def __init__(self,
height,
width,
net=None,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
ignore_threshold=0.5,
dynamic=True,
from_sigmoid=False,
augmentation=False,
make_target=False):
self._height = height
self._width = width
self._mean = mean
self._std = std
self._augmentation = augmentation
self._make_target = make_target
if self._make_target:
self._output1, self._output2, self._output3, self._anchor1, self._anchor2, self._anchor3, _, _, _, _, _, _ = net(
mx.nd.zeros((1, 3, height, width)))
self._target_generator = TargetGenerator(
ignore_threshold=ignore_threshold,
dynamic=dynamic,
from_sigmoid=from_sigmoid)
else:
self._target_generator = None
def __init__(self,
height,
width,
net=None,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
foreground_iou_thresh=0.5,
background_iou_thresh=0.4,
augmentation=False,
make_target=True):
self._height = height
self._width = width
self._mean = mean
self._std = std
self._augmentation = augmentation
self._foreground_iou_thresh = foreground_iou_thresh
self._background_iou_thresh = background_iou_thresh
self._make_target = make_target
if self._make_target:
_, _, self._anchor = net(mx.nd.zeros((1, 3, height, width)))
self._target_generator = TargetGenerator(
foreground_iou_thresh=foreground_iou_thresh,
background_iou_thresh=background_iou_thresh)
else:
self._target_generator = None
def __init__(self, input_size, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], scale_factor=4,
make_target=False, num_classes=3):
self._width = input_size[1]
self._height = input_size[0]
self._mean = mean
self._std = std
self._scale_factor = scale_factor
self._make_target = make_target
if self._make_target:
self._target_generator = TargetGenerator(num_classes=num_classes)
else:
self._target_generator = None
def __init__(self, input_size, input_frame_number=1, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], scale_factor=4,
make_target=False, num_classes=3):
self._width = input_size[1]
self._height = input_size[0]
self._mean = torch.as_tensor(mean*input_frame_number).reshape((3*input_frame_number, 1, 1))
self._std = torch.as_tensor(std*input_frame_number).reshape((3*input_frame_number, 1, 1))
self._toTensor = torchvision.transforms.ToTensor()
self._scale_factor = scale_factor
self._make_target = make_target
if self._make_target:
self._target_generator = TargetGenerator(num_classes=num_classes)
else:
self._target_generator = None