def predict(self, files, model_no, path=None, **kwargs):
model_path = self.models[model_no]
model = self.load_model(model_path)
ds_kwargs = self.ds_kwargs
# Adding extra padding (overlap) for models that have the same input and output shape
if ds_kwargs['padding'][0] == 0:
ds_kwargs['padding'] = (self.extra_padding, ) * 2
ds = TileDataset(files, **ds_kwargs)
dls = DataLoaders.from_dsets(ds,
batch_size=self.bs,
after_batch=self.get_batch_tfms(),
shuffle=False,
drop_last=False,
**self.dl_kwargs)
if torch.cuda.is_available(): dls.cuda()
learn = Learner(dls, model, loss_func=self.loss_fn)
if self.mpt: learn.to_fp16()
if path: path = path / f'model_{model_no}'
return learn.predict_tiles(dl=dls.train, path=path, **kwargs)
def predict(self, files, model_no, bs=None, **kwargs):
bs = bs or self.bs
model_path = self.models[model_no]
model = self.load_model(model_path)
batch_tfms = Normalize.from_stats(*self.stats)
ds = TileDataset(files, **self.ds_kwargs)
dls = DataLoaders.from_dsets(ds,
batch_size=bs,
after_batch=batch_tfms,
shuffle=False,
drop_last=False,
num_workers=0)
if torch.cuda.is_available(): dls.cuda(), model.cuda()
learn = Learner(dls, model, loss_func=self.loss_fn)
if self.mpt: learn.to_fp16()
results = learn.predict_tiles(dl=dls.train, **kwargs)
pth_tmp = self.path / '.tmp' / model_path.name
save_tmp(pth_tmp, files, results)
return results
