chore: import upstream snapshot with attribution
Docker Image CI / build-ubuntu2004 (push) Has been cancelled
Docker Image CI / build-ubuntu2004 (push) Has been cancelled
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,321 @@
|
||||
#
|
||||
# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# 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.
|
||||
#
|
||||
|
||||
|
||||
"""tests of QuantPooling module.
|
||||
Most tests check the functionality of all the combinations in Quant Pooling against the corresponding functionalities
|
||||
in tensor_quant.
|
||||
"""
|
||||
import pytest
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from pytorch_quantization import tensor_quant
|
||||
from pytorch_quantization.nn.modules import quant_pooling
|
||||
|
||||
# make everything run on the GPU
|
||||
torch.set_default_tensor_type('torch.cuda.FloatTensor')
|
||||
|
||||
np.random.seed(1234)
|
||||
torch.manual_seed(1234)
|
||||
|
||||
# pylint:disable=missing-docstring, no-self-use
|
||||
class TestQuantMaxPool1d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool1d(kernel_size=3, stride=1,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool1d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.max_pool1d(quant_input, 3, 1, 0, 1, False, False)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantMaxPool2d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_variable_bits(self):
|
||||
# Repeat checking the output for variable number of bits to QuantDescriptor
|
||||
for bits in [2, 4, 6]:
|
||||
quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
|
||||
|
||||
quant_pooling.QuantMaxPool2d.set_default_quant_desc_input(quant_desc_input)
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits)
|
||||
|
||||
out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_fake_quant_disable(self):
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_pooling_object.input_quantizer.disable()
|
||||
|
||||
out1 = F.max_pool2d(test_input, 3, 1, 0, 1, False, False)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_multi_axis(self):
|
||||
quant_desc_input = tensor_quant.QuantDescriptor(num_bits=8, axis=(0, 1))
|
||||
|
||||
quant_pooling.QuantMaxPool2d.set_default_quant_desc_input(quant_desc_input)
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(16, 7, 5, 5, dtype=torch.double)
|
||||
input_amax = torch.amax(torch.abs(test_input), dim=(2, 3), keepdim=True)
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, input_amax)
|
||||
|
||||
out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantMaxPool3d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool3d(kernel_size=3, stride=1,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantMaxPool3d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(5, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.max_pool3d(quant_input, 3, 1, 0, 1, False, False)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantAvgPool1d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool1d(kernel_size=3, stride=1,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool1d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.avg_pool1d(quant_input, 3, 1, 0, False, True)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantAvgPool2d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.avg_pool2d(quant_input, 3, 1, 0, False, True, None)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_variable_bits(self):
|
||||
# Repeat checking the output for variable number of bits to QuantDescriptor
|
||||
for bits in [2, 4, 6]:
|
||||
quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
|
||||
|
||||
quant_pooling.QuantAvgPool2d.set_default_quant_desc_input(quant_desc_input)
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits)
|
||||
|
||||
out1 = F.avg_pool2d(quant_input, 3, 1, 0, False, True, None)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_fake_quant_disable(self):
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_pooling_object.input_quantizer.disable()
|
||||
|
||||
out1 = F.avg_pool2d(test_input, 3, 1, 0, False, True, None)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantAvgPool3d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool3d(kernel_size=3, stride=1,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantAvgPool3d(kernel_size=3, stride=1)
|
||||
|
||||
test_input = torch.randn(5, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.avg_pool3d(quant_input, 3, 1, 0, False, True, None)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantAdaptiveAvgPool1d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool1d(output_size=3,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool1d(output_size=3)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.adaptive_avg_pool1d(quant_input, 3)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantAdaptiveAvgPool2d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.adaptive_avg_pool2d(quant_input, 3)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_variable_bits(self):
|
||||
# Repeat checking the output for variable number of bits to QuantDescriptor
|
||||
for bits in [2, 4, 6]:
|
||||
quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
|
||||
|
||||
quant_pooling.QuantAdaptiveAvgPool2d.set_default_quant_desc_input(quant_desc_input)
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits)
|
||||
|
||||
out1 = F.adaptive_avg_pool2d(quant_input, 3)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
def test_input_fake_quant_disable(self):
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3)
|
||||
|
||||
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_pooling_object.input_quantizer.disable()
|
||||
|
||||
out1 = F.adaptive_avg_pool2d(test_input, 3)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
|
||||
class TestQuantAdaptiveAvgPool3d():
|
||||
|
||||
def test_raise(self):
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool3d(output_size=3,
|
||||
quant_desc_input=
|
||||
tensor_quant.QuantDescriptor(fake_quant=False))
|
||||
assert "Only fake quantization is supported" in str(excinfo.value)
|
||||
|
||||
# Quantizing activations
|
||||
def test_input_fake_quant(self):
|
||||
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool3d(output_size=3)
|
||||
|
||||
test_input = torch.randn(5, 5, 5, 5, dtype=torch.double)
|
||||
|
||||
quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
|
||||
|
||||
out1 = F.adaptive_avg_pool3d(quant_input, 3)
|
||||
out2 = quant_pooling_object(test_input)
|
||||
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
|
||||
Reference in New Issue
Block a user