wehub-resource-sync
273 lines
8.1 KiB
Python
273 lines
8.1 KiB
Python
# 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 unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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import paddle
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import paddle.base.dygraph as dg
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import paddle.nn.functional as F
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from paddle import base, nn
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class Conv1DTransposeTestCase(unittest.TestCase):
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def __init__(
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self,
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methodName='runTest',
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batch_size=4,
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spartial_shape=16,
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in_channels=6,
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out_channels=8,
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filter_size=3,
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output_size=None,
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padding=0,
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output_padding=0,
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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="NCL",
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dtype="float32",
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):
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super().__init__(methodName)
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self.batch_size = batch_size
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.spartial_shape = spartial_shape
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self.filter_size = filter_size
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self.output_size = output_size
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self.padding = padding
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self.output_padding = output_padding
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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 = False if self.data_format == "NCL" else True
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input_shape = (
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(self.batch_size, self.in_channels, self.spartial_shape)
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if not self.channel_last
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else (
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self.batch_size,
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self.spartial_shape,
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self.in_channels,
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)
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)
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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]
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else:
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filter_size = self.filter_size
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self.weight_shape = weight_shape = (
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self.in_channels,
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self.out_channels // self.groups,
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*filter_size,
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)
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self.weight = np.random.uniform(-1, 1, size=weight_shape).astype(
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self.dtype
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)
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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.out_channels,)
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).astype(self.dtype)
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else:
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self.bias = None
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def functional(self, place):
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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input_shape = (
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(-1, self.in_channels, -1)
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if not self.channel_last
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else (-1, -1, self.in_channels)
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)
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x_var = paddle.static.data("input", input_shape, dtype=self.dtype)
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w_var = paddle.static.data(
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"weight", self.weight_shape, dtype=self.dtype
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)
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if not self.no_bias:
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b_var = paddle.static.data(
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"bias", (self.out_channels,), dtype=self.dtype
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)
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else:
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b_var = None
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y_var = F.conv1d_transpose(
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x_var,
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w_var,
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None if self.no_bias else b_var,
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output_size=self.output_size,
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padding=self.padding,
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output_padding=self.output_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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)
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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 = base.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 = paddle.to_tensor(self.input)
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conv = nn.Conv1DTranspose(
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self.in_channels,
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self.out_channels,
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self.filter_size,
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padding=self.padding,
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output_padding=self.output_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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)
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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, output_size=self.output_size)
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y_np = y_var.numpy()
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return y_np
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def _test_pir_equivalence(self, place):
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with paddle.pir_utils.IrGuard():
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result1 = self.functional(place)
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with dg.guard(place):
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result2 = self.paddle_nn_layer()
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np.testing.assert_array_almost_equal(result1, result2)
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def runTest(self):
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place = base.CPUPlace()
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self._test_pir_equivalence(place)
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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self._test_pir_equivalence(place)
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class Conv1DTransposeErrorTestCase(Conv1DTransposeTestCase):
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def runTest(self):
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place = base.CPUPlace()
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with (
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dg.guard(place),
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self.assertRaises(ValueError),
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):
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self.paddle_nn_layer()
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def add_cases(suite):
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suite.addTest(Conv1DTransposeTestCase(methodName='runTest'))
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suite.addTest(
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Conv1DTransposeTestCase(
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methodName='runTest', stride=[2], no_bias=True, dilation=2
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)
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)
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suite.addTest(
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Conv1DTransposeTestCase(
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methodName='runTest',
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filter_size=(3),
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output_size=[36],
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stride=[2],
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dilation=2,
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)
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)
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suite.addTest(
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Conv1DTransposeTestCase(methodName='runTest', stride=2, dilation=(2))
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)
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suite.addTest(
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Conv1DTransposeTestCase(methodName='runTest', padding="valid")
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)
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suite.addTest(
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Conv1DTransposeTestCase(methodName='runTest', padding='valid')
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)
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suite.addTest(
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Conv1DTransposeTestCase(methodName='runTest', filter_size=1, padding=3)
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)
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suite.addTest(Conv1DTransposeTestCase(methodName='runTest', padding=[2]))
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suite.addTest(
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Conv1DTransposeTestCase(methodName='runTest', data_format="NLC")
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)
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suite.addTest(
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Conv1DTransposeTestCase(methodName='runTest', groups=2, padding="valid")
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)
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suite.addTest(
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Conv1DTransposeTestCase(
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methodName='runTest',
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out_channels=6,
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in_channels=3,
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groups=3,
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padding="valid",
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)
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)
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suite.addTest(
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Conv1DTransposeTestCase(
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methodName='runTest',
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data_format="NLC",
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spartial_shape=16,
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output_size=18,
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)
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)
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suite.addTest(
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Conv1DTransposeTestCase(
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methodName='runTest', data_format="NLC", stride=3, output_padding=2
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)
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)
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suite.addTest(Conv1DTransposeTestCase(methodName='runTest', padding=[1, 2]))
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def add_error_cases(suite):
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suite.addTest(
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Conv1DTransposeErrorTestCase(
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methodName='runTest', data_format="not_valid"
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)
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)
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suite.addTest(
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Conv1DTransposeErrorTestCase(
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methodName='runTest', in_channels=5, groups=2
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)
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)
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suite.addTest(
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Conv1DTransposeErrorTestCase(
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methodName='runTest', stride=2, output_padding=3
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)
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)
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suite.addTest(
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Conv1DTransposeErrorTestCase(
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methodName='runTest', output_size="not_valid"
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)
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)
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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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