wehub-resource-sync
770 lines
24 KiB
Python
770 lines
24 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import itertools
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import unittest
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import numpy as np
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import paddle
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paddle.enable_static()
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from op_test import (
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OpTest,
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copy_bits_from_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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from paddle.base import core
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def convert_float_to_uint16(float_list, data_format="NCHW"):
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if data_format == "NHWC":
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float_list = np.transpose(float_list, [0, 4, 1, 2, 3])
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new_output = []
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for x in np.nditer(float_list):
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new_output.append(np.uint16(copy_bits_from_float_to_uint16(x)))
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new_output = np.reshape(new_output, float_list.shape).view(np.uint16)
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if data_format == "NHWC":
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new_output = np.transpose(new_output, [0, 2, 3, 4, 1])
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return new_output
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def conv3dtranspose_forward_naive(input_, filter_, attrs):
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padding_algorithm = attrs['padding_algorithm']
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if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
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raise ValueError(
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f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
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"It can only be 'SAME' or 'VALID'."
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)
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if attrs['data_format'] == 'NHWC':
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input_ = np.transpose(input_, [0, 4, 1, 2, 3])
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in_n, in_c, in_d, in_h, in_w = input_.shape
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f_c, f_out_c, f_d, f_h, f_w = filter_.shape
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groups = attrs['groups']
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assert in_c == f_c
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out_c = f_out_c * groups
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sub_in_c = in_c // groups
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stride, pad, dilations = (
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attrs['strides'],
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attrs['paddings'],
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attrs['dilations'],
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)
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def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
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padding = []
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for input_size, filter_size, stride_size in zip(
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input_shape, kernel_size, kernel_stride
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):
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out_size = int((input_size + stride_size - 1) / stride_size)
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pad_sum = np.max(
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((out_size - 1) * stride_size + filter_size - input_size, 0)
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)
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pad_0 = int(pad_sum / 2)
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pad_1 = int(pad_sum - pad_0)
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padding.append(pad_0)
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padding.append(pad_1)
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return padding
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ksize = filter_.shape[2:5]
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if padding_algorithm == "VALID":
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pad = [0, 0, 0, 0, 0, 0]
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elif padding_algorithm == "SAME":
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dilations = [1, 1, 1]
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input_data_shape = input_.shape[2:5]
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pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
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pad_d_0, pad_d_1 = pad[0], pad[0]
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pad_h_0, pad_h_1 = pad[1], pad[1]
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pad_w_0, pad_w_1 = pad[2], pad[2]
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if len(pad) == 6:
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pad_d_0, pad_d_1 = pad[0], pad[1]
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pad_h_0, pad_h_1 = pad[2], pad[3]
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pad_w_0, pad_w_1 = pad[4], pad[5]
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d_block_d = dilations[0] * (f_d - 1) + 1
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d_block_h = dilations[1] * (f_h - 1) + 1
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d_block_w = dilations[2] * (f_w - 1) + 1
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out_d = (in_d - 1) * stride[0] + d_block_d
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out_h = (in_h - 1) * stride[1] + d_block_h
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out_w = (in_w - 1) * stride[2] + d_block_w
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out = np.zeros((in_n, out_c, out_d, out_h, out_w))
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for n, d, i, j, g in itertools.product(
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range(in_n),
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range(in_d),
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range(in_h),
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range(in_w),
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range(groups),
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):
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input_masked = input_[
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n, g * sub_in_c : (g + 1) * sub_in_c, d, i, j
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] # (c)
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input_masked = np.reshape(input_masked, (sub_in_c, 1, 1, 1))
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input_masked = np.tile(input_masked, (1, f_d, f_h, f_w))
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for k in range(f_out_c):
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tmp_out = np.sum(
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input_masked
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* filter_[
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g * sub_in_c : (g + 1) * sub_in_c,
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k,
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:,
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:,
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:,
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],
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axis=0,
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)
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d1, d2 = d * stride[0], d * stride[0] + d_block_d
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i1, i2 = i * stride[1], i * stride[1] + d_block_h
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j1, j2 = j * stride[2], j * stride[2] + d_block_w
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out[
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n,
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g * f_out_c + k,
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d1 : d2 : dilations[0],
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i1 : i2 : dilations[1],
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j1 : j2 : dilations[2],
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] += tmp_out
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out = out[
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:,
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:,
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pad_d_0 : out_d - pad_d_1,
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pad_h_0 : out_h - pad_h_1,
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pad_w_0 : out_w - pad_w_1,
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]
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if attrs['data_format'] == 'NHWC':
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out = np.transpose(out, [0, 2, 3, 4, 1])
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return out
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def create_test_cudnn_fp16_class(parent, grad_check=True):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestConv3DTransposeCUDNNFP16(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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def test_check_grad_no_filter(self):
