# 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 from op_test import ( OpTest, convert_float_to_uint16, get_device_place, get_numeric_gradient, is_custom_device, ) from testsuite import create_op import paddle from paddle.base import core def conv3d_forward_naive( input, filter, group, conv_param, padding_algorithm='EXPLICIT', data_format="NCDHW", ): 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 ["NCDHW", "NDHWC"]: raise ValueError( f"Unknown Attr(data_format): '{data_format}' ." "It can only be 'NCDHW' or 'NDHWC'." ) channel_last = data_format == "NDHWC" if channel_last: input = np.transpose(input, [0, 4, 1, 2, 3]) in_n, in_c, in_d, in_h, in_w = input.shape f_n, f_c, f_d, 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[2:5] if padding_algorithm == "VALID": pad = [0, 0, 0, 0, 0, 0] elif padding_algorithm == "SAME": dilation = [1, 1, 1] input_data_shape = input.shape[2:5] pad = _get_padding_with_SAME(input_data_shape, ksize, stride) pad_d_0, pad_d_1 = pad[0], pad[0] pad_h_0, pad_h_1 = pad[1], pad[1] pad_w_0, pad_w_1 = pad[2], pad[2] if len(pad) == 6: pad_d_0, pad_d_1 = pad[0], pad[1] pad_h_0, pad_h_1 = pad[2], pad[3] pad_w_0, pad_w_1 = pad[4], pad[5] out_d = ( 1 + (in_d + pad_d_0 + pad_d_1 - (dilation[0] * (f_d - 1) + 1)) // stride[0] ) out_h = ( 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[1] * (f_h - 1) + 1)) // stride[1] ) out_w = ( 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[2] * (f_w - 1) + 1)) // stride[2] ) out = np.zeros((in_n, out_c, out_d, out_h, out_w)) d_block_d = dilation[0] * (f_d - 1) + 1 d_block_h = dilation[1] * (f_h - 1) + 1 d_block_w = dilation[2] * (f_w - 1) + 1 input_pad = np.pad( input, ( (0, 0), (0, 0), (pad_d_0, pad_d_1), (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_d, d_block_h, d_block_w)) filter_dilation[ :, :, 0 : d_block_d : dilation[0], 0 : d_block_h : dilation[1], 0 : d_block_w : dilation[2], ] = filter for d in range(out_d): 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, d * stride[0] : d * stride[0] + d_block_d, i * stride[1] : i * stride[1] + d_block_h, j * stride[2] : j * stride[2] + d_block_w, ] f_sub = filter_dilation[ g * sub_f_n : (g + 1) * sub_f_n, :, :, :, : ] for k in range(sub_out_c): out[:, g * sub_out_c + k, d, i, j] = np.sum( input_pad_masked * f_sub[k, :, :, :, :], axis=(1, 2, 3, 4), ) if channel_last: out = np.transpose(out, [0, 2, 3, 4, 1]) return out def create_test_cudnn_class(parent): @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "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 = "{}_{}".format(parent.__name__, "CUDNN") TestCUDNNCase.__name__ = cls_name globals()[cls_name] = TestCUDNNCase def create_test_cudnn_bf16_class(parent): @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestConv3DCUDNNBF16(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.dtype = np.uint16 def test_check_output(self): place = get_device_place() self.check_output_with_place( place, check_dygraph=(not self.use_onednn), check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad_no_filter(self): place = get_device_place() numeric_grads = self.get_numeric_grad(place, 'Input') self.check_grad_with_place( place, ['Input'], 'Output', no_grad_set={'Filter'}, check_dygraph=(not self.use_onednn), user_defined_grads=[numeric_grads], check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad_no_input(self): place = get_device_place() numeric_grads = self.get_numeric_grad(place, 'Filter') self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set={'Input'}, check_dygraph=(not self.use_onednn), user_defined_grads=[numeric_grads], check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad(self): place = get_device_place() numeric_input_grads = self.get_numeric_grad(place, 'Input') numeric_filter_grads = self.get_numeric_grad(place, 'Filter') self.check_grad_with_place( place, ['Input', 'Filter'], 'Output', user_defined_grads=[numeric_input_grads, numeric_filter_grads], check_dygraph=(not self.use_onednn), check_pir=True, check_pir_onednn=self.check_pir_onednn, ) cls_name = "{}_{}".format(parent.