first commit

Author: dontLoveBugs

.gitignore

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+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+.idea/
+/.idea
+*.iml
+*.ppt
+*.pptx
+*.caffemodel
+result/
+
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+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+
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+*.jpg
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README.md

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+# Deformable_ConvNet_pytorch

main.py

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+# -*- coding: utf-8 -*-
+"""
+ @Time    : 2019/2/19 16:06
+ @Author  : Wang Xin
+ @Email   : wangxin_buaa@163.com
+"""

network/__init__.py

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+# -*- coding: utf-8 -*-
+"""
+ @Time    : 2019/2/19 16:06
+ @Author  : Wang Xin
+ @Email   : wangxin_buaa@163.com
+"""

network/deform_conv/__init__.py

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+# -*- coding: utf-8 -*-
+"""
+ @Time    : 2019/2/18 15:19
+ @Author  : Wang Xin
+ @Email   : wangxin_buaa@163.com
+"""

network/deform_conv/deform_conv.py

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+# -*- coding: utf-8 -*-
+"""
+ @Time    : 2019/2/18 15:23
+ @Author  : Wang Xin
+ @Email   : wangxin_buaa@163.com
+"""
+
+import numpy as np
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+
+"""
+    https://github.com/ChunhuanLin/deform_conv_pytorch/blob/master/deform_conv.py
+"""
+
+
+class DeformConv2D(nn.Module):
+    def __init__(self, inc, outc, kernel_size=3, padding=1, bias=None):
+        super(DeformConv2D, self).__init__()
+        self.kernel_size = kernel_size
+        self.padding = padding
+        self.zero_padding = nn.ZeroPad2d(padding)
+        self.conv_kernel = nn.Conv2d(inc, outc, kernel_size=kernel_size, stride=kernel_size, bias=bias)
+
+    def forward(self, x, offset):
+        dtype = offset.data.type()
+        ks = self.kernel_size
+        N = offset.size(1) // 2
+
+        # Change offset's order from [x1, x2, ..., y1, y2, ...] to [x1, y1, x2, y2, ...]
+        # Codes below are written to make sure same results of MXNet implementation.
+        # You can remove them, and it won't influence the module's performance.
+        offsets_index = Variable(torch.cat([torch.arange(0, 2 * N, 2), torch.arange(1, 2 * N + 1, 2)]),
+                                 requires_grad=False).type_as(x).long()
+        offsets_index = offsets_index.unsqueeze(dim=0).unsqueeze(dim=-1).unsqueeze(dim=-1).expand(*offset.size())
+        offset = torch.gather(offset, dim=1, index=offsets_index)
+        # ------------------------------------------------------------------------
+
+        if self.padding:
+            x = self.zero_padding(x)
+
+        # (b, 2N, h, w)
+        p = self._get_p(offset, dtype)
+
+        # (b, h, w, 2N)
+        p = p.contiguous().permute(0, 2, 3, 1)
+        q_lt = Variable(p.data, requires_grad=False).floor()
+        q_rb = q_lt + 1
+
+        q_lt = torch.cat([torch.clamp(q_lt[..., :N], 0, x.size(2) - 1), torch.clamp(q_lt[..., N:], 0, x.size(3) - 1)],
+                         dim=-1).long()
+        q_rb = torch.cat([torch.clamp(q_rb[..., :N], 0, x.size(2) - 1), torch.clamp(q_rb[..., N:], 0, x.size(3) - 1)],
+                         dim=-1).long()
+        q_lb = torch.cat([q_lt[..., :N], q_rb[..., N:]], -1)
+        q_rt = torch.cat([q_rb[..., :N], q_lt[..., N:]], -1)
+
+        # (b, h, w, N)
+        mask = torch.cat([p[..., :N].lt(self.padding) + p[..., :N].gt(x.size(2) - 1 - self.padding),
+                          p[..., N:].lt(self.padding) + p[..., N:].gt(x.size(3) - 1 - self.padding)], dim=-1).type_as(p)
+        mask = mask.detach()
