def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
# create the generators
train_generator, validation_generator = create_generators(args)
num_classes = train_generator.num_classes()
model, prediction_model, debug_model = centernet(
num_classes=num_classes, input_size=args.input_size, freeze_bn=False)
# create the model
print(args.resume)
if args.resume:
print('Loading model, this may take a second...')
model.load_weights(args.resume, by_name=True, skip_mismatch=True)
# compile model
model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-3),
loss={
'centernet_loss': lambda y_true, y_pred: y_pred
})
# model.compile(optimizer=SGD(lr=1e-5, momentum=0.9, nesterov=True, decay=1e-5),loss={'centernet_loss': lambda y_true, y_pred:y_pred})
# print model summary
# print(model.summary())
model.summary()
# create the callbacks
callbacks = create_callbacks(
model,
prediction_model,
validation_generator,
args,
)
if not args.compute_val_loss:
validation_generator = None
# start training
model.fit_generator(generator=train_generator,
steps_per_epoch=args.steps,
initial_epoch=0,
epochs=args.epochs,
verbose=1,
callbacks=callbacks,
workers=args.workers,
use_multiprocessing=args.multiprocessing,
max_queue_size=args.max_queue_size,
validation_data=validation_generator)
return 0
'sofa': 17,
'train': 18,
'tvmonitor': 19
}
model_path = '/home/tienduchoang/Videos/models/keras-centernet/checkpoints/pretrain_model.h5'
num_classes = len(voc_classes)
classes = list(voc_classes.keys())
flip_test = True
nms = True
keep_resolution = False
score_threshold = 0.1
colors = [np.random.randint(0, 256, 3).tolist() for i in range(num_classes)]
model, prediction_model, debug_model = centernet(
num_classes=num_classes,
nms=nms,
flip_test=flip_test,
freeze_bn=True,
score_threshold=score_threshold)
prediction_model.load_weights(model_path, by_name=True, skip_mismatch=True)
# image = generator.load_image(i)
image_path = "./data/1.jpg"
image = cv2.imread(image_path)
src_image = image.copy()
c = np.array([image.shape[1] / 2., image.shape[0] / 2.], dtype=np.float32)
s = max(image.shape[0], image.shape[1]) * 1.0
input_size = 512
tgt_w = input_size
from models.resnet import centernet
import cv2
import numpy as np
from generators.utils import get_affine_transform, affine_transform
#get model
trainmodel, deploymodel, debugmodel = centernet(1)
#load model weight
deploymodel.load_weights('checkpoints/2020-04-11/save_model.h5',
by_name=True,
skip_mismatch=True)
#print model struct
debugmodel.summary()
#read img and preprocess
img = cv2.imread("1.jpg")
#pre process image
img_w = img.shape[1]
img_h = img.shape[0]
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
c = np.array([image.shape[1] / 2., image.shape[0] / 2.], dtype=np.float32)
s = max(image.shape[0], image.shape[1]) * 1.0
trans_input = get_affine_transform(c, s, (512, 512))
image = cv2.warpAffine(image, trans_input, (512, 512), flags=cv2.INTER_LINEAR)
image = image.astype(np.float32)
input = image / 255.0
input = input[np.newaxis, :]
#forward
outputs = deploymodel(input)
#get result
bboxs = outputs[0].numpy()
#demo result
def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
# K.set_session(get_session())
# tf.config.gpu.set_per_process_memory_growth(True)
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print('phy', physical_devices)
# if len(physical_devices) > 0:
# for k in range(len(physical_devices)):
# tf.config.experimental.set_memory_growth(physical_devices[k], True)
# print('memory growth:', tf.config.experimental.get_memory_growth(physical_devices[k]))
# else:
# print("Not enough GPU hardware devices available")
# create the generators
train_generator, validation_generator = create_generators(args)
num_classes = train_generator.num_classes()
model, prediction_model, debug_model = centernet(
num_classes=num_classes, input_size=args.input_size, freeze_bn=True)
# create the model
print('Loading model, this may take a second...')
# model.load_weights(args.snapshot, by_name=True, skip_mismatch=True)
model.load_weights(args.snapshot, by_name=True)
# freeze layers
if args.freeze_backbone:
for i in range(190):
# for i in range(175):
model.layers[i].trainable = False
# compile model
model.compile(optimizer=Adam(lr=1e-3),
loss={
'centernet_loss': lambda y_true, y_pred: y_pred
})
# model.compile(optimizer=SGD(lr=1e-5, momentum=0.9, nesterov=True, decay=1e-5),
# loss={'centernet_loss': lambda y_true, y_pred: y_pred})
# print model summary
# print(model.summary())
# create the callbacks
callbacks = create_callbacks(
model,
prediction_model,
validation_generator,
args,
)
if not args.compute_val_loss:
validation_generator = None
# start training
return model.fit_generator(generator=train_generator,
steps_per_epoch=args.steps,
initial_epoch=0,
epochs=args.epochs,
verbose=1,
callbacks=callbacks,
workers=args.workers,
use_multiprocessing=args.multiprocessing,
max_queue_size=args.max_queue_size,
validation_data=validation_generator)
total_loss = hm_loss + wh_loss + reg_loss
return total_loss
if __name__ == '__main__':
from generators.pascal import PascalVocGenerator
from models.resnet import centernet
import numpy as np
generator = PascalVocGenerator('datasets/VOC0712',
'train',
skip_difficult=True,
shuffle_groups=False,
batch_size=1)
model, prediction_model, debug_model = centernet(num_classes=20)
debug_model.load_weights(
'checkpoints/2019-11-06/pascal_31_1.0370_10.7204.h5',
by_name=True,
skip_mismatch=True)
sess = tf.Session()
for inputs, targets in generator:
image_input, hm_true, wh_true, reg_true, reg_mask, indices = inputs
hm_pred, wh_pred, reg_pred = debug_model.predict(image_input)
np.save('debug/1106/hm_true', hm_true)
np.save('debug/1106/wh_true', wh_true)
np.save('debug/1106/reg_true', reg_true)
np.save('debug/1106/reg_mask', reg_mask)
np.save('debug/1106/indices', indices)
np.save('debug/1106/hm_pred', hm_pred)
np.save('debug/1106/wh_pred', wh_pred)
