wehub-resource-sync
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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"""tests of Clip module."""
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import pytest
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import numpy as np
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import torch
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from pytorch_quantization.nn.modules import clip
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# make everything run on the GPU
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torch.set_default_tensor_type('torch.cuda.FloatTensor')
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np.random.seed(1234)
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torch.manual_seed(1234)
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# pylint:disable=missing-docstring, no-self-use
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class TestClip():
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def test_simple_run(self):
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x_np = np.random.rand(1023).astype(np.float32)
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x_torch = torch.Tensor(x_np)
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clip_op = clip.Clip(torch.tensor(0.3), torch.tensor(0.7))
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clip_x_np = np.clip(x_np, 0.3, 0.7)
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clip_x_torch = clip_op(x_torch)
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np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np)
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def test_raise(self):
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with pytest.raises(ValueError, match="must be scalar"):
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clip_op = clip.Clip(torch.tensor(0.3), torch.tensor(0.7), learn_min=True)
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def test_backward(self):
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x = torch.randn(3, 7, requires_grad=True)
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x.retain_grad()
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min_value = 0.3
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max_value = 0.7
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clip_op = clip.Clip(min_value, max_value, learn_min=True, learn_max=True)
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clip_x = clip_op(x)
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clip_x.retain_grad()
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labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda()
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criterion = torch.nn.CrossEntropyLoss()
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loss = criterion(clip_x, labels)
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loss.backward()
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assert x.grad.cpu()[x.cpu() < min_value].sum() == 0
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assert x.grad.cpu()[x.cpu() > max_value].sum() == 0
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assert torch.equal(clip_x.grad[(x > min_value) & (x < max_value)], x.grad[(x > min_value) & (x < max_value)])
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