# copyright (c) 2023 paddlepaddle authors. all rights reserved. # # 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. import unittest import paddle from paddle.nn import Conv2D from paddle.nn.quant import Stub from paddle.quantization import QAT, QuantConfig from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver from paddle.quantization.quanters.abs_max import ( FakeQuanterWithAbsMaxObserverLayer, ) quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9) class Model(paddle.nn.Layer): def __init__(self, num_classes=10): super().__init__() self.quant_in = Stub() self.conv = Conv2D(3, 6, 3, stride=1, padding=1) self.quant = Stub(quanter) self.quant_out = Stub() def forward(self, inputs): out = self.conv(inputs) out = self.quant(out) out = paddle.nn.functional.relu(out) return self.quant_out(out) class TestStub(unittest.TestCase): def test_stub(self): model = Model() q_config = QuantConfig(activation=quanter, weight=quanter) qat = QAT(q_config) q_config.add_layer_config(model.quant_in, activation=None, weight=None) quant_model = qat.quantize(model) image = paddle.rand([1, 3, 32, 32], dtype="float32") out = model(image) out = quant_model(image) out.backward() quanter_count = 0 for _layer in quant_model.sublayers(True): if isinstance(_layer, FakeQuanterWithAbsMaxObserverLayer): quanter_count += 1 self.assertEqual(quanter_count, 5) if __name__ == '__main__': unittest.main()