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| import torch | |
| from collections import OrderedDict | |
| import torch | |
| import torch.nn as nn | |
| def make_layers(block, no_relu_layers): | |
| layers = [] | |
| for layer_name, v in block.items(): | |
| if 'pool' in layer_name: | |
| layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], | |
| padding=v[2]) | |
| layers.append((layer_name, layer)) | |
| else: | |
| conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], | |
| kernel_size=v[2], stride=v[3], | |
| padding=v[4]) | |
| layers.append((layer_name, conv2d)) | |
| if layer_name not in no_relu_layers: | |
| layers.append(('relu_'+layer_name, nn.ReLU(inplace=True))) | |
| return nn.Sequential(OrderedDict(layers)) | |
| class bodypose_model(nn.Module): | |
| def __init__(self): | |
| super(bodypose_model, self).__init__() | |
| # these layers have no relu layer | |
| no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ | |
| 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ | |
| 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ | |
| 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] | |
| blocks = {} | |
| block0 = OrderedDict([ | |
| ('conv1_1', [3, 64, 3, 1, 1]), | |
| ('conv1_2', [64, 64, 3, 1, 1]), | |
| ('pool1_stage1', [2, 2, 0]), | |
| ('conv2_1', [64, 128, 3, 1, 1]), | |
| ('conv2_2', [128, 128, 3, 1, 1]), | |
| ('pool2_stage1', [2, 2, 0]), | |
| ('conv3_1', [128, 256, 3, 1, 1]), | |
| ('conv3_2', [256, 256, 3, 1, 1]), | |
| ('conv3_3', [256, 256, 3, 1, 1]), | |
| ('conv3_4', [256, 256, 3, 1, 1]), | |
| ('pool3_stage1', [2, 2, 0]), | |
| ('conv4_1', [256, 512, 3, 1, 1]), | |
| ('conv4_2', [512, 512, 3, 1, 1]), | |
| ('conv4_3_CPM', [512, 256, 3, 1, 1]), | |
| ('conv4_4_CPM', [256, 128, 3, 1, 1]) | |
| ]) | |
| # Stage 1 | |
| block1_1 = OrderedDict([ | |
| ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), | |
| ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), | |
| ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), | |
| ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), | |
| ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) | |
| ]) | |
| block1_2 = OrderedDict([ | |
| ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), | |
| ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), | |
| ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), | |
| ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), | |
| ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) | |
| ]) | |
| blocks['block1_1'] = block1_1 | |
| blocks['block1_2'] = block1_2 | |
| self.model0 = make_layers(block0, no_relu_layers) | |
| # Stages 2 - 6 | |
| for i in range(2, 7): | |
| blocks['block%d_1' % i] = OrderedDict([ | |
| ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), | |
| ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), | |
| ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) | |
| ]) | |
| blocks['block%d_2' % i] = OrderedDict([ | |
| ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), | |
| ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), | |
| ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) | |
| ]) | |
| for k in blocks.keys(): | |
| blocks[k] = make_layers(blocks[k], no_relu_layers) | |
| self.model1_1 = blocks['block1_1'] | |
| self.model2_1 = blocks['block2_1'] | |
| self.model3_1 = blocks['block3_1'] | |
| self.model4_1 = blocks['block4_1'] | |
| self.model5_1 = blocks['block5_1'] | |
| self.model6_1 = blocks['block6_1'] | |
| self.model1_2 = blocks['block1_2'] | |