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place = get_device_place()
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if core.is_float16_supported(place) and grad_check:
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self.check_grad_with_place(
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place, ['Input'], 'Output', no_grad_set={'Filter'}
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)
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def test_check_grad_no_input(self):
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place = get_device_place()
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if core.is_float16_supported(place) and grad_check:
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self.check_grad_with_place(
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place, ['Filter'], 'Output', no_grad_set={'Input'}
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)
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cls_name = "{}_{}".format(parent.__name__, "CUDNNFP16OP")
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TestConv3DTransposeCUDNNFP16.__name__ = cls_name
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globals()[cls_name] = TestConv3DTransposeCUDNNFP16
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def create_test_cudnn_bf16_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestConv3DTransposeCUDNNBF16(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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{'Input', 'Filter'},
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'Output',
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)
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def test_check_grad_no_filter(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['Input'],
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'Output',
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no_grad_set={'Filter'},
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)
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def test_check_grad_no_input(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['Filter'],
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'Output',
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no_grad_set={'Input'},
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)
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cls_name = "{}_{}".format(parent.__name__, "CUDNNBF16OP")
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TestConv3DTransposeCUDNNBF16.__name__ = cls_name
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globals()[cls_name] = TestConv3DTransposeCUDNNBF16
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def conv3d_transpose_wrapper(
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x,
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weight,
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stride=1,
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padding=0,
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output_padding=[],
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output_size=[],
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padding_algorithm="EXPLICIT",
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groups=1,
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dilation=1,
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data_format="NCDHW",
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):
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if data_format == "AnyLayout":
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data_format = "NCDHW"
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return paddle._C_ops.conv3d_transpose(
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x,
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weight,
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stride,
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padding,
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output_padding,
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output_size,
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padding_algorithm,
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groups,
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dilation,
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data_format,
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)
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class TestConv3DTransposeOp(OpTest):
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def setUp(self):
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# init as conv transpose
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self.use_cudnn = False
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self.check_no_input = False
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self.check_no_filter = False
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self.data_format = 'NCHW'
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self.pad = [0, 0, 0]
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self.padding_algorithm = "EXPLICIT"
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self.init_op_type()
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self.init_kernel_type()
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self.init_test_case()
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if self.is_bfloat16_op():
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input = np.random.random(self.input_size).astype(np.float32)
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filter = np.random.random(self.filter_size).astype(np.float32)
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else:
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input = np.random.random(self.input_size).astype(self.dtype)
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filter = np.random.random(self.filter_size).astype(self.dtype)
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'padding_algorithm': self.padding_algorithm,
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'dilations': self.dilations,
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'groups': self.groups,
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'use_cudnn': self.use_cudnn,
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'data_format': self.data_format,
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}
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output = conv3dtranspose_forward_naive(
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input, filter, self.attrs
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).astype("float32")
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if self.is_bfloat16_op():
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self.inputs = {
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'Input': convert_float_to_uint16(input),
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'Filter': convert_float_to_uint16(filter),
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}
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else:
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self.inputs = {
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'Input': input,
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'Filter': filter,
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}
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output = output.astype(self.dtype)
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self.outputs = {'Output': output}
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def test_check_output(self):
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if self.use_cudnn:
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place = get_device_place()
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self.check_output_with_place(place, atol=1e-5)
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else:
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self.check_output()
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def test_check_grad(self):
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if self.use_cudnn:
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place = get_device_place()
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self.check_grad_with_place(
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place,
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{'Input', 'Filter'},
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'Output',
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max_relative_error=0.03,
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)
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else:
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self.check_grad(
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{'Input', 'Filter'}, 'Output', max_relative_error=0.03
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)
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def test_check_grad_no_filter(self):
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if self.use_cudnn:
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['Input'],
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'Output',
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max_relative_error=0.03,
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no_grad_set={'Filter'},
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)
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elif self.check_no_filter:
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self.check_grad(
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['Input'],
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'Output',
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max_relative_error=0.03,
