wehub-resource-sync
1137 lines
36 KiB
Python
1137 lines
36 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 unittest
|
|
|
|
import numpy as np
|
|
from op_test import (
|
|
OpTest,
|
|
convert_float_to_uint16,
|
|
get_device_place,
|
|
get_numeric_gradient,
|
|
is_custom_device,
|
|
)
|
|
from testsuite import create_op
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core
|
|
|
|
|
|
def conv2d_forward_naive(
|
|
input,
|
|
filter,
|
|
group,
|
|
conv_param,
|
|
padding_algorithm='EXPLICIT',
|
|
data_format='NCHW',
|
|
):
|
|
if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
|
|
raise ValueError(
|
|
f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
|
|
"It can only be 'SAME' or 'VALID'."
|
|
)
|
|
|
|
if data_format not in ["NCHW", "NHWC"]:
|
|
raise ValueError(
|
|
f"Unknown Attr(data_format): '{data_format}' ."
|
|
"It can only be 'NCHW' or 'NHWC'."
|
|
)
|
|
|
|
channel_last = data_format == "NHWC"
|
|
if channel_last:
|
|
input = np.transpose(input, [0, 3, 1, 2])
|
|
|
|
in_n, in_c, in_h, in_w = input.shape
|
|
f_n, f_c, f_h, f_w = filter.shape
|
|
out_n = in_n
|
|
out_c = f_n
|
|
assert f_c * group == in_c
|
|
assert np.mod(out_c, group) == 0
|
|
sub_out_c = out_c // group
|
|
sub_f_n = f_n // group
|
|
|
|
stride, pad, dilation = (
|
|
conv_param['stride'],
|
|
conv_param['pad'],
|
|
conv_param['dilation'],
|
|
)
|
|
|
|
# update pad and dilation
|
|
def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
|
|
padding = []
|
|
for input_size, filter_size, stride_size in zip(
|
|
input_shape, pool_size, pool_stride
|
|
):
|
|
out_size = int((input_size + stride_size - 1) / stride_size)
|
|
pad_sum = np.max(
|
|
((out_size - 1) * stride_size + filter_size - input_size, 0)
|
|
)
|
|
pad_0 = int(pad_sum / 2)
|
|
pad_1 = int(pad_sum - pad_0)
|
|
padding.append(pad_0)
|
|
padding.append(pad_1)
|
|
return padding
|
|
|
|
ksize = filter.shape[2:4]
|
|
if padding_algorithm == "VALID":
|
|
pad = [0, 0, 0, 0]
|
|
elif padding_algorithm == "SAME":
|
|
dilation = [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]
|
|
out_h = (
|
|
1
|
|
+ (in_h + pad_h_0 + pad_h_1 - (dilation[0] * (f_h - 1) + 1))
|
|
// stride[0]
|
|
)
|
|
out_w = (
|
|
1
|
|
+ (in_w + pad_w_0 + pad_w_1 - (dilation[1] * (f_w - 1) + 1))
|
|
// stride[1]
|
|
)
|
|
out = np.zeros((out_n, out_c, out_h, out_w))
|
|
|
|
d_block_h = dilation[0] * (f_h - 1) + 1
|
|
d_block_w = dilation[1] * (f_w - 1) + 1
|
|
|
|
input_pad = np.pad(
|
|
input,
|
|
((0, 0), (0, 0), (pad_h_0, pad_h_1), (pad_w_0, pad_w_1)),
|
|
mode='constant',
|
|
constant_values=0,
|
|
)
|
|
|
|
filter_dilation = np.zeros((f_n, f_c, d_block_h, d_block_w))
|
|
filter_dilation[
|
|
:, :, 0 : d_block_h : dilation[0], 0 : d_block_w : dilation[1]
|
|
] = filter
|
|
|
|
for i in range(out_h):
|
|
for j in range(out_w):
|
|
for g in range(group):
|
|
input_pad_masked = input_pad[
|
|
:,
|
|
g * f_c : (g + 1) * f_c,
|
|
i * stride[0] : i * stride[0] + d_block_h,
|
|
j * stride[1] : j * stride[1] + d_block_w,
|
|
]
|
|
|
|
f_sub = filter_dilation[
|
|
g * sub_f_n : (g + 1) * sub_f_n, :, :, :
|
|
]
|
|
# sub_f_n == sub_out_c
|
|
for k in range(sub_out_c):
|
|
# Multiplication of Corresponding Elements, then sum all
|
|
out[:, g * sub_out_c + k, i, j] = np.sum(
|
|
input_pad_masked * f_sub[k, :, :, :], axis=(1, 2, 3)
|
|
)
|
|
|
|
if channel_last:
|
|
out = np.transpose(out, [0, 2, 3, 1])
|
|
|
|
return out, in_n, out_h, out_w, out_c
|
|
|
|
|
|
def create_test_cudnn_class(parent):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNCase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = (
|
|
np.float32
|
|
if (core.is_compiled_with_rocm() or is_custom_device())
|
|
else np.float64
|
|
)
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CUDNN")
|
|
TestCUDNNCase.__name__ = cls_name
|
|
globals()[cls_name] = TestCUDNNCase
|
|
|
|
|
|
def create_test_cudnn_fp16_class(parent, grad_check=True):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestConv2DCUDNNFp16(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(place, atol=2e-2)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place) and grad_check:
