wehub-resource-sync
322 lines
14 KiB
Python
322 lines
14 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 QuantPooling module.
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Most tests check the functionality of all the combinations in Quant Pooling against the corresponding functionalities
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in tensor_quant.
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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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import torch.nn.functional as F
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from pytorch_quantization import tensor_quant
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from pytorch_quantization.nn.modules import quant_pooling
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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 TestQuantMaxPool1d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantMaxPool1d(kernel_size=3, stride=1,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantMaxPool1d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.max_pool1d(quant_input, 3, 1, 0, 1, False, False)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantMaxPool2d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_variable_bits(self):
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# Repeat checking the output for variable number of bits to QuantDescriptor
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for bits in [2, 4, 6]:
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quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
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quant_pooling.QuantMaxPool2d.set_default_quant_desc_input(quant_desc_input)
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quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits)
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out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_fake_quant_disable(self):
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quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_pooling_object.input_quantizer.disable()
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out1 = F.max_pool2d(test_input, 3, 1, 0, 1, False, False)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_multi_axis(self):
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quant_desc_input = tensor_quant.QuantDescriptor(num_bits=8, axis=(0, 1))
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quant_pooling.QuantMaxPool2d.set_default_quant_desc_input(quant_desc_input)
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quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(16, 7, 5, 5, dtype=torch.double)
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input_amax = torch.amax(torch.abs(test_input), dim=(2, 3), keepdim=True)
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quant_input = tensor_quant.fake_tensor_quant(test_input, input_amax)
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out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantMaxPool3d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantMaxPool3d(kernel_size=3, stride=1,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantMaxPool3d(kernel_size=3, stride=1)
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test_input = torch.randn(5, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.max_pool3d(quant_input, 3, 1, 0, 1, False, False)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantAvgPool1d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantAvgPool1d(kernel_size=3, stride=1,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantAvgPool1d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.avg_pool1d(quant_input, 3, 1, 0, False, True)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantAvgPool2d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.avg_pool2d(quant_input, 3, 1, 0, False, True, None)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_variable_bits(self):
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# Repeat checking the output for variable number of bits to QuantDescriptor
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for bits in [2, 4, 6]:
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quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
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quant_pooling.QuantAvgPool2d.set_default_quant_desc_input(quant_desc_input)
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quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits)
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out1 = F.avg_pool2d(quant_input, 3, 1, 0, False, True, None)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_fake_quant_disable(self):
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quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_pooling_object.input_quantizer.disable()
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out1 = F.avg_pool2d(test_input, 3, 1, 0, False, True, None)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantAvgPool3d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantAvgPool3d(kernel_size=3, stride=1,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantAvgPool3d(kernel_size=3, stride=1)
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test_input = torch.randn(5, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.avg_pool3d(quant_input, 3, 1, 0, False, True, None)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantAdaptiveAvgPool1d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool1d(output_size=3,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool1d(output_size=3)
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test_input = torch.randn(1, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.adaptive_avg_pool1d(quant_input, 3)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantAdaptiveAvgPool2d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.adaptive_avg_pool2d(quant_input, 3)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_variable_bits(self):
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# Repeat checking the output for variable number of bits to QuantDescriptor
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for bits in [2, 4, 6]:
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quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
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quant_pooling.QuantAdaptiveAvgPool2d.set_default_quant_desc_input(quant_desc_input)
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits)
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out1 = F.adaptive_avg_pool2d(quant_input, 3)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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def test_input_fake_quant_disable(self):
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3)
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test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
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quant_pooling_object.input_quantizer.disable()
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out1 = F.adaptive_avg_pool2d(test_input, 3)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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class TestQuantAdaptiveAvgPool3d():
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def test_raise(self):
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with pytest.raises(ValueError) as excinfo:
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool3d(output_size=3,
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quant_desc_input=
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tensor_quant.QuantDescriptor(fake_quant=False))
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assert "Only fake quantization is supported" in str(excinfo.value)
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# Quantizing activations
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def test_input_fake_quant(self):
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quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool3d(output_size=3)
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test_input = torch.randn(5, 5, 5, 5, dtype=torch.double)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = F.adaptive_avg_pool3d(quant_input, 3)
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out2 = quant_pooling_object(test_input)
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np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
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