def train(resume=False):
print('Loading data...')
X_train, X_val, y_train, y_val = DataManager.load_train_val_data("cleaned")
# X_train, y_train = filter_mask_presence(X_train, y_train)
# X_val, y_val = filter_mask_presence(X_train, y_train)
print('Creating and compiling model...')
model = build_model()
if resume:
model.load_weights('./results/net.hdf5')
model_checkpoint = ModelCheckpoint('./results/net.hdf5', monitor='val_loss', save_best_only=True)
print('Training on model')
model.summary()
batch_size = 64
nb_epoch = 200
train_generator = CustomDataGenerator(X_train, y_train, transform, batch_size)
# Use fixed samples instead to visualize histograms. There is currently a bug that prevents it
# when a val generator is used.
# Not aug val samples to keep the eval consistent.
val_generator = CustomDataGenerator(X_val, y_val, lambda x, y: transform(x, y, augment=False), batch_size)
model.fit_generator(train_generator, validation_data=val_generator, nb_val_samples=X_val.shape[0],
samples_per_epoch=X_train.shape[0], nb_epoch=nb_epoch, verbose=2,
callbacks=[model_checkpoint, reduce_lr, tb], max_q_size=1000)
def examine_generator():
X_train, X_val, y_train, y_val = DataManager.load_train_val_data("all")
from generator import CustomDataGenerator
from train import transform, filter_mask_presence
X_val, y_val = filter_mask_presence(X_val, y_val)
generator = CustomDataGenerator(X_val, y_val, lambda x, y: transform(x, y, augment=True), 32)
imgs, outs = generator.next()
for i in range(len(imgs)):
cv2.imshow("image", imgs[i, 0])
cv2.imshow("mask", outs['main_output'][i, 0])
cv2.waitKey(0)
def inspect_set(train=False):
X_train, X_val, y_train, y_val = DataManager.load_train_val_data("all")
X = X_train if train else X_val
y = y_train if train else y_val
from model import build_model
from train import transform
from submission import post_process_mask
model = build_model()
model.load_weights('./results/net.hdf5')
for i in range(X.shape[0]):
img_i, mask_i = transform(X[i], y[i])
masks, has_mask = model.predict(np.array([img_i]), verbose=1)
print has_mask[0, 0]
# print has_masks[0, 0]
cv2.imshow("Image with mask".format(i), generate_image_with_mask(X[i], y[i]))
cv2.imshow("pred mask".format(i), post_process_mask(masks[0, 0]))
cv2.waitKey(0)
