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googlenet_places205.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class GoogLeNetPlaces205(nn.Module):
def __init__(self):
super(GoogLeNetPlaces205, self).__init__()
self.conv1_7x7_s2 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(7, 7), stride=(2, 2), groups=1, bias=True)
self.conv2_3x3_reduce = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.conv2_3x3 = nn.Conv2d(in_channels=64, out_channels=192, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_3a_1x1 = nn.Conv2d(in_channels=192, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3a_5x5_reduce = nn.Conv2d(in_channels=192, out_channels=16, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3a_3x3_reduce = nn.Conv2d(in_channels=192, out_channels=96, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3a_pool_proj = nn.Conv2d(in_channels=192, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3a_5x5 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_3a_3x3 = nn.Conv2d(in_channels=96, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_3b_3x3_reduce = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3b_1x1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3b_5x5_reduce = nn.Conv2d(in_channels=256, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3b_pool_proj = nn.Conv2d(in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_3b_3x3 = nn.Conv2d(in_channels=128, out_channels=192, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_3b_5x5 = nn.Conv2d(in_channels=32, out_channels=96, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_4a_1x1 = nn.Conv2d(in_channels=480, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4a_3x3_reduce = nn.Conv2d(in_channels=480, out_channels=96, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4a_5x5_reduce = nn.Conv2d(in_channels=480, out_channels=16, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4a_pool_proj = nn.Conv2d(in_channels=480, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4a_3x3 = nn.Conv2d(in_channels=96, out_channels=208, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_4a_5x5 = nn.Conv2d(in_channels=16, out_channels=48, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_4b_5x5_reduce = nn.Conv2d(in_channels=512, out_channels=24, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4b_1x1 = nn.Conv2d(in_channels=512, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4b_3x3_reduce = nn.Conv2d(in_channels=512, out_channels=112, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4b_pool_proj = nn.Conv2d(in_channels=512, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.loss1_conv = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4b_5x5 = nn.Conv2d(in_channels=24, out_channels=64, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_4b_3x3 = nn.Conv2d(in_channels=112, out_channels=224, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.loss1_fc_1 = nn.Linear(in_features = 2048, out_features = 1024, bias = True)
self.inception_4c_5x5_reduce = nn.Conv2d(in_channels=512, out_channels=24, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4c_1x1 = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4c_3x3_reduce = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4c_pool_proj = nn.Conv2d(in_channels=512, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4c_5x5 = nn.Conv2d(in_channels=24, out_channels=64, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_4c_3x3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.loss1_classifier_1 = nn.Linear(in_features = 1024, out_features = 205, bias = True)
