未验证 提交 d1a53649 编写于 作者: C Chenxiao Niu 提交者: GitHub

add UTs for mlu interp_v2(nearest). (#43709)

上级 8dd0a3b9
# Copyright (c) 2022 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.
from __future__ import print_function
import unittest
import numpy as np
import sys
sys.path.append('..')
from op_test import OpTest
import paddle.fluid.core as core
import paddle.fluid as fluid
import paddle.nn as nn
import paddle
from paddle.nn.functional import interpolate
paddle.enable_static()
def nearest_neighbor_interp_np(X,
out_h,
out_w,
scale_h=0,
scale_w=0,
out_size=None,
actual_shape=None,
align_corners=True,
data_layout='NCHW'):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if data_layout == "NHWC":
X = np.transpose(X, (0, 3, 1, 2)) # NHWC => NCHW
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
if actual_shape is not None:
out_h = actual_shape[0]
out_w = actual_shape[1]
n, c, in_h, in_w = X.shape
ratio_h = ratio_w = 0.0
if (out_h > 1):
if (align_corners):
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
if scale_h > 0:
ratio_h = 1.0 / scale_h
else:
ratio_h = 1.0 * in_h / out_h
if (out_w > 1):
if (align_corners):
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
if scale_w > 0:
ratio_w = 1.0 / scale_w
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_h, out_w))
if align_corners:
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, i, j] = X[:, :, in_i, in_j]
else:
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, i, j] = X[:, :, in_i, in_j]
if data_layout == "NHWC":
out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC
# out = np.expand_dims(out, 2)
return out.astype(X.dtype)
def nearest_neighbor_interp3d_np(X,
out_d,
out_h,
out_w,
scale_d=0,
scale_h=0,
scale_w=0,
out_size=None,
actual_shape=None,
align_corners=True,
data_layout='NCHW'):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if data_layout == "NHWC":
X = np.transpose(X, (0, 4, 1, 2, 3)) # NDHWC => NCDHW
if out_size is not None:
out_d = out_size[0]
out_h = out_size[1]
out_w = out_size[2]
if actual_shape is not None:
out_d = actual_shape[0]
out_h = actual_shape[1]
out_w = actual_shape[2]
n, c, in_d, in_h, in_w = X.shape
ratio_d = ratio_h = ratio_w = 0.0
if (out_d > 1):
if (align_corners):
ratio_d = (in_d - 1.0) / (out_d - 1.0)
else:
if scale_d > 0:
ratio_d = 1.0 / scale_d
else:
ratio_d = 1.0 * in_d / out_d
if (out_h > 1):
if (align_corners):
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
if scale_h > 0:
ratio_h = 1.0 / scale_h
else:
ratio_h = 1.0 * in_h / out_h
if (out_w > 1):
if (align_corners):
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
if scale_w > 0:
ratio_w = 1.0 / scale_w
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_d, out_h, out_w))
if align_corners:
for d in range(out_d):
in_d = int(ratio_d * d + 0.5)
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
else:
for d in range(out_d):
in_d = int(ratio_d * d)
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
if data_layout == "NDHWC":
out = np.transpose(out, (0, 2, 3, 4, 1)) # NCDHW => NDHWC
return out.astype(X.dtype)
class TestNearestInterpOp(OpTest):
def setUp(self):
self.place = paddle.device.MLUPlace(0)
self.__class__.use_mlu = True
self.out_size = None
self.actual_shape = None
self.init_test_case()
self.data_layout = 'NCHW' if len(self.input_shape) == 4 else 'NCDHW'
self.op_type = "nearest_interp_v2"
input_np = np.random.random(self.input_shape).astype("float32")
if self.data_layout == "NCHW" and len(self.input_shape) == 4:
in_d = 1
in_h = self.input_shape[2]
in_w = self.input_shape[3]
else:
in_d = 1
in_h = self.input_shape[1]
in_w = self.input_shape[2]
if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
in_d = self.input_shape[2]
in_h = self.input_shape[3]
in_w = self.input_shape[4]
else:
in_d = self.input_shape[1]
in_h = self.input_shape[2]
in_w = self.input_shape[3]
scale_d = 0
scale_h = 0
scale_w = 0
if self.scale:
if isinstance(self.scale, float) or isinstance(self.scale, int):
if self.scale > 0:
scale_d = scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_d = scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
if len(self.scale) == 5:
scale_w = self.scale[2]
scale_h = self.scale[1]
scale_d = self.scale[0]
else:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(in_h * scale_h)
out_w = int(in_w * scale_w)
out_d = int(in_d * scale_d)
else:
if len(self.input_shape) == 5:
out_d = self.out_d
out_h = self.out_h
out_w = self.out_w
if len(self.input_shape) == 4:
