wehub-resource-sync
326 lines
9.6 KiB
Python
326 lines
9.6 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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from unittest import TestCase
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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.nn.initializer as I
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class TestDeformConv2D(TestCase):
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batch_size = 4
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spatial_shape = (5, 5)
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dtype = "float32"
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def setUp(self):
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self.in_channels = 2
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [0, 0]
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self.stride = [1, 1]
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self.dilation = [1, 1]
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self.deformable_groups = 1
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self.groups = 1
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self.no_bias = True
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def prepare(self):
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np.random.seed(1)
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paddle.seed(1)
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if isinstance(self.kernel_size, int):
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filter_shape = (self.kernel_size,) * 2
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else:
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filter_shape = tuple(self.kernel_size)
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self.filter_shape = filter_shape
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self.weight = np.random.uniform(
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-1,
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1,
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(self.out_channels, self.in_channels // self.groups, *filter_shape),
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).astype(self.dtype)
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if not self.no_bias:
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self.bias = np.random.uniform(-1, 1, (self.out_channels,)).astype(
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self.dtype
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)
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def out_size(
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in_size, pad_size, dilation_size, kernel_size, stride_size
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):
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return (
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in_size + 2 * pad_size - (dilation_size * (kernel_size - 1) + 1)
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) / stride_size + 1
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out_h = int(
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out_size(
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self.spatial_shape[0],
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self.padding[0],
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self.dilation[0],
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self.kernel_size[0],
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self.stride[0],
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)
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)
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out_w = int(
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out_size(
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self.spatial_shape[1],
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self.padding[1],
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self.dilation[1],
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self.kernel_size[1],
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self.stride[1],
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)
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)
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out_shape = (out_h, out_w)
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self.input_shape = (
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self.batch_size,
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self.in_channels,
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*self.spatial_shape,
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)
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self.offset_shape = (
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self.batch_size,
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self.deformable_groups * 2 * filter_shape[0] * filter_shape[1],
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*out_shape,
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)
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self.mask_shape = (
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self.batch_size,
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self.deformable_groups * filter_shape[0] * filter_shape[1],
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*out_shape,
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)
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self.input = np.random.uniform(-1, 1, self.input_shape).astype(
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self.dtype
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)
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self.offset = np.random.uniform(-1, 1, self.offset_shape).astype(
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self.dtype
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)
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self.mask = np.random.uniform(-1, 1, self.mask_shape).astype(self.dtype)
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def static_graph_case_dcn(self):
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main = paddle.static.Program()
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start = paddle.static.Program()
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paddle.enable_static()
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with paddle.static.program_guard(main, start):
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x = paddle.static.data(
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"input", (-1, self.in_channels, -1, -1), dtype=self.dtype
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)
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offset = paddle.static.data(
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"offset",
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(
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-1,
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self.deformable_groups
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* 2
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* self.filter_shape[0]
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* self.filter_shape[1],
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-1,
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-1,
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),
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dtype=self.dtype,
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)
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mask = paddle.static.data(
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"mask",
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(
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-1,
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self.deformable_groups
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* self.filter_shape[0]
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* self.filter_shape[1],
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-1,
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-1,
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),
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dtype=self.dtype,
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)
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y_v1 = paddle.vision.ops.DeformConv2D(
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in_channels=self.in_channels,
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out_channels=self.out_channels,
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kernel_size=self.filter_shape,
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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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deformable_groups=self.deformable_groups,
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weight_attr=I.Assign(self.weight),
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bias_attr=False if self.no_bias else I.Assign(self.bias),
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)(x, offset, None)
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y_v2 = paddle.vision.ops.DeformConv2D(
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in_channels=self.in_channels,
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out_channels=self.out_channels,
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kernel_size=self.filter_shape,
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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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deformable_groups=self.deformable_groups,
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weight_attr=I.Assign(self.weight),
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bias_attr=False if self.no_bias else I.Assign(self.bias),
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)(x, offset, mask)
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exe = paddle.static.Executor(self.place)
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exe.run(start)
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out_v1, out_v2 = exe.run(
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main,
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feed={
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"input": self.input,
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"offset": self.offset,
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"mask": self.mask,
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},
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fetch_list=[y_v1, y_v2],
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)
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return out_v1, out_v2
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def dygraph_case_dcn(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.input)
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offset = paddle.to_tensor(self.offset)
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mask = paddle.to_tensor(self.mask)
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bias = None if self.no_bias else paddle.to_tensor(self.bias)
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deform_conv2d = paddle.vision.ops.DeformConv2D(
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in_channels=self.in_channels,
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out_channels=self.out_channels,
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kernel_size=self.kernel_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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deformable_groups=self.deformable_groups,
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groups=self.groups,
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weight_attr=I.Assign(self.weight),
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bias_attr=False if self.no_bias else I.Assign(self.bias),
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)
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y_v1 = deform_conv2d(x, offset)
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y_v2 = deform_conv2d(x, offset, mask)
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out_v1 = y_v1.numpy()
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out_v2 = y_v2.numpy()
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return out_v1, out_v2
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def _test_identity(self):
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self.prepare()
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static_dcn_v1, static_dcn_v2 = self.static_graph_case_dcn()
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dy_dcn_v1, dy_dcn_v2 = self.dygraph_case_dcn()
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np.testing.assert_array_almost_equal(static_dcn_v1, dy_dcn_v1)
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np.testing.assert_array_almost_equal(static_dcn_v2, dy_dcn_v2)
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def test_identity(self):
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self.place = paddle.CPUPlace()
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self._test_identity()
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.place = get_device_place()
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self._test_identity()
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# testcases for DeformConv2D
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class TestDeformConv2DWithPadding(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [2, 2]
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self.stride = [1, 1]
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self.dilation = [1, 1]
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self.deformable_groups = 1
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self.groups = 1
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self.no_bias = True
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class TestDeformConv2DWithBias(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [2, 2]
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self.stride = [1, 1]
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self.dilation = [1, 1]
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self.deformable_groups = 1
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self.groups = 1
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self.no_bias = False
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class TestDeformConv2DWithAsynPadding(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [1, 2]
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self.stride = [1, 1]
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self.dilation = [1, 1]
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self.deformable_groups = 1
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self.groups = 1
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self.no_bias = False
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class TestDeformConv2DWithDilation(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [1, 1]
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self.stride = [1, 1]
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self.dilation = [3, 3]
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self.deformable_groups = 1
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self.groups = 1
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self.no_bias = False
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class TestDeformConv2DWithStride(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [1, 1]
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self.stride = [2, 2]
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self.dilation = [1, 1]
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self.deformable_groups = 1
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self.groups = 1
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self.no_bias = False
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class TestDeformConv2DWithDeformable_Groups(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 5
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [1, 1]
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self.stride = [1, 1]
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self.dilation = [1, 1]
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self.deformable_groups = 5
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self.groups = 1
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self.no_bias = False
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class TestDeformConv2DWithGroups(TestDeformConv2D):
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def setUp(self):
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self.in_channels = 5
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self.out_channels = 5
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self.kernel_size = [3, 3]
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self.padding = [1, 1]
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self.stride = [1, 1]
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self.dilation = [1, 1]
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self.deformable_groups = 1
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self.groups = 5
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self.no_bias = False
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if __name__ == "__main__":
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unittest.main()
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