| self.model2_2 = blocks['block2_2'] | |
| self.model3_2 = blocks['block3_2'] | |
| self.model4_2 = blocks['block4_2'] | |
| self.model5_2 = blocks['block5_2'] | |
| self.model6_2 = blocks['block6_2'] | |
| def forward(self, x): | |
| out1 = self.model0(x) | |
| out1_1 = self.model1_1(out1) | |
| out1_2 = self.model1_2(out1) | |
| out2 = torch.cat([out1_1, out1_2, out1], 1) | |
| out2_1 = self.model2_1(out2) | |
| out2_2 = self.model2_2(out2) | |
| out3 = torch.cat([out2_1, out2_2, out1], 1) | |
| out3_1 = self.model3_1(out3) | |
| out3_2 = self.model3_2(out3) | |
| out4 = torch.cat([out3_1, out3_2, out1], 1) | |
| out4_1 = self.model4_1(out4) | |
| out4_2 = self.model4_2(out4) | |
| out5 = torch.cat([out4_1, out4_2, out1], 1) | |
| out5_1 = self.model5_1(out5) | |
| out5_2 = self.model5_2(out5) | |
| out6 = torch.cat([out5_1, out5_2, out1], 1) | |
| out6_1 = self.model6_1(out6) | |
| out6_2 = self.model6_2(out6) | |
| return out6_1, out6_2 | |
| class handpose_model(nn.Module): | |
| def __init__(self): | |
| super(handpose_model, self).__init__() | |
| # these layers have no relu layer | |
| no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ | |
| 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] | |
| # stage 1 | |
| block1_0 = OrderedDict([ | |
| ('conv1_1', [3, 64, 3, 1, 1]), | |
| ('conv1_2', [64, 64, 3, 1, 1]), | |
| ('pool1_stage1', [2, 2, 0]), | |
| ('conv2_1', [64, 128, 3, 1, 1]), | |
| ('conv2_2', [128, 128, 3, 1, 1]), | |
| ('pool2_stage1', [2, 2, 0]), | |
| ('conv3_1', [128, 256, 3, 1, 1]), | |
| ('conv3_2', [256, 256, 3, 1, 1]), | |
| ('conv3_3', [256, 256, 3, 1, 1]), | |
| ('conv3_4', [256, 256, 3, 1, 1]), | |
| ('pool3_stage1', [2, 2, 0]), | |
| ('conv4_1', [256, 512, 3, 1, 1]), | |
| ('conv4_2', [512, 512, 3, 1, 1]), | |
| ('conv4_3', [512, 512, 3, 1, 1]), | |
| ('conv4_4', [512, 512, 3, 1, 1]), | |
| ('conv5_1', [512, 512, 3, 1, 1]), | |
| ('conv5_2', [512, 512, 3, 1, 1]), | |
| ('conv5_3_CPM', [512, 128, 3, 1, 1]) | |
| ]) | |
| block1_1 = OrderedDict([ | |
| ('conv6_1_CPM', [128, 512, 1, 1, 0]), | |
| ('conv6_2_CPM', [512, 22, 1, 1, 0]) | |
| ]) | |
| blocks = {} | |
| blocks['block1_0'] = block1_0 | |
| blocks['block1_1'] = block1_1 | |
| # stage 2-6 | |
| for i in range(2, 7): | |
| blocks['block%d' % i] = OrderedDict([ | |
| ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), | |
| ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), | |
| ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), | |
| ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) | |
| ]) | |
| for k in blocks.keys(): | |
| blocks[k] = make_layers(blocks[k], no_relu_layers) | |
| self.model1_0 = blocks['block1_0'] | |
| self.model1_1 = blocks['block1_1'] | |
| self.model2 = blocks['block2'] | |
| self.model3 = blocks['block3'] | |
| self.model4 = blocks['block4'] | |
| self.model5 = blocks['block5'] | |
| self.model6 = blocks['block6'] | |
| def forward(self, x): | |
| out1_0 = self.model1_0(x) | |
| out1_1 = self.model1_1(out1_0) | |
| concat_stage2 = torch.cat([out1_1, out1_0], 1) | |
| out_stage2 = self.model2(concat_stage2) | |
| concat_stage3 = torch.cat([out_stage2, out1_0], 1) | |
| out_stage3 = self.model3(concat_stage3) | |
| concat_stage4 = torch.cat([out_stage3, out1_0], 1) | |
| out_stage4 = self.model4(concat_stage4) | |
| concat_stage5 = torch.cat([out_stage4, out1_0], 1) | |
| out_stage5 = self.model5(concat_stage5) | |
| concat_stage6 = torch.cat([out_stage5, out1_0], 1) | |
| out_stage6 = self.model6(concat_stage6) | |
| return out_stage6 | |