+        floor_p = p - (p - torch.floor(p))
+        p = p * (1 - mask) + floor_p * mask
+        p = torch.cat([torch.clamp(p[..., :N], 0, x.size(2) - 1), torch.clamp(p[..., N:], 0, x.size(3) - 1)], dim=-1)
+
+        # bilinear kernel (b, h, w, N)
+        g_lt = (1 + (q_lt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_lt[..., N:].type_as(p) - p[..., N:]))
+        g_rb = (1 - (q_rb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_rb[..., N:].type_as(p) - p[..., N:]))
+        g_lb = (1 + (q_lb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_lb[..., N:].type_as(p) - p[..., N:]))
+        g_rt = (1 - (q_rt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_rt[..., N:].type_as(p) - p[..., N:]))
+
+        # (b, c, h, w, N)
+        x_q_lt = self._get_x_q(x, q_lt, N)
+        x_q_rb = self._get_x_q(x, q_rb, N)
+        x_q_lb = self._get_x_q(x, q_lb, N)
+        x_q_rt = self._get_x_q(x, q_rt, N)
+
+        # (b, c, h, w, N)
+        x_offset = g_lt.unsqueeze(dim=1) * x_q_lt + \
+                   g_rb.unsqueeze(dim=1) * x_q_rb + \
+                   g_lb.unsqueeze(dim=1) * x_q_lb + \
+                   g_rt.unsqueeze(dim=1) * x_q_rt
+
+        x_offset = self._reshape_x_offset(x_offset, ks)
+        out = self.conv_kernel(x_offset)
+
+        return out
+
+    def _get_p_n(self, N, dtype):
+        p_n_x, p_n_y = np.meshgrid(range(-(self.kernel_size - 1) // 2, (self.kernel_size - 1) // 2 + 1),
+                                   range(-(self.kernel_size - 1) // 2, (self.kernel_size - 1) // 2 + 1), indexing='ij')
+        # (2N, 1)
+        p_n = np.concatenate((p_n_x.flatten(), p_n_y.flatten()))
+        p_n = np.reshape(p_n, (1, 2 * N, 1, 1))
+        p_n = Variable(torch.from_numpy(p_n).type(dtype), requires_grad=False)
+
+        return p_n
+
+    @staticmethod
+    def _get_p_0(h, w, N, dtype):
+        p_0_x, p_0_y = np.meshgrid(range(1, h + 1), range(1, w + 1), indexing='ij')
+        p_0_x = p_0_x.flatten().reshape(1, 1, h, w).repeat(N, axis=1)
+        p_0_y = p_0_y.flatten().reshape(1, 1, h, w).repeat(N, axis=1)
+        p_0 = np.concatenate((p_0_x, p_0_y), axis=1)
+        p_0 = Variable(torch.from_numpy(p_0).type(dtype), requires_grad=False)
+
+        return p_0
+
+    def _get_p(self, offset, dtype):
+        N, h, w = offset.size(1) // 2, offset.size(2), offset.size(3)
+
+        # (1, 2N, 1, 1)
+        p_n = self._get_p_n(N, dtype)
+        # (1, 2N, h, w)
+        p_0 = self._get_p_0(h, w, N, dtype)
+        p = p_0 + p_n + offset
+        return p
+
+    def _get_x_q(self, x, q, N):
+        b, h, w, _ = q.size()
+        padded_w = x.size(3)
+        c = x.size(1)
+        # (b, c, h*w)
+        x = x.contiguous().view(b, c, -1)
+
+        # (b, h, w, N)
+        index = q[..., :N] * padded_w + q[..., N:]  # offset_x*w + offset_y
+        # (b, c, h*w*N)
+        index = index.contiguous().unsqueeze(dim=1).expand(-1, c, -1, -1, -1).contiguous().view(b, c, -1)
+
+        x_offset = x.gather(dim=-1, index=index).contiguous().view(b, c, h, w, N)
+
+        return x_offset
+
+    @staticmethod
+    def _reshape_x_offset(x_offset, ks):
+        b, c, h, w, N = x_offset.size()
+        x_offset = torch.cat([x_offset[..., s:s + ks].contiguous().view(b, c, h, w * ks) for s in range(0, N, ks)],
+                             dim=-1)
+        x_offset = x_offset.contiguous().view(b, c, h * ks, w * ks)
+
+        return x_offset
+
+
+if __name__ == '__main__':
+    x = torch.randn(4, 3, 5, 5)
+
+    p_conv = nn.Conv2d(3, 2 * 3 * 3, kernel_size=3, padding=1, stride=1)
+    d_conv2 = DeformConv2D(3, 64)
+
+    offset = p_conv(x)
+    y = d_conv2(x, offset)
+
+    print(y.size())
+    print('y = ', y)
+    print("offset:")
+    print(offset)