wehub-resource-sync
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
# copyright (c) 2023 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 paddle
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from paddle.nn import Conv2D
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from paddle.nn.quant import Stub
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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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quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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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.quant_in = Stub()
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self.conv = Conv2D(3, 6, 3, stride=1, padding=1)
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self.quant = Stub(quanter)
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self.quant_out = Stub()
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def forward(self, inputs):
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out = self.conv(inputs)
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out = self.quant(out)
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out = paddle.nn.functional.relu(out)
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return self.quant_out(out)
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class TestStub(unittest.TestCase):
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def test_stub(self):
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model = Model()
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q_config = QuantConfig(activation=quanter, weight=quanter)
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qat = QAT(q_config)
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q_config.add_layer_config(model.quant_in, activation=None, weight=None)
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quant_model = qat.quantize(model)
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image = paddle.rand([1, 3, 32, 32], dtype="float32")
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out = model(image)
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out = quant_model(image)
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out.backward()
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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, 5)
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if __name__ == '__main__':
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unittest.main()
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