dim; // Random embeddings diff --git a/encoderfile/proto/image_classification.proto b/encoderfile/proto/image_classification.proto index af028275..065a3fa3 100644 --- a/encoderfile/proto/image_classification.proto +++ b/encoderfile/proto/image_classification.proto @@ -16,6 +16,6 @@ message ImageClassificationRequest { } message ImageClassificationResponse { - repeated encoderfile.image_types.ImageLabels labels = 1; + repeated encoderfile.image_types.ImageLabels results = 1; map metadata = 11; } diff --git a/encoderfile/src/builder/transforms/validation/image_classification.rs b/encoderfile/src/builder/transforms/validation/image_classification.rs index 1ec5ebab..9f5fb81a 100644 --- a/encoderfile/src/builder/transforms/validation/image_classification.rs +++ b/encoderfile/src/builder/transforms/validation/image_classification.rs @@ -1,6 +1,6 @@ use super::{ TransformValidatorExt, - utils::{BATCH_SIZE, SEQ_LEN, random_tensor, validation_err, validation_err_ctx}, + utils::{BATCH_SIZE, random_tensor, validation_err, validation_err_ctx}, }; use crate::{ common::ModelConfig, @@ -8,6 +8,8 @@ use crate::{ }; use anyhow::{Context, Result}; +const TEST_NUM_LABELS: usize = 16; + impl TransformValidatorExt for ImageClassificationTransform { fn dry_run(&self, model_config: &ModelConfig) -> Result<()> { let num_labels = match model_config.num_labels() { @@ -17,7 +19,7 @@ impl TransformValidatorExt for ImageClassificationTransform { )?, }; - let dummy_logits = random_tensor(&[BATCH_SIZE, SEQ_LEN, num_labels], (-1.0, 1.0))?; + let dummy_logits = random_tensor(&[BATCH_SIZE, TEST_NUM_LABELS], (-1.0, 1.0))?; let shape = dummy_logits.shape().to_owned(); let res = self.postprocess(dummy_logits) diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index 4eb44e54..89647488 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -3,7 +3,6 @@ use std::{collections::HashMap, io::Read}; use utoipa::ToSchema; use anyhow::Result; use crate::common::FromReadInput; -use image::ImageFormat; use bytes::Bytes; use crate::transport::http::multipart_openapi::{FromMultipart, MultipartApiError}; use crate::common::image_types::{ImageInfo, ImageLabelScore}; @@ -99,6 +98,7 @@ pub struct ImageClassificationResult { mod tests { use super::*; use std::fs::File; + use image::ImageFormat; #[test] fn test_image_classification_request_from_read_input() { diff --git a/encoderfile/src/common/image_types.rs b/encoderfile/src/common/image_types.rs index 5a0a2040..1448243e 100644 --- a/encoderfile/src/common/image_types.rs +++ b/encoderfile/src/common/image_types.rs @@ -1,11 +1,7 @@ use serde::{Deserialize, Serialize}; -use std::{collections::HashMap, io::Read}; use utoipa::ToSchema; -use anyhow::Result; -use crate::common::FromReadInput; use image::ImageFormat; use bytes::Bytes; -use crate::transport::http::multipart_openapi::{FromMultipart, MultipartApiError}; #[derive(Debug, Serialize, Deserialize)] pub struct ImageInfo { diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index 5e354f36..a7203d59 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -1,6 +1,6 @@ use crate::{ common::{ - Config, TokenizerConfig, + ModelConfig, Config, TokenizerConfig, model_type::{self, ModelTypeSpec}, }, runtime::{ @@ -21,14 +21,14 @@ use std::str::FromStr; use std::{fs::File, io::BufReader, fmt::Debug}; const EMBEDDING_DIR: &str = "../models/embedding"; -// CHECK sentence embedding???? const SEQUENCE_CLASSIFICATION_DIR: &str = "../models/sequence_classification"; const TOKEN_CLASSIFICATION_DIR: &str = "../models/token_classification"; +const IMAGE_CLASSIFICATION_DIR: &str = "../models/image_classification"; pub fn get_state<'a, T: ModelTypeSpec + InputType + TaskType>(dir: &'a str) -> AppState - where ::State: TryFrom<&'a str> + Debug, + where ::State: TryFrom<&'a str>, <::State as TryFrom<&'a str>>::Error: Debug, - ::State: TryFrom<&'a str> + Debug, + ::State: TryFrom<&'a str>, <::State as TryFrom<&'a str>>::Error: Debug, { let config = Config { @@ -130,6 +130,11 @@ pub fn token_classification_state() -> AppState get_state(TOKEN_CLASSIFICATION_DIR) } +pub fn image_classification_state() -> AppState +{ + get_state(IMAGE_CLASSIFICATION_DIR) +} + fn get_tokenizer(dir: &str) -> crate::runtime::TokenizerService { let tokenizer_str = std::fs::read_to_string(format!("{}/{}", dir, "tokenizer.json")) .expect("Tokenizer json not found"); diff --git a/encoderfile/src/generated/image_classification.rs b/encoderfile/src/generated/image_classification.rs index bedfde9c..7cdf3f9b 100644 --- a/encoderfile/src/generated/image_classification.rs +++ b/encoderfile/src/generated/image_classification.rs @@ -20,7 +20,10 @@ impl From for common::ImageClassificationRequest { impl From for ImageClassificationResponse { fn from(val: common::ImageClassificationResponse) -> Self { Self { - labels: val.results.into_iter().map(|result| result.into()).collect(), + results: val + .results + .into_iter() + .map(|result| result.into()).collect(), metadata: val.metadata.unwrap_or_default(), } } diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index fc6523b7..ddbfcfea 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -24,7 +24,6 @@ pub fn image_classification<'a>( .unwrap() .to_owned(); let raw_outputs = crate::run_cv_model!(session, grouped_images)?; - println!("Raw outputs: {:?}", raw_outputs.keys().collect::>()); let mut outputs = raw_outputs .get("logits") .expect("Model does not return logits") @@ -33,9 +32,7 @@ pub fn image_classification<'a>( .into_dimensionality::() .expect("Model does not return tensor of shape [n_batch, n_classes]") .into_owned(); - println!("Model outputs: {:?}", outputs); outputs.mapv_inplace(logit_to_prob); - println!("Model outputs: {:?}", outputs); Ok(postprocess(outputs, classes)) } diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 908e982f..9c903bd3 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -1,7 +1,8 @@ use std::{ marker::PhantomData, sync::Arc, - io::{Read, Seek, Write}, + io::{Read, Seek}, + fmt::Debug, }; use serde::{Deserialize, Serialize}; @@ -35,7 +36,7 @@ pub trait TaskType { fn task_type() -> Task { Self::TASK } - type State; + type State: Debug; } pub trait InputType { @@ -46,7 +47,7 @@ pub trait InputType { fn input_type() -> Input { Self::INPUT } - type State; + type State: Debug; } #[derive(Debug, Deserialize, Serialize)] @@ -222,8 +223,6 @@ input_type_impl![ #[derive(Debug)] pub struct EncoderfileState - where ::State: std::fmt::Debug, - ::State: std::fmt::Debug, { pub config: Config, pub session: Mutex, @@ -234,8 +233,6 @@ pub struct EncoderfileState } impl EncoderfileState - where ::State: std::fmt::Debug, - ::State: std::fmt::Debug, { pub fn new( config: Config, diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index cbc62f04..d8084987 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -10,7 +10,7 @@ use crate::{ runtime::AppState, }; use image::RgbImage; -use ndarray::{Array4, s}; +use ndarray::{Array4}; use super::inference::Inference; use crate::inference::image_classification::image_classification; @@ -26,6 +26,10 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); + if request.images.is_empty() { + return Err(ApiError::InputError("Cannot tokenize empty string")); + } + println!("--> Received request for image classification inference: {:?}", request); let rescale_factor = 0.00392156862745098 as f32; let image_mean = 0.5; let image_std = 0.5; @@ -34,7 +38,6 @@ impl Inference for AppState // convert input image into flattened rbg let images: Vec = (&request.images).into_iter().map(|image_info| { let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); - println!("Height x width: {:?} x {:?}", img.height(), img.width()); img .resize_exact( self.model_input_state.width.unwrap(), @@ -66,10 +69,8 @@ impl Inference for AppState } } } - println!("Some sample slice of the input array (pre scale, post reshape): {:?}", images_array.slice(s![.., .., 0..5, 0..5])); // TODO make parallel images_array.mapv_inplace(|x| ((x * rescale_factor) - image_mean) / image_std); - println!("Some sample slice of the input array (post scale): {:?}", images_array.slice(s![.., .., 0..5, 0..5])); let label_map = self.task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); @@ -125,9 +126,20 @@ mod tests { let response = state.inference(request).expect("Inference failed"); assert_eq!(response.results.len(), 1); assert_eq!(response.results[0].labels.len(), 2); - assert_eq!(response.results[0].labels[0].label, "normal"); - assert_eq!(response.results[0].labels[0].score, Some(1.5378942)); - assert_eq!(response.results[0].labels[1].label, "nsfw"); - assert_eq!(response.results[0].labels[1].score, Some(-1.6556994)); + assert!(response.results[0].labels.iter().any(|x| x.label == "normal")); + assert!(response.results[0].labels.iter().any(|x| x.label == "nsfw")); + } + + #[test] + fn test_image_classification_empty() { + init_tracing(); + + let state = dev_utils::get_state::("../models/image_classification"); + let request = ImageClassificationRequest { + images: vec![], + metadata: Default::default(), + }; + let response = state.inference(request); + assert!(response.is_err()); } } \ No newline at end of file diff --git a/encoderfile/src/services/model_metadata.rs b/encoderfile/src/services/model_metadata.rs index e002097a..93fbc828 100644 --- a/encoderfile/src/services/model_metadata.rs +++ b/encoderfile/src/services/model_metadata.rs @@ -1,20 +1,21 @@ use std::collections::HashMap; -use std::fmt::Debug; use crate::{ - common::{GetModelMetadataResponse, model_type::{ModelType, ModelTypeSpec}}, runtime::{AppState, TaskType, InputType}, + common::{ + GetModelMetadataResponse, + model_type::{ModelType, ModelTypeSpec} + }, + runtime::{ + AppState, ClassifierState, FeatureExtractorState, InputType, TaskType + }, }; -pub trait ClassifierMetadata { - fn id2label(&self) -> Option>; -} - pub trait Metadata { fn metadata(&self) -> GetModelMetadataResponse { GetModelMetadataResponse { model_id: self.model_id(), model_type: self.model_type(), - id2label: None, + id2label: self.id2label(), } } @@ -22,11 +23,29 @@ pub trait Metadata { fn model_type(&self) -> ModelType; + fn id2label(&self) -> Option>; +} + +trait TaskStateMetadata { + fn id2label(&self) -> Option>; +} + +impl TaskStateMetadata for ClassifierState { + fn id2label(&self) -> Option> { + println!("ClassifierState: {:?}", self); + self.id2label.clone() + } +} + +impl TaskStateMetadata for FeatureExtractorState { + fn id2label(&self) -> Option> { + None + } } impl Metadata for AppState - where ::State: Debug, - ::State: Debug, +where + ::State: TaskStateMetadata { fn model_id(&self) -> String { self.config.name.clone() @@ -36,4 +55,7 @@ impl Metadata for AppState T::enum_val() } + fn id2label(&self) -> Option> { + self.task_state.id2label() + } } diff --git a/encoderfile/src/transport/grpc/mod.rs b/encoderfile/src/transport/grpc/mod.rs index 871b5c3f..6b9b3fad 100644 --- a/encoderfile/src/transport/grpc/mod.rs +++ b/encoderfile/src/transport/grpc/mod.rs @@ -71,6 +71,7 @@ macro_rules! generate_grpc_server { tonic::Response<$crate::generated::metadata::GetModelMetadataResponse>, tonic::Status, > { + println!("And the metadata is...: {:?}", self.state.metadata()); Ok(tonic::Response::new(self.state.metadata().into())) } } diff --git a/encoderfile/tests/test_grpc.rs b/encoderfile/tests/test_grpc.rs index 5cd95276..707f4c84 100644 --- a/encoderfile/tests/test_grpc.rs +++ b/encoderfile/tests/test_grpc.rs @@ -1,5 +1,8 @@ use std::collections::HashMap; +use std::fs::File; +use std::io::Read; +use bytes::Bytes; use encoderfile::{ dev_utils::*, generated::{ @@ -19,6 +22,11 @@ use encoderfile::{ TokenClassificationRequest, TokenClassificationResponse, token_classification_inference_server::TokenClassificationInference, }, + image_classification::{ + ImageClassificationRequest, ImageClassificationResponse, + image_classification_inference_server::ImageClassificationInference, + }, + image_types::{ImageInput} }, transport::grpc::GrpcService, }; @@ -46,8 +54,6 @@ macro_rules! test_grpc_service { .unwrap() .into_inner(); - println!("Model metadata: {:?}", response); - if $has_labels { assert!(!response.id2label.is_empty(), "id2label is an empty dict") } else { @@ -140,3 +146,26 @@ test_grpc_service!( }, SentenceEmbeddingResponse ); + +const TEST_IMAGE_PATH: &str = "../test-pictures/w3c_home.jpg"; + +fn get_file_bytes(filename: &str) -> Vec { + let mut file = File::open(filename).expect("Failed to open test image"); + let mut buffer = Vec::new(); + file.read_to_end(&mut buffer).expect("Failed to read test image"); + buffer +} + +test_grpc_service!( + image_classification_tests, + { GrpcService::new(image_classification_state()) }, + true, + ImageClassificationRequest { + inputs: vec![ + TEST_IMAGE_PATH, + TEST_IMAGE_PATH + ].iter().map(|s| ImageInput { image: get_file_bytes(s) }).collect(), + metadata: HashMap::new(), + }, + ImageClassificationResponse +); diff --git a/encoderfile/tests/test_models.rs b/encoderfile/tests/test_models.rs index c95840cc..f30c8162 100644 --- a/encoderfile/tests/test_models.rs +++ b/encoderfile/tests/test_models.rs @@ -11,7 +11,7 @@ fn test_embedding_model() { let state = embedding_state(); let encodings = state - .per_model_input_state + .model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -36,7 +36,7 @@ fn test_embedding_inference_with_bad_model() { let state = token_classification_state(); let encodings = state - .per_model_input_state + .model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -57,7 +57,7 @@ fn test_sequence_classification_model() { let state = sequence_classification_state(); let encodings = state - .per_model_input_state + .model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -73,7 +73,7 @@ fn test_sequence_classification_model() { let results = sequence_classification( session_lock, &transform, - &state.per_task_state, + &state.task_state, encodings.clone(), ) .expect("Failed to compute results"); @@ -117,7 +117,7 @@ fn test_token_classification_model() { let state = token_classification_state(); let encodings = state - .per_model_input_state + .model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -133,7 +133,7 @@ fn test_token_classification_model() { let results = token_classification( session_lock, &transform, - &state.per_task_state, + &state.task_state, encodings.clone(), ) .expect("Failed to compute results"); @@ -147,7 +147,7 @@ fn test_token_classification_inference_with_bad_model() { let state = sequence_classification_state(); let encodings = state - .per_model_input_state + .model_input_state .tokenizer .encode_text(vec![ "hello world".to_string(), @@ -163,7 +163,7 @@ fn test_token_classification_inference_with_bad_model() { token_classification( session_lock, &transform, - &state.per_task_state, + &state.task_state, encodings.clone(), ) .expect("Failed to compute results"); From 83ec21c6bcf7a73ad757b99e2fea136ff37503bd Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Tue, 19 May 2026 09:58:52 +0200 Subject: [PATCH 11/21] Fix lint --- encoderfile/src/builder/builder.rs | 2 +- encoderfile/src/builder/config.rs | 2 -- .../src/common/image_classification.rs | 33 ------------------ encoderfile/src/dev_utils/mod.rs | 4 +-- encoderfile/src/format/codec/encoder.rs | 2 +- .../src/inference/image_classification.rs | 4 +-- .../src/inference/sequence_classification.rs | 2 +- .../src/inference/token_classification.rs | 2 +- .../src/services/image_classification.rs | 8 ++--- .../src/transport/http/multipart_openapi.rs | 34 +++++++++++++++++++ encoderfile/tests/test_grpc.rs | 6 +--- encoderfile/tests/test_models.rs | 1 - 12 files changed, 46 insertions(+), 54 deletions(-) diff --git a/encoderfile/src/builder/builder.rs b/encoderfile/src/builder/builder.rs index 20bdf93b..80bed39e 100644 --- a/encoderfile/src/builder/builder.rs +++ b/encoderfile/src/builder/builder.rs @@ -27,7 +27,7 @@ pub struct EncoderfileBuilder { pub config: BuildConfig, } -pub fn validate(input: &Input) -> Result<()> { +pub fn validate(_input: &Input) -> Result<()> { Ok(()) } diff --git a/encoderfile/src/builder/config.rs b/encoderfile/src/builder/config.rs index b967401e..5bb6d240 100644 --- a/encoderfile/src/builder/config.rs +++ b/encoderfile/src/builder/config.rs @@ -1,6 +1,4 @@ use crate::common::{Config as EmbeddedConfig, LuaLibs, ModelConfig, model_type::ModelType}; -use crate::runtime::TaskType; -use crate::runtime::InputType; use anyhow::{Context, Result, bail}; use schemars::JsonSchema; use std::string::String; diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index 89647488..8889c9ee 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -4,7 +4,6 @@ use utoipa::ToSchema; use anyhow::Result; use crate::common::FromReadInput; use bytes::Bytes; -use crate::transport::http::multipart_openapi::{FromMultipart, MultipartApiError}; use crate::common::image_types::{ImageInfo, ImageLabelScore}; #[derive(Debug, Serialize, Deserialize)] @@ -13,7 +12,6 @@ pub struct ImageClassificationRequest { pub metadata: Option>, } -// FIXME check if we need to reorganize the from*input traits impl super::FromCliInput for ImageClassificationRequest { fn from_cli_input(inputs: Vec) -> Self { let images = inputs.into_iter().map(|path| { @@ -51,37 +49,6 @@ impl FromReadInput for ImageClassificationRequest { } } -impl FromMultipart for ImageClassificationRequest { - fn from_multipart( - payload: serde_json::Value, - attachments: Vec<(Option, Option, bytes::Bytes)>, - ) -> Result { - let images = attachments - .into_iter() - .map(|(_file_name, _content_type, image_bytes)| { - let format = image::guess_format(&image_bytes) - .map_err(|e| MultipartApiError::RequestConstruction( - format!("Failed to detect image format: {}", e) - ))?; - Ok(ImageInfo { - image_bytes, - image_format: format, - }) - }) - .collect::, _>>()?; - - let metadata = if payload.is_null() || payload == serde_json::json!({}) { - Some(HashMap::default()) - } else { - serde_json::from_value(payload) - .ok() - .or(Some(HashMap::default())) - }; - - Ok(Self { images, metadata }) - } -} - #[derive(Debug, Serialize, Deserialize, ToSchema, utoipa::ToResponse)] pub struct ImageClassificationResponse { pub results: Vec, diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index a7203d59..0d3281e7 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -1,6 +1,6 @@ use crate::{ common::{ - ModelConfig, Config, TokenizerConfig, + Config, TokenizerConfig, model_type::{self, ModelTypeSpec}, }, runtime::{ @@ -15,7 +15,7 @@ use crate::{ TaskType, }, }; -use ort::session::{Input, Session}; +use ort::session::Session; use parking_lot::Mutex; use std::str::FromStr; use std::{fs::File, io::BufReader, fmt::Debug}; diff --git a/encoderfile/src/format/codec/encoder.rs b/encoderfile/src/format/codec/encoder.rs index ca8b798d..5627927e 100644 --- a/encoderfile/src/format/codec/encoder.rs +++ b/encoderfile/src/format/codec/encoder.rs @@ -9,7 +9,7 @@ use crate::{ assets::{AssetPlan, AssetPolicySpec}, footer::EncoderfileFooter, }, - generated::manifest::{Artifact, EncoderfileManifest}, runtime::InputType, + generated::manifest::{Artifact, EncoderfileManifest}, }; use prost::Message; diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index ddbfcfea..524495f4 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -1,6 +1,4 @@ -use std::os::raw; - -use ndarray::{Array2, Array4, Ix2, Axis, s}; +use ndarray::{Array2, Array4, Ix2, Axis}; use crate::{ error::ApiError, diff --git a/encoderfile/src/inference/sequence_classification.rs b/encoderfile/src/inference/sequence_classification.rs index 1a37a3f7..ceb309ac 100644 --- a/encoderfile/src/inference/sequence_classification.rs +++ b/encoderfile/src/inference/sequence_classification.rs @@ -1,5 +1,5 @@ use crate::{ - common::{ModelConfig, SequenceClassificationResult}, error::ApiError, runtime::ClassifierState, transforms::{Postprocessor, SequenceClassificationTransform} + common::{SequenceClassificationResult}, error::ApiError, runtime::ClassifierState, transforms::{Postprocessor, SequenceClassificationTransform} }; use ndarray::{Array2, Axis, Ix2}; use ndarray_stats::QuantileExt; diff --git a/encoderfile/src/inference/token_classification.rs b/encoderfile/src/inference/token_classification.rs index 3d5181fa..f6825aa6 100644 --- a/encoderfile/src/inference/token_classification.rs +++ b/encoderfile/src/inference/token_classification.rs @@ -1,5 +1,5 @@ use crate::{ - common::{ModelConfig, TokenClassification, TokenClassificationResult, TokenInfo}, error::ApiError, runtime::ClassifierState, transforms::{Postprocessor, TokenClassificationTransform} + common::{TokenClassification, TokenClassificationResult, TokenInfo}, error::ApiError, runtime::ClassifierState, transforms::{Postprocessor, TokenClassificationTransform} }; use ndarray::{Array3, Axis, Ix3}; use ndarray_stats::QuantileExt; diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index d8084987..c5f69021 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -27,16 +27,16 @@ impl Inference for AppState fn inference(&self, request: impl Into) -> Result { let request = request.into(); if request.images.is_empty() { - return Err(ApiError::InputError("Cannot tokenize empty string")); + return Err(ApiError::InputError("Cannot classify empty image list")); } println!("--> Received request for image classification inference: {:?}", request); - let rescale_factor = 0.00392156862745098 as f32; + let rescale_factor = 0.003_921_569_f32; let image_mean = 0.5; let image_std = 0.5; // bilinear resampling // convert input image into flattened rbg - let images: Vec = (&request.images).into_iter().map(|image_info| { + let images: Vec = request.images.iter().map(|image_info| { let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); img .resize_exact( @@ -74,7 +74,7 @@ impl Inference for AppState let label_map = self.task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); - entries.sort_by(|x, y| x.0.cmp(&y.0)); + entries.sort_by(|x, y| x.0.cmp(y.0)); let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); let labels_batch = image_classification( diff --git a/encoderfile/src/transport/http/multipart_openapi.rs b/encoderfile/src/transport/http/multipart_openapi.rs index 6b5d11b8..8591a8de 100644 --- a/encoderfile/src/transport/http/multipart_openapi.rs +++ b/encoderfile/src/transport/http/multipart_openapi.rs @@ -7,6 +7,8 @@ use axum::{ use serde::{Deserialize, Serialize}; use utoipa::OpenApi; use crate::common::model_type::ImageClassification; +use crate::common::{ImageClassificationRequest, ImageInfo}; +use std::collections::HashMap; use crate::runtime::AppState; pub const MULTIPART_PREDICT_ENDPOINT: &str = "/predict/multipart"; @@ -69,6 +71,38 @@ pub trait FromMultipart: Sized { ) -> Result; } +impl FromMultipart for ImageClassificationRequest { + fn from_multipart( + payload: serde_json::Value, + attachments: Vec<(Option, Option, bytes::Bytes)>, + ) -> Result { + let images = attachments + .into_iter() + .map(|(_file_name, _content_type, image_bytes)| { + let format = image::guess_format(&image_bytes) + .map_err(|e| MultipartApiError::RequestConstruction( + format!