wehub-resource-sync
1624 lines
50 KiB
Python
1624 lines
50 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
import unittest
|
|
from unittest import TestCase
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.base.dygraph as dg
|
|
import paddle.static
|
|
from paddle import nn
|
|
|
|
paddle.enable_static()
|
|
|
|
from op_test import (
|
|
OpTest,
|
|
convert_float_to_uint16,
|
|
get_device_place,
|
|
get_numeric_gradient,
|
|
get_places,
|
|
is_custom_device,
|
|
)
|
|
from test_attribute_var import UnittestBase
|
|
from testsuite import create_op
|
|
|
|
from paddle import base
|
|
from paddle.base import Program, core, program_guard
|
|
|
|
|
|
def conv2dtranspose_forward_naive(input_, filter_, attrs):
|
|
padding_algorithm = attrs['padding_algorithm']
|
|
if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
|
|
raise ValueError(
|
|
f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
|
|
"It can only be 'SAME' or 'VALID'."
|
|
)
|
|
|
|
if attrs['data_format'] == 'NHWC':
|
|
input_ = np.transpose(input_, [0, 3, 1, 2])
|
|
in_n, in_c, in_h, in_w = input_.shape
|
|
f_c, f_out_c, f_h, f_w = filter_.shape
|
|
groups = attrs['groups']
|
|
assert in_c == f_c
|
|
out_c = f_out_c * groups
|
|
sub_in_c = in_c // groups
|
|
|
|
stride, pad, dilations = (
|
|
attrs['strides'],
|
|
attrs['paddings'],
|
|
attrs['dilations'],
|
|
)
|
|
|
|
# update pad and dilation
|
|
def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
|
|
padding = []
|
|
for input_size, filter_size, stride_size in zip(
|
|
input_shape, kernel_size, kernel_stride
|
|
):
|
|
out_size = int((input_size + stride_size - 1) / stride_size)
|
|
pad_sum = np.max(
|
|
((out_size - 1) * stride_size + filter_size - input_size, 0)
|
|
)
|
|
pad_0 = int(pad_sum / 2)
|
|
pad_1 = int(pad_sum - pad_0)
|
|
padding.append(pad_0)
|
|
padding.append(pad_1)
|
|
return padding
|
|
|
|
ksize = filter_.shape[2:4]
|
|
if padding_algorithm == "VALID":
|
|
pad = [0, 0, 0, 0]
|
|
elif padding_algorithm == "SAME":
|
|
dilations = [1, 1]
|
|
input_data_shape = input_.shape[2:4]
|
|
pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
|
|
|
|
pad_h_0, pad_h_1 = pad[0], pad[0]
|
|
pad_w_0, pad_w_1 = pad[1], pad[1]
|
|
if len(pad) == 4:
|
|
pad_h_0, pad_h_1 = pad[0], pad[1]
|
|
pad_w_0, pad_w_1 = pad[2], pad[3]
|
|
|
|
d_block_h = dilations[0] * (f_h - 1) + 1
|
|
d_block_w = dilations[1] * (f_w - 1) + 1
|
|
out_h = (in_h - 1) * stride[0] + d_block_h
|
|
out_w = (in_w - 1) * stride[1] + d_block_w
|
|
if 'output_size' in attrs:
|
|
output_size = attrs['output_size']
|
|
out_h = output_size[0] + pad_h_0 + pad_h_1
|
|
out_w = output_size[1] + pad_w_0 + pad_w_1
|
|
out_pad_h = 0
|
|
out_pad_w = 0
|
|
if 'output_padding' in attrs:
|
|
out_pad_h = attrs['output_padding'][0]
|
|
out_pad_w = attrs['output_padding'][1]
|
|
out = np.zeros(
|
|
(in_n, out_c, out_h + out_pad_h, out_w + out_pad_w), dtype=input_.dtype
|
|
)
|
|
|
|
for n in range(in_n):
|
|
for i in range(in_h):
|
|
for j in range(in_w):
|
|
for g in range(groups):
|
|
input_masked = input_[
|
|
n, g * sub_in_c : (g + 1) * sub_in_c, i, j
|
|
] # (c)
|
|
input_masked = np.reshape(input_masked, (sub_in_c, 1, 1))
|
|
input_masked = np.tile(input_masked, (1, f_h, f_w))
|
|
|
|
for k in range(f_out_c):
|
|
tmp_out = np.sum(
|
|
input_masked
|
|
* filter_[
|
|
g * sub_in_c : (g + 1) * sub_in_c, k, :, :
|
|
],
|
|
axis=0,
|
|
)
|
|
i1, i2 = i * stride[0], i * stride[0] + d_block_h
|
|
j1, j2 = j * stride[1], j * stride[1] + d_block_w
|
|
out[
|
|
n,
|
|
g * f_out_c + k,
|
|
i1 : i2 : dilations[0],
|
|
j1 : j2 : dilations[1],
|
|
] += tmp_out
|
|
|
|
out = out[
|
|
:,
|
|
:,
|
|
pad_h_0 : out_h - pad_h_1 + out_pad_h,
|
|
pad_w_0 : out_w - pad_w_1 + out_pad_w,
|
|
]
|
|
if attrs['data_format'] == 'NHWC':
|
|
out = np.transpose(out, [0, 2, 3, 1])
|
|
return out
|
|
|
|
|
|
def conv2dtranspose_wrapper(
|
|
x,
|
|
weight,
|
|
stride=1,
|
|
padding=0,
|
|
output_padding=[],
|
|
output_size=[],
|
|
padding_algorithm="EXPLICIT",
|
|
groups=1,
|
|
dilation=1,
|
|
data_format="NCDHW",
|
|
):
|
|
if data_format == "AnyLayout":
|
|
data_format = "NCDHW"
|
|
if padding_algorithm is None:
|
|
padding_algorithm = "EXPLICIT"
|
|
return paddle._C_ops.conv2d_transpose(
|
|
x,
|
|
weight,
|
|
stride,
|
|
padding,
|
|
output_padding,
|
|
output_size,
|
|
padding_algorithm,
|
|
groups,
|
|
dilation,
|
|
