# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from paddle import fluid, nn import paddle.fluid.dygraph as dg import paddle.nn.functional as F import paddle.fluid.initializer as I import unittest class Conv2DTransposeTestCase(unittest.TestCase): def __init__(self, methodName='runTest', batch_size=4, spartial_shape=(16, 16), num_channels=6, num_filters=8, filter_size=3, output_size=None, output_padding=0, padding=0, stride=1, dilation=1, groups=1, no_bias=False, data_format="NCHW", dtype="float32"): super(Conv2DTransposeTestCase, self).__init__(methodName) self.batch_size = batch_size self.num_channels = num_channels self.num_filters = num_filters self.spartial_shape = spartial_shape self.filter_size = filter_size self.output_size = output_size self.output_padding = output_padding self.padding = padding self.stride = stride self.dilation = dilation self.groups = groups self.no_bias = no_bias self.data_format = data_format self.dtype = dtype def setUp(self): self.channel_last = self.data_format == "NHWC" if self.channel_last: input_shape = (self.batch_size, ) + self.spartial_shape + ( self.num_channels, ) else: input_shape = (self.batch_size, self.num_channels ) + self.spartial_shape self.input = np.random.randn(*input_shape).astype(self.dtype) if isinstance(self.filter_size, int): filter_size = [self.filter_size] * 2 else: filter_size = self.filter_size self.weight_shape = weight_shape = (self.num_channels, self.num_filters // self.groups) + tuple(filter_size) self.weight = np.random.uniform( -1, 1, size=weight_shape).astype(self.dtype) if not self.no_bias: self.bias = np.random.uniform( -1, 1, size=(self.num_filters, )).astype(self.dtype) else: self.bias = None def fluid_layer(self, place): main = fluid.Program() start = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, start): input_shape = (-1, -1, -1,self.num_channels) \ if self.channel_last else (-1, self.num_channels, -1, -1) x_var = fluid.data("input", input_shape, dtype=self.dtype) weight_attr = I.NumpyArrayInitializer(self.weight) if self.bias is None: bias_attr = False else: bias_attr = I.NumpyArrayInitializer(self.bias) y_var = fluid.layers.conv2d_transpose( x_var, self.num_filters, filter_size=self.filter_size, output_size=self.output_size, padding=self.padding, stride=self.stride, dilation=self.dilation, groups=self.groups, param_attr=weight_attr, bias_attr=bias_attr, data_format=self.data_format) feed_dict = {"input": self.input} exe = fluid.Executor(place) exe.run(start) y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var]) return y_np def functional(self, place): main = fluid.Program() start = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, start): input_shape = (-1, -1, -1,self.num_channels) \ if self.channel_last else (-1, self.num_channels, -1, -1) x_var = fluid.data("input", input_shape, dtype=self.dtype) w_var = fluid.data( "weight", self.weight_shape, dtype=self.dtype) b_var = fluid.data( "bias", (self.num_filters, ), dtype=self.dtype) if self.output_padding != 0: output_size = None else: output_size = self.output_size y_var = F.conv2d_transpose( x_var, w_var, None if self.no_bias else b_var, output_size=output_size, padding=self.padding, output_padding=self.output_padding, stride=self.stride, dilation=self.dilation, groups=self.groups, data_format=self.data_format) feed_dict = {"input": self.input, "weight": self.weight} if self.bias is not None: feed_dict["bias"] = self.bias exe = fluid.Executor(place) exe.run(start) y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var]) return y_np def paddle_nn_layer(self): x_var = dg.to_variable(self.input) if self.output_padding != 0: output_size = None else: output_size = self.output_size conv = nn.Conv2DTranspose( self.num_channels, self.num_filters, self.filter_size, padding=self.padding, output_padding=self.output_padding, stride=self.stride, dilation=self.dilation, groups=self.groups, data_format=self.data_format) conv.weight.set_value(self.weight) if not self.no_bias: conv.bias.set_value(self.bias) y_var = conv(x_var, output_size) y_np = y_var.numpy() return y_np def _test_equivalence(self, place): place = fluid.CPUPlace() result1 = self.fluid_layer(place) result2 = self.functional(place) with dg.guard(place): result3 = self.paddle_nn_layer() np.testing.assert_array_almost_equal(result1, result2) np.testing.assert_array_almost_equal(result2, result3) def runTest(self): place = fluid.CPUPlace() self._test_equivalence(place) if fluid.core.is_compiled_with_cuda(): place = fluid.CUDAPlace(0) self._test_equivalence(place) class Conv2DTransposeErrorTestCase(Conv2DTransposeTestCase): def runTest(self): place = fluid.CPUPlace() with dg.guard(place): with self.assertRaises(ValueError): self.paddle_nn_layer() def add_cases(suite): suite.addTest(Conv2DTransposeTestCase(methodName='runTest')) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', stride=[1, 2], no_bias=True, dilation=2)) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', filter_size=(3, 3), output_size=[20, 36], stride=[1, 2], dilation=2)) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', stride=2, dilation=(2, 1))) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', padding="valid")) suite.addTest(Conv2DTransposeTestCase(methodName='runTest', padding="same")) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', filter_size=1, padding=(2, 3))) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', padding=[1, 2, 2, 1])) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', padding=[[0, 0], [0, 0], [1, 2], [2, 1]])) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', data_format="NHWC")) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', data_format="NHWC", padding=[[0, 0], [1, 1], [2, 2], [0, 0]])) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', groups=2, padding="valid")) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', num_filters=6, num_channels=3, groups=3, padding="valid")) suite.addTest( Conv2DTransposeTestCase( methodName='runTest', num_filters=6, num_channels=3, spartial_shape=(7, 7), filter_size=[5, 5], groups=1, padding=2, stride=2, output_size=[14, 14], output_padding=[1, 1], )) def add_error_cases(suite): suite.addTest( Conv2DTransposeErrorTestCase( methodName='runTest', num_channels=5, groups=2)) suite.addTest( Conv2DTransposeErrorTestCase( methodName='runTest', output_size="not_valid")) def load_tests(loader, standard_tests, pattern): suite = unittest.TestSuite() add_cases(suite) add_error_cases(suite) return suite if __name__ == '__main__': unittest.main()