# Copyright (c) 2018 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 unittest import numpy as np import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid import Program, program_guard from paddle.fluid.tests.unittests.op_test import ( OpTest, convert_float_to_uint16, get_numeric_gradient, ) from paddle.fluid.tests.unittests.testsuite import create_op def conv2d_forward_naive( input, filter, group, conv_param, padding_algorithm='EXPLICIT', data_format='NCHW', ): if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]: raise ValueError( "Unknown Attr(padding_algorithm): '%s'. " "It can only be 'SAME' or 'VALID'." % str(padding_algorithm) ) if data_format not in ["NCHW", "NHWC"]: raise ValueError( "Unknown Attr(data_format): '%s' ." "It can only be 'NCHW' or 'NHWC'." % str(data_format) ) channel_last = data_format == "NHWC" if channel_last: input = np.transpose(input, [0, 3, 1, 2]) in_n, in_c, in_h, in_w = input.shape f_n, f_c, f_h, f_w = filter.shape out_n = in_n out_c = f_n assert f_c * group == in_c assert np.mod(out_c, group) == 0 sub_out_c = out_c // group sub_f_n = f_n // group stride, pad, dilation = ( conv_param['stride'], conv_param['pad'], conv_param['dilation'], ) # update pad and dilation def _get_padding_with_SAME(input_shape, pool_size, pool_stride): padding = [] for input_size, filter_size, stride_size in zip( input_shape, pool_size, pool_stride ): out_size = int((input_size + stride_size - 1) / stride_size) pad_sum = np.max( ((out_size - 1) * stride_size + filter_size - input_size, 0) ) pad_0 = int(pad_sum / 2) pad_1 = int(pad_sum - pad_0) padding.append(pad_0) padding.append(pad_1) return padding ksize = filter.shape[2:4] if padding_algorithm == "VALID": pad = [0, 0, 0, 0] elif padding_algorithm == "SAME": dilation = [1, 1] input_data_shape = input.shape[2:4] pad = _get_padding_with_SAME(input_data_shape, ksize, stride) pad_h_0, pad_h_1 = pad[0], pad[0] pad_w_0, pad_w_1 = pad[1], pad[1] if len(pad) == 4: pad_h_0, pad_h_1 = pad[0], pad[1] pad_w_0, pad_w_1 = pad[2], pad[3] out_h = ( 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[0] * (f_h - 1) + 1)) // stride[0] ) out_w = ( 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[1] * (f_w - 1) + 1)) // stride[1] ) out = np.zeros((out_n, out_c, out_h, out_w)) d_bolck_h = dilation[0] * (f_h - 1) + 1 d_bolck_w = dilation[1] * (f_w - 1) + 1 input_pad = np.pad( input, ((0, 0), (0, 0), (pad_h_0, pad_h_1), (pad_w_0, pad_w_1)), mode='constant', constant_values=0, ) filter_dilation = np.zeros((f_n, f_c, d_bolck_h, d_bolck_w)) filter_dilation[ :, :, 0 : d_bolck_h : dilation[0], 0 : d_bolck_w : dilation[1] ] = filter for i in range(out_h): for j in range(out_w): for g in range(group): input_pad_masked = input_pad[ :, g * f_c : (g + 1) * f_c, i * stride[0] : i * stride[0] + d_bolck_h, j * stride[1] : j * stride[1] + d_bolck_w, ] f_sub = filter_dilation[ g * sub_f_n : (g + 1) * sub_f_n, :, :, : ] # sub_f_n == sub_out_c for k in range(sub_out_c): # Multiplication of Corresponding Elements, then sum all out[:, g * sub_out_c + k, i, j] = np.sum( input_pad_masked * f_sub[k, :, :, :], axis=(1, 2, 3) ) if channel_last: out = np.transpose(out, [0, 2, 3, 1]) return out, in_n, out_h, out_w, out_c def create_test_cudnn_class(parent): @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestCUDNNCase(parent): def init_kernel_type(self): self.use_cudnn = True self.dtype = ( np.float32 if core.is_compiled_with_rocm() else np.float64 ) cls_name = "{0}_{1}".format(parent.__name__, "CUDNN") TestCUDNNCase.__name__ = cls_name globals()[cls_name] = TestCUDNNCase def create_test_cudnn_fp16_class(parent, grad_check=True): @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestConv2DCUDNNFp16(parent): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=2e-2) def test_check_grad_no_filter(self): place = core.CUDAPlace(0) if core.is_float16_supported(place) and grad_check: self.check_grad_with_place( place, ['Input'], 'Output', no_grad_set=set(['Filter']) ) def test_check_grad_no_input(self): place = core.CUDAPlace(0) if core.is_float16_supported(place) and grad_check: self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set=set(['Input']) ) cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16") TestConv2DCUDNNFp16.