__name__, "CUDNNBF16OP") TestConv3DCUDNNBF16.__name__ = cls_name globals()[cls_name] = TestConv3DCUDNNBF16 def create_test_padding_SAME_class(parent): class TestPaddingSAMECase(parent): def init_paddings(self): self.pad = [0, 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, 1] self.padding_algorithm = "VALID" cls_name = "{}_{}".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() or is_custom_device()), "core is not compiled with CUDA", ) class TestCUDNNPaddingSAMECase(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, 1] self.padding_algorithm = "SAME" cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingSAMEOp") TestCUDNNPaddingSAMECase.__name__ = cls_name globals()[cls_name] = TestCUDNNPaddingSAMECase def create_test_cudnn_padding_VALID_class(parent): @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "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, 1] self.padding_algorithm = "VALID" cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingVALIDOp") TestCUDNNPaddingVALIDCase.__name__ = cls_name globals()[cls_name] = TestCUDNNPaddingVALIDCase def create_test_channel_last_class(parent): class TestChannelLastCase(parent): def init_data_format(self): self.data_format = "NDHWC" def init_test_case_2(self): N, C, D, H, W = self.input_size self.input_size = [N, D, H, W, C] cls_name = "{}_{}".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() or is_custom_device()), "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 = "NDHWC" def init_test_case_2(self): N, C, D, H, W = self.input_size self.input_size = [N, D, H, W, C] cls_name = "{}_{}".format(parent.__name__, "CudnnChannelLast") TestCudnnChannelLastCase.__name__ = cls_name globals()[cls_name] = TestCudnnChannelLastCase def conv3d_wrapper( x, weight, stride=1, padding=0, padding_algorithm="EXPLICIT", groups=1, dilation=1, data_format="NCDHW", ): if data_format == "AnyLayout": data_format = "NCDHW" if padding_algorithm is None: padding_algorithm = "EXPLICIT" return paddle._C_ops.conv3d( x, weight, stride, padding, padding_algorithm, groups, dilation, data_format, ) class TestConv3DOp(OpTest): def setUp(self): self.op_type = "conv3d" self.python_api = conv3d_wrapper self.use_cudnn = False self.use_onednn = False self.data_format = "AnyLayout" self.dtype = np.float64 self.init_kernel_type() self.init_group() self.init_dilation() self.init_test_case() conv3d_param = { 'stride': self.stride, 'pad': self.pad, 'dilation': self.dilation, } if self.is_bfloat16_op(): input = np.random.random(self.input_size).astype(np.float32) filter = np.random.random(self.filter_size).astype(np.float32) else: input = np.random.random(self.input_size).astype(self.dtype) filter = np.random.random(self.filter_size).astype(self.dtype) output = conv3d_forward_naive( input, filter, self.groups, conv3d_param, ) if self.is_bfloat16_op(): output = convert_float_to_uint16(output) self.inputs = { 'Input': convert_float_to_uint16(input), 'Filter': convert_float_to_uint16(filter), } self.inputs_fp32 = { 'Input': OpTest.np_dtype_to_base_dtype(input), 'Filter': OpTest.np_dtype_to_base_dtype(filter), } else: output = output.astype(self.dtype) self.inputs = { 'Input': OpTest.np_dtype_to_base_dtype(input), 'Filter': OpTest.np_dtype_to_base_dtype(filter), } self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'groups': self.groups, 'dilation': self.dilation, 'use_cudnn': self.use_cudnn, 'use_onednn': self.use_onednn, 'data_format': self.data_format, } self.outputs = {'Output': output} def has_cudnn(self): return ( core.is_compiled_with_cuda() or is_custom_device() ) and self.use_cudnn def test_check_output(self): # TODO(wangzhongpu): support onednn op in dygraph mode place = get_device_place() if self.has_cudnn() else core.CPUPlace() self.check_output_with_place( place, atol=1e-5, check_dygraph=(not self.use_onednn), check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad(self): place = get_device_place() if self.has_cudnn() else core.CPUPlace() # TODO(wangzhongpu): support onednn op in dygraph mode self.check_grad_with_place( place, {'Input', 'Filter'}, 'Output', max_relative_error=0.03, check_dygraph=(not self.use_onednn), check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad_no_filter(self): place = get_device_place() if self.has_cudnn() else core.CPUPlace() # TODO(wangzhongpu): support onednn op in dygraph mode self.check_grad_with_place( place, ['Input'], 'Output', max_relative_error=0.03, no_grad_set={'Filter'}, check_dygraph=(not self.use_onednn), check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad_no_input(self): place = get_device_place() if self.has_cudnn() else core.CPUPlace() # TODO(wangzhongpu): support onednn op in dygraph mode self.check_grad_with_place( place, ['Filter'], 'Output', max_relative_error=0.03, no_grad_set={'Input'}, check_dygraph=(not self.use_onednn), check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] def init_test_case_2(self): pass def init_dilation(self): self.dilation = [1, 1, 1] def init_group(self): self.groups = 1 def init_kernel_type(self): pass class TestCase1(TestConv3DOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] class TestWithGroup1(TestConv3DOp): def init_group(self): self.groups = 3 class TestWithGroup2(TestCase1): def init_group(self): self.groups = 3 class TestWith1x1(TestConv3DOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] 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, 1] def init_dilation(self): self.dilation = [1, 1, 1] def init_group(self): self.groups = 3 class TestWithInput1x1Filter1x1(TestConv3DOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [40, 3, 1, 1, 1] 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, 1] def init_dilation(self): self.dilation = [1, 1, 1] def init_group(self): self.groups = 3 class TestWithDilation(TestConv3DOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [2, 3, 6, 6, 6] assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [24, f_c, 2, 2, 2] def init_dilation(self): self.dilation = [2, 2, 2] def init_group(self): self.groups = 3 # ---------------- Conv3DCUDNN ---------------- @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestCUDNN(TestConv3DOp): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestFP16CUDNN(TestConv3DOp): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() if core.is_float16_supported(place): self.check_output_with_place( place, atol=2e-2, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestWithGroup1CUDNN(TestWithGroup1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestFP16WithGroup1CUDNN(TestWithGroup1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() if core.is_float16_supported(place): self.check_output_with_place( place, atol=2e-2, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestWithGroup2CUDNN(TestWithGroup2): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestFP16WithGroup2CUDNN(TestWithGroup2): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() if core.is_float16_supported(place): self.check_output_with_place( place, atol=2e-2, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestWith1x1CUDNN(TestWith1x1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestFP16With1x1CUDNN(TestWith1x1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() if core.is_float16_supported(place): self.check_output_with_place( place, atol=2e-2, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestWithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestFP16WithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() if core.is_float16_supported(place): self.check_output_with_place( place, atol=2e-2, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) class TestCUDNNExhaustiveSearch(TestCUDNN): 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 # ----------------Conv3DCUDNN