def main(): mode = 'colour' if mode == 'gray': with open('./validation_ids_gray.pickle', 'rb') as fp: validation_paths = pickle.load(fp) elif mode == 'recoloured': with open('./validation_ids_recolour.pickle', 'rb') as fp: validation_paths = pickle.load(fp) elif mode == 'colour': with open('./validation_ids_tiny.pickle', 'rb') as fp: validation_paths = pickle.load(fp) dim_in = (64, 64, 3) shuffle = True batch_size = 32 n_classes = 30 dim_out = (n_classes) training_generator = data_utils.DataGenerator(validation_paths, batch_size, dim_in, dim_out, shuffle, mode, 'validation') model = keras.models.load_model(model_path) total = 0 correct = 0 for batch in training_generator: X, y = batch total += X.shape[0] y = np.argmax(y, axis=1) p = np.argmax(model.predict(X), axis=1) c = (y == p).sum() correct += c print("acc:", correct / total) pass
def _main(): # Set testing mode (dropout/batchnormalization) K.set_learning_phase(TEST_PHASE) # Output dimension (empty place probability) output_dim = 1 # Generate testing data test_datagen = data_utils.DataGenerator(rescale=1. / 255) # Iterator object containing testing data to be generated batch by batch test_generator = test_datagen.flow_from_directory( FLAGS.test_dir, output_dim, shuffle=False, img_mode=FLAGS.img_mode, target_size=(FLAGS.img_height, FLAGS.img_width), batch_size=FLAGS.batch_size) # Load json and create model json_model_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.json_model_fname) model = utils.jsonToModel(json_model_path) # Load weights weights_load_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.weights_fname) try: model.load_weights(weights_load_path) print("Loaded model from {}".format(weights_load_path)) except: print("Impossible to find weight path. Returning untrained model") # Compile model model.compile(loss='categorical_crossentropy', optimizer='adam') # Get predictions and ground truth n_samples = test_generator.samples nb_batches = int(np.ceil(n_samples / FLAGS.batch_size)) probs_per_class, ground_truth = utils.compute_predictions_and_gt( model, test_generator, nb_batches, verbose=1) # Predicted probabilities pred_probs = np.max(probs_per_class, axis=-1) # Prediced labels pred_labels = np.argmax(probs_per_class, axis=-1) # Real labels (ground truth) real_labels = np.argmax(ground_truth, axis=-1) # Evaluate predictions: Average accuracy and highest errors print("-----------------------------------------------") print("Evalutaion:") evaluation = evaluate_classification(pred_probs, pred_labels, real_labels) print("-----------------------------------------------") # Save evaluation utils.write_to_file( evaluation, os.path.join(FLAGS.experiment_rootdir, 'test_results.json')) # Save predicted and real steerings as a dictionary labels_dict = { 'pred_labels': pred_labels.tolist(), 'real_labels': real_labels.tolist() } utils.write_to_file( labels_dict, os.path.join(FLAGS.experiment_rootdir, 'predicted_and_real_labels.json')) # Visualize confusion matrix utils.plot_confusion_matrix(FLAGS.experiment_rootdir, real_labels, pred_labels, CLASSES, normalize=True)
def _main(): # Set training phase k.set_learning_phase(TRAIN_PHASE) # Create the experiment rootdir if not already there: if not os.path.exists(FLAGS.experiment_rootdir): os.makedirs(FLAGS.experiment_rootdir) # Split the data into training, validation and test sets if FLAGS.initial_epoch == 0: data_utils.cross_val_create(FLAGS.data_path) # Input image dimensions img_width, img_height = FLAGS.img_width, FLAGS.img_height print('TamaƱo de los mel-espectrogramas: Alto: {}, Ancho: {}'.format( img_height, img_width)) # Generate training data with real-time augmentation train_data_gen = data_utils.DataGenerator() # Iterator object containing training data to be generated batch by batch train_generator = train_data_gen.flow_from_directory( 'train', shuffle=True, target_size=(img_height, img_width), classes=FLAGS.num_classes, batch_size=FLAGS.batch_size) # Generate validation data with real-time augmentation val_data_gen = data_utils.DataGenerator() # Iterator object containing validation data to be generated batch by batch val_generator = val_data_gen.flow_from_directory( 'val', shuffle=False, target_size=(img_height, img_width), classes=FLAGS.num_classes, batch_size=FLAGS.batch_size) # Check if the number of classes in data corresponds to the one specified assert train_generator.num_classes == FLAGS.num_classes, \ " Not matching output dimensions in training