__name__ = cls_name globals()[cls_name] = TestConv2DCUDNNFp16 def create_test_cudnn_bf16_class(parent): @unittest.skipIf( not core.is_compiled_with_cuda() or not core.is_bfloat16_supported(core.CUDAPlace(0)), "core is not compiled with CUDA and do not support bfloat16", ) class TestConv2DCUDNNBF16(parent): def get_numeric_grad(self, place, check_name): scope = core.Scope() self._check_grad_helper() op = create_op( scope, self.op_type, self.inputs, self.outputs, self.attrs ) return get_numeric_gradient( place, scope, op, self.inputs_fp32, check_name, ['Output'] ) def init_kernel_type(self): self.use_cudnn = True self.no_need_check_grad = True self.dtype = np.uint16 def test_check_output(self): place = core.CUDAPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad_no_filter(self): place = core.CUDAPlace(0) numeric_grads = self.get_numeric_grad(place, 'Input') self.check_grad_with_place( place, ['Input'], 'Output', no_grad_set=set(['Filter']), user_defined_grads=[numeric_grads], ) def test_check_grad_no_input(self): place = core.CUDAPlace(0) numeric_grads = self.get_numeric_grad(place, 'Filter') self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set=set(['Input']), user_defined_grads=[numeric_grads], ) cls_name = "{0}_{1}".format(parent.__name__, "CUDNNBF16") TestConv2DCUDNNBF16.__name__ = cls_name globals()[cls_name] = TestConv2DCUDNNBF16 def create_test_channel_last_class(parent): class TestChannelLastCase(parent): def init_data_format(self): self.data_format = "NHWC" def init_test_case_2(self): N, C, H, W = self.input_size self.input_size = [N, H, W, C] cls_name = "{0}_{1}".format(parent.__name__, "ChannelLast") TestChannelLastCase.__name__ = cls_name globals()[cls_name] = TestChannelLastCase def create_test_cudnn_channel_last_class(parent): @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestCudnnChannelLastCase(parent): def init_kernel_type(self): self.use_cudnn = True self.dtype = ( np.float32 if core.is_compiled_with_rocm() else np.float64 ) def init_data_format(self): self.data_format = "NHWC" def init_test_case_2(self): N, C, H, W = self.input_size self.input_size = [N, H, W, C] cls_name = "{0}_{1}".format(parent.__name__, "CudnnChannelLast") TestCudnnChannelLastCase.__name__ = cls_name globals()[cls_name] = TestCudnnChannelLastCase def create_test_cudnn_channel_last_fp16_class(parent, grad_check=True): @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestCudnnChannelLastFp16(parent): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=2e-2) def test_check_grad_no_filter(self): place = core.CUDAPlace(0) if core.is_float16_supported(place) and grad_check: self.check_grad_with_place( place, ['Input'], 'Output', no_grad_set=set(['Filter']) ) def test_check_grad_no_input(self): place = core.CUDAPlace(0) if core.is_float16_supported(place) and grad_check: self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set=set(['Input']) ) def init_data_format(self): self.data_format = "NHWC" def init_test_case_2(self): N, C, H, W = self.input_size self.input_size = [N, H, W, C] cls_name = "{0}_{1}".format(parent.__name__, "CudnnChannelLastFp16") TestCudnnChannelLastFp16.__name__ = cls_name globals()[cls_name] = TestCudnnChannelLastFp16 def create_test_padding_SAME_class(parent): class TestPaddingSMAECase(parent): def init_paddings(self): self.pad = [0, 0] self.padding_algorithm = "SAME" cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp") TestPaddingSMAECase.__name__ = cls_name globals()[cls_name] = TestPaddingSMAECase def create_test_padding_VALID_class(parent): class TestPaddingVALIDCase(parent): def init_paddings(self): self.pad = [1, 1] self.padding_algorithm = "VALID" cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp") TestPaddingVALIDCase.