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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import math |
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_weights_dict = dict() |
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def load_weights(weight_file): |
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if weight_file == None: |
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return |
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try: |
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weights_dict = np.load(weight_file, allow_pickle=True).item() |
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except: |
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weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() |
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return weights_dict |
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class irn50_pytorch(nn.Module): |
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def __init__(self, weight_file): |
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super(irn50_pytorch, self).__init__() |
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global _weights_dict |
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_weights_dict = load_weights(weight_file) |
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self.Convolution1 = self.__conv(2, name='Convolution1', in_channels=3, out_channels=32, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=False) |
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self.BatchNorm1 = self.__batch_normalization(2, 'BatchNorm1', num_features=32, eps=9.999999747378752e-06, momentum=0.0) |
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self.Convolution2 = self.__conv(2, name='Convolution2', in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.BatchNorm2 = self.__batch_normalization(2, 'BatchNorm2', num_features=32, eps=9.999999747378752e-06, momentum=0.0) |
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self.Convolution3 = self.__conv(2, name='Convolution3', in_channels=32, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.BatchNorm3 = self.__batch_normalization(2, 'BatchNorm3', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.Convolution4 = self.__conv(2, name='Convolution4', in_channels=64, out_channels=80, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.BatchNorm4 = self.__batch_normalization(2, 'BatchNorm4', num_features=80, eps=9.999999747378752e-06, momentum=0.0) |
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self.Convolution5 = self.__conv(2, name='Convolution5', in_channels=80, out_channels=192, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.BatchNorm5 = self.__batch_normalization(2, 'BatchNorm5', num_features=192, eps=9.999999747378752e-06, momentum=0.0) |
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self.Convolution6 = self.__conv(2, name='Convolution6', in_channels=192, out_channels=256, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=False) |
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self.BatchNorm6 = self.__batch_normalization(2, 'BatchNorm6', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res1_proj = self.__conv(2, name='conv2_res1_proj', in_channels=256, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res1_conv1 = self.__conv(2, name='conv2_res1_conv1', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res1_conv1_bn = self.__batch_normalization(2, 'conv2_res1_conv1_bn', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res1_conv2 = self.__conv(2, name='conv2_res1_conv2', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res1_conv2_bn = self.__batch_normalization(2, 'conv2_res1_conv2_bn', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res1_conv3 = self.__conv(2, name='conv2_res1_conv3', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res2_pre_bn = self.__batch_normalization(2, 'conv2_res2_pre_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res2_conv1 = self.__conv(2, name='conv2_res2_conv1', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res2_conv1_bn = self.__batch_normalization(2, 'conv2_res2_conv1_bn', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res2_conv2 = self.__conv(2, name='conv2_res2_conv2', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res2_conv2_bn = self.__batch_normalization(2, 'conv2_res2_conv2_bn', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res2_conv3 = self.__conv(2, name='conv2_res2_conv3', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res3_pre_bn = self.__batch_normalization(2, 'conv2_res3_pre_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res3_conv1 = self.__conv(2, name='conv2_res3_conv1', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res3_conv1_bn = self.__batch_normalization(2, 'conv2_res3_conv1_bn', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res3_conv2 = self.__conv(2, name='conv2_res3_conv2', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv2_res3_conv2_bn = self.__batch_normalization(2, 'conv2_res3_conv2_bn', num_features=64, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv2_res3_conv3 = self.__conv(2, name='conv2_res3_conv3', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res1_pre_bn = self.__batch_normalization(2, 'conv3_res1_pre_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res1_proj = self.__conv(2, name='conv3_res1_proj', in_channels=256, out_channels=512, kernel_size=(1, 1), stride=(2, 2), groups=1, bias=False) |
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self.conv3_res1_conv1 = self.__conv(2, name='conv3_res1_conv1', in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(2, 2), groups=1, bias=False) |
