fix depthconv and sepconv

Signed-off-by: zehao-intel <[email protected]>

neural_compressor/tensorflow/keras/layers/conv2d.py

@@ -30,7 +30,7 @@
 else:
     from keras.layers.convolutional.base_conv import Conv  # pylint: disable=E0401
 
-if version1_gte_version2(tf.__version__, "2.16.1"):
+if version1_gte_version2(tf.__version__, "2.16.1"):  # pragma: no cover
 
     class QConv2D(BaseConv):
         def __init__(
@@ -190,7 +190,7 @@ def get_config(self):
 
             return config
 
-else:  # pragma: no cover
+else:
 
     class QConv2D(Conv):
         def __init__(

neural_compressor/tensorflow/keras/layers/depthwise_conv2d.py

@@ -34,12 +34,10 @@
 
 if version1_gte_version2(tf.__version__, "2.16.1"):
 
-    class QDepthwiseConv2D(BaseDepthwiseConv):
+    class QDepthwiseConv2D(BaseDepthwiseConv):  # pragma: no cover
         def __init__(
             self,
             kernel_size,
-            min_value,
-            max_value,
             strides=(1, 1),
             padding="valid",
             depth_multiplier=1,
@@ -189,14 +187,12 @@ def get_config(self):
 
             return config
 
-else:  # pragma: no cover
+else:
 
     class QDepthwiseConv2D(DepthwiseConv):
         def __init__(
             self,
             kernel_size,
-            min_value,
-            max_value,
             strides=(1, 1),
             padding="valid",
             depth_multiplier=1,

neural_compressor/tensorflow/keras/layers/separable_conv2d.py

@@ -32,15 +32,13 @@
     from keras.layers.convolutional.base_separable_conv import SeparableConv  # pylint: disable=E0401
     from keras.utils import conv_utils  # pylint: disable=E0401
 
-if version1_gte_version2(tf.__version__, "2.16.1"):
+if version1_gte_version2(tf.__version__, "2.16.1"):  # pragma: no cover
 
     class QSeparableConv2D(BaseSeparableConv):
         def __init__(
             self,
             filters,
             kernel_size,
-            min_value,
-            max_value,
             strides=(1, 1),
             padding="valid",
             data_format=None,
@@ -198,15 +196,13 @@ def get_config(self):
 
             return config
 
-else:  # pragma: no cover
+else:
 
     class QSeparableConv2D(SeparableConv):
         def __init__(
             self,
             filters,
             kernel_size,
-            min_value,
-            max_value,
             strides=(1, 1),
             padding="valid",
             data_format=None,

test/3x/tensorflow/keras/test_layers.py

@@ -0,0 +1,149 @@
+!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# 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 math
+import os
+import shutil
+import unittest
+
+import keras
+import numpy as np
+import tensorflow as tf
+
+from neural_compressor.common import Logger
+from neural_compressor.tensorflow.utils import version1_gte_version2
+
+logger = Logger().get_logger()
+
+
+def build_model():
+    # Load MNIST dataset
+    mnist = keras.datasets.mnist
+
+    # 60000 images in train and 10000 images in test, but we don't need so much for ut
+    (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+    train_images, train_labels = train_images[:1000], train_labels[:1000]
+    test_images, test_labels = test_images[:200], test_labels[:200]
+
+    # Normalize the input image so that each pixel value is between 0 to 1.
+    train_images = train_images / 255.0
+    test_images = test_images / 255.0
+
+    # Define the model architecture.
+    model = keras.Sequential(
+        [
+            keras.layers.InputLayer(input_shape=(28, 28)),
+            keras.layers.Reshape(target_shape=(28, 28, 1)),
+            keras.layers.DepthwiseConv2D(3, 3, activation='relu', name="conv2d")(x)
+            keras.layers.MaxPooling2D(pool_size=(2, 2)),
+            keras.layers.Flatten(),
+            keras.layers.Dense(10, name="dense"),
+        ]
+    )
+    # Train the digit classification model
+    model.compile(
+        optimizer="adam", loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"]
+    )
+
+    model.fit(
+        train_images,
+        train_labels,
+        epochs=1,
+        validation_split=0.1,
+    )
+
+    _, baseline_model_accuracy = model.evaluate(test_images, test_labels, verbose=0)
+
+    print("Baseline test accuracy:", baseline_model_accuracy)
+    if version1_gte_version2(tf.__version__, "2.16.1"):
+        model.save("baseline_model.keras")
+    else:
+        model.save("baseline_model")
+
+
+class Dataset(object):
+    def __init__(self, batch_size=1):
+        self.batch_size = batch_size
+        mnist = keras.datasets.mnist
+        (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+        train_images, train_labels = train_images[:1000], train_labels[:1000]
+        test_images, test_labels = test_images[:200], test_labels[:200]
+        # Normalize the input image so that each pixel value is between 0 to 1.
+        self.train_images = train_images / 255.0
+        self.test_images = test_images / 255.0
+        self.train_labels = train_labels
+        self.test_labels = test_labels
+
+    def __len__(self):
+        return len(self.test_images)
+
+    def __getitem__(self, idx):
+        return self.test_images[idx], self.test_labels[idx]
+
+
+class MyDataloader:
+    def __init__(self, dataset, batch_size=1):
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.length = math.ceil(len(dataset) / self.batch_size)
+
+    def __iter__(self):
+        for _, (images, labels) in enumerate(self.dataset):
+            images = np.expand_dims(images, axis=0)
+            labels = np.expand_dims(labels, axis=0)
+            yield (images, labels)
+
+    def __len__(self):
+        return self.length
+
+
+class TestTF3xNewApi(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        build_model()
+        os.environ["ITEX_ONEDNN_GRAPH"] = "1"
+        self.fp32_model_path = (
+            "baseline_model.keras" if version1_gte_version2(tf.__version__, "2.16.1") else "baseline_model"
+        )
+
+    @classmethod
+    def tearDownClass(self):
+        if self.fp32_model_path.endswith(".keras"):
+            os.remove(self.fp32_model_path)
+        else:
+            shutil.rmtree(self.fp32_model_path, ignore_errors=True)
+        os.environ["ITEX_ONEDNN_GRAPH"] = "0"
+
+    def test_static_quant_from_dict_default(self):
+        logger.info("test_static_quant_from_dict_default")
+        from neural_compressor.tensorflow import quantize_model
+        from neural_compressor.tensorflow.keras import get_default_static_quant_config
+
+        calib_dataloader = MyDataloader(dataset=Dataset())
+        fp32_model = keras.models.load_model(self.fp32_model_path)
+        qmodel = quantize_model(fp32_model, get_default_static_quant_config(), calib_dataloader)
+        self.assertIsNotNone(qmodel)
+
+        for layer in qmodel.layers:
+            if layer.name == "conv2d":
+                self.assertEqual(layer.__class__.__name__, "QDepthwiseConv2D")
+                break
+
+
+
+if __name__ == "__main__":
+    unittest.main()