__name__ = cls_name globals()[cls_name] = TestPaddingVALIDCase def create_test_cudnn_padding_SAME_class(parent): @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestCUDNNPaddingSMAECase(parent): def init_kernel_type(self): self.use_cudnn = True self.dtype = ( np.float32 if core.is_compiled_with_rocm() else np.float64 ) def init_paddings(self): self.pad = [1, 1] self.padding_algorithm = "SAME" cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingSAMEOp") TestCUDNNPaddingSMAECase.__name__ = cls_name globals()[cls_name] = TestCUDNNPaddingSMAECase def create_test_cudnn_padding_VALID_class(parent): @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestCUDNNPaddingVALIDCase(parent): def init_kernel_type(self): self.use_cudnn = True self.dtype = ( np.float32 if core.is_compiled_with_rocm() else np.float64 ) def init_paddings(self): self.pad = [1, 1] self.padding_algorithm = "VALID" cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingVALIDOp") TestCUDNNPaddingVALIDCase.__name__ = cls_name globals()[cls_name] = TestCUDNNPaddingVALIDCase class TestConv2DOp(OpTest): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False self.exhaustive_search = False self.use_cuda = False self.use_mkldnn = False self.fuse_relu_before_depthwise_conv = False self.data_format = "AnyLayout" self.dtype = np.float64 self.init_kernel_type() self.init_group() self.init_dilation() self.init_test_case() conv2d_param = { 'stride': self.stride, 'pad': self.pad, 'dilation': self.dilations, } if self.is_bfloat16_op(): input = np.random.random(self.input_size).astype(np.float32) filter = np.random.uniform(-1, 1, self.filter_size).astype( np.float32 ) else: input = np.random.random(self.input_size).astype(self.dtype) filter = np.random.uniform(-1, 1, self.filter_size).astype( self.dtype ) if not self.has_cuda(): self.fuse_relu_before_depthwise_conv = False if self.fuse_relu_before_depthwise_conv: input = input - 0.5 input -= (input < 0) * 0.1 input += (input >= 0) * 0.1 input2 = np.maximum(input, 0.0) else: input2 = input output, _, _, _, _ = conv2d_forward_naive( input2, filter, self.groups, conv2d_param ) if self.is_bfloat16_op(): output = output.astype(np.float32) self.inputs = { 'Input': convert_float_to_uint16(input), 'Filter': convert_float_to_uint16(filter), } self.inputs_fp32 = { 'Input': OpTest.np_dtype_to_fluid_dtype(input), 'Filter': OpTest.np_dtype_to_fluid_dtype(filter), } else: output = output.astype(self.dtype) self.inputs = { 'Input': OpTest.np_dtype_to_fluid_dtype(input), 'Filter': OpTest.np_dtype_to_fluid_dtype(filter), } self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'groups': self.groups, 'dilations': self.dilations, 'use_cudnn': self.use_cudnn, 'use_mkldnn': self.use_mkldnn, 'data_format': self.data_format, 'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv, 'exhaustive_search': self.exhaustive_search, } self.outputs = {'Output': output} def has_cuda(self): return core.is_compiled_with_cuda() and ( self.use_cudnn or self.use_cuda ) def test_check_output(self): place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_output_with_place( place, atol=1e-5, check_dygraph=(not self.use_mkldnn) ) def test_check_grad(self): if self.dtype == np.float16 or ( hasattr(self, "no_need_check_grad") and self.no_need_check_grad ): return place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_grad_with_place( place, {'Input', 'Filter'}, 'Output', max_relative_error=0.02, check_dygraph=(not self.use_mkldnn), ) def test_check_grad_no_filter(self): if self.dtype == np.float16 or ( hasattr(self, "no_need_check_grad") and self.no_need_check_grad ): return place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_grad_with_place( place, ['Input'], 'Output', max_relative_error=0.02, no_grad_set=set(['Filter']), check_dygraph=(not self.use_mkldnn), ) def test_check_grad_no_input(self): if self.dtype == np.float16 or ( hasattr(self, "no_need_check_grad") and self.no_need_check_grad ): return