|
|
self.check_grad_with_place(
|
|
place, ['Input'], 'Output', no_grad_set={'Filter'}
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place) and grad_check:
|
|
self.check_grad_with_place(
|
|
place, ['Filter'], 'Output', no_grad_set={'Input'}
|
|
)
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CUDNNFp16")
|
|
TestConv2DCUDNNFp16.__name__ = cls_name
|
|
globals()[cls_name] = TestConv2DCUDNNFp16
|
|
|
|
|
|
def create_test_cudnn_bf16_class(parent):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and do not support bfloat16",
|
|
)
|
|
class TestConv2DCUDNNBF16(parent):
|
|
def get_numeric_grad(self, place, check_name):
|
|
scope = core.Scope()
|
|
self._check_grad_helper()
|
|
op = create_op(
|
|
scope, self.op_type, self.inputs, self.outputs, self.attrs
|
|
)
|
|
return get_numeric_gradient(
|
|
place, scope, op, self.inputs_fp32, check_name, ['Output']
|
|
)
|
|
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.no_need_check_grad = True
|
|
self.dtype = np.uint16
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_output_with_place(place, atol=1e-2)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
place = get_device_place()
|
|
numeric_grads = self.get_numeric_grad(place, 'Input')
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
no_grad_set={'Filter'},
|
|
user_defined_grads=[numeric_grads],
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
place = get_device_place()
|
|
numeric_grads = self.get_numeric_grad(place, 'Filter')
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
no_grad_set={'Input'},
|
|
user_defined_grads=[numeric_grads],
|
|
)
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CUDNNBF16")
|
|
TestConv2DCUDNNBF16.__name__ = cls_name
|
|
globals()[cls_name] = TestConv2DCUDNNBF16
|
|
|
|
|
|
def create_test_channel_last_class(parent):
|
|
class TestChannelLastCase(parent):
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_test_case_2(self):
|
|
N, C, H, W = self.input_size
|
|
self.input_size = [N, H, W, C]
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "ChannelLast")
|
|
TestChannelLastCase.__name__ = cls_name
|
|
globals()[cls_name] = TestChannelLastCase
|
|
|
|
|
|
def create_test_cudnn_channel_last_class(parent):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCudnnChannelLastCase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = (
|
|
np.float32
|
|
if (core.is_compiled_with_rocm() or is_custom_device())
|
|
else np.float64
|
|
)
|
|
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_test_case_2(self):
|
|
N, C, H, W = self.input_size
|
|
self.input_size = [N, H, W, C]
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CudnnChannelLast")
|
|
TestCudnnChannelLastCase.__name__ = cls_name
|
|
globals()[cls_name] = TestCudnnChannelLastCase
|
|
|
|
|
|
def create_test_cudnn_channel_last_fp16_class(parent, grad_check=True):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCudnnChannelLastFp16(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(place, atol=2e-2)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place) and grad_check:
|
|
self.check_grad_with_place(
|
|
place, ['Input'], 'Output', no_grad_set={'Filter'}
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place) and grad_check:
|
|
self.check_grad_with_place(
|
|
place, ['Filter'], 'Output', no_grad_set={'Input'}
|
|
)
|
|
|
|
def init_data_format(self):
|
|
self.data_format = "NHWC"
|
|
|
|
def init_test_case_2(self):
|
|
N, C, H, W = self.input_size
|
|
self.input_size = [N, H, W, C]
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CudnnChannelLastFp16")
|
|
TestCudnnChannelLastFp16.__name__ = cls_name
|
|
globals()[cls_name] = TestCudnnChannelLastFp16
|
|
|
|
|
|
def create_test_padding_SAME_class(parent):
|
|
class TestPaddingSAMECase(parent):
|
|
def init_paddings(self):
|
|
self.pad = [0, 0]
|
|
self.padding_algorithm = "SAME"
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "PaddingSAMEOp")
|
|
TestPaddingSAMECase.__name__ = cls_name
|
|
globals()[cls_name] = TestPaddingSAMECase
