def __init__(self, pretrained_path):
super().__init__()
self.pretrained_path = pretrained_path
self.output_dim = 800 * 800
# ------------------
# PRE-TRAINED MODEL
# ------------------
ae = AE.load_from_checkpoint(pretrained_path)
ae.freeze()
self.backbone = ae.encoder
self.backbone.c3_only = True
self.backbone.out_channels = 32
# ------------------
# FAST RCNN
# ------------------
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512), ),
aspect_ratios=((0.5, 1.0, 2.0), ))
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
output_size=7,
sampling_ratio=2)
self.fast_rcnn = FasterRCNN(self.backbone,
num_classes=9,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
# for unfreezing encoder later
self.frozen = True
required=True,
help='root directory that contains captions')
parser.add_argument('--fasttext_model',
type=str,
required=True,
help='pretrained fastText model (binary file)')
parser.add_argument('--max_nwords',
type=int,
default=50,
help='maximum number of words (default: 50)')
parser.add_argument('--img_model',
type=str,
required=True,
help='pretrained autoencoder model')
args = parser.parse_args()
if __name__ == '__main__':
caption_root = args.caption_root.split('/')[-1]
if (caption_root + '_vec') not in os.listdir(
args.caption_root.replace(caption_root, '')):
os.makedirs(args.caption_root + '_vec')
print('Loading a pretrained image model...')
img_model = AE.load_from_checkpoint(checkpoint_path=args.img_model)
model = nn.Sequential(img_model.encoder, img_model.fc)
model = model.eval()
print('Loading a pretrained fastText model...')
word_embedding = fastText.load_model(args.fasttext_model)
print('Making vectorized caption data files...')
ConvertCapVec().convert_and_save3(args.caption_root, word_embedding,
args.max_nwords, model)