data_format,
|
|
)
|
|
|
|
|
|
class TestConv2DTransposeOp(OpTest):
|
|
def setUp(self):
|
|
# init as conv transpose
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
self.need_check_grad = True
|
|
self.is_test = False
|
|
self.use_cudnn = False
|
|
self.use_onednn = False
|
|
self.output_size = None
|
|
self.output_padding = []
|
|
self.data_format = "NCHW"
|
|
self.pad = [0, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
self.init_op_type()
|
|
self.init_test_case()
|
|
|
|
if self.is_bfloat16_op():
|
|
input_ = np.random.random(self.input_size).astype(np.float32)
|
|
filter_ = np.random.random(self.filter_size).astype(np.float32)
|
|
else:
|
|
input_ = np.random.random(self.input_size).astype(self.dtype)
|
|
filter_ = np.random.random(self.filter_size).astype(self.dtype)
|
|
|
|
self.attrs = {
|
|
'strides': self.stride,
|
|
'paddings': self.pad,
|
|
'padding_algorithm': self.padding_algorithm,
|
|
'groups': self.groups,
|
|
'dilations': self.dilations,
|
|
'use_cudnn': self.use_cudnn,
|
|
'is_test': self.is_test,
|
|
'use_onednn': self.use_onednn,
|
|
'data_format': self.data_format,
|
|
}
|
|
if self.output_size is not None:
|
|
self.attrs['output_size'] = self.output_size
|
|
|
|
if len(self.output_padding) > 0:
|
|
self.attrs['output_padding'] = self.output_padding
|
|
|
|
output = conv2dtranspose_forward_naive(input_, filter_, self.attrs)
|
|
|
|
if self.is_bfloat16_op():
|
|
output = output.astype(np.float32)
|
|
self.inputs = {
|
|
'Input': convert_float_to_uint16(input_),
|
|
'Filter': convert_float_to_uint16(filter_),
|
|
}
|
|
self.inputs_fp32 = {'Input': input_, 'Filter': filter_}
|
|
else:
|
|
output = output.astype(self.dtype)
|
|
self.inputs = {'Input': input_, 'Filter': filter_}
|
|
|
|
self.outputs = {'Output': output}
|
|
|
|
def test_check_output(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=1e-5,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_output(
|
|
check_dygraph=(not self.use_onednn), check_pir=True
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
if self.need_check_grad:
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Input'},
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['Filter'], 'Output', no_grad_set={'Input'}, check_pir=True
|
|
)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
if self.need_check_grad:
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
no_grad_set={'Filter'},
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['Input'], 'Output', no_grad_set={'Filter'}, check_pir=True
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
if self.need_check_grad:
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
check_pir=True,
|
|
)
|
|
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 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.op_type = "conv2d_transpose"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
|
|
class TestWithSymmetricPad(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
|
|
class TestWithAsymmetricPad(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 0, 1, 2]
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
|
|
class TestWithSAMEPad(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.stride = [2, 1]
|
|
self.dilations = [1, 2]
|
|
self.groups = 1
|
|
self.input_size = [2, 3, 6, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 4, 3]
|
|
self.padding_algorithm = 'SAME'
|
|
|
|
|
|
class TestWithVALIDPad(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.padding_algorithm = 'VALID'
|
|
|
|
|
|
class TestWithGroups(TestConv2DTransposeOp):
|
|
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]
|
|
|
|
|
|
class TestWithStride(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
|
|
|
|
class TestWithDilation(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.groups = 1
|
|
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]
|
|
|
|
|
|
class TestWithEvenUpsample(TestConv2DTransposeOp):
|
|
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, 3, 7, 7] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 5, 5]
|
|
|
|
|
|
class TestWithEvenUpsampleOutputPadding(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [2, 2]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.output_padding = [1, 1]
|
|
self.input_size = [2, 3, 7, 7] # NCHW
|
|
f_c = self.input_size[1]
|
|
self.filter_size = [f_c, 6, 5, 5]
|
|
|
|
|
|
