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import torch
import numpy as np
import copy
def org_tcl_rois(batch_size, pos_lists, pos_masks, label_lists, tcl_bs):
""" """
pos_lists_, pos_masks_, label_lists_ = [], [], []
img_bs = batch_size
ngpu = int(batch_size / img_bs)
img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
pos_lists_split, pos_masks_split, label_lists_split = [], [], []
for i in range(ngpu):
pos_lists_split.append([])
pos_masks_split.append([])
label_lists_split.append([])
for i in range(img_ids.shape[0]):
img_id = img_ids[i]
gpu_id = int(img_id / img_bs)
img_id = img_id % img_bs
pos_list = pos_lists[i].copy()
pos_list[:, 0] = img_id
pos_lists_split[gpu_id].append(pos_list)
pos_masks_split[gpu_id].append(pos_masks[i].copy())
label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
# repeat or delete
for i in range(ngpu):
vp_len = len(pos_lists_split[i])
if vp_len <= tcl_bs:
for j in range(0, tcl_bs - vp_len):
pos_list = pos_lists_split[i][j].copy()
pos_lists_split[i].append(pos_list)
pos_mask = pos_masks_split[i][j].copy()
pos_masks_split[i].append(pos_mask)
label_list = copy.deepcopy(label_lists_split[i][j])
label_lists_split[i].append(label_list)
else:
for j in range(0, vp_len - tcl_bs):
c_len = len(pos_lists_split[i])
pop_id = np.random.permutation(c_len)[0]
pos_lists_split[i].pop(pop_id)
pos_masks_split[i].pop(pop_id)
label_lists_split[i].pop(pop_id)
# merge
for i in range(ngpu):
pos_lists_.extend(pos_lists_split[i])
pos_masks_.extend(pos_masks_split[i])
label_lists_.extend(label_lists_split[i])
return pos_lists_, pos_masks_, label_lists_
def pre_process(label_list, pos_list, pos_mask, max_text_length, max_text_nums,
pad_num, tcl_bs):
label_list = label_list.numpy()
batch, _, _, _ = label_list.shape
pos_list = pos_list.numpy()
pos_mask = pos_mask.numpy()
pos_list_t = []
pos_mask_t = []
label_list_t = []
for i in range(batch):
for j in range(max_text_nums):
if pos_mask[i, j].any():
pos_list_t.append(pos_list[i][j])
pos_mask_t.append(pos_mask[i][j])
label_list_t.append(label_list[i][j])
pos_list, pos_mask, label_list = org_tcl_rois(batch, pos_list_t, pos_mask_t,
label_list_t, tcl_bs)
label = []
tt = [l.tolist() for l in label_list]
for i in range(tcl_bs):
k = 0
for j in range(max_text_length):
if tt[i][j][0] != pad_num:
k += 1
else:
break
label.append(k)
label = torch.tensor(label)
label = label.long()
pos_list = torch.tensor(pos_list)
pos_mask = torch.tensor(pos_mask)
label_list = torch.squeeze(torch.tensor(label_list), dim=2)
label_list = label_list.int()
return pos_list, pos_mask, label_list, label
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