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903 lines
28 KiB
903 lines
28 KiB
# 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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from __future__ import print_function
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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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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from op_test import OpTest
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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("Unknown Attr(padding_algorithm): '%s'. "
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"It can only be 'SAME' or 'VALID'." %
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str(padding_algorithm))
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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 = attrs['strides'], attrs['paddings'], attrs[
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'dilations']
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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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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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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_bolck_h = dilations[0] * (f_h - 1) + 1
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d_bolck_w = dilations[1] * (f_w - 1) + 1
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out_h = (in_h - 1) * stride[0] + d_bolck_h
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out_w = (in_w - 1) * stride[1] + d_bolck_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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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_[n, g * sub_in_c:(g + 1) * sub_in_c, i,
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j] # (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_[g * sub_in_c:(g + 1) * sub_in_c, k, :, :],
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axis=0)
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i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
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j1, j2 = j * stride[1], j * stride[1] + d_bolck_w
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out[n, g * f_out_c + k, i1:i2:dilations[0], j1:j2:
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dilations[1]] += tmp_out
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out = out[:, :, pad_h_0:out_h - pad_h_1 + out_pad_h, pad_w_0:out_w - pad_w_1
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+ out_pad_w]
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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 TestConv2DTransposeOp(OpTest):
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def setUp(self):
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# init as conv transpose
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self.dtype = np.float64
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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_mkldnn = 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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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_mkldnn': self.use_mkldnn,
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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(input_, filter_,
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self.attrs).astype(self.dtype)
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self.outputs = {'Output': output}
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def test_check_output(self):
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# TODO(wangzhongpu): support mkldnn op in dygraph mode
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if self.use_cudnn:
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place = core.CUDAPlace(0)
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self.check_output_with_place(
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place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
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else:
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self.check_output(check_dygraph=(self.use_mkldnn == False))
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def test_check_grad_no_input(self):
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if self.need_check_grad:
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if self.use_cudnn:
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place, ['Filter'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Input']))
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else:
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self.check_grad(
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['Filter'], 'Output', no_grad_set=set(['Input']))
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def test_check_grad_no_filter(self):
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if self.need_check_grad:
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if self.use_cudnn:
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place, ['Input'], 'Output', no_grad_set=set(['Filter']))
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else:
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self.check_grad(
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['Input'], 'Output', no_grad_set=set(['Filter']))
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def test_check_grad(self):
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if self.need_check_grad:
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if self.use_cudnn:
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place,
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set(['Input', 'Filter']),
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'Output',
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max_relative_error=0.02)
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else:
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self.check_grad(
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set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
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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.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 TestWithDilation(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.groups = 1
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self.dilations = [2, 2]
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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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class TestWithEvenUpsampleOutputPadding(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_padding = [1, 1]
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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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class Test_NHWC(TestConv2DTransposeOp):
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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, 5, 5, 3] # NHWC
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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.data_format = 'NHWC'
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class TestWithSymmetricPad_NHWC(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, 5, 5, 3] # NHWC
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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.data_format = 'NHWC'
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class TestWithAsymmetricPad_NHWC(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, 5, 5, 3] # NHWC
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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.data_format = 'NHWC'
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class TestWithGroups_NHWC(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, 5, 5, 4] # NHWC
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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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self.data_format = 'NHWC'
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class TestWithStride_NHWC(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, 5, 5, 3] # 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.data_format = 'NHWC'
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class TestWithDilation_NHWC(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.groups = 1
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self.dilations = [2, 2]
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self.input_size = [2, 5, 5, 3] # NHWC
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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.data_format = 'NHWC'
