File size: 6,334 Bytes
bb46e5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | from PIL import Image
from scipy import special
import torch
import numpy as np
from math import e
from param import output
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessorMixin
class UMMProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
self.image_token = "<image>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
if getattr(tokenizer, "image_token_id", None):
self.image_token_id = tokenizer.image_token_id
else:
tokenizer.add_tokens(["<image>"], special_tokens=True)
self.image_token_id = -200
self.image_gen_token = "<image_gen>" if not hasattr(tokenizer, "image_gen_token") else tokenizer.image_gen_token
if getattr(tokenizer, "image_gen_token_id", None):
self.image_gen_token_id = tokenizer.image_gen_token_id
else:
tokenizer.add_tokens(["<image_gen>"], special_tokens=True)
self.image_gen_token_id = -300
self.image_gen_start_token = "<im_start>" if not hasattr(tokenizer, "image_gen_start") else tokenizer.image_gen_start
if getattr(tokenizer, "image_gen_start_token_id", None):
self.image_gen_start_token_id = tokenizer.image_gen_start_token_id
else:
tokenizer.add_tokens(["<im_start>"], special_tokens=True)
self.image_gen_start_token_id = tokenizer.convert_tokens_to_ids(self.image_gen_start_token)
self.image_gen_end_token = "<im_end>" if not hasattr(tokenizer, "image_gen_end") else tokenizer.image_gen_end
if getattr(tokenizer, "image_gen_end_token_id", None):
self.image_gen_end_token_id = tokenizer.image_gen_end_token_id
else:
tokenizer.add_tokens(["<im_end>"], special_tokens=True)
self.image_gen_end_token_id = tokenizer.convert_tokens_to_ids(self.image_gen_end_token)
self.no_mean_token = "<no_mean>" if not hasattr(tokenizer, "no_mean") else tokenizer.no_mean
if getattr(tokenizer, "no_mean_id", None):
self.no_mean_token_id = tokenizer.no_mean_id
else:
tokenizer.add_tokens(["<no_mean>"], special_tokens=True)
self.no_mean_token_id = tokenizer.convert_tokens_to_ids(self.no_mean_token)
if chat_template is None and hasattr(tokenizer, "chat_template"):
chat_template = tokenizer.chat_template
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(self, images=None, text=None, max_resolution=None, add_im_start_id=False, **kwargs):
if "padding" not in kwargs:
kwargs["padding"] = True
if "truncation" not in kwargs:
kwargs["truncation"] = True
if not isinstance(text, list):
text = [text]
text = text.copy()
return_tensors = kwargs.pop("return_tensors", None)
text_inputs = self.tokenizer(text, **kwargs, return_tensors=return_tensors)
img_token_id = self.tokenizer.convert_tokens_to_ids(self.image_token)
img_gen_token_id = self.tokenizer.convert_tokens_to_ids(self.image_gen_token)
if add_im_start_id:
B, T = text_inputs["input_ids"].shape
new_input_ids = torch.full((B, T+1), self.tokenizer.pad_token_id)
new_input_ids[:, :T] = text_inputs["input_ids"]
is_valid = (text_inputs["input_ids"] != self.tokenizer.pad_token_id)
valid_len = is_valid.sum(dim=1)
else:
new_input_ids = text_inputs["input_ids"]
t = []
und_gen_mask_list = []
for i, ids in enumerate(text_inputs["input_ids"]):
for j, token_id in enumerate(ids):
if token_id == img_token_id:
new_input_ids[i][j] = self.image_token_id
t.append(torch.tensor([1.0]))
und_gen_mask_list.append(1)
elif token_id == img_gen_token_id:
new_input_ids[i][j] = self.image_gen_token_id
t.append(torch.rand(1))
und_gen_mask_list.append(0)
image_inputs = {}
pixel_values, grid_hws = [], []
if images is not None:
image_idx = 0
for per_images in images if isinstance(images, list) else [images]:
if per_images is None:
dummy_image = Image.fromarray(np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8))
image_info = self.image_processor(images=dummy_image)
else:
for per_image in per_images if isinstance(per_images, list) else[per_images]:
if und_gen_mask_list[image_idx] == 0:
image_info = self.image_processor(images=per_image, max_resolution=max_resolution, und=False)
else:
image_info = self.image_processor(images=per_image, max_resolution=max_resolution)
image_idx += 1
pixel_values.append(image_info.pixel_values)
grid_hws.append(image_info.grid_hws)
pixel_values = torch.concat(pixel_values, dim=0)
grid_hws = torch.concat(grid_hws, dim=0)
image_inputs.update({'pixel_values': pixel_values, 'grid_hws': grid_hws})
if len(t) > 0:
t = torch.cat(t)
image_inputs.update({"t":t})
if add_im_start_id:
for b in range(B):
pos = valid_len[b].item()
new_input_ids[b, pos] = self.image_gen_start_token_id
attention_mask = torch.cat([
text_inputs["attention_mask"],
(new_input_ids[:, -1] != self.tokenizer.pad_token_id).long().unsqueeze(1)
], dim=1)
text_inputs["attention_mask"] = attention_mask
text_inputs["input_ids"] = new_input_ids
return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
__all__ = ["UMMProcessor"] |