("Failed to detect image format: {}", e) + ))?; + Ok(ImageInfo { + image_bytes, + image_format: format, + }) + }) + .collect::, _>>()?; + + let metadata = if payload.is_null() || payload == serde_json::json!({}) { + Some(HashMap::default()) + } else { + serde_json::from_value(payload) + .ok() + .or(Some(HashMap::default())) + }; + + Ok(Self { images, metadata }) + } +} + + #[derive(Debug, utoipa::OpenApi)] #[openapi(paths(post_multipart), components(schemas(MultipartPredictBody, MultipartPredictResponse, ParsedAttachment)))] pub struct MultipartApiDoc; diff --git a/encoderfile/tests/test_grpc.rs b/encoderfile/tests/test_grpc.rs index 707f4c84..542a0ec4 100644 --- a/encoderfile/tests/test_grpc.rs +++ b/encoderfile/tests/test_grpc.rs @@ -2,7 +2,6 @@ use std::collections::HashMap; use std::fs::File; use std::io::Read; -use bytes::Bytes; use encoderfile::{ dev_utils::*, generated::{ @@ -161,10 +160,7 @@ test_grpc_service!( { GrpcService::new(image_classification_state()) }, true, ImageClassificationRequest { - inputs: vec![ - TEST_IMAGE_PATH, - TEST_IMAGE_PATH - ].iter().map(|s| ImageInput { image: get_file_bytes(s) }).collect(), + inputs: [TEST_IMAGE_PATH, TEST_IMAGE_PATH].iter().map(|s| ImageInput { image: get_file_bytes(s) }).collect(), metadata: HashMap::new(), }, ImageClassificationResponse diff --git a/encoderfile/tests/test_models.rs b/encoderfile/tests/test_models.rs index f30c8162..f5994a14 100644 --- a/encoderfile/tests/test_models.rs +++ b/encoderfile/tests/test_models.rs @@ -4,7 +4,6 @@ use encoderfile::inference::{ token_classification::token_classification, }; use encoderfile::transforms::{DEFAULT_LIBS, Transform}; -use encoderfile::runtime::{InputType, TaskType}; #[test] fn test_embedding_model() { From 50cb6f6deeaf036ced5483b82b8cc63dee0f79d4 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Wed, 20 May 2026 11:23:29 +0200 Subject: [PATCH 12/21] Fix image classifier implementation --- encoderfile/proto/manifest.proto | 3 + encoderfile/src/builder/builder.rs | 18 +++- encoderfile/src/builder/config.rs | 18 ++++ encoderfile/src/builder/image_preprocessor.rs | 93 ++++++++++++++++++ encoderfile/src/builder/mod.rs | 1 + encoderfile/src/builder/tokenizer.rs | 1 + encoderfile/src/dev_utils/mod.rs | 47 ++++++--- encoderfile/src/format/assets/kind.rs | 6 ++ encoderfile/src/format/codec/encoder.rs | 1 + encoderfile/src/format/codec/mod.rs | 3 + encoderfile/src/runtime/loader.rs | 17 +++- encoderfile/src/runtime/mod.rs | 3 + encoderfile/src/runtime/state.rs | 45 ++++++++- .../src/services/image_classification.rs | 33 ++++--- encoderfile/src/transport/cli.rs | 5 +- encoderfile/src/transport/mcp/mod.rs | 40 +++++++- encoderfile/src/transport/server.rs | 19 ++-- encoderfile/tests/test_mcp.rs | 2 +- models/image_classification/config.json | 45 +++++++++ .../preprocessor_config.json | 22 +++++ test-pictures/yoga01.jpg | Bin 0 -> 18509 bytes test-pictures/yoga02.jpg | Bin 0 -> 70107 bytes test_img_class_config.yml | 4 +- 23 files changed, 371 insertions(+), 55 deletions(-) create mode 100644 encoderfile/src/builder/image_preprocessor.rs create mode 100644 models/image_classification/config.json create mode 100644 models/image_classification/preprocessor_config.json create mode 100644 test-pictures/yoga01.jpg create mode 100644 test-pictures/yoga02.jpg diff --git a/encoderfile/proto/manifest.proto b/encoderfile/proto/manifest.proto index 16878fd4..f0ca23ca 100644 --- a/encoderfile/proto/manifest.proto +++ b/encoderfile/proto/manifest.proto @@ -55,6 +55,9 @@ message EncoderfileManifest { // Tokenizer data (vocab, merges, config). // Serialized runtime::tokenizer::TokenizerService optional Artifact tokenizer = 130; + + // Image preprocessor configuration. + optional Artifact image_preprocessor = 140; } message LuaLibs { diff --git a/encoderfile/src/builder/builder.rs b/encoderfile/src/builder/builder.rs index 80bed39e..6cc2466d 100644 --- a/encoderfile/src/builder/builder.rs +++ b/encoderfile/src/builder/builder.rs @@ -95,11 +95,19 @@ impl EncoderfileBuilder { } // validate tokenizer - if self.config.encoderfile.model_type.input_type() == crate::runtime::Input::Text { - let tokenizer_asset = - crate::builder::tokenizer::validate_tokenizer(&self.config.encoderfile)?; - planned_assets.push(tokenizer_asset); - terminal::success("Tokenizer validated"); + match self.config.encoderfile.model_type.input_type() { + Input::Text => { + let tokenizer_asset = + crate::builder::tokenizer::validate_tokenizer(&self.config.encoderfile)?; + planned_assets.push(tokenizer_asset); + terminal::success("Tokenizer validated"); + } + Input::Image => { + let image_preprocessor_asset = + crate::builder::image_preprocessor::validate_image_preprocessor(&self.config.encoderfile)?; + planned_assets.push(image_preprocessor_asset); + terminal::success("Image preprocessor validated"); + } } // initialize final binary diff --git a/encoderfile/src/builder/config.rs b/encoderfile/src/builder/config.rs index 5bb6d240..c8b3e155 100644 --- a/encoderfile/src/builder/config.rs +++ b/encoderfile/src/builder/config.rs @@ -268,6 +268,7 @@ pub enum ModelPath { model_weights_path: PathBuf, tokenizer_path: PathBuf, tokenizer_config_path: Option, + preprocessor_config_path: Option, }, } @@ -330,6 +331,7 @@ macro_rules! asset_path { impl ModelPath { asset_path!(model_config_path, "config.json", "model config"); asset_path!(tokenizer_path, "tokenizer.json", "tokenizer"); + asset_path!(@Optional preprocessor_config_path, "preprocessor_config.json", "image preprocessing"); asset_path!(model_weights_path, "model.onnx", "model weights"); asset_path!(@Optional tokenizer_config_path, "tokenizer_config.json", "tokenizer config"); } @@ -416,6 +418,22 @@ mod tests { tokenizer_path: base.join("tokenizer.json"), model_weights_path: base.join("model.onnx"), tokenizer_config_path: Some(base.join("tokenizer_config.json")), + preprocessor_config_path: None, + }; + + assert!(mp.model_config_path().is_ok()); + + cleanup(&base); + } + + fn test_modelpath_explicit_paths_image() { + let base = create_temp_model_dir(); + let mp = ModelPath::Paths { + model_config_path: base.join("config.json"), + tokenizer_path: PathBuf::new(), // not needed for image model + model_weights_path: base.join("model.onnx"), + tokenizer_config_path: None, + preprocessor_config_path: Some(base.join("preprocessor_config.json")), }; assert!(mp.model_config_path().is_ok()); diff --git a/encoderfile/src/builder/image_preprocessor.rs b/encoderfile/src/builder/image_preprocessor.rs new file mode 100644 index 00000000..b13cff81 --- /dev/null +++ b/encoderfile/src/builder/image_preprocessor.rs @@ -0,0 +1,93 @@ +// IMPORTANT NOTE: +// +// Image preprocessor configuration is NOT a stable, self-contained artifact (see tokenizer situation). +// +// It seems to vary widely between models and is often not even explicitly defined anywhere, so for now we just +// require users to provide the config for the model they are using, and we will deal with new +// models on a case-by-case basis as they come in. + +use crate::{ + format::assets::{AssetKind, AssetSource, PlannedAsset}, +}; +use anyhow::Result; +use std::str::FromStr; + +use super::config::{ + EncoderfileConfig +}; +use crate::runtime::{ + ImagePreprocessing +}; + +pub fn validate_image_preprocessor<'a>(efconfig: &'a EncoderfileConfig) -> Result> { + let mut config = match efconfig.path.preprocessor_config_path()? { + // if preprocessor_config.json is provided, use that + Some(preprocessor_config_path) => { + // open preprocessor_config + let contents = std::fs::read_to_string(preprocessor_config_path)?; + let preprocessor_config: ImagePreprocessing = serde_json::from_str(contents.as_str())?; + preprocessor_config + } + // some values may be present in config.json + None => { + // from_model_config(&image_preprocessing.config)?; + anyhow::bail!("FATAL: No preprocessor config provided"); + } + }; + let model_config = efconfig.model_config()?; + let serialized = serde_json::to_vec(&config)?; + + // num_channels must be same as len for mean and std + if let Some(num_channels) = model_config.num_channels { + if let Some(image_mean) = config.image_mean.as_ref() { + if image_mean.len() != num_channels as usize { + anyhow::bail!("num_channels must match length of image_mean"); + } + } + if let Some(image_std) = config.image_std.as_ref() { + if image_std.len() != num_channels as usize { + anyhow::bail!("num_channels must match length of image_std"); + } + } + } + + PlannedAsset::from_asset_source( + AssetSource::InMemory(std::borrow::Cow::Owned(serialized)), + AssetKind::ImagePreprocessor, + ) +} + + + +#[cfg(test)] +mod tests { + use crate::builder::config::ModelPath; + use crate::common::model_type::ModelType; + + use super::*; + + #[test] + fn test_validate_preprocessor_config() { + let config = EncoderfileConfig { + name: "my-model".into(), + version: "0.0.1".into(), + path: ModelPath::Directory("../models/image_classification".into()), + model_type: ModelType::Embedding, + output_path: None, + cache_dir: None, + transform: None, + lua_libs: None, + tokenizer: None, + validate_transform: false, + base_binary_path: None, + target: None, + }; + + let preprocessor_config = validate_image_preprocessor(&config) + .expect("Failed to validate image preprocessor config"); + + println!("Validated image preprocessor config: {:?}", preprocessor_config); + } + + +} diff --git a/encoderfile/src/builder/mod.rs b/encoderfile/src/builder/mod.rs index 96afa60b..6bed4cb9 100644 --- a/encoderfile/src/builder/mod.rs +++ b/encoderfile/src/builder/mod.rs @@ -11,3 +11,4 @@ pub mod templates; pub mod terminal; pub mod tokenizer; pub mod transforms; +pub mod image_preprocessor; diff --git a/encoderfile/src/builder/tokenizer.rs b/encoderfile/src/builder/tokenizer.rs index bfabf14a..a8e32800 100644 --- a/encoderfile/src/builder/tokenizer.rs +++ b/encoderfile/src/builder/tokenizer.rs @@ -452,6 +452,7 @@ mod tests { model_weights_path: path.model_weights_path().unwrap(), tokenizer_path: path.tokenizer_path().unwrap(), tokenizer_config_path: None, + preprocessor_config_path: None, }; let config = EncoderfileConfig { diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index 0d3281e7..e55ed24a 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -5,14 +5,15 @@ use crate::{ }, runtime::{ AppState, + ClassifierState, EncoderfileState, FeatureExtractorState, - ORTSessionBuilder, - ClassifierState, - TextInputState, ImageInputState, + ImageConfig, + ImagePreprocessing, + ImageSize, InputType, - TaskType, + ORTSessionBuilder, TaskType, TextInputState }, }; use ort::session::Session; @@ -56,14 +57,19 @@ pub trait TaskTypeFromFile: TaskType { fn get_task_state(dir: &str) -> Result; } -pub fn get_reader(dir: &str) -> BufReader { +pub fn get_config_reader(dir: &str) -> BufReader { let file = File::open(format!("{}/{}", dir, "config.json")).expect("Config not found"); BufReader::new(file) } +pub fn get_preproc_reader(dir: &str) -> BufReader { + let file = File::open(format!("{}/{}", dir, "preprocessor_config.json")).expect("Preprocessing config not found"); + BufReader::new(file) +} + // Input types fn get_text_input_state(dir: &str) -> Result { - let reader = get_reader(dir); + let reader = get_config_reader(dir); let tokenizer = get_tokenizer(dir); let model_config = serde_json::from_reader(reader)?; @@ -71,13 +77,28 @@ fn get_text_input_state(dir: &str) -> Result { } fn get_image_input_state(dir: &str) -> Result { - let reader = get_reader(dir); - let incomplete_state: ImageInputState = serde_json::from_reader(reader)?; + let config_reader = get_config_reader(dir); + let preproc_reader = get_preproc_reader(dir); + let config_state: ImageConfig = serde_json::from_reader(config_reader)?; + let preproc_state: ImagePreprocessing = serde_json::from_reader(preproc_reader)?; Ok(ImageInputState { - num_channels: incomplete_state.num_channels, - height: incomplete_state.height.or(incomplete_state.image_size), - width: incomplete_state.width.or(incomplete_state.image_size), - image_size: incomplete_state.image_size, + config: ImageConfig { + num_channels: config_state.num_channels, + image_size: config_state.image_size }, + preprocessing: ImagePreprocessing { + do_normalize: preproc_state.do_normalize, + do_rescale: preproc_state.do_rescale, + do_resize: preproc_state.do_resize, + image_processor_type: preproc_state.image_processor_type, + rescale_factor: preproc_state.rescale_factor, + image_mean: preproc_state.image_mean, + image_std: preproc_state.image_std, + size: preproc_state.size.or( + Some( + ImageSize{ width: config_state.image_size, height: config_state.image_size, shortest_edge: None } + ) + ) + } }) } @@ -100,7 +121,7 @@ state_impl!(FeatureExtractorState, get_feature_task_state); // Task types fn get_class_task_state(dir: &str) -> Result { - let reader = get_reader(dir); + let reader = get_config_reader(dir); let state: ClassifierState = serde_json::from_reader(reader)?; Ok(state) } diff --git a/encoderfile/src/format/assets/kind.rs b/encoderfile/src/format/assets/kind.rs index 9ea67f32..2a9485b1 100644 --- a/encoderfile/src/format/assets/kind.rs +++ b/encoderfile/src/format/assets/kind.rs @@ -39,6 +39,9 @@ pub enum AssetKind { /// Tokenizer data required for text-based models. Tokenizer, + + /// Optional image preprocessing configuration. + ImagePreprocessor, } impl AssetKind { @@ -47,6 +50,7 @@ impl AssetKind { AssetKind::Transform, AssetKind::ModelConfig, AssetKind::Tokenizer, + AssetKind::ImagePreprocessor, ]; } @@ -66,10 +70,12 @@ pub trait AssetPolicySpec: ModelTypeSpec + InputType + TaskType { (Input::Image, Task::Classification) => &[ AssetKind::ModelWeights, AssetKind::ModelConfig, + AssetKind::ImagePreprocessor, ], (Input::Image, Task::FeatureExtraction) => &[ AssetKind::ModelWeights, AssetKind::ModelConfig, + AssetKind::ImagePreprocessor, ], } } diff --git a/encoderfile/src/format/codec/encoder.rs b/encoderfile/src/format/codec/encoder.rs index 5627927e..71666300 100644 --- a/encoderfile/src/format/codec/encoder.rs +++ b/encoderfile/src/format/codec/encoder.rs @@ -100,6 +100,7 @@ impl EncoderfileCodec { weights: None, transform: None, tokenizer: None, + image_preprocessor: None, }; // Populate artifacts with length + hash diff --git a/encoderfile/src/format/codec/mod.rs b/encoderfile/src/format/codec/mod.rs index 6878500e..b153dbb8 100644 --- a/encoderfile/src/format/codec/mod.rs +++ b/encoderfile/src/format/codec/mod.rs @@ -41,6 +41,7 @@ impl EncoderfileManifest { AssetKind::ModelConfig => &mut self.model_config, AssetKind::Transform => &mut self.transform, AssetKind::Tokenizer => &mut self.tokenizer, + AssetKind::ImagePreprocessor => &mut self.image_preprocessor, } } @@ -50,6 +51,7 @@ impl EncoderfileManifest { AssetKind::ModelConfig => &self.model_config, AssetKind::Transform => &self.transform, AssetKind::Tokenizer => &self.tokenizer, + AssetKind::ImagePreprocessor => &self.image_preprocessor, } } @@ -74,6 +76,7 @@ mod tests { weights: None, transform: None, tokenizer: None, + image_preprocessor: None, } } diff --git a/encoderfile/src/runtime/loader.rs b/encoderfile/src/runtime/loader.rs index 74557d3a..c5af9257 100644 --- a/encoderfile/src/runtime/loader.rs +++ b/encoderfile/src/runtime/loader.rs @@ -8,7 +8,7 @@ use crate::{ common::{Config, LuaLibs, ModelConfig, model_type::ModelType}, format::{assets::AssetKind, codec::EncoderfileCodec, container::Encoderfile}, generated::manifest::{self, TransformType}, - runtime::{ORTExecutionProvider, ORTSessionBuilder, TokenizerService}, + runtime::{ORTExecutionProvider, ORTSessionBuilder, TokenizerService, ImagePreprocessing}, }; pub struct EncoderfileLoader<'a, R: Read + Seek> { @@ -129,6 +129,21 @@ impl<'a, R: Read + Seek> EncoderfileLoader<'a, R> { Err(e) => bail!("Error loading model config: {e:?}"), } } + + pub fn image_preprocessor_config(&mut self) -> Result { + match self + .encoderfile + .open_required(self.reader, AssetKind::ImagePreprocessor) + { + Ok(mut r) => { + let mut buf = vec![0u8; r.len() as usize]; + r.read_exact(&mut buf)?; + + Ok(serde_json::from_slice(buf.as_slice())?) + } + Err(e) => bail!("Error loading image preprocessor config: {e:?}"), + } + } } pub fn load_assets<'a, R: Read + Seek>(file: &'a mut R) -> Result> { diff --git a/encoderfile/src/runtime/mod.rs b/encoderfile/src/runtime/mod.rs index 6032ea12..739dd60c 100644 --- a/encoderfile/src/runtime/mod.rs +++ b/encoderfile/src/runtime/mod.rs @@ -19,6 +19,9 @@ pub use state::{ FeatureExtractorState, ImageInputState, TextInputState, + ImageConfig, + ImageSize, + ImagePreprocessing }; pub use tokenizer::TokenizerService; diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 9c903bd3..4d0efe2e 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -56,12 +56,36 @@ pub struct TextInputState { pub tokenizer: TokenizerService, pub model_config: ModelConfig, } + #[derive(Debug, Serialize, Deserialize, Clone)] pub struct ImageInputState { + pub config: ImageConfig, + pub preprocessing: ImagePreprocessing, +} + +#[derive(Debug, Serialize, Deserialize, Clone)] +pub struct ImageConfig { pub num_channels: u32, + pub image_size: Option, +} + +#[derive(Debug, Serialize, Deserialize, Clone)] +pub struct ImagePreprocessing { + pub rescale_factor: Option, + pub image_mean: Option>, + pub image_std: Option>, + pub do_normalize: Option, + pub do_rescale: Option, + pub do_resize: Option, + pub image_processor_type: Option, + pub size: Option +} + +#[derive(Debug, Serialize, Deserialize, Clone)] +pub struct ImageSize { pub height: Option, pub width: Option, - pub image_size: Option, + pub shortest_edge: Option, } #[derive(Debug, Serialize, Deserialize, Clone)] @@ -116,11 +140,22 @@ where R: Read + Seek, { let model_config = loader.model_config()?; + let preprocessor_config = loader.image_preprocessor_config()?; Ok(ImageInputState { - num_channels: model_config.num_channels.ok_or_else(|| anyhow::anyhow!("num_channels is required for image models"))?, - height: model_config.height, - width: model_config.width, - image_size: model_config.image_size, + config: ImageConfig { + num_channels: model_config.num_channels.ok_or_else(|| anyhow::anyhow!("num_channels is required for image models"))?, + image_size: model_config.image_size, + }, + preprocessing: ImagePreprocessing { + rescale_factor: preprocessor_config.rescale_factor, + image_mean: preprocessor_config.image_mean, + image_std: preprocessor_config.image_std, + do_normalize: preprocessor_config.do_normalize, + do_rescale: preprocessor_config.do_rescale, + do_resize: preprocessor_config.do_resize, + image_processor_type: preprocessor_config.image_processor_type, + size: preprocessor_config.size, + }, }) } diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index c5f69021..d0ea83ef 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -10,7 +10,7 @@ use crate::{ runtime::AppState, }; use image::RgbImage; -use ndarray::{Array4}; +use ndarray::{Array4, s}; use super::inference::Inference; use crate::inference::image_classification::image_classification; @@ -29,10 +29,9 @@ impl Inference for AppState if request.images.is_empty() { return Err(ApiError::InputError("Cannot classify empty image list")); } - println!("--> Received request for image classification inference: {:?}", request); - let rescale_factor = 0.003_921_569_f32; - let image_mean = 0.5; - let image_std = 0.5; + let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; + let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; + let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; // bilinear resampling // convert input image into flattened rbg @@ -40,16 +39,16 @@ impl Inference for AppState let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); img .resize_exact( - self.model_input_state.width.unwrap(), - self.model_input_state.height.unwrap(), + self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap(), + self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap(), DEFAULT_FILTER_TYPE ) .to_rgb8() }).collect(); let batch_size = request.images.len(); - let num_channels = self.model_input_state.num_channels as usize; - let height = self.model_input_state.height.unwrap() as usize; - let width = self.model_input_state.width.unwrap() as usize; + let num_channels = self.model_input_state.config.num_channels as usize; + let height = self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap() as usize; + let width = self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap() as usize; if num_channels != 3 { return Err(ApiError::InputError("Image classification currently expects 3 RGB channels")); @@ -70,7 +69,11 @@ impl Inference for AppState } } // TODO make parallel - images_array.mapv_inplace(|x| ((x * rescale_factor) - image_mean) / image_std); + for c in 0..num_channels { + let mean = image_mean[c]; + let std = image_std[c]; + images_array.slice_mut(s![.., c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); + } let label_map = self.task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); @@ -120,14 +123,14 @@ mod tests { init_tracing(); let state = dev_utils::get_state::("../models/image_classification"); - let mut file = File::open("../test-pictures/w3c_home.jpg").expect("Failed to open test image"); + let mut file = File::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); let file_vec = vec![&mut file]; let request = ImageClassificationRequest::from_read_input(file_vec).expect("Failed to create request from read input"); let response = state.inference(request).expect("Inference failed"); + println!