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import torch
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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def make_layers(block, no_relu_layers):
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layers = []
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for layer_name, v in block.items():
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if 'pool' in layer_name:
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layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
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padding=v[2])
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layers.append((layer_name, layer))
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else:
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conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
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kernel_size=v[2], stride=v[3],
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padding=v[4])
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layers.append((layer_name, conv2d))
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if layer_name not in no_relu_layers:
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layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
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return nn.Sequential(OrderedDict(layers))
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class bodypose_model(nn.Module):
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def __init__(self):
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super(bodypose_model, self).__init__()
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no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
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'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
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'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
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'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
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blocks = {}
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block0 = OrderedDict([
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('conv1_1', [3, 64, 3, 1, 1]),
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('conv1_2', [64, 64, 3, 1, 1]),
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('pool1_stage1', [2, 2, 0]),
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('conv2_1', [64, 128, 3, 1, 1]),
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('conv2_2', [128, 128, 3, 1, 1]),
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('pool2_stage1', [2, 2, 0]),
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('conv3_1', [128, 256, 3, 1, 1]),
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('conv3_2', [256, 256, 3, 1, 1]),
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('conv3_3', [256, 256, 3, 1, 1]),
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('conv3_4', [256, 256, 3, 1, 1]),
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('pool3_stage1', [2, 2, 0]),
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('conv4_1', [256, 512, 3, 1, 1]),
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('conv4_2', [512, 512, 3, 1, 1]),
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('conv4_3_CPM', [512, 256, 3, 1, 1]),
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('conv4_4_CPM', [256, 128, 3, 1, 1])
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])
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block1_1 = OrderedDict([
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('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
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('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
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('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
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('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
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('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
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])
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block1_2 = OrderedDict([
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('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
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('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
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('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
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('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
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('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
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])
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blocks['block1_1'] = block1_1
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blocks['block1_2'] = block1_2
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self.model0 = make_layers(block0, no_relu_layers)
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for i in range(2, 7):
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blocks['block%d_1' % i] = OrderedDict([
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('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
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('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
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('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
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('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
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('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
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('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
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('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
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])
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blocks['block%d_2' % i] = OrderedDict([
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('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
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('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
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('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
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('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
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('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
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('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
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('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
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])
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for k in blocks.keys():
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blocks[k] = make_layers(blocks[k], no_relu_layers)
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self.model1_1 = blocks['block1_1']
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self.model2_1 = blocks['block2_1']
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self.model3_1 = blocks['block3_1']
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self.model4_1 = blocks['block4_1']
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self.model5_1 = blocks['block5_1']
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self.model6_1 = blocks['block6_1']
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self.model1_2 = blocks['block1_2']
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self.model2_2 = blocks['block2_2']
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self.model3_2 = blocks['block3_2']
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self.model4_2 = blocks['block4_2']
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self.model5_2 = blocks['block5_2']
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self.model6_2 = blocks['block6_2']
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def forward(self, x):
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out1 = self.model0(x)
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out1_1 = self.model1_1(out1)
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out1_2 = self.model1_2(out1)
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out2 = torch.cat([out1_1, out1_2, out1], 1)
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out2_1 = self.model2_1(out2)
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out2_2 = self.model2_2(out2)
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out3 = torch.cat([out2_1, out2_2, out1], 1)
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out3_1 = self.model3_1(out3)
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out3_2 = self.model3_2(out3)
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out4 = torch.cat([out3_1, out3_2, out1], 1)
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out4_1 = self.model4_1(out4)
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out4_2 = self.model4_2(out4)
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out5 = torch.cat([out4_1, out4_2, out1], 1)
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out5_1 = self.model5_1(out5)
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out5_2 = self.model5_2(out5)
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out6 = torch.cat([out5_1, out5_2, out1], 1)
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out6_1 = self.model6_1(out6)
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out6_2 = self.model6_2(out6)
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return out6_1, out6_2
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class handpose_model(nn.Module):
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def __init__(self):
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super(handpose_model, self).__init__()
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no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
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'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
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block1_0 = OrderedDict([
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('conv1_1', [3, 64, 3, 1, 1]),
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('conv1_2', [64, 64, 3, 1, 1]),
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('pool1_stage1', [2, 2, 0]),
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('conv2_1', [64, 128, 3, 1, 1]),
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('conv2_2', [128, 128, 3, 1, 1]),
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('pool2_stage1', [2, 2, 0]),
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('conv3_1', [128, 256, 3, 1, 1]),
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('conv3_2', [256, 256, 3, 1, 1]),
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('conv3_3', [256, 256, 3, 1, 1]),
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('conv3_4', [256, 256, 3, 1, 1]),
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('pool3_stage1', [2, 2, 0]),
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('conv4_1', [256, 512, 3, 1, 1]),
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('conv4_2', [512, 512, 3, 1, 1]),
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('conv4_3', [512, 512, 3, 1, 1]),
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('conv4_4', [512, 512, 3, 1, 1]),
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('conv5_1', [512, 512, 3, 1, 1]),
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('conv5_2', [512, 512, 3, 1, 1]),
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('conv5_3_CPM', [512, 128, 3, 1, 1])
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])
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block1_1 = OrderedDict([
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('conv6_1_CPM', [128, 512, 1, 1, 0]),
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('conv6_2_CPM', [512, 22, 1, 1, 0])
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])
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blocks = {}
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blocks['block1_0'] = block1_0
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blocks['block1_1'] = block1_1
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for i in range(2, 7):
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blocks['block%d' % i] = OrderedDict([
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('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
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('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
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('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
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('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
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('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
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('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
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('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
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])
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for k in blocks.keys():
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blocks[k] = make_layers(blocks[k], no_relu_layers)
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self.model1_0 = blocks['block1_0']
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self.model1_1 = blocks['block1_1']
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self.model2 = blocks['block2']
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self.model3 = blocks['block3']
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self.model4 = blocks['block4']
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self.model5 = blocks['block5']
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self.model6 = blocks['block6']
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def forward(self, x):
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out1_0 = self.model1_0(x)
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out1_1 = self.model1_1(out1_0)
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concat_stage2 = torch.cat([out1_1, out1_0], 1)
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out_stage2 = self.model2(concat_stage2)
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concat_stage3 = torch.cat([out_stage2, out1_0], 1)
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out_stage3 = self.model3(concat_stage3)
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concat_stage4 = torch.cat([out_stage3, out1_0], 1)
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out_stage4 = self.model4(concat_stage4)
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concat_stage5 = torch.cat([out_stage4, out1_0], 1)
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out_stage5 = self.model5(concat_stage5)
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concat_stage6 = torch.cat([out_stage5, out1_0], 1)
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out_stage6 = self.model6(concat_stage6)
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return out_stage6
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