# 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. from __future__ import print_function import unittest import numpy as np import paddle.fluid.core as core from op_test import OpTest import paddle.fluid as fluid 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 = [] if data_format == "NCHW": input_data_shape = input.shape[2:4] elif data_format == "NHWC": input_data_shape = input.shape[1:3] 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 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', max_relative_error=0.02, 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', max_relative_error=0.02, no_grad_set=set(['Input'])) cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16") TestConv2DCUDNNFp16.__name__ = cls_name globals()[cls_name] = TestConv2DCUDNNFp16 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 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_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 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 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.float32 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 } 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) 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() self.check_output_with_place(place, atol=1e-5) def test_check_grad(self): 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) def test_check_grad_no_filter(self): 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'])) def test_check_grad_no_input(self): 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', max_relative_error=0.02, no_grad_set=set(['Input'])) 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_group(self): self.groups = 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 = [6, 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 = [8, 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 = [6, 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 = [2, 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 = [6, 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) #----------------TestDepthwiseConv ----- class TestDepthwiseConv(TestConv2dOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" class TestDepthwiseConv2(TestConv2dOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" class TestDepthwiseConv3(TestConv2dOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 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] self.op_type = "depthwise_conv2d" class TestDepthwiseConvWithDilation(TestConv2dOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" class TestDepthwiseConvWithDilation2(TestConv2dOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" class TestDepthwiseConvandFuse(TestConv2dOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" class TestDepthwiseConv2andFuse(TestConv2dOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" class TestDepthwiseConv3andFuse(TestConv2dOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 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] self.op_type = "depthwise_conv2d" class TestDepthwiseConvWithDilationandFuse(TestConv2dOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" class TestDepthwiseConvWithDilation2andFuse(TestConv2dOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" class TestCUDNNExhaustiveSearch(TestConv2dOp): def init_kernel_type(self): self.use_cudnn = True self.exhaustive_search = True # 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.float32 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): place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace() self.check_output_with_place(place, atol=1e-5) def test_check_grad(self): 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) def test_check_grad_no_filter(self): 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'])) def test_check_grad_no_input(self): 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', max_relative_error=0.02, no_grad_set=set(['Input'])) 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_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_group(self): self.groups = 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 = [6, 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 = [8, 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 = [6, 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 = [2, 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 = [6, 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) class TestDepthwiseConv_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.use_cuda = True self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [1, 1, 0, 1] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConv2_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.use_cuda = True self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [0, 1, 0, 2] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConv3_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.use_cuda = True self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 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] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [1, 1, 0, 0] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConvWithDilation_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [1, 1, 2, 1] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [0, 1, 1, 0] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConvandFuse_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [2, 1, 2, 3] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConv2andFuse_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [3, f_c, 3, 3] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [1, 1, 1, 2] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConv3andFuse_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 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] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [1, 2, 0, 2] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [2, 1, 1, 0] self.padding_algorithm = "EXPLICIT" class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2dOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True self.pad = [1, 1] self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW self.groups = 3 self.dilations = [2, 2] 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] self.op_type = "depthwise_conv2d" def init_paddings(self): self.pad = [1, 3, 1, 3] self.padding_algorithm = "EXPLICIT" #---------- 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) # depthwise conv2d create_test_padding_SAME_class(TestDepthwiseConv_AsyPadding) create_test_padding_SAME_class(TestDepthwiseConvWithDilation_AsyPadding) create_test_padding_SAME_class(TestDepthwiseConvandFuse_AsyPadding) create_test_padding_SAME_class(TestDepthwiseConvWithDilationandFuse_AsyPadding) create_test_padding_VALID_class(TestDepthwiseConv_AsyPadding) create_test_padding_VALID_class(TestDepthwiseConvWithDilation_AsyPadding) create_test_padding_VALID_class(TestDepthwiseConvandFuse_AsyPadding) create_test_padding_VALID_class(TestDepthwiseConvWithDilationandFuse_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_channel_last_class(TestDepthwiseConv_AsyPadding) create_test_channel_last_class(TestDepthwiseConvWithDilation2_AsyPadding) create_test_channel_last_class(TestDepthwiseConvandFuse_AsyPadding) create_test_channel_last_class(TestDepthwiseConvWithDilationandFuse_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) # --------- test python API --------------- class TestConv2dAPI(OpTest): def test_api(self): input_NHWC = fluid.layers.data( name="input_NHWC", shape=[2, 5, 5, 3], append_batch_size=False, dtype="float32") input_NCHW = fluid.layers.data( name="input_NCHW", shape=[2, 3, 5, 5], append_batch_size=False, dtype="float32") fluid.layers.conv2d( input=input_NHWC, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d( input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[1, 2, 1, 0], dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d( input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 0], [0, 0], [1, 1], [1, 1]], dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d( input=input_NHWC, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0]], dilation=[1, 1], groups=1, data_format="NHWC") fluid.layers.conv2d( input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding="SAME", dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d( input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding="VALID", dilation=[1, 1], groups=1, data_format="NCHW") class TestConv2dAPI_Error(OpTest): def test_api(self): input = fluid.layers.data( name="input", shape=[2, 5, 5, 5], append_batch_size=False, dtype="float32") # ValueError: cudnn def run_1(): fluid.layers.conv2d( input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, use_cudnn=[0], data_format="NCHW") self.assertRaises(ValueError, run_1) # ValueError: data_format def run_2(): fluid.layers.conv2d( input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, use_cudnn=False, data_format="NCHWC") self.assertRaises(ValueError, run_2) # ValueError: padding def run_3(): fluid.layers.conv2d( input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding="SAMEE", dilation=[1, 1], groups=1, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_3) def run_4(): fluid.layers.conv2d( input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 1], [0, 1], [0, 1], [0, 1]], dilation=[1, 1], groups=1, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_4) def run_5(): fluid.layers.conv2d( input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 1], [0, 1], [0, 1], [0, 1]], dilation=[1, 1], groups=1, use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_5) # ValueError: channel dimmention x = fluid.layers.data( name="x", shape=[2, 5, 5, -1], append_batch_size=False, dtype="float32") def run_6(): fluid.layers.conv2d( input=x, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_6) # ValueError: groups def run_7(): fluid.layers.conv2d( input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=3, use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_7) if __name__ == '__main__': unittest.main()