wehub-resource-sync
383 lines
12 KiB
Python
383 lines
12 KiB
Python
# Copyright (c) 2019 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,
|
|
is_custom_device,
|
|
)
|
|
|
|
import paddle
|
|
import paddle.nn.functional as F
|
|
from paddle.base import core
|
|
|
|
|
|
def pixel_shuffle_np(x, up_factor, data_format="NCHW"):
|
|
if data_format == "NCHW":
|
|
n, c, h, w = x.shape
|
|
new_shape = (
|
|
n,
|
|
c // (up_factor * up_factor),
|
|
up_factor,
|
|
up_factor,
|
|
h,
|
|
w,
|
|
)
|
|
# reshape to (num,output_channel,upscale_factor,upscale_factor,h,w)
|
|
npresult = np.reshape(x, new_shape)
|
|
# transpose to (num,output_channel,h,upscale_factor,w,upscale_factor)
|
|
npresult = npresult.transpose(0, 1, 4, 2, 5, 3)
|
|
oshape = [n, c // (up_factor * up_factor), h * up_factor, w * up_factor]
|
|
npresult = np.reshape(npresult, oshape)
|
|
return npresult
|
|
else:
|
|
n, h, w, c = x.shape
|
|
new_shape = (
|
|
n,
|
|
h,
|
|
w,
|
|
c // (up_factor * up_factor),
|
|
up_factor,
|
|
up_factor,
|
|
)
|
|
# reshape to (num,h,w,output_channel,upscale_factor,upscale_factor)
|
|
npresult = np.reshape(x, new_shape)
|
|
# transpose to (num,h,upscale_factor,w,upscale_factor,output_channel)
|
|
npresult = npresult.transpose(0, 1, 4, 2, 5, 3)
|
|
oshape = [n, h * up_factor, w * up_factor, c // (up_factor * up_factor)]
|
|
npresult = np.reshape(npresult, oshape)
|
|
return npresult
|
|
|
|
|
|
class TestPixelShuffleOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "pixel_shuffle"
|
|
self.python_api = paddle.nn.functional.pixel_shuffle
|
|
self.init_dtype()
|
|
self.init_shape()
|
|
self.init_data_format()
|
|
n, c, h, w = self.shape
|
|
|
|
if self.format == "NCHW":
|
|
shape = [n, c, h, w]
|
|
if self.format == "NHWC":
|
|
shape = [n, h, w, c]
|
|
|
|
up_factor = 3
|
|
|
|
x = np.random.random(shape).astype(self.dtype)
|
|
npresult = pixel_shuffle_np(x, up_factor, self.format)
|
|
|
|
self.inputs = {'X': x}
|
|
self.outputs = {'Out': npresult}
|
|
self.attrs = {'upscale_factor': up_factor, "data_format": self.format}
|
|
|
|
def init_shape(self):
|
|
self.shape = [2, 9, 4, 4]
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
|
|
def init_data_format(self):
|
|
self.format = "NCHW"
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
class TestChannelLast(TestPixelShuffleOp):
|
|
def init_data_format(self):
|
|
self.format = "NHWC"
|
|
|
|
|
|
class TestPixelShuffleFP16Op(TestPixelShuffleOp):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
class TestPixelShuffleOp_ZeroSize(TestPixelShuffleOp):
|
|
def init_shape(self):
|
|
self.shape = [2, 0, 0, 4]
|
|
|
|
|
|
@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 or not support bfloat16",
|
|
)
|
|
class TestPixelShuffleBF16Op(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "pixel_shuffle"
|
|
self.python_api = paddle.nn.functional.pixel_shuffle
|
|
self.init_dtype()
|
|
self.init_data_format()
|
|
n, c, h, w = 2, 9, 4, 4
|
|
|
|
if self.format == "NCHW":
|
|
shape = [n, c, h, w]
|
|
if self.format == "NHWC":
|
|
