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examples/5_DataManagement/build_an_image_dataset.py
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Mickaël Schoentgen
Fix ResourceWarning: unclosed file (#295)
15 янв 2019, 22:10
15 янв 2019, 22:10
b5a9df9
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""" Build an Image Dataset in TensorFlow. For this example, you need to make your own set of images (JPEG). We will show 2 different ways to build that dataset: - From a root folder, that will have a sub-folder containing images for each class ``` ROOT_FOLDER |-------- SUBFOLDER (CLASS 0) | | | | ----- image1.jpg | | ----- image2.jpg | | ----- etc... | |-------- SUBFOLDER (CLASS 1) | | | | ----- image1.jpg | | ----- image2.jpg | | ----- etc... ``` - From a plain text file, that will list all images with their class ID: ``` /path/to/image/1.jpg CLASS_ID /path/to/image/2.jpg CLASS_ID /path/to/image/3.jpg CLASS_ID /path/to/image/4.jpg CLASS_ID etc... ``` Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), such as the dataset path. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import print_function import tensorflow as tf import os # Dataset Parameters - CHANGE HERE MODE = 'folder' # or 'file', if you choose a plain text file (see above). DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path. # Image Parameters N_CLASSES = 2 # CHANGE HERE, total number of classes IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to CHANNELS = 3 # The 3 color channels, change to 1 if grayscale # Reading the dataset # 2 modes: 'file' or 'folder' def read_images(dataset_path, mode, batch_size): imagepaths, labels = list(), list() if mode == 'file': # Read dataset file with open(dataset_path) as f: data = f.read().splitlines() for d in data: imagepaths.append(d.split(' ')[0]) labels.append(int(d.split(' ')[1])) elif mode == 'folder': # An ID will be affected to each sub-folders by alphabetical order label = 0 # List the directory try: # Python 2 classes = sorted(os.walk(dataset_path).next()[1]) except Exception: # Python 3 classes = sorted(os.walk(dataset_path).__next__()[1]) # List each sub-directory (the classes) for c in classes: c_dir = os.path.join(dataset_path, c) try: # Python 2 walk = os.walk(c_dir).next() except Exception: # Python 3 walk = os.walk(c_dir).__next__() # Add each image to the training set for sample in walk[2]: # Only keeps jpeg images if sample.endswith('.jpg') or sample.endswith('.jpeg'): imagepaths.append(os.path.join(c_dir, sample)) labels.append(label) label += 1 else: raise Exception("Unknown mode.") # Convert to Tensor imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string) labels = tf.convert_to_tensor(labels, dtype=tf.int32) # Build a TF Queue, shuffle data image, label = tf.train.slice_input_producer([imagepaths, labels], shuffle=True) # Read images from disk image = tf.read_file(image) image = tf.image.decode_jpeg(image, channels=CHANNELS) # Resize images to a common size image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH]) # Normalize image = image * 1.0/127.5 - 1.0 # Create batches X, Y = tf.train.batch([image, label], batch_size=batch_size, capacity=batch_size * 8, num_threads=4) return X, Y # ----------------------------------------------- # THIS IS A CLASSIC CNN (see examples, section 3) # ----------------------------------------------- # Note that a few elements have changed (usage of queues). # Parameters learning_rate = 0.001 num_steps = 10000 batch_size = 128 display_step = 100 # Network Parameters dropout = 0.75 # Dropout, probability to keep units # Build the data input X, Y = read_images(DATASET_PATH, MODE, batch_size) # Create model def conv_net(x, n_classes, dropout, reuse, is_training): # Define a scope for reusing the variables with tf.variable_scope('ConvNet', reuse=reuse): # Convolution Layer with 32 filters and a kernel size of 5 conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) # Convolution Layer with 32 filters and a kernel size of 5 conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) # Flatten the data to a 1-D vector for the fully connected layer fc1 = tf.contrib.layers.flatten(conv2) # Fully connected layer (in contrib folder for now) fc1 = tf.layers.dense(fc1, 1024) # Apply Dropout (if is_training is False, dropout is not applied) fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) # Output layer, class prediction out = tf.layers.dense(fc1, n_classes) # Because 'softmax_cross_entropy_with_logits' already apply softmax, # we only apply softmax to testing network out = tf.nn.softmax(out) if not is_training else out return out # Because Dropout have different behavior at training and prediction time, we # need to create 2 distinct computation graphs that share the same weights. # Create a graph for training logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True) # Create another graph for testing that reuse the same weights logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False) # Define loss and optimizer (with train logits, for dropout to take effect) loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits_train, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Evaluate model (with test logits, for dropout to be disabled) correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Saver object saver = tf.train.Saver() # Start training with tf.Session() as sess: # Run the initializer sess.run(init) # Start the data queue tf.train.start_queue_runners() # Training cycle for step in range(1, num_steps+1): if step % display_step == 0: # Run optimization and calculate batch loss and accuracy _, loss, acc = sess.run([train_op, loss_op, accuracy]) print("Step " + str(step) + ", Minibatch Loss= " + \ "{:.4f}".format(loss) + ", Training Accuracy= " + \ "{:.3f}".format(acc)) else: # Only run the optimization op (backprop) sess.run(train_op) print("Optimization Finished!") # Save your model saver.save(sess, 'my_tf_model')