class Test_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'
|
|
|
|
|
|
class TestWithSymmetricPad_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
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'
|
|
|
|
|
|
class TestWithAsymmetricPad_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 0, 1, 2]
|
|
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'
|
|
|
|
|
|
class TestWithGroups_NHWC(TestConv2DTransposeOp):
|
|
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] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 3, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
class TestWithStride_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [2, 5, 5, 3] # NCHW
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 6, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
class TestWithDilation_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.groups = 1
|
|
self.dilations = [2, 2]
|
|
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'
|
|
|
|
|
|
class TestWithEvenUpsample_NHWC(TestConv2DTransposeOp):
|
|
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'
|
|
|
|
|
|
class TestWithEvenUpsample_NHWC_output_padding(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.pad = [2, 2]
|
|
self.stride = [2, 2]
|
|
self.groups = 1
|
|
self.dilations = [1, 1]
|
|
self.output_padding = [1, 1]
|
|
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'
|
|
|
|
|
|
# ------------ test_cudnn ------------
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNN(TestConv2DTransposeOp):
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithSymmetricPad(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, 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"
|
|
self.python_api = conv2dtranspose_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, 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"
|
|
self.python_api = conv2dtranspose_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.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"
|
|
self.python_api = conv2dtranspose_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.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"
|
|
self.python_api = conv2dtranspose_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]
|
|
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"
|
|
self.python_api = conv2dtranspose_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]
|
|
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"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
|
|
# ------------ test_cudnn ------------
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNWithEvenUpsample(TestWithEvenUpsample):
|
|
def init_op_type(self):
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
self.python_api = conv2dtranspose_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]
|
|
# 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() or is_custom_device()),
|
|
"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"
|
|
self.python_api = conv2dtranspose_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]
|
|
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"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"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"
|
|
self.python_api = conv2dtranspose_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]
|
|
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"
|
|
self.python_api = conv2dtranspose_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]
|
|
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"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"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"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"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 = True
|
|
self.use_cudnn = True
|
|
self.op_type = "conv2d_transpose"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
def test_check_output(self):
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=0.02,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_output(
|
|
check_dygraph=(not self.use_onednn), check_pir=True
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
if self.need_check_grad:
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Input'},
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['Filter'], 'Output', no_grad_set={'Input'}, check_pir=True
|
|
)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
