# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle import paddle.nn.functional as F import paddle.nn.initializer as I import numpy as np import unittest from unittest import TestCase class TestDeformConv2D(TestCase): batch_size = 4 spatial_shape = (16, 16) dtype = "float32" def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [0, 0] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = True def prepare(self): if isinstance(self.kernel_size, int): filter_shape = (self.kernel_size, ) * 2 else: filter_shape = tuple(self.kernel_size) self.filter_shape = filter_shape self.weight = np.random.uniform( -1, 1, (self.out_channels, self.in_channels // self.groups ) + filter_shape).astype(self.dtype) if not self.no_bias: self.bias = np.random.uniform(-1, 1, ( self.out_channels, )).astype(self.dtype) def out_size(in_size, pad_size, dilation_size, kernel_size, stride_size): return (in_size + 2 * pad_size - (dilation_size * (kernel_size - 1) + 1)) / stride_size + 1 out_h = int( out_size(self.spatial_shape[0], self.padding[0], self.dilation[0], self.kernel_size[0], self.stride[0])) out_w = int( out_size(self.spatial_shape[1], self.padding[1], self.dilation[1], self.kernel_size[1], self.stride[1])) out_shape = (out_h, out_w) self.input_shape = (self.batch_size, self.in_channels ) + self.spatial_shape self.offset_shape = (self.batch_size, 2 * filter_shape[0] * filter_shape[1]) + out_shape self.mask_shape = (self.batch_size, filter_shape[0] * filter_shape[1] ) + out_shape self.input = np.random.uniform(-1, 1, self.input_shape).astype(self.dtype) self.offset = np.random.uniform(-1, 1, self.offset_shape).astype(self.dtype) self.mask = np.random.uniform(-1, 1, self.mask_shape).astype(self.dtype) def static_graph_case_dcn(self): main = paddle.static.Program() start = paddle.static.Program() paddle.enable_static() with paddle.static.program_guard(main, start): x = paddle.static.data( "input", (-1, self.in_channels, -1, -1), dtype=self.dtype) offset = paddle.static.data( "offset", (-1, 2 * self.filter_shape[0] * self.filter_shape[1], -1, -1), dtype=self.dtype) mask = paddle.static.data( "mask", (-1, self.filter_shape[0] * self.filter_shape[1], -1, -1), dtype=self.dtype) y_v1 = paddle.fluid.layers.deformable_conv( input=x, offset=offset, mask=None, num_filters=self.out_channels, filter_size=self.filter_shape, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, deformable_groups=1, im2col_step=1, param_attr=I.Assign(self.weight), bias_attr=False if self.no_bias else I.Assign(self.bias), modulated=False) y_v2 = paddle.fluid.layers.deformable_conv( input=x, offset=offset, mask=mask, num_filters=self.out_channels, filter_size=self.filter_shape, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, deformable_groups=1, im2col_step=1, param_attr=I.Assign(self.weight), bias_attr=False if self.no_bias else I.Assign(self.bias)) exe = paddle.static.Executor(self.place) exe.run(start) out_v1, out_v2 = exe.run(main, feed={ "input": self.input, "offset": self.offset, "mask": self.mask }, fetch_list=[y_v1, y_v2]) return out_v1, out_v2 def dygraph_case_dcn(self): paddle.disable_static() x = paddle.to_tensor(self.input) offset = paddle.to_tensor(self.offset) mask = paddle.to_tensor(self.mask) bias = None if self.no_bias else paddle.to_tensor(self.bias) deform_conv2d = paddle.vision.ops.DeformConv2D( in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, weight_attr=I.Assign(self.weight), bias_attr=False if self.no_bias else I.Assign(self.bias)) y_v1 = deform_conv2d(x, offset) y_v2 = deform_conv2d(x, offset, mask) out_v1 = y_v1.numpy() out_v2 = y_v2.numpy() return out_v1, out_v2 def _test_identity(self): self.prepare() static_dcn_v1, static_dcn_v2 = self.static_graph_case_dcn() dy_dcn_v1, dy_dcn_v2 = self.dygraph_case_dcn() np.testing.assert_array_almost_equal(static_dcn_v1, dy_dcn_v1) np.testing.assert_array_almost_equal(static_dcn_v2, dy_dcn_v2) def test_identity(self): self.place = paddle.CPUPlace() self._test_identity() if paddle.is_compiled_with_cuda(): self.place = paddle.CUDAPlace(0) self._test_identity() class TestDeformConv2DFunctional(TestCase): batch_size = 4 spatial_shape = (16, 16) dtype = "float32" def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [0, 0] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = True def prepare(self): if isinstance(self.kernel_size, int): filter_shape = (self.kernel_size, ) * 2 else: filter_shape = tuple(self.kernel_size) self.filter_shape = filter_shape self.weight = np.random.uniform( -1, 1, (self.out_channels, self.in_channels // self.groups ) + filter_shape).astype(self.dtype) if not self.no_bias: self.bias = np.random.uniform(-1, 1, ( self.out_channels, )).astype(self.dtype) def out_size(in_size, pad_size, dilation_size, kernel_size, stride_size): return (in_size + 2 * pad_size - (dilation_size * (kernel_size - 1) + 1)) / stride_size + 1 out_h = int( out_size(self.spatial_shape[0], self.padding[0], self.dilation[0], self.kernel_size[0], self.stride[0])) out_w = int( out_size(self.spatial_shape[1], self.padding[1], self.dilation[1], self.kernel_size[1], self.stride[1])) out_shape = (out_h, out_w) self.input_shape = (self.batch_size, self.in_channels ) + self.spatial_shape self.offset_shape = (self.batch_size, 2 * filter_shape[0] * filter_shape[1]) + out_shape self.mask_shape = (self.batch_size, filter_shape[0] * filter_shape[1] ) + out_shape self.input = np.random.uniform(-1, 1, self.input_shape).astype(self.dtype) self.offset = np.random.uniform(-1, 1, self.offset_shape).astype(self.dtype) self.mask = np.random.uniform(-1, 1, self.mask_shape).astype(self.dtype) def static_graph_case_dcn(self): main = paddle.static.Program() start = paddle.static.Program() paddle.enable_static() with paddle.static.program_guard(main, start): x = paddle.static.data( "input", (-1, self.in_channels, -1, -1), dtype=self.dtype) offset = paddle.static.data( "offset", (-1, 2 * self.filter_shape[0] * self.filter_shape[1], -1, -1), dtype=self.dtype) mask = paddle.static.data( "mask", (-1, self.filter_shape[0] * self.filter_shape[1], -1, -1), dtype=self.dtype) y_v1 = paddle.fluid.layers.deformable_conv( input=x, offset=offset, mask=None, num_filters=self.out_channels, filter_size=self.filter_shape, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, deformable_groups=1, im2col_step=1, param_attr=I.Assign(self.weight), bias_attr=False if self.no_bias else I.Assign(self.bias), modulated=False) y_v2 = paddle.fluid.layers.deformable_conv( input=x, offset=offset, mask=mask, num_filters=self.out_channels, filter_size=self.filter_shape, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, deformable_groups=1, im2col_step=1, param_attr=I.Assign(self.weight), bias_attr=False if self.no_bias else I.Assign(self.bias)) exe = paddle.static.Executor(self.place) exe.run(start) out_v1, out_v2 = exe.run(main, feed={ "input": self.input, "offset": self.offset, "mask": self.mask }, fetch_list=[y_v1, y_v2]) return out_v1, out_v2 def dygraph_case_dcn(self): paddle.disable_static() x = paddle.to_tensor(self.input) offset = paddle.to_tensor(self.offset) mask = paddle.to_tensor(self.mask) weight = paddle.to_tensor(self.weight) bias = None if self.no_bias else paddle.to_tensor(self.bias) y_v1 = paddle.vision.ops.deform_conv2d( x=x, offset=offset, weight=weight, bias=bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, ) y_v2 = paddle.vision.ops.deform_conv2d( x=x, offset=offset, mask=mask, weight=weight, bias=bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, ) out_v1 = y_v1.numpy() out_v2 = y_v2.numpy() return out_v1, out_v2 def new_api_static_graph_case_dcn(self): main = paddle.static.Program() start = paddle.static.Program() paddle.enable_static() with paddle.static.program_guard(main, start): x = paddle.static.data( "input", (-1, self.in_channels, -1, -1), dtype=self.dtype) offset = paddle.static.data( "offset", (-1, 2 * self.filter_shape[0] * self.filter_shape[1], -1, -1), dtype=self.dtype) mask = paddle.static.data( "mask", (-1, self.filter_shape[0] * self.filter_shape[1], -1, -1), dtype=self.dtype) weight = paddle.static.data( "weight", list(self.weight.shape), dtype=self.dtype) if not self.no_bias: bias = paddle.static.data("bias", [-1], dtype=self.dtype) y_v1 = paddle.vision.ops.deform_conv2d( x=x, offset=offset, weight=weight, bias=None if self.no_bias else bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, ) y_v2 = paddle.vision.ops.deform_conv2d( x=x, offset=offset, mask=mask, weight=weight, bias=None if self.no_bias else bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, ) exe = paddle.static.Executor(self.place) exe.run(start) feed_dict = { "input": self.input, "offset": self.offset, "mask": self.mask, "weight": self.weight } if not self.no_bias: feed_dict["bias"] = self.bias out_v1, out_v2 = exe.run(main, feed=feed_dict, fetch_list=[y_v1, y_v2]) return out_v1, out_v2 def _test_identity(self): self.prepare() static_dcn_v1, static_dcn_v2 = self.static_graph_case_dcn() dy_dcn_v1, dy_dcn_v2 = self.dygraph_case_dcn() new_static_dcn_v1, new_static_dcn_v2 = self.new_api_static_graph_case_dcn( ) np.testing.assert_array_almost_equal(static_dcn_v1, dy_dcn_v1) np.testing.assert_array_almost_equal(static_dcn_v2, dy_dcn_v2) np.testing.assert_array_almost_equal(static_dcn_v1, new_static_dcn_v1) np.testing.assert_array_almost_equal(static_dcn_v2, new_static_dcn_v2) def test_identity(self): self.place = paddle.CPUPlace() self._test_identity() if paddle.is_compiled_with_cuda(): self.place = paddle.CUDAPlace(0) self._test_identity() # testcases for DeformConv2D class TestDeformConv2DWithPadding(TestDeformConv2D): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [2, 2] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = True class TestDeformConv2DWithBias(TestDeformConv2D): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [2, 2] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = False class TestDeformConv2DWithAsynPadding(TestDeformConv2D): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 2] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = False class TestDeformConv2DWithDilation(TestDeformConv2D): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 1] self.stride = [1, 1] self.dilation = [3, 3] self.groups = 1 self.no_bias = False class TestDeformConv2DWithStride(TestDeformConv2D): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 1] self.stride = [2, 2] self.dilation = [1, 1] self.groups = 1 self.no_bias = False class TestDeformConv2DWithGroups(TestDeformConv2D): def setUp(self): self.in_channels = 5 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 1] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 5 self.no_bias = False # testcases for deform_conv2d class TestDeformConv2DFunctionalWithPadding(TestDeformConv2DFunctional): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [2, 2] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = True class TestDeformConv2DFunctionalWithBias(TestDeformConv2DFunctional): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [2, 2] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = False class TestDeformConv2DFunctionalWithAsynPadding(TestDeformConv2DFunctional): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 2] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 1 self.no_bias = False class TestDeformConv2DFunctionalWithDilation(TestDeformConv2DFunctional): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 1] self.stride = [1, 1] self.dilation = [3, 3] self.groups = 1 self.no_bias = False class TestDeformConv2DFunctionalWithStride(TestDeformConv2DFunctional): def setUp(self): self.in_channels = 3 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 1] self.stride = [2, 2] self.dilation = [1, 1] self.groups = 1 self.no_bias = False class TestDeformConv2DFunctionalWithGroups(TestDeformConv2DFunctional): def setUp(self): self.in_channels = 5 self.out_channels = 5 self.kernel_size = [3, 3] self.padding = [1, 1] self.stride = [1, 1] self.dilation = [1, 1] self.groups = 5 self.no_bias = False if __name__ == "__main__": unittest.main()