forked from mindspore-Ecosystem/mindspore
136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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test msssim
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"""
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import _executor
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_MSSSIM_WEIGHTS = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333)
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class MSSSIMNet(nn.Cell):
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def __init__(self, max_val=1.0, power_factors=_MSSSIM_WEIGHTS, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
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super(MSSSIMNet, self).__init__()
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self.net = nn.MSSSIM(max_val, power_factors, filter_size, filter_sigma, k1, k2)
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def construct(self, img1, img2):
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return self.net(img1, img2)
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def test_compile():
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factors = (0.033, 0.033, 0.033)
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net = MSSSIMNet(power_factors=factors)
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img1 = Tensor(np.random.random((8, 3, 128, 128)))
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img2 = Tensor(np.random.random((8, 3, 128, 128)))
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_executor.compile(net, img1, img2)
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def test_compile_grayscale():
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max_val = 255
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factors = (0.033, 0.033, 0.033)
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net = MSSSIMNet(max_val=max_val, power_factors=factors)
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img1 = Tensor(np.random.randint(0, 256, (8, 3, 128, 128), np.uint8))
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img2 = Tensor(np.random.randint(0, 256, (8, 3, 128, 128), np.uint8))
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_executor.compile(net, img1, img2)
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def test_msssim_max_val_negative():
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max_val = -1
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with pytest.raises(ValueError):
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_ = MSSSIMNet(max_val)
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def test_msssim_max_val_bool():
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max_val = True
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with pytest.raises(TypeError):
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_ = MSSSIMNet(max_val)
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def test_msssim_max_val_zero():
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max_val = 0
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with pytest.raises(ValueError):
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_ = MSSSIMNet(max_val)
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def test_msssim_power_factors_set():
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with pytest.raises(TypeError):
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_ = MSSSIMNet(power_factors={0.033, 0.033, 0.033})
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def test_msssim_filter_size_float():
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with pytest.raises(TypeError):
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_ = MSSSIMNet(filter_size=1.1)
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def test_msssim_filter_size_zero():
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with pytest.raises(ValueError):
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_ = MSSSIMNet(filter_size=0)
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def test_msssim_filter_sigma_zero():
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with pytest.raises(ValueError):
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_ = MSSSIMNet(filter_sigma=0.0)
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def test_msssim_filter_sigma_negative():
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with pytest.raises(ValueError):
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_ = MSSSIMNet(filter_sigma=-0.1)
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def test_msssim_different_shape():
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shape_1 = (8, 3, 128, 128)
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shape_2 = (8, 3, 256, 256)
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factors = (0.033, 0.033, 0.033)
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img1 = Tensor(np.random.random(shape_1))
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img2 = Tensor(np.random.random(shape_2))
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net = MSSSIMNet(power_factors=factors)
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with pytest.raises(ValueError):
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_executor.compile(net, img1, img2)
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def test_msssim_different_dtype():
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dtype_1 = mstype.float32
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dtype_2 = mstype.float16
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factors = (0.033, 0.033, 0.033)
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img1 = Tensor(np.random.random((8, 3, 128, 128)), dtype=dtype_1)
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img2 = Tensor(np.random.random((8, 3, 128, 128)), dtype=dtype_2)
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net = MSSSIMNet(power_factors=factors)
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with pytest.raises(TypeError):
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_executor.compile(net, img1, img2)
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def test_msssim_invalid_5d_input():
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shape_1 = (8, 3, 128, 128)
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shape_2 = (8, 3, 256, 256)
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invalid_shape = (8, 3, 128, 128, 1)
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factors = (0.033, 0.033, 0.033)
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img1 = Tensor(np.random.random(shape_1))
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invalid_img1 = Tensor(np.random.random(invalid_shape))
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img2 = Tensor(np.random.random(shape_2))
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invalid_img2 = Tensor(np.random.random(invalid_shape))
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net = MSSSIMNet(power_factors=factors)
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with pytest.raises(ValueError):
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_executor.compile(net, invalid_img1, img2)
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with pytest.raises(ValueError):
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_executor.compile(net, img1, invalid_img2)
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with pytest.raises(ValueError):
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_executor.compile(net, invalid_img1, invalid_img2)
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