tensorflow conv2-same 理解

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数字理解

输出结果的shape计算：
‘SAME’ 类型的padding,其输出的height和width计算如下：
out_height = ceil(float(in_height) / float(strides[1])) ceil：向上取整
out_width = ceil(float(in_width) / float(strides[2]))

‘VALID’类型的padding, 其输出的height和width计算如下：
out_height = ceil(float(in_height – filter_height + 1) / float(strides[1]))
out_width = ceil(float(in_width – filter_width + 1) / float(strides[2]))

图片理解

具体计算理解

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x =
0 1 2 3
1 2 3 4
2 3 4 5
3 4 5 6

w = 
0 1 2
1 2 3
2 3 4
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def testConv2DSameEven(self):
   n, n2 = 4, 2

   # Input image.
   x = create_test_input(1, n, n, 1)

   # Convolution kernel.
   w = create_test_input(1, 3, 3, 1)
   w = array_ops.reshape(w, [3, 3, 1, 1])

   variable_scope.get_variable('Conv/weights', initializer=w)
   variable_scope.get_variable('Conv/biases', initializer=array_ops.zeros([1]))
   variable_scope.get_variable_scope().reuse_variables()

   y1 = layers.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
   y1_expected = math_ops.to_float([[14, 28, 43, 26], [28, 48, 66, 37],
                                    [43, 66, 84, 46], [26, 37, 46, 22]])
   y1_expected = array_ops.reshape(y1_expected, [1, n, n, 1])

   y2 = resnet_utils.subsample(y1, 2)
   y2_expected = math_ops.to_float([[14, 43], [43, 84]])
   y2_expected = array_ops.reshape(y2_expected, [1, n2, n2, 1])

   y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
   y3_expected = y2_expected

   y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
   y4_expected = math_ops.to_float([[48, 37], [37, 22]])
   y4_expected = array_ops.reshape(y4_expected, [1, n2, n2, 1])

   with self.test_session() as sess:
     sess.run(variables.global_variables_initializer())
     self.assertAllClose(y1.eval(), y1_expected.eval())
     self.assertAllClose(y2.eval(), y2_expected.eval())
     self.assertAllClose(y3.eval(), y3_expected.eval())
     self.assertAllClose(y4.eval(), y4_expected.eval())

总结

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1. net = conv2d_same(inputs, num_outputs, 3, stride=stride)
2. net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME')
   net = subsample(net, factor=stride)
3. net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME')
1和2是等价的，当inputs的高或宽是偶数时3与1,2不一样。