data." # Check if the number of classes in data corresponds to the one specified assert val_generator.num_classes == FLAGS.num_classes, \ " Not matching output dimensions in validation data." # Weights to restore weights_path = FLAGS.initial_weights # Epoch from which training starts initial_epoch = FLAGS.initial_epoch if FLAGS.restore_model: # In this case weights are initialized as specified in pre-trained model # initial_epoch = FLAGS.initial_epoch try: # Carga estructura de la red json_model_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.json_model_fname) model = json_to_model(json_model_path) # Carga los pesos model.load_weights(weights_path) print("Loaded model from {}".format(weights_path)) except ImportError: print("Impossible to find weight path. Returning untrained model") else: # In this case weights are initialized randomly weights_path = None # Define model bot_model = InceptionV3(weights=None, include_top=False, input_shape=[img_height, img_width, 1], classes=train_generator.num_classes) bot_model.summary() input = Input(shape=[img_height, img_width, 1]) top = bot_model(input) # intermediate = Dropout()(top) # top = Flatten()(intermediate) top = Flatten()(top) top = Dense(FLAGS.num_classes, activation='softmax', name='predictions')(top) model = Model(inputs=input, outputs=top) model.summary() # Serialize model into json json_model_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.json_model_fname) model_to_json(model, json_model_path) # Train model train_model(train_generator, val_generator, model, initial_epoch) # Plot training and validation losses utils.plot_loss(FLAGS.experiment_rootdir)
def main(dataset, batch_size, patch_size, epochs, label_smoothing, label_flipping): print(project_dir) config = tf.ConfigProto() config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU sess = tf.Session(config=config) K.tensorflow_backend.set_session( sess) # set this TensorFlow session as the default session for Keras image_data_format = "channels_first" K.set_image_data_format(image_data_format) save_images_every_n_batches = 30 save_model_every_n_epochs = 0 # configuration parameters print("Config params:") print(" dataset = {}".format(dataset)) print(" batch_size = {}".format(batch_size)) print(" patch_size = {}".format(patch_size)) print(" epochs = {}".format(epochs)) print(" label_smoothing = {}".format(label_smoothing)) print(" label_flipping = {}".format(label_flipping)) print(" save_images_every_n_batches = {}".format( save_images_every_n_batches)) print(" save_model_every_n_epochs = {}".format(save_model_every_n_epochs)) model_name = datetime.strftime(datetime.now(), '%y%m%d-%H%M') model_dir = os.path.join(project_dir, "models", model_name) fig_dir = os.path.join(project_dir, "reports", "figures") logs_dir = os.path.join(project_dir, "reports", "logs", model_name) os.makedirs(model_dir) # Load and rescale data ds_train_gen = data_utils.DataGenerator(file_path=dataset, dataset_type="train", batch_size=batch_size) ds_train_disc = data_utils.DataGenerator(file_path=dataset, dataset_type="train", batch_size=batch_size) ds_val = data_utils.DataGenerator(file_path=dataset, dataset_type="val", batch_size=batch_size) enq_train_gen = OrderedEnqueuer(ds_train_gen, use_multiprocessing=True, shuffle=True) enq_train_disc = OrderedEnqueuer(ds_train_disc, use_multiprocessing=True, shuffle=True) enq_val = OrderedEnqueuer(ds_val, use_multiprocessing=True, shuffle=False) img_dim = ds_train_gen[0][0].shape[-3:] n_batch_per_epoch = len(ds_train_gen) epoch_size = n_batch_per_epoch * batch_size print("Derived params:") print(" n_batch_per_epoch = {}".format(n_batch_per_epoch)) print(" epoch_size = {}".format(epoch_size)) print(" n_batches_val = {}".format(len(ds_val))) # Get the number of non overlapping patch and the size of input image to the discriminator nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size) tensorboard = TensorBoard(log_dir=logs_dir, histogram_freq=0, batch_size=batch_size, write_graph=True, write_grads=True, update_freq='batch') try: # Create optimizers opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08) # opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True) opt_discriminator = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08) # Load generator model generator_model = models.generator_unet_upsampling(img_dim) generator_model.summary() plot_model(generator_model, to_file=os.path.join(fig_dir, "generator_model.png"), show_shapes=True, show_layer_names=True) # Load