def evaluate_pascal_voc(root_dir, download, batch_size, conf_thres, log_every,
limit, plot, save, images_results_dir):
cfg_file = os.path.join(HERE, '../cfg/yolov3.cfg')
weight_file = os.path.join(HERE, '../cfg/yolov3.weights')
namesfile = os.path.join(HERE, '../cfg/coco.names')
class_names = load_class_names(namesfile)
model = Yolo(cfg_file=cfg_file,
class_names=class_names,
batch_size=batch_size)
model.load_weights(weight_file)
image_and_target_transform = Compose([SquashResize(416), ToTensor()])
dataset = YaYoloVocDataset(root_dir=root_dir,
batch_size=batch_size,
transforms=image_and_target_transform,
image_set='val',
download=download,
class_names=class_names)
summary_writer = SummaryWriter(comment=f' evaluate={batch_size}')
evaluate(model,
dataset,
summary_writer,
images_results_dir,
iou_thres=0.5,
conf_thres=conf_thres,
nms_thres=0.5,
log_every=log_every,
limit=limit,
plot=plot,
save=save)
summary_writer.close()
def train_car_make(car_make_json_file, batch_size, lr, conf_thres,
gradient_accumulations, clip_gradients, epochs, limit,
log_every, save_every, model_dir, parameters,
lambda_no_obj):
image_and_target_transform = Compose([SquashResize(416), ToTensor()])
dataset = DetectedCareMakeDataset(json_file=car_make_json_file,
transforms=image_and_target_transform,
batch_size=batch_size)
cfg_file = os.path.join(HERE, '../cfg/yolov3.cfg')
weight_file = os.path.join(HERE, '../cfg/yolov3.weights')
model = Yolo(cfg_file=cfg_file,
class_names=dataset.class_names,
batch_size=batch_size,
coreml_mode=False)
model.load_weights(weight_file)
if parameters is not None:
logger.info(f"loading model parameters from {parameters}")
model.load_state_dict(torch.load(parameters, map_location=DEVICE))
summary_writer = SummaryWriter(comment=f' evaluate={batch_size}')
# with neptune.create_experiment(name='start-with-neptune',
# params=PARAMS):
# neptune.append_tag('first-example')
train(model=model,
dataset=dataset,
model_dir=model_dir,
summary_writer=summary_writer,
epochs=epochs,
lr=lr,
conf_thres=conf_thres,
nms_thres=0.5,
iou_thres=0.5,
lambda_coord=5,
lambda_no_obj=lambda_no_obj,
gradient_accumulations=gradient_accumulations,
clip_gradients=clip_gradients,
limit=limit,
debug=False,
print_every=log_every,
save_every=save_every)
summary_writer.close()
def run_detect_cars(in_dir, out_dir, batch_size, limit, conf_thres, nms_thres, debug):
assert os.path.isdir(out_dir), "directory {} does not exist".format(out_dir)
now = datetime.datetime.now()
now_str = now.strftime("%Y-%m-%dT%H-%M-%S")
detected_dataset_dir = os.path.join(out_dir, now_str)
os.mkdir(detected_dataset_dir)
feed_file = os.path.join(detected_dataset_dir, "feed.json")
detected_dataset_images_dir = os.path.join(detected_dataset_dir, "images")
os.mkdir(detected_dataset_images_dir)
cfg_file = os.path.join(HERE, '../cfg/yolov3.cfg')
weight_file = os.path.join(HERE, '../cfg/yolov3.weights')
namesfile = os.path.join(HERE, '../cfg/coco.names')
class_names = load_class_names(namesfile)
with torch.no_grad():
model = Yolo(cfg_file=cfg_file, class_names=class_names, batch_size=batch_size)
model.load_weights(weight_file)
model.to(DEVICE)
model.eval()
image_and_target_transform = Compose([
SquashResize(416),
ToTensor()
])
dataset = SimpleCarDataset(
root_dir=in_dir,
transforms=image_and_target_transform,
batch_size=batch_size)
with FileWriter(file_path=feed_file) as file_writer:
car_dataset_writer = DetectedCarDatasetWriter(file_writer)
detected_dataset_helper = DetectedCarDatasetHelper(car_dataset_writer=car_dataset_writer,
class_names=model.class_names,
conf_thres=conf_thres,
nms_thres=nms_thres,
batch_size=batch_size,
debug=debug)
cnt = detect_and_process(model=model,
dataset=dataset,
processor=detected_dataset_helper.process_detections,
limit=limit)
logger.info("Ran detection of {} images".format(cnt))
def train_coco(coco_images_dir, coco_annotations_file, batch_size, lr,
conf_thres, gradient_accumulations, clip_gradients, epochs,
limit, log_every, save_every, model_dir, parameters):
cfg_file = os.path.join(HERE, '../cfg/yolov3.cfg')
weight_file = os.path.join(HERE, '../cfg/yolov3.weights')
namesfile = os.path.join(HERE, '../cfg/coco.names')
class_names = load_class_names(namesfile)
model = Yolo(cfg_file=cfg_file,
class_names=class_names,
batch_size=batch_size)
model.load_weights(weight_file)
model.freeze_parameters_of_all_old_layers()
if parameters is not None:
logger.info(f"loading model parameters from {parameters}")
model.load_state_dict(torch.load(parameters, map_location=DEVICE))
image_and_target_transform = Compose([SquashResize(416), ToTensor()])
dataset = YaYoloCocoDataset(images_dir=coco_images_dir,
annotations_file=coco_annotations_file,
transforms=image_and_target_transform,
batch_size=batch_size)
summary_writer = SummaryWriter(comment=f' evaluate={batch_size}')
train(model=model,
dataset=dataset,
model_dir=model_dir,
summary_writer=summary_writer,
epochs=epochs,
lr=lr,
conf_thres=conf_thres,
nms_thres=0.5,
iou_thres=0.5,
lambda_coord=5,
lambda_no_obj=0.5,
gradient_accumulations=gradient_accumulations,
clip_gradients=clip_gradients,
limit=limit,
debug=False,
print_every=log_every,
save_every=save_every)
summary_writer.close()