wehub-resource-sync
299 lines
10 KiB
Python
299 lines
10 KiB
Python
# Copyright (c) 2022 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 unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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paddle.enable_static()
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def conv2dtranspose_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, 3, 1, 2])
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in_n, in_c, in_h, in_w = input_.shape
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f_c, f_out_c, 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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# update pad and dilation
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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[2:4]
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if padding_algorithm == "VALID":
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pad = [0, 0, 0, 0]
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elif padding_algorithm == "SAME":
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dilations = [1, 1]
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input_data_shape = input_.shape[2:4]
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pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
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pad_h_0, pad_h_1 = pad[0], pad[0]
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pad_w_0, pad_w_1 = pad[1], pad[1]
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if len(pad) == 4:
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pad_h_0, pad_h_1 = pad[0], pad[1]
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pad_w_0, pad_w_1 = pad[2], pad[3]
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d_block_h = dilations[0] * (f_h - 1) + 1
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d_block_w = dilations[1] * (f_w - 1) + 1
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out_h = (in_h - 1) * stride[0] + d_block_h
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out_w = (in_w - 1) * stride[1] + d_block_w
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if 'output_size' in attrs:
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output_size = attrs['output_size']
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out_h = output_size[0] + pad_h_0 + pad_h_1
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out_w = output_size[1] + pad_w_0 + pad_w_1
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out_pad_h = 0
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out_pad_w = 0
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if 'output_padding' in attrs:
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out_pad_h = attrs['output_padding'][0]
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out_pad_w = attrs['output_padding'][1]
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out = np.zeros(
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(in_n, out_c, out_h + out_pad_h, out_w + out_pad_w), dtype=input_.dtype
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)
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for n in range(in_n):
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for i in range(in_h):
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for j in range(in_w):
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for g in range(groups):
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input_masked = input_[
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n, g * sub_in_c : (g + 1) * sub_in_c, i, j
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] # (c)
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input_masked = np.reshape(input_masked, (sub_in_c, 1, 1))
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input_masked = np.tile(input_masked, (1, 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, k, :, :
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],
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axis=0,
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)
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i1, i2 = i * stride[0], i * stride[0] + d_block_h
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j1, j2 = j * stride[1], j * stride[1] + 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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i1 : i2 : dilations[0],
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j1 : j2 : dilations[1],
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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_h_0 : out_h - pad_h_1 + out_pad_h,
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pad_w_0 : out_w - pad_w_1 + out_pad_w,
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]
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if attrs['data_format'] == 'NHWC':
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out = np.transpose(out, [0, 2, 3, 1])
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return out
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class XPUTestConv2DTransposeOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'conv2d_transpose'
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self.use_dynamic_create_class = False
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class TestConv2DTransposeOp(XPUOpTest):
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def setUp(self):
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# init as conv transpose
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self.need_check_grad = True
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self.is_test = False
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self.use_cudnn = False
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self.use_onednn = False
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self.output_size = None
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self.output_padding = []
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self.data_format = "NCHW"
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self.pad = [0, 0]
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self.padding_algorithm = "EXPLICIT"
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self.init_op_type()
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self.init_test_case()
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self.__class__.op_type = "conv2d_transpose"
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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.inputs = {'Input': input_, 'Filter': filter_}
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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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'groups': self.groups,
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'dilations': self.dilations,
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'use_cudnn': self.use_cudnn,
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'is_test': self.is_test,
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'use_onednn': self.use_onednn,
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'data_format': self.data_format,
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}
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if self.output_size is not None:
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self.attrs['output_size'] = self.output_size
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if len(self.output_padding) > 0:
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self.attrs['output_padding'] = self.output_padding
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output = conv2dtranspose_forward_naive(
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input_, filter_, self.attrs
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).astype(self.dtype)
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self.outputs = {'Output': output}
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad_no_input(self):
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if self.need_check_grad:
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self.check_grad_with_place(
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self.place, ['Filter'], 'Output', no_grad_set={'Input'}
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)
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def test_check_grad_no_filter(self):
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if self.need_check_grad:
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self.check_grad_with_place(
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self.place, ['Input'], 'Output', no_grad_set={'Filter'}
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)
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def test_check_grad(self):
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if self.need_check_grad:
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self.check_grad_with_place(
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self.place, {'Input', 'Filter'}, 'Output'
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)
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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def init_op_type(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.op_type = "conv2d_transpose"
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class TestWithSymmetricPad(TestConv2DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithAsymmetricPad(TestConv2DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 0, 1, 2]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithSAMEPad(TestConv2DTransposeOp):
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def init_test_case(self):
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self.stride = [2, 1]
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self.dilations = [1, 2]
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self.groups = 1
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self.input_size = [2, 3, 6, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 4, 3]
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self.padding_algorithm = 'SAME'
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class TestWithVALIDPad(TestConv2DTransposeOp):
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def init_test_case(self):
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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self.padding_algorithm = 'VALID'
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class TestWithGroups(TestConv2DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.groups = 2
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self.input_size = [2, 4, 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]
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class TestWithStride(TestConv2DTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithEvenUpsample(TestConv2DTransposeOp):
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def init_test_case(self):
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self.pad = [2, 2]
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self.stride = [2, 2]
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self.groups = 1
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self.dilations = [1, 1]
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self.output_size = [14, 14]
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self.input_size = [2, 3, 7, 7] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 5, 5]
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support_types = get_xpu_op_support_types('conv2d_transpose')
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for stype in support_types:
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create_test_class(globals(), XPUTestConv2DTransposeOp, stype)
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if __name__ == '__main__':
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unittest.main()
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