def load_model_unet(_model_weights, is_inference=False):
print('Using weights {}'.format(_model_weights))
if _model_weights == 'imagenet':
model = Unet(unet_encoder,
encoder_weights="imagenet",
classes=4,
activation=None,
attention_type=ATTENTION_TYPE)
if is_inference:
model.eval()
return model
else:
model = Unet(
unet_encoder,
encoder_weights=None, # "imagenet",
classes=4,
activation=None,
attention_type=ATTENTION_TYPE)
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"])
optimizer_state = state['optimizer']
return model, optimizer_state
# 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 unet(backbone, pretrained_weights=None, classes=1, activation='sigmoid'):
device = torch.device("cuda")
model = Unet(encoder_name=backbone,
encoder_weights=pretrained_weights,
classes=classes,
activation=activation)
model.to(device)
model.eval() # 위치 확인해볼것
return model
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
df = pd.read_csv(sample_submission_path)
testset = DataLoader(
TestDataset(test_data_folder, df, mean, std),
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True
)
# Initialize mode and load trained weights
ckpt_path = "../input/model_dump1/model.pth"
device = torch.device("cuda")
model = Unet("efficientnet-b5", encoder_weights=None, classes=4, activation=None)
model.to(device)
model.eval()
state = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
model.load_state_dict(state["state_dict"])
# start prediction
predictions = []
for i, batch in enumerate(tqdm(testset)):
fnames, images = batch
batch_preds = torch.sigmoid(model(images.to(device)))
batch_preds = batch_preds.detach().cpu().numpy()
for fname, preds in zip(fnames, batch_preds):
for cls, pred in enumerate(preds):
pred, num = post_process(pred, best_threshold[cls], min_size[cls])
rle = mask2rle(pred)
name = fname + f"_{cls+1}"
classes=4,
activation='sigmoid',
)
state = torch.load("../input/bce-clf/unet_res34_525.pth")
unet_resnet34.load_state_dict(state['model_state_dict'])
unet_resnet34 = unet_resnet34.cuda()
unet_resnet34 = unet_resnet34.eval()
device = torch.device("cuda")
model_senet = Unet('se_resnext50_32x4d',
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',