Create my_pooling.py

my_pooling.py

@@ -0,0 +1,83 @@
+import torch
+import numpy as np
+import random
+from torch.autograd import Variable
+from torch.nn.modules.module import Module
+from torch.nn.modules.utils import _single, _pair, _triple
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+
+
+
+class my_MaxPool2d(Module):
+
+
+    def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
+                 return_indices=False, ceil_mode=False):
+        super(my_MaxPool2d, self).__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride or kernel_size
+        self.padding = padding
+        self.dilation = dilation
+        self.return_indices = return_indices
+        self.ceil_mode = ceil_mode
+
+    def forward(self, input):
+        input = input.transpose(3,1)
+
+
+        input = F.max_pool2d(input, self.kernel_size, self.stride,
+                            self.padding, self.dilation, self.ceil_mode,
+                            self.return_indices)
+        input = input.transpose(3,1).contiguous()
+
+        return input
+
+    def __repr__(self):
+        kh, kw = _pair(self.kernel_size)
+        dh, dw = _pair(self.stride)
+        padh, padw = _pair(self.padding)
+        dilh, dilw = _pair(self.dilation)
+        padding_str = ', padding=(' + str(padh) + ', ' + str(padw) + ')' \
+            if padh != 0 or padw != 0 else ''
+        dilation_str = (', dilation=(' + str(dilh) + ', ' + str(dilw) + ')'
+                        if dilh != 0 and dilw != 0 else '')
+        ceil_str = ', ceil_mode=' + str(self.ceil_mode)
+        return self.__class__.__name__ + '(' \
+            + 'kernel_size=(' + str(kh) + ', ' + str(kw) + ')' \
+            + ', stride=(' + str(dh) + ', ' + str(dw) + ')' \
+            + padding_str + dilation_str + ceil_str + ')'
+
+
+class my_AvgPool2d(Module):
+    def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False,
+                 count_include_pad=True):
+        super(my_AvgPool2d, self).__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride or kernel_size
+        self.padding = padding
+        self.ceil_mode = ceil_mode
+        self.count_include_pad = count_include_pad
+
+    def forward(self, input):
+        input = input.transpose(3,1)
+        input = F.avg_pool2d(input, self.kernel_size, self.stride,
+                            self.padding, self.ceil_mode, self.count_include_pad)
+        input = input.transpose(3,1).contiguous()
+
+        return input
+
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(' \
+            + 'kernel_size=' + str(self.kernel_size) \
+            + ', stride=' + str(self.stride) \
+            + ', padding=' + str(self.padding) \
+            + ', ceil_mode=' + str(self.ceil_mode) \
+            + ', count_include_pad=' + str(self.count_include_pad) + ')'
+
+
+m = my_MaxPool2d((1, 32), stride=(1, 32))
+input = Variable(torch.randn(3, 2208, 7, 7))
+output = m(input)
+print(output.size())