Udacity-Computer-Vision-Nanodegree-Program/project_2_image_captioning_project/CNN_model_COCO_Dataset.py at master · sauravraghuvanshi/Udacity-Computer-Vision-Nanodegree-Program · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
#!/usr/bin/env python
# coding: utf-8

# In[ ]:


class ConvNet(nn.Module):


    def __init__(self, num_classes = 101):
        super(ConvNet, self).__init__()

        self.features = nn.Sequential(
            nn.Conv3d(3, 64, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.Conv3d(64, 64, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1,2,2), stride=(1,2,2)),

            nn.Conv3d(64, 128, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.Conv3d(128, 128, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1,2,2), stride=(1,2,2)),

            nn.Conv3d(128, 256, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1,2,2), stride=(1,2,2)),

            nn.Conv3d(256, 512, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.Conv3d(512, 512, kernel_size=(1,3,3), padding=(0,1,1)),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1,2,2), stride=(1,2,2)),

        )


        self.conv3d = nn.Sequential(
            nn.Conv3d(512, 512, kernel_size=(3,1,1), padding=(1,0,0)),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(3,1,1), stride=(2,1,1), padding=(1,0,0)),

            nn.Conv3d(512, 512, kernel_size=(3,3,3), padding=(1,1,1)),
            nn.ReLU(),
            nn.Conv3d(512, 512, kernel_size=(3,3,3), padding=(1,1,1)),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(2,2,2), stride=(2,2,2)),

            nn.Conv3d(512, 512, kernel_size=(3,3,3), padding=(1,1,1)),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)),

        )


        self.st_classifier = nn.Sequential(
            nn.Linear(9216, 4096),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(4096, num_classes),
        )

    def forward(self, input):

        x = self.features(input)
        x = self.conv3d(x)

        x = x.view(-1, 9216)
        x = self.st_classifier(x)
        return x