def collate_fn(batch):
text, x, bbox_num, y = [], [], [], []
n = len(batch)
for (_text, _ids), _y in batch:
t = []
for i in _text:
t += i
text.append(t)
t2 = []
for _id in _ids:
feature = load_rcnn_features(_id)
t2.append(feature["x"])
x.append(np.vstack(t2))
bbox_num.append(len(x[-1])) # bbox num
y.append(_y)
padded_text, lens = padding_text_1d(text, config["max_tokens"])
max_lens = max(lens)
max_bbox = max(bbox_num)
padded_x = torch.zeros((n, max_bbox, config["img_input_size"]),
dtype=torch.float32)
img_key_padding_mask = torch.ones((n, max_bbox), dtype=torch.bool)
text_key_padding_mask = torch.ones((n, max_lens), dtype=torch.bool)
for i, (l, s) in enumerate(zip(lens, bbox_num)):
padded_x[i, :s] = torch.from_numpy(x[i])
img_key_padding_mask[i, :s] = False
text_key_padding_mask[i, :l] = False
bbox_num = torch.LongTensor(bbox_num)
y = torch.tensor(y)
return (padded_text, lens, text_key_padding_mask, padded_x,
img_key_padding_mask), y
def collate_fn(batch):
text, imgs, y = [], [], []
for (_text, _id), _y in batch:
text.append(_text)
imgs.append(load_vgg(_id))
y.append(_y)
padded_text, lens = padding_text_1d(text)
imgs = torch.tensor(imgs) # 转为张量
y = torch.tensor(y) #
res = ((padded_text, lens, imgs), y)
return res
def collate_fn(batch):
global config
text, imgs, y = [], [], []
for (_text, _imgs), _y in batch:
t = []
for i in _text:
t += i
text.append(t)
for i in _imgs:
feature = load_vgg_features(i)
if feature is not None:
imgs.append(feature)
# imgs += _imgs # _imgs: List[4096, 4096, 4096]
y.append(_y)
padded_text, lens = padding_text_1d(text, config["max_tokens"])
imgs = torch.tensor(imgs) # 转为张量
y = torch.tensor(y) #
res = ((padded_text, lens, imgs), y)
return res
def collate_fn(batch):
text, x, bbox_num, y = [], [], [], []
n = len(batch)
for (_text, _id), _y in batch:
text.append(_text)
feature = load_rcnn(_id)["x"]
x.append(feature)
bbox_num.append(len(feature)) # bbox num
y.append(_y)
padded_text, lens = padding_text_1d(text)
max_lens = max(lens)
max_bbox = max(bbox_num)
padded_x = torch.zeros((n, max_bbox, config["img_input_size"]),
dtype=torch.float32)
for i, (l, s) in enumerate(zip(lens, bbox_num)):
padded_x[i, :s] = torch.from_numpy(x[i])
bbox_num = torch.LongTensor(bbox_num)
y = torch.tensor(y)
return (padded_text, lens, padded_x), y