shape = [n, h, w, c]
|
|
|
|
up_factor = 3
|
|
|
|
x = np.random.random(shape).astype(self.np_dtype)
|
|
npresult = pixel_shuffle_np(x, up_factor, self.format)
|
|
|
|
self.inputs = {'X': x}
|
|
self.outputs = {'Out': npresult}
|
|
self.attrs = {'upscale_factor': up_factor, "data_format": self.format}
|
|
|
|
self.place = get_device_place()
|
|
self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
|
|
self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.uint16
|
|
self.np_dtype = np.float32
|
|
|
|
def init_data_format(self):
|
|
self.format = "NCHW"
|
|
|
|
def test_check_output(self):
|
|
self.check_output_with_place(self.place, check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad_with_place(
|
|
self.place,
|
|
['X'],
|
|
'Out',
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
class TestPixelShuffleAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_1_np = np.random.random([2, 9, 4, 4]).astype("float64")
|
|
self.x_2_np = np.random.random([2, 4, 4, 9]).astype("float64")
|
|
self.out_1_np = pixel_shuffle_np(self.x_1_np, 3)
|
|
self.out_2_np = pixel_shuffle_np(self.x_2_np, 3, "NHWC")
|
|
|
|
def test_static_graph_functional(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
|
|
paddle.enable_static()
|
|
x_1 = paddle.static.data(
|
|
name="x", shape=[2, 9, 4, 4], dtype="float64"
|
|
)
|
|
x_2 = paddle.static.data(
|
|
name="x2", shape=[2, 4, 4, 9], dtype="float64"
|
|
)
|
|
out_1 = F.pixel_shuffle(x_1, 3)
|
|
out_2 = F.pixel_shuffle(x_2, 3, "NHWC")
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
res_1 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": self.x_1_np, "x2": self.x_2_np},
|
|
fetch_list=out_1,
|
|
use_prune=True,
|
|
)[0]
|
|
|
|
res_2 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": self.x_1_np, "x2": self.x_2_np},
|
|
fetch_list=out_2,
|
|
use_prune=True,
|
|
)[0]
|
|
|
|
np.testing.assert_allclose(res_1, self.out_1_np)
|
|
np.testing.assert_allclose(res_2, self.out_2_np)
|
|
|
|
def test_api_fp16(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
self.x_1_np = np.random.random([2, 9, 4, 4]).astype("float16")
|
|
self.x_2_np = np.random.random([2, 4, 4, 9]).astype("float16")
|
|
x_1 = paddle.static.data(
|
|
name="x", shape=[2, 9, 4, 4], dtype="float16"
|
|
)
|
|
x_2 = paddle.static.data(
|
|
name="x2", shape=[2, 4, 4, 9], dtype="float16"
|
|
)
|
|
# init instance
|
|
ps_1 = paddle.nn.PixelShuffle(3)
|
|
ps_2 = paddle.nn.PixelShuffle(3, "NHWC")
|
|
out_1 = ps_1(x_1)
|
|
out_2 = ps_2(x_2)
|
|
out_1_np = pixel_shuffle_np(self.x_1_np, 3)
|
|
out_2_np = pixel_shuffle_np(self.x_2_np, 3, "NHWC")
|
|
exe = paddle.static.Executor(place=place)
|
|
res_1 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": self.x_1_np, "x2": self.x_2_np},
|
|
fetch_list=out_1,
|
|
use_prune=True,
|
|
)[0]
|
|
res_2 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": self.x_1_np, "x2": self.x_2_np},
|
|
fetch_list=out_2,
|
|
use_prune=True,
|
|
)[0]
|
|
np.testing.assert_allclose(res_1, out_1_np)
|
|
np.testing.assert_allclose(res_2, out_2_np)