if self.need_check_grad:
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Filter'},
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['Input'], 'Output', no_grad_set={'Filter'}, check_pir=True
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
if self.need_check_grad:
|
|
if self.use_cudnn:
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_grad_with_place(
|
|
place,
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
check_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"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() or is_custom_device()),
|
|
"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() or is_custom_device()),
|
|
"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() or is_custom_device()),
|
|
"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() or is_custom_device()),
|
|
"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() or is_custom_device()),
|
|
"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'
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNN_BF16(TestConv2DTransposeOp):
|
|
def get_numeric_grad(self, place, check_name):
|
|
scope = core.Scope()
|
|
self._check_grad_helper()
|
|
op = create_op(
|
|
scope, self.op_type, self.inputs, self.outputs, self.attrs
|
|
)
|
|
return get_numeric_gradient(
|
|
place, scope, op, self.inputs_fp32, check_name, ['Output']
|
|
)
|
|
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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"
|
|
self.python_api = conv2dtranspose_wrapper
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=0.02,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir=True,
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
place = get_device_place()
|
|
numeric_grads = self.get_numeric_grad(place, 'Filter')
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Input'},
|
|
user_defined_grads=[numeric_grads],
|
|
check_pir=True,
|
|
)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
place = get_device_place()
|
|
numeric_grads = self.get_numeric_grad(place, 'Input')
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Filter'},
|
|
user_defined_grads=[numeric_grads],
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNN_NHWC_BF16(TestCUDNN_BF16):
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNNWithSymmetricPad_NHWC_BF16(TestCUDNN_BF16):
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNNWithAsymmetricPad_NHWC_BF16(TestCUDNN_BF16):
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNNWithStride_NHWC_BF16(TestCUDNN_BF16):
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNNWithGroups_NHWC_BF16(TestCUDNN_BF16):
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestCUDNNWithEvenUpsample_NHWC_BF16(TestCUDNN_BF16):
|
|
def init_test_case(self):
|
|
self.dtype = np.uint16
|
|
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 = paddle.static.data(
|
|
name='data1', shape=[-1, 3, 5, 5], dtype='float32'
|
|
)
|
|
data2 = paddle.static.data(
|
|
name='data2', shape=[-1, 5, 5, 3], dtype='float32'
|
|
)
|
|
out1 = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
groups=1,
|
|
data_format='NCHW',
|
|
)(data1)
|
|
out2 = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
groups=1,
|
|
data_format='NHWC',
|
|
)(data2)
|
|
out3 = paddle.nn.Conv2DTranspose(
|
|
in_channels=5,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
groups=1,
|
|
padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
|
|
data_format='NHWC',
|
|
)(data1)
|
|
out4 = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
groups=3,
|
|
padding=[[0, 0], [0, 0], [2, 1], [0, 0]],
|
|
data_format='NCHW',
|
|
)(data1)
|
|
out5 = paddle.nn.Conv2DTranspose(
|
|
in_channels=5,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
groups=1,
|
|
padding='SAME',
|
|
data_format='NCHW',
|
|
)(data2)
|
|
out6 = paddle.nn.Conv2DTranspose(
|
|
in_channels=5,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
groups=1,
|
|
padding='VALID',
|
|
data_format='NHWC',
|
|
)(data1)
|
|
out7 = paddle.nn.Conv2DTranspose(
|
|
in_channels=5,
|
|
out_channels=6,
|
|
kernel_size=[5, 3],
|
|
groups=1,
|
|
padding=[0, 0],
|
|
data_format='NHWC',
|
|
)(data1, [7, 7])
|
|
|
|
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() or is_custom_device():
|
|
place = get_device_place()
|
|
else:
|
|
place = core.CPUPlace()
|
|
exe = base.Executor(place)
|
|