self.inception_4d_3x3_reduce = nn.Conv2d(in_channels=512, out_channels=144, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4d_1x1 = nn.Conv2d(in_channels=512, out_channels=112, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4d_5x5_reduce = nn.Conv2d(in_channels=512, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4d_pool_proj = nn.Conv2d(in_channels=512, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4d_3x3 = nn.Conv2d(in_channels=144, out_channels=288, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_4d_5x5 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_4e_1x1 = nn.Conv2d(in_channels=528, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4e_5x5_reduce = nn.Conv2d(in_channels=528, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4e_3x3_reduce = nn.Conv2d(in_channels=528, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.loss2_conv = nn.Conv2d(in_channels=528, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4e_pool_proj = nn.Conv2d(in_channels=528, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_4e_5x5 = nn.Conv2d(in_channels=32, out_channels=128, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_4e_3x3 = nn.Conv2d(in_channels=160, out_channels=320, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.loss2_fc_1 = nn.Linear(in_features = 2048, out_features = 1024, bias = True)
self.inception_5a_1x1 = nn.Conv2d(in_channels=832, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5a_5x5_reduce = nn.Conv2d(in_channels=832, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5a_3x3_reduce = nn.Conv2d(in_channels=832, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5a_pool_proj = nn.Conv2d(in_channels=832, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.loss2_classifier_1 = nn.Linear(in_features = 1024, out_features = 205, bias = True)
self.inception_5a_5x5 = nn.Conv2d(in_channels=32, out_channels=128, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
self.inception_5a_3x3 = nn.Conv2d(in_channels=160, out_channels=320, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_5b_3x3_reduce = nn.Conv2d(in_channels=832, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5b_5x5_reduce = nn.Conv2d(in_channels=832, out_channels=48, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5b_1x1 = nn.Conv2d(in_channels=832, out_channels=384, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5b_pool_proj = nn.Conv2d(in_channels=832, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
self.inception_5b_3x3 = nn.Conv2d(in_channels=192, out_channels=384, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True)
self.inception_5b_5x5 = nn.Conv2d(in_channels=48, out_channels=128, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=True)
def forward(self, x):
self.training = False
conv1_7x7_s2_pad = F.pad(x, (3, 3, 3, 3))
conv1_7x7_s2 = self.conv1_7x7_s2(conv1_7x7_s2_pad)
conv1_relu_7x7 = F.relu(conv1_7x7_s2)
pool1_3x3_s2_pad = F.pad(conv1_relu_7x7, (0, 1, 0, 1), value=float('-inf'))
pool1_3x3_s2 = F.max_pool2d(pool1_3x3_s2_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
pool1_norm1 = F.local_response_norm(pool1_3x3_s2, size=5, alpha=9.999999747378752e-05, beta=0.75, k=1.0)