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class TestWithEvenUpsample_NHWC(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, 7, 7, 3] # NHWC
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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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self.data_format = 'NHWC'
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class TestWithEvenUpsample_NHWC_output_padding(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_padding = [1, 1]
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self.input_size = [2, 7, 7, 3] # NHWC
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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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self.data_format = 'NHWC'
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# ------------ test_cudnn ------------
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNN(TestConv2DTransposeOp):
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv2d_transpose"
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNNWithSymmetricPad(TestWithSymmetricPad):
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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.groups = 1
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self.dilations = [1, 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.use_cudnn = True
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self.op_type = "conv2d_transpose"
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
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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.groups = 1
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self.dilations = [1, 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.use_cudnn = True
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self.op_type = "conv2d_transpose"
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNNWithSAMEPad(TestWithSAMEPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 0, 1, 2]
|
|
self.stride = [1, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithVALIDPad(TestWithVALIDPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 0, 1, 2]
|
|
self.stride = [1, 1]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithStride(TestWithStride):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithGroups(TestWithGroups):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 2
|
|
self.input_size = [2, 4, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 3, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
# ------------ test_cudnn ------------
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithEvenUpsample(TestWithEvenUpsample):
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
# 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]
|
|
# self.stride = [2, 2]
|
|
# self.dilations = [2, 2]
|
|
# self.input_size = [2, 3, 5, 5] # NCHW
|
|
# f_c = self.input_size[1]
|
|
# self.filter_size = [f_c, 6, 3, 3]
|
|
#
|
|
# def init_op_type(self):
|
|
# self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNN_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithSymmetricPad_NHWC(TestWithSymmetricPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithAsymmetricPad_NHWC(TestWithSymmetricPad):
|
|
def init_test_case(self):
|
|
self.pad = [1, 0, 2, 3]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithStride_NHWC(TestWithStride):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithGroups_NHWC(TestWithGroups):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 2
|
|
self.input_size = [2, 5, 5, 4] # NCHW
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithEvenUpsample_NHWC(TestWithEvenUpsample):
|
|
def init_test_case(self):
|
|
self.pad = [2, 2]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.output_size = [14, 14]
|
|
self.input_size = [2, 7, 7, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 5, 5]
|
|
self.data_format = 'NHWC'
|
|
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNN_FP16(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
def init_op_type(self):
|
|
self.need_check_grad = False
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
|
|
def test_check_output(self):
|
|
if self.use_cudnn:
|
|
place = core.CUDAPlace(0)
|
|
self.check_output_with_place(
|
|
place, atol=0.02, check_dygraph=(self.use_mkldnn == False))
|
|
else:
|
|
self.check_output(check_dygraph=(self.use_mkldnn == False))
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNN_NHWC_FP16(TestCUDNN_FP16):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithSymmetricPad_NHWC_FP16(TestCUDNN_FP16):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithAsymmetricPad_NHWC_FP16(TestCUDNN_FP16):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [1, 0, 2, 3]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithStride_NHWC_FP16(TestCUDNN_FP16):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.input_size = [2, 5, 5, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithGroups_NHWC_FP16(TestCUDNN_FP16):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 2
|
|
self.input_size = [2, 5, 5, 4] # NCHW
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestCUDNNWithEvenUpsample_NHWC_FP16(TestCUDNN_FP16):
|
|
def init_test_case(self):
|
|
self.dtype = np.float16
|
|
self.pad = [2, 2]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.output_size = [14, 14]
|
|
self.input_size = [2, 7, 7, 3] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 5, 5]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
class TestConv2DTransposeAPI(unittest.TestCase):
|
|
def test_case1(self):
|
|
data1 = fluid.layers.data(
|
|
name='data1', shape=[3, 5, 5], dtype='float32')
|
|
data2 = fluid.layers.data(
|
|
name='data2', shape=[5, 5, 3], dtype='float32')
|
|
out1 = fluid.layers.conv2d_transpose(
|
|
input=data1,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
data_format='NCHW')
|
|
out2 = fluid.layers.conv2d_transpose(
|
|
input=data2,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
data_format='NHWC')
|
|
out3 = fluid.layers.conv2d_transpose(
|
|
input=data1,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
|
|
data_format='NHWC')
|
|
out4 = fluid.layers.conv2d_transpose(
|
|
input=data1,
|
|
groups=3,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding=[[0, 0], [0, 0], [2, 1], [0, 0]],
|
|
data_format='NCHW')
|
|
out5 = fluid.layers.conv2d_transpose(
|
|
input=data2,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding='SAME',
|
|
data_format='NCHW')
|
|
out6 = fluid.layers.conv2d_transpose(
|
|
input=data1,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding='VALID',
|
|
data_format='NHWC')
|
|
out7 = fluid.layers.conv2d_transpose(
|
|
input=data1,
|
|
groups=1,
|
|
num_filters=6,
|
|
output_size=[7, 7],
|
|
padding=[0, 0],
|
|
data_format='NHWC')
|
|
|
|
data1_np = np.random.random((2, 3, 5, 5)).astype("float32")
|
|
data2_np = np.random.random((2, 5, 5, 3)).astype("float32")
|
|
|
|
if core.is_compiled_with_cuda():
|
|
place = core.CUDAPlace(0)
|
|
else:
|
|
place = core.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
exe.run(fluid.default_startup_program())
|
|
results = exe.run(
|
|
fluid.default_main_program(),
|
|
feed={"data1": data1_np,
|
|
"data2": data2_np},
|
|
fetch_list=[out1, out2, out3, out4, out5, out6, out7],
|
|
return_numpy=True)
|
|
self.assertIsNotNone(results[0])
|
|
self.assertIsNotNone(results[1])
|
|
self.assertIsNotNone(results[2])
|
|
self.assertIsNotNone(results[3])
|
|
self.assertIsNotNone(results[4])
|
|
self.assertIsNotNone(results[5])
|
|
self.assertIsNotNone(results[6])
|
|
|
|
|
|
class TestConv2DTransposeOpException(unittest.TestCase):
|
|
def test_exception(self):
|
|
data = fluid.layers.data(name='data', shape=[3, 5, 5], dtype="float32")
|
|
|
|
def attr_data_format():
|
|
out = fluid.layers.conv2d_transpose(
|
|
input=data,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
data_format="NCDHW")
|
|
|
|
self.assertRaises(ValueError, attr_data_format)
|
|
|
|
def attr_padding_str():
|
|
out = fluid.layers.conv2d_transpose(
|
|
input=data,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding='Vald')
|
|
|
|
self.assertRaises(ValueError, attr_padding_str)
|
|
|
|
def attr_padding_list():
|
|
out = fluid.layers.conv2d_transpose(
|
|
input=data,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding=[[1, 1], [1, 1], [0, 0], [0, 0]])
|
|
|
|
self.assertRaises(ValueError, attr_padding_list)
|
|
|
|
def attr_padding_with_data_format():
|
|
out = fluid.layers.conv2d_transpose(
|
|
input=data,
|
|
groups=1,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
padding=[[1, 1], [0, 0], [0, 0], [1, 1]],
|
|
data_format='NHWC')
|
|
|
|
self.assertRaises(ValueError, attr_padding_with_data_format)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|