# 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 unittest import numpy as np from get_test_cover_info import ( XPUOpTestWrapper, create_test_class, get_xpu_op_support_types, ) from op_test_xpu import XPUOpTest import paddle from paddle.base import core 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( f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. " "It can only be 'SAME' or 'VALID'." ) if data_format not in ["NCHW", "NHWC"]: raise ValueError( f"Unknown Attr(data_format): '{data_format}' ." "It can only be 'NCHW' or 'NHWC'." ) 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_block_h = dilation[0] * (f_h - 1) + 1 d_block_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_block_h, d_block_w)) filter_dilation[ :, :, 0 : d_block_h : dilation[0], 0 : d_block_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_block_h, j * stride[1] : j * stride[1] + d_block_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_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 = "{}_{}".format(parent.__name__, "ChannelLast") TestChannelLastCase.__name__ = cls_name globals()[cls_name] = TestChannelLastCase def create_test_padding_SAME_class(parent): class TestPaddingSAMECase(parent): def init_paddings(self): self.pad = [0, 0] self.padding_algorithm = "SAME" cls_name = "{}_{}".format(parent.__name__, "PaddingSAMEOp") TestPaddingSAMECase.__name__ = cls_name globals()[cls_name] = TestPaddingSAMECase def create_test_padding_VALID_class(parent): class TestPaddingVALIDCase(parent): def init_paddings(self): self.pad = [1, 1] self.padding_algorithm = "VALID" cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp") TestPaddingVALIDCase.__name__ = cls_name globals()[cls_name] = TestPaddingVALIDCase class XPUTestConv2DOp(XPUOpTestWrapper): def __init__(self): self.op_name = 'conv2d' self.use_dynamic_create_class = False class TestConv2DOp(XPUOpTest): def setUp(self): self.dtype = self.in_type self.place = paddle.XPUPlace(0) self.op_type = "conv2d" self.use_cudnn = False self.exhaustive_search = False self.use_cuda = False self.use_onednn = False self.fuse_relu_before_depthwise_conv = False self.data_format = "AnyLayout" 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, } np.random.seed(100) 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 np.random.seed(1) 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': XPUOpTest.np_dtype_to_base_dtype(input), 'Filter': XPUOpTest.np_dtype_to_base_dtype(filter), } self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'groups': self.groups, 'dilations': self.dilations, 'use_cudnn': self.use_cudnn, 'use_onednn': self.use_onednn, '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): if core.is_compiled_with_xpu(): paddle.enable_static() self.check_output_with_place(self.place, atol=0.005, rtol=0.005) def test_check_grad(self): if hasattr(self, "no_need_check_grad") and self.no_need_check_grad: return if core.is_compiled_with_xpu(): paddle.enable_static() self.check_grad_with_place( self.place, {'Input', 'Filter'}, 'Output' ) def test_check_grad_no_filter(self): if hasattr(self, "no_need_check_grad") and self.no_need_check_grad: return if core.is_compiled_with_xpu(): paddle.enable_static() self.check_grad_with_place( self.place, ['Input'], 'Output', no_grad_set={'Filter'} ) def test_check_grad_no_input(self): if hasattr(self, "no_need_check_grad") and self.no_need_check_grad: return if core.is_compiled_with_xpu(): paddle.enable_static() self.check_grad_with_place( self.place, ['Filter'], 'Output', no_grad_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 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 = 1 # ---- test asymmetric padding ---- class XPUTestConv2DOp_v2(XPUOpTestWrapper): def __init__(self): self.op_name = 'conv2d' self.use_dynamic_create_class = False class TestConv2DOp_v2(XPUOpTest): def setUp(self): self.dtype = self.in_type self.place = paddle.XPUPlace(0) self.op_type = "conv2d" self.use_cudnn = False self.exhaustive_search = False self.use_cuda = False self.use_onednn = False self.fuse_relu_before_depthwise_conv = False 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, } np.random.seed(100) 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 np.random.seed(8) 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': XPUOpTest.np_dtype_to_base_dtype(input), 'Filter': XPUOpTest.np_dtype_to_base_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_onednn': self.use_onednn, '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 onednn op in dygraph mode if core.is_compiled_with_xpu(): paddle.enable_static() self.check_output_with_place( place=self.place, atol=0.005, rtol=0.005 ) def test_check_grad(self): # TODO(wangzhongpu): support onednn op in dygraph mode if hasattr(self, "no_need_check_grad") and self.no_need_check_grad: return if core.is_compiled_with_xpu(): paddle.enable_static() self.check_grad_with_place( self.place, {'Input', 'Filter'}, 'Output' ) def test_check_grad_no_filter(self): # TODO(wangzhongpu): support onednn op in dygraph mode if hasattr(self, "no_need_check_grad") and self.no_need_check_grad: return if core.is_compiled_with_xpu(): paddle.enable_static() self.check_grad_with_place( self.place, ['Input'], 'Output', no_grad_set={'Filter'} ) def test_check_grad_no_input(self): # TODO(wangzhongpu): support onednn op in dygraph mode if hasattr(self, "no_need_check_grad") and self.no_need_check_grad: return if core.is_compiled_with_xpu(): paddle.enable_static() self.check_grad_with_place( self.place, ['Filter'], 'Output', no_grad_set={'Input'} ) 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, 0, 0] 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 = [1, 1, 1, 1] 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 = [1, 1, 1, 1] self.padding_algorithm = "EXPLICIT" class XPUTestConv2DOp_NHWC(XPUOpTestWrapper): def __init__(self): self.op_name = 'conv2d' self.use_dynamic_create_class = False class TestConv2DOp_AsyPadding_NHWC( XPUTestConv2DOp_v2.TestConv2DOp_AsyPadding ): 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] class TestWithPad_AsyPadding_NHWC( XPUTestConv2DOp_v2.TestWithPad_AsyPadding ): 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] support_types = get_xpu_op_support_types('conv2d') for stype in ['float32']: create_test_class(globals(), XPUTestConv2DOp, stype) create_test_class(globals(), XPUTestConv2DOp_v2, stype) create_test_class( globals(), XPUTestConv2DOp_NHWC, stype, ignore_device_version=[core.XPUVersion.XPU1], ) # ---------- 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_VALID_class(TestConv2DOp_AsyPadding) # create_test_padding_VALID_class(TestWithPad_AsyPadding) # create_test_padding_VALID_class(TestWithStride_AsyPadding) if __name__ == '__main__': unittest.main()