| | from PIL import Image |
| | from io import BytesIO |
| | import base64 |
| |
|
| | import torch |
| | from transformers import StoppingCriteria |
| | from psalm.constants import IMAGE_TOKEN_INDEX, SEG_TOKEN_INDEX |
| |
|
| |
|
| | def load_image_from_base64(image): |
| | return Image.open(BytesIO(base64.b64decode(image))) |
| |
|
| |
|
| | def process_images(images, image_processor, model_cfg): |
| | return image_processor(images, return_tensors='pt')['pixel_values'] |
| |
|
| |
|
| | def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): |
| | prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')] |
| |
|
| | def insert_separator(X, sep): |
| | return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1] |
| |
|
| | input_ids = [] |
| | offset = 0 |
| | if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: |
| | offset = 1 |
| | input_ids.append(prompt_chunks[0][0]) |
| |
|
| | for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): |
| | input_ids.extend(x[offset:]) |
| |
|
| | if return_tensors is not None: |
| | if return_tensors == 'pt': |
| | return torch.tensor(input_ids, dtype=torch.long) |
| | raise ValueError(f'Unsupported tensor type: {return_tensors}') |
| | return input_ids |
| |
|
| |
|
| | def get_model_name_from_path(model_path): |
| | model_path = model_path.strip("/") |
| | model_paths = model_path.split("/") |
| | if model_paths[-1].startswith('checkpoint-'): |
| | return model_paths[-2] + "_" + model_paths[-1] |
| | else: |
| | return model_paths[-1] |
| |
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| |
|
| |
|
| |
|
| | class KeywordsStoppingCriteria(StoppingCriteria): |
| | def __init__(self, keywords, tokenizer, input_ids): |
| | self.keywords = keywords |
| | self.keyword_ids = [] |
| | for keyword in keywords: |
| | cur_keyword_ids = tokenizer(keyword).input_ids |
| | if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: |
| | cur_keyword_ids = cur_keyword_ids[1:] |
| | self.keyword_ids.append(torch.tensor(cur_keyword_ids)) |
| | self.tokenizer = tokenizer |
| | self.start_len = input_ids.shape[1] |
| |
|
| | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
| | assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" |
| | offset = min(output_ids.shape[1] - self.start_len, 3) |
| | self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] |
| | for keyword_id in self.keyword_ids: |
| | if output_ids[0, -keyword_id.shape[0]:] == keyword_id: |
| | return True |
| | outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] |
| | for keyword in self.keywords: |
| | if keyword in outputs: |
| | return True |
| | return False |
| |
|
