def collate(data_list):
"""
collate
"""
batch = Pack()
# 手写各个结构
# num_src
batch['num_src'] = list2tensor([x['num_src'] for x in data_list])
# num_tgt_input
batch['num_tgt_input'] = list2tensor(
[x['num_tgt_input'] for x in data_list])
# tgt_output
batch['tgt_output'] = list2tensor(
[x['tgt_output'] for x in data_list])
batch['tgt_emo'] = list2tensor([x['tgt_emo'] for x in data_list])
# mask
batch['mask'] = list2tensor([x['mask'] for x in data_list])
batch['raw_src'] = [x['raw_src'] for x in data_list]
batch['raw_tgt'] = [x['raw_tgt'] for x in data_list]
if 'id' in data_list[0].keys():
batch['id'] = [x['id'] for x in data_list]
if device >= 0:
batch = batch.cuda(device=device)
return batch
def collate(data_list):
batch = Pack()
for key in data_list[0].keys():
batch[key] = list2tensor([x[key] for x in data_list])
if device >= 0:
batch = batch.cuda(device=device)
return batch
def collate(data_list):
"""
collate
"""
data_list1, data_list2 = zip(*data_list)
batch1 = Pack()
batch2 = Pack()
data_list1 = list(data_list1)
data_list2 = list(data_list2)
for key in data_list1[0].keys():
batch1[key] = list2tensor([x[key] for x in data_list1])
if device >= 0:
batch1 = batch1.cuda(device=device)
for key in list(data_list2)[0].keys():
batch2[key] = list2tensor([x[key] for x in data_list2])
if device >= 0:
batch2 = batch2.cuda(device=device)
return batch1, batch2
def collate(
data_list): # data_list的长度就是一个batch_size，每个元素都是__getitem__得到的
"""
collate
"""
batch = Pack()
for key in data_list[0].keys(): # keys(): src, tgt, cue
batch[key] = list2tensor([x[key] for x in data_list
]) # 所有的src, tgt, cue分别整合在一起
if device >= 0:
batch = batch.cuda(device=device)
return batch
def collate(data_list):
"""
collate
"""
batch = Pack()
# batch is a dict
for key in data_list[0].keys():
# data_list: a list of dict
# so one sample is one dict
batch[key] = list2tensor([x[key] for x in data_list])
if device >= 0:
batch = batch.cuda(device=device)
return batch
def interact(self, src, cue=None):
if src == "":
return None
inputs = Pack()
src = self.src_field.numericalize([src])
inputs.add(src=list2tensor(src))
if cue is not None:
cue = self.cue_field.numericalize([cue])
inputs.add(cue=list2tensor(cue))
if self.use_gpu:
inputs = inputs.cuda()
_, preds, _, _ = self.forward(inputs=inputs, num_candidates=1)
pred = self.tgt_field.denumericalize(preds[0][0])
return pred
def collate(data_list):
"""
collate
"""
batch = Pack()
for key in data_list[0].keys():
if key == 'topic':
continue
batch[key] = list2tensor([x[key] for x in data_list])
batch_bow = []
for x in data_list:
v = torch.zeros(bow_vocab_size, dtype=torch.float)
x_bow = x['topic'] # dict
for w, f in x_bow:
v[w] += f
batch_bow.append(v)
batch['bow'] = torch.stack(batch_bow)
if device >= 0:
batch = batch.cuda(device=device)
return batch
def collate(data_list):
"""
collate
---
data_list: List[Dict]
"""
batch = Pack()
for key in data_list[0].keys():
batch[key] = list2tensor([x[key] for x in data_list])
if device >= 0:
batch = batch.cuda(device=device)
# copy mechanism prepare
raw_src = [x['raw_src'].split() for x in data_list]
token2idx, idx2token, batch_pos_idx_map, idx2idx_mapping \
= build_copy_mapping(raw_src, vocab)
batch['token2idx'] = token2idx
batch['idx2token'] = idx2token
batch['batch_pos_idx_map'] = batch_pos_idx_map
batch['idx2idx_mapping'] = idx2idx_mapping
batch['output'] = '???'
return batch