def load_model_fpn(_model_weights, is_inference=False):
print("Using weights {}".format(_model_weights))
if _model_weights == "imagenet":
model = FPN(unet_encoder,
encoder_weights="imagenet",
classes=4,
activation=None)
if is_inference:
model.eval()
return model
else:
model = FPN(unet_encoder,
encoder_weights=None,
classes=4,
activation=None)
if is_inference:
model.eval()
if _model_weights is not None:
device = torch.device("cuda")
model.to(device)
state = torch.load(
_model_weights) # , map_location=lambda storage, loc: storage)
model.load_state_dict(state["state_dict"])
# new_state_dict = OrderedDict()
#
# for k, v in state['state_dict'].items():
# if k in model.state_dict():
# new_state_dict[k] = v
# model = model.load_state_dict(new_state_dict)
return model
def fpn(backbone, pretrained_weights=None, classes=1, activation='sigmoid'):
device = torch.device("cuda")
model = FPN(encoder_name=backbone,
encoder_weights=pretrained_weights,
classes=classes,
activation=activation)
model.to(device)
model.eval() # 위치 확인해볼것
return model
encoder_weights=None,
classes=4,
activation=None)
model_senet.to(device)
model_senet.eval()
state = torch.load(
'../input/senetmodels/senext50_30_epochs_high_threshold.pth',
map_location=lambda storage, loc: storage)
model_senet.load_state_dict(state["state_dict"])
model_fpn91lb = FPN(encoder_name="se_resnext50_32x4d",
classes=4,
activation=None,
encoder_weights=None)
model_fpn91lb.to(device)
model_fpn91lb.eval()
#state = torch.load('../input/fpnseresnext/model_se_resnext50_32x4d_fold_0_epoch_7_dice_0.935771107673645.pth', map_location=lambda storage, loc: storage)
state = torch.load(
'../input/fpnse50dice944/model_se_resnext50_32x4d_fold_0_epoch_26_dice_0.94392.pth',
map_location=lambda storage, loc: storage)
model_fpn91lb.load_state_dict(state["state_dict"])
model_fpn91lb_pseudo = FPN(encoder_name="se_resnext50_32x4d",
classes=4,
activation=None,
encoder_weights=None)
model_fpn91lb_pseudo.to(device)
model_fpn91lb_pseudo.eval()
#state = torch.load('../input/fpnseresnext/model_se_resnext50_32x4d_fold_0_epoch_7_dice_0.935771107673645.pth', map_location=lambda storage, loc: storage)
state = torch.load(
'../input/942-finetuned-on-pseudo-to9399/pseudo_fpn_se_resnext50_32x4d_fold_0_epoch_22_dice_0.944/pseudo_fpn_se_resnext50_32x4d_fold_0_epoch_22_dice_0.9446276426315308.pth',