def main(args):
# set the necessary list
# train_list = pd.read_csv(args.train_list,header=None)
# val_list = pd.read_csv(args.val_list,header=None)
#
# # set the necessary directories
# trainimg_dir = args.trainimg_dir
# trainmsk_dir = args.trainmsk_dir
# valimg_dir = args.valimg_dir
# valmsk_dir = args.valmsk_dir
#
# train_gen = data_gen_small(trainimg_dir, trainmsk_dir, train_list, args.batch_size, [args.input_shape[0], args.input_shape[1]], args.n_labels)
# val_gen = data_gen_small(valimg_dir, valmsk_dir, val_list, args.batch_size, [args.input_shape[0], args.input_shape[1]], args.n_labels)
train_gen, val_gen = train_val_generator(args.batch_size)
# segnet = CreateSegNet(args.input_shape, args.n_labels, args.kernel, args.pool_size, args.output_mode)
from keras_fcn import FCN
segnet = FCN(input_shape=args.input_shape,
classes=args.n_labels,
weights='imagenet',
trainable_encoder=True)
print(segnet.summary())
checkpointer = ModelCheckpoint(
filepath="model/weights.{epoch:02d}-{val_acc:.4f}.hdf5",
verbose=1,
save_best_only=False)
mycallback = MyCallBack()
segnet.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
segnet.fit_generator(train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
callbacks=[checkpointer, mycallback])
segnet.save_weights("model/finalSegNet" + str(args.n_epochs) + ".hdf5")
print("sava weight done..")
json_string = segnet.to_json()
open("model/LIP_SegNet.json", "w").write(json_string)
classes=21,
weight_decay=3e-3,
weights='imagenet',
trainable_encoder=True)
optimizer = keras.optimizers.Adam(1e-4)
fcn_vgg16.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(class_mode='categorical',
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=train_loader),
steps_per_epoch=1112,
epochs=100,
validation_data=datagen.flow_from_imageset(class_mode='categorical',
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=val_loader),
validation_steps=1111,
verbose=1,
callbacks=[
lr_reducer, early_stopper, csv_logger, checkpointer, nan_terminator
])
fcn_vgg16.save('output/fcn_vgg16.h5')
history = model.fit(X_train_all, y_train_all, batch_size=32, epochs=10)
#%%
fcn_vgg16 = FCN(input_shape=X_train[0].shape, classes=3,
weights='None', trainable_encoder=True)
sgd = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
fcn_vgg16.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
# history = fcn_vgg16.fit(X_train_all, y_train_all, batch_size=32, epochs=20)
batch_size = 64
epochs = 50
history = fcn_vgg16.fit_generator(generator.flow(X_train_all, y_train_all, batch_size=batch_size),
steps_per_epoch=len(X_train_all) / batch_size, epochs=epochs)
#%%
plot_history(history)
#%%
y_pred = fcn_vgg16.predict(X_test[0][np.newaxis, ...])
y_pred_cls = np.argmax(y_pred, axis=3)
print(y_pred_cls.shape)
#%%
print(X_test[0][np.newaxis, ...].shape)
#%%
read_test_dataset_and_predict(model, X_mean, X_std)