bf16---------------- create_test_cudnn_bf16_class(TestConv3DOp) create_test_cudnn_bf16_class(TestWithGroup1) create_test_cudnn_bf16_class(TestWithGroup2) create_test_cudnn_bf16_class(TestWith1x1) create_test_cudnn_bf16_class(TestWithInput1x1Filter1x1) # ---- test asymmetric padding ---- class TestConv3DOp_2(OpTest): def setUp(self): self.op_type = "conv3d" self.python_api = conv3d_wrapper self.use_cudnn = False self.use_onednn = False self.data_format = "NCDHW" 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() conv3d_param = { 'stride': self.stride, 'pad': self.pad, 'dilation': self.dilation, } input = np.random.random(self.input_size).astype(self.dtype) filter = np.random.random(self.filter_size).astype(self.dtype) output = conv3d_forward_naive( input, filter, self.groups, conv3d_param, self.padding_algorithm, self.data_format, ).astype(self.dtype) self.inputs = { 'Input': OpTest.np_dtype_to_base_dtype(input), 'Filter': OpTest.np_dtype_to_base_dtype(filter), } self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'padding_algorithm': self.padding_algorithm, 'groups': self.groups, 'dilation': self.dilation, 'use_cudnn': self.use_cudnn, 'use_onednn': self.use_onednn, 'data_format': self.data_format, } self.outputs = {'Output': output} def has_cudnn(self): return ( core.is_compiled_with_cuda() or is_custom_device() ) and self.use_cudnn def test_check_output(self): place = get_device_place() if self.has_cudnn() else core.CPUPlace() self.check_output_with_place( place, atol=1e-5, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad(self): if self.dtype == np.float16: return place = get_device_place() if self.has_cudnn() else core.CPUPlace() self.check_grad_with_place( place, {'Input', 'Filter'}, 'Output', max_relative_error=0.03, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad_no_filter(self): if self.dtype == np.float16: return place = get_device_place() if self.has_cudnn() else core.CPUPlace() self.check_grad_with_place( place, ['Input'], 'Output', max_relative_error=0.03, no_grad_set={'Filter'}, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def test_check_grad_no_input(self): if self.dtype == np.float16: return place = get_device_place() if self.has_cudnn() else core.CPUPlace() self.check_grad_with_place( place, ['Filter'], 'Output', max_relative_error=0.03, no_grad_set={'Input'}, check_pir=True, check_pir_onednn=self.check_pir_onednn, ) def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] def init_test_case_2(self): pass def init_dilation(self): self.dilation = [1, 1, 1] def init_group(self): self.groups = 1 def init_kernel_type(self): pass def init_paddings(self): self.pad = [0, 0, 0] self.padding_algorithm = "EXPLICIT" def init_data_format(self): self.data_format = "NCDHW" class TestConv3DOp_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 2] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] def init_paddings(self): self.pad = [1, 0, 1, 0, 0, 2] self.padding_algorithm = "EXPLICIT" class TestConv3DOp_DiffDataInDiffDim(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 2] self.input_size = [2, 3, 4, 5, 5] # NCDHW 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, 4, 3] def init_paddings(self): self.pad = [1, 0, 1, 0, 0, 2] self.padding_algorithm = "EXPLICIT" create_test_padding_SAME_class(TestConv3DOp_DiffDataInDiffDim) create_test_padding_VALID_class(TestConv3DOp_DiffDataInDiffDim) create_test_channel_last_class(TestConv3DOp_DiffDataInDiffDim) class TestCase1_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] def init_paddings(self): self.pad = [0, 0, 1, 0, 0, 2] self.padding_algorithm = "EXPLICIT" class TestWithGroup1_AsyPadding(TestConv3DOp_2): def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [1, 1, 1, 0, 0, 2] self.padding_algorithm = "EXPLICIT" class TestWithGroup2_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [1, 1, 0, 1, 0, 2] self.padding_algorithm = "EXPLICIT" class TestWith1x1_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] 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, 1] def init_dilation(self): self.dilation = [1, 1, 