("Inference response: {:?}", response); assert_eq!(response.results.len(), 1); - assert_eq!(response.results[0].labels.len(), 2); - assert!(response.results[0].labels.iter().any(|x| x.label == "normal")); - assert!(response.results[0].labels.iter().any(|x| x.label == "nsfw")); + assert_eq!(response.results[0].labels.len(), 9); + assert!(response.results[0].labels.iter().enumerate().max_by(|a, b| a.1.score.partial_cmp(&b.1.score).unwrap()).unwrap().1.label == "Downward-Dog"); // top label should be "yoga mat" } #[test] diff --git a/encoderfile/src/transport/cli.rs b/encoderfile/src/transport/cli.rs index a03ffb88..eddc3327 100644 --- a/encoderfile/src/transport/cli.rs +++ b/encoderfile/src/transport/cli.rs @@ -132,7 +132,8 @@ impl Commands { .await } ModelType::ImageClassification => { - Err(anyhow::anyhow!("Image classification is not yet supported in the CLI transport")) + self.execute_from_loader::(loader) + .await } } } @@ -236,7 +237,7 @@ impl Commands { config, session, ::State::try_from(loader).expect("could not load model input state from file"), - ::State::try_from(loader).expect("could not load model input state from file"), + ::State::try_from(loader).expect("could not load model task state from file"), )); setup_tracing(None)?; diff --git a/encoderfile/src/transport/mcp/mod.rs b/encoderfile/src/transport/mcp/mod.rs index be53d3cc..1681b148 100644 --- a/encoderfile/src/transport/mcp/mod.rs +++ b/encoderfile/src/transport/mcp/mod.rs @@ -3,6 +3,9 @@ use rmcp::transport::streamable_http_server::{ StreamableHttpService, session::local::LocalSessionManager, }; +use crate::common::model_type; +use crate::runtime::AppState; + mod error; pub trait McpRouter @@ -13,7 +16,7 @@ where const NEW_TOOL: fn(Self) -> Self::Tool; // TODO figure out the lifetimes of a state so a ref can be safely passed - fn mcp_router(self) -> axum::Router + fn mcp_router(self) -> Result where ::Tool: rmcp::ServerHandler, { @@ -23,7 +26,40 @@ where Default::default(), ); - axum::Router::new().nest_service("/mcp", service) + Ok(axum::Router::new().nest_service("/mcp", service)) + } +} + +pub struct DummyTool {} + +impl ServerHandler for DummyTool { + fn get_info(&self) -> rmcp::model::ServerInfo { + rmcp::model::ServerInfo { + protocol_version: rmcp::model::ProtocolVersion::V_2025_06_18, + capabilities: rmcp::model::ServerCapabilities::default(), + server_info: rmcp::model::Implementation::default(), + instructions: None, + } + } + + async fn initialize( + &self, + _request: rmcp::model::InitializeRequestParam, + _context: rmcp::service::RequestContext, + ) -> Result { + Err(rmcp::ErrorData { + code: rmcp::model::ErrorCode::INTERNAL_ERROR, + message: std::borrow::Cow::Borrowed("This is a dummy tool with no functionality."), + data: None, + }) + } +} + +impl McpRouter for AppState { + type Tool = DummyTool; + const NEW_TOOL: fn(Self) -> Self::Tool = |_state| Self::Tool {}; + fn mcp_router(self) -> Result { + Err(crate::error::ApiError::InternalError("MCP not implemented for ImageClassification model type")) } } diff --git a/encoderfile/src/transport/server.rs b/encoderfile/src/transport/server.rs index 6f6cda1f..29f7dbc6 100644 --- a/encoderfile/src/transport/server.rs +++ b/encoderfile/src/transport/server.rs @@ -23,14 +23,14 @@ pub async fn run_grpc( "gRPC", state, |state| { - state + Ok(state .clone() .grpc_router() .layer( tower_http::trace::TraceLayer::new_for_grpc() .on_response(DefaultOnResponse::new().level(tracing::Level::INFO)), ) - .into_make_service_with_connect_info::() + .into_make_service_with_connect_info::()) }, ) .await @@ -51,14 +51,14 @@ pub async fn run_http( "HTTP", state, |state| { - state + Ok(state .clone() .http_router() .layer( tower_http::trace::TraceLayer::new_for_http() .on_response(DefaultOnResponse::new().level(tracing::Level::INFO)), ) - .into_make_service_with_connect_info::() + .into_make_service_with_connect_info::()) }, ) .await @@ -79,16 +79,15 @@ pub async fn run_mcp( "MCP", state, |state| { - state - .clone() - .mcp_router() + let mcp_router = state.clone().mcp_router()?; + Ok(mcp_router .layer( tower_http::trace::TraceLayer::new_for_http() // TODO check if otel is enabled // .make_span_with(crate::middleware::format_span) .on_response(DefaultOnResponse::new().level(tracing::Level::INFO)), ) - .into_make_service_with_connect_info::() + .into_make_service_with_connect_info::()) }, ) .await @@ -101,11 +100,11 @@ async fn serve_with_optional_tls( maybe_key_file: Option, server_type_str: &str, state: S, - into_service_fn: impl Fn(&S) -> IntoMakeServiceWithConnectInfo, + into_service_fn: impl Fn(&S) -> Result, crate::error::ApiError>, ) -> Result<()> { let addr = format!("{}:{}", &hostname, &port); - let router = into_service_fn(&state); + let router = into_service_fn(&state)?; let model_type = state.model_type(); diff --git a/encoderfile/tests/test_mcp.rs b/encoderfile/tests/test_mcp.rs index 8608f5b2..38e81c85 100644 --- a/encoderfile/tests/test_mcp.rs +++ b/encoderfile/tests/test_mcp.rs @@ -16,7 +16,7 @@ async fn run_mcp( where AppState: McpRouter, { - let router = state.mcp_router().layer( + let router = state.mcp_router()?.layer( tower_http::trace::TraceLayer::new_for_http() // TODO check if otel is enabled // .make_span_with(crate::middleware::format_span) diff --git a/models/image_classification/config.json b/models/image_classification/config.json new file mode 100644 index 00000000..e2309f2a --- /dev/null +++ b/models/image_classification/config.json @@ -0,0 +1,45 @@ +{ + "_name_or_path": "dima806/yoga_pose_image_classification", + "architectures": [ + "ViTForImageClassification" + ], + "attention_probs_dropout_prob": 0.0, + "encoder_stride": 16, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 768, + "id2label": { + "0": "Bridge", + "1": "Child", + "2": "Cobra", + "3": "Downward-Dog", + "4": "Pigeon", + "5": "Standing-Mountain", + "6": "Tree", + "7": "Triangle", + "8": "Warrior" + }, + "image_size": 224, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "Bridge": 0, + "Child": 1, + "Cobra": 2, + "Downward-Dog": 3, + "Pigeon": 4, + "Standing-Mountain": 5, + "Tree": 6, + "Triangle": 7, + "Warrior": 8 + }, + "layer_norm_eps": 1e-12, + "model_type": "vit", + "num_attention_heads": 12, + "num_channels": 3, + "num_hidden_layers": 12, + "patch_size": 16, + "problem_type": "single_label_classification", + "qkv_bias": true, + "transformers_version": "4.37.2" +} diff --git a/models/image_classification/preprocessor_config.json b/models/image_classification/preprocessor_config.json new file mode 100644 index 00000000..02018dec --- /dev/null +++ b/models/image_classification/preprocessor_config.json @@ -0,0 +1,22 @@ +{ + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.5, + 0.5, + 0.5 + ], + "image_processor_type": "ViTFeatureExtractor", + "image_std": [ + 0.5, + 0.5, + 0.5 + ], + "resample": 2, + "rescale_factor": 0.00392156862745098, + "size": { + "height": 224, + "width": 224 + } +} diff --git a/test-pictures/yoga01.jpg 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+0200 Subject: [PATCH 13/21] Fix lint --- encoderfile/src/builder/config.rs | 1 + encoderfile/src/builder/image_preprocessor.rs | 13 +++++-------- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/encoderfile/src/builder/config.rs b/encoderfile/src/builder/config.rs index c8b3e155..30dcc423 100644 --- a/encoderfile/src/builder/config.rs +++ b/encoderfile/src/builder/config.rs @@ -426,6 +426,7 @@ mod tests { cleanup(&base); } + #[test] fn test_modelpath_explicit_paths_image() { let base = create_temp_model_dir(); let mp = ModelPath::Paths { diff --git a/encoderfile/src/builder/image_preprocessor.rs b/encoderfile/src/builder/image_preprocessor.rs index b13cff81..80b2b9b7 100644 --- a/encoderfile/src/builder/image_preprocessor.rs +++ b/encoderfile/src/builder/image_preprocessor.rs @@ -10,7 +10,6 @@ use crate::{ format::assets::{AssetKind, AssetSource, PlannedAsset}, }; use anyhow::Result; -use std::str::FromStr; use super::config::{ EncoderfileConfig @@ -20,7 +19,7 @@ use crate::runtime::{ }; pub fn validate_image_preprocessor<'a>(efconfig: &'a EncoderfileConfig) -> Result> { - let mut config = match efconfig.path.preprocessor_config_path()? { + let config = match efconfig.path.preprocessor_config_path()? { // if preprocessor_config.json is provided, use that Some(preprocessor_config_path) => { // open preprocessor_config @@ -39,16 +38,14 @@ pub fn validate_image_preprocessor<'a>(efconfig: &'a EncoderfileConfig) -> Resul // num_channels must be same as len for mean and std if let Some(num_channels) = model_config.num_channels { - if let Some(image_mean) = config.image_mean.as_ref() { - if image_mean.len() != num_channels as usize { + if let Some(image_mean) = config.image_mean.as_ref() && + image_mean.len() != num_channels as usize { anyhow::bail!("num_channels must match length of image_mean"); } - } - if let Some(image_std) = config.image_std.as_ref() { - if image_std.len() != num_channels as usize { + if let Some(image_std) = config.image_std.as_ref() && + image_std.len() != num_channels as usize { anyhow::bail!("num_channels must match length of image_std"); } - } } PlannedAsset::from_asset_source( From d5d7e66abbe45e7676e98a070935493805ed0528 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Thu, 21 May 2026 10:23:06 +0200 Subject: [PATCH 14/21] Preliminary Lua bindings for images --- .../src/inference/image_classification.rs | 2 +- encoderfile/src/transforms/image/mod.rs | 84 +++++++++++++++++++ encoderfile/src/transforms/mod.rs | 2 + 3 files changed, 87 insertions(+), 1 deletion(-) create mode 100644 encoderfile/src/transforms/image/mod.rs diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index 524495f4..bcae25d8 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -30,7 +30,7 @@ pub fn image_classification<'a>( .into_dimensionality::() .expect("Model does not return tensor of shape [n_batch, n_classes]") .into_owned(); - outputs.mapv_inplace(logit_to_prob); + // outputs.mapv_inplace(logit_to_prob); Ok(postprocess(outputs, classes)) } diff --git a/encoderfile/src/transforms/image/mod.rs b/encoderfile/src/transforms/image/mod.rs new file mode 100644 index 00000000..dc730974 --- /dev/null +++ b/encoderfile/src/transforms/image/mod.rs @@ -0,0 +1,84 @@ +use ndarray::Array3; +use image::DynamicImage; +use mlua::prelude::*; +use super::Tensor; + +const DEFAULT_FILTER_TYPE: image::imageops::FilterType = image::imageops::FilterType::Triangle; + +#[derive(Debug, Clone)] +pub struct Image(pub DynamicImage); + +impl Image { + pub fn into_inner(&self) -> &DynamicImage { + &self.0 + } +} + +impl FromLua for Image { + fn from_lua(value: LuaValue, _lua: &Lua) -> Result { + match value { + LuaValue::UserData(data) => data.borrow::().map(|i| i.to_owned()), + _ => Err(LuaError::external( + format!("Unknown type: {}", value.type_name()).as_str(), + )), + } + } +} + +fn dyn_image_to_array3(image: &DynamicImage, height: u32, width: u32, num_channels: u32) -> Array3 { + let raw = image.to_rgb8().into_raw(); + let h_us: usize = height as usize; + let w_us: usize = width as usize; + let nc_us: usize = num_channels as usize; + + // Build CHW array directly from raw HWC bytes, avoiding an intermediate array and transpose. + Array3::from_shape_fn((nc_us, h_us, w_us), |(c, y, x)| { + raw[y * w_us * nc_us + x * nc_us + c] as f32 + }) +} + +fn resize_image(image: &DynamicImage, height: u32, width: u32) -> DynamicImage { + image.resize_exact(width, height, DEFAULT_FILTER_TYPE) +} + +impl LuaUserData for Image { + fn add_methods>(methods: &mut M) { + // tensor ops + methods.add_method("to_array", |_, this, (height, width, num_channels)| Ok(Tensor(dyn_image_to_array3(this.into_inner(), height, width, num_channels).into_dyn()))); + methods.add_method("resize", |_, this, (height, width)| Ok(Image(resize_image(this.into_inner(), height, width)))); + } +} + +#[cfg(test)] +fn load_env() -> Lua { + Lua::new() +} + +#[test] +fn test_resize_image() { + use image::GenericImageView; + let img = image::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + assert_ne!(img.dimensions(), (224, 224)); + let lua = load_env(); + let img_val = Image(img); + lua.globals().set("img", img_val).unwrap(); + let resized: Image = lua + .load("return img:resize(224, 224)") + .eval() + .unwrap(); + assert_eq!(resized.into_inner().dimensions(), (224, 224)); +} + +#[test] +fn test_image_to_array() { + let img = image::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + let lua = load_env(); + let img_val = Image(img); + lua.globals().set("img", img_val).unwrap(); + let array: Tensor = lua + .load("return img:to_array(224, 224, 3)") + .eval() + .unwrap(); + assert_eq!(array.into_inner().shape(), &[3, 224, 224]); +} + diff --git a/encoderfile/src/transforms/mod.rs b/encoderfile/src/transforms/mod.rs index 6f00903a..98840d95 100644 --- a/encoderfile/src/transforms/mod.rs +++ b/encoderfile/src/transforms/mod.rs @@ -1,9 +1,11 @@ mod engine; mod tensor; +mod image; mod utils; pub use engine::*; pub use tensor::Tensor; +pub use image::Image; pub const DEFAULT_LIBS: [mlua::StdLib; 3] = [ mlua::StdLib::TABLE, From 88993e886c388394f9852ca1401e47e6f980ae41 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Thu, 21 May 2026 17:43:49 +0200 Subject: [PATCH 15/21] Lua preproc WIP --- encoderfile/src/runtime/state.rs | 34 ++++++++++ .../src/services/image_classification.rs | 68 +++++++++++++++++++ .../transforms/engine/image_classification.rs | 56 ++++++++++++++- encoderfile/src/transforms/engine/mod.rs | 27 +++++++- encoderfile/src/transforms/image/mod.rs | 14 ++-- test_img_class_config.yml | 2 +- 6 files changed, 190 insertions(+), 11 deletions(-) diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index 4d0efe2e..fc69e6f5 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -5,6 +5,7 @@ use std::{ fmt::Debug, }; use serde::{Deserialize, Serialize}; +use mlua::prelude::*; use ort::session::Session; use parking_lot::Mutex; @@ -88,6 +89,39 @@ pub struct ImageSize { pub shortest_edge: Option, } +impl LuaUserData for ImageInputState { + fn add_fields>(fields: &mut F) { + fields.add_field_method_get("num_channels", |_, this| Ok(this.config.num_channels)); + fields.add_field_method_get("image_size", |_, this| Ok(this.config.image_size)); + fields.add_field_method_get("rescale_factor", |_, this| Ok(this.preprocessing.rescale_factor)); + fields.add_field_method_get("image_mean", |_, this| Ok(this.preprocessing.image_mean.clone())); + fields.add_field_method_get("image_std", |_, this| Ok(this.preprocessing.image_std.clone())); + fields.add_field_method_get("do_normalize", |_, this| Ok(this.preprocessing.do_normalize)); + fields.add_field_method_get("do_rescale", |_, this| Ok(this.preprocessing.do_rescale)); + fields.add_field_method_get("do_resize", |_, this| Ok(this.preprocessing.do_resize)); + fields.add_field_method_get("size_height", |_, this| Ok(this.preprocessing.size.as_ref().and_then(|s| s.height))); + fields.add_field_method_get("size_width", |_, this| Ok(this.preprocessing.size.as_ref().and_then(|s| s.width))); + fields.add_field_method_get("size_shortest_edge", |_, this| Ok(this.preprocessing.size.as_ref().and_then(|s| s.shortest_edge))); + } +} + +impl LuaUserData for TextInputState { + fn add_fields>(fields: &mut F) { + fields.add_field_method_get("model_type", |_, this| Ok(this.model_config.model_type.clone())); + fields.add_field_method_get("num_labels", |_, this| Ok(this.model_config.num_labels())); + fields.add_field_method_get("id2label", |_, this| Ok(this.model_config.id2label.clone())); + fields.add_field_method_get("label2id", |_, this| Ok(this.model_config.label2id.clone())); + } +} + +impl LuaUserData for ClassifierState { + fn add_fields>(fields: &mut F) { + fields.add_field_method_get("num_labels", |_, this| Ok(this.num_labels())); + fields.add_field_method_get("id2label", |_, this| Ok(this.id2label.clone())); + fields.add_field_method_get("label2id", |_, this| Ok(this.label2id.clone())); + } +} + #[derive(Debug, Serialize, Deserialize, Clone)] pub struct ClassifierState { pub id2label: Option>, diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index d0ea83ef..f0f8d8f3 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -25,10 +25,19 @@ impl Inference for AppState type Output = ImageClassificationResponse; fn inference(&self, request: impl Into) -> Result { + // let transform = ImageClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; + let request = request.into(); if request.images.is_empty() { return Err(ApiError::InputError("Cannot classify empty image list")); } + /* + transform.preprocessor().as_ref().map(|pre| { + pre.call::<_, ()>(()) + .map_err(|e| ApiError::LuaError(format!("Preprocessor error: {e}"))) + })?; + */ + let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; @@ -92,6 +101,65 @@ impl Inference for AppState metadata: request.metadata, }) } + + + + + + /* + fn new_inference(&self, request: impl Into) -> Result { + let transform = ImageClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; + + let request = request.into(); + if request.images.is_empty() { + return Err(ApiError::InputError("Cannot classify empty image list")); + } + + let postprocess_code = r##" + function Preprocess(img) + return img:resize(224,224):to_array(3) + end + "##.to_string(); + + let engine = ImageClassificationTransform::new( + DEFAULT_LIBS.to_vec(), + Some(postprocess_code), + ) + .expect("Failed to create engine"); + + transform.preprocessor().as_ref().map(|pre| { + pre.call::<_, ()>(()) + .map_err(|e| ApiError::LuaError(format!("Preprocessor error: {e}"))) + })?; + + + + // TODO make parallel + for c in 0..num_channels { + let mean = image_mean[c]; + let std = image_std[c]; + images_array.slice_mut(s![.., c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); + } + + let label_map = self.task_state.id2label.clone().unwrap(); + let mut entries: Vec<_> = label_map.iter().collect(); + entries.sort_by(|x, y| x.0.cmp(y.0)); + let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); + + let labels_batch = image_classification( + self.session.lock(), + images_array, + // COMMENT having optional fields complicates things later on, but otoh + // it allows models with variations of these fields + classes)?; + + Ok(ImageClassificationResponse { + results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), + metadata: request.metadata, + }) + } + */ + } #[cfg(test)] diff --git a/encoderfile/src/transforms/engine/image_classification.rs b/encoderfile/src/transforms/engine/image_classification.rs index 6da82b8c..2b00324f 100644 --- a/encoderfile/src/transforms/engine/image_classification.rs +++ b/encoderfile/src/transforms/engine/image_classification.rs @@ -1,6 +1,6 @@ -use crate::{common::model_type, error::ApiError}; +use crate::{common::model_type, error::ApiError, runtime::ImageInputState}; -use super::{super::tensor::Tensor, Postprocessor, Transform}; +use super::{super::tensor::Tensor, super::image::Image, Postprocessor, Preprocessor, Transform}; use ndarray::{Array2, Ix2}; impl Postprocessor for Transform { @@ -44,6 +44,25 @@ impl Postprocessor for Transform { } } +impl Preprocessor for Transform { + type Input = (Image, ImageInputState); + type Output = Tensor; + + fn preprocess(&self, (image, config): Self::Input) -> Result { + let func = match self.preprocessor() { + Some(p) => p, + None => return Err(ApiError::InternalError("No preprocessor defined for this model")), + }; + + self.lua.globals() + .set("input_config", config) + .map_err(|e| ApiError::LuaError(e.to_string()))?; + + func.call::(image) + .map_err(|e| ApiError::LuaError(e.to_string())) + } +} + #[cfg(test)] mod tests { use super::*; @@ -137,4 +156,37 @@ mod tests { } } } + + + #[test] + fn test_image_preprocess() { + let engine = Transform::::new( + DEFAULT_LIBS.to_vec(), + Some( + r##" + function Preprocess(img) + return img:resize(input_config.size_height, input_config.size_width):to_array(input_config.num_channels) + end + "## + .to_string(), + ), + ) + .expect("Failed to create engine"); + + let img = image::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + + let config = ImageInputState { + config: crate::runtime::ImageConfig { num_channels: 3, image_size: Some(224) }, + preprocessing: crate::runtime::ImagePreprocessing { + rescale_factor: None, image_mean: None, image_std: None, + do_normalize: None, do_rescale: None, do_resize: None, + image_processor_type: None, + size: Some(crate::runtime::ImageSize { height: Some(224), width: Some(224), shortest_edge: None }), + }, + }; + let result = engine.preprocess((Image(img), config)).expect("Failed"); + + assert!(result.into_inner().shape() == &[3, 224, 224]); + } + } diff --git a/encoderfile/src/transforms/engine/mod.rs b/encoderfile/src/transforms/engine/mod.rs index 3a4510ee..5a10be7b 100644 --- a/encoderfile/src/transforms/engine/mod.rs +++ b/encoderfile/src/transforms/engine/mod.rs @@ -89,6 +89,11 @@ transform!(TokenClassificationTransform, TokenClassification); transform!(SentenceEmbeddingTransform, SentenceEmbedding); transform!