use segmentation-data-generator in example

examples/pascal_voc_jaccard_loss.py

@@ -9,34 +9,28 @@ from __future__ import division
 
 import numpy as np
 import os
+import shutil
 
 from keras import backend as K
 from keras.utils.np_utils import to_categorical
-from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, CSVLogger
 from keras.optimizers import Adam
 from keras.preprocessing.image import ImageDataGenerator
-from keras.utils import np_utils
 from keras.losses import categorical_crossentropy
 from keras_contrib.losses.jaccard import jaccard_distance
 from keras_contrib.datasets import pascal_voc
 from keras_contrib.applications.densenet import DenseNetFCN
 
-batch_size = 64
+batch_size = 2
 nb_classes = 21
 epochs = 100
-
-img_rows, img_cols = 32, 32
+img_rows, img_cols = 256, 256
 img_channels = 3
 
 # Parameters for the DenseNet model builder
 img_dim = (img_channels, img_rows,
            img_cols) if K.image_data_format() == 'channels_first' else (
                img_rows, img_cols, img_channels)
-depth = 40
-nb_dense_block = 3
-growth_rate = 12
-nb_filter = 16
-dropout_rate = 0.0  # 0.0 for data augmentation
 
 conf = pascal_voc.voc_config()
 pascal_folder = os.path.join(conf['pascal_berkeley_root'], 'dataset')
@@ -45,30 +39,32 @@ pascal_folder = os.path.join(conf['pascal_berkeley_root'], 'dataset')
 if not os.path.isdir(pascal_folder):
     pascal_voc.data_pascal_voc.run()
 
-# load the data using dual imagedata generators
-# we create two instances with the same arguments
-data_gen_args = dict()
-image_datagen = ImageDataGenerator(**data_gen_args)
-mask_datagen = ImageDataGenerator(**data_gen_args)
-
-# Provide the same seed and keyword arguments to the fit and flow methods
-seed = 1
-image_generator = image_datagen.flow_from_directory(
-    pascal_folder,
-    target_size=(img_rows, img_cols),
-    class_mode=None,
-    classes=["img"],
-    seed=seed)
-mask_generator = image_datagen.flow_from_directory(
-    pascal_folder,
-    target_size=(img_rows, img_cols),
-    class_mode=None,
-    classes=["cls_png"],
-    seed=seed)
+# move validation files to diff folder
+val_names = [
+    l.strip()
+    for l in open(
+        os.path.join(conf['pascal_berkeley_root'], 'dataset', 'val.txt'))
+]
+val_x_folder = os.path.join(conf['pascal_berkeley_root'], 'dataset', 'img_val')
+val_y_folder = os.path.join(conf['pascal_berkeley_root'], 'dataset',
+                            'cls_png_val')
+if not os.path.isdir(val_x_folder):
+    os.makedirs(val_x_folder)
+    for val_name in val_names:
+        from_path = os.path.join(conf['pascal_berkeley_root'], 'dataset',
+                                 'img', val_name + '.png')
+        to_path = os.path.join(val_x_folder, val_name + '.jpg')
+        shutil.move(from_path, to_path)
+if not os.path.isdir(val_y_folder):
+    os.makedirs(val_y_folder)
+    for val_name in val_names:
+        from_path = os.path.join(conf['pascal_berkeley_root'], 'dataset',
+                                 'cls_png', val_name + '.png')
+        to_path = os.path.join(val_y_folder, val_name + '.jpg')
+        shutil.move(from_path, to_path)
 
 
 def transform(gen_x, gen_y):
-    """Combine two generatorsand one-hot transform the labels."""
     while True:
         x_batch = next(gen_x) / 255.0
         y_batch = next(gen_y)
@@ -77,18 +73,81 @@ def transform(gen_x, gen_y):
             num_classes=nb_classes).reshape(y_batch.shape[:3] + (nb_classes, ))
         yield x_batch, y_batch
 