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self.conv3_res1_conv1_bn = self.__batch_normalization(2, 'conv3_res1_conv1_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res1_conv2 = self.__conv(2, name='conv3_res1_conv2', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res1_conv2_bn = self.__batch_normalization(2, 'conv3_res1_conv2_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res1_conv3 = self.__conv(2, name='conv3_res1_conv3', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res2_pre_bn = self.__batch_normalization(2, 'conv3_res2_pre_bn', num_features=512, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res2_conv1 = self.__conv(2, name='conv3_res2_conv1', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res2_conv1_bn = self.__batch_normalization(2, 'conv3_res2_conv1_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res2_conv2 = self.__conv(2, name='conv3_res2_conv2', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res2_conv2_bn = self.__batch_normalization(2, 'conv3_res2_conv2_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res2_conv3 = self.__conv(2, name='conv3_res2_conv3', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res3_pre_bn = self.__batch_normalization(2, 'conv3_res3_pre_bn', num_features=512, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res3_conv1 = self.__conv(2, name='conv3_res3_conv1', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res3_conv1_bn = self.__batch_normalization(2, 'conv3_res3_conv1_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res3_conv2 = self.__conv(2, name='conv3_res3_conv2', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res3_conv2_bn = self.__batch_normalization(2, 'conv3_res3_conv2_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res3_conv3 = self.__conv(2, name='conv3_res3_conv3', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res4_pre_bn = self.__batch_normalization(2, 'conv3_res4_pre_bn', num_features=512, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res4_conv1 = self.__conv(2, name='conv3_res4_conv1', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res4_conv1_bn = self.__batch_normalization(2, 'conv3_res4_conv1_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res4_conv2 = self.__conv(2, name='conv3_res4_conv2', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv3_res4_conv2_bn = self.__batch_normalization(2, 'conv3_res4_conv2_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv3_res4_conv3 = self.__conv(2, name='conv3_res4_conv3', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res1_pre_bn = self.__batch_normalization(2, 'conv4_res1_pre_bn', num_features=512, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res1_proj = self.__conv(2, name='conv4_res1_proj', in_channels=512, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res1_conv1 = self.__conv(2, name='conv4_res1_conv1', in_channels=512, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res1_conv1_bn = self.__batch_normalization(2, 'conv4_res1_conv1_bn', num_features=128, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res1_conv2 = self.__conv(2, name='conv4_res1_conv2', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res2_pre_bn = self.__batch_normalization(2, 'conv4_res2_pre_bn', num_features=512, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res2_conv1_proj = self.__conv(2, name='conv4_res2_conv1_proj', in_channels=512, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res2_conv1 = self.__conv(2, name='conv4_res2_conv1', in_channels=512, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res2_conv1_bn = self.__batch_normalization(2, 'conv4_res2_conv1_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res2_conv2 = self.__conv(2, name='conv4_res2_conv2', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res2_conv2_bn = self.__batch_normalization(2, 'conv4_res2_conv2_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res2_conv3 = self.__conv(2, name='conv4_res2_conv3', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res3_pre_bn = self.__batch_normalization(2, 'conv4_res3_pre_bn', num_features=1024, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res3_conv1 = self.__conv(2, name='conv4_res3_conv1', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res3_conv1_bn = self.__batch_normalization(2, 'conv4_res3_conv1_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res3_conv2 = self.__conv(2, name='conv4_res3_conv2', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=False) |
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self.conv4_res3_conv2_bn = self.__batch_normalization(2, 'conv4_res3_conv2_bn', num_features=256, eps=9.999999747378752e-06, momentum=0.0) |
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self.conv4_res3_conv3 = self.__conv(2, name='conv4_res3_conv3', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=False) |
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self.conv5_bn = self.__batch_normalization(2, 'conv5_bn', num_features=1024, eps=9.999999747378752e-06, momentum=0.0) |
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self.fc1_1 = self.__dense(name = 'fc1_1', in_features = 16384, out_features = 512, bias = False) |
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self.bn_fc1 = self.__batch_normalization(1, 'bn_fc1', num_features=512, eps=9.999999747378752e-06, momentum=0.0) |
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def forward(self, x): |
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Convolution1 = self.Convolution1(x) |
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BatchNorm1 = self.BatchNorm1(Convolution1) |
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ReLU1 = F.relu(BatchNorm1) |
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Convolution2 = self.Convolution2(ReLU1) |