1] def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [0, 0, 1, 0, 0, 2] self.padding_algorithm = "EXPLICIT" class TestWithDilation_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 6, 6, 6] assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [24, f_c, 2, 2, 2] def init_dilation(self): self.dilation = [2, 2, 2] def init_group(self): self.groups = 3 def init_paddings(self): self.pad = [0, 0, 1, 0, 1, 0] self.padding_algorithm = "EXPLICIT" create_test_cudnn_class(TestConv3DOp_AsyPadding) create_test_cudnn_class(TestWithGroup1_AsyPadding) create_test_cudnn_class(TestWithGroup2_AsyPadding) create_test_cudnn_class(TestWith1x1_AsyPadding) create_test_cudnn_class(TestWithDilation_AsyPadding) create_test_padding_SAME_class(TestConv3DOp_AsyPadding) create_test_padding_SAME_class(TestWithGroup1_AsyPadding) create_test_padding_SAME_class(TestWith1x1_AsyPadding) create_test_padding_VALID_class(TestConv3DOp_AsyPadding) create_test_padding_VALID_class(TestWithGroup1_AsyPadding) create_test_padding_VALID_class(TestWith1x1_AsyPadding) create_test_cudnn_padding_SAME_class(TestConv3DOp_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithGroup1_AsyPadding) create_test_cudnn_padding_SAME_class(TestWith1x1_AsyPadding) create_test_cudnn_padding_VALID_class(TestConv3DOp_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithGroup1_AsyPadding) create_test_cudnn_padding_VALID_class(TestWith1x1_AsyPadding) create_test_channel_last_class(TestConv3DOp_AsyPadding) create_test_channel_last_class(TestWithGroup1_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) create_test_channel_last_class(TestConv3DOp_AsyPadding) create_test_channel_last_class(TestWithGroup1_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding) create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding) create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding) create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding) create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding) create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding) # FIXME(typhoonzero): find a way to determine if # using cudnn > 6 in python # class TestWithDilationCUDNN(TestWithDilation): # def init_op_type(self): # self.op_type = "conv3d" # --------- test python API --------------- class TestConv3DAPI(unittest.TestCase): def api_run(self): input_NDHWC = paddle.static.data( name="input_NDHWC", shape=[2, 5, 5, 5, 3], dtype="float32", ) input_NDHWC_in_channel = 5 input_NCDHW = paddle.static.data( name="input_NCDHW", shape=[2, 3, 5, 5, 3], dtype="float32", ) input_NCDHW_in_channel = 3 paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=0, dilation=[1, 1, 1], groups=1, data_format="NCDHW", )(input_NCDHW) paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 2, 1, 0, 1, 0], dilation=[1, 1, 1], groups=1, data_format="NCDHW", )(input_NCDHW) paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]], dilation=[1, 1, 1], groups=1, data_format="NCDHW", )(input_NCDHW) paddle.nn.Conv3D( in_channels=input_NDHWC_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]], dilation=[1, 1, 1], groups=1, data_format="NDHWC", )(input_NDHWC) paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding="SAME", dilation=[1, 1, 1], groups=1, data_format="NCDHW", )(input_NCDHW) paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding="VALID", dilation=[1, 1, 1], groups=1, data_format="NCDHW", )(input_NCDHW) def test_api(self): with paddle.pir_utils.OldIrGuard(): self.api_run() with paddle.pir_utils.IrGuard(): self.api_run() class TestConv3DAPI_Error(unittest.TestCase): def test_api(self): with paddle.pir_utils.OldIrGuard(): input = paddle.static.data( name="input", shape=[2, 5, 5, 5, 4], dtype="float32", ) # ValueError: cudnn def run_1(): paddle.static.nn.conv3d( input=input, num_filters=3, filter_size=3, stride=1, padding=0, dilation=1, groups=1, use_cudnn=[0], data_format="NCDHW", ) self.assertRaises(ValueError, run_1) # ValueError: data_format def