updated the sample to use INC 2.0 and TF 2.0 (#2301)

AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/alexnet.py

@@ -81,7 +81,7 @@ def create_model(w, c, classes):
     model.add(Dense(classes))
     model.add(Activation('softmax'))
 
-    opt = Adam(lr=0.001)
+    opt = Adam(learning_rate=0.001)
     model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
     return model
 

AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/inc_quantize_model.py

@@ -1,29 +1,18 @@
-import sys
-
-try:
-    import neural_compressor as inc
-    print("neural_compressor version {}".format(inc.__version__))
-except:
-    try:
-        import lpot as inc
-        print("LPOT version {}".format(inc.__version__))
-    except:
-        import ilit as inc
-        print("iLiT version {}".format(inc.__version__))
-
-if inc.__version__ == '1.2':
-    print("This script doesn't support LPOT 1.2, please install LPOT 1.1, 1.2.1 or newer")
-    sys.exit(1)
+import neural_compressor as inc
+print("neural_compressor version {}".format(inc.__version__))
 
 import alexnet
 import math
+import yaml
 import mnist_dataset
+from neural_compressor.quantization import fit
+from neural_compressor.config import PostTrainingQuantConfig, TuningCriterion, AccuracyCriterion 
 
 
 def save_int8_frezon_pb(q_model, path):
     from tensorflow.python.platform import gfile
     f = gfile.GFile(path, 'wb')
-    f.write(q_model.as_graph_def().SerializeToString())
+    f.write(q_model.graph.as_graph_def().SerializeToString())
     print("Save to {}".format(path))
 
 
@@ -44,23 +33,31 @@ def __iter__(self):
         yield x_test[begin:], label_test[begin:]
 
 
-def auto_tune(input_graph_path, yaml_config, batch_size):
+def auto_tune(input_graph_path, config, batch_size):    
     fp32_graph = alexnet.load_pb(input_graph_path)
-    quan = inc.Quantization(yaml_config)
     dataloader = Dataloader(batch_size)
-
-    q_model = quan(
-        fp32_graph,
-        q_dataloader=dataloader,
-        eval_func=None,
-        eval_dataloader=dataloader)
+    assert(dataloader)
+
+    tuning_criterion = TuningCriterion(**config["tuning_criterion"])
+    accuracy_criterion = AccuracyCriterion(**config["accuracy_criterion"])
+    q_model = fit(
+            model=input_graph_path,
+            conf=PostTrainingQuantConfig(**config["quant_config"],
+                        tuning_criterion=tuning_criterion,
+                        accuracy_criterion=accuracy_criterion,
+                ),
+            calib_dataloader=dataloader,
+        )
     return q_model
 
 
-yaml_file = "alexnet.yaml"
 batch_size = 200
 fp32_frezon_pb_file = "fp32_frezon.pb"
 int8_pb_file = "alexnet_int8_model.pb"
 
-q_model = auto_tune(fp32_frezon_pb_file, yaml_file, batch_size)
+with open("quant_config.yaml") as f:
+    config = yaml.safe_load(f.read())
+config
+
+q_model = auto_tune(fp32_frezon_pb_file, config, batch_size)
 save_int8_frezon_pb(q_model, int8_pb_file)