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BatchNorm2 = self.BatchNorm2(Convolution2) |
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ReLU2 = F.relu(BatchNorm2) |
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Convolution3_pad = F.pad(ReLU2, (1, 1, 1, 1)) |
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Convolution3 = self.Convolution3(Convolution3_pad) |
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BatchNorm3 = self.BatchNorm3(Convolution3) |
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ReLU3 = F.relu(BatchNorm3) |
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Pooling1_pad = F.pad(ReLU3, (0, 1, 0, 1), value=float('-inf')) |
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Pooling1, Pooling1_idx = F.max_pool2d(Pooling1_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) |
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Convolution4 = self.Convolution4(Pooling1) |
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BatchNorm4 = self.BatchNorm4(Convolution4) |
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ReLU4 = F.relu(BatchNorm4) |
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Convolution5 = self.Convolution5(ReLU4) |
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BatchNorm5 = self.BatchNorm5(Convolution5) |
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ReLU5 = F.relu(BatchNorm5) |
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Convolution6_pad = F.pad(ReLU5, (1, 1, 1, 1)) |
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Convolution6 = self.Convolution6(Convolution6_pad) |
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BatchNorm6 = self.BatchNorm6(Convolution6) |
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ReLU6 = F.relu(BatchNorm6) |
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conv2_res1_proj = self.conv2_res1_proj(ReLU6) |
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conv2_res1_conv1 = self.conv2_res1_conv1(ReLU6) |
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conv2_res1_conv1_bn = self.conv2_res1_conv1_bn(conv2_res1_conv1) |
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conv2_res1_conv1_relu = F.relu(conv2_res1_conv1_bn) |
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conv2_res1_conv2_pad = F.pad(conv2_res1_conv1_relu, (1, 1, 1, 1)) |
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conv2_res1_conv2 = self.conv2_res1_conv2(conv2_res1_conv2_pad) |
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conv2_res1_conv2_bn = self.conv2_res1_conv2_bn(conv2_res1_conv2) |
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conv2_res1_conv2_relu = F.relu(conv2_res1_conv2_bn) |
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conv2_res1_conv3 = self.conv2_res1_conv3(conv2_res1_conv2_relu) |
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conv2_res1 = conv2_res1_proj + conv2_res1_conv3 |
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conv2_res2_pre_bn = self.conv2_res2_pre_bn(conv2_res1) |
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conv2_res2_pre_relu = F.relu(conv2_res2_pre_bn) |
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conv2_res2_conv1 = self.conv2_res2_conv1(conv2_res2_pre_relu) |
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conv2_res2_conv1_bn = self.conv2_res2_conv1_bn(conv2_res2_conv1) |
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conv2_res2_conv1_relu = F.relu(conv2_res2_conv1_bn) |
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conv2_res2_conv2_pad = F.pad(conv2_res2_conv1_relu, (1, 1, 1, 1)) |
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conv2_res2_conv2 = self.conv2_res2_conv2(conv2_res2_conv2_pad) |
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conv2_res2_conv2_bn = self.conv2_res2_conv2_bn(conv2_res2_conv2) |
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conv2_res2_conv2_relu = F.relu(conv2_res2_conv2_bn) |
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conv2_res2_conv3 = self.conv2_res2_conv3(conv2_res2_conv2_relu) |
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conv2_res2 = conv2_res1 + conv2_res2_conv3 |
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conv2_res3_pre_bn = self.conv2_res3_pre_bn(conv2_res2) |
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conv2_res3_pre_relu = F.relu(conv2_res3_pre_bn) |
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conv2_res3_conv1 = self.conv2_res3_conv1(conv2_res3_pre_relu) |
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conv2_res3_conv1_bn = self.conv2_res3_conv1_bn(conv2_res3_conv1) |
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conv2_res3_conv1_relu = F.relu(conv2_res3_conv1_bn) |
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conv2_res3_conv2_pad = F.pad(conv2_res3_conv1_relu, (1, 1, 1, 1)) |
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conv2_res3_conv2 = self.conv2_res3_conv2(conv2_res3_conv2_pad) |
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conv2_res3_conv2_bn = self.conv2_res3_conv2_bn(conv2_res3_conv2) |
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conv2_res3_conv2_relu = F.relu(conv2_res3_conv2_bn) |
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conv2_res3_conv3 = self.conv2_res3_conv3(conv2_res3_conv2_relu) |
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conv2_res3 = conv2_res2 + conv2_res3_conv3 |
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conv3_res1_pre_bn = self.conv3_res1_pre_bn(conv2_res3) |
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conv3_res1_pre_relu = F.relu(conv3_res1_pre_bn) |
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conv3_res1_proj = self.conv3_res1_proj(conv3_res1_pre_relu) |
