def batchify_image_features(self, batch: Batch) -> Batch:
"""
Return the image features as a Tensor of the correct type.
Fill in missing feature vectors. Here, we require image features to be saved in
`batch` as a Tensor for passing through the image encoder. This is required for
data_parallel.
"""
# Checks/formatting of batch.image
bsz = self._get_batch_size(batch)
if batch.image is None or len(batch.image) == 0:
batch.image = [None] * bsz
else:
assert len(batch.image) == bsz
# Process all image feature vectors, or add in zero vectors if missing
processed_features_list = []
processed_zero_features = self._process_image_features(
torch.zeros((self.image_features_dim, )))
for orig_features in batch.image:
if isinstance(orig_features, torch.Tensor):
processed_features_list.append(
self._process_image_features(orig_features))
else:
if orig_features is not None:
warn_once(
'Unsupported image feature format. Image features will be ignored!'
)
processed_features_list.append(processed_zero_features)
# Turn into batchsize x image_features_dim for DataParallel
batch.image = torch.stack(processed_features_list)
return batch
def batchify_image_features(self, batch: Batch) -> Batch:
"""
Format and return the batched image features.
Image features represented by tensors will set to the right type.
"""
if type(batch.image) == list and any(b is not None for b in batch.image):
images = []
for img in batch.image:
if isinstance(img, torch.Tensor):
img = self._process_image_features(img)
images.append(img)
batch.image = images
else:
images = [None] * len(batch.valid_indices)
batch.image = images
return batch
