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test_tipc/supplementary/slim/slim_quant.py

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+import paddle
+import numpy as np
+import os
+import paddle.nn as nn
+import paddleslim
+
+
+class PACT(paddle.nn.Layer):
+    def __init__(self):
+        super(PACT, self).__init__()
+        alpha_attr = paddle.ParamAttr(
+            name=self.full_name() + ".pact",
+            initializer=paddle.nn.initializer.Constant(value=20),
+            learning_rate=1.0,
+            regularizer=paddle.regularizer.L2Decay(2e-5),
+        )
+
+        self.alpha = self.create_parameter(shape=[1], attr=alpha_attr, dtype="float32")
+
+    def forward(self, x):
+        out_left = paddle.nn.functional.relu(x - self.alpha)
+        out_right = paddle.nn.functional.relu(-self.alpha - x)
+        x = x - out_left + out_right
+        return x
+
+
+quant_config = {
+    # weight preprocess type, default is None and no preprocessing is performed.
+    "weight_preprocess_type": None,
+    # activation preprocess type, default is None and no preprocessing is performed.
+    "activation_preprocess_type": None,
+    # weight quantize type, default is 'channel_wise_abs_max'
+    "weight_quantize_type": "channel_wise_abs_max",
+    # activation quantize type, default is 'moving_average_abs_max'
+    "activation_quantize_type": "moving_average_abs_max",
+    # weight quantize bit num, default is 8
+    "weight_bits": 8,
+    # activation quantize bit num, default is 8
+    "activation_bits": 8,
+    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+    "dtype": "int8",
+    # window size for 'range_abs_max' quantization. default is 10000
+    "window_size": 10000,
+    # The decay coefficient of moving average, default is 0.9
+    "moving_rate": 0.9,
+    # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
+    "quantizable_layer_type": ["Conv2D", "Linear"],
+}