output_np = nearest_neighbor_interp_np(
input_np, out_h, out_w, scale_h, scale_w, self.out_size,
self.actual_shape, self.align_corners, self.data_layout)
elif len(self.input_shape) == 5:
output_np = nearest_neighbor_interp3d_np(input_np, out_d, out_h,
out_w, scale_d, scale_h,
scale_w, self.out_size,
self.actual_shape,
self.align_corners,
self.data_layout)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
if len(self.input_shape) == 5:
self.attrs = {
'out_d': self.out_d,
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout
}
else:
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout
}
if self.scale:
if isinstance(self.scale, float) or isinstance(self.scale, int):
if self.scale > 0:
self.scale = [self.scale]
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
self.outputs = {'Out': output_np}
def test_check_output(self):
self.check_output_with_place(self.place)
def test_check_grad(self):
self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
# class TestNearestNeighborInterpCase1(TestNearestInterpOp):
# def init_test_case(self):
# self.interp_method = 'nearest'
# self.input_shape = [4, 1, 1, 7, 8]
# self.out_d = 1
# self.out_h = 1
# self.out_w = 1
# self.scale = 0.
# self.align_corners = True
class TestNearestNeighborInterpCase2(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.align_corners = True
class TestNearestNeighborInterpCase3(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [1, 1, 32, 64]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.align_corners = True
class TestNearestNeighborInterpCase4(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpCase5(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpCase6(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [1, 1, 32, 64]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpSame(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 32, 64]
self.out_h = 32
self.out_w = 64
self.scale = 0.
self.align_corners = True
class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 4, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = np.array([3, 8]).astype("int32")
self.align_corners = True
self.data_layout = "NHWC"
class TestNearestInterpWithoutCorners(TestNearestInterpOp):
def set_align_corners(self):
self.align_corners = False
class TestNearestNeighborInterpScale1(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 7, 5]
self.out_h = 64
self.out_w = 32
self.scale = 2.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpScale2(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 5, 7]
self.out_h = 64
self.out_w = 32
self.scale = 1.5
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpScale3(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 7, 5]
self.out_h = 64
self.out_w = 32
self.scale = [2.0, 3.0]
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestInterpOp_attr_tensor(OpTest):
def setUp(self):
self.place = paddle.device.MLUPlace(0)
self.__class__.use_mlu = True
self.out_size = None
self.actual_shape = None
self.init_test_case()
self.op_type = "nearest_interp_v2"
self.shape_by_1Dtensor = False
self.scale_by_1Dtensor = False
self.attrs = {
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
input_np = np.random.random(self.input_shape).astype("float32")
self.inputs = {'X': input_np}
if self.scale_by_1Dtensor:
self.inputs['Scale'] = np.array([self.scale]).astype("float32")
elif self.scale:
if isinstance(self.scale, float) or isinstance(self.scale, int):
if self.scale > 0:
scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(self.input_shape[2] * scale_h)
out_w = int(self.input_shape[3] * scale_w)
else:
out_h = self.out_h
out_w = self.out_w
if self.shape_by_1Dtensor:
self.inputs['OutSize'] = self.out_size
elif self.out_size is not None:
size_tensor = []
for index, ele in enumerate(self.out_size):
size_tensor.append(("x" + str(index), np.ones(
(1)).astype('int32') * ele))
self.inputs['SizeTensor'] = size_tensor
self.attrs['out_h'] = self.out_h
self.attrs['out_w'] = self.out_w
if self.scale:
if isinstance(self.scale, float) or isinstance(self.scale, int):
if self.scale > 0:
self.scale = [self.scale]
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, 0, 0,
self.out_size, self.actual_shape,
self.align_corners)
self.outputs = {'Out': output_np}
def test_check_output(self):
self.check_output_with_place(self.place)
def test_check_grad(self):
self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 5, 4, 4]
self.out_h = 3
self.out_w = 3
self.scale = 0.
