wehub-resource-sync
428 lines
14 KiB
Python
428 lines
14 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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from test_conv3d_op import conv3d_forward_naive
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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 Conv3DTestCase(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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spatial_shape=(8, 8, 8),
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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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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="NCDHW",
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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.num_channels = num_channels
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self.num_filters = num_filters
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self.spatial_shape = spatial_shape
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self.filter_size = filter_size
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self.padding = 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 = self.data_format == "NDHWC"
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if self.channel_last:
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input_shape = (
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self.batch_size,
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*self.spatial_shape,
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self.num_channels,
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)
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else:
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input_shape = (
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self.batch_size,
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self.num_channels,
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*self.spatial_shape,
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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] * 3
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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 base_layer(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, -1, -1, -1, self.num_channels)
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if self.channel_last
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else (-1, self.num_channels, -1, -1, -1)
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)
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x_var = paddle.static.data("input", input_shape, dtype=self.dtype)
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weight_attr = paddle.nn.initializer.Assign(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 = paddle.nn.initializer.Assign(self.bias)
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y_var = paddle.nn.Conv3D(
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in_channels=self.num_channels,
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out_channels=self.num_filters,
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kernel_size=self.filter_size,
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stride=self.stride,
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padding=self.padding,
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dilation=self.dilation,
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groups=self.groups,
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padding_mode="zeros",
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weight_attr=weight_attr,
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bias_attr=bias_attr,
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data_format=self.data_format,
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)(x_var)
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feed_dict = {"input": self.input}
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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 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, -1, -1, -1, self.num_channels)
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if self.channel_last
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else (-1, self.num_channels, -1, -1, -1)
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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.conv3d(
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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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x_var.stop_gradient = False
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conv = nn.Conv3D(
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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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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_var.backward()
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y_np = y_var.numpy()
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t1 = x_var.gradient()
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return y_np, t1
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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.base_layer(place)
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result2 = self.functional(place)
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with dg.guard(place):
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result3, g1 = 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 = 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 Conv3DErrorTestCase(Conv3DTestCase):
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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(Conv3DTestCase(methodName='runTest'))
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suite.addTest(
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Conv3DTestCase(methodName='runTest', stride=[1, 2, 1], dilation=2)
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)
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suite.addTest(
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Conv3DTestCase(methodName='runTest', stride=2, dilation=(2, 1, 2))
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)
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suite.addTest(
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Conv3DTestCase(methodName='runTest', padding="same", no_bias=True)
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)
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suite.addTest(
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Conv3DTestCase(
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methodName='runTest', filter_size=(3, 2, 3), padding='valid'
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)
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)
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suite.addTest(Conv3DTestCase(methodName='runTest', padding=(2, 3, 1)))
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suite.addTest(
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Conv3DTestCase(methodName='runTest', padding=[1, 2, 2, 1, 2, 3])
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)
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suite.addTest(
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Conv3DTestCase(
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methodName='runTest',
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padding=[[0, 0], [0, 0], [1, 2], [2, 1], [2, 2]],
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)
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)
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suite.addTest(Conv3DTestCase(methodName='runTest', data_format="NDHWC"))
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suite.addTest(
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Conv3DTestCase(
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methodName='runTest',
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data_format="NDHWC",
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padding=[[0, 0], [1, 1], [3, 3], [2, 2], [0, 0]],
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)
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)
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suite.addTest(
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Conv3DTestCase(methodName='runTest', groups=2, padding="valid")
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)
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suite.addTest(
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Conv3DTestCase(
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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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)
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)
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suite.addTest(Conv3DZeroSizeXPUTestCase(methodName='runTest'))
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def add_error_cases(suite):
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suite.addTest(
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Conv3DErrorTestCase(methodName='runTest', num_channels=5, groups=2)
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)
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suite.addTest(
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Conv3DErrorTestCase(
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methodName='runTest', num_channels=5, groups=2, padding=[-1, 1, 3]
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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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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 TestConv3dAPI_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, 8, 8, 8] # NCDHW
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self.shape_w = [6, 3, 3, 3, 3] # Co, Cin, kD, kH, kW
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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 = {
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"stride": [1, 1, 1],
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"pad": [0, 0, 0],
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"dilation": [1, 1, 1],
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}
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self.np_ref_out = conv3d_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.conv3d(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.conv3d(x=x, weight=w)
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paddle_dygraph_out.append(out2)
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# Keywords args for alias compatibility
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out3 = paddle.nn.functional.conv3d(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.conv3d(x, weight=w)
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paddle_dygraph_out.append(out4)
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# refer to test/xpu/test_conv3d_op_xpu.py
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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.conv3d(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.conv3d(x=x, weight=w)
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fetch_list.append(out2)
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# Keywords args for alias compatibility
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out3 = paddle.nn.functional.conv3d(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.conv3d(x, weight=w)
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fetch_list.append(out4)
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for place in self.places:
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# refer to test/xpu/test_conv2d_op_xpu.py
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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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class Conv3DZeroSizeXPUTestCase(unittest.TestCase):
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def runTest(self):
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if not core.is_compiled_with_xpu():
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return
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paddle.device.set_device('xpu')
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paddle.disable_static()
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x = paddle.randn([4, 3, 0, 8, 8], dtype='float32')
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w = paddle.randn([5, 3, 3, 3, 3], dtype='float32')
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b = paddle.randn([5], dtype='float32')
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out = paddle.nn.functional.conv3d(
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x,
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w,
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b,
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padding=[[0, 0], [0, 0], [1, 1], [2, 2], [2, 2]],
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stride=1,
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dilation=1,
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groups=1,
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data_format='NCDHW',
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)
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self.assertEqual(list(out.shape), [4, 5, 0, 10, 10])
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paddle.enable_static()
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if __name__ == '__main__':
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unittest.main()
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