81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
"""
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from https://github.com/DavideA/c3d-pytorch/blob/master/C3D_model.py
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"""
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import torch.nn as nn
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class C3D(nn.Module):
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"""
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The C3D network as described in [1].
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"""
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def __init__(self):
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super(C3D, self).__init__()
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self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
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self.conv2 = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
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self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
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self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
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self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
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self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1))
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self.fc6 = nn.Linear(8192, 4096)
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self.fc7 = nn.Linear(4096, 4096)
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self.fc8 = nn.Linear(4096, 487)
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self.dropout = nn.Dropout(p=0.5)
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self.relu = nn.ReLU()
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self.softmax = nn.Softmax()
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def forward(self, x):
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h = self.relu(self.conv1(x))
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h = self.pool1(h)
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h = self.relu(self.conv2(h))
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h = self.pool2(h)
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h = self.relu(self.conv3a(h))
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h = self.relu(self.conv3b(h))
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h = self.pool3(h)
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h = self.relu(self.conv4a(h))
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h = self.relu(self.conv4b(h))
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h = self.pool4(h)
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h = self.relu(self.conv5a(h))
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h = self.relu(self.conv5b(h))
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h = self.pool5(h)
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h = h.view(-1, 8192)
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h = self.relu(self.fc6(h))
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h = self.dropout(h)
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h = self.relu(self.fc7(h))
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# h = self.dropout(h)
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# logits = self.fc8(h)
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# probs = self.softmax(logits)
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return h
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"""
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References
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----------
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[1] Tran, Du, et al. "Learning spatiotemporal features with 3d convolutional networks."
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Proceedings of the IEEE international conference on computer vision. 2015.
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"""
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