|
|
|
|
|
|
def create_test_padding_VALID_class(parent):
|
|
class TestPaddingVALIDCase(parent):
|
|
def init_paddings(self):
|
|
self.pad = [1, 1]
|
|
self.padding_algorithm = "VALID"
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp")
|
|
TestPaddingVALIDCase.__name__ = cls_name
|
|
globals()[cls_name] = TestPaddingVALIDCase
|
|
|
|
|
|
def create_test_cudnn_padding_SAME_class(parent):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNPaddingSAMECase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = (
|
|
np.float32
|
|
if (core.is_compiled_with_rocm() or is_custom_device())
|
|
else np.float64
|
|
)
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1]
|
|
self.padding_algorithm = "SAME"
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingSAMEOp")
|
|
TestCUDNNPaddingSAMECase.__name__ = cls_name
|
|
globals()[cls_name] = TestCUDNNPaddingSAMECase
|
|
|
|
|
|
def create_test_cudnn_padding_VALID_class(parent):
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNNPaddingVALIDCase(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = (
|
|
np.float32
|
|
if (core.is_compiled_with_rocm() or is_custom_device())
|
|
else np.float64
|
|
)
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1]
|
|
self.padding_algorithm = "VALID"
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingVALIDOp")
|
|
TestCUDNNPaddingVALIDCase.__name__ = cls_name
|
|
globals()[cls_name] = TestCUDNNPaddingVALIDCase
|
|
|
|
|
|
def conv2d_wrapper(
|
|
x,
|
|
weight,
|
|
stride=1,
|
|
padding=0,
|
|
padding_algorithm="EXPLICIT",
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
):
|
|
if data_format == "AnyLayout":
|
|
data_format = "NCDHW"
|
|
if padding_algorithm is None:
|
|
padding_algorithm = "EXPLICIT"
|
|
return paddle._C_ops.conv2d(
|
|
x,
|
|
weight,
|
|
stride,
|
|
padding,
|
|
padding_algorithm,
|
|
dilation,
|
|
groups,
|
|
data_format,
|
|
)
|
|
|
|
|
|
class TestConv2DOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "conv2d"
|
|
self.python_api = conv2d_wrapper
|
|
self.use_cudnn = False
|
|
self.exhaustive_search = False
|
|
self.use_cuda = False
|
|
self.use_onednn = False
|
|
self.fuse_relu_before_depthwise_conv = False
|
|
self.data_format = "AnyLayout"
|
|
self.dtype = np.float64 if not is_custom_device() else np.float32
|
|
self.init_kernel_type()
|
|
self.init_group()
|
|
self.init_dilation()
|
|
self.init_test_case()
|
|
|
|
conv2d_param = {
|
|
'stride': self.stride,
|
|
'pad': self.pad,
|
|
'dilation': self.dilations,
|
|
}
|
|
|
|
if self.is_bfloat16_op():
|
|
input = np.random.random(self.input_size).astype(np.float32)
|
|
filter = np.random.uniform(-1, 1, self.filter_size).astype(
|
|
np.float32
|
|
)
|
|
else:
|
|
input = np.random.random(self.input_size).astype(self.dtype)
|
|
filter = np.random.uniform(-1, 1, self.filter_size).astype(
|
|
self.dtype
|
|
)
|
|
|
|
if not self.has_cuda():
|
|
self.fuse_relu_before_depthwise_conv = False
|
|
if self.fuse_relu_before_depthwise_conv:
|
|
input = input - 0.5
|
|
input -= (input < 0) * 0.1
|
|
input += (input >= 0) * 0.1
|
|
input2 = np.maximum(input, 0.0)
|
|
else:
|
|
input2 = input
|
|
|
|
output, _, _, _, _ = conv2d_forward_naive(
|
|
input2, filter, self.groups, conv2d_param
|
|
)
|
|
|
|
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': OpTest.np_dtype_to_base_dtype(input),
|
|
'Filter': OpTest.np_dtype_to_base_dtype(filter),
|
|
}
|
|
else:
|
|
output = output.astype(self.dtype)
|
|
self.inputs = {
|
|
'Input': OpTest.np_dtype_to_base_dtype(input),
|
|
'Filter': OpTest.np_dtype_to_base_dtype(filter),
|
|
}
|
|
|
|
self.attrs = {
|
|
'strides': self.stride,
|
|
'paddings': self.pad,
|
|
'groups': self.groups,
|
|
'dilations': self.dilations,
|
|
'use_cudnn': self.use_cudnn,
|
|
'use_onednn': self.use_onednn,
|
|
'data_format': self.data_format,
|
|
'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv,
|
|
'exhaustive_search': self.exhaustive_search,
|
|
}
|
|
self.outputs = {'Output': output}
|
|
|
|
def has_cuda(self):
|
|
return (core.is_compiled_with_cuda() or is_custom_device()) and (
