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- """
- Retrain the YOLO model for your own dataset.
- """
- import os
- import numpy as np
- import keras.backend as K
- from keras.layers import Input, Lambda
- from keras.models import Model
- from keras.optimizers import Adam
- from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
-
- from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
- from yolo3.utils import get_random_data
-
-
- def _main():
- annotation_path = 'train.txt'
- log_dir = 'logs/000/'
- classes_path = 'model_data/coco_classes.txt'
- anchors_path = 'model_data/yolo_anchors.txt'
- class_names = get_classes(classes_path)
- num_classes = len(class_names)
- anchors = get_anchors(anchors_path)
-
- input_shape = (416,416) # multiple of 32, hw
-
- model, bottleneck_model, last_layer_model = create_model(input_shape, anchors, num_classes,
- freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze
-
- logging = TensorBoard(log_dir=log_dir)
- checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
- monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
- reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
- early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
-
- val_split = 0.1
- with open(annotation_path) as f:
- lines = f.readlines()
- np.random.seed(10101)
- np.random.shuffle(lines)
- np.random.seed(None)
- num_val = int(len(lines)*val_split)
- num_train = len(lines) - num_val
-
- # Train with frozen layers first, to get a stable loss.
- # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
- if True:
- # perform bottleneck training
- if not os.path.isfile("bottlenecks.npz"):
- print("calculating bottlenecks")
- batch_size=8
- bottlenecks=bottleneck_model.predict_generator(data_generator_wrapper(lines, batch_size, input_shape, anchors, num_classes, random=False, verbose=True),
- steps=(len(lines)//batch_size)+1, max_queue_size=1)
- np.savez("bottlenecks.npz", bot0=bottlenecks[0], bot1=bottlenecks[1], bot2=bottlenecks[2])
-
- # load bottleneck features from file
- dict_bot=np.load("bottlenecks.npz")
- bottlenecks_train=[dict_bot["bot0"][:num_train], dict_bot["bot1"][:num_train], dict_bot["bot2"][:num_train]]
- bottlenecks_val=[dict_bot["bot0"][num_train:], dict_bot["bot1"][num_train:], dict_bot["bot2"][num_train:]]
-
- # train last layers with fixed bottleneck features
- batch_size=8
- print("Training last layers with bottleneck features")
- print('with {} samples, val on {} samples and batch size {}.'.format(num_train, num_val, batch_size))
- last_layer_model.compile(optimizer='adam', loss={'yolo_loss': lambda y_true, y_pred: y_pred})
- last_layer_model.fit_generator(bottleneck_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, bottlenecks_train),
- steps_per_epoch=max(1, num_train//batch_size),
- validation_data=bottleneck_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, bottlenecks_val),
- validation_steps=max(1, num_val//batch_size),
- epochs=30,
- initial_epoch=0, max_queue_size=1)
- model.save_weights(log_dir + 'trained_weights_stage_0.h5')
-
- # train last layers with random augmented data
- model.compile(optimizer=Adam(lr=1e-3), loss={
- # use custom yolo_loss Lambda layer.
- 'yolo_loss': lambda y_true, y_pred: y_pred})
- batch_size = 16
- print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
- model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
- steps_per_epoch=max(1, num_train//batch_size),
- validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
- validation_steps=max(1, num_val//batch_size),
- epochs=50,
- initial_epoch=0,
- callbacks=[logging, checkpoint])
- model.save_weights(log_dir + 'trained_weights_stage_1.h5')
-
- # Unfreeze and continue training, to fine-tune.
- # Train longer if the result is not good.
- if True:
- for i in range(len(model.layers)):
- model.layers[i].trainable = True
- model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
- print('Unfreeze all of the layers.')
-
- batch_size = 4 # note that more GPU memory is required after unfreezing the body
- print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
- model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
- steps_per_epoch=max(1, num_train//batch_size),
- validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
- validation_steps=max(1, num_val//batch_size),
- epochs=100,
- initial_epoch=50,
- callbacks=[logging, checkpoint, reduce_lr, early_stopping])
- model.save_weights(log_dir + 'trained_weights_final.h5')
-
- # Further training if needed.
