Update test_examples

examples/quantization_aware_training/tensorflow/mobilenet_v2/main.py

@@ -113,7 +113,7 @@ def preprocess_for_train(image, label):
 
 
 train_dataset = tfds.load("imagenette/320px-v2", split="train", shuffle_files=True, as_supervised=True)
-train_dataset = train_dataset.map(preprocess_for_train).shuffle(1024).batch(128)
+train_dataset = train_dataset.map(preprocess_for_train).batch(64)
 
 val_dataset = tfds.load("imagenette/320px-v2", split="validation", shuffle_files=False, as_supervised=True)
 val_dataset = val_dataset.map(preprocess_for_eval).batch(128)
@@ -150,12 +150,12 @@ def transform_fn(data_item):
 tf_quantized_model = nncf.quantize(tf_model, calibration_dataset)
 
 tf_quantized_model.compile(
-    optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),
+    optimizer=tf.keras.optimizers.RMSprop(learning_rate=1e-4),
     loss=tf.keras.losses.CategoricalCrossentropy(),
     metrics=[tf.keras.metrics.CategoricalAccuracy()],
 )
 
-tf_quantized_model.fit(train_dataset, epochs=3, verbose=1)
+tf_quantized_model.fit(train_dataset, epochs=1, verbose=1)
 
 # Removes auxiliary layers and operations added during the quantization process,
 # resulting in a clean, fully quantized model ready for deployment.

tests/cross_fw/examples/.test_durations

@@ -14,5 +14,6 @@
     "tests/cross_fw/examples/test_examples.py::test_examples[quantization_aware_training_torch_anomalib]": 478.797,
     "tests/cross_fw/examples/test_examples.py::test_examples[quantization_aware_training_torch_resnet18]": 1251.144,
     "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_torch_fx_resnet18]": 412.243,
-    "tests/cross_fw/examples/test_examples.py::test_examples[fp8_llm_quantization]": 229.69
+    "tests/cross_fw/examples/test_examples.py::test_examples[fp8_llm_quantization]": 229.69,
+    "tests.cross_fw.examples.test_examples.test_examples[quantization_aware_training_tensorflow_mobilenet_v2]": 1500.00
 }

tests/cross_fw/examples/example_scope.json

@@ -273,5 +273,25 @@
                 "Tokyo."
             ]
         }
+    },
+    "quantization_aware_training_tensorflow_mobilenet_v2": {
+        "backend": "tf",
+        "requirements": "examples/quantization_aware_training/tensorflow/mobilenet_v2/requirements.txt",
+        "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz",
+        "accuracy_metrics": {
+            "fp32_top1": 0.987770676612854,
+            "int8_top1": 0.9727388620376587,
+            "accuracy_drop": 0.015031814575195312
+        },
+        "performance_metrics": {
+            "fp32_fps": 1703.04,
+            "int8_fps": 5796.3,
+            "performance_speed_up": 3.403501972942503
+        },
+        "model_size_metrics": {
+            "fp32_model_size": 8.596238136291504,
+            "int8_model_size": 2.69466495513916,
+            "model_compression_rate": 3.1900953474371994
+        }
     }
 }

tests/cross_fw/examples/run_example.py

@@ -279,6 +279,15 @@ def quantization_aware_training_torch_anomalib(data: Union[str, None]):
     }
 
 
+def quantization_aware_training_tensorflow_mobilenet_v2() -> Dict[str, float]:
+    import tensorflow_datasets as tfds
+
+    tfds.display_progress_bar(enable=False)
+
+    example_root = str(PROJECT_ROOT / "examples" / "quantization_aware_training" / "tensorflow" / "mobilenet_v2")
+    return post_training_quantization_mobilenet_v2(example_root)
+
+
 def main(argv):
     parser = ArgumentParser()
     parser.add_argument("--name", help="Example name", required=True)