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from __future__ import absolute_import, division, print_function |
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import torch |
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import torch.nn as nn |
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class PoseCNN(nn.Module): |
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def __init__(self, num_input_frames): |
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super(PoseCNN, self).__init__() |
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self.num_input_frames = num_input_frames |
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self.convs = {} |
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self.convs[0] = nn.Conv2d(3 * num_input_frames, 16, 7, 2, 3) |
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self.convs[1] = nn.Conv2d(16, 32, 5, 2, 2) |
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self.convs[2] = nn.Conv2d(32, 64, 3, 2, 1) |
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self.convs[3] = nn.Conv2d(64, 128, 3, 2, 1) |
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self.convs[4] = nn.Conv2d(128, 256, 3, 2, 1) |
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self.convs[5] = nn.Conv2d(256, 256, 3, 2, 1) |
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self.convs[6] = nn.Conv2d(256, 256, 3, 2, 1) |
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self.pose_conv = nn.Conv2d(256, 6 * (num_input_frames - 1), 1) |
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self.num_convs = len(self.convs) |
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self.relu = nn.ReLU(True) |
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self.net = nn.ModuleList(list(self.convs.values())) |
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def forward(self, out): |
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for i in range(self.num_convs): |
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out = self.convs[i](out) |
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out = self.relu(out) |
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out = self.pose_conv(out) |
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out = out.mean(3).mean(2) |
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out = 0.01 * out.view(-1, self.num_input_frames - 1, 1, 6) |
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axisangle = out[..., :3] |
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translation = out[..., 3:] |
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return axisangle, translation |
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