-
-
- def get_classes(classes_path):
- '''loads the classes'''
- with open(classes_path) as f:
- class_names = f.readlines()
- class_names = [c.strip() for c in class_names]
- return class_names
-
- def get_anchors(anchors_path):
- '''loads the anchors from a file'''
- with open(anchors_path) as f:
- anchors = f.readline()
- anchors = [float(x) for x in anchors.split(',')]
- return np.array(anchors).reshape(-1, 2)
-
-
- def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
- weights_path='model_data/yolo_weights.h5'):
- '''create the training model'''
- K.clear_session() # get a new session
- image_input = Input(shape=(None, None, 3))
- h, w = input_shape
- num_anchors = len(anchors)
-
- y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
- num_anchors//3, num_classes+5)) for l in range(3)]
-
- model_body = yolo_body(image_input, num_anchors//3, num_classes)
- print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
-
- if load_pretrained:
- model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
- print('Load weights {}.'.format(weights_path))
- if freeze_body in [1, 2]:
- # Freeze darknet53 body or freeze all but 3 output layers.
- num = (185, len(model_body.layers)-3)[freeze_body-1]
- for i in range(num): model_body.layers[i].trainable = False
- print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
-
- # get output of second last layers and create bottleneck model of it
- out1=model_body.layers[246].output
- out2=model_body.layers[247].output
- out3=model_body.layers[248].output
- bottleneck_model = Model([model_body.input, *y_true], [out1, out2, out3])
-
- # create last layer model of last layers from yolo model
- in0 = Input(shape=bottleneck_model.output[0].shape[1:].as_list())
- in1 = Input(shape=bottleneck_model.output[1].shape[1:].as_list())
- in2 = Input(shape=bottleneck_model.output[2].shape[1:].as_list())
- last_out0=model_body.layers[249](in0)
- last_out1=model_body.layers[250](in1)
- last_out2=model_body.layers[251](in2)
- model_last=Model(inputs=[in0, in1, in2], outputs=[last_out0, last_out1, last_out2])
- model_loss_last =Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
- arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
- [*model_last.output, *y_true])
- last_layer_model = Model([in0,in1,in2, *y_true], model_loss_last)
-
-
- model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
- arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
- [*model_body.output, *y_true])
- model = Model([model_body.input, *y_true], model_loss)
-
- return model, bottleneck_model, last_layer_model
-
- def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False):
- '''data generator for fit_generator'''
- n = len(annotation_lines)
- i = 0
- while True:
- image_data = []
- box_data = []
- for b in range(batch_size):
- if i==0 and random:
- np.random.shuffle(annotation_lines)
- image, box = get_random_data(annotation_lines[i], input_shape, random=random)
- image_data.append(image)
- box_data.append(box)
- i = (i+1) % n
- image_data = np.array(image_data)
- if verbose:
- print("Progress: ",i,"/",n)
- box_data = np.array(box_data)
- y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
- yield [image_data, *y_true], np.zeros(batch_size)
-
- def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False):
- n = len(annotation_lines)
- if n==0 or batch_size<=0: return None
- return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random, verbose)
-
- def bottleneck_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, bottlenecks):
- n = len(annotation_lines)
- i = 0
- while True:
- box_data = []
- b0=np.zeros((batch_size,bottlenecks[0].shape[1],bottlenecks[0].shape[2],bottlenecks[0].shape[3]))
- b1=np.zeros((batch_size,bottlenecks[1].shape[1],bottlenecks[1].shape[2],bottlenecks[1].shape[3]))
- b2=np.zeros((batch_size,bottlenecks[2].shape[1],bottlenecks[2].shape[2],bottlenecks[2].shape[3]))
- for b in range(batch_size):
- _, box = get_random_data(annotation_lines[i], input_shape, random=False, proc_img=False)
- box_data.append(box)
- b0[b]=bottlenecks[0][i]
- b1[b]=bottlenecks[1][i]
- b2[b]=bottlenecks[2][i]
- i = (i+1) % n
- box_data = np.array(box_data)
- y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
- yield [b0, b1, b2, *y_true], np.zeros(batch_size)
-
- if __name__ == '__main__':
- _main()
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