self.out_size = [3, 3]
self.align_corners = True
# out_size is a tensor list
class TestNearestInterp_attr_tensor_Case1(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.out_size = [8, 12]
self.align_corners = True
# out_size is a 1-D tensor
class TestNearestInterp_attr_tensor_Case2(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
self.shape_by_1Dtensor = True
# scale is a 1-D tensor
class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 2.0
self.out_size = None
self.align_corners = True
self.scale_by_1Dtensor = True
#TODO: comment this test for now until nearest_interp_op added.
# class TestNearestAPI(unittest.TestCase):
# def test_case(self):
# x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
# y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
# dim = fluid.data(name="dim", shape=[1], dtype="int32")
# shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
# actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
# scale_tensor = fluid.data(
# name="scale_tensor", shape=[1], dtype="float32")
# out1 = fluid.layers.resize_nearest(
# y, out_shape=[12, 12], data_format='NHWC', align_corners=False)
# out2 = fluid.layers.resize_nearest(
# x, out_shape=[12, dim], align_corners=False)
# out3 = fluid.layers.resize_nearest(
# x, out_shape=shape_tensor, align_corners=False)
# out4 = fluid.layers.resize_nearest(
# x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False)
# out5 = fluid.layers.resize_nearest(
# x, scale=scale_tensor, align_corners=False)
# x_data = np.random.random((2, 3, 6, 6)).astype("float32")
# dim_data = np.array([12]).astype("int32")
# shape_data = np.array([12, 12]).astype("int32")
# actual_size_data = np.array([12, 12]).astype("int32")
# scale_data = np.array([2.0]).astype("float32")
# place = paddle.MLUPlace(0)
# exe = fluid.Executor(place)
# exe.run(fluid.default_startup_program())
# results = exe.run(fluid.default_main_program(),
# feed={
# "x": x_data,
# "y": np.transpose(x_data, (0, 2, 3, 1)),
# "dim": dim_data,
# "shape_tensor": shape_data,
# "actual_size": actual_size_data,
# "scale_tensor": scale_data
# },
# fetch_list=[out1, out2, out3, out4, out5],
# return_numpy=True)
# expect_res = nearest_neighbor_interp_np(
# x_data, out_h=12, out_w=12, align_corners=False)
# self.assertTrue(
# np.allclose(results[0], np.transpose(expect_res, (0, 2, 3, 1))))
# for i in range(len(results) - 1):
# self.assertTrue(np.allclose(results[i + 1], expect_res))
class TestNearestInterpException(unittest.TestCase):
def test_exception(self):
import paddle
input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
def attr_data_format():
# for 4-D input, data_format can only be NCHW or NHWC
out = fluid.layers.resize_nearest(input,
out_shape=[4, 8],
data_format='NDHWC')
def attr_scale_type():
out = fluid.layers.resize_nearest(input, scale='scale')
def attr_scale_value():
out = fluid.layers.resize_nearest(input, scale=-0.3)
def input_shape_error():
x = paddle.randn([1, 3])
out = paddle.nn.functional.interpolate(x, scale_factor='scale')
def mode_error():
x = paddle.randn([1, 3])
out = paddle.nn.functional.interpolate(x,
scale_factor='scale',
mode="BILINEAR")
self.assertRaises(ValueError, attr_data_format)
self.assertRaises(TypeError, attr_scale_type)
self.assertRaises(ValueError, attr_scale_value)
self.assertRaises(ValueError, input_shape_error)
self.assertRaises(ValueError, mode_error)
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册