add homework4, part1

homework04/README.md

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+In this homework you will combine your knowledge of convolutional and recurrent neural networks to build an image captioning model.
+
+As usual, there are two tracks:
+* Basic: salary prediction, than image captioning
+* Advanced: image captioning with attention

homework04/beheaded_inception3.py

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+import torch, torch.nn as nn
+import torch.nn.functional as F
+from torchvision.models.inception import Inception3
+from torch.utils.model_zoo import load_url
+from warnings import warn
+
+class BeheadedInception3(Inception3):
+    """ Like torchvision.models.inception.Inception3 but the head goes separately """
+
+    def forward(self, x):
+        if self.transform_input:
+            x = x.clone()
+            x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
+            x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
+            x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
+        else: warn("Input isn't transformed")
+        x = self.Conv2d_1a_3x3(x)
+        x = self.Conv2d_2a_3x3(x)
+        x = self.Conv2d_2b_3x3(x)
+        x = F.max_pool2d(x, kernel_size=3, stride=2)
+        x = self.Conv2d_3b_1x1(x)
+        x = self.Conv2d_4a_3x3(x)
+        x = F.max_pool2d(x, kernel_size=3, stride=2)
+        x = self.Mixed_5b(x)
+        x = self.Mixed_5c(x)
+        x = self.Mixed_5d(x)
+        x = self.Mixed_6a(x)
+        x = self.Mixed_6b(x)
+        x = self.Mixed_6c(x)
+        x = self.Mixed_6d(x)
+        x = self.Mixed_6e(x)
+        x = self.Mixed_7a(x)
+        x = self.Mixed_7b(x)
+        x_for_attn = x = self.Mixed_7c(x)
+        # 8 x 8 x 2048
+        x = F.avg_pool2d(x, kernel_size=8)
+        # 1 x 1 x 2048
+        x_for_capt = x = x.view(x.size(0), -1)
+        # 2048
+        x = self.fc(x)
+        # 1000 (num_classes)
+        return x_for_attn, x_for_capt, x
+
+
+def beheaded_inception_v3(transform_input=True):
+    model= BeheadedInception3(transform_input=transform_input)
+    inception_url = 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'
+    model.load_state_dict(load_url(inception_url))
+    return model