run_2(): paddle.static.nn.conv3d( input=input, num_filters=3, filter_size=[3, 3, 3], stride=[1, 1, 1], padding=0, dilation=[1, 1, 1], groups=1, use_cudnn=False, data_format="NCHWC", ) self.assertRaises(ValueError, run_2) # ValueError: padding def run_3(): paddle.static.nn.conv3d( input=input, num_filters=3, filter_size=3, stride=1, padding="SAMEE", dilation=1, groups=1, use_cudnn=False, data_format="NCDHW", ) self.assertRaises(ValueError, run_3) def run_4(): paddle.static.nn.conv3d( input=input, num_filters=3, filter_size=3, stride=1, padding=[[0, 1], [0, 0], [0, 1], [0, 1], [0, 1]], dilation=1, groups=1, use_cudnn=False, data_format="NCDHW", ) self.assertRaises(ValueError, run_4) def run_5(): paddle.static.nn.conv3d( input=input, num_filters=3, filter_size=0, stride=0, padding=[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1]], dilation=1, groups=1, use_cudnn=False, data_format="NDHWC", ) self.assertRaises(ValueError, run_5) # ValueError: channel dimension x = paddle.static.data( name="x", shape=[2, 5, 5, 5, -1], dtype="float32", ) def run_6(): paddle.static.nn.conv3d( input=x, num_filters=3, filter_size=3, stride=1, padding=0, dilation=1, groups=1, use_cudnn=False, data_format="NDHWC", ) self.assertRaises(ValueError, run_6) # ValueError: groups def run_7(): paddle.static.nn.conv3d( input=input, num_filters=3, filter_size=3, stride=1, padding=0, dilation=1, groups=3, use_cudnn=False, data_format="NDHWC", ) self.assertRaises(ValueError, run_7) # ValueError: filter num def run_8(): paddle.static.nn.conv3d( input=input, num_filters=0, filter_size=0, stride=0, padding=0, dilation=0, groups=1, use_cudnn=False, data_format="NDHWC", ) self.assertRaises(ValueError, run_8) class TestPIRConv3DAPI_Error(unittest.TestCase): def test_api(self): with paddle.pir_utils.IrGuard(): input = paddle.static.data( name="input", shape=[2, 5, 5, 5, 4], dtype="float32", ) input_NCDHW_in_channel = 5 input_NDHWC_in_channel = 4 # ValueError: cudnn # def run_1(): # model = paddle.nn.Conv3D( # in_channels=input_NCDHW_in_channel, # out_channels=3, # kernel_size=3, # stride=1, # padding=0, # dilation=1, # groups=1, # data_format="NCDHW", # ) # model._use_cudnn = [0] # model(input) # # self.assertRaises(ValueError, run_1) # ValueError: data_format def run_2(): paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=0, dilation=[1, 1, 1], groups=1, data_format="NCHWC", )(input) self.assertRaises(ValueError, run_2) # ValueError: padding def run_3(): paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=3, stride=1, padding="SAMEE", dilation=1, groups=1, data_format="NCDHW", )(input) self.assertRaises(ValueError, run_3) def run_4(): paddle.nn.Conv3D( in_channels=input_NCDHW_in_channel, out_channels=3, kernel_size=3, stride=1, padding=[[0, 1], [0, 0], [0, 1], [0, 1], [0, 1]], dilation=1, groups=1, data_format="NCDHW", )(input) self.assertRaises(ValueError, run_4) def run_5(): paddle.nn.Conv3D( in_channels=input_NDHWC_in_channel, out_channels=3, kernel_size=0, stride=0, padding=[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1]], dilation=1, groups=1, data_format="NDHWC", )(input) self.assertRaises(ValueError, run_5) # ValueError: channel dimension x = paddle.static.data( name="x", shape=[2, 5, 5, 5, -1], dtype="float32", ) x_NCDHW_in_channel = 5 x_NDHWC_in_channel = -1 def run_6(): paddle.nn.Conv3D( in_channels=x_NDHWC_in_channel, out_channels=3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, data_format="NDHWC", )(x) self.assertRaises(AssertionError, run_6) # ValueError: groups def run_7(): paddle.nn.Conv3D( in_channels=x_NDHWC_in_channel, out_channels=3, kernel_size=3, stride=1, padding=0, dilation=1, groups=3, data_format="NDHWC", )(x) self.assertRaises(ValueError, run_7) # ValueError: filter num def run_8(): paddle.nn.Conv3D( in_channels=x_NDHWC_in_channel, out_channels=0, kernel_size=0, stride=0, padding=0, dilation=0, groups=1, data_format="NDHWC", )(x) self.assertRaises(AssertionError, run_8) if __name__ == '__main__': unittest.main()