conv2_3x3_reduce = self.conv2_3x3_reduce(pool1_norm1)
conv2_relu_3x3_reduce = F.relu(conv2_3x3_reduce)
conv2_3x3_pad = F.pad(conv2_relu_3x3_reduce, (1, 1, 1, 1))
conv2_3x3 = self.conv2_3x3(conv2_3x3_pad)
conv2_relu_3x3 = F.relu(conv2_3x3)
conv2_norm2 = F.local_response_norm(conv2_relu_3x3, size=5, alpha=9.999999747378752e-05, beta=0.75, k=1.0)
pool2_3x3_s2_pad = F.pad(conv2_norm2, (0, 1, 0, 1), value=float('-inf'))
pool2_3x3_s2 = F.max_pool2d(pool2_3x3_s2_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
inception_3a_pool_pad = F.pad(pool2_3x3_s2, (1, 1, 1, 1), value=float('-inf'))
inception_3a_pool = F.max_pool2d(inception_3a_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_3a_1x1 = self.inception_3a_1x1(pool2_3x3_s2)
inception_3a_5x5_reduce = self.inception_3a_5x5_reduce(pool2_3x3_s2)
inception_3a_3x3_reduce = self.inception_3a_3x3_reduce(pool2_3x3_s2)
inception_3a_pool_proj = self.inception_3a_pool_proj(inception_3a_pool)
inception_3a_relu_1x1 = F.relu(inception_3a_1x1)
inception_3a_relu_5x5_reduce = F.relu(inception_3a_5x5_reduce)
inception_3a_relu_3x3_reduce = F.relu(inception_3a_3x3_reduce)
inception_3a_relu_pool_proj = F.relu(inception_3a_pool_proj)
inception_3a_5x5_pad = F.pad(inception_3a_relu_5x5_reduce, (2, 2, 2, 2))
inception_3a_5x5 = self.inception_3a_5x5(inception_3a_5x5_pad)
inception_3a_3x3_pad = F.pad(inception_3a_relu_3x3_reduce, (1, 1, 1, 1))
inception_3a_3x3 = self.inception_3a_3x3(inception_3a_3x3_pad)
inception_3a_relu_5x5 = F.relu(inception_3a_5x5)
inception_3a_relu_3x3 = F.relu(inception_3a_3x3)
inception_3a_output = torch.cat((inception_3a_relu_1x1, inception_3a_relu_3x3, inception_3a_relu_5x5, inception_3a_relu_pool_proj), 1)
inception_3b_3x3_reduce = self.inception_3b_3x3_reduce(inception_3a_output)
inception_3b_pool_pad = F.pad(inception_3a_output, (1, 1, 1, 1), value=float('-inf'))
inception_3b_pool = F.max_pool2d(inception_3b_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_3b_1x1 = self.inception_3b_1x1(inception_3a_output)
inception_3b_5x5_reduce = self.inception_3b_5x5_reduce(inception_3a_output)
inception_3b_relu_3x3_reduce = F.relu(inception_3b_3x3_reduce)
inception_3b_pool_proj = self.inception_3b_pool_proj(inception_3b_pool)
inception_3b_relu_1x1 = F.relu(inception_3b_1x1)
inception_3b_relu_5x5_reduce = F.relu(inception_3b_5x5_reduce)
inception_3b_3x3_pad = F.pad(inception_3b_relu_3x3_reduce, (1, 1, 1, 1))
inception_3b_3x3 = self.inception_3b_3x3(inception_3b_3x3_pad)
inception_3b_relu_pool_proj = F.relu(inception_3b_pool_proj)
inception_3b_5x5_pad = F.pad(inception_3b_relu_5x5_reduce, (2, 2, 2, 2))
inception_3b_5x5 = self.inception_3b_5x5(inception_3b_5x5_pad)
inception_3b_relu_3x3 = F.relu(inception_3b_3x3)
inception_3b_relu_5x5 = F.relu(inception_3b_5x5)
inception_3b_output = torch.cat((inception_3b_relu_1x1, inception_3b_relu_3x3, inception_3b_relu_5x5, inception_3b_relu_pool_proj), 1)
pool3_3x3_s2_pad = F.pad(inception_3b_output, (0, 1, 0, 1), value=float('-inf'))
pool3_3x3_s2 = F.max_pool2d(pool3_3x3_s2_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
inception_4a_1x1 = self.inception_4a_1x1(pool3_3x3_s2)
inception_4a_3x3_reduce = self.inception_4a_3x3_reduce(pool3_3x3_s2)
inception_4a_5x5_reduce = self.inception_4a_5x5_reduce(pool3_3x3_s2)
inception_4a_pool_pad = F.pad(pool3_3x3_s2, (1, 1, 1, 1), value=float('-inf'))
