mirror of
https://github.com/OpenBMB/MiniCPM-V.git
synced 2026-02-04 09:49:20 +08:00
291 lines
10 KiB
Python
291 lines
10 KiB
Python
import os
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import math
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import json
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import copy
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import logging
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import numpy as np
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import torch
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from torch.nn.utils.rnn import pad_sequence
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from typing import Dict, Optional, List
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from PIL import Image
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from dataclasses import dataclass, field
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from transformers import AutoTokenizer, AutoProcessor
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from torch.utils.data import Dataset
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class SupervisedDataset(Dataset):
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"""Dataset for supervised fine-tuning."""
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def __init__(self, raw_data, transform, tokenizer, slice_config):
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super(SupervisedDataset, self).__init__()
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self.raw_data = raw_data
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self.tokenizer = tokenizer
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self.transform = transform
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self.slice_config = slice_config
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def __len__(self):
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return len(self.raw_data)
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def __getitem__(self, i) -> Dict[str, torch.Tensor]:
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image = Image.open(self.raw_data[i]["image"]).convert("RGB")
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ret = preprocess(image, self.raw_data[i]["conversations"], self.tokenizer, self.transform, slice_config=self.slice_config)
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ret = dict(
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input_ids=ret["input_ids"],
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labels=ret["target"],
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attention_mask=ret["input_ids"].ne(self.tokenizer.pad_token_id),
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pixel_values=ret["pixel_values"],
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image_bound=ret["image_bound"],
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)
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return ret
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def data_collator(examples, padding_value=0):
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input_ids = pad_sequence([example["input_ids"] for example in examples], batch_first=True, padding_value=padding_value)
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targets = pad_sequence([example["labels"] for example in examples], batch_first=True, padding_value=padding_value)
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attention_mask = pad_sequence([example["attention_mask"] for example in examples], batch_first=True, padding_value=padding_value)
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pixel_values = [example["pixel_values"] for example in examples]
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image_bound = [example["image_bound"] for example in examples]
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return {"input_ids": input_ids, "labels":targets, "attention_mask": attention_mask, "image_bound": image_bound, "pixel_values": pixel_values}
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def conversation_to_ids(conversation, tokenizer):
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"""
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for single image multi-turn conversation
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conversation: [{'role': 'user', 'content': 'Describe this image'},
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{'role': 'assistant', 'content': 'This is a cat.'}]
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"""
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raw_msg = ''
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input_ids = []
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context = []
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for idx, msg in enumerate(conversation):
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role = msg['role']
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message = msg['content']
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assert role in ['user', 'assistant']
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if role == 'user':
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prefix = '<用户>'
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else:
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prefix = '<AI>'
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# append eos
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if idx == len(conversation) - 1:
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message = message + tokenizer.eos_token
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prefix_ids = tokenizer.encode(prefix)[1:] # remove bos
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message_ids = tokenizer.encode(message)[1:]
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input_ids.append(prefix_ids)
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input_ids.append(message_ids)
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context.append(np.ones((len(prefix_ids),), dtype=np.int8))
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if role == 'assistant':
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context.append(np.zeros((len(message_ids),), dtype=np.int8))
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else:
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context.append(np.ones((len(message_ids),), dtype=np.int8))
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raw_msg += (prefix + message)
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ids = torch.from_numpy(np.hstack(input_ids, dtype=np.int32))
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context = torch.from_numpy(np.hstack(context, dtype=np.int8))
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# build target
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target = torch.full_like(ids, -100, dtype=torch.int32)
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for i in range(1, len(ids)):
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if context[i] == 0:
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target[i - 1] = ids[i]
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if context[i] == 1 and context[i - 1] == 0:
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target[i - 1] = tokenizer.eos_id
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# build image bound
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image_start_tokens = torch.where(ids == tokenizer.im_start_id)[0]
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image_start_tokens += 1
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image_end_tokens = torch.where(ids == tokenizer.im_end_id)[0]
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if len(image_start_tokens) != len(image_end_tokens):
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print('image start token != image end tokens')
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if len(image_start_tokens)>0:
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image_bound = torch.hstack([image_start_tokens.unsqueeze(-1), image_end_tokens.unsqueeze(-1)])
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else:
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image_bound = []
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return {
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'input_ids': ids,
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'target': target,
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'image_bound': image_bound,
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'raw_msg': raw_msg,
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}
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def preprocess(image, conversation, tokenizer, transform, query_nums=64, slice_config=None):
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"""