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conv3_res1_conv1 = self.conv3_res1_conv1(conv3_res1_pre_relu) |
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conv3_res1_conv1_bn = self.conv3_res1_conv1_bn(conv3_res1_conv1) |
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conv3_res1_conv1_relu = F.relu(conv3_res1_conv1_bn) |
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conv3_res1_conv2_pad = F.pad(conv3_res1_conv1_relu, (1, 1, 1, 1)) |
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conv3_res1_conv2 = self.conv3_res1_conv2(conv3_res1_conv2_pad) |
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conv3_res1_conv2_bn = self.conv3_res1_conv2_bn(conv3_res1_conv2) |
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conv3_res1_conv2_relu = F.relu(conv3_res1_conv2_bn) |
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conv3_res1_conv3 = self.conv3_res1_conv3(conv3_res1_conv2_relu) |
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conv3_res1 = conv3_res1_proj + conv3_res1_conv3 |
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conv3_res2_pre_bn = self.conv3_res2_pre_bn(conv3_res1) |
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conv3_res2_pre_relu = F.relu(conv3_res2_pre_bn) |
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conv3_res2_conv1 = self.conv3_res2_conv1(conv3_res2_pre_relu) |
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conv3_res2_conv1_bn = self.conv3_res2_conv1_bn(conv3_res2_conv1) |
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conv3_res2_conv1_relu = F.relu(conv3_res2_conv1_bn) |
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conv3_res2_conv2_pad = F.pad(conv3_res2_conv1_relu, (1, 1, 1, 1)) |
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conv3_res2_conv2 = self.conv3_res2_conv2(conv3_res2_conv2_pad) |
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conv3_res2_conv2_bn = self.conv3_res2_conv2_bn(conv3_res2_conv2) |
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conv3_res2_conv2_relu = F.relu(conv3_res2_conv2_bn) |
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conv3_res2_conv3 = self.conv3_res2_conv3(conv3_res2_conv2_relu) |
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conv3_res2 = conv3_res1 + conv3_res2_conv3 |
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conv3_res3_pre_bn = self.conv3_res3_pre_bn(conv3_res2) |
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conv3_res3_pre_relu = F.relu(conv3_res3_pre_bn) |
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conv3_res3_conv1 = self.conv3_res3_conv1(conv3_res3_pre_relu) |
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conv3_res3_conv1_bn = self.conv3_res3_conv1_bn(conv3_res3_conv1) |
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conv3_res3_conv1_relu = F.relu(conv3_res3_conv1_bn) |
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conv3_res3_conv2_pad = F.pad(conv3_res3_conv1_relu, (1, 1, 1, 1)) |
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conv3_res3_conv2 = self.conv3_res3_conv2(conv3_res3_conv2_pad) |
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conv3_res3_conv2_bn = self.conv3_res3_conv2_bn(conv3_res3_conv2) |
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conv3_res3_conv2_relu = F.relu(conv3_res3_conv2_bn) |
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conv3_res3_conv3 = self.conv3_res3_conv3(conv3_res3_conv2_relu) |
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conv3_res3 = conv3_res2 + conv3_res3_conv3 |
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conv3_res4_pre_bn = self.conv3_res4_pre_bn(conv3_res3) |
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conv3_res4_pre_relu = F.relu(conv3_res4_pre_bn) |
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conv3_res4_conv1 = self.conv3_res4_conv1(conv3_res4_pre_relu) |
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conv3_res4_conv1_bn = self.conv3_res4_conv1_bn(conv3_res4_conv1) |
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conv3_res4_conv1_relu = F.relu(conv3_res4_conv1_bn) |
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conv3_res4_conv2_pad = F.pad(conv3_res4_conv1_relu, (1, 1, 1, 1)) |
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conv3_res4_conv2 = self.conv3_res4_conv2(conv3_res4_conv2_pad) |
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conv3_res4_conv2_bn = self.conv3_res4_conv2_bn(conv3_res4_conv2) |
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conv3_res4_conv2_relu = F.relu(conv3_res4_conv2_bn) |
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conv3_res4_conv3 = self.conv3_res4_conv3(conv3_res4_conv2_relu) |
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conv3_res4 = conv3_res3 + conv3_res4_conv3 |
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conv4_res1_pre_bn = self.conv4_res1_pre_bn(conv3_res4) |
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conv4_res1_pre_relu = F.relu(conv4_res1_pre_bn) |
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conv4_res1_proj = self.conv4_res1_proj(conv4_res1_pre_relu) |
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conv4_res1_conv1_pad = F.pad(conv4_res1_pre_relu, (1, 1, 1, 1)) |
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conv4_res1_conv1 = self.conv4_res1_conv1(conv4_res1_conv1_pad) |
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conv4_res1_conv1_bn = self.conv4_res1_conv1_bn(conv4_res1_conv1) |
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conv4_res1_conv1_relu = F.relu(conv4_res1_conv1_bn) |
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conv4_res1_conv2 = self.conv4_res1_conv2(conv4_res1_conv1_relu) |
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conv4_res1 = conv4_res1_proj + conv4_res1_conv2 |