inception_4a_pool = F.max_pool2d(inception_4a_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_4a_relu_1x1 = F.relu(inception_4a_1x1)
inception_4a_relu_3x3_reduce = F.relu(inception_4a_3x3_reduce)
inception_4a_relu_5x5_reduce = F.relu(inception_4a_5x5_reduce)
inception_4a_pool_proj = self.inception_4a_pool_proj(inception_4a_pool)
inception_4a_3x3_pad = F.pad(inception_4a_relu_3x3_reduce, (1, 1, 1, 1))
inception_4a_3x3 = self.inception_4a_3x3(inception_4a_3x3_pad)
inception_4a_5x5_pad = F.pad(inception_4a_relu_5x5_reduce, (2, 2, 2, 2))
inception_4a_5x5 = self.inception_4a_5x5(inception_4a_5x5_pad)
inception_4a_relu_pool_proj = F.relu(inception_4a_pool_proj)
inception_4a_relu_3x3 = F.relu(inception_4a_3x3)
inception_4a_relu_5x5 = F.relu(inception_4a_5x5)
inception_4a_output = torch.cat((inception_4a_relu_1x1, inception_4a_relu_3x3, inception_4a_relu_5x5, inception_4a_relu_pool_proj), 1)
inception_4b_pool_pad = F.pad(inception_4a_output, (1, 1, 1, 1), value=float('-inf'))
inception_4b_pool = F.max_pool2d(inception_4b_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
#loss1_ave_pool = F.avg_pool2d(inception_4a_output, kernel_size=(5, 5), stride=(3, 3), padding=(0,), ceil_mode=True, count_include_pad=False)
inception_4b_5x5_reduce = self.inception_4b_5x5_reduce(inception_4a_output)
inception_4b_1x1 = self.inception_4b_1x1(inception_4a_output)
inception_4b_3x3_reduce = self.inception_4b_3x3_reduce(inception_4a_output)
inception_4b_pool_proj = self.inception_4b_pool_proj(inception_4b_pool)
#loss1_conv = self.loss1_conv(loss1_ave_pool)
inception_4b_relu_5x5_reduce = F.relu(inception_4b_5x5_reduce)
inception_4b_relu_1x1 = F.relu(inception_4b_1x1)
inception_4b_relu_3x3_reduce = F.relu(inception_4b_3x3_reduce)
inception_4b_relu_pool_proj = F.relu(inception_4b_pool_proj)
#loss1_relu_conv = F.relu(loss1_conv)
inception_4b_5x5_pad = F.pad(inception_4b_relu_5x5_reduce, (2, 2, 2, 2))
inception_4b_5x5 = self.inception_4b_5x5(inception_4b_5x5_pad)
inception_4b_3x3_pad = F.pad(inception_4b_relu_3x3_reduce, (1, 1, 1, 1))
inception_4b_3x3 = self.inception_4b_3x3(inception_4b_3x3_pad)
#loss1_fc_0 = loss1_relu_conv.view(loss1_relu_conv.size(0), -1)
inception_4b_relu_5x5 = F.relu(inception_4b_5x5)
inception_4b_relu_3x3 = F.relu(inception_4b_3x3)
#loss1_fc_1 = self.loss1_fc_1(loss1_fc_0)
inception_4b_output = torch.cat((inception_4b_relu_1x1, inception_4b_relu_3x3, inception_4b_relu_5x5, inception_4b_relu_pool_proj), 1)
#loss1_relu_fc = F.relu(loss1_fc_1)
inception_4c_5x5_reduce = self.inception_4c_5x5_reduce(inception_4b_output)
inception_4c_pool_pad = F.pad(inception_4b_output, (1, 1, 1, 1), value=float('-inf'))
inception_4c_pool = F.max_pool2d(inception_4c_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_4c_1x1 = self.inception_4c_1x1(inception_4b_output)
inception_4c_3x3_reduce = self.inception_4c_3x3_reduce(inception_4b_output)
#loss1_drop_fc = F.dropout(input = loss1_relu_fc, p = 0.699999988079071, training = self.training, inplace = True)
inception_4c_relu_5x5_reduce = F.relu(inception_4c_5x5_reduce)
inception_4c_pool_proj = self.inception_4c_pool_proj(inception_4c_pool)
inception_4c_relu_1x1 = F.relu(inception_4c_1x1)
inception_4c_relu_3x3_reduce = F.relu(inception_4c_3x3_reduce)
#loss1_classifier_0 = loss1_drop_fc.view(loss1_drop_fc.size(0), -1)
inception_4c_5x5_pad = F.pad(inception_4c_relu_5x5_reduce, (2, 2, 2, 2))
