wehub-resource-sync
487 lines
15 KiB
Python
487 lines
15 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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from paddle.base import core
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class Conv1DTestCase(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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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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dtype="float32",
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data_format="NCL",
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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.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.data_format = data_format
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self.channel_last = self.data_format == "NLC"
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self.padding = padding
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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.dtype = dtype
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def setUp(self):
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input_shape = (
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(self.batch_size, self.num_channels, *self.spartial_shape)
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if not self.channel_last
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else (self.batch_size, *self.spartial_shape, self.num_channels)
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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.num_filters,
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self.num_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.num_filters,)
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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.num_channels, -1)
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if not self.channel_last
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else (-1, -1, self.num_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.num_filters,), 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(
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x_var,
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w_var,
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b_var,
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padding=self.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.Conv1D(
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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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)
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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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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_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_equivalence(place)
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class Conv1DErrorTestCase(Conv1DTestCase):
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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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class Conv1DTypeErrorTestCase(Conv1DTestCase):
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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(TypeError),
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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(Conv1DTestCase(methodName='runTest'))
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suite.addTest(Conv1DTestCase(methodName='runTest', stride=[1], dilation=2))
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suite.addTest(Conv1DTestCase(methodName='runTest', stride=2, dilation=(1)))
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suite.addTest(
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Conv1DTestCase(methodName='runTest', padding="same", no_bias=True)
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)
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suite.addTest(
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Conv1DTestCase(methodName='runTest', filter_size=3, padding='valid')
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)
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suite.addTest(
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Conv1DTestCase(methodName='runTest', num_filters=512, padding='valid')
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)
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suite.addTest(
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Conv1DTestCase(methodName='runTest', num_filters=512, padding=[1, 2])
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)
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suite.addTest(
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Conv1DTestCase(methodName='runTest', padding=2, data_format='NLC')
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)
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suite.addTest(Conv1DTestCase(methodName='runTest', padding=[1]))
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suite.addTest(Conv1DTestCase(methodName='runTest', padding=[1, 2]))
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suite.addTest(
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Conv1DTestCase(methodName='runTest', padding=[1, 2], data_format='NLC')
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)
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suite.addTest(Conv1DTestCase(methodName='runTest', padding=2))
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suite.addTest(Conv1DTestCase(methodName='runTest'))
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suite.addTest(
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Conv1DTestCase(methodName='runTest', groups=2, padding="valid")
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)
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suite.addTest(
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Conv1DTestCase(
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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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data_format='NLC',
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)
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)
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def add_error_cases(suite):
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suite.addTest(
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Conv1DTypeErrorTestCase(
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methodName='runTest', padding_mode="reflect", padding="valid"
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)
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)
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suite.addTest(
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Conv1DErrorTestCase(methodName='runTest', data_format="VALID")
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)
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suite.addTest(
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Conv1DErrorTestCase(methodName='runTest', padding_mode="VALID")
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)
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suite.addTest(
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Conv1DErrorTestCase(methodName='runTest', num_channels=5, groups=2)
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)
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suite.addTest(
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Conv1DErrorTestCase(
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methodName='runTest', num_filters=8, num_channels=15, groups=3
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)
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)
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suite.addTest(
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Conv1DErrorTestCase(methodName='runTest', padding=[1, 2, 3, 4, 5])
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)
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suite.addTest(
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Conv1DErrorTestCase(
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methodName='runTest', padding=[1, 2, 3, 4, 5], data_format='NLC'
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)
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)
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suite.addTest(
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Conv1DErrorTestCase(
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methodName='runTest', num_filters=512, padding=[1, 2, 3, 4, 5]
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)
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)
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suite.addTest(Conv1DErrorTestCase(methodName='runTest', dilation=-10))
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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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def conv1d_forward_naive(
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input,
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filter,
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group,
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conv_param,
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padding_algorithm="EXPLICIT",
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data_format="NCL",
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):
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if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
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raise ValueError(
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f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
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"It can only be 'SAME' or 'VALID'."
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)
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if data_format not in ["NCL", "NLC"]:
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raise ValueError(
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f"Unknown Attr(data_format): '{data_format}' ."
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"It can only be 'NCL' or 'NLC'."
