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168 lines (132 loc) · 5.28 KB
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from keras.models import Model
from keras.layers.merge import Concatenate
from keras.layers import Activation, Input, Lambda
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import Multiply
from keras.regularizers import l2
def relu(x): return Activation('relu')(x)
def conv(x, nf, ks, name, weight_decay):
kernel_reg = l2(weight_decay[0]) if weight_decay else None
bias_reg = l2(weight_decay[1]) if weight_decay else None
x = Conv2D(nf, (ks, ks), padding='same', name=name,
kernel_regularizer=kernel_reg,
bias_regularizer=bias_reg)(x)
return x
def pooling(x, ks, st, name):
x = MaxPooling2D((ks, ks), strides=(st, st), name=name)(x)
return x
def vgg_block(x, weight_decay):
# Block 1
x = conv(x, 64, 3, "conv1_1", (weight_decay, 0))
x = relu(x)
x = conv(x, 64, 3, "conv1_2", (weight_decay, 0))
x = relu(x)
x = pooling(x, 2, 2, "pool1_1")
# Block 2
x = conv(x, 128, 3, "conv2_1", (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 3, "conv2_2", (weight_decay, 0))
x = relu(x)
x = pooling(x, 2, 2, "pool2_1")
# Block 3
x = conv(x, 256, 3, "conv3_1", (weight_decay, 0))
x = relu(x)
x = conv(x, 256, 3, "conv3_2", (weight_decay, 0))
x = relu(x)
x = conv(x, 256, 3, "conv3_3", (weight_decay, 0))
x = relu(x)
x = conv(x, 256, 3, "conv3_4", (weight_decay, 0))
x = relu(x)
x = pooling(x, 2, 2, "pool3_1")
# Block 4
x = conv(x, 512, 3, "conv4_1", (weight_decay, 0))
x = relu(x)
x = conv(x, 512, 3, "conv4_2", (weight_decay, 0))
x = relu(x)
# Additional non vgg layers
x = conv(x, 256, 3, "conv4_3_CPM", (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 3, "conv4_4_CPM", (weight_decay, 0))
x = relu(x)
return x
def stage1_block(x, x1, x2, num_p, branch, weight_decay):
# Block 1
x = conv(x, 128, 3, "Mconv1_stage1_L%d" % branch, (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 3, "Mconv2_stage1_L%d" % branch, (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 3, "Mconv3_stage1_L%d" % branch, (weight_decay, 0))
x = relu(x)
x = conv(x, 512, 1, "Mconv4_stage1_L%d" % branch, (weight_decay, 0))
x = relu(x)
x = conv(x, num_p, 1, "Mconv5_stage1_L%d" % branch, (weight_decay, 0))
w_name = "weight_stage1_L%d" % branch
if num_p == 38:
w = Multiply(name=w_name)([x, x1]) # vec_weight
else:
w = Multiply(name=w_name)([x, x2]) # vec_heat
return x, w
def stageT_block(x, x1, x2, num_p, stage, branch, weight_decay):
# Block 1
x = conv(x, 128, 7, "Mconv1_stage%d_L%d" % (stage, branch), (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 7, "Mconv2_stage%d_L%d" % (stage, branch), (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 7, "Mconv3_stage%d_L%d" % (stage, branch), (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 7, "Mconv4_stage%d_L%d" % (stage, branch), (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 7, "Mconv5_stage%d_L%d" % (stage, branch), (weight_decay, 0))
x = relu(x)
x = conv(x, 128, 1, "Mconv6_stage%d_L%d" % (stage, branch), (weight_decay, 0))
x = relu(x)
x = conv(x, num_p, 1, "Mconv7_stage%d_L%d" % (stage, branch), (weight_decay, 0))
w_name = "weight_stage%d_L%d" % (stage, branch)
if num_p == 38:
w = Multiply(name=w_name)([x, x1]) # vec_weight
else:
w = Multiply(name=w_name)([x, x2]) # vec_heat
return x, w
def get_model(training=True, weight_decay=None):
stages = 6
np_branch1 = 38
np_branch2 = 19
img_input_shape = (None, None, 3)
vec_input_shape = (None, None, 38)
heat_input_shape = (None, None, 19)
inputs = []
outputs = []
img_input = Input(shape=img_input_shape)
vec_weight_input = Input(shape=vec_input_shape)
heat_weight_input = Input(shape=heat_input_shape)
inputs.append(img_input)
inputs.append(vec_weight_input)
inputs.append(heat_weight_input)
img_normalized = Lambda(lambda x: x / 127.5 - 1.0)(img_input)
# VGG
stage0_out = vgg_block(img_normalized, weight_decay)
# stage 1
stage1_branch1_out,w1 = stage1_block(stage0_out, vec_weight_input,
heat_weight_input, np_branch1, 1, weight_decay)
stage1_branch2_out,w2 = stage1_block(stage0_out, vec_weight_input,
heat_weight_input, np_branch2, 2, weight_decay)
x = Concatenate()([stage1_branch1_out, stage1_branch2_out, stage0_out])
outputs.append(w1)
outputs.append(w2)
# stage t >= 2
#stageT_branch1_out = None
#stageT_branch2_out = None
for sn in range(2, stages + 1):
stageT_branch1_out, w1 = stageT_block(x, vec_weight_input,
heat_weight_input, np_branch1, sn, 1, weight_decay)
stageT_branch2_out, w2 = stageT_block(x, vec_weight_input,
heat_weight_input, np_branch2, sn, 2, weight_decay)
outputs.append(w1)
outputs.append(w2)
if (sn < stages):
x = Concatenate()([stageT_branch1_out, stageT_branch2_out, stage0_out])
#outputs.insert(0, stageT_branch1_out)
#outputs.insert(1, stageT_branch2_out)
model = Model(inputs=inputs, outputs=outputs)
return model