(ImageClassificationTransform, ImageClassification); +pub trait TransformSpec { + fn has_postprocessor(&self) -> bool; + fn has_preprocessor(&self) -> bool; +} + pub trait Postprocessor: TransformSpec { type Input; type Output; @@ -96,14 +101,18 @@ pub trait Postprocessor: TransformSpec { fn postprocess(&self, data: Self::Input) -> Result; } -pub trait TransformSpec { - fn has_postprocessor(&self) -> bool; +pub trait Preprocessor: TransformSpec { + type Input; + type Output; + + fn preprocess(&self, data: Self::Input) -> Result; } #[derive(Debug)] pub struct Transform { #[allow(dead_code)] lua: Lua, + preprocessor: Option, postprocessor: Option, _marker: PhantomData, } @@ -113,6 +122,10 @@ impl Transform { &self.postprocessor } + fn preprocessor(&self) -> &Option { + &self.preprocessor + } + #[tracing::instrument(name = "new_transform", skip_all)] pub fn new(libs: Vec, transform: Option) -> Result { let lua = new_lua(libs)?; @@ -126,8 +139,14 @@ impl Transform { .get::>("Postprocess") .map_err(|e| ApiError::LuaError(e.to_string()))?; + let preprocessor = lua + .globals() + .get::>("Preprocess") + .map_err(|e| ApiError::LuaError(e.to_string()))?; + Ok(Self { lua, + preprocessor, postprocessor, _marker: PhantomData, }) @@ -138,6 +157,10 @@ impl TransformSpec for Transform { fn has_postprocessor(&self) -> bool { self.postprocessor.is_some() } + + fn has_preprocessor(&self) -> bool { + self.preprocessor.is_some() + } } fn new_lua(libs: Vec) -> Result { diff --git a/encoderfile/src/transforms/image/mod.rs b/encoderfile/src/transforms/image/mod.rs index dc730974..fc521765 100644 --- a/encoderfile/src/transforms/image/mod.rs +++ b/encoderfile/src/transforms/image/mod.rs @@ -1,5 +1,5 @@ use ndarray::Array3; -use image::DynamicImage; +use image::{DynamicImage, GenericImageView}; use mlua::prelude::*; use super::Tensor; @@ -25,10 +25,12 @@ impl FromLua for Image { } } -fn dyn_image_to_array3(image: &DynamicImage, height: u32, width: u32, num_channels: u32) -> Array3 { +fn dyn_image_to_array3(image: &DynamicImage, num_channels: u32) -> Array3 { + // TODO num_channels is tied to the format we convert to let raw = image.to_rgb8().into_raw(); - let h_us: usize = height as usize; - let w_us: usize = width as usize; + let (h_us, w_us) = image.dimensions(); + let h_us: usize = h_us as usize; + let w_us: usize = w_us as usize; let nc_us: usize = num_channels as usize; // Build CHW array directly from raw HWC bytes, avoiding an intermediate array and transpose. @@ -44,7 +46,7 @@ fn resize_image(image: &DynamicImage, height: u32, width: u32) -> DynamicImage { impl LuaUserData for Image { fn add_methods>(methods: &mut M) { // tensor ops - methods.add_method("to_array", |_, this, (height, width, num_channels)| Ok(Tensor(dyn_image_to_array3(this.into_inner(), height, width, num_channels).into_dyn()))); + methods.add_method("to_array", |_, this, num_channels| Ok(Tensor(dyn_image_to_array3(this.into_inner(), num_channels).into_dyn()))); methods.add_method("resize", |_, this, (height, width)| Ok(Image(resize_image(this.into_inner(), height, width)))); } } @@ -76,7 +78,7 @@ fn test_image_to_array() { let img_val = Image(img); lua.globals().set("img", img_val).unwrap(); let array: Tensor = lua - .load("return img:to_array(224, 224, 3)") + .load("return img:resize(224,224):to_array(3)") .eval() .unwrap(); assert_eq!(array.into_inner().shape(), &[3, 224, 224]); diff --git a/test_img_class_config.yml b/test_img_class_config.yml index 8551825b..8653088f 100644 --- a/test_img_class_config.yml +++ b/test_img_class_config.yml @@ -1,4 +1,4 @@ -# optimum-cli export onnx --model phuong-tk-nguyen/resnet-50-finetuned-cifar10 --task image-classification ./cifar10 +# optimum-cli export onnx --model dima806/yoga_pose_image_classification --task image-classification ./yoga encoderfile: name: test-img-class path: models/image_classification From 0bae7f99758b189435c6b53cbeb8fae2b24e27cf Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Fri, 22 May 2026 15:01:35 +0200 Subject: [PATCH 16/21] Introduce Lua preprocessing --- .../src/inference/image_classification.rs | 6 +- .../src/services/image_classification.rs | 119 +++++++++--------- 2 files changed, 62 insertions(+), 63 deletions(-) diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index bcae25d8..2d39f1d2 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -24,11 +24,11 @@ pub fn image_classification<'a>( let raw_outputs = crate::run_cv_model!(session, grouped_images)?; let mut outputs = raw_outputs .get("logits") - .expect("Model does not return logits") + .ok_or(ApiError::InternalError("Model does not return logits"))? .try_extract_array::() - .expect("Model does not return tensor extractable to f32") + .map_err(|_| ApiError::InternalError("Model does not return tensor extractable to f32"))? .into_dimensionality::() - .expect("Model does not return tensor of shape [n_batch, n_classes]") + .map_err(|_| ApiError::InternalError("Model does not return tensor of shape [n_batch, n_classes]"))? .into_owned(); // outputs.mapv_inplace(logit_to_prob); diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index f0f8d8f3..9dc92cf5 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -5,12 +5,11 @@ use crate::{ ImageClassificationResult, model_type }, - + transforms::{ImageClassificationTransform, DEFAULT_LIBS, Preprocessor, Image}, error::ApiError, runtime::AppState, }; -use image::RgbImage; -use ndarray::{Array4, s}; +use ndarray::{ArrayD, Axis, Ix4, s}; use super::inference::Inference; use crate::inference::image_classification::image_classification; @@ -38,6 +37,62 @@ impl Inference for AppState })?; */ + let transform = ImageClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; + + let postprocess_code = r##" + function Preprocess(img) + return img:resize(224,224):to_array(3) + end + "##.to_string(); + + let engine = ImageClassificationTransform::new( + DEFAULT_LIBS.to_vec(), + Some(postprocess_code), + ) + .expect("Failed to create engine"); + + let num_channels = self.model_input_state.config.num_channels as usize; + let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; + let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; + let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; + + let images: Vec> = request.images.iter().map(|image_info| { + let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); + let mut res = engine.preprocess((Image(img), self.model_input_state.clone())).expect("Failed").into_inner(); + for c in 0..num_channels { + let mean = image_mean[c]; + let std = image_std[c]; + res.slice_mut(s![c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); + } + res + }).collect(); + + let images_array = ndarray::stack(Axis(0), &images.iter().map(|x| x.view()).collect::>()) + .unwrap().into_dimensionality::().unwrap(); + + // TODO make parallel??? + // TODO _maybe + + let label_map = self.task_state.id2label.clone().unwrap(); + let mut entries: Vec<_> = label_map.iter().collect(); + entries.sort_by(|x, y| x.0.cmp(y.0)); + let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); + + let labels_batch = image_classification( + self.session.lock(), + images_array, + // COMMENT having optional fields complicates things later on, but otoh + // it allows models with variations of these fields + classes)?; + + Ok(ImageClassificationResponse { + results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), + metadata: request.metadata, + }) + + +/* + let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; @@ -100,66 +155,10 @@ impl Inference for AppState results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), metadata: request.metadata, }) +*/ } - - - - /* - fn new_inference(&self, request: impl Into) -> Result { - let transform = ImageClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; - - let request = request.into(); - if request.images.is_empty() { - return Err(ApiError::InputError("Cannot classify empty image list")); - } - - let postprocess_code = r##" - function Preprocess(img) - return img:resize(224,224):to_array(3) - end - "##.to_string(); - - let engine = ImageClassificationTransform::new( - DEFAULT_LIBS.to_vec(), - Some(postprocess_code), - ) - .expect("Failed to create engine"); - - transform.preprocessor().as_ref().map(|pre| { - pre.call::<_, ()>(()) - .map_err(|e| ApiError::LuaError(format!("Preprocessor error: {e}"))) - })?; - - - - // TODO make parallel - for c in 0..num_channels { - let mean = image_mean[c]; - let std = image_std[c]; - images_array.slice_mut(s![.., c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); - } - - let label_map = self.task_state.id2label.clone().unwrap(); - let mut entries: Vec<_> = label_map.iter().collect(); - entries.sort_by(|x, y| x.0.cmp(y.0)); - let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); - - let labels_batch = image_classification( - self.session.lock(), - images_array, - // COMMENT having optional fields complicates things later on, but otoh - // it allows models with variations of these fields - classes)?; - - Ok(ImageClassificationResponse { - results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), - metadata: request.metadata, - }) - } - */ - } #[cfg(test)] From 64c9499ff3e714bdc9bd4e94e4ba2047654b6bda Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Fri, 22 May 2026 15:09:29 +0200 Subject: [PATCH 17/21] Fix lint --- encoderfile/src/builder/builder.rs | 7 +- encoderfile/src/builder/config.rs | 2 - encoderfile/src/builder/image_preprocessor.rs | 43 ++- encoderfile/src/builder/mod.rs | 2 +- .../src/builder/transforms/validation/mod.rs | 2 +- .../src/common/image_classification.rs | 58 ++-- encoderfile/src/common/image_types.rs | 7 +- encoderfile/src/common/mod.rs | 5 +- encoderfile/src/common/model_config.rs | 2 +- encoderfile/src/dev_utils/mod.rs | 76 +++--- encoderfile/src/format/assets/kind.rs | 5 +- encoderfile/src/format/codec/encoder.rs | 3 +- .../src/generated/image_classification.rs | 25 +- encoderfile/src/generated/image_types.rs | 2 +- encoderfile/src/generated/metadata.rs | 4 +- encoderfile/src/generated/mod.rs | 4 +- .../src/inference/image_classification.rs | 33 +-- .../src/inference/sequence_classification.rs | 5 +- .../src/inference/token_classification.rs | 5 +- encoderfile/src/inference/utils.rs | 3 +- encoderfile/src/runtime/loader.rs | 2 +- encoderfile/src/runtime/mod.rs | 14 +- encoderfile/src/runtime/state.rs | 143 +++++++--- encoderfile/src/services/embedding.rs | 8 +- .../src/services/image_classification.rs | 254 ++++++++++-------- encoderfile/src/services/inference.rs | 4 +- encoderfile/src/services/mod.rs | 2 +- encoderfile/src/services/model_metadata.rs | 8 +- .../src/services/sentence_embedding.rs | 8 +- .../src/services/sequence_classification.rs | 8 +- .../src/services/token_classification.rs | 8 +- .../transforms/engine/image_classification.rs | 34 ++- encoderfile/src/transforms/engine/mod.rs | 2 +- encoderfile/src/transforms/image/mod.rs | 20 +- encoderfile/src/transforms/mod.rs | 4 +- encoderfile/src/transport/cli.rs | 35 ++- encoderfile/src/transport/grpc/mod.rs | 5 +- .../src/transport/http/multipart_openapi.rs | 39 +-- encoderfile/src/transport/mcp/mod.rs | 4 +- encoderfile/src/transport/server.rs | 7 +- encoderfile/tests/test_grpc.rs | 22 +- encoderfile/tests/test_mcp.rs | 2 +- encoderfile/tests/test_model_validation.rs | 1 - encoderfile/tests/test_models.rs | 1 - 44 files changed, 549 insertions(+), 379 deletions(-) diff --git a/encoderfile/src/builder/builder.rs b/encoderfile/src/builder/builder.rs index 6cc2466d..f5691e31 100644 --- a/encoderfile/src/builder/builder.rs +++ b/encoderfile/src/builder/builder.rs @@ -27,11 +27,10 @@ pub struct EncoderfileBuilder { pub config: BuildConfig, } -pub fn validate(_input: &Input) -> Result<()> { +pub fn validate(_input: &Input) -> Result<()> { Ok(()) } - impl EncoderfileBuilder { pub fn new(config: BuildConfig) -> EncoderfileBuilder { Self { config } @@ -104,7 +103,9 @@ impl EncoderfileBuilder { } Input::Image => { let image_preprocessor_asset = - crate::builder::image_preprocessor::validate_image_preprocessor(&self.config.encoderfile)?; + crate::builder::image_preprocessor::validate_image_preprocessor( + &self.config.encoderfile, + )?; planned_assets.push(image_preprocessor_asset); terminal::success("Image preprocessor validated"); } diff --git a/encoderfile/src/builder/config.rs b/encoderfile/src/builder/config.rs index 30dcc423..751fc968 100644 --- a/encoderfile/src/builder/config.rs +++ b/encoderfile/src/builder/config.rs @@ -272,8 +272,6 @@ pub enum ModelPath { }, } - - impl ModelPath { fn resolve( &self, diff --git a/encoderfile/src/builder/image_preprocessor.rs b/encoderfile/src/builder/image_preprocessor.rs index 80b2b9b7..b97638d8 100644 --- a/encoderfile/src/builder/image_preprocessor.rs +++ b/encoderfile/src/builder/image_preprocessor.rs @@ -6,19 +6,15 @@ // require users to provide the config for the model they are using, and we will deal with new // models on a case-by-case basis as they come in. -use crate::{ - format::assets::{AssetKind, AssetSource, PlannedAsset}, -}; +use crate::format::assets::{AssetKind, AssetSource, PlannedAsset}; use anyhow::Result; -use super::config::{ - EncoderfileConfig -}; -use crate::runtime::{ - ImagePreprocessing -}; +use super::config::EncoderfileConfig; +use crate::runtime::ImagePreprocessing; -pub fn validate_image_preprocessor<'a>(efconfig: &'a EncoderfileConfig) -> Result> { +pub fn validate_image_preprocessor<'a>( + efconfig: &'a EncoderfileConfig, +) -> Result> { let config = match efconfig.path.preprocessor_config_path()? { // if preprocessor_config.json is provided, use that Some(preprocessor_config_path) => { @@ -38,14 +34,16 @@ pub fn validate_image_preprocessor<'a>(efconfig: &'a EncoderfileConfig) -> Resul // num_channels must be same as len for mean and std if let Some(num_channels) = model_config.num_channels { - if let Some(image_mean) = config.image_mean.as_ref() && - image_mean.len() != num_channels as usize { - anyhow::bail!("num_channels must match length of image_mean"); - } - if let Some(image_std) = config.image_std.as_ref() && - image_std.len() != num_channels as usize { - anyhow::bail!("num_channels must match length of image_std"); - } + if let Some(image_mean) = config.image_mean.as_ref() + && image_mean.len() != num_channels as usize + { + anyhow::bail!("num_channels must match length of image_mean"); + } + if let Some(image_std) = config.image_std.as_ref() + && image_std.len() != num_channels as usize + { + anyhow::bail!("num_channels must match length of image_std"); + } } PlannedAsset::from_asset_source( @@ -54,8 +52,6 @@ pub fn validate_image_preprocessor<'a>(efconfig: &'a EncoderfileConfig) -> Resul ) } - - #[cfg(test)] mod tests { use crate::builder::config::ModelPath; @@ -83,8 +79,9 @@ mod tests { let preprocessor_config = validate_image_preprocessor(&config) .expect("Failed to validate image preprocessor config"); - println!("Validated image preprocessor config: {:?}", preprocessor_config); + println!( + "Validated image preprocessor config: {:?}", + preprocessor_config + ); } - - } diff --git a/encoderfile/src/builder/mod.rs b/encoderfile/src/builder/mod.rs index 6bed4cb9..463a2e67 100644 --- a/encoderfile/src/builder/mod.rs +++ b/encoderfile/src/builder/mod.rs @@ -5,10 +5,10 @@ pub mod builder; pub mod cache; pub mod cli; pub mod config; +pub mod image_preprocessor; pub mod model; pub mod templates; /// Terminal logging utilities. pub mod terminal; pub mod tokenizer; pub mod transforms; -pub mod image_preprocessor; diff --git a/encoderfile/src/builder/transforms/validation/mod.rs b/encoderfile/src/builder/transforms/validation/mod.rs index 39089444..0b74cb05 100644 --- a/encoderfile/src/builder/transforms/validation/mod.rs +++ b/encoderfile/src/builder/transforms/validation/mod.rs @@ -10,10 +10,10 @@ use crate::builder::config::EncoderfileConfig; use prost::Message; mod embedding; +mod image_classification; mod sentence_embedding; mod sequence_classification; mod token_classification; -mod image_classification; mod utils; pub trait TransformValidatorExt: TransformSpec { diff --git a/encoderfile/src/common/image_classification.rs b/encoderfile/src/common/image_classification.rs index 8889c9ee..0af0e264 100644 --- a/encoderfile/src/common/image_classification.rs +++ b/encoderfile/src/common/image_classification.rs @@ -1,10 +1,10 @@ +use crate::common::FromReadInput; +use crate::common::image_types::{ImageInfo, ImageLabelScore}; +use anyhow::Result; +use bytes::Bytes; use serde::{Deserialize, Serialize}; use std::{collections::HashMap, io::Read}; use utoipa::ToSchema; -use anyhow::Result; -use crate::common::FromReadInput; -use bytes::Bytes; -use crate::common::image_types::{ImageInfo, ImageLabelScore}; #[derive(Debug, Serialize, Deserialize)] pub struct ImageClassificationRequest { @@ -14,14 +14,18 @@ pub struct ImageClassificationRequest { impl super::FromCliInput for ImageClassificationRequest { fn from_cli_input(inputs: Vec) -> Self { - let images = inputs.into_iter().map(|path| { - let image_data = std::fs::read(path).expect("Failed to read image file"); - let format = image::guess_format(&image_data).expect("Failed to guess image format"); - ImageInfo { - image_bytes: Bytes::from(image_data), - image_format: format, - } - }).collect(); + let images = inputs + .into_iter() + .map(|path| { + let image_data = std::fs::read(path).expect("Failed to read image file"); + let format = + image::guess_format(&image_data).expect("Failed to guess image format"); + ImageInfo { + image_bytes: Bytes::from(image_data), + image_format: format, + } + }) + .collect(); Self { images, @@ -32,15 +36,21 @@ impl super::FromCliInput for ImageClassificationRequest { impl FromReadInput for ImageClassificationRequest { fn from_read_input(input: Vec<&mut impl Read>) -> Result { - let images = input.into_iter().map(|reader| { - let mut image_data = Vec::new(); - reader.read_to_end(&mut image_data).map_err(|e| anyhow::anyhow!("Failed to read image data: {}", e))?; - let format = image::guess_format(&image_data).map_err(|e| anyhow::anyhow!("Failed to guess image format: {}", e))?; - Ok(ImageInfo { - image_bytes: Bytes::from(image_data), - image_format: format, + let images = input + .into_iter() + .map(|reader| { + let mut image_data = Vec::new(); + reader + .read_to_end(&mut image_data) + .map_err(|e| anyhow::anyhow!("Failed to read image data: {}", e))?; + let format = image::guess_format(&image_data) + .map_err(|e| anyhow::anyhow!("Failed to guess image format: {}", e))?; + Ok(ImageInfo { + image_bytes: Bytes::from(image_data), + image_format: format, + }) }) - }).collect::>>()?; + .collect::>>()?; Ok(Self { images, @@ -64,14 +74,16 @@ pub struct ImageClassificationResult { #[cfg(test)] mod tests { use super::*; - use std::fs::File; use image::ImageFormat; + use std::fs::File; #[test] fn test_image_classification_request_from_read_input() { - let mut file = File::open("../test-pictures/w3c_home.jpg").expect("Failed to open test image"); + let mut file = + File::open("../test-pictures/yoga01.jpg").expect("Failed to open test image"); let file_vec = vec![&mut file]; - let request = ImageClassificationRequest::from_read_input(file_vec).expect("Failed to create request from read input"); + let request = ImageClassificationRequest::from_read_input(file_vec) + .expect("Failed to create request from read input"); assert_eq!(request.images.len(), 1); assert_eq!(request.images[0].image_format, ImageFormat::Jpeg); diff --git a/encoderfile/src/common/image_types.rs b/encoderfile/src/common/image_types.rs index 1448243e..7423cbeb 100644 --- a/encoderfile/src/common/image_types.rs +++ b/encoderfile/src/common/image_types.rs @@ -1,7 +1,7 @@ +use bytes::Bytes; +use image::ImageFormat; use serde::{Deserialize, Serialize}; use utoipa::ToSchema; -use image::ImageFormat; -use bytes::Bytes; #[derive(Debug, Serialize, Deserialize)] pub struct ImageInfo { @@ -15,10 +15,7 @@ pub struct ImageLabelScore { pub score: Option, } - #[derive(Debug, Serialize, Deserialize, ToSchema, Clone)] pub struct ImageLabels { pub labels: Vec, } - - diff --git a/encoderfile/src/common/mod.rs b/encoderfile/src/common/mod.rs index ea81671d..2990ae73 100644 --- a/encoderfile/src/common/mod.rs +++ b/encoderfile/src/common/mod.rs @@ -22,10 +22,10 @@ pub use token::*; pub use token_classification::*; // CV +use anyhow::Result; pub use image_classification::*; pub use image_types::*; use std::io::Read; -use anyhow::Result; pub trait FromCliInput { fn from_cli_input(inputs: Vec) -> Self; @@ -33,5 +33,6 @@ pub trait FromCliInput { pub trait FromReadInput { fn from_read_input(input: Vec<&mut impl Read>) -> Result - where Self: Sized; + where + Self: Sized; } diff --git a/encoderfile/src/common/model_config.rs b/encoderfile/src/common/model_config.rs index 2e9d08c7..16233c62 100644 --- a/encoderfile/src/common/model_config.rs +++ b/encoderfile/src/common/model_config.rs @@ -31,7 +31,7 @@ impl ModelConfig { if let Some(id2label) = &self.id2label { return Some(id2label.len()); - } + } if let Some(label2id) = &self.label2id { return Some(label2id.len()); diff --git a/encoderfile/src/dev_utils/mod.rs b/encoderfile/src/dev_utils/mod.rs index e55ed24a..6f145700 100644 --- a/encoderfile/src/dev_utils/mod.rs +++ b/encoderfile/src/dev_utils/mod.rs @@ -4,22 +4,15 @@ use crate::{ model_type::{self, ModelTypeSpec}, }, runtime::{ - AppState, - ClassifierState, - EncoderfileState, - FeatureExtractorState, - ImageInputState, - ImageConfig, - ImagePreprocessing, - ImageSize, - InputType, - ORTSessionBuilder, TaskType, TextInputState + AppState, ClassifierState, EncoderfileState, FeatureExtractorState, ImageConfig, + ImageInputState, ImagePreprocessing, ImageSize, InputType, ORTSessionBuilder, TaskType, + TextInputState, }, }; use ort::session::Session; use parking_lot::Mutex; use std::str::FromStr; -use std::{fs::File, io::BufReader, fmt::Debug}; +use std::{fmt::Debug, fs::File, io::BufReader}; const EMBEDDING_DIR: &str = "../models/embedding"; const SEQUENCE_CLASSIFICATION_DIR: &str = "../models/sequence_classification"; @@ -27,10 +20,11 @@ const TOKEN_CLASSIFICATION_DIR: &str = "../models/token_classification"; const IMAGE_CLASSIFICATION_DIR: &str = "../models/image_classification"; pub fn get_state<'a, T: ModelTypeSpec + InputType + TaskType>(dir: &'a str) -> AppState - where ::State: TryFrom<&'a str>, - <::State as TryFrom<&'a str>>::Error: Debug, - ::State: TryFrom<&'a str>, - <::State as TryFrom<&'a str>>::Error: Debug, +where + ::State: TryFrom<&'a str>, + <::State as TryFrom<&'a str>>::Error: Debug, + ::State: TryFrom<&'a str>, + <::State as TryFrom<&'a str>>::Error: Debug, { let config = Config { name: "my-model".to_string(), @@ -42,15 +36,12 @@ pub fn get_state<'a, T: ModelTypeSpec + InputType + TaskType>(dir: &'a str) -> A let session = get_model(dir); - let model_input_state = ::State::try_from(dir).expect("could not load model input state from file"); - let model_task_state = ::State::try_from(dir).expect("could not load model task state from file"); + let model_input_state = + ::State::try_from(dir).expect("could not load model input state from file"); + let model_task_state = + ::State::try_from(dir).expect("could not load model task state from file"); - EncoderfileState::new( - config, - session, - model_input_state, - model_task_state, - ).into() + EncoderfileState::new(config, session, model_input_state, model_task_state).into() } pub trait TaskTypeFromFile: TaskType { @@ -63,7 +54,8 @@ pub fn get_config_reader(dir: &str) -> BufReader { } pub fn get_preproc_reader(dir: &str) -> BufReader { - let file = File::open(format!