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no_grad_set={'Filter'},
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)
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def test_check_grad_no_input(self):
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if self.use_cudnn:
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['Filter'],
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'Output',
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max_relative_error=0.03,
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no_grad_set={'Input'},
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)
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elif self.check_no_input:
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self.check_grad(
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['Filter'],
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'Output',
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max_relative_error=0.03,
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no_grad_set={'Input'},
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)
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def init_test_case(self):
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self.pad = [0, 0, 0]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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def init_op_type(self):
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self.op_type = "conv3d_transpose"
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self.python_api = conv3d_transpose_wrapper
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def init_kernel_type(self):
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self.dtype = np.float32
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class TestWithSymmetricPad(TestConv3DTransposeOp):
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def init_test_case(self):
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self.check_no_input = True
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [1, 2, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class TestWithAsymmetricPad(TestConv3DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 0, 1, 0, 1, 2]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [1, 2, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class TestWithSAMEPad(TestConv3DTransposeOp):
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def init_test_case(self):
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self.stride = [1, 1, 2]
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self.dilations = [1, 2, 1]
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self.groups = 1
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self.input_size = [1, 2, 5, 5, 6] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 4]
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self.padding_algorithm = 'SAME'
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class TestWithVALIDPad(TestConv3DTransposeOp):
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def init_test_case(self):
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self.stride = [2, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [1, 2, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 4, 3]
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self.padding_algorithm = 'VALID'
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class TestWithStride(TestConv3DTransposeOp):
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def init_test_case(self):
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self.check_no_filter = True
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self.pad = [1, 1, 1]
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self.stride = [2, 2, 2]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [1, 2, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class TestWithGroups(TestConv3DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 2
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self.input_size = [1, 2, 5, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 3, 3, 3, 3]
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class TestWithDilation(TestConv3DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [2, 2, 2]
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self.groups = 1
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self.input_size = [1, 2, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class Test_NHWC(TestConv3DTransposeOp):
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def init_test_case(self):
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self.pad = [0, 0, 0]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [1, 5, 5, 5, 2] # NDHWC
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f_c = self.input_size[-1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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self.data_format = 'NHWC'
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# ------------ test_cudnn ------------
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestCUDNN(TestConv3DTransposeOp):
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv3d_transpose"
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self.python_api = conv3d_transpose_wrapper
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
|
|
class TestCUDNNWithSymmetricPad(TestWithSymmetricPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 2, 5, 5, 5] # NCDHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1, 0, 0, 2]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 2, 4, 4, 4] # NCDHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithSAMEPad(TestWithSAMEPad):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 2]
|
|
self.dilations = [1, 2, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 2, 5, 5, 5] # NCDHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 4, 3]
|
|
self.padding_algorithm = 'SAME'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithVALIDPad(TestWithVALIDPad):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 2, 5, 5, 5] # NCDHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
self.padding_algorithm = 'VALID'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithStride(TestWithStride):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [2, 2, 2]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 2, 5, 5, 5] # NCDHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithGroups(TestWithGroups):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 2
|
|
self.input_size = [1, 2, 5, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 3, 3, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
# Please Don't remove the following code.
|
|
# Currently, CI use cudnn V5.0 which not support dilation conv.
|
|
# class TestCUDNNWithDilation(TestWithDilation):
|
|
# def init_test_case(self):
|
|
# self.pad = [1, 1, 1]
|
|
# self.stride = [2, 2, 2]
|
|
# self.dilations = [2, 2, 2]
|
|
# self.input_size = [2, 3, 5, 5, 5] # NCDHW
|
|
# f_c = self.input_size[1]
|
|
# self.filter_size = [f_c, 6, 3, 3, 3]
|
|
#
|
|
# def init_op_type(self):
|
|
# self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNN_NHWC(TestConv3DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0, 0]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 5, 5, 5, 2] # NDHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithSymmetricPad_NHWC(TestWithSymmetricPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 5, 5, 5, 2] # NDHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithAsymmetricPad_NHWC(TestWithAsymmetricPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 0, 1, 0, 0, 2]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 5, 5, 5, 2] # NDHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithStride_NHWC(TestWithStride):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [2, 2, 2]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 5, 5, 5, 2] # NDHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithGroups_NHWC(TestWithGroups):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [1, 1, 1]
|
|
self.dilations = [1, 1, 1]
|
|
self.groups = 2
|
|
self.input_size = [1, 5, 5, 5, 2] # NDHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 3, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv3d_transpose"
|
|
self.python_api = conv3d_transpose_wrapper
|
|
|
|
|
|
# ----------------Conv3DTransposeCUDNN fp16----------------
|
|
create_test_cudnn_fp16_class(TestConv3DTransposeOp)
|
|
create_test_cudnn_fp16_class(TestWithSymmetricPad)
|
|
create_test_cudnn_fp16_class(TestWithAsymmetricPad)
|
|
create_test_cudnn_fp16_class(TestWithSAMEPad)
|
|
create_test_cudnn_fp16_class(TestWithVALIDPad)
|
|
create_test_cudnn_fp16_class(TestWithStride)
|
|
create_test_cudnn_fp16_class(TestWithGroups)
|
|
create_test_cudnn_fp16_class(TestWithDilation)
|
|
create_test_cudnn_fp16_class(Test_NHWC)
|
|
|
|
|
|
# ----------------Conv3DTransposeCUDNN bf16----------------
|
|
create_test_cudnn_bf16_class(TestConv3DTransposeOp)
|
|
create_test_cudnn_bf16_class(TestWithSymmetricPad)
|
|
create_test_cudnn_bf16_class(TestWithAsymmetricPad)
|
|
create_test_cudnn_bf16_class(TestWithSAMEPad)
|
|
create_test_cudnn_bf16_class(TestWithVALIDPad)
|
|
create_test_cudnn_bf16_class(TestWithStride)
|
|
create_test_cudnn_bf16_class(TestWithGroups)
|
|
create_test_cudnn_bf16_class(TestWithDilation)
|
|
create_test_cudnn_bf16_class(Test_NHWC)
|
|
|
|
|
|
class TestConv3dTranspose(unittest.TestCase):
|
|
def error_weight_input(self):
|
|
array = np.array([1], dtype=np.float32)
|
|
x = paddle.to_tensor(
|
|
np.reshape(array, [1, 1, 1, 1, 1]), dtype='float32'
|
|
)
|
|
weight = paddle.to_tensor(np.reshape(array, [1]), dtype='float32')
|
|
paddle.nn.functional.conv3d_transpose(x, weight, bias=0)
|
|
|
|
def test_type_error(self):
|
|
self.assertRaises(ValueError, self.error_weight_input)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|