inception_4c_5x5 = self.inception_4c_5x5(inception_4c_5x5_pad)
inception_4c_relu_pool_proj = F.relu(inception_4c_pool_proj)
inception_4c_3x3_pad = F.pad(inception_4c_relu_3x3_reduce, (1, 1, 1, 1))
inception_4c_3x3 = self.inception_4c_3x3(inception_4c_3x3_pad)
#loss1_classifier_1 = self.loss1_classifier_1(loss1_classifier_0)
inception_4c_relu_5x5 = F.relu(inception_4c_5x5)
inception_4c_relu_3x3 = F.relu(inception_4c_3x3)
inception_4c_output = torch.cat((inception_4c_relu_1x1, inception_4c_relu_3x3, inception_4c_relu_5x5, inception_4c_relu_pool_proj), 1)
inception_4d_pool_pad = F.pad(inception_4c_output, (1, 1, 1, 1), value=float('-inf'))
inception_4d_pool = F.max_pool2d(inception_4d_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_4d_3x3_reduce = self.inception_4d_3x3_reduce(inception_4c_output)
inception_4d_1x1 = self.inception_4d_1x1(inception_4c_output)
inception_4d_5x5_reduce = self.inception_4d_5x5_reduce(inception_4c_output)
inception_4d_pool_proj = self.inception_4d_pool_proj(inception_4d_pool)
inception_4d_relu_3x3_reduce = F.relu(inception_4d_3x3_reduce)
inception_4d_relu_1x1 = F.relu(inception_4d_1x1)
inception_4d_relu_5x5_reduce = F.relu(inception_4d_5x5_reduce)
inception_4d_relu_pool_proj = F.relu(inception_4d_pool_proj)
inception_4d_3x3_pad = F.pad(inception_4d_relu_3x3_reduce, (1, 1, 1, 1))
inception_4d_3x3 = self.inception_4d_3x3(inception_4d_3x3_pad)
inception_4d_5x5_pad = F.pad(inception_4d_relu_5x5_reduce, (2, 2, 2, 2))
inception_4d_5x5 = self.inception_4d_5x5(inception_4d_5x5_pad)
inception_4d_relu_3x3 = F.relu(inception_4d_3x3)
inception_4d_relu_5x5 = F.relu(inception_4d_5x5)
inception_4d_output = torch.cat((inception_4d_relu_1x1, inception_4d_relu_3x3, inception_4d_relu_5x5, inception_4d_relu_pool_proj), 1)
inception_4e_1x1 = self.inception_4e_1x1(inception_4d_output)
inception_4e_5x5_reduce = self.inception_4e_5x5_reduce(inception_4d_output)
#loss2_ave_pool = F.avg_pool2d(inception_4d_output, kernel_size=(5, 5), stride=(3, 3), padding=(0,), ceil_mode=True, count_include_pad=False)
inception_4e_3x3_reduce = self.inception_4e_3x3_reduce(inception_4d_output)
inception_4e_pool_pad = F.pad(inception_4d_output, (1, 1, 1, 1), value=float('-inf'))
inception_4e_pool = F.max_pool2d(inception_4e_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_4e_relu_1x1 = F.relu(inception_4e_1x1)
inception_4e_relu_5x5_reduce = F.relu(inception_4e_5x5_reduce)
#loss2_conv = self.loss2_conv(loss2_ave_pool)
inception_4e_relu_3x3_reduce = F.relu(inception_4e_3x3_reduce)
inception_4e_pool_proj = self.inception_4e_pool_proj(inception_4e_pool)
inception_4e_5x5_pad = F.pad(inception_4e_relu_5x5_reduce, (2, 2, 2, 2))
inception_4e_5x5 = self.inception_4e_5x5(inception_4e_5x5_pad)
#loss2_relu_conv = F.relu(loss2_conv)
inception_4e_3x3_pad = F.pad(inception_4e_relu_3x3_reduce, (1, 1, 1, 1))
inception_4e_3x3 = self.inception_4e_3x3(inception_4e_3x3_pad)
inception_4e_relu_pool_proj = F.relu(inception_4e_pool_proj)
inception_4e_relu_5x5 = F.relu(inception_4e_5x5)
#loss2_fc_0 = loss2_relu_conv.view(loss2_relu_conv.size(0), -1)
inception_4e_relu_3x3 = F.relu(inception_4e_3x3)
#loss2_fc_1 = self.loss2_fc_1(loss2_fc_0)
inception_4e_output = torch.cat((inception_4e_relu_1x1, inception_4e_relu_3x3, inception_4e_relu_5x5, inception_4e_relu_pool_proj), 1)
#loss2_relu_fc = F.relu(loss2_fc_1)