|
|
self.use_cudnn or self.use_cuda
|
|
)
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=1e-5,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16 or (
|
|
hasattr(self, "no_need_check_grad") and self.no_need_check_grad
|
|
):
|
|
return
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad_with_place(
|
|
place,
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
if self.dtype == np.float16 or (
|
|
hasattr(self, "no_need_check_grad") and self.no_need_check_grad
|
|
):
|
|
return
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Filter'},
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
if self.dtype == np.float16 or (
|
|
hasattr(self, "no_need_check_grad") and self.no_need_check_grad
|
|
):
|
|
return
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
no_grad_set={'Input'},
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3]
|
|
|
|
def init_test_case_2(self):
|
|
pass
|
|
|
|
def init_dilation(self):
|
|
self.dilations = [1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 1
|
|
|
|
def init_kernel_type(self):
|
|
pass
|
|
|
|
|
|
class TestWithPad(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3]
|
|
|
|
|
|
class TestWithStride(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 6, 6] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3]
|
|
|
|
|
|
class TestWithGroup(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.group = 3
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [18, f_c, 3, 3]
|
|
|
|
|
|
class TestWith1x1(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
class TestWithDepthWise3x3(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [3, 4, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [12, f_c, 3, 3]
|
|
|
|
def init_dilation(self):
|
|
self.dilations = [2, 2]
|
|
|
|
def init_group(self):
|
|
self.groups = 4
|
|
|
|
|
|
class TestWithDepthWise5x5(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 4, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [8, f_c, 5, 5]
|
|
|
|
def init_group(self):
|
|
self.groups = 4
|
|
|
|
|
|
class TestWithDepthWise7x7(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 8, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [16, f_c, 7, 7]
|
|
|
|
def init_group(self):
|
|
self.groups = 8
|
|
|
|
|
|
class TestWithDilation(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [12, f_c, 3, 3]
|
|
|
|
def init_dilation(self):
|
|
self.dilations = [2, 2]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
class TestWithInput1x1Filter1x1(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 1]
|
|
self.input_size = [100, 3, 1, 1] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
# #----------------Conv2DCUDNN----------------
|
|
|
|
create_test_cudnn_class(TestConv2DOp)
|
|
create_test_cudnn_class(TestWithPad)
|
|
create_test_cudnn_class(TestWithStride)
|
|
create_test_cudnn_class(TestWithGroup)
|
|
create_test_cudnn_class(TestWith1x1)
|
|
create_test_cudnn_class(TestWithInput1x1Filter1x1)
|
|
|
|
# ----------------Conv2DCUDNN fp16----------------
|
|
|
|
create_test_cudnn_fp16_class(TestConv2DOp)
|
|
create_test_cudnn_fp16_class(TestWithPad)
|
|
create_test_cudnn_fp16_class(TestWithStride)
|
|
create_test_cudnn_fp16_class(TestWithGroup)
|
|
create_test_cudnn_fp16_class(TestWith1x1)
|
|
create_test_cudnn_fp16_class(TestWithInput1x1Filter1x1)
|
|
|
|
# ----------------Conv2DCUDNN bf16----------------
|
|
|
|
create_test_cudnn_bf16_class(TestConv2DOp)
|
|
create_test_cudnn_bf16_class(TestWithPad)
|
|
create_test_cudnn_bf16_class(TestWithStride)
|
|
create_test_cudnn_bf16_class(TestWithGroup)
|
|
create_test_cudnn_bf16_class(TestWith1x1)
|
|
create_test_cudnn_bf16_class(TestWithInput1x1Filter1x1)
|
|
|
|
|
|
class TestCUDNNExhaustiveSearch(TestConv2DOp):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.exhaustive_search = True
|
|
self.dtype = (
|
|
np.float32
|
|
if (core.is_compiled_with_rocm() or is_custom_device())
|
|
else np.float64
|
|
)
|
|
|
|
|
|
class TestConv2DOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
|
|
def test_Variable():