discriminator model # TODO: modify disc to accept real input as well discriminator_model = models.DCGAN_discriminator( img_dim_disc, nb_patch) discriminator_model.summary() plot_model(discriminator_model, to_file=os.path.join(fig_dir, "discriminator_model.png"), show_shapes=True, show_layer_names=True) # TODO: pretty sure this is unnecessary generator_model.compile(loss='mae', optimizer=opt_discriminator) discriminator_model.trainable = False DCGAN_model = models.DCGAN(generator_model, discriminator_model, img_dim, patch_size, image_data_format) # L1 loss applies to generated image, cross entropy applies to predicted label loss = [models.l1_loss, 'binary_crossentropy'] loss_weights = [1E1, 1] DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan) discriminator_model.trainable = True discriminator_model.compile(loss='binary_crossentropy', optimizer=opt_discriminator) tensorboard.set_model(DCGAN_model) # Start training enq_train_gen.start(workers=1, max_queue_size=20) enq_train_disc.start(workers=1, max_queue_size=20) enq_val.start(workers=1, max_queue_size=20) out_train_gen = enq_train_gen.get() out_train_disc = enq_train_disc.get() out_val = enq_val.get() print("Start training") for e in range(1, epochs + 1): # Initialize progbar and batch counter progbar = generic_utils.Progbar(epoch_size) start = time.time() for batch_counter in range(1, n_batch_per_epoch + 1): X_transformed_batch, X_orig_batch = next(out_train_disc) # Create a batch to feed the discriminator model X_disc, y_disc = data_utils.get_disc_batch( X_transformed_batch, X_orig_batch, generator_model, batch_counter, patch_size, label_smoothing=label_smoothing, label_flipping=label_flipping) # Update the discriminator disc_loss = discriminator_model.train_on_batch(X_disc, y_disc) # Create a batch to feed the generator model X_gen_target, X_gen = next(out_train_gen) y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8) # Set labels to 1 (real) to maximize the discriminator loss y_gen[:, 1] = 1 # Freeze the discriminator discriminator_model.trainable = False gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen]) # Unfreeze the discriminator discriminator_model.trainable = True metrics = [("D logloss", disc_loss), ("G tot", gen_loss[0]), ("G L1", gen_loss[1]), ("G logloss", gen_loss[2])] progbar.add(batch_size, values=metrics) logs = {k: v for (k, v) in metrics} logs["size"] = batch_size tensorboard.on_batch_end(batch_counter, logs=logs) # Save images for visualization if batch_counter % save_images_every_n_batches == 0: # Get new images from validation data_utils.plot_generated_batch( X_transformed_batch, X_orig_batch, generator_model, os.path.join(logs_dir, "current_batch_training.png")) X_transformed_batch, X_orig_batch = next(out_val) data_utils.plot_generated_batch( X_transformed_batch, X_orig_batch, generator_model, os.path.join(logs_dir, "current_batch_validation.png")) print("") print('Epoch %s/%s, Time: %s' % (e, epochs, time.time() - start)) tensorboard.on_epoch_end(e, logs=logs) if (save_model_every_n_epochs >= 1 and e % save_model_every_n_epochs == 0) or \ (e == epochs): print("Saving model for epoch {}...".format(e), end="") sys.stdout.flush() gen_weights_path = os.path.join( model_dir, 'gen_weights_epoch{:03d}.h5'.format(e)) generator_model.save_weights(gen_weights_path, overwrite=True) disc_weights_path = os.path.join( model_dir, 'disc_weights_epoch{:03d}.h5'.format(e)) discriminator_model.save_weights(disc_weights_path, overwrite=True) DCGAN_weights_path = os.path.join( model_dir, 'DCGAN_weights_epoch{:03d}.h5'.format(e)) DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True) print("done") except KeyboardInterrupt: pass enq_train_gen.stop() enq_train_disc.stop() enq_val.stop()
def _main(): # Set random seed if FLAGS.random_seed: seed = np.random.randint(0,2*31-1) else: seed = 5 np.random.seed(seed) tf.set_random_seed(seed) # Set training phase K.set_learning_phase(TRAIN_PHASE) # Create the experiment rootdir if not already there if not os.path.exists(FLAGS.experiment_rootdir): os.makedirs(FLAGS.experiment_rootdir) # Input image dimensions img_width, img_height = FLAGS.img_width, FLAGS.img_height # Image mode (RGB or grayscale) if FLAGS.img_mode=='rgb': img_channels = 3 elif FLAGS.img_mode == 'grayscale': img_channels = 1 else: raise IOError("Unidentified image mode: use 'grayscale' or 'rgb'") # Output dimension (empty place probability) output_dim=1 # Generate