wehub-resource-sync
200 lines
9.4 KiB
Python
200 lines
9.4 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 QuantInstanceNorm module.
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Mose tests check the functionality of all the combinations in Quant instancenorm against the corresponding functionalities in
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tensor_quant. There are tests for all the three QuantInstaceNorm1D, QuantInstanceNorm2D, and QuantInstanceNorm3D
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"""
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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 torch import nn
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import torch.nn.functional as F
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from pytorch_quantization import tensor_quant
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from pytorch_quantization.tensor_quant import QuantDescriptor
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from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer
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from pytorch_quantization import utils as quant_utils
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from pytorch_quantization.nn.modules import quant_instancenorm
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#import tests.utils as test_utils
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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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torch.backends.cudnn.deterministic = True
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np.random.seed(1234)
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# pylint:disable=missing-docstring, no-self-use
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NUM_CHANNELS = 15
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class TestQuantInstanceNorm1D():
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def test_no_quant(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True)
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quant_instancenorm_object.input_quantizer.disable()
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test_input = torch.randn(8, NUM_CHANNELS, 128)
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(test_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_fake_quant_per_tensor(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True,
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quant_desc_input=QuantDescriptor())
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test_input = torch.randn(8, NUM_CHANNELS, 128)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(quant_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_fake_quant_per_channel(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True,
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quant_desc_input=QuantDescriptor(axis=(1)))
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test_input = torch.randn(8, NUM_CHANNELS, 128)
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quant_input = tensor_quant.fake_tensor_quant(test_input,
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torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0])
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(quant_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantInstanceNorm2D():
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def test_no_quant(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True)
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quant_instancenorm_object.input_quantizer.disable()
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test_input = torch.randn(8, NUM_CHANNELS, 128, 128)
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(test_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_fake_quant_per_tensor(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True,
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quant_desc_input=QuantDescriptor())
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test_input = torch.randn(8, NUM_CHANNELS, 128, 128)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(quant_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_fake_quant_per_channel(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True,
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quant_desc_input=QuantDescriptor(axis=(1)))
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test_input = torch.randn(8, NUM_CHANNELS, 128, 128)
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quant_input = tensor_quant.fake_tensor_quant(test_input,
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torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0].max(3, keepdim=True)[0])
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(quant_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantInstanceNorm3D():
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def test_no_quant(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True)
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quant_instancenorm_object.input_quantizer.disable()
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test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128)
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(test_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_fake_quant_per_tensor(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True,
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quant_desc_input=QuantDescriptor())
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test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(quant_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_fake_quant_per_channel(self):
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quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True,
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quant_desc_input=QuantDescriptor(axis=(1)))
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test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128)
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quant_input = tensor_quant.fake_tensor_quant(test_input,
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torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0]
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.max(3, keepdim=True)[0].max(4, keepdim=True)[0])
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out1 = quant_instancenorm_object(test_input)
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out2 = F.instance_norm(quant_input,
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quant_instancenorm_object.running_mean,
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quant_instancenorm_object.running_var,
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quant_instancenorm_object.weight,
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quant_instancenorm_object.bias)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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