Add windows support

demo_imitator.py

@@ -1,6 +1,7 @@
 import numpy as np
 from tqdm import tqdm
 import os
+import sys
 import glob
 
 from models.imitator import Imitator
@@ -45,7 +46,7 @@ def generate_actor_result(test_opt, src_img_path):
     src_img_name = os.path.split(src_img_path)[-1][:-4]
     test_opt.src_path = src_img_path
 
-    if test_opt.post_tune:
+    if test_opt.post_tune and sys.platform in ("darwin", "linux", "linux2"):
         adaptive_personalize(test_opt, imitator, visualizer=None)
     else:
         imitator.personalize(test_opt.src_path, visualizer=None)

networks/batch_smpl.py

@@ -5,7 +5,7 @@
 'lsp': Returns H3.6M-LSP 14 joints
 Note: To get original smpl joints, use self.J_transformed
 """
-
+import sys
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -55,7 +55,10 @@ def batch_skew(vec, batch_size=None, device="cpu"):
     ).view(-1).to(device)
 
     res = torch.zeros(batch_size * 9, dtype=vec.dtype).to(device)
-    res[indices] = updates
+    if sys.platform in ("darwin", "linux", "linux2"):
+        res[indices] = updates
+    elif sys.platform == "win32":
+        res[indices.long()] = updates
     res = res.view(batch_size, 3, 3)
 
     return res

run_imitator.py

@@ -3,6 +3,7 @@
 import torch.utils.data
 from tqdm import tqdm
 import os
+import sys
 import glob
 
 from data.dataset import PairSampleDataset
@@ -183,12 +184,15 @@ class Config(object):
 
     meta_cycle_ds = MetaCycleDataSet(opt=config)
     length = len(meta_cycle_ds)
-
+    if sys.platform in ("darwin", "linux", "linux2"):
+        num_workers = 4
+    elif sys.platform == "win32":
+        num_workers = 0
     data_loader = torch.utils.data.DataLoader(
         meta_cycle_ds,
         batch_size=min(length, opt.batch_size),
         shuffle=False,
-        num_workers=4,
+        num_workers=num_workers,
         drop_last=True)
 
     return data_loader

thirdparty/neural_renderer/examples/example2.py

@@ -69,7 +69,10 @@ def main():
     model = Model(args.filename_obj, args.filename_ref)
     model.cuda()
 
-    optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
+    def lambda_req(p):
+        return p.requires_grad
+
+    optimizer = torch.optim.Adam(filter(lambda_req, model.parameters()))
     # optimizer.setup(model)
     loop = tqdm.tqdm(range(300))
     for i in loop:

thirdparty/neural_renderer/neural_renderer/cuda/rasterize_cuda_kernel.cu

@@ -19,6 +19,24 @@ static __inline__ __device__ double atomicAdd(double* address, double val) {
 }
 #endif
 
+// for windows installation please change the upper function with this.
+// #if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
+
+// #else
+// static __inline__ __device__ double atomicAdd(double *address, double val) {
+// unsigned long long int* address_as_ull = (unsigned long long int*)address;
+// unsigned long long int old = *address_as_ull, assumed;
+// if (val==0.0)
+//     return __longlong_as_double(old);
+// do {
+//     assumed = old;
+//     old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val +__longlong_as_double(assumed)));
+// } while (assumed != old);
+// return __longlong_as_double(old);
+// }
+
+#endif
+
 namespace{
 template <typename scalar_t>
 __global__ void forward_face_index_map_cuda_kernel_1(