+
+# load the data using dual imagedata generators
+data_gen_args = dict()
+image_datagen = ImageDataGenerator(**data_gen_args)
+mask_datagen = ImageDataGenerator(**data_gen_args)
+
+# Provide the same seed and keyword arguments to the fit and flow methods
+seed = 1
+image_generator = image_datagen.flow_from_directory(
+    pascal_folder,
+    class_mode=None,
+    classes=["img"],
+    batch_size=batch_size,
+    seed=seed)
+mask_generator = image_datagen.flow_from_directory(
+    pascal_folder,
+    class_mode=None,
+    classes=["cls_png"],
+    batch_size=batch_size,
+    seed=seed)
+# combine generators into one which yields image and masks
 train_generator = transform(image_generator, mask_generator)
 
-# Create the model
+image_generator_val = image_datagen.flow_from_directory(
+    pascal_folder,
+    class_mode=None,
+    classes=["img_val"],
+    batch_size=batch_size,
+    seed=seed)
+
+mask_generator_val = image_datagen.flow_from_directory(
+    pascal_folder,
+    class_mode=None,
+    classes=["cls_png_val"],
+    batch_size=batch_size,
+    seed=seed)
+# combine generators into one which yields image and masks
+val_generator = transform(image_generator_val, mask_generator_val)
+
+# # view some images if you like
+# %matplotlib inline
+# from mpl_toolkits.axes_grid1 import ImageGrid
+# from matplotlib import pyplot as plt
+# X, y = next(train_generator)
+# figure = plt.figure(figsize=(10, 10))
+# n=5
+# grid = ImageGrid(figure, 111, (n ,2), axes_pad=0.3)
+# for i in range(n):
+#     xx = X[i]
+#     yy = y[i]
+#     grid[i*2].imshow(xx)
+#     grid[i*2+1].imshow(yy.argmax(-1)*nb_classes)
+
+# Create the model, a quick and small one
 model = DenseNetFCN(
-    #     depth=depth,
-    nb_dense_block=nb_dense_block,
-    growth_rate=growth_rate,
-    #     nb_filter=nb_filter,
-    dropout_rate=dropout_rate,
+    nb_dense_block=3,
+    growth_rate=10,
+    nb_layers_per_block=2,
+    reduction=0.0,
+    dropout_rate=0.2,
     input_shape=img_dim,
-    classes=nb_classes
-)
+    upsampling_conv=128,
+    init_conv_filters=48,
+    classes=nb_classes,
+    upsampling_type='upsampling')
+print('Model created')
+
+model.summary()
+
+optimizer = Adam(lr=1e-3)  # Using Adam instead of SGD to speed up training
+model.compile(
+    loss=jaccard_distance,
+    optimizer=optimizer,
+    metrics=['acc', categorical_crossentropy])
+print('Finished compiling')
 print('Model created')
 model.summary()
 
@@ -99,9 +158,10 @@ model.compile(
     metrics=['acc', categorical_crossentropy])
 print('Finished compiling')
 
+# Setup some callbacks
 weights_file = 'DenseNet-40-12-PASCAL-10.h5'
-
-# setup some callbacks
+csv_file = 'DenseNet-40-12-PASCAL-10.csv'
+csv_logger = CSVLogger(csv_file)
 lr_reducer = ReduceLROnPlateau(
     monitor='val_loss',
     factor=np.sqrt(0.1),
@@ -117,13 +177,18 @@ model_checkpoint = ModelCheckpoint(
     mode='auto')
 callbacks = [lr_reducer, early_stopper, model_checkpoint]
 
-model.fit_generator(
+# Train
+history = model.fit_generator(
     train_generator,
     steps_per_epoch=image_generator.n // batch_size,
     epochs=epochs,
     callbacks=callbacks,
-    verbose=2)
+    validation_data=val_generator,
+    validation_steps=image_generator_val.n // batch_size,
+    verbose=1)
 
-scores = model.evaluate(testX, Y_test, batch_size=batch_size)
-print('Test loss : ', scores[0])
-print('Test accuracy : ', scores[1])
+# Get the score from the validation data
+scores = model.evaluate_generator(
+    val_generator, steps=image_generator_val.n // batch_size)
+print('Validation loss : ', scores[0])
+print('Validation accuracy : ', scores[1])