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)
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channel_last = data_format == "NLC"
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if channel_last:
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input = np.transpose(input, [0, 2, 1])
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in_n, in_c, in_l = input.shape
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f_n, f_c, f_l = filter.shape
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out_n = in_n
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out_c = f_n
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assert f_c * group == in_c
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assert np.mod(out_c, group) == 0
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sub_out_c = out_c // group
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sub_f_n = f_n // group
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stride, pad, dilation = (
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conv_param["stride"],
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conv_param["pad"],
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conv_param["dilation"],
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)
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# update pad and dilation
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def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
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padding = []
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for input_size, filter_size, stride_size in zip(
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input_shape, pool_size, pool_stride
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):
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out_size = int((input_size + stride_size - 1) / stride_size)
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pad_sum = np.max(
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((out_size - 1) * stride_size + filter_size - input_size, 0)
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)
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pad_0 = int(pad_sum / 2)
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pad_1 = int(pad_sum - pad_0)
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padding.append(pad_0)
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padding.append(pad_1)
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return padding
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ksize = [filter.shape[2]] # 1D kernel size
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if padding_algorithm == "VALID":
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pad = [0, 0]
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elif padding_algorithm == "SAME":
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dilation = [1]
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input_data_shape = [input.shape[2]] # 1D input shape
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pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
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pad_l_0, pad_l_1 = pad[0], pad[0]
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if len(pad) == 2:
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pad_l_0, pad_l_1 = pad[0], pad[1]
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out_l = (
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1
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+ (in_l + pad_l_0 + pad_l_1 - (dilation[0] * (f_l - 1) + 1))
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// stride[0]
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)
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out = np.zeros((out_n, out_c, out_l))
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d_block_l = dilation[0] * (f_l - 1) + 1
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input_pad = np.pad(
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input,
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((0, 0), (0, 0), (pad_l_0, pad_l_1)),
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mode="constant",
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constant_values=0,
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)
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filter_dilation = np.zeros((f_n, f_c, d_block_l))
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filter_dilation[:, :, 0 : d_block_l : dilation[0]] = filter
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for i in range(out_l):
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for g in range(group):
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input_pad_masked = input_pad[
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:,
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g * f_c : (g + 1) * f_c,
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i * stride[0] : i * stride[0] + d_block_l,
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]
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f_sub = filter_dilation[g * sub_f_n : (g + 1) * sub_f_n, :, :]
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# sub_f_n == sub_out_c
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for k in range(sub_out_c):
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# Multiplication of Corresponding Elements, then sum all
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out[:, g * sub_out_c + k, i] = np.sum(
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input_pad_masked * f_sub[k, :, :], axis=(1, 2)
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)
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if channel_last:
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out = np.transpose(out, [0, 2, 1])
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return out, in_n, out_l, out_c
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def get_places():
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places = []
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if core.is_compiled_with_xpu():
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places.append(paddle.device.XPUPlace(0))
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elif core.is_compiled_with_cuda():
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places.append(paddle.CUDAPlace(0))
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places.append(paddle.CPUPlace())
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return places
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class TestConv1dAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(2025)
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self.places = get_places()
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self.shape_x = [2, 3, 16] # NCL
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self.shape_w = [6, 3, 3] # Co, Cin, kL
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self.dtype = "float32"
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self.init_data()
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def init_data(self):
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self.np_x = np.random.rand(*self.shape_x).astype(self.dtype)
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self.np_w = np.random.rand(*self.shape_w).astype(self.dtype)
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conv_param = {"stride": [1], "pad": [0], "dilation": [1]}
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self.np_ref_out, _, _, _ = conv1d_forward_naive(
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self.np_x, self.np_w, 1, conv_param
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)
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def test_dygraph_Compatibility(self):
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for place in self.places:
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paddle.device.set_device(place)
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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w = paddle.to_tensor(self.np_w)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.nn.functional.conv1d(x, w)
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paddle_dygraph_out.append(out1)
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# Keywords args (kwargs) for paddle
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out2 = paddle.nn.functional.conv1d(x=x, weight=w)
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paddle_dygraph_out.append(out2)
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# Keywords args for alias compatibility - testing x->input
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out3 = paddle.nn.functional.conv1d(input=x, weight=w)
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paddle_dygraph_out.append(out3)
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# Combined args and kwargs
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out4 = paddle.nn.functional.conv1d(x, weight=w)
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paddle_dygraph_out.append(out4)
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if isinstance(place, core.XPUPlace):
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rtol = 5e-3
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atol = 5e-3
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else:
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rtol = 1e-5
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atol = 0
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# Check all dygraph results against reference
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(
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self.np_ref_out, out.numpy(), rtol=rtol, atol=atol
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)
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paddle.enable_static()
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def test_static_Compatibility(self):
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paddle.enable_static()
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fetch_list = []
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x = paddle.static.data(
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name="x", shape=self.shape_x, dtype=self.dtype
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)
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w = paddle.static.data(
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name="w", shape=self.shape_w, dtype=self.dtype
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)
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# Position args (args)
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out1 = paddle.nn.functional.conv1d(x, w)
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fetch_list.append(out1)
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# Keywords args (kwargs) for paddle
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out2 = paddle.nn.functional.conv1d(x=x, weight=w)
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fetch_list.append(out2)
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# Keywords args for alias compatibility - testing x->input
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out3 = paddle.nn.functional.conv1d(input=x, weight=w)
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fetch_list.append(out3)
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# Combined args and kwargs
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out4 = paddle.nn.functional.conv1d(x, weight=w)
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fetch_list.append(out4)
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for place in self.places:
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if isinstance(place, core.XPUPlace):
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rtol = 5e-3
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atol = 5e-3
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else:
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rtol = 1e-5
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atol = 0
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exe = base.Executor(place)
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fetches = exe.run(
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main,
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feed={"x": self.np_x, "w": self.np_w},
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fetch_list=fetch_list,
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)
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for out in fetches:
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np.testing.assert_allclose(
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out, self.np_ref_out, rtol=rtol, atol=atol
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)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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