Fixed pylint and added docstrings

official/legacy/image_classification/classifier_trainer.py

@@ -36,7 +36,7 @@ from official.modeling import hyperparams
 from official.modeling import performance
 from official.utils import hyperparams_flags
 from official.utils.misc import keras_utils
-from official.vision.image_classification.vgg16 import vgg_model
+from official.legacy.image_classification.vgg16 import vgg_model
 
 
 def get_models() -> Mapping[str, tf.keras.Model]:

official/legacy/image_classification/vgg16/vgg_config.py

@@ -19,9 +19,8 @@ from __future__ import division
 from __future__ import print_function
 
 import dataclasses
-
+from official.legacy.image_classification.configs import base_configs
 from official.modeling.hyperparams import base_config
-from official.vision.image_classification.configs import base_configs
 
 
 @dataclasses.dataclass

official/legacy/image_classification/vgg16/vgg_model.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+"""VGG16 model for Keras.
+
+Adapted from tf.keras.applications.vgg16.VGG16().
+
+Related papers/blogs:
+- https://arxiv.org/abs/1409.1556
+
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -6,16 +28,30 @@ import tensorflow as tf
 
 layers = tf.keras.layers
 
+
 def _gen_l2_regularizer(use_l2_regularizer=True, l2_weight_decay=1e-4):
   return tf.keras.regularizers.L2(
       l2_weight_decay) if use_l2_regularizer else None
 
+
 def vgg16(num_classes,
           batch_size=None,
           use_l2_regularizer=True,
           batch_norm_decay=0.9,
           batch_norm_epsilon=1e-5):
+    """Instantiates the VGG16 architecture
+
+    Args:
+      num_classes: `int` number of classes for image classification.
+      batch_size: Size of the batches for each step.
+      use_l2_regularizer: whether to use L2 regularizer on Conv/Dense layer.
+      batch_norm_decay: Moment of batch norm layers.
+      batch_norm_epsilon: Epsilon of batch borm layers.
+
+    Returns:
+        A Keras model instance.
     
+    """
     input_shape = (224, 224, 3)
     img_input = layers.Input(shape=input_shape, batch_size=batch_size)
     
@@ -26,22 +62,23 @@ def vgg16(num_classes,
         bn_axis = 1
     else:  # channels_last
         bn_axis = 3
-        
     # Block 1
-    x = layers.Conv2D(64, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block1_conv1')(x)
+    x = layers.Conv2D(
+        64, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block1_conv1')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv1')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(64, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block1_conv2')(x)
+    x = layers.Conv2D(
+        64, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block1_conv2')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
@@ -51,20 +88,22 @@ def vgg16(num_classes,
     x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
     
     # Block 2
-    x = layers.Conv2D(128, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block2_conv1')(x)
+    x = layers.Conv2D(
+        128, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block2_conv1')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv3')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(128, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block2_conv2')(x)
+    x = layers.Conv2D(
+        128, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block2_conv2')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
@@ -74,30 +113,33 @@ def vgg16(num_classes,
     x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
     
     # Block 3
-    x = layers.Conv2D(256, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block3_conv1')(x)
+    x = layers.Conv2D(
+        256, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block3_conv1')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv5')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(256, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block3_conv2')(x)
+    x = layers.Conv2D(
+        256, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block3_conv2')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv6')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(256, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block3_conv3')(x)
+    x = layers.Conv2D(
+        256, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block3_conv3')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
@@ -107,30 +149,33 @@ def vgg16(num_classes,
     x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
     
     # Block 4
-    x = layers.Conv2D(512, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block4_conv1')(x)
+    x = layers.Conv2D(
+        512, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block4_conv1')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv8')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(512, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block4_conv2')(x)
+    x = layers.Conv2D(
+        512, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block4_conv2')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv9')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(512, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block4_conv3')(x)
+    x = layers.Conv2D(
+        512, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block4_conv3')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
@@ -140,30 +185,33 @@ def vgg16(num_classes,
     x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
     
     # Block 5
-    x = layers.Conv2D(512, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block5_conv1')(x)
+    x = layers.Conv2D(
+        512, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block5_conv1')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv11')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(512, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block5_conv2')(x)
+    x = layers.Conv2D(
+        512, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block5_conv2')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
         epsilon=batch_norm_epsilon,
         name='bn_conv12')(x)
     x = layers.Activation('relu')(x)
-    x = layers.Conv2D(512, (3, 3),
-                      padding='same',
-                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                      name='block5_conv3')(x)
+    x = layers.Conv2D(
+        512, (3, 3),
+        padding='same',
+        kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+        name='block5_conv3')(x)
     x = layers.BatchNormalization(
         axis=bn_axis,
         momentum=batch_norm_decay,
@@ -174,23 +222,17 @@ def vgg16(num_classes,
     
     x = layers.Flatten(name='flatten')(x)
     x = layers.Dense(4096,
-                     #kernel_initializer=tf.initializers.random_normal(stddev=0.01),
                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                     #bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
                      name='fc1')(x)
     x = layers.Activation('relu')(x)
     x = layers.Dropout(0.5)(x)
     x = layers.Dense(4096,
-                     #kernel_initializer=tf.initializers.random_normal(stddev=0.01),
                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                     #bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
                      name='fc2')(x)
     x = layers.Activation('relu')(x)
     x = layers.Dropout(0.5)(x)
     x = layers.Dense(num_classes,
-                     #kernel_initializer=tf.initializers.random_normal(stddev=0.01),
                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
-                     #bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),                     
                      name='fc1000')(x)
     
     # A softmax that is followed by the model loss must be done cannot be done
@@ -199,4 +241,3 @@ def vgg16(num_classes,
     
     # Create model.
     return tf.keras.Model(img_input, x, name='vgg16')
-   
\ No newline at end of file