exe.run(base.default_startup_program())
|
|
results = exe.run(
|
|
base.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):
|
|
with paddle.pir_utils.OldIrGuard():
|
|
data = paddle.static.data(
|
|
name='data', shape=[-1, 3, 5, 5], dtype="float32"
|
|
)
|
|
|
|
def attr_data_format():
|
|
out = paddle.static.nn.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 = paddle.static.nn.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 = paddle.static.nn.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 = paddle.static.nn.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)
|
|
|
|
error_input = paddle.static.data(
|
|
name='error_data', shape=[-1, 1], dtype="float32"
|
|
)
|
|
|
|
def error_input_size():
|
|
out = paddle.static.nn.conv2d_transpose(
|
|
input=error_input, groups=1, num_filters=6, filter_size=3
|
|
)
|
|
|
|
self.assertRaises(ValueError, error_input_size)
|
|
|
|
def error_groups():
|
|
out = paddle.static.nn.conv2d_transpose(
|
|
input=data,
|
|
groups=0,
|
|
num_filters=6,
|
|
filter_size=3,
|
|
data_format='NHWC',
|
|
)
|
|
|
|
self.assertRaises(ValueError, error_groups)
|
|
|
|
def error_0_filter_number():
|
|
out = paddle.static.nn.conv2d_transpose(
|
|
input=data,
|
|
groups=1,
|
|
num_filters=0,
|
|
filter_size=3,
|
|
data_format='NCHW',
|
|
)
|
|
|
|
self.assertRaises(ValueError, error_0_filter_number)
|
|
|
|
def test_pir_exception(self):
|
|
with paddle.pir_utils.IrGuard():
|
|
data = paddle.static.data(
|
|
name='data', shape=[-1, 3, 5, 5], dtype="float32"
|
|
)
|
|
|
|
def attr_data_format():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
groups=1,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
data_format='NCDHW',
|
|
)(data)
|
|
|
|
self.assertRaises(ValueError, attr_data_format)
|
|
|
|
def attr_padding_str():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
groups=1,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
padding='Vald',
|
|
)(data)
|
|
|
|
self.assertRaises(ValueError, attr_padding_str)
|
|
|
|
def attr_padding_list():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
groups=1,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
padding=[[1, 1], [1, 1], [0, 0], [0, 0]],
|
|
)(data)
|
|
|
|
self.assertRaises(ValueError, attr_padding_list)
|
|
|
|
def attr_padding_with_data_format():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=5,
|
|
groups=1,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
padding=[[1, 1], [0, 0], [0, 0], [1, 1]],
|
|
data_format='NHWC',
|
|
)(data)
|
|
|
|
self.assertRaises(ValueError, attr_padding_with_data_format)
|
|
|
|
error_input = paddle.static.data(
|
|
name='error_data', shape=[-1, 1], dtype="float32"
|
|
)
|
|
|
|
def error_input_size():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=1,
|
|
groups=1,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
)(error_input)
|
|
|
|
self.assertRaises(ValueError, error_input_size)
|
|
|
|
def error_groups():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=5,
|
|
groups=0,
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
)(data)
|
|
|
|
self.assertRaises(ZeroDivisionError, error_groups)
|
|
|
|
def error_0_filter_number():
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=3,
|
|
groups=1,
|
|
out_channels=0,
|
|
kernel_size=3,
|
|
)(data)
|
|
|
|
self.assertRaises(AssertionError, error_0_filter_number)
|
|
|
|
|
|
class TestConv2DTransposeRepr(unittest.TestCase):
|
|
def test_case(self):
|
|
paddle.disable_static()
|
|
x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1.0, max=1.0)
|
|
conv = nn.Conv2DTranspose(4, 6, (3, 3), output_padding=1, stride=2)
|
|
print(conv)
|
|
y_var = conv(x_var)
|
|
y_np = y_var.numpy()
|
|
self.assertIsNotNone(y_np)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestConv2dTranspose(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]), dtype='float32')
|
|
weight = paddle.to_tensor(np.reshape(array, [1]), dtype='float32')
|
|
paddle.nn.functional.conv2d_transpose(x, weight, bias=0)
|
|
|
|
def test_type_error(self):
|
|
self.assertRaises(ValueError, self.error_weight_input)
|
|
|
|
|
|
class TestTensorOutputSize1(UnittestBase):
|
|
def init_info(self):
|
|
self.shapes = [[2, 3, 8, 8]]
|
|
self.save_path = os.path.join(self.temp_dir.name, self.path_prefix())
|
|
|
|
def path_prefix(self):
|
|
return 'conv2d_transpose_tensor_output_size1'
|
|
|
|
def var_prefix(self):