("{}/{}", dir, "preprocessor_config.json")).expect("Preprocessing config not found"); + let file = File::open(format!("{}/{}", dir, "preprocessor_config.json")) + .expect("Preprocessing config not found"); BufReader::new(file) } @@ -73,7 +65,10 @@ fn get_text_input_state(dir: &str) -> Result { let tokenizer = get_tokenizer(dir); let model_config = serde_json::from_reader(reader)?; - Ok(TextInputState { tokenizer, model_config }) + Ok(TextInputState { + tokenizer, + model_config, + }) } fn get_image_input_state(dir: &str) -> Result { @@ -84,7 +79,8 @@ fn get_image_input_state(dir: &str) -> Result { Ok(ImageInputState { config: ImageConfig { num_channels: config_state.num_channels, - image_size: config_state.image_size }, + image_size: config_state.image_size, + }, preprocessing: ImagePreprocessing { do_normalize: preproc_state.do_normalize, do_rescale: preproc_state.do_rescale, @@ -93,12 +89,12 @@ fn get_image_input_state(dir: &str) -> Result { rescale_factor: preproc_state.rescale_factor, image_mean: preproc_state.image_mean, image_std: preproc_state.image_std, - size: preproc_state.size.or( - Some( - ImageSize{ width: config_state.image_size, height: config_state.image_size, shortest_edge: None } - ) - ) - } + size: preproc_state.size.or(Some(ImageSize { + width: config_state.image_size, + height: config_state.image_size, + shortest_edge: None, + })), + }, }) } @@ -130,29 +126,23 @@ fn get_feature_task_state(_dir: &str) -> Result AppState -{ +pub fn embedding_state() -> AppState { get_state(EMBEDDING_DIR) } -pub fn sentence_embedding_state() -> AppState -{ +pub fn sentence_embedding_state() -> AppState { get_state(EMBEDDING_DIR) } -pub fn sequence_classification_state() -> AppState -{ +pub fn sequence_classification_state() -> AppState { get_state(SEQUENCE_CLASSIFICATION_DIR) } -pub fn token_classification_state() -> AppState -{ +pub fn token_classification_state() -> AppState { get_state(TOKEN_CLASSIFICATION_DIR) } -pub fn image_classification_state() -> AppState -{ +pub fn image_classification_state() -> AppState { get_state(IMAGE_CLASSIFICATION_DIR) } diff --git a/encoderfile/src/format/assets/kind.rs b/encoderfile/src/format/assets/kind.rs index 2a9485b1..6c5be0ab 100644 --- a/encoderfile/src/format/assets/kind.rs +++ b/encoderfile/src/format/assets/kind.rs @@ -1,4 +1,7 @@ -use crate::{common::model_type::ModelTypeSpec, runtime::{Input, InputType, Task, TaskType}}; +use crate::{ + common::model_type::ModelTypeSpec, + runtime::{Input, InputType, Task, TaskType}, +}; /// Identifies the semantic role of an embedded artifact. /// diff --git a/encoderfile/src/format/codec/encoder.rs b/encoderfile/src/format/codec/encoder.rs index 71666300..c79d9c99 100644 --- a/encoderfile/src/format/codec/encoder.rs +++ b/encoderfile/src/format/codec/encoder.rs @@ -3,7 +3,8 @@ use anyhow::{Result, bail}; use crate::{ common::model_type::{ - Embedding, ImageClassification, ModelType, SentenceEmbedding, SequenceClassification, TokenClassification, + Embedding, ImageClassification, ModelType, SentenceEmbedding, SequenceClassification, + TokenClassification, }, format::{ assets::{AssetPlan, AssetPolicySpec}, diff --git a/encoderfile/src/generated/image_classification.rs b/encoderfile/src/generated/image_classification.rs index 7cdf3f9b..0ca34dd6 100644 --- a/encoderfile/src/generated/image_classification.rs +++ b/encoderfile/src/generated/image_classification.rs @@ -4,15 +4,23 @@ tonic::include_proto!("encoderfile.image_classification"); impl From for common::ImageClassificationRequest { fn from(val: ImageClassificationRequest) -> Self { - let images = val.inputs.into_iter().map(|input| { - common::ImageInfo { - image_bytes: bytes::Bytes::from(input.image), - image_format: image::ImageFormat::Png, // TODO: detect format properly - } - }).collect(); + let images = val + .inputs + .into_iter() + .map(|input| { + common::ImageInfo { + image_bytes: bytes::Bytes::from(input.image), + image_format: image::ImageFormat::Png, // TODO: detect format properly + } + }) + .collect(); Self { images, - metadata: if val.metadata.is_empty() { None } else { Some(val.metadata) }, + metadata: if val.metadata.is_empty() { + None + } else { + Some(val.metadata) + }, } } } @@ -23,7 +31,8 @@ impl From for ImageClassificationResponse { results: val .results .into_iter() - .map(|result| result.into()).collect(), + .map(|result| result.into()) + .collect(), metadata: val.metadata.unwrap_or_default(), } } diff --git a/encoderfile/src/generated/image_types.rs b/encoderfile/src/generated/image_types.rs index d8b9a452..5fbdd38e 100644 --- a/encoderfile/src/generated/image_types.rs +++ b/encoderfile/src/generated/image_types.rs @@ -18,7 +18,7 @@ impl From for ImageLabelScore { } } } - + impl From for ImageLabels { fn from(val: common::ImageLabels) -> Self { ImageLabels { diff --git a/encoderfile/src/generated/metadata.rs b/encoderfile/src/generated/metadata.rs index e7d68479..d240d1f2 100644 --- a/encoderfile/src/generated/metadata.rs +++ b/encoderfile/src/generated/metadata.rs @@ -28,7 +28,9 @@ impl From for common::model_type::ModelType { fn from(val: ModelType) -> Self { match val { ModelType::Embedding => common::model_type::ModelType::Embedding, - ModelType::SequenceClassification => common::model_type::ModelType::SequenceClassification, + ModelType::SequenceClassification => { + common::model_type::ModelType::SequenceClassification + } ModelType::TokenClassification => common::model_type::ModelType::TokenClassification, ModelType::SentenceEmbedding => common::model_type::ModelType::SentenceEmbedding, ModelType::ImageClassification => common::model_type::ModelType::ImageClassification, diff --git a/encoderfile/src/generated/mod.rs b/encoderfile/src/generated/mod.rs index e617876e..4b5a8dbc 100644 --- a/encoderfile/src/generated/mod.rs +++ b/encoderfile/src/generated/mod.rs @@ -1,9 +1,9 @@ pub mod embedding; +pub mod image_classification; +pub mod image_types; pub mod manifest; pub mod metadata; pub mod sentence_embedding; pub mod sequence_classification; pub mod token; pub mod token_classification; -pub mod image_classification; -pub mod image_types; diff --git a/encoderfile/src/inference/image_classification.rs b/encoderfile/src/inference/image_classification.rs index 2d39f1d2..f29b00c1 100644 --- a/encoderfile/src/inference/image_classification.rs +++ b/encoderfile/src/inference/image_classification.rs @@ -1,14 +1,14 @@ -use ndarray::{Array2, Array4, Ix2, Axis}; +use ndarray::{Array2, Array4, Axis, Ix2}; -use crate::{ - error::ApiError, -}; +use crate::error::ApiError; -use crate::common::{ImageLabelScore}; +use crate::common::ImageLabelScore; +/* fn logit_to_prob(logit: f32) -> f32 { 1.0 / (1.0 + (-logit).exp()) } +*/ #[tracing::instrument(skip_all)] pub fn image_classification<'a>( @@ -17,18 +17,19 @@ pub fn image_classification<'a>( images: Array4, classes: Vec, ) -> Result>, ApiError> { - let grouped_images = ort::value::TensorRef::from_array_view( - &images) + let grouped_images = ort::value::TensorRef::from_array_view(&images) .unwrap() .to_owned(); let raw_outputs = crate::run_cv_model!(session, grouped_images)?; - let mut outputs = raw_outputs + let /*mut*/ outputs = raw_outputs .get("logits") .ok_or(ApiError::InternalError("Model does not return logits"))? .try_extract_array::() .map_err(|_| ApiError::InternalError("Model does not return tensor extractable to f32"))? .into_dimensionality::() - .map_err(|_| ApiError::InternalError("Model does not return tensor of shape [n_batch, n_classes]"))? + .map_err(|_| { + ApiError::InternalError("Model does not return tensor of shape [n_batch, n_classes]") + })? .into_owned(); // outputs.mapv_inplace(logit_to_prob); @@ -40,14 +41,14 @@ pub fn postprocess(outputs: Array2, classes: Vec) -> Vec, classes: Vec) -> Vec {{ - $session.run(ort::inputs!($image_bytes)) - .map_err(|e| { + $session.run(ort::inputs!($image_bytes)).map_err(|e| { tracing::error!("Error running model: {:?}", e); $crate::error::ApiError::InternalError("Error running model") }) diff --git a/encoderfile/src/runtime/loader.rs b/encoderfile/src/runtime/loader.rs index c5af9257..213350ca 100644 --- a/encoderfile/src/runtime/loader.rs +++ b/encoderfile/src/runtime/loader.rs @@ -8,7 +8,7 @@ use crate::{ common::{Config, LuaLibs, ModelConfig, model_type::ModelType}, format::{assets::AssetKind, codec::EncoderfileCodec, container::Encoderfile}, generated::manifest::{self, TransformType}, - runtime::{ORTExecutionProvider, ORTSessionBuilder, TokenizerService, ImagePreprocessing}, + runtime::{ImagePreprocessing, ORTExecutionProvider, ORTSessionBuilder, TokenizerService}, }; pub struct EncoderfileLoader<'a, R: Read + Seek> { diff --git a/encoderfile/src/runtime/mod.rs b/encoderfile/src/runtime/mod.rs index 739dd60c..886b4356 100644 --- a/encoderfile/src/runtime/mod.rs +++ b/encoderfile/src/runtime/mod.rs @@ -9,19 +9,9 @@ mod tokenizer; pub use loader::{EncoderfileLoader, load_assets}; pub use session::{ORTExecutionProvider, ORTSessionBuilder}; pub use state::{ - AppState, - EncoderfileState, - Input, - Task, - InputType, - TaskType, - ClassifierState, - FeatureExtractorState, - ImageInputState, + AppState, ClassifierState, EncoderfileState, FeatureExtractorState, ImageConfig, + ImageInputState, ImagePreprocessing, ImageSize, Input, InputType, Task, TaskType, TextInputState, - ImageConfig, - ImageSize, - ImagePreprocessing }; pub use tokenizer::TokenizerService; diff --git a/encoderfile/src/runtime/state.rs b/encoderfile/src/runtime/state.rs index fc69e6f5..d3d65787 100644 --- a/encoderfile/src/runtime/state.rs +++ b/encoderfile/src/runtime/state.rs @@ -1,18 +1,23 @@ +use mlua::prelude::*; +use serde::{Deserialize, Serialize}; use std::{ + fmt::Debug, + io::{Read, Seek}, marker::PhantomData, sync::Arc, - io::{Read, Seek}, - fmt::Debug, }; -use serde::{Deserialize, Serialize}; -use mlua::prelude::*; use ort::session::Session; use parking_lot::Mutex; use crate::{ - common::{Config, ModelConfig, model_type::{ModelType, ModelTypeSpec, self}}, runtime::TokenizerService, transforms::DEFAULT_LIBS, + common::{ + Config, ModelConfig, + model_type::{self, ModelType, ModelTypeSpec}, + }, + runtime::TokenizerService, runtime::loader::EncoderfileLoader, + transforms::DEFAULT_LIBS, }; pub type AppState = Arc>; @@ -79,7 +84,7 @@ pub struct ImagePreprocessing { pub do_rescale: Option, pub do_resize: Option, pub image_processor_type: Option, - pub size: Option + pub size: Option, } #[derive(Debug, Serialize, Deserialize, Clone)] @@ -93,21 +98,41 @@ impl LuaUserData for ImageInputState { fn add_fields>(fields: &mut F) { fields.add_field_method_get("num_channels", |_, this| Ok(this.config.num_channels)); fields.add_field_method_get("image_size", |_, this| Ok(this.config.image_size)); - fields.add_field_method_get("rescale_factor", |_, this| Ok(this.preprocessing.rescale_factor)); - fields.add_field_method_get("image_mean", |_, this| Ok(this.preprocessing.image_mean.clone())); - fields.add_field_method_get("image_std", |_, this| Ok(this.preprocessing.image_std.clone())); - fields.add_field_method_get("do_normalize", |_, this| Ok(this.preprocessing.do_normalize)); + fields.add_field_method_get("rescale_factor", |_, this| { + Ok(this.preprocessing.rescale_factor) + }); + fields.add_field_method_get("image_mean", |_, this| { + Ok(this.preprocessing.image_mean.clone()) + }); + fields.add_field_method_get("image_std", |_, this| { + Ok(this.preprocessing.image_std.clone()) + }); + fields.add_field_method_get("do_normalize", |_, this| { + Ok(this.preprocessing.do_normalize) + }); fields.add_field_method_get("do_rescale", |_, this| Ok(this.preprocessing.do_rescale)); fields.add_field_method_get("do_resize", |_, this| Ok(this.preprocessing.do_resize)); - fields.add_field_method_get("size_height", |_, this| Ok(this.preprocessing.size.as_ref().and_then(|s| s.height))); - fields.add_field_method_get("size_width", |_, this| Ok(this.preprocessing.size.as_ref().and_then(|s| s.width))); - fields.add_field_method_get("size_shortest_edge", |_, this| Ok(this.preprocessing.size.as_ref().and_then(|s| s.shortest_edge))); + fields.add_field_method_get("size_height", |_, this| { + Ok(this.preprocessing.size.as_ref().and_then(|s| s.height)) + }); + fields.add_field_method_get("size_width", |_, this| { + Ok(this.preprocessing.size.as_ref().and_then(|s| s.width)) + }); + fields.add_field_method_get("size_shortest_edge", |_, this| { + Ok(this + .preprocessing + .size + .as_ref() + .and_then(|s| s.shortest_edge)) + }); } } impl LuaUserData for TextInputState { fn add_fields>(fields: &mut F) { - fields.add_field_method_get("model_type", |_, this| Ok(this.model_config.model_type.clone())); + fields.add_field_method_get("model_type", |_, this| { + Ok(this.model_config.model_type.clone()) + }); fields.add_field_method_get("num_labels", |_, this| Ok(this.model_config.num_labels())); fields.add_field_method_get("id2label", |_, this| Ok(this.model_config.id2label.clone())); fields.add_field_method_get("label2id", |_, this| Ok(this.model_config.label2id.clone())); @@ -157,7 +182,9 @@ impl ClassifierState { #[derive(Debug, Serialize, Deserialize, Clone)] pub struct FeatureExtractorState {} -fn text_input_state_try_from_loader<'a, R>(loader: &mut EncoderfileLoader<'a, R>) -> Result +fn text_input_state_try_from_loader<'a, R>( + loader: &mut EncoderfileLoader<'a, R>, +) -> Result where R: Read + Seek, { @@ -169,7 +196,9 @@ where }) } -fn image_input_state_try_from_loader<'a, R>(loader: &mut EncoderfileLoader<'a, R>) -> Result +fn image_input_state_try_from_loader<'a, R>( + loader: &mut EncoderfileLoader<'a, R>, +) -> Result where R: Read + Seek, { @@ -177,7 +206,9 @@ where let preprocessor_config = loader.image_preprocessor_config()?; Ok(ImageInputState { config: ImageConfig { - num_channels: model_config.num_channels.ok_or_else(|| anyhow::anyhow!("num_channels is required for image models"))?, + num_channels: model_config + .num_channels + .ok_or_else(|| anyhow::anyhow!("num_channels is required for image models"))?, image_size: model_config.image_size, }, preprocessing: ImagePreprocessing { @@ -193,7 +224,9 @@ where }) } -fn classifier_state_try_from_loader<'a, R>(loader: &mut EncoderfileLoader<'a, R>) -> Result +fn classifier_state_try_from_loader<'a, R>( + loader: &mut EncoderfileLoader<'a, R>, +) -> Result where R: Read + Seek, { @@ -205,7 +238,9 @@ where }) } -fn feature_extractor_state_try_from_loader<'a, R>(_loader: &mut EncoderfileLoader<'a, R>) -> Result +fn feature_extractor_state_try_from_loader<'a, R>( + _loader: &mut EncoderfileLoader<'a, R>, +) -> Result where R: Read + Seek, { @@ -215,11 +250,14 @@ where macro_rules! state_from_source_impl { ($base_type:tt, $state_type:ty, $state_fun:ident) => { impl<'a, 'borrow, R> TryFrom<&'borrow mut EncoderfileLoader<'a, R>> for $state_type - where R: Read + Seek, + where + R: Read + Seek, { type Error = anyhow::Error; - fn try_from(loader: &'borrow mut EncoderfileLoader<'a, R>) -> Result { + fn try_from( + loader: &'borrow mut EncoderfileLoader<'a, R>, + ) -> Result { $state_fun::(loader) } } @@ -227,10 +265,17 @@ macro_rules! state_from_source_impl { } state_from_source_impl!(InputType, TextInputState, text_input_state_try_from_loader); -state_from_source_impl!(InputType, ImageInputState, image_input_state_try_from_loader); +state_from_source_impl!( + InputType, + ImageInputState, + image_input_state_try_from_loader +); state_from_source_impl!(TaskType, ClassifierState, classifier_state_try_from_loader); -state_from_source_impl!(TaskType, FeatureExtractorState, feature_extractor_state_try_from_loader); - +state_from_source_impl!( + TaskType, + FeatureExtractorState, + feature_extractor_state_try_from_loader +); macro_rules! input_state_impl { ($model_type:ty, $state_type:ty, $input:expr) => { @@ -243,9 +288,17 @@ macro_rules! input_state_impl { input_state_impl!(model_type::Embedding, TextInputState, Input::Text); input_state_impl!(model_type::SentenceEmbedding, TextInputState, Input::Text); -input_state_impl!(model_type::SequenceClassification, TextInputState, Input::Text); +input_state_impl!( + model_type::SequenceClassification, + TextInputState, + Input::Text +); input_state_impl!(model_type::TokenClassification, TextInputState, Input::Text); -input_state_impl!(model_type::ImageClassification, ImageInputState, Input::Image); +input_state_impl!( + model_type::ImageClassification, + ImageInputState, + Input::Image +); macro_rules! task_state_impl { ($model_type:ty, $state_type:ty, $task:expr) => { @@ -256,11 +309,31 @@ macro_rules! task_state_impl { }; } -task_state_impl!(model_type::SequenceClassification, ClassifierState, Task::Classification); -task_state_impl!(model_type::TokenClassification, ClassifierState, Task::Classification); -task_state_impl!(model_type::ImageClassification, ClassifierState, Task::Classification); -task_state_impl!(model_type::Embedding, FeatureExtractorState, Task::FeatureExtraction); -task_state_impl!(model_type::SentenceEmbedding, FeatureExtractorState, Task::FeatureExtraction); +task_state_impl!( + model_type::SequenceClassification, + ClassifierState, + Task::Classification +); +task_state_impl!( + model_type::TokenClassification, + ClassifierState, + Task::Classification +); +task_state_impl!( + model_type::ImageClassification, + ClassifierState, + Task::Classification +); +task_state_impl!( + model_type::Embedding, + FeatureExtractorState, + Task::FeatureExtraction +); +task_state_impl!( + model_type::SentenceEmbedding, + FeatureExtractorState, + Task::FeatureExtraction +); macro_rules! input_type_impl { [ $( $x:ident ),* $(,)? ] => { @@ -279,7 +352,7 @@ macro_rules! input_type_impl { )* } } - } + } } } input_type_impl![ @@ -291,8 +364,7 @@ input_type_impl![ ]; #[derive(Debug)] -pub struct EncoderfileState -{ +pub struct EncoderfileState { pub config: Config, pub session: Mutex, pub model_input_state: ::State, @@ -301,8 +373,7 @@ pub struct EncoderfileState _marker: PhantomData, } -impl EncoderfileState -{ +impl EncoderfileState { pub fn new( config: Config, session: Mutex, diff --git a/encoderfile/src/services/embedding.rs b/encoderfile/src/services/embedding.rs index 153c4ee7..cfc846f7 100644 --- a/encoderfile/src/services/embedding.rs +++ b/encoderfile/src/services/embedding.rs @@ -8,15 +8,17 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState -{ +impl Inference for AppState { type Input = EmbeddingRequest; type Output = EmbeddingResponse; fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self + .model_input_state + .tokenizer + .encode_text(request.inputs)?; let transform = EmbeddingTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 9dc92cf5..c5218518 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -1,13 +1,11 @@ use crate::{ common::{ - ImageClassificationRequest, - ImageClassificationResponse, - ImageClassificationResult, - model_type + ImageClassificationRequest, ImageClassificationResponse, ImageClassificationResult, + model_type, }, - transforms::{ImageClassificationTransform, DEFAULT_LIBS, Preprocessor, Image}, error::ApiError, runtime::AppState, + transforms::{DEFAULT_LIBS, Image, ImageClassificationTransform, Preprocessor}, }; use ndarray::{ArrayD, Axis, Ix4, s}; @@ -16,10 +14,7 @@ use crate::inference::image_classification::image_classification; // No service impl yet -const DEFAULT_FILTER_TYPE: image::imageops::FilterType = image::imageops::FilterType::Triangle; - -impl Inference for AppState -{ +impl Inference for AppState { type Input = ImageClassificationRequest; type Output = ImageClassificationResponse; @@ -37,139 +32,170 @@ impl Inference for AppState })?; */ - let transform = ImageClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; - let postprocess_code = r##" function Preprocess(img) return img:resize(224,224):to_array(3) end - "##.to_string(); + "## + .to_string(); - let engine = ImageClassificationTransform::new( - DEFAULT_LIBS.to_vec(), - Some(postprocess_code), - ) - .expect("Failed to create engine"); + let engine = + ImageClassificationTransform::new(DEFAULT_LIBS.to_vec(), Some(postprocess_code)) + .expect("Failed to create engine"); let num_channels = self.model_input_state.config.num_channels as usize; - let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; - let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; - let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; - - let images: Vec> = request.images.iter().map(|image_info| { - let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); - let mut res = engine.preprocess((Image(img), self.model_input_state.clone())).expect("Failed").into_inner(); - for c in 0..num_channels { - let mean = image_mean[c]; - let std = image_std[c]; - res.slice_mut(s![c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); - } - res - }).collect(); - - let images_array = ndarray::stack(Axis(0), &images.iter().map(|x| x.view()).collect::>()) - .unwrap().into_dimensionality::().unwrap(); + let rescale_factor = self + .model_input_state + .preprocessing + .rescale_factor + .ok_or(ApiError::InternalError("missing rescale factor"))?; + let image_mean = self + .model_input_state + .preprocessing + .image_mean + .as_ref() + .ok_or(ApiError::InternalError("missing image mean"))?; + let image_std = self + .model_input_state + .preprocessing + .image_std + .as_ref() + .ok_or(ApiError::InternalError("missing image std"))?; + + let images: Vec> = request + .images + .iter() + .map(|image_info| { + let img = image::load_from_memory(&image_info.image_bytes) + .expect("Failed to load image from bytes"); + let mut res = engine + .preprocess((Image(img), self.model_input_state.clone())) + .expect("Failed") + .into_inner(); + for c in 0..num_channels { + let mean = image_mean[c]; + let std = image_std[c]; + res.slice_mut(s![c, .., ..]) + .mapv_inplace(|x| ((x * rescale_factor) - mean) / std); + } + res + }) + .collect(); + + let images_array = ndarray::stack( + Axis(0), + &images.iter().map(|x| x.view()).collect::>(), + ) + .unwrap() + .into_dimensionality::() + .unwrap(); // TODO make parallel??? - // TODO _maybe + // TODO _maybe let label_map = self.task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); entries.sort_by(|x, y| x.0.cmp(y.0)); - let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); + let classes: Vec = entries + .into_iter() + .map(|(_, label)| label.clone()) + .collect(); let labels_batch = image_classification( self.session.lock(), images_array, // COMMENT having optional fields complicates things later on, but otoh // it allows models with variations of these fields - classes)?; + classes, + )?; Ok(ImageClassificationResponse { - results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), + results: labels_batch + .iter() + .map(|labels| ImageClassificationResult { + labels: labels.clone(), + }) + .collect(), metadata: request.metadata, }) + /* -/* - - let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; - let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; - let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; - // bilinear resampling - - // convert input image into flattened rbg - let images: Vec = request.images.iter().map(|image_info| { - let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); - img - .resize_exact( - self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap(), - self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap(), - DEFAULT_FILTER_TYPE - ) - .to_rgb8() - }).collect(); - let batch_size = request.images.len(); - let num_channels = self.model_input_state.config.num_channels as usize; - let height = self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap() as usize; - let width = self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap() as usize; - - if num_channels != 3 { - return Err(ApiError::InputError("Image classification currently expects 3 RGB channels")); - } - - let mut images_array = Array4::::zeros((batch_size, num_channels, height, width)); - for (image_idx, img) in images.into_iter().enumerate() { - let raw = img.into_raw(); + let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; + let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; + let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; + // bilinear resampling + + // convert input image into flattened rbg + let images: Vec = request.images.iter().map(|image_info| { + let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); + img + .resize_exact( + self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap(), + self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap(), + DEFAULT_FILTER_TYPE + ) + .to_rgb8() + }).collect(); + let batch_size = request.images.len(); + let num_channels = self.model_input_state.config.num_channels as usize; + let height = self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap() as usize; + let width = self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap() as usize; + + if num_channels != 3 { + return Err(ApiError::InputError("Image classification currently expects 3 RGB channels")); + } - // The image crate stores RGB bytes in HWC order; rewrite into NCHW. - for y in 0..height { - for x in 0..width { - let pixel_offset = (y * width + x) * num_channels; - for c in 0..num_channels { - images_array[[image_idx, c, y, x]] = raw[pixel_offset + c] as f32; + let mut images_array = Array4::::zeros((batch_size, num_channels, height, width)); + for (image_idx, img) in images.into_iter().enumerate() { + let raw = img.into_raw(); + + // The image crate stores RGB bytes in HWC order; rewrite into NCHW. + for y in 0..height { + for x in 0..width { + let pixel_offset = (y * width + x) * num_channels; + for c in 0..num_channels { + images_array[[image_idx, c, y, x]] = raw[pixel_offset + c] as f32; + } + } } } - } - } - // TODO make parallel - for c in 0..num_channels { - let mean = image_mean[c]; - let std = image_std[c]; - images_array.slice_mut(s![.., c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); - } - - let label_map = self.task_state.id2label.clone().unwrap(); - let mut entries: Vec<_> = label_map.iter().collect(); - entries.sort_by(|x, y| x.0.cmp(y.0)); - let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); - - let labels_batch = image_classification( - self.session.lock(), - images_array, - // COMMENT having optional fields complicates things later on, but otoh - // it allows models with variations of these fields - classes)?; + // TODO make parallel + for c in 0..num_channels { + let mean = image_mean[c]; + let std = image_std[c]; + images_array.slice_mut(s![.., c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); + } - Ok(ImageClassificationResponse { - results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), - metadata: request.metadata, - }) -*/ + let label_map = self.task_state.id2label.clone().unwrap(); + let mut entries: Vec<_> = label_map.iter().collect(); + entries.sort_by(|x, y| x.0.cmp(y.0)); + let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); + + let labels_batch = image_classification( + self.session.lock(), + images_array, + // COMMENT having optional fields complicates things later on, but otoh + // it allows models with variations of these fields + classes)?; + + Ok(ImageClassificationResponse { + results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), + metadata: request.metadata, + }) + */ } - - } #[cfg(test)] mod tests { + use super::*; + use crate::common::FromReadInput; + use crate::common::ImageClassificationRequest; use crate::common::model_type::ImageClassification; use crate::dev_utils; - use crate::common::ImageClassificationRequest; - use crate::common::FromReadInput; use std::fs::File; use std::sync::Once; - use super::*; fn init_tracing() { static TRACING: Once = Once::new(); @@ -190,14 +216,26 @@ mod tests { init_tracing(); let state = dev_utils::get_state::("../models/image_classification"); - let mut file = File::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + let mut file = + File::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); let file_vec = vec![&mut file]; - let request = ImageClassificationRequest::from_read_input(file_vec).expect("Failed to create request from read input"); + let request = ImageClassificationRequest::from_read_input(file_vec) + .expect("Failed to create request from read input"); let response = state.inference(request).expect("Inference failed"); println!("Inference response: {:?}", response); assert_eq!(response.results.len(), 1); assert_eq!(response.results[0].labels.len(), 9); - assert!(response.results[0].labels.iter().enumerate().max_by(|a, b| a.1.score.partial_cmp(&b.1.score).unwrap()).unwrap().1.label == "Downward-Dog"); // top label should be "yoga mat" + assert!( + response.results[0] + .labels + .iter() + .enumerate() + .max_by(|a, b| a.1.score.partial_cmp(&b.1.score).unwrap()) + .unwrap() + .1 + .label + == "Downward-Dog" + ); // top label should be "yoga mat" } #[test] @@ -212,4 +250,4 @@ mod tests { let response = state.inference(request); assert!(response.is_err()); } -} \ No newline at end of file +} diff --git a/encoderfile/src/services/inference.rs b/encoderfile/src/services/inference.rs index 60fb0095..d1832431 100644 --- a/encoderfile/src/services/inference.rs +++ b/encoderfile/src/services/inference.rs @@ -2,8 +2,8 @@ use crate::{common::FromCliInput, error::ApiError, services::Metadata}; // FIXME enforce the openapi schema later on pub trait Inference: Metadata { - type Input: FromCliInput + serde::de::DeserializeOwned + Sync + Send /* + utoipa::ToSchema */; - type Output: serde::Serialize + Sync + Send /* + utoipa::ToSchema */; + type Input: FromCliInput + serde::de::DeserializeOwned + Sync + Send; + type Output: serde::Serialize + Sync + Send; fn inference(&self, request: impl Into) -> Result; } diff --git a/encoderfile/src/services/mod.rs b/encoderfile/src/services/mod.rs index 6d88d6db..4e7db263 100644 --- a/encoderfile/src/services/mod.rs +++ b/encoderfile/src/services/mod.rs @@ -1,10 +1,10 @@ mod embedding; +mod image_classification; mod inference; mod model_metadata; mod sentence_embedding; mod sequence_classification; mod token_classification; -mod image_classification; pub use inference::Inference; pub use model_metadata::Metadata; diff --git a/encoderfile/src/services/model_metadata.rs b/encoderfile/src/services/model_metadata.rs index 93fbc828..8d4637d3 100644 --- a/encoderfile/src/services/model_metadata.rs +++ b/encoderfile/src/services/model_metadata.rs @@ -3,11 +3,9 @@ use std::collections::HashMap; use crate::{ common::{ GetModelMetadataResponse, - model_type::{ModelType, ModelTypeSpec} - }, - runtime::{ - AppState, ClassifierState, FeatureExtractorState, InputType, TaskType + model_type::{ModelType, ModelTypeSpec}, }, + runtime::{AppState, ClassifierState, FeatureExtractorState, InputType, TaskType}, }; pub trait Metadata { @@ -45,7 +43,7 @@ impl TaskStateMetadata for FeatureExtractorState { impl Metadata for AppState where - ::State: TaskStateMetadata + ::State: TaskStateMetadata, { fn model_id(&self) -> String { self.config.name.clone() diff --git a/encoderfile/src/services/sentence_embedding.rs b/encoderfile/src/services/sentence_embedding.rs index b9e8f205..465e3424 100644 --- a/encoderfile/src/services/sentence_embedding.rs +++ b/encoderfile/src/services/sentence_embedding.rs @@ -8,15 +8,17 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState -{ +impl Inference for AppState { type Input = SentenceEmbeddingRequest; type Output = SentenceEmbeddingResponse; fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self + .model_input_state + .tokenizer + .encode_text(request.inputs)?; let transform = SentenceEmbeddingTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/sequence_classification.rs b/encoderfile/src/services/sequence_classification.rs index bc9d7533..f354cb21 100644 --- a/encoderfile/src/services/sequence_classification.rs +++ b/encoderfile/src/services/sequence_classification.rs @@ -8,15 +8,17 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState -{ +impl Inference for AppState { type Input = SequenceClassificationRequest; type Output = SequenceClassificationResponse; fn inference(&self, request: impl Into) -> Result { let request = request.into(); - let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self + .model_input_state + .tokenizer + .encode_text(request.inputs)?; let transform = SequenceClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/services/token_classification.rs b/encoderfile/src/services/token_classification.rs index fed061bd..fe7e3402 100644 --- a/encoderfile/src/services/token_classification.rs +++ b/encoderfile/src/services/token_classification.rs @@ -8,8 +8,7 @@ use crate::{ use super::inference::Inference; -impl Inference for AppState -{ +impl Inference for AppState { type Input = TokenClassificationRequest; type Output = TokenClassificationResponse; @@ -18,7 +17,10 @@ impl Inference for AppState let session = self.session.lock(); - let encodings = self.model_input_state.tokenizer.encode_text(request.inputs)?; + let encodings = self + .model_input_state + .tokenizer + .encode_text(request.inputs)?; let transform = TokenClassificationTransform::new(self.lua_libs.clone(), self.transform_str())?; diff --git a/encoderfile/src/transforms/engine/image_classification.rs b/encoderfile/src/transforms/engine/image_classification.rs index 2b00324f..be9fd8a6 100644 --- a/encoderfile/src/transforms/engine/image_classification.rs +++ b/encoderfile/src/transforms/engine/image_classification.rs @@ -1,6 +1,6 @@ use crate::{common::model_type, error::ApiError, runtime::ImageInputState}; -use super::{super::tensor::Tensor, super::image::Image, Postprocessor, Preprocessor, Transform}; +use super::{super::image::Image, super::tensor::Tensor, Postprocessor, Preprocessor, Transform}; use ndarray::{Array2, Ix2}; impl Postprocessor for Transform { @@ -51,10 +51,15 @@ impl Preprocessor for Transform { fn preprocess(&self, (image, config): Self::Input) -> Result { let func = match self.preprocessor() { Some(p) => p, - None => return Err(ApiError::InternalError("No preprocessor defined for this model")), + None => { + return Err(ApiError::InternalError( + "No preprocessor defined for this model", + )); + } }; - self.lua.globals() + self.lua + .globals() .set("input_config", config) .map_err(|e| ApiError::LuaError(e.to_string()))?; @@ -157,7 +162,6 @@ mod tests { } } - #[test] fn test_image_preprocess() { let engine = Transform::::new( @@ -176,17 +180,27 @@ mod tests { let img = image::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); let config = ImageInputState { - config: crate::runtime::ImageConfig { num_channels: 3, image_size: Some(224) }, + config: crate::runtime::ImageConfig { + num_channels: 3, + image_size: Some(224), + }, preprocessing: crate::runtime::ImagePreprocessing { - rescale_factor: None, image_mean: None, image_std: None, - do_normalize: None, do_rescale: None, do_resize: None, + rescale_factor: None, + image_mean: None, + image_std: None, + do_normalize: None, + do_rescale: None, + do_resize: None, image_processor_type: None, - size: Some(crate::runtime::ImageSize { height: Some(224), width: Some(224), shortest_edge: None }), + size: Some(crate::runtime::ImageSize { + height: Some(224), + width: Some(224), + shortest_edge: None, + }), }, }; let result = engine.preprocess((Image(img), config)).expect("Failed"); - assert!(result.into_inner().shape() == &[3, 224, 224]); + assert!(result.into_inner().shape() == [3, 224, 224]); } - } diff --git a/encoderfile/src/transforms/engine/mod.rs b/encoderfile/src/transforms/engine/mod.rs index 5a10be7b..bc6e9712 100644 --- a/encoderfile/src/transforms/engine/mod.rs +++ b/encoderfile/src/transforms/engine/mod.rs @@ -13,10 +13,10 @@ use super::tensor::Tensor; use mlua::prelude::*; mod embedding; +mod image_classification; mod sentence_embedding; mod sequence_classification; mod token_classification; -mod image_classification; impl From<&LuaLibs> for Vec { fn from(value: &LuaLibs) -> Self { diff --git a/encoderfile/src/transforms/image/mod.rs b/encoderfile/src/transforms/image/mod.rs index fc521765..a01573a3 100644 --- a/encoderfile/src/transforms/image/mod.rs +++ b/encoderfile/src/transforms/image/mod.rs @@ -1,7 +1,7 @@ -use ndarray::Array3; +use super::Tensor; use image::{DynamicImage, GenericImageView}; use mlua::prelude::*; -use super::Tensor; +use ndarray::Array3; const DEFAULT_FILTER_TYPE: image::imageops::FilterType = image::imageops::FilterType::Triangle; @@ -46,8 +46,14 @@ fn resize_image(image: &DynamicImage, height: u32, width: u32) -> DynamicImage { impl LuaUserData for Image { fn add_methods>(methods: &mut M) { // tensor ops - methods.add_method("to_array", |_, this, num_channels| Ok(Tensor(dyn_image_to_array3(this.into_inner(), num_channels).into_dyn()))); - methods.add_method("resize", |_, this, (height, width)| Ok(Image(resize_image(this.into_inner(), height, width)))); + methods.add_method("to_array", |_, this, num_channels| { + Ok(Tensor( + dyn_image_to_array3(this.into_inner(), num_channels).into_dyn(), + )) + }); + methods.add_method("resize", |_, this, (height, width)| { + Ok(Image(resize_image(this.into_inner(), height, width))) + }); } } @@ -64,10 +70,7 @@ fn test_resize_image() { let lua = load_env(); let img_val = Image(img); lua.globals().set("img", img_val).unwrap(); - let resized: Image = lua - .load("return img:resize(224, 224)") - .eval() - .unwrap(); + let resized: Image = lua.load("return img:resize(224, 224)").eval().unwrap(); assert_eq!(resized.into_inner().dimensions(), (224, 224)); } @@ -83,4 +86,3 @@ fn test_image_to_array() { .unwrap(); assert_eq!(array.into_inner().shape(), &[3, 224, 224]); } - diff --git a/encoderfile/src/transforms/mod.rs b/encoderfile/src/transforms/mod.rs index 98840d95..e1489a7c 100644 --- a/encoderfile/src/transforms/mod.rs +++ b/encoderfile/src/transforms/mod.rs @@ -1,11 +1,11 @@ mod engine; -mod tensor; mod image; +mod tensor; mod utils; pub use engine::*; -pub use tensor::Tensor; pub use image::Image; +pub use tensor::Tensor; pub const DEFAULT_LIBS: [mlua::StdLib; 3] = [ mlua::StdLib::TABLE, diff --git a/encoderfile/src/transport/cli.rs b/encoderfile/src/transport/cli.rs index eddc3327..c15f3681 100644 --- a/encoderfile/src/transport/cli.rs +++ b/encoderfile/src/transport/cli.rs @@ -3,7 +3,7 @@ use crate::{ FromCliInput, model_type::{self, ModelType, ModelTypeSpec}, }, - runtime::{EncoderfileLoader, EncoderfileState, ORTExecutionProvider, InputType, TaskType}, + runtime::{EncoderfileLoader, EncoderfileState, InputType, ORTExecutionProvider, TaskType}, services::{Inference, Metadata}, transport::{ grpc::GrpcRouter, @@ -18,7 +18,7 @@ use opentelemetry::trace::TracerProvider as _; use opentelemetry_otlp::{Protocol, WithExportConfig}; use opentelemetry_sdk::trace::SdkTracerProvider; use std::{ - fmt::{Display, Debug}, + fmt::{Debug, Display}, io::{Read, Seek, Write}, sync::Arc, }; @@ -137,7 +137,11 @@ impl Commands { } } } - pub async fn execute_from_loader<'loader, R: Read + Seek, T: ModelTypeSpec + InputType + TaskType>( + pub async fn execute_from_loader< + 'loader, + R: Read + Seek, + T: ModelTypeSpec + InputType + TaskType, + >( self, loader: &mut EncoderfileLoader<'loader, R>, ) -> Result<()> @@ -145,8 +149,10 @@ impl Commands { Arc>: Inference + GrpcRouter + HttpRouter + McpRouter + CliRoute, ::State: Debug, ::State: Debug, - for<'b> ::State: TryFrom<&'b mut EncoderfileLoader<'loader, R>, Error = anyhow::Error>, - for<'b> ::State: TryFrom<&'b mut EncoderfileLoader<'loader, R>, Error = anyhow::Error>, + for<'b> ::State: + TryFrom<&'b mut EncoderfileLoader<'loader, R>, Error = anyhow::Error>, + for<'b> ::State: + TryFrom<&'b mut EncoderfileLoader<'loader, R>, Error = anyhow::Error>, { match self { Commands::Serve { @@ -174,8 +180,10 @@ impl Commands { let state = Arc::new(EncoderfileState::::new( config, session, - ::State::try_from(loader).expect("could not load model input state from file"), - ::State::try_from(loader).expect("could not load model task state from file") + ::State::try_from(loader) + .expect("could not load model input state from file"), + ::State::try_from(loader) + .expect("could not load model task state from file"), )); let banner = crate::get_banner(state.model_id().as_str()); @@ -236,8 +244,10 @@ impl Commands { let state = Arc::new(EncoderfileState::::new( config, session, - ::State::try_from(loader).expect("could not load model input state from file"), - ::State::try_from(loader).expect("could not load model task state from file"), + ::State::try_from(loader) + .expect("could not load model input state from file"), + ::State::try_from(loader) + .expect("could not load model task state from file"), )); setup_tracing(None)?; @@ -264,8 +274,10 @@ impl Commands { let state = Arc::new(EncoderfileState::::new( config, session, - ::State::try_from(loader).expect("could not load model input state from file"), - ::State::try_from(loader).expect("could not load model input state from file"), + ::State::try_from(loader) + .expect("could not load model input state from file"), + ::State::try_from(loader) + .expect("could not load model input state from file"), )); let banner = crate::get_banner(state.model_id().as_str()); @@ -278,7 +290,6 @@ impl Commands { } } - #[derive(Clone, Args)] pub struct ONNXArgs { #[arg(long, default_value_t = false)] diff --git a/encoderfile/src/transport/grpc/mod.rs b/encoderfile/src/transport/grpc/mod.rs index 6b9b3fad..9cc81ee2 100644 --- a/encoderfile/src/transport/grpc/mod.rs +++ b/encoderfile/src/transport/grpc/mod.rs @@ -1,6 +1,9 @@ use crate::{ common::model_type, - generated::{embedding, sentence_embedding, sequence_classification, token_classification, image_classification}, + generated::{ + embedding, image_classification, sentence_embedding, sequence_classification, + token_classification, + }, runtime::AppState, services::{Inference, Metadata}, }; diff --git a/encoderfile/src/transport/http/multipart_openapi.rs b/encoderfile/src/transport/http/multipart_openapi.rs index 8591a8de..2ad85e54 100644 --- a/encoderfile/src/transport/http/multipart_openapi.rs +++ b/encoderfile/src/transport/http/multipart_openapi.rs @@ -1,3 +1,6 @@ +use crate::common::model_type::ImageClassification; +use crate::common::{ImageClassificationRequest, ImageInfo}; +use crate::runtime::AppState; use axum::{ Json, extract::{Multipart, State}, @@ -5,11 +8,8 @@ use axum::{ response::{IntoResponse, Response}, }; use serde::{Deserialize, Serialize}; -use utoipa::OpenApi; -use crate::common::model_type::ImageClassification; -use crate::common::{ImageClassificationRequest, ImageInfo}; use std::collections::HashMap; -use crate::runtime::AppState; +use utoipa::OpenApi; pub const MULTIPART_PREDICT_ENDPOINT: &str = "/predict/multipart"; pub const MULTIPART_OPENAPI_ENDPOINT: &str = "/predict/multipart/openapi.json"; @@ -79,10 +79,12 @@ impl FromMultipart for ImageClassificationRequest { let images = attachments .into_iter() .map(|(_file_name, _content_type, image_bytes)| { - let format = image::guess_format(&image_bytes) - .map_err(|e| MultipartApiError::RequestConstruction( - format!("Failed to detect image format: {}", e) - ))?; + let format = image::guess_format(&image_bytes).map_err(|e| { + MultipartApiError::RequestConstruction(format!