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‎inception5h.pth‎

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‎inception5h.py‎

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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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class Inception5h(nn.Module):
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def __init__(self):
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super(Inception5h, self).__init__()
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self.conv2d0_pre_relu_conv = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(7, 7), stride=(2, 2), groups=1, bias=True)
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self.conv2d1_pre_relu_conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.conv2d2_pre_relu_conv = nn.Conv2d(in_channels=64, out_channels=192, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
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self.mixed3a_1x1_pre_relu_conv = nn.Conv2d(in_channels=192, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3a_3x3_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=192, out_channels=96, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3a_5x5_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=192, out_channels=16, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3a_pool_reduce_pre_relu_conv = nn.Conv2d(in_channels=192, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3a_3x3_pre_relu_conv = nn.Conv2d(in_channels=96, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
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self.mixed3a_5x5_pre_relu_conv = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
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self.mixed3b_1x1_pre_relu_conv = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3b_3x3_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3b_5x5_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=256, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3b_pool_reduce_pre_relu_conv = nn.Conv2d(in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed3b_3x3_pre_relu_conv = nn.Conv2d(in_channels=128, out_channels=192, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
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self.mixed3b_5x5_pre_relu_conv = nn.Conv2d(in_channels=32, out_channels=96, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
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self.mixed4a_1x1_pre_relu_conv = nn.Conv2d(in_channels=480, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed4a_3x3_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=480, out_channels=96, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed4a_5x5_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=480, out_channels=16, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed4a_pool_reduce_pre_relu_conv = nn.Conv2d(in_channels=480, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.mixed4a_3x3_pre_relu_conv = nn.Conv2d(in_channels=96, out_channels=204, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
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self.mixed4a_5x5_pre_relu_conv = nn.Conv2d(in_channels=16, out_channels=48, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
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self.head0_bottleneck_pre_relu_conv = nn.Conv2d(in_channels=508, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
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self.nn0_pre_relu_matmul = nn.Linear(in_features = 2048, out_features = 1024, bias = True)
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self.softmax0_pre_activation_matmul = nn.Linear(in_features = 1024, out_features = 1008, bias = True)
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def forward(self, x):
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conv2d0_pre_relu_conv_pad = F.pad(x, (2, 3, 2, 3))
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conv2d0_pre_relu_conv = self.conv2d0_pre_relu_conv(conv2d0_pre_relu_conv_pad)
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conv2d0 = F.relu(conv2d0_pre_relu_conv)
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maxpool0_pad = F.pad(conv2d0, (0, 1, 0, 1), value=float('-inf'))
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maxpool0 = F.max_pool2d(maxpool0_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
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localresponsenorm0 = F.local_response_norm(maxpool0, size=9, alpha=9.999999747378752e-05, beta=0.5, k=1)
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conv2d1_pre_relu_conv = self.conv2d1_pre_relu_conv(localresponsenorm0)
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conv2d1 = F.relu(conv2d1_pre_relu_conv)
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conv2d2_pre_relu_conv_pad = F.pad(conv2d1, (1, 1, 1, 1))
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conv2d2_pre_relu_conv = self.conv2d2_pre_relu_conv(conv2d2_pre_relu_conv_pad)
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conv2d2 = F.relu(conv2d2_pre_relu_conv)
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localresponsenorm1 = F.local_response_norm(conv2d2, size=9, alpha=9.999999747378752e-05, beta=0.5, k=1)
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maxpool1_pad = F.pad(localresponsenorm1, (0, 1, 0, 1), value=float('-inf'))
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maxpool1 = F.max_pool2d(maxpool1_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
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mixed3a_1x1_pre_relu_conv = self.mixed3a_1x1_pre_relu_conv(maxpool1)
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mixed3a_3x3_bottleneck_pre_relu_conv = self.mixed3a_3x3_bottleneck_pre_relu_conv(maxpool1)
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mixed3a_5x5_bottleneck_pre_relu_conv = self.mixed3a_5x5_bottleneck_pre_relu_conv(maxpool1)
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mixed3a_pool_pad = F.pad(maxpool1, (1, 1, 1, 1), value=float('-inf'))
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mixed3a_pool = F.max_pool2d(mixed3a_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
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mixed3a_1x1 = F.relu(mixed3a_1x1_pre_relu_conv)
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mixed3a_3x3_bottleneck = F.relu(mixed3a_3x3_bottleneck_pre_relu_conv)
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mixed3a_5x5_bottleneck = F.relu(mixed3a_5x5_bottleneck_pre_relu_conv)
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mixed3a_pool_reduce_pre_relu_conv = self.mixed3a_pool_reduce_pre_relu_conv(mixed3a_pool)
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mixed3a_3x3_pre_relu_conv_pad = F.pad(mixed3a_3x3_bottleneck, (1, 1, 1, 1))