|
|
return "Vars["
|
|
|
|
def call_func(self, x):
|
|
w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
|
|
output_size = paddle.assign([17])
|
|
out = paddle.nn.functional.conv2d_transpose(
|
|
x, w_var, stride=2, output_size=output_size
|
|
)
|
|
return out
|
|
|
|
def test_static(self):
|
|
main_prog = Program()
|
|
startup_prog = Program()
|
|
with program_guard(main_prog, startup_prog):
|
|
fc = paddle.nn.Linear(8, 8)
|
|
x = paddle.randn([2, 3, 8, 8])
|
|
x.stop_gradient = False
|
|
feat = fc(x)
|
|
out = self.call_func(feat)
|
|
|
|
sgd = paddle.optimizer.SGD()
|
|
sgd.minimize(paddle.mean(out))
|
|
if not paddle.framework.use_pir_api():
|
|
self.assertTrue(self.var_prefix() in str(main_prog))
|
|
|
|
exe = paddle.static.Executor()
|
|
exe.run(startup_prog)
|
|
res = exe.run(fetch_list=[feat, out])
|
|
np.testing.assert_allclose(res[1].shape, (2, 6, 17, 17))
|
|
|
|
paddle.static.save_inference_model(
|
|
self.save_path, [x], [feat, out], exe
|
|
)
|
|
# Test for Inference Predictor
|
|
infer_outs = self.infer_prog()
|
|
np.testing.assert_allclose(infer_outs[1].shape, (2, 6, 17, 17))
|
|
|
|
|
|
class TestTensorOutputSize2(TestTensorOutputSize1):
|
|
def path_prefix(self):
|
|
return 'conv2d_transpose_tensor_output_size2'
|
|
|
|
def call_func(self, x):
|
|
w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
|
|
output_size = [17, paddle.assign([17])]
|
|
out = paddle.nn.functional.conv2d_transpose(
|
|
x, w_var, stride=2, output_size=output_size
|
|
)
|
|
return out
|
|
|
|
|
|
class TestTensorOutputSize3(TestTensorOutputSize1):
|
|
def path_prefix(self):
|
|
return 'conv2d_transpose_tensor_output_size3'
|
|
|
|
def call_func(self, x):
|
|
w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
|
|
output_size = paddle.assign([17])
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=x.shape[1],
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
stride=2,
|
|
)(x, output_size)
|
|
|
|
return out
|
|
|
|
|
|
class TestTensorOutputSize4(TestTensorOutputSize1):
|
|
def path_prefix(self):
|
|
return 'conv2d_transpose_tensor_output_size4'
|
|
|
|
def call_func(self, x):
|
|
output_size = [17, paddle.assign([17])]
|
|
out = paddle.nn.Conv2DTranspose(
|
|
in_channels=x.shape[1],
|
|
out_channels=6,
|
|
kernel_size=3,
|
|
stride=2,
|
|
)(x, output_size)
|
|
|
|
return out
|
|
|
|
|
|
class TestFunctionalConv2DTranspose_ZeroSize(TestCase):
|
|
def init_data(self):
|
|
self.input = np.random.randn(0, 4, 16, 4)
|
|
self.filter = np.random.randn(4, 3, 3, 3)
|
|
self.np_out = np.zeros([0, 3, 18, 6])
|
|
|
|
def setUp(self):
|
|
self.init_data()
|
|
self.bias = None
|
|
self.padding = 0
|
|
self.stride = 1
|
|
self.dilation = 1
|
|
self.groups = 1
|
|
self.data_format = "NCHW"
|
|
self.places = get_places()
|
|
|
|
def test_dygraph(self):
|
|
for place in self.places:
|
|
with dg.guard(place):
|
|
input = paddle.to_tensor(self.input)
|
|
input.stop_gradient = False
|
|
filter = paddle.to_tensor(self.filter)
|
|
filter.stop_gradient = False
|
|
y = paddle.nn.functional.conv2d_transpose(
|
|
input,
|
|
filter,
|
|
self.bias,
|
|
padding=self.padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
data_format=self.data_format,
|
|
)
|
|
np.testing.assert_allclose(y.numpy(), self.np_out)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
np.testing.assert_allclose(input.grad.shape, input.shape)
|
|
np.testing.assert_allclose(filter.grad, np.zeros(filter.shape))
|
|
|
|
|
|
class TestFunctionalConv2DTranspose_ZeroSize2(
|
|
TestFunctionalConv2DTranspose_ZeroSize
|
|
):
|
|
def init_data(self):
|
|
self.input = np.random.randn(4, 5, 3, 3)
|
|
self.filter = np.random.randn(5, 0, 4, 4)
|
|
self.np_out = np.zeros([4, 0, 6, 6])
|
|
|
|
|
|
class TestWithSAMEPad_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 1
|
|
self.input_size = [1, 3, 3, 1] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 2, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
self.padding_algorithm = 'SAME'
|
|
|
|
|
|
class TestWithSAMEPadGroups_NHWC(TestConv2DTransposeOp):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.dilations = [1, 1]
|
|
self.groups = 2
|
|
self.input_size = [1, 3, 3, 2] # NHWC
|
|
f_c = self.input_size[-1]
|
|
self.filter_size = [f_c, 1, 3, 3]
|
|
self.data_format = 'NHWC'
|
|
self.padding_algorithm = 'SAME'
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|