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feat: Added judgment logic to support training with plain text data. #281

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feat: Added judgment logic to support training with plain text data. #281

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63 changes: 37 additions & 26 deletions finetune/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,29 +43,40 @@ def __len__(self):
return len(self.raw_data)

def __getitem__(self, i) -> Dict[str, torch.Tensor]:
image = Image.open(self.raw_data[i]["image"]).convert("RGB")
ret = preprocess(
image,
self.raw_data[i]["conversations"],
self.tokenizer,
self.transform,
query_nums=self.query_nums,
slice_config=self.slice_config,
llm_type=self.llm_type,
patch_size=self.patch_size,
batch_vision=self.batch_vision,
)
ret = dict(
input_ids=ret["input_ids"],
position_ids=ret["position_ids"],
labels=ret["target"],
attention_mask=torch.ones_like(ret["input_ids"], dtype=torch.bool),
pixel_values=ret["pixel_values"],
tgt_sizes=ret["tgt_sizes"],
image_bound=ret["image_bound"],
)

return ret
if "image" in self.raw_data[i]:
image = Image.open(self.raw_data[i]["image"]).convert("RGB")
ret = preprocess(
image,
self.raw_data[i]["conversations"],
self.tokenizer,
self.transform,
query_nums=self.query_nums,
slice_config=self.slice_config,
llm_type=self.llm_type,
patch_size=self.patch_size,
batch_vision=self.batch_vision,
)
ret = dict(
input_ids=ret["input_ids"],
position_ids=ret["position_ids"],
labels=ret["target"],
attention_mask=torch.ones_like(ret["input_ids"], dtype=torch.bool),
pixel_values=ret["pixel_values"],
tgt_sizes=ret["tgt_sizes"],
image_bound=ret["image_bound"],
)

return ret
else:
# Processing plain text data
ret = conversation_to_ids(self.raw_data[i]["conversations"], self.tokenizer, self.llm_type)
ret = dict(
input_ids=ret["input_ids"],
position_ids=ret["position_ids"],
labels=ret["target"],
attention_mask=torch.ones_like(ret["input_ids"], dtype=torch.bool),
)
return ret

def data_collator(examples, padding_value=0, max_length=2048):
def trim_and_pad(seq, batch_first, padding_value):
Expand All @@ -91,9 +102,9 @@ def trim_and_pad(seq, batch_first, padding_value):
batch_first=True,
padding_value=padding_value,
)
pixel_values = [example["pixel_values"] for example in examples]
image_bound = [example["image_bound"] for example in examples]
tgt_sizes = [example["tgt_sizes"] for example in examples]
pixel_values = [example["pixel_values"] if "pixel_values" in example else torch.tensor([]) for example in examples]
image_bound = [example["image_bound"] if "image_bound" in example else torch.tensor([]) for example in examples]
tgt_sizes = [example["tgt_sizes"] if "tgt_sizes" in example else torch.tensor([]) for example in examples]
return {
"input_ids": input_ids,
"position_ids": position_ids,
Expand Down