wehub-resource-sync
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
# copyright (c) 2022 paddlepaddle authors. all rights reserved.
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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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import unittest
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle.io import Dataset
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from paddle.nn import Conv2D, Linear, ReLU, Sequential
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from paddle.quantization import QAT, QuantConfig
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from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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from paddle.quantization.quanters.abs_max import (
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FakeQuanterWithAbsMaxObserverLayer,
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)
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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data = np.random.random([3, 32, 32]).astype('float32')
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return data
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def __len__(self):
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return self.num_samples
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class Model(paddle.nn.Layer):
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def __init__(self, num_classes=10):
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super().__init__()
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self.num_classes = num_classes
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self.features = Sequential(
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Conv2D(3, 6, 3, stride=1, padding=1),
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ReLU(),
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paddle.nn.MaxPool2D(2, stride=2),
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Conv2D(6, 16, 5, stride=1, padding=0),
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ReLU(),
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paddle.nn.MaxPool2D(2, stride=2),
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)
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if num_classes > 0:
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self.fc = Sequential(
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Linear(576, 120), Linear(120, 84), Linear(84, 10)
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)
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def forward(self, inputs):
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x = self.features(inputs)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(x)
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out = F.relu(x)
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return out
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class TestQAT(unittest.TestCase):
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def test_qat(self):
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nums_batch = 100
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batch_size = 32
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dataset = RandomDataset(nums_batch * batch_size)
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loader = paddle.io.DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=False,
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drop_last=True,
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num_workers=0,
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)
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model = Model()
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quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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q_config = QuantConfig(activation=quanter, weight=quanter)
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qat = QAT(q_config)
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print(model)
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quant_model = qat.quantize(model)
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print(quant_model)
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quanter_count = 0
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for _layer in quant_model.sublayers(True):
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if isinstance(_layer, FakeQuanterWithAbsMaxObserverLayer):
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quanter_count += 1
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self.assertEqual(quanter_count, 14)
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for _, data in enumerate(loader):
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out = quant_model(data)
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out.backward()
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if __name__ == '__main__':
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unittest.main()
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