( + "Failed to detect image format: {}", + e + )) + })?; Ok(ImageInfo { image_bytes, image_format: format, @@ -102,9 +104,11 @@ impl FromMultipart for ImageClassificationRequest { } } - #[derive(Debug, utoipa::OpenApi)] -#[openapi(paths(post_multipart), components(schemas(MultipartPredictBody, MultipartPredictResponse, ParsedAttachment)))] +#[openapi( + paths(post_multipart), + components(schemas(MultipartPredictBody, MultipartPredictResponse, ParsedAttachment)) +)] pub struct MultipartApiDoc; #[utoipa::path( @@ -162,7 +166,8 @@ pub async fn parse_multipart( match name.as_deref() { Some("payload") => { payload = Some( - serde_json::from_slice(&bytes).map_err(|_| MultipartApiError::InvalidPayload)?, + serde_json::from_slice(&bytes) + .map_err(|_| MultipartApiError::InvalidPayload)?, ); } Some("files") => { @@ -210,7 +215,8 @@ pub async fn post_multipart_typed( match name.as_deref() { Some("payload") => { payload = Some( - serde_json::from_slice(&bytes).map_err(|_| MultipartApiError::InvalidPayload)?, + serde_json::from_slice(&bytes) + .map_err(|_| MultipartApiError::InvalidPayload)?, ); } Some("files") => { @@ -239,7 +245,10 @@ pub async fn post_multipart_typed( pub fn router() -> axum::Router { axum::Router::new() - .route(MULTIPART_PREDICT_ENDPOINT, axum::routing::post(post_multipart)) + .route( + MULTIPART_PREDICT_ENDPOINT, + axum::routing::post(post_multipart), + ) .route(MULTIPART_OPENAPI_ENDPOINT, axum::routing::get(openapi)) } @@ -274,9 +283,7 @@ async fn post_multipart_image_classification( /// Standard predict endpoint for ImageClassification. async fn predict_handler( State(state): State>, - Json(req): Json< - as crate::services::Inference>::Input, - >, + Json(req): Json< as crate::services::Inference>::Input>, ) -> impl IntoResponse { super::base::predict(State(state), Json(req)).await } diff --git a/encoderfile/src/transport/mcp/mod.rs b/encoderfile/src/transport/mcp/mod.rs index 1681b148..b9fb2241 100644 --- a/encoderfile/src/transport/mcp/mod.rs +++ b/encoderfile/src/transport/mcp/mod.rs @@ -59,7 +59,9 @@ impl McpRouter for AppState { type Tool = DummyTool; const NEW_TOOL: fn(Self) -> Self::Tool = |_state| Self::Tool {}; fn mcp_router(self) -> Result { - Err(crate::error::ApiError::InternalError("MCP not implemented for ImageClassification model type")) + Err(crate::error::ApiError::InternalError( + "MCP not implemented for ImageClassification model type", + )) } } diff --git a/encoderfile/src/transport/server.rs b/encoderfile/src/transport/server.rs index 29f7dbc6..15f7c13d 100644 --- a/encoderfile/src/transport/server.rs +++ b/encoderfile/src/transport/server.rs @@ -100,7 +100,12 @@ async fn serve_with_optional_tls( maybe_key_file: Option, server_type_str: &str, state: S, - into_service_fn: impl Fn(&S) -> Result, crate::error::ApiError>, + into_service_fn: impl Fn( + &S, + ) -> Result< + IntoMakeServiceWithConnectInfo, + crate::error::ApiError, + >, ) -> Result<()> { let addr = format!("{}:{}", &hostname, &port); diff --git a/encoderfile/tests/test_grpc.rs b/encoderfile/tests/test_grpc.rs index 542a0ec4..5927bb46 100644 --- a/encoderfile/tests/test_grpc.rs +++ b/encoderfile/tests/test_grpc.rs @@ -8,6 +8,11 @@ use encoderfile::{ embedding::{ EmbeddingRequest, EmbeddingResponse, embedding_inference_server::EmbeddingInference, }, + image_classification::{ + ImageClassificationRequest, ImageClassificationResponse, + image_classification_inference_server::ImageClassificationInference, + }, + image_types::ImageInput, metadata::{GetModelMetadataRequest, GetModelMetadataResponse}, sentence_embedding::{ SentenceEmbeddingRequest, SentenceEmbeddingResponse, @@ -21,11 +26,6 @@ use encoderfile::{ TokenClassificationRequest, TokenClassificationResponse, token_classification_inference_server::TokenClassificationInference, }, - image_classification::{ - ImageClassificationRequest, ImageClassificationResponse, - image_classification_inference_server::ImageClassificationInference, - }, - image_types::{ImageInput} }, transport::grpc::GrpcService, }; @@ -146,12 +146,13 @@ test_grpc_service!( SentenceEmbeddingResponse ); -const TEST_IMAGE_PATH: &str = "../test-pictures/w3c_home.jpg"; +const TEST_IMAGE_PATH: &str = "../test-pictures/yoga01.jpg"; fn get_file_bytes(filename: &str) -> Vec { let mut file = File::open(filename).expect("Failed to open test image"); let mut buffer = Vec::new(); - file.read_to_end(&mut buffer).expect("Failed to read test image"); + file.read_to_end(&mut buffer) + .expect("Failed to read test image"); buffer } @@ -160,7 +161,12 @@ test_grpc_service!( { GrpcService::new(image_classification_state()) }, true, ImageClassificationRequest { - inputs: [TEST_IMAGE_PATH, TEST_IMAGE_PATH].iter().map(|s| ImageInput { image: get_file_bytes(s) }).collect(), + inputs: [TEST_IMAGE_PATH, TEST_IMAGE_PATH] + .iter() + .map(|s| ImageInput { + image: get_file_bytes(s) + }) + .collect(), metadata: HashMap::new(), }, ImageClassificationResponse diff --git a/encoderfile/tests/test_mcp.rs b/encoderfile/tests/test_mcp.rs index 38e81c85..864181f1 100644 --- a/encoderfile/tests/test_mcp.rs +++ b/encoderfile/tests/test_mcp.rs @@ -1,11 +1,11 @@ use anyhow::Result; use encoderfile::AppState; use encoderfile::common::model_type::ModelTypeSpec; +use encoderfile::runtime::{InputType, TaskType}; use encoderfile::transport::mcp::McpRouter; use tokio::net::TcpListener; use tokio::sync::oneshot; use tower_http::trace::DefaultOnResponse; -use encoderfile::runtime::{InputType, TaskType}; async fn run_mcp( addr: String, diff --git a/encoderfile/tests/test_model_validation.rs b/encoderfile/tests/test_model_validation.rs index 11d819c6..edb9aa64 100644 --- a/encoderfile/tests/test_model_validation.rs +++ b/encoderfile/tests/test_model_validation.rs @@ -57,4 +57,3 @@ pub fn test_image_classification() { .is_err() ); } - diff --git a/encoderfile/tests/test_models.rs b/encoderfile/tests/test_models.rs index f5994a14..fed03a82 100644 --- a/encoderfile/tests/test_models.rs +++ b/encoderfile/tests/test_models.rs @@ -194,4 +194,3 @@ fn test_image_classification_model() { assert!(results.len() == encodings.len()); */ } - From f8d2f14cb7562183e3dc2d8b45955391edb49c0b Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Thu, 4 Jun 2026 17:03:31 +0200 Subject: [PATCH 18/21] Fix image classification http service --- encoderfile/src/builder/image_preprocessor.rs | 8 +- .../src/services/image_classification.rs | 93 ++++--------------- .../src/transport/http/multipart_openapi.rs | 62 ++++++------- encoderfile/tests/test_http.rs | 82 ++++++++++++++++ 4 files changed, 131 insertions(+), 114 deletions(-) diff --git a/encoderfile/src/builder/image_preprocessor.rs b/encoderfile/src/builder/image_preprocessor.rs index b97638d8..80c09a11 100644 --- a/encoderfile/src/builder/image_preprocessor.rs +++ b/encoderfile/src/builder/image_preprocessor.rs @@ -13,9 +13,9 @@ use super::config::EncoderfileConfig; use crate::runtime::ImagePreprocessing; pub fn validate_image_preprocessor<'a>( - efconfig: &'a EncoderfileConfig, + encoderfile_config: &'a EncoderfileConfig, ) -> Result> { - let config = match efconfig.path.preprocessor_config_path()? { + let config = match encoderfile_config.path.preprocessor_config_path()? { // if preprocessor_config.json is provided, use that Some(preprocessor_config_path) => { // open preprocessor_config @@ -26,10 +26,10 @@ pub fn validate_image_preprocessor<'a>( // some values may be present in config.json None => { // from_model_config(&image_preprocessing.config)?; - anyhow::bail!("FATAL: No preprocessor config provided"); + anyhow::bail!("FATAL: No preprocessor_config.json provided"); } }; - let model_config = efconfig.model_config()?; + let model_config = encoderfile_config.model_config()?; let serialized = serde_json::to_vec(&config)?; // num_channels must be same as len for mean and std diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index c5218518..7a86ddd2 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -7,7 +7,7 @@ use crate::{ runtime::AppState, transforms::{DEFAULT_LIBS, Image, ImageClassificationTransform, Preprocessor}, }; -use ndarray::{ArrayD, Axis, Ix4, s}; +use ndarray::{ArrayD, Axis, Ix4, Zip}; use super::inference::Inference; use crate::inference::image_classification::image_classification; @@ -72,12 +72,20 @@ impl Inference for AppState { .preprocess((Image(img), self.model_input_state.clone())) .expect("Failed") .into_inner(); - for c in 0..num_channels { - let mean = image_mean[c]; - let std = image_std[c]; - res.slice_mut(s![c, .., ..]) - .mapv_inplace(|x| ((x * rescale_factor) - mean) / std); - } + let mean_arr = ndarray::Array::from_shape_vec( + (num_channels, 1, 1), + image_mean.to_vec(), + ) + .expect("mean shape mismatch"); + let std_arr = ndarray::Array::from_shape_vec( + (num_channels, 1, 1), + image_std.to_vec(), + ) + .expect("std shape mismatch"); + Zip::from(&mut res) + .and_broadcast(&mean_arr) + .and_broadcast(&std_arr) + .for_each(|x, &m, &s| *x = (*x * rescale_factor - m) / s); res }) .collect(); @@ -90,8 +98,10 @@ impl Inference for AppState { .into_dimensionality::() .unwrap(); - // TODO make parallel??? - // TODO _maybe + // TODO overlap preprocessing and inference, but for now just do it sequentially + // Since we are adding gpu providers now, preprocessing could run in cpu while inference + // is running. Using some sort of task queue will pave the way for more efficient batch + // processing. However, it will not be implemented right now. let label_map = self.task_state.id2label.clone().unwrap(); let mut entries: Vec<_> = label_map.iter().collect(); @@ -119,71 +129,6 @@ impl Inference for AppState { metadata: request.metadata, }) - /* - - let rescale_factor = self.model_input_state.preprocessing.rescale_factor.ok_or(ApiError::InternalError("missing rescale factor"))?; - let image_mean = self.model_input_state.preprocessing.image_mean.as_ref().ok_or(ApiError::InternalError("missing image mean"))?; - let image_std = self.model_input_state.preprocessing.image_std.as_ref().ok_or(ApiError::InternalError("missing image std"))?; - // bilinear resampling - - // convert input image into flattened rbg - let images: Vec = request.images.iter().map(|image_info| { - let img = image::load_from_memory(&image_info.image_bytes).expect("Failed to load image from bytes"); - img - .resize_exact( - self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap(), - self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap(), - DEFAULT_FILTER_TYPE - ) - .to_rgb8() - }).collect(); - let batch_size = request.images.len(); - let num_channels = self.model_input_state.config.num_channels as usize; - let height = self.model_input_state.preprocessing.size.as_ref().unwrap().height.unwrap() as usize; - let width = self.model_input_state.preprocessing.size.as_ref().unwrap().width.unwrap() as usize; - - if num_channels != 3 { - return Err(ApiError::InputError("Image classification currently expects 3 RGB channels")); - } - - let mut images_array = Array4::::zeros((batch_size, num_channels, height, width)); - for (image_idx, img) in images.into_iter().enumerate() { - let raw = img.into_raw(); - - // The image crate stores RGB bytes in HWC order; rewrite into NCHW. - for y in 0..height { - for x in 0..width { - let pixel_offset = (y * width + x) * num_channels; - for c in 0..num_channels { - images_array[[image_idx, c, y, x]] = raw[pixel_offset + c] as f32; - } - } - } - } - // TODO make parallel - for c in 0..num_channels { - let mean = image_mean[c]; - let std = image_std[c]; - images_array.slice_mut(s![.., c, .., ..]).mapv_inplace(|x| ((x * rescale_factor) - mean) / std); - } - - let label_map = self.task_state.id2label.clone().unwrap(); - let mut entries: Vec<_> = label_map.iter().collect(); - entries.sort_by(|x, y| x.0.cmp(y.0)); - let classes: Vec = entries.into_iter().map(|(_, label)| label.clone()).collect(); - - let labels_batch = image_classification( - self.session.lock(), - images_array, - // COMMENT having optional fields complicates things later on, but otoh - // it allows models with variations of these fields - classes)?; - - Ok(ImageClassificationResponse { - results: labels_batch.iter().map(|labels| ImageClassificationResult { labels: labels.clone() }).collect(), - metadata: request.metadata, - }) - */ } } diff --git a/encoderfile/src/transport/http/multipart_openapi.rs b/encoderfile/src/transport/http/multipart_openapi.rs index 2ad85e54..f520a84b 100644 --- a/encoderfile/src/transport/http/multipart_openapi.rs +++ b/encoderfile/src/transport/http/multipart_openapi.rs @@ -1,6 +1,7 @@ use crate::common::model_type::ImageClassification; -use crate::common::{ImageClassificationRequest, ImageInfo}; +use crate::common::{ImageClassificationRequest, ImageClassificationResponse, ImageInfo}; use crate::runtime::AppState; +use crate::services::Inference; use axum::{ Json, extract::{Multipart, State}, @@ -31,13 +32,6 @@ pub struct ParsedAttachment { pub size_bytes: usize, } -#[derive(Debug, Serialize, Deserialize, utoipa::ToSchema, utoipa::ToResponse)] -pub struct MultipartPredictResponse { - pub payload: serde_json::Value, - pub attachment_count: usize, - pub attachments: Vec, -} - #[derive(Debug, thiserror::Error)] pub enum MultipartApiError { #[error("missing required multipart field 'payload'")] @@ -107,7 +101,7 @@ impl FromMultipart for ImageClassificationRequest { #[derive(Debug, utoipa::OpenApi)] #[openapi( paths(post_multipart), - components(schemas(MultipartPredictBody, MultipartPredictResponse, ParsedAttachment)) + components(schemas(MultipartPredictBody, ImageClassificationResponse, ParsedAttachment)) )] pub struct MultipartApiDoc; @@ -131,21 +125,23 @@ pub async fn openapi() -> impl IntoResponse { description = "Multipart payload with a JSON part named 'payload' and 0..N binary parts named 'files'" ), responses( - (status = 200, body = MultipartPredictResponse), + (status = 200, body = ImageClassificationResponse), (status = 422, description = "Missing or invalid payload JSON"), (status = 400, description = "Invalid multipart body") ) )] pub async fn post_multipart( + state: State>, mut multipart: Multipart, -) -> Result, MultipartApiError> { - parse_multipart(&mut multipart).await +) -> Result, MultipartApiError> { + parse_multipart(state, &mut multipart).await } /// Generic multipart parser that extracts payload and attachments. pub async fn parse_multipart( + State(state): State>, multipart: &mut Multipart, -) -> Result, MultipartApiError> { +) -> Result, MultipartApiError> { let mut payload: Option = None; let mut attachments = Vec::new(); let mut attachment_metadata = Vec::new(); @@ -184,17 +180,20 @@ pub async fn parse_multipart( let payload = payload.ok_or(MultipartApiError::MissingPayload)?; - Ok(Json(MultipartPredictResponse { - payload, - attachment_count: attachment_metadata.len(), - attachments: attachment_metadata, - })) + // Convert to typed request + let request = ImageClassificationRequest::from_multipart(payload.clone(), attachments)?; + let result = state.inference(request).map(Json).map_err(|e| { + MultipartApiError::RequestConstruction(format!("Inference error: {}", e.to_string())) + })?; + + Ok(result) } /// Generic handler that converts multipart request into typed request. pub async fn post_multipart_typed( + State(state): State>, mut multipart: Multipart, -) -> Result, MultipartApiError> { +) -> Result, MultipartApiError> { let mut payload: Option = None; let mut attachments = Vec::new(); let mut attachment_metadata = Vec::new(); @@ -234,22 +233,12 @@ pub async fn post_multipart_typed( let payload = payload.ok_or(MultipartApiError::MissingPayload)?; // Convert to typed request - let _request: R = R::from_multipart(payload.clone(), attachments)?; - - Ok(Json(MultipartPredictResponse { - payload, - attachment_count: attachment_metadata.len(), - attachments: attachment_metadata, - })) -} + let request = ImageClassificationRequest::from_multipart(payload.clone(), attachments)?; + let result = state.inference(request).map(Json).map_err(|e| { + MultipartApiError::RequestConstruction(format!("Inference error: {}", e.to_string())) + })?; -pub fn router() -> axum::Router { - axum::Router::new() - .route( - MULTIPART_PREDICT_ENDPOINT, - axum::routing::post(post_multipart), - ) - .route(MULTIPART_OPENAPI_ENDPOINT, axum::routing::get(openapi)) + Ok(result) } /// HttpRouter implementation for ImageClassification model type. @@ -275,9 +264,10 @@ impl super::HttpRouter for crate::runtime::AppState { /// Multipart handler specialized for ImageClassificationRequest. async fn post_multipart_image_classification( + state: State>, multipart: Multipart, -) -> Result, MultipartApiError> { - post_multipart_typed::(multipart).await +) -> Result, MultipartApiError> { + post_multipart_typed::(state, multipart).await } /// Standard predict endpoint for ImageClassification. diff --git a/encoderfile/tests/test_http.rs b/encoderfile/tests/test_http.rs index 441d001b..005df01d 100644 --- a/encoderfile/tests/test_http.rs +++ b/encoderfile/tests/test_http.rs @@ -94,6 +94,7 @@ macro_rules! test_router_mod { }; } + test_router_mod!( Embedding, embedding_tests, @@ -130,3 +131,84 @@ test_router_mod!( metadata: None, } ); + + +mod image_classification_tests { + use axum::http::{Request, StatusCode}; + use encoderfile::{ + dev_utils, + transport::http::HttpRouter, + }; + use tower::ServiceExt; + + fn router() -> axum::Router { + let state = dev_utils::image_classification_state(); + state.http_router() + } + + + #[tokio::test] + async fn test_predict_route() { + let router = router(); + let img_loc1 = "../test-pictures/yoga01.jpg"; + let img_loc2 = "../test-pictures/yoga02.jpg"; + let img_bytes1 = std::fs::read(img_loc1).unwrap(); + let img_bytes2 = std::fs::read(img_loc2).unwrap(); + let payload = serde_json::json!({ + "inputs": ["yoga01.jpg", "yoga02.jpg"], + "metadata": {} + }); + + let boundary = "----encoderfile-boundary"; + let mut multipart_body = Vec::new(); + + multipart_body.extend_from_slice( + format!( + "--{boundary}\r\nContent-Disposition: form-data; name=\"payload\"\r\nContent-Type: application/json\r\n\r\n{}\r\n", + payload + ) + .as_bytes(), + ); + + multipart_body.extend_from_slice( + format!( + "--{boundary}\r\nContent-Disposition: form-data; name=\"files\"; filename=\"yoga01.jpg\"\r\nContent-Type: image/jpeg\r\n\r\n" + ) + .as_bytes(), + ); + multipart_body.extend_from_slice(&img_bytes1); + multipart_body.extend_from_slice(b"\r\n"); + + multipart_body.extend_from_slice( + format!( + "--{boundary}\r\nContent-Disposition: form-data; name=\"files\"; filename=\"yoga02.jpg\"\r\nContent-Type: image/jpeg\r\n\r\n" + ) + .as_bytes(), + ); + multipart_body.extend_from_slice(&img_bytes2); + multipart_body.extend_from_slice(b"\r\n"); + + multipart_body.extend_from_slice(format!("--{boundary}--\r\n").as_bytes()); + + let request = Request::post("/predict/multipart") + .header( + "Content-Type", + format!("multipart/form-data; boundary={boundary}"), + ) + .body(axum::body::Body::from(multipart_body)) + .unwrap(); + + let resp = router.oneshot(request).await.unwrap(); + + if resp.status() != StatusCode::OK { + panic!("{} {:#?}", resp.status(), resp.body()) + } + + assert_eq!(resp.status(), StatusCode::OK); + + // gather the body into a single bytes object and convert it into a string for easier debugging if the test fails + let body_bytes = axum::body::to_bytes(resp.into_body(), usize::MAX).await.unwrap(); + let body_string = String::from_utf8(body_bytes.to_vec()).unwrap(); + println!("Response body: {}", body_string); + } +} \ No newline at end of file From 8cc396b1a6b621bb42109863eca65f25e804e3ca Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Mon, 22 Jun 2026 09:02:28 +0200 Subject: [PATCH 19/21] Fix lint --- .../src/services/image_classification.rs | 108 ++++++++++++++---- .../src/transport/http/multipart_openapi.rs | 16 +-- encoderfile/tests/test_http.rs | 14 +-- 3 files changed, 97 insertions(+), 41 deletions(-) diff --git a/encoderfile/src/services/image_classification.rs b/encoderfile/src/services/image_classification.rs index 7a86ddd2..c8969955 100644 --- a/encoderfile/src/services/image_classification.rs +++ b/encoderfile/src/services/image_classification.rs @@ -25,12 +25,6 @@ impl Inference for AppState { if request.images.is_empty() { return Err(ApiError::InputError("Cannot classify empty image list")); } - /* - transform.preprocessor().as_ref().map(|pre| { - pre.call::<_, ()>(()) - .map_err(|e| ApiError::LuaError(format!("Preprocessor error: {e}"))) - })?; - */ let postprocess_code = r##" function Preprocess(img) @@ -72,16 +66,12 @@ impl Inference for AppState { .preprocess((Image(img), self.model_input_state.clone())) .expect("Failed") .into_inner(); - let mean_arr = ndarray::Array::from_shape_vec( - (num_channels, 1, 1), - image_mean.to_vec(), - ) - .expect("mean shape mismatch"); - let std_arr = ndarray::Array::from_shape_vec( - (num_channels, 1, 1), - image_std.to_vec(), - ) - .expect("std shape mismatch"); + let mean_arr = + ndarray::Array::from_shape_vec((num_channels, 1, 1), image_mean.to_vec()) + .expect("mean shape mismatch"); + let std_arr = + ndarray::Array::from_shape_vec((num_channels, 1, 1), image_std.to_vec()) + .expect("std shape mismatch"); Zip::from(&mut res) .and_broadcast(&mean_arr) .and_broadcast(&std_arr) @@ -111,13 +101,7 @@ impl Inference for AppState { .map(|(_, label)| label.clone()) .collect(); - let labels_batch = image_classification( - self.session.lock(), - images_array, - // COMMENT having optional fields complicates things later on, but otoh - // it allows models with variations of these fields - classes, - )?; + let labels_batch = image_classification(self.session.lock(), images_array, classes)?; Ok(ImageClassificationResponse { results: labels_batch @@ -128,7 +112,6 @@ impl Inference for AppState { .collect(), metadata: request.metadata, }) - } } @@ -140,7 +123,7 @@ mod tests { use crate::common::model_type::ImageClassification; use crate::dev_utils; use std::fs::File; - use std::sync::Once; + use std::sync::{Arc, Once}; fn init_tracing() { static TRACING: Once = Once::new(); @@ -195,4 +178,79 @@ mod tests { let response = state.inference(request); assert!(response.is_err()); } + + #[test] + fn test_image_classification_missing_rescale_factor() { + init_tracing(); + + let mut state = + dev_utils::get_state::("../models/image_classification"); + Arc::get_mut(&mut state) + .expect("state should not be shared") + .model_input_state + .preprocessing + .rescale_factor = None; + + let mut file = + File::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + let file_vec = vec![&mut file]; + let request = ImageClassificationRequest::from_read_input(file_vec) + .expect("Failed to create request from read input"); + + let response = state.inference(request); + assert!(matches!( + response, + Err(ApiError::InternalError("missing rescale factor")) + )); + } + + #[test] + fn test_image_classification_missing_image_mean() { + init_tracing(); + + let mut state = + dev_utils::get_state::("../models/image_classification"); + Arc::get_mut(&mut state) + .expect("state should not be shared") + .model_input_state + .preprocessing + .image_mean = None; + + let mut file = + File::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + let file_vec = vec![&mut file]; + let request = ImageClassificationRequest::from_read_input(file_vec) + .expect("Failed to create request from read input"); + + let response = state.inference(request); + assert!