|
|
# the input of conv2d must be Variable.
|
|
x1 = base.create_lod_tensor(
|
|
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], base.CPUPlace()
|
|
)
|
|
paddle.nn.Conv2D(x1.shape[1], 1, 1)(x1)
|
|
|
|
self.assertRaises(TypeError, test_Variable)
|
|
|
|
def test_dtype():
|
|
# the input dtype of conv2d must be float16 or float32 or float64
|
|
# float16 only can be set on GPU place
|
|
x2 = paddle.static.data(
|
|
name='x2', shape=[-1, 3, 4, 5, 6], dtype="int32"
|
|
)
|
|
paddle.nn.Conv2D(x2.shape[1], 1, 1)(x2)
|
|
|
|
self.assertRaises(TypeError, test_dtype)
|
|
paddle.disable_static()
|
|
|
|
|
|
# Please Don't remove the following code.
|
|
# Currently, CI use cudnn V5.0 which not support dilation conv.
|
|
# class TestCUDNNWithDilation(TestWithDilation):
|
|
# def init_op_type(self):
|
|
# self.op_type = "conv_cudnn"
|
|
|
|
# ---- test asymmetric padding ----
|
|
|
|
|
|
class TestConv2DOp_v2(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "conv2d"
|
|
self.python_api = conv2d_wrapper
|
|
self.use_cudnn = False
|
|
self.exhaustive_search = False
|
|
self.use_cuda = False
|
|
self.use_onednn = False
|
|
self.fuse_relu_before_depthwise_conv = False
|
|
self.dtype = np.float64 if not is_custom_device() else np.float32
|
|
self.init_kernel_type()
|
|
self.init_group()
|
|
self.init_dilation()
|
|
self.init_data_format()
|
|
self.init_test_case()
|
|
self.init_paddings()
|
|
self.init_test_case_2()
|
|
|
|
conv2d_param = {
|
|
'stride': self.stride,
|
|
'pad': self.pad,
|
|
'dilation': self.dilations,
|
|
}
|
|
|
|
input = np.random.random(self.input_size).astype(self.dtype)
|
|
if not self.has_cuda():
|
|
self.fuse_relu_before_depthwise_conv = False
|
|
if self.fuse_relu_before_depthwise_conv:
|
|
input = input - 0.5
|
|
input -= (input < 0) * 0.1
|
|
input += (input >= 0) * 0.1
|
|
input2 = np.maximum(input, 0.0)
|
|
else:
|
|
input2 = input
|
|
filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
|
|
output, _, _, _, _ = conv2d_forward_naive(
|
|
input2,
|
|
filter,
|
|
self.groups,
|
|
conv2d_param,
|
|
self.padding_algorithm,
|
|
self.data_format,
|
|
)
|
|
output = output.astype(self.dtype)
|
|
|
|
self.inputs = {
|
|
'Input': OpTest.np_dtype_to_base_dtype(input),
|
|
'Filter': OpTest.np_dtype_to_base_dtype(filter),
|
|
}
|
|
self.attrs = {
|
|
'strides': self.stride,
|
|
'paddings': self.pad,
|
|
'padding_algorithm': self.padding_algorithm,
|
|
'groups': self.groups,
|
|
'dilations': self.dilations,
|
|
'use_cudnn': self.use_cudnn,
|
|
'use_onednn': self.use_onednn,
|
|
'data_format': self.data_format,
|
|
'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv,
|
|
'exhaustive_search': self.exhaustive_search,
|
|
}
|
|
self.outputs = {'Output': output}
|
|
|
|
def has_cuda(self):
|
|
return (core.is_compiled_with_cuda() or is_custom_device()) and (
|
|
self.use_cudnn or self.use_cuda
|
|
)
|
|
|
|
def test_check_output(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=1e-5,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
if self.dtype == np.float16:
|
|
return
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
self.check_grad_with_place(
|
|
place,
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
if self.dtype == np.float16:
|
|
return
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
max_relative_error=0.02,
|
|
no_grad_set={'Filter'},
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
if self.dtype == np.float16:
|
|
return
|
|
place = get_device_place() if self.has_cuda() else core.CPUPlace()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
no_grad_set={'Input'},
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 4, 3]
|
|
|
|
def init_dilation(self):
|
|
self.dilations = [1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 1
|
|
|
|
def init_kernel_type(self):
|
|
pass
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
def init_data_format(self):
|
|
self.data_format = "NCHW"
|
|
|
|
def init_test_case_2(self):
|
|
pass
|
|
|
|
|
|
class TestConv2DOp_AsyPadding(TestConv2DOp_v2):
|
|
def init_paddings(self):
|
|
self.pad = [0, 0, 1, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithPad_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [2, 1, 3, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithStride_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 6, 6] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [2, 1, 3, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithGroup_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0]
|
|
