init

test_tipc/supplementary/slim/slim_fpgm.py

@@ -0,0 +1,22 @@
+import paddleslim
+import paddle
+import numpy as np
+
+from paddleslim.dygraph import FPGMFilterPruner
+
+
+def prune_model(model, input_shape, prune_ratio=0.1):
+
+    flops = paddle.flops(model, input_shape)
+    pruner = FPGMFilterPruner(model, input_shape)
+
+    params_sensitive = {}
+    for param in model.parameters():
+        if 'transpose' not in param.name and 'linear' not in param.name:
+            # set prune ratio as 10%. The larger the value, the more convolution weights will be cropped
+            params_sensitive[param.name] = prune_ratio
+
+    plan = pruner.prune_vars(params_sensitive, [0])
+
+    flops = paddle.flops(model, input_shape)
+    return model

test_tipc/supplementary/slim/slim_quant.py

@@ -0,0 +1,48 @@
+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'],
+}