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conv4_res2_pre_bn = self.conv4_res2_pre_bn(conv4_res1) |
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conv4_res2_pre_relu = F.relu(conv4_res2_pre_bn) |
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conv4_res2_conv1_proj = self.conv4_res2_conv1_proj(conv4_res2_pre_relu) |
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conv4_res2_conv1 = self.conv4_res2_conv1(conv4_res2_pre_relu) |
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conv4_res2_conv1_bn = self.conv4_res2_conv1_bn(conv4_res2_conv1) |
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conv4_res2_conv1_relu = F.relu(conv4_res2_conv1_bn) |
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conv4_res2_conv2_pad = F.pad(conv4_res2_conv1_relu, (1, 1, 1, 1)) |
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conv4_res2_conv2 = self.conv4_res2_conv2(conv4_res2_conv2_pad) |
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conv4_res2_conv2_bn = self.conv4_res2_conv2_bn(conv4_res2_conv2) |
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conv4_res2_conv2_relu = F.relu(conv4_res2_conv2_bn) |
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conv4_res2_conv3 = self.conv4_res2_conv3(conv4_res2_conv2_relu) |
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conv4_res2 = conv4_res2_conv1_proj + conv4_res2_conv3 |
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conv4_res3_pre_bn = self.conv4_res3_pre_bn(conv4_res2) |
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conv4_res3_pre_relu = F.relu(conv4_res3_pre_bn) |
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conv4_res3_conv1 = self.conv4_res3_conv1(conv4_res3_pre_relu) |
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conv4_res3_conv1_bn = self.conv4_res3_conv1_bn(conv4_res3_conv1) |
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conv4_res3_conv1_relu = F.relu(conv4_res3_conv1_bn) |
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conv4_res3_conv2_pad = F.pad(conv4_res3_conv1_relu, (1, 1, 1, 1)) |
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conv4_res3_conv2 = self.conv4_res3_conv2(conv4_res3_conv2_pad) |
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conv4_res3_conv2_bn = self.conv4_res3_conv2_bn(conv4_res3_conv2) |
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conv4_res3_conv2_relu = F.relu(conv4_res3_conv2_bn) |
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conv4_res3_conv3 = self.conv4_res3_conv3(conv4_res3_conv2_relu) |
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conv4_res3 = conv4_res2 + conv4_res3_conv3 |
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conv5_bn = self.conv5_bn(conv4_res3) |
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conv5_relu = F.relu(conv5_bn) |
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pool5 = F.avg_pool2d(conv5_relu, kernel_size=(4, 4), stride=(1, 1), padding=(0,), ceil_mode=False, count_include_pad=False) |
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fc1_0 = pool5.view(pool5.size(0), -1) |
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fc1_1 = self.fc1_1(fc1_0) |
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bn_fc1 = self.bn_fc1(fc1_1) |
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|
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bn_fc1 = bn_fc1.reshape(bn_fc1.size()[0], bn_fc1.size()[1]) |
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slice_fc1, slice_fc2 = bn_fc1[:, :256], bn_fc1[:, 256:] |
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eltwise_fc1 = torch.max(slice_fc1, slice_fc2) |
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|
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return eltwise_fc1 |
|
|
|
@staticmethod |
|
def __conv(dim, name, **kwargs): |
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if dim == 1: layer = nn.Conv1d(**kwargs) |
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elif dim == 2: layer = nn.Conv2d(**kwargs) |
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elif dim == 3: layer = nn.Conv3d(**kwargs) |
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else: raise NotImplementedError() |
|
|
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layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['weights'])) |
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if 'bias' in _weights_dict[name]: |
|
layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) |
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return layer |
|
|
|
@staticmethod |
|
def __batch_normalization(dim, name, **kwargs): |
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if dim == 0 or dim == 1: layer = nn.BatchNorm1d(**kwargs) |
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elif dim == 2: layer = nn.BatchNorm2d(**kwargs) |
|
elif dim == 3: layer = nn.BatchNorm3d(**kwargs) |
|
else: raise NotImplementedError() |
|
|
|
if 'scale' in _weights_dict[name]: |
|
layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['scale'])) |
|
else: |
|
layer.weight.data.fill_(1) |
|
|
|
if 'bias' in _weights_dict[name]: |
|
layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) |
|
else: |
|
layer.bias.data.fill_(0) |
|
|
|
layer.state_dict()['running_mean'].copy_(torch.from_numpy(_weights_dict[name]['mean'])) |
|
layer.state_dict()['running_var'].copy_(torch.from_numpy(_weights_dict[name]['var'])) |
|
return layer |
|
|
|
@staticmethod |
|
def __dense(name, **kwargs): |
|
layer = nn.Linear(**kwargs) |
|
layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['weights'])) |
|
if 'bias' in _weights_dict[name]: |
|
layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) |
|
return layer |
|
|
|
|