self.stride = [1, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.group = 3
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [24, f_c, 4, 3]
|
|
|
|
|
|
class TestWith1x1_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [2, 2, 4, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithDepthWise3x3_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.input_size = [3, 4, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [16, f_c, 3, 3]
|
|
|
|
def init_dilation(self):
|
|
self.dilations = [2, 2]
|
|
|
|
def init_group(self):
|
|
self.groups = 4
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 3, 2, 1]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithDepthWise5x5_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 4, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [8, f_c, 5, 5]
|
|
|
|
def init_group(self):
|
|
self.groups = 4
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 1, 1, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithDepthWise7x7_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 8, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [16, f_c, 7, 7]
|
|
|
|
def init_group(self):
|
|
self.groups = 8
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 3, 4, 1]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithDilation_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 10, 10] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [24, f_c, 3, 3]
|
|
|
|
def init_dilation(self):
|
|
self.dilations = [2, 2]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 1, 3, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1]
|
|
self.input_size = [40, 3, 1, 1] # NCHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 3, 4, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
create_test_cudnn_class(TestConv2DOp_AsyPadding)
|
|
create_test_cudnn_class(TestWithPad_AsyPadding)
|
|
create_test_cudnn_class(TestWithStride_AsyPadding)
|
|
create_test_cudnn_class(TestWithGroup_AsyPadding)
|
|
create_test_cudnn_class(TestWith1x1_AsyPadding)
|
|
create_test_cudnn_class(TestWithInput1x1Filter1x1_AsyPadding)
|
|
|
|
# ---------- test SAME VALID -----------
|
|
create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
|
|
create_test_padding_SAME_class(TestWithPad_AsyPadding)
|
|
create_test_padding_SAME_class(TestWithStride_AsyPadding)
|
|
create_test_padding_SAME_class(TestWithGroup_AsyPadding)
|
|
create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding)
|
|
|
|
create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
|
|
create_test_padding_VALID_class(TestWithPad_AsyPadding)
|
|
create_test_padding_VALID_class(TestWithStride_AsyPadding)
|
|
create_test_padding_VALID_class(TestWithGroup_AsyPadding)
|
|
create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding)
|
|
|
|
create_test_cudnn_padding_SAME_class(TestConv2DOp_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(TestWithPad_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(TestWithStride_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(TestWithGroup_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding)
|
|
|
|
create_test_cudnn_padding_VALID_class(TestConv2DOp_AsyPadding)
|
|
create_test_cudnn_padding_VALID_class(TestWithPad_AsyPadding)
|
|
create_test_cudnn_padding_VALID_class(TestWithStride_AsyPadding)
|
|
create_test_cudnn_padding_VALID_class(TestWithGroup_AsyPadding)
|
|
create_test_cudnn_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding)
|
|
|
|
# ------------ test channel last ---------
|
|
create_test_channel_last_class(TestConv2DOp_AsyPadding)
|
|
create_test_channel_last_class(TestWithPad_AsyPadding)
|
|
create_test_channel_last_class(TestWithGroup_AsyPadding)
|
|
create_test_channel_last_class(TestWith1x1_AsyPadding)
|
|
create_test_channel_last_class(TestWithInput1x1Filter1x1_AsyPadding)
|
|
|
|
create_test_cudnn_channel_last_class(TestConv2DOp_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWithPad_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWithStride_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWithGroup_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWithDilation_AsyPadding)
|
|
|
|
create_test_cudnn_channel_last_fp16_class(TestConv2DOp_AsyPadding)
|
|
create_test_cudnn_channel_last_fp16_class(TestWithPad_AsyPadding)
|
|
create_test_cudnn_channel_last_fp16_class(TestWithStride_AsyPadding)
|
|
create_test_cudnn_channel_last_fp16_class(TestWithGroup_AsyPadding)
|
|
create_test_cudnn_channel_last_fp16_class(TestWithDilation_AsyPadding)
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|