training data with real-time augmentation train_datagen = data_utils.DataGenerator(rescale = 1./255) # Iterator object containing training data to be generated batch by batch train_generator = train_datagen.flow_from_directory(FLAGS.train_dir, output_dim, shuffle = True, img_mode = FLAGS.img_mode, target_size=(img_height, img_width), batch_size = FLAGS.batch_size) # Check if the number of classes in dataset corresponds to the one specified # assert train_generator.num_classes == num_classes, \ # " Not macthing output dimensions in training data." # Generate validation data with real-time augmentation val_datagen = data_utils.DataGenerator(rescale = 1./255) # Iterator object containing validation data to be generated batch by batch val_generator = val_datagen.flow_from_directory(FLAGS.val_dir, output_dim, shuffle = False, img_mode = FLAGS.img_mode, target_size=(img_height, img_width), batch_size = FLAGS.batch_size) # Check if the number of classes in dataset corresponds to the one specified # assert val_generator.num_classes == num_classes, \ # " Not macthing output dimensions in validation data." # Weights to restore weights_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.weights_fname) # Epoch from which training starts initial_epoch = 0 if not FLAGS.restore_model: # In this case weights are initialized randomly weights_path = None else: # In this case weigths are initialized as specified in pre-trained model initial_epoch = FLAGS.initial_epoch # Define model model = getModelResnet(FLAGS.n_layers, FLAGS.rn_version, img_width, img_height, img_channels, output_dim, weights_path) # Serialize model into json json_model_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.json_model_fname) utils.modelToJson(model, json_model_path) # Train model trainModel(train_generator, val_generator, model, initial_epoch) # Plot training and validation losses utils.plot_loss(FLAGS.experiment_rootdir)
def main(model, input, truth, output, image_size, concat, batch_size, dataset): K.set_image_data_format("channels_first") # Load the model img_dim = (3,) + image_size generator_model = models.generator_unet_upsampling(img_dim) generator_model.load_weights(str(model)) # Setup the data generator if h5py.is_hdf5(input): generator = data_utils.DataGenerator(file_path=input, dataset_type=dataset, batch_size=batch_size) batch_gen = get_batch_from_hdf5(generator) count = len(generator) * batch_size elif os.path.isdir(input): # Directory of images input_paths = [str(img) for img in sorted(Path(input).glob('**/*.jpg'))] if truth is not None: truth_paths = [str(img) for img in sorted(Path(truth).glob('**/*.jpg'))] else: truth_paths = [] batch_gen = get_batch_from_images(input_paths, truth_paths, batch_size, image_size) count = len(input_paths) else: # Single image file input_paths = [input] if truth is not None: truth_paths = [truth] else: truth_paths = [] batch_size = 1 batch_gen = get_batch_from_images(input_paths, truth_paths, batch_size, image_size) count = 1 # Setup the output if output is not None: # If input is multiple images, create directory for output images if count > 1: if os.path.exists(output): raise IOError('Output directory already exists') os.makedirs(output) # Inference with tqdm(None, total=count) as progress_bar: for batch_record in batch_gen: input_batch = batch_record.input truth_batch = batch_record.truth image_names = batch_record.image_names out_batch = generator_model.predict(input_batch) progress_bar.update(input_batch.shape[0]) if output is not None: # Individually save each image in the batch for i in range(len(image_names)): input_b = input_batch[i:i+1] output_b = out_batch[i:i+1] truth_b = truth_batch[i:i+1] if truth_batch is not None else None image_name = image_names[i] out_image = generate_output_image(input_b, output_b, truth_b, concat) if count > 1: out_path = os.path.join(str(output), image_name) else: out_path = output plt.imsave(out_path, out_image) else: # Show the image out_image = generate_output_image(input_batch, out_batch, truth_batch, concat) plt.figure() plt.imshow(out_image) plt.show() plt.clf() plt.close()
