steps_per_epoch = int(len(train_idx) / batch_size)
if validation_steps == 0 or steps_per_epoch == 0:
print("No data for city", cities[city_id])
continue
print('Training city', cities[city_id], 'fold', it)
print('steps_per_epoch', steps_per_epoch, 'validation_steps', validation_steps)
np.random.seed(it+1)
random.seed(it+1)
tf.set_random_seed(it+1)
print('Training model', it, cities[city_id])
model = get_inception_resnet_v2_unet(input_shape)
model.compile(loss=dice_logloss3,
optimizer=SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True),
metrics=[dice_coef, dice_coef_rounded, metrics.binary_crossentropy])
model_checkpoint = ModelCheckpoint(path.join(models_folder, 'inc_v2_520_model_weights_{0}_{1}.h5'.format(cities[city_id], it)), monitor='val_dice_coef_rounded',
save_best_only=True, save_weights_only=True, mode='max')
model.fit_generator(generator=batch_data_generator(train_idx, batch_size),
epochs=15, steps_per_epoch=steps_per_epoch, verbose=2,
validation_data=val_data_generator(val_idx, batch_size, validation_steps),
validation_steps=validation_steps,
callbacks=[model_checkpoint])
for l in model.layers:
l.trainable = True
model.compile(loss=dice_logloss3,
steps_per_epoch = int(len(train_idx) / batch_size)
if validation_steps == 0 or steps_per_epoch == 0:
print("No data for city", cities[city_id])
continue
print('Training city', cities[city_id], 'fold', it)
print('steps_per_epoch', steps_per_epoch, 'validation_steps', validation_steps)
np.random.seed(it+1)
random.seed(it+1)
tf.set_random_seed(it+1)
print('Training model', it, cities[city_id])
model = get_inception_resnet_v2_unet(input_shape)
model.compile(loss=dice_logloss3,
optimizer=SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True),
metrics=[dice_coef, dice_coef_rounded, metrics.binary_crossentropy])
model_checkpoint = ModelCheckpoint(path.join(models_folder, 'inception_small_model_weights_{0}_{1}.h5'.format(cities[city_id], it)), monitor='val_dice_coef_rounded',
save_best_only=True, save_weights_only=True, mode='max')
model.fit_generator(generator=batch_data_generator(train_idx, batch_size),
epochs=15, steps_per_epoch=steps_per_epoch, verbose=2,
validation_data=val_data_generator(val_idx, batch_size, validation_steps),
validation_steps=validation_steps,
callbacks=[model_checkpoint])
for l in model.layers:
l.trainable = True
model.compile(loss=dice_logloss3,
if not path.isdir(path.join(pred_folder, model_name)):
mkdir(path.join(pred_folder, model_name))
for it in [0, 1]:
models = []
if not path.isdir(path.join(pred_folder, model_name, str(it))):
mkdir(path.join(pred_folder, model_name, str(it)))
for i in range(4):
if i in ignored_cities or not path.isfile(path.join(models_folder, 'inception_smallest_model_weights4_{0}_{1}.h5'.format(cities[i], it))):
models.append(None)
continue
if not path.isdir(path.join(path.join(pred_folder, model_name, str(it), cities[i]))):
mkdir(path.join(path.join(pred_folder, model_name, str(it), cities[i])))
model = get_inception_resnet_v2_unet(input_shape, weights=None)
model.load_weights(path.join(models_folder, 'inception_smallest_model_weights4_{0}_{1}.h5'.format(cities[i], it)))
models.append(model)
print('Predictiong fold', it)
for d in test_folders:
for f in tqdm(sorted(listdir(path.join(d, 'MUL')))):
if path.isfile(path.join(d, 'MUL', f)) and '.tif' in f:
img_id = f.split('MUL_')[1].split('.')[0]
cinp = np.zeros((4,))
cinp[cities.index(img_id.split('_')[2])] = 1.0
cid = cinp.argmax()
if cid in ignored_cities:
continue
fpath = path.join(d, 'MUL', f)
img = skimage.io.imread(fpath, plugin='tifffile')
