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import logging
from typing import List, Tuple
from dataclasses import dataclass
from transformers import ProcessorMixin, AutoProcessor, AutoTokenizer
from src.arguments import DataArguments, ModelArguments
import torch
logger = logging.getLogger(__name__)
@dataclass
class TrainCollator:
data_args: DataArguments
model_args: ModelArguments
processor: ProcessorMixin
def __call__(self, examples):
"""
:param examples: [{qry:..., qry_image:..., pos_text:..., pos_image:...}] * batch_size
"""
qry_inputs = self._get_batch_inputs(examples, 0, 1)
pos_inputs = self._get_batch_inputs(examples, 2, 3)
if "hard_neg" in self.data_args.dataset_name:
hard_neg_inputs = self._get_batch_inputs(examples, 4, 5)
return qry_inputs, pos_inputs, hard_neg_inputs
return qry_inputs, pos_inputs
def _get_batch_inputs(self, examples, text_idx, image_idx):
input_ids, pixel_values = [], []
image_mask, image_sizes, image_grid_thw = [], [], []
for example in examples:
text, image = example[text_idx], example[image_idx]
has_image = image is not None
image_mask.append(1 if has_image else 0)
# 统一processor调用逻辑
if self.model_args.model_backbone == "llava_next":
inputs = self.processor(
text=text,
images=image if has_image else None,
return_tensors="pt",
max_length=self.data_args.max_len,
truncation=True
)
elif self.model_args.model_backbone in ["qwen", "qwen2_vl"]:
inputs = self.processor(
text=[text],
images=[image] if has_image else None,
return_tensors="pt",
max_length=self.data_args.max_len,
truncation=True
)
else:
inputs = self.processor(
text=text,
images=[image] if has_image else None,
return_tensors="pt",
max_length=self.data_args.max_len,
truncation=True
)
if has_image:
if self.model_args.model_backbone == "qwen":
pixel_values.append(inputs['pixel_values'].unsqueeze(0))
else:
pixel_values.append(inputs['pixel_values'])
input_ids.append(inputs["input_ids"].squeeze(0).unsqueeze(1))
if "image_sizes" in inputs:
image_sizes.append(inputs['image_sizes'])
if "image_grid_thw" in inputs:
image_grid_thw.append(inputs['image_grid_thw'])
input_ids = torch._C._nn.pad_sequence(
input_ids,
batch_first=True,
padding_value=self.processor.tokenizer.pad_token_id
).squeeze(2)
attention_mask = input_ids.ne(self.processor.tokenizer.pad_token_id)
inputs = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'image_mask': torch.tensor(image_mask, dtype=torch.float)
}
if any(image_mask):
if pixel_values:
inputs['pixel_values'] = torch.cat(pixel_values, dim=0)
if image_sizes:
inputs['image_sizes'] = torch.cat(image_sizes, dim=0)
if image_grid_thw:
inputs['image_grid_thw'] = torch.cat(image_grid_thw, dim=0)
if self.model_args.model_backbone == "internvl_2_5":
inputs['image_flags'] = inputs['image_mask'].to(torch.long)
return inputs
@dataclass
class EvalCollator:
data_args: DataArguments
model_args: ModelArguments
processor: ProcessorMixin
def __call__(self, examples):
"""
:param examples: qry, qry_image, pos_text, pos_image
"""
inputs = self._get_batch_inputs(examples)
return inputs
def _get_batch_inputs(self, examples):
input_ids, pixel_values, image_sizes = [], [], []
image_mask = []
image_exist = False
for example in examples:
text, image = example
has_image = image is not None
image_mask.append(1 if has_image else 0)
if self.model_args.model_backbone == "internvl_2_5":
inputs = self.processor(
text=text,
images=[image] if has_image else None,
return_tensors="pt",
max_length=self.data_args.max_len,
truncation=True
)
input_ids.append(inputs["input_ids"].squeeze(0).unsqueeze(1))
if has_image:
pixel_values.append(inputs['pixel_values'])
if 'image_sizes' in inputs:
image_sizes.append(inputs['image_sizes'])
continue
if image is None:
if self.model_args.model_backbone == "llava_next":
inputs = self.processor(images=None, text=text, return_tensors="pt")
else:
inputs = self.processor(text, None, return_tensors="pt", max_length=self.data_args.max_len,
truncation=True)
input_ids.append(inputs["input_ids"].squeeze(0).unsqueeze(1))
pixel_values.append(None)
image_sizes.append(None)
else:
image_exist = True
if self.model_args.model_backbone == "llava_next":
inputs = self.processor(images=image, text=text, return_tensors="pt")
