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282 lines
9.9 KiB
282 lines
9.9 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from paddle import fluid, nn
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import paddle.fluid.dygraph as dg
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import paddle.nn.functional as F
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import paddle.fluid.initializer as I
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import unittest
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def _reverse_repeat_list(t, n):
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return list(x for x in reversed(t) for _ in range(n))
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class Conv2DTestCase(unittest.TestCase):
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def __init__(self,
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methodName='runTest',
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batch_size=4,
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spartial_shape=(16, 16),
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num_channels=6,
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num_filters=8,
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filter_size=3,
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padding=0,
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padding_mode='zeros',
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stride=1,
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dilation=1,
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groups=1,
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no_bias=False,
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data_format="NCHW",
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dtype="float32"):
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super(Conv2DTestCase, self).__init__(methodName)
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.num_filters = num_filters
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self.spartial_shape = spartial_shape
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self.filter_size = filter_size
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self.padding = padding
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if padding_mode in {'reflect', 'replicate', 'circular'}:
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_paired_padding = fluid.layers.utils.convert_to_list(padding, 2,
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'padding')
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self._reversed_padding_repeated_twice = _reverse_repeat_list(
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_paired_padding, 2)
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self.padding_mode = padding_mode
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self.stride = stride
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self.dilation = dilation
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self.groups = groups
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self.no_bias = no_bias
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self.data_format = data_format
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self.dtype = dtype
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def setUp(self):
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self.channel_last = self.data_format == "NHWC"
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if self.channel_last:
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input_shape = (self.batch_size, ) + self.spartial_shape + (
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self.num_channels, )
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else:
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input_shape = (self.batch_size, self.num_channels
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) + self.spartial_shape
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self.input = np.random.randn(*input_shape).astype(self.dtype)
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if isinstance(self.filter_size, int):
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filter_size = [self.filter_size] * 2
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else:
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filter_size = self.filter_size
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self.weight_shape = weight_shape = (self.num_filters, self.num_channels
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// self.groups) + tuple(filter_size)
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self.weight = np.random.uniform(
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-1, 1, size=weight_shape).astype(self.dtype)
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if not self.no_bias:
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self.bias = np.random.uniform(
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-1, 1, size=(self.num_filters, )).astype(self.dtype)
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else:
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self.bias = None
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def fluid_layer(self, place):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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input_shape = (-1, -1, -1,self.num_channels) \
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if self.channel_last else (-1, self.num_channels, -1, -1)
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x_var = fluid.data("input", input_shape, dtype=self.dtype)
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weight_attr = I.NumpyArrayInitializer(self.weight)
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if self.bias is None:
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bias_attr = False
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else:
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bias_attr = I.NumpyArrayInitializer(self.bias)
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if self.padding_mode != 'zeros':
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x_var = F.pad(x_var,
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self._reversed_padding_repeated_twice,
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mode=self.padding_mode,
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data_format=self.data_format)
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padding = 0
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else:
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padding = self.padding
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y_var = fluid.layers.conv2d(
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x_var,
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self.num_filters,
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self.filter_size,
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padding=padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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param_attr=weight_attr,
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bias_attr=bias_attr,
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data_format=self.data_format)
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feed_dict = {"input": self.input}
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exe = fluid.Executor(place)
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exe.run(start)
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y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
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return y_np
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def functional(self, place):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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input_shape = (-1, -1, -1,self.num_channels) \
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if self.channel_last else (-1, self.num_channels, -1, -1)
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x_var = fluid.data("input", input_shape, dtype=self.dtype)
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w_var = fluid.data(
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"weight", self.weight_shape, dtype=self.dtype)
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b_var = fluid.data(
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"bias", (self.num_filters, ), dtype=self.dtype)
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if self.padding_mode != 'zeros':
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x_var = F.pad(x_var,
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self._reversed_padding_repeated_twice,
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mode=self.padding_mode,
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data_format=self.data_format)
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padding = 0
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else:
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padding = self.padding
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y_var = F.conv2d(
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x_var,
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w_var,
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b_var if not self.no_bias else None,
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padding=padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format)
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feed_dict = {"input": self.input, "weight": self.weight}
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if self.bias is not None:
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feed_dict["bias"] = self.bias
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exe = fluid.Executor(place)
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exe.run(start)
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y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
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return y_np
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def paddle_nn_layer(self):
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x_var = dg.to_variable(self.input)
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conv = nn.Conv2d(
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self.num_channels,
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self.num_filters,
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self.filter_size,
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padding=self.padding,
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padding_mode=self.padding_mode,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format)
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conv.weight.set_value(self.weight)
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if not self.no_bias:
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conv.bias.set_value(self.bias)
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y_var = conv(x_var)
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y_np = y_var.numpy()
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return y_np
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def _test_equivalence(self, place):
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place = fluid.CPUPlace()
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result1 = self.fluid_layer(place)
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result2 = self.functional(place)
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with dg.guard(place):
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result3 = self.paddle_nn_layer()
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np.testing.assert_array_almost_equal(result1, result2)
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np.testing.assert_array_almost_equal(result2, result3)
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def runTest(self):
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place = fluid.CPUPlace()
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self._test_equivalence(place)
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if fluid.core.is_compiled_with_cuda():
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place = fluid.CUDAPlace(0)
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self._test_equivalence(place)
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class Conv2DErrorTestCase(Conv2DTestCase):
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def runTest(self):
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place = fluid.CPUPlace()
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with dg.guard(place):
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with self.assertRaises(ValueError):
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self.paddle_nn_layer()
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def add_cases(suite):
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suite.addTest(Conv2DTestCase(methodName='runTest'))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest', stride=[1, 2], dilation=2))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest', stride=2, dilation=(2, 1)))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest', padding="same", no_bias=True))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest', filter_size=(3, 3), padding='valid'))
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suite.addTest(Conv2DTestCase(methodName='runTest', padding=(2, 3)))
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suite.addTest(Conv2DTestCase(methodName='runTest', padding=[1, 2, 2, 1]))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest', padding=[[0, 0], [0, 0], [1, 2], [2, 1]]))
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suite.addTest(Conv2DTestCase(methodName='runTest', data_format="NHWC"))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest',
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data_format="NHWC",
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padding=[[0, 0], [1, 1], [2, 2], [0, 0]]))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest', groups=2, padding="valid"))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest',
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num_filters=6,
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num_channels=3,
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groups=3,
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padding="valid"))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest',
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filter_size=(3, 3),
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padding=1,
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padding_mode='reflect'))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest',
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filter_size=(3, 3),
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padding=1,
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padding_mode='replicate'))
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suite.addTest(
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Conv2DTestCase(
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methodName='runTest',
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filter_size=(3, 3),
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padding=1,
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padding_mode='circular'))
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def add_error_cases(suite):
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suite.addTest(
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Conv2DErrorTestCase(
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methodName='runTest', num_channels=5, groups=2))
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def load_tests(loader, standard_tests, pattern):
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suite = unittest.TestSuite()
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add_cases(suite)
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add_error_cases(suite)
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return suite
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if __name__ == '__main__':
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unittest.main()
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