def main():
in_arg = get_input_args()
data_dir = in_arg.data_dir
save_dir = in_arg.save_dir
arch = in_arg.arch
learning_rate = in_arg.learning_rate
hidden_units = in_arg.hidden_units
epochs = in_arg.epochs
#processing_unit = in_arg.gpu
if torch.cuda.is_available() and in_arg.gpu == 'gpu':
print('GPU will be used')
processing_unit = 'gpu'
elif torch.cuda.is_available() == False:
print('CPU will be used')
processing_unit = 'cpu'
print(in_arg)
training_dataloaders, validation_dataloaders, testing_dataloaders, class_to_idx = load_datas(
data_dir)
pre_model = pretrained_model(arch)
model = classifier(pre_model, hidden_units)
after_train_model = train_model(model, training_dataloaders,
validation_dataloaders, learning_rate,
epochs, processing_unit)
valid_model(after_train_model, testing_dataloaders, processing_unit)
save_checkpoint(model, save_dir, class_to_idx)
def main():
assert FLAGS.MODE in ('train', 'valid', 'demo')
if FLAGS.MODE == 'demo':
demo(FLAGS.checkpoint_dir, FLAGS.show_box)
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
def main():
assert FLAGS.MODE in ('train', 'valid', 'demo')
if FLAGS.MODE == 'demo':
demo(FLAGS.checkpoint_dir, FLAGS.show_box)
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
def main():
if SET_MODE == 'video':
video(ckpt_dir, show_box)
elif SET_MODE == 'train':
train_model(train_data)
elif SET_MODE == 'test':
valid_model(ckpt_dir, valid_data)
else:
print("Wrong MODE Selected")
def main():
assert FLAGS.MODE in ('train', 'valid', 'demo')
if FLAGS.MODE == 'demo':
x = demo(FLAGS.checkpoint_dir, FLAGS.show_box, SAMPLE_IMAGE_PATH)
print("test: = " + str(x))
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
def main():
assert FLAGS.MODE in ('train', 'valid', 'demo')
# FLAGS.MODE 中含有‘train’，‘valid’，‘demo’ 三个字符串
if FLAGS.MODE == 'demo':
demo(FLAGS.checkpoint_dir, FLAGS.show_box)
# 运行 demo.demo()
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
def main():
assert FLAGS.MODE in ('train', 'valid', 'demo')
tf.compat.v1.disable_eager_execution()
if FLAGS.MODE == 'demo':
demo(FLAGS.checkpoint_dir, FLAGS.show_box)
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
def main():
f = input("please input you choose/n")
if f == "demo":
run_demo()
elif f == "model":
a1 = input("train?valid?")
if a1 == "train":
train_model()
else:
valid_model()
else:
print("usage: python3 main.py <function>")
def post(self):
x = ""
self.bytes_to_img(request.form['img'])
if FLAGS.MODE == 'demo':
img = Image.open('temp_file.jpg')
open_cv_image = numpy.array(img)
open_cv_image = open_cv_image[:, :, ::-1].copy()
x = demo(FLAGS.checkpoint_dir, FLAGS.show_box, open_cv_image)
print("test: = " + str(x))
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
return json.loads(x)
def post(self):
x = ""
if not request.files:
return 'File Not Found'
img = Image.open(request.files['file'])
if FLAGS.MODE == 'demo':
img = Image.open(request.files['file'])
open_cv_image = numpy.array(img)
open_cv_image = open_cv_image[:, :, ::-1].copy()
x = demo(FLAGS.checkpoint_dir, FLAGS.show_box, open_cv_image)
print("test: = " + str(x))
elif FLAGS.MODE == 'train':
train_model(FLAGS.train_data)
elif FLAGS.MODE == 'valid':
valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data)
return json.loads(x)
def run_model(args):
if args.train_or_valid == 'train':
train_model()
elif args.train_or_valid == 'valid':
valid_model()