else:
inputs = self.processor(text, [image], return_tensors="pt", max_length=self.data_args.max_len, truncation=True)
input_ids.append(inputs["input_ids"].squeeze(0).unsqueeze(1))
pixel_values.append(inputs['pixel_values'])
image_sizes.append(inputs['image_sizes'])
input_ids = torch._C._nn.pad_sequence(
input_ids, batch_first=True, padding_value=self.processor.tokenizer.pad_token_id
).squeeze(2)
attention_mask = input_ids.ne(self.processor.tokenizer.pad_token_id)
if self.model_args.model_backbone == "internvl_2_5":
inputs = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'image_mask': torch.tensor(image_mask, dtype=torch.float)
}
if any(image_mask):
if pixel_values:
inputs['pixel_values'] = torch.cat(pixel_values, dim=0)
if image_sizes:
inputs['image_sizes'] = torch.cat(image_sizes, dim=0)
inputs['image_flags'] = inputs['image_mask'].to(torch.long)
del inputs['image_mask']
else:
if not image_exist:
dummy_pixel_values = torch.zeros(input_ids.shape[0], 1)
dummy_image_sizes = torch.ones(input_ids.shape[0], 1)
inputs = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'pixel_values': dummy_pixel_values,
'image_sizes': dummy_image_sizes,
}
else:
pixel_values_shape = list(set(v.shape for v in pixel_values if v is not None))[0]
pixel_values = [v if v is not None else torch.zeros(pixel_values_shape) for v in pixel_values]
pixel_values = torch.cat(pixel_values, dim=0)
image_sizes_shape = list(set(v.shape for v in image_sizes if v is not None))[0]
image_sizes = [v if v is not None else torch.ones(image_sizes_shape) for v in image_sizes]
image_sizes = torch.cat(image_sizes, dim=0)
inputs = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'pixel_values': pixel_values,
'image_sizes': image_sizes,
}
return inputs
@dataclass
class CLIPCollator:
data_args: DataArguments
vis_processors: AutoProcessor
txt_processors: AutoTokenizer
def __call__(self, examples):
"""
:param examples: qry, qry_image, pos_text, pos_image
"""
inputs = self._get_batch_inputs(examples)
return inputs
def _get_batch_inputs(self, examples):
input_ids, pixel_values, attention_mask = [], [], []
image_exist, text_exist = False, False
for example in examples:
text, image = example
if image is not None:
if image.mode == 'L':
image = image.convert('RGB')
image_inputs = self.vis_processors(images=image, return_tensors="pt")
image_exist = True
pixel_values.append(image_inputs['pixel_values'])
if text:
text_exist = True
text_inputs = self.txt_processors(text, padding=getattr(self.data_args, "padding", True), max_length=self.data_args.max_len, truncation=True, return_tensors="pt")
input_ids.append(text_inputs["input_ids"].squeeze(0))
if text_exist:
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.txt_processors.pad_token_id
)
attention_mask = input_ids.ne(self.txt_processors.pad_token_id)
if image_exist:
pixel_values = torch.cat(pixel_values, dim=0)
if text_exist and image_exist:
assert input_ids.size()[0]==pixel_values.size()[0]
inputs = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'pixel_values': pixel_values,
}
return inputs
@dataclass
class OpenCLIPCollator:
data_args: DataArguments
vis_processors: AutoProcessor
txt_processors: AutoTokenizer
def __call__(self, examples):
"""
:param examples: qry, qry_image, pos_text, pos_image
"""
inputs = self._get_batch_inputs(examples)
return inputs
def _get_batch_inputs(self, examples):
input_ids, pixel_values, attention_mask = [], [], []
image_exist, text_exist = False, False
for example in examples:
text, image = example
if image is not None:
if image.mode == 'L':
image = image.convert('RGB')
image_inputs = self.vis_processors(image).unsqueeze(0)
image_exist = True
pixel_values.append(image_inputs)
if text:
text_exist = True
text_inputs = self.txt_processors(text)
input_ids.append(text_inputs)
if text_exist:
input_ids = torch.cat(input_ids, dim=0)
attention_mask = input_ids.ne(0)
if image_exist:
pixel_values = torch.cat(pixel_values, dim=0)
if text_exist and image_exist:
assert input_ids.size()[0]==pixel_values.size()[0]
inputs = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'pixel_values': pixel_values,
}
return inputs
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