place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set=set(['Input']), check_dygraph=(not self.use_mkldnn), ) def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [6, f_c, 3, 3] def init_test_case_2(self): pass def init_dilation(self): self.dilations = [1, 1] def init_group(self): self.groups = 1 def init_kernel_type(self): pass class TestWithPad(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [6, f_c, 3, 3] class TestWithStride(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 6, 6] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [6, f_c, 3, 3] class TestWithGroup(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.group = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [18, f_c, 3, 3] class TestWith1x1(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [120, f_c, 1, 1] def init_group(self): self.groups = 3 class TestWithDepthWise3x3(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.input_size = [3, 4, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [12, f_c, 3, 3] def init_dilation(self): self.dilations = [2, 2] def init_group(self): self.groups = 4 class TestWithDepthWise5x5(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.input_size = [2, 4, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [8, f_c, 5, 5] def init_group(self): self.groups = 4 class TestWithDepthWise7x7(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 8, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [16, f_c, 7, 7] def init_group(self): self.groups = 8 class TestWithDilation(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [12, f_c, 3, 3] def init_dilation(self): self.dilations = [2, 2] def init_group(self): self.groups = 3 class TestWithInput1x1Filter1x1(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.input_size = [100, 3, 1, 1] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [120, f_c, 1, 1] def init_group(self): self.groups = 3 # #----------------Conv2DCUDNN---------------- create_test_cudnn_class(TestConv2DOp) create_test_cudnn_class(TestWithPad) create_test_cudnn_class(TestWithStride) create_test_cudnn_class(TestWithGroup) create_test_cudnn_class(TestWith1x1) create_test_cudnn_class(TestWithInput1x1Filter1x1) # ----------------Conv2DCUDNN fp16---------------- create_test_cudnn_fp16_class(TestConv2DOp, grad_check=False) create_test_cudnn_fp16_class(TestWithPad, grad_check=False) create_test_cudnn_fp16_class(TestWithStride, grad_check=False) create_test_cudnn_fp16_class(TestWithGroup, grad_check=False) create_test_cudnn_fp16_class(TestWith1x1, grad_check=False) create_test_cudnn_fp16_class(TestWithInput1x1Filter1x1, grad_check=False) # ----------------Conv2DCUDNN bf16---------------- create_test_cudnn_bf16_class(TestConv2DOp) create_test_cudnn_bf16_class(TestWithPad) create_test_cudnn_bf16_class(TestWithStride) create_test_cudnn_bf16_class(TestWithGroup) create_test_cudnn_bf16_class(TestWith1x1) create_test_cudnn_bf16_class(TestWithInput1x1Filter1x1) class TestCUDNNExhaustiveSearch(TestConv2DOp): def init_kernel_type(self): self.use_cudnn = True self.exhaustive_search = True self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 class TestConv2DOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): def test_Variable(): # the input of conv2d must be Variable. x1 = fluid.create_lod_tensor( np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace() ) fluid.layers.conv2d(x1, 1, 1) self.assertRaises(TypeError, test_Variable) def test_dtype(): # the input dtype of conv2d must be float16 or float32 or float64 # float16 only can be set on GPU place x2 = fluid.layers.data( name='x2', shape=[3, 4, 5, 6], dtype="int32" ) fluid.layers.conv2d(x2, 1, 1) self.assertRaises(TypeError, test_dtype) # Please Don't remove the following code. # Currently, CI use cudnn V5.0 which not support dilation conv. # class TestCUDNNWithDilation(TestWithDilation): # def init_op_type(self): # self.op_type = "conv_cudnn" # ---- test asymmetric padding ---- class