wehub-resource-sync
603 lines
20 KiB
Python
603 lines
20 KiB
Python
# Copyright (c) 2021 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
|
|
|
|
import paddle
|
|
|
|
paddle.enable_static()
|
|
import sys
|
|
|
|
from op_test import get_device_place, get_numeric_gradient, is_custom_device
|
|
|
|
sys.path.append("../../legacy_test")
|
|
from test_conv2d_op import (
|
|
TestConv2DOp,
|
|
TestConv2DOp_v2,
|
|
create_test_channel_last_class,
|
|
create_test_cudnn_channel_last_class,
|
|
create_test_cudnn_padding_SAME_class,
|
|
create_test_padding_SAME_class,
|
|
create_test_padding_VALID_class,
|
|
)
|
|
from testsuite import create_op
|
|
|
|
from paddle.base import core
|
|
|
|
# ----------------TestDepthwiseConv -----
|
|
|
|
|
|
def depthwise_conv2d_wrapper(
|
|
x,
|
|
weight,
|
|
stride=1,
|
|
padding=0,
|
|
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.depthwise_conv2d(
|
|
x,
|
|
weight,
|
|
stride,
|
|
padding,
|
|
padding_algorithm,
|
|
groups,
|
|
dilation,
|
|
data_format,
|
|
)
|
|
|
|
|
|
class TestDepthwiseConv(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConv2(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConv3(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 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, 3, 3]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConvWithDilation(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConvWithDilation2(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConvandFuse(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConv2andFuse(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConv3andFuse(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 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, 3, 3]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConvWithDilationandFuse(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConvWithDilation2andFuse(TestConv2DOp):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
|
|
class TestDepthwiseConv_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 0, 1]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConv2_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 1, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConv3_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 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, 3, 3]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 0, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConvWithDilation_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 2, 1]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 1, 1, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConvandFuse_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [2, 1, 2, 3]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConv2andFuse_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 1, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConv3andFuse_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 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, 3, 3]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 2, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [2, 2]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [2, 1, 1, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2DOp_v2):
|
|
def init_test_case(self):
|
|
self.fuse_relu_before_depthwise_conv = True
|
|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
|
|
self.input_size = [2, 3, 5, 5] # NCHW
|
|
self.groups = 3
|
|
self.dilations = [2, 2]
|
|
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]
|
|
self.op_type = "depthwise_conv2d"
|
|
self.python_api = depthwise_conv2d_wrapper
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 3, 1, 3]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
def create_test_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 TestDepthwiseConvFP16(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cuda = 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'}
|
|
)
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "FP16OP")
|
|
TestDepthwiseConvFP16.__name__ = cls_name
|
|
globals()[cls_name] = TestDepthwiseConvFP16
|
|
|
|
|
|
def create_test_bf16_class(parent, atol=1e-2):
|
|
@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 TestDepthwiseConvBF16(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_cuda = 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=atol)
|
|
|
|
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__, "BF16OP")
|
|
TestDepthwiseConvBF16.__name__ = cls_name
|
|
globals()[cls_name] = TestDepthwiseConvBF16
|
|
|
|
|
|
def create_test_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 TestChannelLastFP16(parent):
|
|
def init_kernel_type(self):
|
|
self.use_cuda = 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__, "ChannelLastFP16")
|
|
TestChannelLastFP16.__name__ = cls_name
|
|
globals()[cls_name] = TestChannelLastFP16
|
|
|
|
|
|
# depthwise conv2d fp16
|
|
|
|
create_test_fp16_class(TestDepthwiseConv)
|
|
create_test_fp16_class(TestDepthwiseConv2)
|
|
create_test_fp16_class(TestDepthwiseConv3)
|
|
create_test_fp16_class(TestDepthwiseConvWithDilation)
|
|
create_test_fp16_class(TestDepthwiseConvWithDilation2)
|
|
create_test_fp16_class(TestDepthwiseConvandFuse)
|
|
create_test_fp16_class(TestDepthwiseConv2andFuse)
|
|
create_test_fp16_class(TestDepthwiseConv3andFuse)
|
|
create_test_fp16_class(TestDepthwiseConvWithDilationandFuse)
|
|
create_test_fp16_class(TestDepthwiseConvWithDilation2andFuse)
|
|
|
|
# depthwise conv2d bf16
|
|
|
|
create_test_bf16_class(TestDepthwiseConv)
|
|
create_test_bf16_class(TestDepthwiseConv2)
|
|
create_test_bf16_class(TestDepthwiseConv3, atol=4e-2)
|
|
create_test_bf16_class(TestDepthwiseConvWithDilation)
|
|
create_test_bf16_class(TestDepthwiseConvWithDilation2)
|
|
create_test_bf16_class(TestDepthwiseConvandFuse)
|
|
create_test_bf16_class(TestDepthwiseConv2andFuse)
|
|
create_test_bf16_class(TestDepthwiseConv3andFuse)
|
|
create_test_bf16_class(TestDepthwiseConvWithDilationandFuse)
|
|
create_test_bf16_class(TestDepthwiseConvWithDilation2andFuse)
|
|
|
|
# depthwise conv2d
|
|
|
|
create_test_padding_SAME_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_padding_SAME_class(TestDepthwiseConvWithDilation_AsyPadding)
|
|
create_test_padding_SAME_class(TestDepthwiseConvandFuse_AsyPadding)
|
|
create_test_padding_SAME_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
|
|
|
|
create_test_padding_VALID_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_padding_VALID_class(TestDepthwiseConvWithDilation_AsyPadding)
|
|
create_test_padding_VALID_class(TestDepthwiseConvandFuse_AsyPadding)
|
|
create_test_padding_VALID_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
|
|
|
|
# channel last
|
|
|
|
create_test_channel_last_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_channel_last_class(TestDepthwiseConvWithDilation2_AsyPadding)
|
|
create_test_channel_last_class(TestDepthwiseConvandFuse_AsyPadding)
|
|
create_test_channel_last_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
|
|
|
|
# channel last fp16
|
|
create_test_channel_last_fp16_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_channel_last_fp16_class(TestDepthwiseConvWithDilation2_AsyPadding)
|
|
create_test_channel_last_fp16_class(TestDepthwiseConvandFuse_AsyPadding)
|
|
create_test_channel_last_fp16_class(
|
|
TestDepthwiseConvWithDilationandFuse_AsyPadding
|
|
)
|
|
|
|
|
|
# ------------ depthwise conv2d in MIOPEN ---------
|
|
if core.is_compiled_with_rocm():
|
|
create_test_cudnn_padding_SAME_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(
|
|
TestDepthwiseConvWithDilation_AsyPadding
|
|
)
|
|
create_test_padding_VALID_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_padding_VALID_class(TestDepthwiseConvWithDilation_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestDepthwiseConv_AsyPadding)
|
|
create_test_cudnn_channel_last_class(
|
|
TestDepthwiseConvWithDilation2_AsyPadding
|
|
)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|