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mixed3a_3x3_pre_relu_conv = self.mixed3a_3x3_pre_relu_conv(mixed3a_3x3_pre_relu_conv_pad)
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mixed3a_5x5_pre_relu_conv_pad = F.pad(mixed3a_5x5_bottleneck, (2, 2, 2, 2))
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mixed3a_5x5_pre_relu_conv = self.mixed3a_5x5_pre_relu_conv(mixed3a_5x5_pre_relu_conv_pad)
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mixed3a_pool_reduce = F.relu(mixed3a_pool_reduce_pre_relu_conv)
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mixed3a_3x3 = F.relu(mixed3a_3x3_pre_relu_conv)
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mixed3a_5x5 = F.relu(mixed3a_5x5_pre_relu_conv)
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mixed3a = torch.cat((mixed3a_1x1, mixed3a_3x3, mixed3a_5x5, mixed3a_pool_reduce), 1)
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mixed3b_1x1_pre_relu_conv = self.mixed3b_1x1_pre_relu_conv(mixed3a)
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mixed3b_3x3_bottleneck_pre_relu_conv = self.mixed3b_3x3_bottleneck_pre_relu_conv(mixed3a)
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mixed3b_5x5_bottleneck_pre_relu_conv = self.mixed3b_5x5_bottleneck_pre_relu_conv(mixed3a)
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mixed3b_pool_pad = F.pad(mixed3a, (1, 1, 1, 1), value=float('-inf'))
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mixed3b_pool = F.max_pool2d(mixed3b_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
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mixed3b_1x1 = F.relu(mixed3b_1x1_pre_relu_conv)
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mixed3b_3x3_bottleneck = F.relu(mixed3b_3x3_bottleneck_pre_relu_conv)
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mixed3b_5x5_bottleneck = F.relu(mixed3b_5x5_bottleneck_pre_relu_conv)
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mixed3b_pool_reduce_pre_relu_conv = self.mixed3b_pool_reduce_pre_relu_conv(mixed3b_pool)
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mixed3b_3x3_pre_relu_conv_pad = F.pad(mixed3b_3x3_bottleneck, (1, 1, 1, 1))
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mixed3b_3x3_pre_relu_conv = self.mixed3b_3x3_pre_relu_conv(mixed3b_3x3_pre_relu_conv_pad)
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mixed3b_5x5_pre_relu_conv_pad = F.pad(mixed3b_5x5_bottleneck, (2, 2, 2, 2))
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mixed3b_5x5_pre_relu_conv = self.mixed3b_5x5_pre_relu_conv(mixed3b_5x5_pre_relu_conv_pad)
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mixed3b_pool_reduce = F.relu(mixed3b_pool_reduce_pre_relu_conv)
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mixed3b_3x3 = F.relu(mixed3b_3x3_pre_relu_conv)
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mixed3b_5x5 = F.relu(mixed3b_5x5_pre_relu_conv)
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mixed3b = torch.cat((mixed3b_1x1, mixed3b_3x3, mixed3b_5x5, mixed3b_pool_reduce), 1)
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maxpool4_pad = F.pad(mixed3b, (0, 1, 0, 1), value=float('-inf'))
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maxpool4 = F.max_pool2d(maxpool4_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
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mixed4a_1x1_pre_relu_conv = self.mixed4a_1x1_pre_relu_conv(maxpool4)
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mixed4a_3x3_bottleneck_pre_relu_conv = self.mixed4a_3x3_bottleneck_pre_relu_conv(maxpool4)
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mixed4a_5x5_bottleneck_pre_relu_conv = self.mixed4a_5x5_bottleneck_pre_relu_conv(maxpool4)
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mixed4a_pool_pad = F.pad(maxpool4, (1, 1, 1, 1), value=float('-inf'))
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mixed4a_pool = F.max_pool2d(mixed4a_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
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mixed4a_1x1 = F.relu(mixed4a_1x1_pre_relu_conv)
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mixed4a_3x3_bottleneck = F.relu(mixed4a_3x3_bottleneck_pre_relu_conv)
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mixed4a_5x5_bottleneck = F.relu(mixed4a_5x5_bottleneck_pre_relu_conv)
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mixed4a_pool_reduce_pre_relu_conv = self.mixed4a_pool_reduce_pre_relu_conv(mixed4a_pool)
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mixed4a_3x3_pre_relu_conv_pad = F.pad(mixed4a_3x3_bottleneck, (1, 1, 1, 1))
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mixed4a_3x3_pre_relu_conv = self.mixed4a_3x3_pre_relu_conv(mixed4a_3x3_pre_relu_conv_pad)
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mixed4a_5x5_pre_relu_conv_pad = F.pad(mixed4a_5x5_bottleneck, (2, 2, 2, 2))
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mixed4a_5x5_pre_relu_conv = self.mixed4a_5x5_pre_relu_conv(mixed4a_5x5_pre_relu_conv_pad)
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mixed4a_pool_reduce = F.relu(mixed4a_pool_reduce_pre_relu_conv)
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mixed4a_3x3 = F.relu(mixed4a_3x3_pre_relu_conv)
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mixed4a_5x5 = F.relu(mixed4a_5x5_pre_relu_conv)
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mixed4a = torch.cat((mixed4a_1x1, mixed4a_3x3, mixed4a_5x5, mixed4a_pool_reduce), 1)
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head0_pool = F.avg_pool2d(mixed4a, kernel_size=(5, 5), stride=(3, 3), padding=(0,), ceil_mode=False, count_include_pad=False)
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head0_bottleneck_pre_relu_conv = self.head0_bottleneck_pre_relu_conv(head0_pool)
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head0_bottleneck = F.relu(head0_bottleneck_pre_relu_conv)
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avgpool_2d = nn.AdaptiveAvgPool2d((4, 4))
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x = avgpool_2d(head0_bottleneck)
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x = torch.flatten(x, 1)
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nn0_pre_relu_matmul = self.nn0_pre_relu_matmul(x)
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nn0 = F.relu(nn0_pre_relu_matmul)
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nn0_reshape = torch.reshape(input = nn0, shape = (-1,1024))
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softmax0_pre_activation_matmul = self.softmax0_pre_activation_matmul(nn0_reshape)
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softmax0 = F.softmax(softmax0_pre_activation_matmul)
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return softmax0

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