def train(model: Sequential, mode): if mode == 'gray': with open('./train_ids_gray.pickle', 'rb') as fp: training_paths = pickle.load(fp) with open('./validation_ids_gray.pickle', 'rb') as fp: validation_paths = pickle.load(fp) elif mode == 'recoloured': with open('./train_ids_recolour.pickle', 'rb') as fp: training_paths = pickle.load(fp) with open('./validation_ids_recolour.pickle', 'rb') as fp: validation_paths = pickle.load(fp) elif mode == 'colour': with open('./train_ids_tiny.pickle', 'rb') as fp: training_paths = pickle.load(fp) with open('./validation_ids_tiny.pickle', 'rb') as fp: validation_paths = pickle.load(fp) # params dim_in = (64, 64, 3) shuffle = True batch_size = 64 n_classes = 30 dim_out = (n_classes) training_generator = data_utils.DataGenerator(training_paths, batch_size, dim_in, dim_out, shuffle, mode, 'training') validation_generator = data_utils.DataGenerator(validation_paths, batch_size, dim_in, dim_out, shuffle, mode, 'validation') callback_list = list() directory = create_result_dir('./vgg_classification') print('Directory:', directory) print("using tensorboard") tensor_directory = directory + '/' + 'tensorboard' tb_callback = callbacks.TensorBoard(log_dir=tensor_directory) callback_list.append(tb_callback) saving_period = 3 print("saving model every {} epochs".format(saving_period)) model_dir = os.path.join(directory, 'models', 'model.{epoch:02d}-loss_{val_loss:.2f}.hdf5') p_save_callback = callbacks.ModelCheckpoint(model_dir, period=saving_period) callback_list.append(p_save_callback) print("saving best model") bestmodels_dir = directory + '/' + 'bestmodels/best_model.hdf5' best_save_callback = callbacks.ModelCheckpoint(bestmodels_dir, save_best_only=True, mode='val_acc') callback_list.append(best_save_callback) lr_reduce = callbacks.ReduceLROnPlateau(monitor='val_acc', factor=0.2, patience=4) callback_list.append(lr_reduce) n_workers = 2 n_epochs = 30 model.fit_generator(generator=training_generator, validation_data=validation_generator, use_multiprocessing=True, workers=n_workers, verbose=1, epochs=n_epochs, callbacks=callback_list)
def _main(): # Set testing mode (dropout/batch normalization) k.set_learning_phase(TEST_PHASE) # Split the data into training, validation and test sets if FLAGS.initial_epoch == 0: data_utils.cross_val_create(FLAGS.data_path) # Generate testing data test_data_gen = data_utils.DataGenerator() # Iterator object containing testing data to be generated batch by batch test_generator = test_data_gen.flow_from_directory( 'test', shuffle=False, target_size=(FLAGS.img_height, FLAGS.img_width), batch_size=FLAGS.batch_size) # Load json and create model json_model_path = os.path.join(FLAGS.experiment_rootdir, FLAGS.json_model_fname) model = utils.json_to_model(json_model_path) # Load weights weights_load_path = os.path.abspath('./experiment_6/weights_039.h5') try: model.load_weights(weights_load_path) print("Loaded model from {}".format(weights_load_path)) except ImportError: print("Impossible to find weight path. Returning untrained model") # Compile model model.compile(optimizer='adam', loss='mean_squared_error') # Get predictions and ground truth n_samples = test_generator.samples nb_batches = int(np.ceil(n_samples / FLAGS.batch_size)) probs_per_class, ground_truth = utils.compute_predictions_and_gt( model, test_generator, nb_batches, verbose=FLAGS.verbose) # Predicted probabilities pred_probs = np.max(probs_per_class, axis=-1) # Predicted labels pred_labels = np.argmax(probs_per_class, axis=-1) # Real labels (ground truth) real_labels = np.argmax(ground_truth, axis=-1) # Evaluate predictions: Average accuracy and highest errors print("-----------------------------------------------") print("Evaluation:") evaluation = evaluate_classification(pred_probs, pred_labels, real_labels) print("-----------------------------------------------") # Save evaluation utils.write_to_file( evaluation, os.path.join(FLAGS.experiment_rootdir, 'test_results.json')) # Save predicted and real steerings as a dictionary labels_dict = { 'pred_labels': pred_labels.tolist(), 'real_labels': real_labels.tolist() } utils.write_to_file( labels_dict, os.path.join(FLAGS.experiment_rootdir, 'predicted_and_real_labels.json')) # Visualize confusion matrix utils.plot_confusion_matrix('test', FLAGS.experiment_rootdir, real_labels, pred_labels, CLASSES, normalize=True) print('Accuracy:', accuracy_score(real_labels, pred_labels)) print('F1 score:', f1_score(real_labels, pred_labels, average='micro')) print('Recall:', recall_score(real_labels, pred_labels, average='micro')) print('Precision:', precision_score(real_labels, pred_labels, average='micro')) print('\n clasification report:\n', classification_report(real_labels, pred_labels)) print('\n confussion matrix:\n', confusion_matrix(real_labels, pred_labels))