(matches!( + response, + Err(ApiError::InternalError("missing image mean")) + )); + } + + #[test] + fn test_image_classification_missing_image_std() { + init_tracing(); + + let mut state = + dev_utils::get_state::("../models/image_classification"); + Arc::get_mut(&mut state) + .expect("state should not be shared") + .model_input_state + .preprocessing + .image_std = None; + + let mut file = + File::open("../test-pictures/yoga02.jpg").expect("Failed to open test image"); + let file_vec = vec![&mut file]; + let request = ImageClassificationRequest::from_read_input(file_vec) + .expect("Failed to create request from read input"); + + let response = state.inference(request); + assert!(matches!( + response, + Err(ApiError::InternalError("missing image std")) + )); + } } diff --git a/encoderfile/src/transport/http/multipart_openapi.rs b/encoderfile/src/transport/http/multipart_openapi.rs index f520a84b..a07a793d 100644 --- a/encoderfile/src/transport/http/multipart_openapi.rs +++ b/encoderfile/src/transport/http/multipart_openapi.rs @@ -180,11 +180,12 @@ pub async fn parse_multipart( let payload = payload.ok_or(MultipartApiError::MissingPayload)?; - // Convert to typed request + // Convert to typed request let request = ImageClassificationRequest::from_multipart(payload.clone(), attachments)?; - let result = state.inference(request).map(Json).map_err(|e| { - MultipartApiError::RequestConstruction(format!("Inference error: {}", e.to_string())) - })?; + let result = state + .inference(request) + .map(Json) + .map_err(|e| MultipartApiError::RequestConstruction(format!("Inference error: {}", e)))?; Ok(result) } @@ -234,9 +235,10 @@ pub async fn post_multipart_typed( // Convert to typed request let request = ImageClassificationRequest::from_multipart(payload.clone(), attachments)?; - let result = state.inference(request).map(Json).map_err(|e| { - MultipartApiError::RequestConstruction(format!("Inference error: {}", e.to_string())) - })?; + let result = state + .inference(request) + .map(Json) + .map_err(|e| MultipartApiError::RequestConstruction(format!("Inference error: {}", e)))?; Ok(result) } diff --git a/encoderfile/tests/test_http.rs b/encoderfile/tests/test_http.rs index 005df01d..77d575ae 100644 --- a/encoderfile/tests/test_http.rs +++ b/encoderfile/tests/test_http.rs @@ -94,7 +94,6 @@ macro_rules! test_router_mod { }; } - test_router_mod!( Embedding, embedding_tests, @@ -132,13 +131,9 @@ test_router_mod!( } ); - mod image_classification_tests { use axum::http::{Request, StatusCode}; - use encoderfile::{ - dev_utils, - transport::http::HttpRouter, - }; + use encoderfile::{dev_utils, transport::http::HttpRouter}; use tower::ServiceExt; fn router() -> axum::Router { @@ -146,7 +141,6 @@ mod image_classification_tests { state.http_router() } - #[tokio::test] async fn test_predict_route() { let router = router(); @@ -207,8 +201,10 @@ mod image_classification_tests { assert_eq!(resp.status(), StatusCode::OK); // gather the body into a single bytes object and convert it into a string for easier debugging if the test fails - let body_bytes = axum::body::to_bytes(resp.into_body(), usize::MAX).await.unwrap(); + let body_bytes = axum::body::to_bytes(resp.into_body(), usize::MAX) + .await + .unwrap(); let body_string = String::from_utf8(body_bytes.to_vec()).unwrap(); println!("Response body: {}", body_string); } -} \ No newline at end of file +} From 951ff9f48f38ce2f1b497b6d3f8b93b52d120c11 Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Mon, 22 Jun 2026 09:25:40 +0200 Subject: [PATCH 20/21] Download and cache image classification model --- .github/workflows/ci.yml | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3dde5382..fa7e8245 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -39,6 +39,18 @@ jobs: path: ~/.cache/uv key: ${{ runner.os }}-uv-${{ hashFiles('pyproject.toml') }} + - name: Cache image classification model + id: cache-img-model + uses: actions/cache@v4 + with: + path: models/image_classification/model.onnx + key: img-class-model + + - name: Download ONNX model + if: steps.cache-img-model.outputs.cache-hit != 'true' + run: | + curl -L -o models/image_classification/model.onnx https://huggingface.co/dima806/yoga_pose_image_classification/resolve/main/onnx/model.onnx + - name: Project setup uses: ./.github/actions/project-setup From 684c883a7f83fadba9c975efb07dd9e01c36210c Mon Sep 17 00:00:00 2001 From: Javier Torres Date: Mon, 22 Jun 2026 09:40:17 +0200 Subject: [PATCH 21/21] Update Cargo.lock --- Cargo.lock | 591 ++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 590 insertions(+), 1 deletion(-) diff --git a/Cargo.lock b/Cargo.lock index 5fe17a05..e19ad472 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -31,6 +31,24 @@ dependencies = [ "memchr", ] +[[package]] +name = "aligned" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ee4508988c62edf04abd8d92897fca0c2995d907ce1dfeaf369dac3716a40685" +dependencies = [ + "as-slice", +] + +[[package]] +name = "aligned-vec" +version = "0.6.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dc890384c8602f339876ded803c97ad529f3842aba97f6392b3dba0dd171769b" +dependencies = [ + "equator", +] + [[package]] name = "android_system_properties" version = "0.1.5" @@ -105,6 +123,12 @@ dependencies = [ "num-traits", ] +[[package]] +name = "arbitrary" +version = "1.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c3d036a3c4ab069c7b410a2ce876bd74808d2d0888a82667669f8e783a898bf1" + [[package]] name = "arc-swap" version = "1.9.1" @@ -114,6 +138,32 @@ dependencies = [ "rustversion", ] +[[package]] +name = "arg_enum_proc_macro" +version = "0.3.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + +[[package]] +name = "arrayvec" +version = "0.7.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f02882884d3e1bc524fb12c79f107f6ad0e1cfd498c536ffb494301740995dfe" + +[[package]] +name = "as-slice" +version = "0.2.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "516b6b4f0e40d50dcda9365d53964ec74560ad4284da2e7fc97122cd83174516" +dependencies = [ + "stable_deref_trait", +] + [[package]] name = "async-trait" version = "0.1.89" @@ -146,6 +196,49 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" +[[package]] +name = "av-scenechange" +version = "0.14.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0f321d77c20e19b92c39e7471cf986812cbb46659d2af674adc4331ef3f18394" +dependencies = [ + "aligned", + "anyhow", + "arg_enum_proc_macro", + "arrayvec", + "log", + "num-rational", + "num-traits", + "pastey", + "rayon", + "thiserror 2.0.18", + "v_frame", + "y4m", +] + +[[package]] +name = "av1-grain" +version = "0.2.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8cfddb07216410377231960af4fcab838eaa12e013417781b78bd95ee22077f8" +dependencies = [ + "anyhow", + "arrayvec", + "log", + "nom 8.0.0", + "num-rational", + "v_frame", +] + +[[package]] +name = "avif-serialize" +version = "0.8.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e7178fe5f7d460b13895ebb9dcb28a3a6216d2df2574a0806cb51b555d297f38" +dependencies = [ + "arrayvec", +] + [[package]] name = "aws-lc-rs" version = "1.16.2" @@ -187,6 +280,7 @@ dependencies = [ "matchit", "memchr", "mime", + "multer", "percent-encoding", "pin-project-lite", "serde_core", @@ -260,12 +354,27 @@ version = "1.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2af50177e190e07a26ab74f8b1efbfe2ef87da2116221318cb1c2e82baf7de06" +[[package]] +name = "bit_field" +version = "0.10.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1e4b40c7323adcfc0a41c4b88143ed58346ff65a288fc144329c5c45e05d70c6" + [[package]] name = "bitflags" version = "2.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "843867be96c8daad0d758b57df9392b6d8d271134fce549de6ce169ff98a92af" +[[package]] +name = "bitstream-io" +version = "4.10.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7eff00be299a18769011411c9def0d827e8f2d7bf0c3dbf53633147a8867fd1f" +dependencies = [ + "no_std_io2", +] + [[package]] name = "block-buffer" version = "0.10.4" @@ -285,6 +394,12 @@ dependencies = [ "serde", ] +[[package]] +name = "built" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5c0e531d93d39c34eef561e929e8a7f86d77a5af08aac4f6d6e39976c51858e9" + [[package]] name = "bumpalo" version = "3.20.2" @@ -303,11 +418,20 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" +[[package]] +name = "byteorder-lite" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8f1fe948ff07f4bd06c30984e69f5b4899c516a3ef74f34df92a2df2ab535495" + [[package]] name = "bytes" version = "1.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1e748733b7cbc798e1434b6ac524f0c1ff2ab456fe201501e6497c8417a4fc33" +dependencies = [ + "serde", +] [[package]] name = "castaway" @@ -471,6 +595,12 @@ dependencies = [ "regex-lite", ] +[[package]] +name = "color_quant" +version = "1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b" + [[package]] name = "colorchoice" version = "1.0.5" @@ -599,6 +729,12 @@ version = "0.8.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28" +[[package]] +name = "crunchy" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "460fbee9c2c2f33933d720630a6a0bac33ba7053db5344fac858d4b8952d77d5" + [[package]] name = "crypto-common" version = "0.1.7" @@ -819,6 +955,7 @@ dependencies = [ "anyhow", "axum", "axum-server", + "bytes", "clap", "clap_derive", "codspeed-divan-compat", @@ -827,6 +964,8 @@ dependencies = [ "dotenv", "figment", "flate2", + "image", + "image-ndarray", "mlua", "ndarray", "ndarray-stats", @@ -918,6 +1057,26 @@ dependencies = [ "log", ] +[[package]] +name = "equator" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4711b213838dfee0117e3be6ac926007d7f433d7bbe33595975d4190cb07e6fc" +dependencies = [ + "equator-macro", +] + +[[package]] +name = "equator-macro" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "44f23cf4b44bfce11a86ace86f8a73ffdec849c9fd00a386a53d278bd9e81fb3" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + [[package]] name = "equivalent" version = "1.0.2" @@ -943,12 +1102,42 @@ dependencies = [ "cc", ] +[[package]] +name = "exr" +version = "1.74.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4300e043a56aa2cb633c01af81ca8f699a321879a7854d3896a0ba89056363be" +dependencies = [ + "bit_field", + "half", + "lebe", + "miniz_oxide", + "rayon-core", + "smallvec 1.15.1", + "zune-inflate", +] + [[package]] name = "fastrand" version = "2.4.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9f1f227452a390804cdb637b74a86990f2a7d7ba4b7d5693aac9b4dd6defd8d6" +[[package]] +name = "fax" +version = "0.2.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "caf1079563223d5d59d83c85886a56e586cfd5c1a26292e971a0fa266531ac5a" + +[[package]] +name = "fdeflate" +version = "0.3.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1e6853b52649d4ac5c0bd02320cddc5ba956bdb407c4b75a2c6b75bf51500f8c" +dependencies = [ + "simd-adler32", +] + [[package]] name = "figment" version = "0.10.19" @@ -1186,6 +1375,16 @@ dependencies = [ "wasip3", ] +[[package]] +name = "gif" +version = "0.14.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ee8cfcc411d9adbbaba82fb72661cc1bcca13e8bba98b364e62b2dba8f960159" +dependencies = [ + "color_quant", + "weezl", +] + [[package]] name = "glob" version = "0.3.3" @@ -1211,6 +1410,17 @@ dependencies = [ "tracing", ] +[[package]] +name = "half" +version = "2.7.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" +dependencies = [ + "cfg-if", + "crunchy", + "zerocopy", +] + [[package]] name = "hashbrown" version = "0.15.5" @@ -1492,6 +1702,59 @@ dependencies = [ "icu_properties", ] +[[package]] +name = "image" +version = "0.25.10" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "85ab80394333c02fe689eaf900ab500fbd0c2213da414687ebf995a65d5a6104" +dependencies = [ + "bytemuck", + "byteorder-lite", + "color_quant", + "exr", + "gif", + "image-webp", + "moxcms", + "num-traits", + "png", + "qoi", + "ravif", + "rayon", + "rgb", + "serde", + "tiff", + "zune-core", + "zune-jpeg", +] + +[[package]] +name = "image-ndarray" +version = "0.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "366ec4e7613badea5930852b9fc8781fdbb010a59845a3a5c1cf61d0ccc3f133" +dependencies = [ + "image", + "ndarray", + "num-traits", + "thiserror 2.0.18", +] + +[[package]] +name = "image-webp" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "525e9ff3e1a4be2fbea1fdf0e98686a6d98b4d8f937e1bf7402245af1909e8c3" +dependencies = [ + "byteorder-lite", + "quick-error", +] + +[[package]] +name = "imgref" +version = "1.12.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "89194689a993ab15268672e99e7b0e19da2da3268ac682e8f02d29d4d1434cd7" + [[package]] name = "indexmap" version = "2.14.0" @@ -1532,6 +1795,17 @@ version = "0.1.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c8fae54786f62fb2918dcfae3d568594e50eb9b5c25bf04371af6fe7516452fb" +[[package]] +name = "interpolate_name" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c34819042dc3d3971c46c2190835914dfbe0c3c13f61449b2997f4e9722dfa60" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + [[package]] name = "ipnet" version = "2.12.0" @@ -1656,12 +1930,28 @@ version = "0.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "09edd9e8b54e49e587e4f6295a7d29c3ea94d469cb40ab8ca70b288248a81db2" +[[package]] +name = "lebe" +version = "0.5.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7a79a3332a6609480d7d0c9eab957bca6b455b91bb84e66d19f5ff66294b85b8" + [[package]] name = "libc" version = "0.2.185" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "52ff2c0fe9bc6cb6b14a0592c2ff4fa9ceb83eea9db979b0487cd054946a2b8f" +[[package]] +name = "libfuzzer-sys" +version = "0.4.13" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a9fd2f41a1cba099f79a0b6b6c35656cf7c03351a7bae8ff0f28f25270f929d2" +dependencies = [ + "arbitrary", + "cc", +] + [[package]] name = "libredox" version = "0.1.16" @@ -1701,6 +1991,15 @@ version = "0.4.29" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897" +[[package]] +name = "loop9" +version = "0.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0fae87c125b03c1d2c0150c90365d7d6bcc53fb73a9acaef207d2d065860f062" +dependencies = [ + "imgref", +] + [[package]] name = "lru-slab" version = "0.1.2" @@ -1767,6 +2066,16 @@ dependencies = [ "rawpointer", ] +[[package]] +name = "maybe-rayon" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8ea1f30cedd69f0a2954655f7188c6a834246d2bcf1e315e2ac40c4b24dc9519" +dependencies = [ + "cfg-if", + "rayon", +] + [[package]] name = "memchr" version = "2.8.0" @@ -1867,6 +2176,33 @@ dependencies = [ "syn", ] +[[package]] +name = "moxcms" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bb85c154ba489f01b25c0d36ae69a87e4a1c73a72631fc6c0eb6dde34a73e44b" +dependencies = [ + "num-traits", + "pxfm", +] + +[[package]] +name = "multer" +version = "3.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "83e87776546dc87511aa5ee218730c92b666d7264ab6ed41f9d215af9cd5224b" +dependencies = [ + "bytes", + "encoding_rs", + "futures-util", + "http", + "httparse", + "memchr", + "mime", + "spin", + "version_check", +] + [[package]] name = "multimap" version = "0.10.1" @@ -1920,6 +2256,12 @@ dependencies = [ "rand 0.8.5", ] +[[package]] +name = "new_debug_unreachable" +version = "1.0.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086" + [[package]] name = "nix" version = "0.31.2" @@ -1932,6 +2274,15 @@ dependencies = [ "libc", ] +[[package]] +name = "no_std_io2" +version = "0.9.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "418abd1b6d34fbf6cae440dc874771b0525a604428704c76e48b29a5e67b8003" +dependencies = [ + "memchr", +] + [[package]] name = "noisy_float" version = "0.2.1" @@ -1951,6 +2302,21 @@ dependencies = [ "minimal-lexical", ] +[[package]] +name = "nom" +version = "8.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "df9761775871bdef83bee530e60050f7e54b1105350d6884eb0fb4f46c2f9405" +dependencies = [ + "memchr", +] + +[[package]] +name = "noop_proc_macro" +version = "0.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0676bb32a98c1a483ce53e500a81ad9c3d5b3f7c920c28c24e9cb0980d0b5bc8" + [[package]] name = "nu-ansi-term" version = "0.50.3" @@ -1960,6 +2326,16 @@ dependencies = [ "windows-sys 0.61.2", ] +[[package]] +name = "num-bigint" +version = "0.4.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a5e44f723f1133c9deac646763579fdb3ac745e418f2a7af9cd0c431da1f20b9" +dependencies = [ + "num-integer", + "num-traits", +] + [[package]] name = "num-complex" version = "0.4.6" @@ -1969,6 +2345,17 @@ dependencies = [ "num-traits", ] +[[package]] +name = "num-derive" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + [[package]] name = "num-integer" version = "0.1.46" @@ -1978,6 +2365,17 @@ dependencies = [ "num-traits", ] +[[package]] +name = "num-rational" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824" +dependencies = [ + "num-bigint", + "num-integer", + "num-traits", +] + [[package]] name = "num-traits" version = "0.2.19" @@ -2216,6 +2614,12 @@ version = "1.0.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a" +[[package]] +name = "pastey" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "35fb2e5f958ec131621fdd531e9fc186ed768cbe395337403ae56c17a74c68ec" + [[package]] name = "pear" version = "0.2.9" @@ -2303,6 +2707,19 @@ version = "0.2.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b4596b6d070b27117e987119b4dac604f3c58cfb0b191112e24771b2faeac1a6" +[[package]] +name = "png" +version = "0.18.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60769b8b31b2a9f263dae2776c37b1b28ae246943cf719eb6946a1db05128a61" +dependencies = [ + "bitflags", + "crc32fast", + "fdeflate", + "flate2", + "miniz_oxide", +] + [[package]] name = "portable-atomic" version = "1.13.1" @@ -2377,6 +2794,25 @@ dependencies = [ "yansi", ] +[[package]] +name = "profiling" +version = "1.0.18" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3d595e54a326bc53c1c197b32d295e14b169e3cfeaa8dc82b529f947fba6bcf5" +dependencies = [ + "profiling-procmacros", +] + +[[package]] +name = "profiling-procmacros" +version = 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"a993555f31e5a609f617c12db6250dedcac1b0a85076912c436e6fc9b2c8e6a3" + [[package]] name = "quinn" version = "0.11.9" @@ -2647,6 +3104,56 @@ dependencies = [ "getrandom 0.3.4", ] +[[package]] +name = "rav1e" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "43b6dd56e85d9483277cde964fd1bdb0428de4fec5ebba7540995639a21cb32b" +dependencies = [ + "aligned-vec", + "arbitrary", + "arg_enum_proc_macro", + "arrayvec", + "av-scenechange", + "av1-grain", + "bitstream-io", + "built", + "cfg-if", + "interpolate_name", + "itertools 0.14.0", + "libc", + "libfuzzer-sys", + "log", + "maybe-rayon", + "new_debug_unreachable", + "noop_proc_macro", + "num-derive", + "num-traits", + "paste", + "profiling", + "rand 0.9.3", + "rand_chacha 0.9.0", + "simd_helpers", + "thiserror 2.0.18", + "v_frame", + "wasm-bindgen", +] + +[[package]] +name = "ravif" +version = "0.13.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e52310197d971b0f5be7fe6b57530dcd27beb35c1b013f29d66c1ad73fbbcc45" +dependencies = [ + "avif-serialize", + "imgref", + "loop9", + "quick-error", + "rav1e", + "rayon", + "rgb", +] + [[package]] name = "rawpointer" version = "0.2.1" @@ -2852,6 +3359,12 @@ dependencies = [ "web-sys", ] +[[package]] +name = "rgb" +version = "0.8.53" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "47b34b781b31e5d73e9fbc8689c70551fd1ade9a19e3e28cfec8580a79290cc4" + [[package]] name = "ring" version = "0.17.14" @@ -3237,6 +3750,15 @@ version = "0.3.9" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "703d5c7ef118737c72f1af64ad2f6f8c5e1921f818cdcb97b8fe6fc69bf66214" +[[package]] +name = "simd_helpers" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "95890f873bec569a0362c235787f3aca6e1e887302ba4840839bcc6459c42da6" +dependencies = [ + "quote", +] + [[package]] name = "slab" version = "0.4.12" @@ -3276,6 +3798,12 @@ dependencies = [ "winapi", ] +[[package]] +name = "spin" +version = "0.9.8" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67" + [[package]] name = "spm_precompiled" version = "0.1.4" @@ -3283,7 +3811,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5851699c4033c63636f7ea4cf7b7c1f1bf06d0cc03cfb42e711de5a5c46cf326" dependencies = [ "base64 0.13.1", - "nom", + "nom 7.1.3", "serde", "unicode-segmentation", ] @@ -3498,6 +4026,20 @@ dependencies = [ "cfg-if", ] +[[package]] +name = "tiff" +version = "0.11.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b63feaf3343d35b6ca4d50483f94843803b0f51634937cc2ec519fc32232bc52" +dependencies = [ + "fax", + "flate2", + "half", + "quick-error", + "weezl", + "zune-jpeg", +] + [[package]] name = "tinystr" version = "0.8.3" @@ -4071,6 +4613,17 @@ dependencies = [ "wasm-bindgen", ] +[[package]] +name = "v_frame" +version = "0.3.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "666b7727c8875d6ab5db9533418d7c764233ac9c0cff1d469aec8fa127597be2" +dependencies = [ + "aligned-vec", + "num-traits", + "wasm-bindgen", +] + [[package]] name = "valuable" version = "0.1.1" @@ -4272,6 +4825,12 @@ dependencies = [ "rustls-pki-types", ] +[[package]] +name = "weezl" +version = "0.1.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a28ac98ddc8b9274cb41bb4d9d4d5c425b6020c50c46f25559911905610b4a88" + [[package]] name = "which" version = "8.0.2" @@ -4726,6 +5285,12 @@ dependencies = [ "rustix", ] +[[package]] +name = "y4m" +version = "0.8.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7a5a4b21e1a62b67a2970e6831bc091d7b87e119e7f9791aef9702e3bef04448" + [[package]] name = "yansi" version = "1.0.1" @@ -4840,3 +5405,27 @@ name = "zmij" version = "1.0.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" + +[[package]] +name = "zune-core" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "cb8a0807f7c01457d0379ba880ba6322660448ddebc890ce29bb64da71fb40f9" + +[[package]] +name = "zune-inflate" +version = "0.2.54" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "73ab332fe2f6680068f3582b16a24f90ad7096d5d39b974d1c0aff0125116f02" +dependencies = [ + "simd-adler32", +] + +[[package]] +name = "zune-jpeg" +version = "0.5.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "27bc9d5b815bc103f142aa054f561d9187d191692ec7c2d1e2b4737f8dbd7296" +dependencies = [ + "zune-core", +]