pool4_3x3_s2_pad = F.pad(inception_4e_output, (0, 1, 0, 1), value=float('-inf'))
pool4_3x3_s2 = F.max_pool2d(pool4_3x3_s2_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
#loss2_drop_fc = F.dropout(input = loss2_relu_fc, p = 0.699999988079071, training = self.training, inplace = True)
inception_5a_1x1 = self.inception_5a_1x1(pool4_3x3_s2)
inception_5a_5x5_reduce = self.inception_5a_5x5_reduce(pool4_3x3_s2)
inception_5a_pool_pad = F.pad(pool4_3x3_s2, (1, 1, 1, 1), value=float('-inf'))
inception_5a_pool = F.max_pool2d(inception_5a_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_5a_3x3_reduce = self.inception_5a_3x3_reduce(pool4_3x3_s2)
#loss2_classifier_0 = loss2_drop_fc.view(loss2_drop_fc.size(0), -1)
inception_5a_relu_1x1 = F.relu(inception_5a_1x1)
inception_5a_relu_5x5_reduce = F.relu(inception_5a_5x5_reduce)
inception_5a_pool_proj = self.inception_5a_pool_proj(inception_5a_pool)
inception_5a_relu_3x3_reduce = F.relu(inception_5a_3x3_reduce)
#loss2_classifier_1 = self.loss2_classifier_1(loss2_classifier_0)
inception_5a_5x5_pad = F.pad(inception_5a_relu_5x5_reduce, (2, 2, 2, 2))
inception_5a_5x5 = self.inception_5a_5x5(inception_5a_5x5_pad)
inception_5a_relu_pool_proj = F.relu(inception_5a_pool_proj)
inception_5a_3x3_pad = F.pad(inception_5a_relu_3x3_reduce, (1, 1, 1, 1))
inception_5a_3x3 = self.inception_5a_3x3(inception_5a_3x3_pad)
inception_5a_relu_5x5 = F.relu(inception_5a_5x5)
inception_5a_relu_3x3 = F.relu(inception_5a_3x3)
inception_5a_output = torch.cat((inception_5a_relu_1x1, inception_5a_relu_3x3, inception_5a_relu_5x5, inception_5a_relu_pool_proj), 1)
inception_5b_3x3_reduce = self.inception_5b_3x3_reduce(inception_5a_output)
inception_5b_pool_pad = F.pad(inception_5a_output, (1, 1, 1, 1), value=float('-inf'))
inception_5b_pool = F.max_pool2d(inception_5b_pool_pad, kernel_size=(3, 3), stride=(1, 1), padding=0, ceil_mode=False)
inception_5b_5x5_reduce = self.inception_5b_5x5_reduce(inception_5a_output)
inception_5b_1x1 = self.inception_5b_1x1(inception_5a_output)
inception_5b_relu_3x3_reduce = F.relu(inception_5b_3x3_reduce)
inception_5b_pool_proj = self.inception_5b_pool_proj(inception_5b_pool)
inception_5b_relu_5x5_reduce = F.relu(inception_5b_5x5_reduce)
inception_5b_relu_1x1 = F.relu(inception_5b_1x1)
inception_5b_3x3_pad = F.pad(inception_5b_relu_3x3_reduce, (1, 1, 1, 1))
inception_5b_3x3 = self.inception_5b_3x3(inception_5b_3x3_pad)
inception_5b_relu_pool_proj = F.relu(inception_5b_pool_proj)
inception_5b_5x5_pad = F.pad(inception_5b_relu_5x5_reduce, (2, 2, 2, 2))
inception_5b_5x5 = self.inception_5b_5x5(inception_5b_5x5_pad)
inception_5b_relu_3x3 = F.relu(inception_5b_3x3)
inception_5b_relu_5x5 = F.relu(inception_5b_5x5)
inception_5b_output = torch.cat((inception_5b_relu_1x1, inception_5b_relu_3x3, inception_5b_relu_5x5, inception_5b_relu_pool_proj), 1)
pool5_7x7_s1 = F.avg_pool2d(inception_5b_output, kernel_size=(7, 7), stride=(1, 1), padding=(0,), ceil_mode=False, count_include_pad=False)
pool5_drop_7x7_s1 = F.dropout(input = pool5_7x7_s1, p = 0.4000000059604645, training = self.training, inplace = True)
return pool5_drop_7x7_s1
#return pool5_drop_7x7_s1#, loss2_classifier_1, loss1_classifier_1
# The Aux classifiers were causing errors with classification, so I commented them out in the forward function.
cnn = GoogLeNetPlaces205()
cnn.load_state_dict(torch.load("googlenet_places205.pth"))
cnn.eval()
print(cnn)