TestConv2DOp_v2(OpTest): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False self.exhaustive_search = False self.use_cuda = False self.use_mkldnn = False self.fuse_relu_before_depthwise_conv = False self.dtype = np.float64 self.init_kernel_type() self.init_group() self.init_dilation() self.init_data_format() self.init_test_case() self.init_paddings() self.init_test_case_2() conv2d_param = { 'stride': self.stride, 'pad': self.pad, 'dilation': self.dilations, } input = np.random.random(self.input_size).astype(self.dtype) if not self.has_cuda(): self.fuse_relu_before_depthwise_conv = False if self.fuse_relu_before_depthwise_conv: input = input - 0.5 input -= (input < 0) * 0.1 input += (input >= 0) * 0.1 input2 = np.maximum(input, 0.0) else: input2 = input filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype) output, _, _, _, _ = conv2d_forward_naive( input2, filter, self.groups, conv2d_param, self.padding_algorithm, self.data_format, ) output = output.astype(self.dtype) self.inputs = { 'Input': OpTest.np_dtype_to_fluid_dtype(input), 'Filter': OpTest.np_dtype_to_fluid_dtype(filter), } self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'padding_algorithm': self.padding_algorithm, 'groups': self.groups, 'dilations': self.dilations, 'use_cudnn': self.use_cudnn, 'use_mkldnn': self.use_mkldnn, 'data_format': self.data_format, 'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv, 'exhaustive_search': self.exhaustive_search, } self.outputs = {'Output': output} def has_cuda(self): return core.is_compiled_with_cuda() and ( self.use_cudnn or self.use_cuda ) def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() self.check_output_with_place( place, atol=1e-5, check_dygraph=(not self.use_mkldnn) ) def test_check_grad(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.dtype == np.float16: return place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() self.check_grad_with_place( place, {'Input', 'Filter'}, 'Output', max_relative_error=0.02, check_dygraph=(not self.use_mkldnn), ) def test_check_grad_no_filter(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.dtype == np.float16: return place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() self.check_grad_with_place( place, ['Input'], 'Output', max_relative_error=0.02, no_grad_set=set(['Filter']), check_dygraph=(not self.use_mkldnn), ) def test_check_grad_no_input(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.dtype == np.float16: return place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set=set(['Input']), check_dygraph=(not self.use_mkldnn), ) def init_test_case(self): self.pad = [0, 0] self.stride = [1, 2] self.input_size = [2, 3, 5, 5] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [6, f_c, 4, 3] def init_dilation(self): self.dilations = [1, 1] def init_group(self): self.groups = 1 def init_kernel_type(self): pass def init_paddings(self): self.pad = [0, 0] self.padding_algorithm = "EXPLICIT" def init_data_format(self): self.data_format = "NCHW" def init_test_case_2(self): pass class TestConv2DOp_AsyPadding(TestConv2DOp_v2): def init_paddings(self): self.pad = [0, 0, 1, 2] self.padding_algorithm = "EXPLICIT" class TestWithPad_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [6, f_c, 3, 3] def init_paddings(self): self.pad = [2, 1, 3, 2] self.padding_algorithm = "EXPLICIT" class TestWithStride_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 3, 6, 6] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [6, f_c, 3, 3] def init_paddings(self): self.pad = [2, 1, 3, 2] self.padding_algorithm = "EXPLICIT" class TestWithGroup_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 2] self.input_size = [2, 3, 5, 5] # NCHW self.group = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [24, f_c, 4, 3] class TestWith1x1_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [120, f_c, 1, 1] def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [2, 2, 4, 0] self.padding_algorithm = "EXPLICIT" class TestWithDepthWise3x3_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [3, 4, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [16, f_c, 3, 3] def init_dilation(self): self.dilations = [2, 2] def init_group(self): self.groups = 4 def init_paddings(self): self.pad = [1, 3, 2, 1] self.padding_algorithm = "EXPLICIT" class TestWithDepthWise5x5_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 4, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [8, f_c, 5, 5] def init_group(self): self.groups = 4 def init_paddings(self): self.pad = [0, 1, 1, 0] self.padding_algorithm = "EXPLICIT" class TestWithDepthWise7x7_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 8, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [16, f_c, 7, 7] def init_group(self): self.groups = 8 def init_paddings(self): self.pad = [1, 3, 4, 1] self.padding_algorithm = "EXPLICIT" class TestWithDilation_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [24, f_c, 3, 3] def init_dilation(self): self.dilations = [2, 2] def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [0, 1, 3, 0] self.padding_algorithm = "EXPLICIT" class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [40, 3, 1, 1] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [120, f_c, 1, 1] def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [0, 3, 4, 0] self.padding_algorithm = "EXPLICIT" create_test_cudnn_class(TestConv2DOp_AsyPadding) create_test_cudnn_class(TestWithPad_AsyPadding) create_test_cudnn_class(TestWithStride_AsyPadding) create_test_cudnn_class(TestWithGroup_AsyPadding) create_test_cudnn_class(TestWith1x1_AsyPadding) create_test_cudnn_class(TestWithInput1x1Filter1x1_AsyPadding) # ---------- test SAME VALID ----------- create_test_padding_SAME_class(TestConv2DOp_AsyPadding) create_test_padding_SAME_class(TestWithPad_AsyPadding) create_test_padding_SAME_class(TestWithStride_AsyPadding) create_test_padding_SAME_class(TestWithGroup_AsyPadding) create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding) create_test_padding_VALID_class(TestConv2DOp_AsyPadding) create_test_padding_VALID_class(TestWithPad_AsyPadding) create_test_padding_VALID_class(TestWithStride_AsyPadding) create_test_padding_VALID_class(TestWithGroup_AsyPadding) create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding) create_test_cudnn_padding_SAME_class(TestConv2DOp_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithPad_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithStride_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithGroup_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding) create_test_cudnn_padding_VALID_class(TestConv2DOp_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithPad_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithStride_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithGroup_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding) # ------------ test channel last --------- create_test_channel_last_class(TestConv2DOp_AsyPadding) create_test_channel_last_class(TestWithPad_AsyPadding) create_test_channel_last_class(TestWithGroup_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) create_test_channel_last_class(TestWithInput1x1Filter1x1_AsyPadding) create_test_cudnn_channel_last_class(TestConv2DOp_AsyPadding) create_test_cudnn_channel_last_class(TestWithPad_AsyPadding) create_test_cudnn_channel_last_class(TestWithStride_AsyPadding) create_test_cudnn_channel_last_class(TestWithGroup_AsyPadding) create_test_cudnn_channel_last_class(TestWithDilation_AsyPadding) create_test_cudnn_channel_last_fp16_class( TestConv2DOp_AsyPadding, grad_check=False ) create_test_cudnn_channel_last_fp16_class( TestWithPad_AsyPadding, grad_check=False ) create_test_cudnn_channel_last_fp16_class( TestWithStride_AsyPadding, grad_check=False ) create_test_cudnn_channel_last_fp16_class( TestWithGroup_AsyPadding, grad_check=False ) create_test_cudnn_channel_last_fp16_class( TestWithDilation_AsyPadding, grad_check=False ) if __name__ == '__main__': paddle.enable_static() unittest.main()