|
|
|
|
# same test between layer and functional in this op.
|
|
|
|
def test_static_graph_layer(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
|
|
paddle.enable_static()
|
|
x_1 = paddle.static.data(
|
|
name="x", shape=[2, 9, 4, 4], dtype="float64"
|
|
)
|
|
x_2 = paddle.static.data(
|
|
name="x2", shape=[2, 4, 4, 9], dtype="float64"
|
|
)
|
|
# init instance
|
|
ps_1 = paddle.nn.PixelShuffle(3)
|
|
ps_2 = paddle.nn.PixelShuffle(3, "NHWC")
|
|
out_1 = ps_1(x_1)
|
|
out_2 = ps_2(x_2)
|
|
out_1_np = pixel_shuffle_np(self.x_1_np, 3)
|
|
out_2_np = pixel_shuffle_np(self.x_2_np, 3, "NHWC")
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
res_1 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": self.x_1_np, "x2": self.x_2_np},
|
|
fetch_list=out_1,
|
|
use_prune=True,
|
|
)[0]
|
|
|
|
res_2 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": self.x_1_np, "x2": self.x_2_np},
|
|
fetch_list=out_2,
|
|
use_prune=True,
|
|
)[0]
|
|
|
|
np.testing.assert_allclose(res_1, out_1_np, rtol=1e-5, atol=1e-8)
|
|
np.testing.assert_allclose(res_2, out_2_np, rtol=1e-5, atol=1e-8)
|
|
|
|
def run_dygraph(self, up_factor, data_format):
|
|
n, c, h, w = 2, 9, 4, 4
|
|
|
|
if data_format == "NCHW":
|
|
shape = [n, c, h, w]
|
|
if data_format == "NHWC":
|
|
shape = [n, h, w, c]
|
|
|
|
x = np.random.random(shape).astype("float64")
|
|
|
|
npresult = pixel_shuffle_np(x, up_factor, data_format)
|
|
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
|
|
paddle.disable_static(place=place)
|
|
|
|
pixel_shuffle = paddle.nn.PixelShuffle(
|
|
up_factor, data_format=data_format
|
|
)
|
|
result = pixel_shuffle(paddle.to_tensor(x))
|
|
|
|
np.testing.assert_allclose(result.numpy(), npresult, rtol=1e-05)
|
|
|
|
result_functional = F.pixel_shuffle(
|
|
paddle.to_tensor(x), 3, data_format
|
|
)
|
|
np.testing.assert_allclose(
|
|
result_functional.numpy(), npresult, rtol=1e-05
|
|
)
|
|
|
|
def test_dygraph1(self):
|
|
self.run_dygraph(3, "NCHW")
|
|
|
|
def test_dygraph2(self):
|
|
self.run_dygraph(3, "NHWC")
|
|
|
|
|
|
class TestPixelShuffleError(unittest.TestCase):
|
|
def test_error_functional(self):
|
|
def error_upscale_factor():
|
|
with paddle.base.dygraph.guard():
|
|
x = np.random.random([2, 9, 4, 4]).astype("float64")
|
|
pixel_shuffle = F.pixel_shuffle(paddle.to_tensor(x), 3.33)
|
|
|
|
self.assertRaises(TypeError, error_upscale_factor)
|
|
|
|
def error_0_upscale_factor():
|
|
with paddle.base.dygraph.guard():
|
|
x = paddle.uniform([1, 1, 1, 1], dtype='float64')
|
|
pixel_shuffle = F.pixel_shuffle(x, 0)
|
|
|
|
self.assertRaises(ValueError, error_0_upscale_factor)
|
|
|
|
def error_data_format():
|
|
with paddle.base.dygraph.guard():
|
|
x = np.random.random([2, 9, 4, 4]).astype("float64")
|
|
pixel_shuffle = F.pixel_shuffle(paddle.to_tensor(x), 3, "WOW")
|
|
|
|
self.assertRaises(ValueError, error_data_format)
|
|
|
|
def test_error_layer(self):
|
|
def error_upscale_factor_layer():
|
|
with paddle.base.dygraph.guard():
|
|
x = np.random.random([2, 9, 4, 4]).astype("float64")
|
|
ps = paddle.nn.PixelShuffle(3.33)
|
|
|
|
self.assertRaises(TypeError, error_upscale_factor_layer)
|
|
|
|
def error_data_format_layer():
|
|
with paddle.base.dygraph.guard():
|
|
x = np.random.random([2, 9, 4, 4]).astype("float64")
|
|
ps = paddle.nn.PixelShuffle(3, "MEOW")
|
|
|
|
self.assertRaises(ValueError, error_data_format_layer)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|