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@ -5,6 +5,7 @@ from op_test import OpTest
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class TestConv2dOp(OpTest):
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def setUp(self):
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self.init_groups()
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self.op_type = "conv2d"
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batch_size = 2
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input_channels = 3
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@ -15,7 +16,6 @@ class TestConv2dOp(OpTest):
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filter_width = 3
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stride = 1
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padding = 0
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groups = 3
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output_height = (input_height - filter_height + 2 * padding
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) / stride + 1
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output_width = (input_width - filter_width + 2 * padding) / stride + 1
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@ -23,18 +23,22 @@ class TestConv2dOp(OpTest):
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input_width)).astype("float32")
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filter = np.random.random(
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(output_channels, input_channels / groups, filter_height,
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(output_channels, input_channels / self.groups, filter_height,
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filter_width)).astype("float32")
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output = np.ndarray(
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(batch_size, output_channels, output_height, output_width))
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self.inputs = {'Input': input, 'Filter': filter}
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self.attrs = {'strides': [1, 1], 'paddings': [0, 0], 'groups': groups}
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self.attrs = {
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'strides': [1, 1],
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'paddings': [0, 0],
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'groups': self.groups
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}
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output_group_channels = output_channels / groups
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input_group_channels = input_channels / groups
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output_group_channels = output_channels / self.groups
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input_group_channels = input_channels / self.groups
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for batchid in xrange(batch_size):
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for group in xrange(groups):
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for group in xrange(self.groups):
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for outchannelid in range(group * output_group_channels,
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(group + 1) * output_group_channels):
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for rowid in xrange(output_height):
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@ -71,6 +75,14 @@ class TestConv2dOp(OpTest):
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def test_check_grad(self):
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self.check_grad(set(['Input', 'Filter']), 'Output')
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def init_groups(self):
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self.groups = 1
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class TestWithGroup(TestConv2dOp):
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def init_groups(self):
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self.groups = 3
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if __name__ == '__main__':
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unittest.main()
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