def test_train(setup_tf_eager, setup_darknet_weights, setup_generator):
# 1. create generator
generator = setup_generator
# 2. create model
model = Yolonet(n_classes=1)
model.load_darknet_params(setup_darknet_weights, True)
# 3. training
loss_history = train_fn(model,
generator,
valid_generator=setup_generator,
num_epoches=3)
assert loss_history[0] > loss_history[-1]
def test_detect(setup_tf_eager, setup_darknet_weights):
darknet_weights = setup_darknet_weights
image_path = os.path.join(PROJECT_ROOT, "tests", "samples", "sample.jpeg")
# 1. create yolo model & load weights
yolov3 = Yolonet()
yolov3.load_darknet_params(darknet_weights)
# 2. preprocess the image
image = cv2.imread(image_path)
image = image[:,:,::-1]
d = YoloDetector(yolov3, COCO_ANCHORS, net_size=416)
boxes, labels, probs = d.detect(image)
assert len(boxes) == 31
def test_train(setup_tf_eager, setup_darknet_weights, setup_train_dirs):
ann_fnames, image_root = setup_train_dirs
darknet_weights = setup_darknet_weights
# 1. create generator
generator = BatchGenerator(ann_fnames,
image_root,
batch_size=2,
labels_naming=["raccoon"],
jitter=False)
valid_generator = BatchGenerator(ann_fnames,
image_root,
batch_size=2,
labels_naming=["raccoon"],
jitter=False)
# 2. create model
model = Yolonet(n_classes=1)
model.load_darknet_params(darknet_weights, True)
# 3. training
loss_history = train_fn(model, generator, valid_generator, num_epoches=3)
assert loss_history[0] > loss_history[-1]
import tensorflow as tf
import json
import os
tf.config.gpu.set_per_process_memory_growth(True)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
opt = Options()
args = opt.opt
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
args.experiment_name = 'yolov3_coco'
args.total_epoch = 100
args.dataset_root='/home/gwl/datasets/coco2017'
args.dataset_name='COCO'
# args.resume = 'load_yolov3'
# args.net_size=608
net = Yolonet(n_classes=80,freeze_backbone=args.freeze_darknet)
#the total size of your dataset
lensVOC=117266
lr_schedule = keras.experimental.CosineDecay(
initial_learning_rate=args.lr_initial,
decay_steps=args.total_epoch*(lensVOC//args.batch_size),
alpha=0.01
)
optimizer = keras.optimizers.SGD(learning_rate=lr_schedule,
momentum=args.momentum)
_Trainer = Trainer(args=args,
model=net,
optimizer=optimizer,
)
def _read_bytes(self, size):
self.offset = self.offset + size
return self.all_weights[self.offset - size:self.offset]
def _load_1d_var(self, variable):
size = np.prod(variable.shape)
value = self._read_bytes(size) # bias
variable.assign(value)
def _load_4d_var(self, variable):
size = np.prod(variable.shape)
value = self._read_bytes(size) # scale
value = value.reshape(list(reversed(variable.shape)))
value = value.transpose([2, 3, 1, 0])
variable.assign(value)
if __name__ == '__main__':
from yolo.net.yolonet import Yolonet
from yolo import YOLOV3_WEIGHTS
yolonet = Yolonet(18)
reader = WeightReader(YOLOV3_WEIGHTS)
reader.load_weights(yolonet, True)
yolonet.load_darknet_params(YOLOV3_WEIGHTS, skip_detect_layer=True)
yolonet = Yolonet(255)
yolonet.load_darknet_params(YOLOV3_WEIGHTS, skip_detect_layer=True)
yolonet.load_darknet_params(YOLOV3_WEIGHTS, skip_detect_layer=False)
help='path to image file')
if __name__ == '__main__':
args = argparser.parse_args()
image_path = args.image
import json
with open(args.config) as data_file:
config = json.load(data_file)
# Download if not exits weight file
download_if_not_exists(config["pretrained"]["darknet_format"],
"https://pjreddie.com/media/files/yolov3.weights")
# 1. create yolo model & load weights
yolov3 = Yolonet()
yolov3.load_darknet_params(config["pretrained"]["darknet_format"])
# 2. preprocess the image
image = cv2.imread(image_path)
image = image[:, :, ::-1]
d = YoloDetector(yolov3)
boxes = d.detect(image, COCO_ANCHORS, net_size=416)
# 4. draw detected boxes
image = draw_boxes(image, boxes, config["model"]["labels"], obj_thresh=0.5)
# 5. plot
plt.imshow(image)
plt.show()
image_path = args.image
# set some parameters
labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", \
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", \
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", \
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", \
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", \
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", \
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", \
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", \
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", \
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
# 1. create yolo model & load weights
yolov3 = Yolonet()
yolov3.load_darknet_params(weights_path)
# 2. preprocess the image
image = cv2.imread(image_path)
image = image[:, :, ::-1]
d = YoloDetector(yolov3)
boxes = d.detect(image, COCO_ANCHORS, net_size=416)
# 4. draw detected boxes
image = draw_boxes(image, boxes, labels, obj_thresh=0.5)
# 5. plot
plt.imshow(image)
plt.show()