wehub-resource-sync
93 lines
3.4 KiB
Python
93 lines
3.4 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 calibrators"""
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import inspect
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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 import enable_onnx_export
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from pytorch_quantization import utils as quant_utils
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from pytorch_quantization import calib
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from pytorch_quantization import nn as quant_nn
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import tests.utils as test_utils
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from examples.torchvision.models.classification import *
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from tests.fixtures import verbose
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from tests.fixtures.models import QuantLeNet
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np.random.seed(12345)
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torch.manual_seed(12345)
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# pylint:disable=missing-docstring, no-self-use
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class TestExampleModels():
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def test_resnet50(self):
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model = resnet50(pretrained=True, quantize=True)
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model.eval()
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for name, module in model.named_modules():
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if name.endswith('_quantizer'):
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module.amax = 2.50
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model.cuda()
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dummy_input = torch.randn(1, 3, 224, 224, device='cuda')
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with enable_onnx_export():
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if "enable_onnx_checker" in inspect.signature(torch.onnx.export).parameters:
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torch.onnx.export(model,
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dummy_input,
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"/tmp/resnet50.onnx",
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verbose=False,
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opset_version=13,
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enable_onnx_checker=False,
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do_constant_folding=True)
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else:
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torch.onnx.export(model,
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dummy_input,
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"/tmp/resnet50.onnx",
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verbose=False,
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opset_version=13,
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do_constant_folding=True)
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def test_resnet50_cpu(self):
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model = resnet50(pretrained=True, quantize=True)
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model.eval()
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for name, module in model.named_modules():
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if name.endswith('_quantizer'):
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module.amax = 2.50
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dummy_input = torch.randn(1, 3, 224, 224)
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with enable_onnx_export():
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if "enable_onnx_checker" in inspect.signature(torch.onnx.export).parameters:
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torch.onnx.export(model,
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dummy_input,
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"/tmp/resnet50_cpu.onnx",
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verbose=False,
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opset_version=13,
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enable_onnx_checker=False,
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do_constant_folding=True)
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else:
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torch.onnx.export(model,
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dummy_input,
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"/tmp/resnet50.onnx",
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verbose=False,
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opset_version=13,
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do_constant_folding=True)
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