def create_model(cfg, conf_distr):
'''
'''
#
base = networks_map[cfg.NETS]
#number_box= [2*len(aspect_ratios) if isinstance(aspect_ratios[0], int) else len(aspect_ratios) for aspect_ratios in cfg.ASPECT_RATIOS]
#number_box= [2*len(aspect_ratios) if isinstance(aspect_ratios[0], int) else len(aspect_ratios)+1 for aspect_ratios in cfg.ASPECT_RATIOS]
#number_box= [2*len(aspect_ratios) if isinstance(aspect_ratios[0], int) else len(aspect_ratios)+2 for aspect_ratios in cfg.ASPECT_RATIOS]
#number_box= [2*len(aspect_ratios) if isinstance(aspect_ratios[0], int) else (len(aspect_ratios)+1)*2 for aspect_ratios in cfg.ASPECT_RATIOS]
number_box = PriorBox.get_anchor_number(cfg.ASPECT_RATIOS)
print('==>AnchorBox:', number_box)
model = ssds_map[cfg.SSDS](base=base,
feature_layer=cfg.FEATURE_LAYER,
mbox=number_box,
num_classes=cfg.NUM_CLASSES,
conf_distr=conf_distr)
#
feature_maps = _forward_features_size(model, cfg.IMAGE_SIZE)
print('==>Feature map size:')
print(feature_maps)
#
priorbox = PriorBox(image_size=cfg.IMAGE_SIZE,
feature_maps=feature_maps,
aspect_ratios=cfg.ASPECT_RATIOS,
scale=cfg.SIZES,
archor_stride=cfg.STEPS,
clip=cfg.CLIP)
# priors = Variable(priorbox.forward(), volatile=True)
return model, priorbox
def create_model(cfg):
'''
'''
#
base = networks_map[cfg.NETS]
number_box = [
2 + 2 * len(aspect_ratios) for aspect_ratios in cfg.ASPECT_RATIOS
]
model = ssds_map[cfg.SSDS](base=base,
feature_layer=cfg.FEATURE_LAYER,
mbox=number_box,
num_classes=cfg.NUM_CLASSES)
#
feature_maps = _forward_features_size(model, cfg.IMAGE_SIZE)
print('==>Feature map size:')
print(feature_maps)
#
priorbox = PriorBox(image_size=cfg.IMAGE_SIZE,
feature_maps=feature_maps,
aspect_ratios=cfg.ASPECT_RATIOS,
scale=cfg.SIZES,
archor_stride=cfg.STEPS,
clip=cfg.CLIP)
# priors = Variable(priorbox.forward(), volatile=True)
return model, priorbox
def create_model(cfg):
'''
'''
#
if cfg.NETS == 'mobilenet_v3':
base = MobileNetV3_Large()
model = torch.load("./Student/mbv3_large.old.pth.tar", map_location='cpu')
weight = model["state_dict"]
weights_norm = {i[7:]:weight[i] for i in weight}
base.load_state_dict(weights_norm)
elif cfg.NETS == 'mobilenet_v2':
base = torchvision.models.mobilenet_v2(True).features[:18]
elif cfg.NETS == 'resnet_18':
base = torchvision.models.resnet18(True)
base.fc = Identity()
else:
base = networks_map[cfg.NETS]
number_box= [2*len(aspect_ratios) if isinstance(aspect_ratios[0], int) else len(aspect_ratios) for aspect_ratios in cfg.ASPECT_RATIOS]
if cfg.NETS == 'mobilenet_v3':
model = ssd_lite_MobileNetV3.build_ssd_lite(base=base, feature_layer=cfg.FEATURE_LAYER, mbox=number_box, num_classes=cfg.NUM_CLASSES)
elif cfg.NETS == 'mobilenet_v2':
model = ssd_lite_MobileNetV2.build_ssd_lite(base=base, feature_layer=cfg.FEATURE_LAYER, mbox=number_box, num_classes=cfg.NUM_CLASSES)
elif cfg.NETS == 'resnet_18':
model = ssd_ResNet18.build_ssd(base=base, feature_layer=cfg.FEATURE_LAYER, mbox=number_box, num_classes=cfg.NUM_CLASSES)
else:
model = ssds_map[cfg.SSDS](base=base, feature_layer=cfg.FEATURE_LAYER, mbox=number_box, num_classes=cfg.NUM_CLASSES)
#
feature_maps = _forward_features_size(model, cfg.IMAGE_SIZE)
print('==>Feature map size:')
print(feature_maps)
#
if cfg.PRIOR:
priorbox = PriorBox(image_size=cfg.IMAGE_SIZE, feature_maps=feature_maps, aspect_ratios=cfg.ASPECT_RATIOS,
scale=cfg.SIZES, archor_stride=cfg.STEPS, clip=cfg.CLIP)
return model, priorbox, feature_maps
else:
return model
def create_model(cfg):
'''
'''
# Base of network takes in image and outputs
# a list of feautre maps at different scales
net_base = networks_map[cfg.NETS]
# Head of network takes in a list of feature maps
# and outputs a list of tensors corresponding to
# the location and class predictions of objects.
ssd_head = ssds_map[cfg.SSDS]
number_box = [(2 * len(aspect_ratios) if isinstance(aspect_ratios[0], int)
else len(aspect_ratios))
for aspect_ratios in cfg.ASPECT_RATIOS]
# Combine base and head of network
model = ssd_head(base=net_base,
feature_layer=cfg.FEATURE_LAYER,
mbox=number_box,
num_classes=cfg.NUM_CLASSES)
logger.info('Model blocks:')
logger.info(model)
feature_maps = _forward_features_size(model, cfg.IMAGE_SIZE)
logger.info('Feature map size:')
logger.info(feature_maps)
# PriorBox takes in a list of feature maps and outputs
# a tensor containing a list of bounding box priors used
# to match location and class predictions
priorbox = PriorBox(image_size=cfg.IMAGE_SIZE,
feature_maps=feature_maps,
aspect_ratios=cfg.ASPECT_RATIOS,
scale=cfg.SIZES,
archor_stride=cfg.STEPS,
clip=cfg.CLIP)
return model, priorbox