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single image preprocess, the image will be placed at the top of the conversation
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"""
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conversation = copy.deepcopy(conversation)
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assert len(conversation) > 1, "conversation length must large than 2"
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assert conversation[0]['role'] == 'user', "the first role must be user"
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if slice_config is not None:
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assert isinstance(slice_config, Dict)
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assert 'patch_size' in slice_config
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assert 'max_slice_nums' in slice_config
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assert 'scale_resolution' in slice_config
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default_image_placeholder = tokenizer.im_start + tokenizer.unk_token * query_nums + tokenizer.im_end
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if slice_config:
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images = []
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source_image, patches, best_grid = slice_image(
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image, slice_config['max_slice_nums'], slice_config['scale_resolution'], slice_config['patch_size']
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)
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images.append(source_image)
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image_placeholder = default_image_placeholder
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if len(patches) > 0:
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for i in range(len(patches)):
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for j in range(len(patches[0])):
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images.append(patches[i][j])
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image_placeholder += get_grid_placeholder(
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tokenizer, best_grid, query_nums
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)
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images = [transform(i) for i in images]
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else:
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images = [transform(image)]
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image_placeholder = default_image_placeholder
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if '<image>' in conversation[0]['content']:
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conversation[0]['content'] = conversation[0]['content'].replace('<image>', image_placeholder)
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else:
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conversation[0]['content'] = image_placeholder + '\n' + conversation[0]['content']
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input_dict = conversation_to_ids(conversation, tokenizer)
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input_dict['pixel_values'] = images
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return input_dict
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def slice_image(
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image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False
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):
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original_size = image.size
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original_width, original_height = original_size
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log_ratio = math.log(original_width / original_height)
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ratio = original_width * original_height / (scale_resolution * scale_resolution)
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multiple = min(math.ceil(ratio), max_slice_nums)
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source_image = None
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best_grid = None
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patches = []
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if multiple <= 1 or never_split:
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# dont need to slice, upsample
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best_size = find_best_resize(
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original_size, scale_resolution, patch_size, allow_upscale=True
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)
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source_image = image.resize(best_size, Image.Resampling.BICUBIC)
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else:
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candidate_split_grids_nums = []
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for i in [multiple - 1, multiple, multiple + 1]:
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if i == 1 or i > max_slice_nums:
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continue
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candidate_split_grids_nums.append(i)
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# source image, down-sampling and ensure divided by patch_size
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best_resize = find_best_resize(original_size, scale_resolution, patch_size)
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source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
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candidate_grids = []
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# find best grid
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for split_grids_nums in candidate_split_grids_nums:
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m = 1
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while m <= split_grids_nums:
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if split_grids_nums % m == 0:
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candidate_grids.append([m, split_grids_nums // m])
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m += 1
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best_grid = [1, 1]
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min_error = float("inf")
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for grid in candidate_grids:
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error = abs(log_ratio - math.log(grid[0] / grid[1]))
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if error < min_error:
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best_grid = grid
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min_error = error
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refine_size = get_refine_size(
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original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
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)
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refine_image = image.resize(refine_size, Image.Resampling.BICUBIC)
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patches = split_to_patches(refine_image, best_grid)
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return source_image, patches, best_grid
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def ensure_divide(length, patch_size):
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return max(round(length / patch_size) * patch_size, patch_size)
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def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False):
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width, height = original_size
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if (width * height > scale_resolution * scale_resolution) or allow_upscale:
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r = width / height
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height = int(scale_resolution / math.sqrt(r))
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width = int(height * r)
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best_width = ensure_divide(width, patch_size)
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best_height = ensure_divide(height, patch_size)
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return (best_width, best_height)
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def get_refine_size(
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original_size, grid, scale_resolution, patch_size, allow_upscale=False
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):
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width, height = original_size
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grid_x, grid_y = grid
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refine_width = ensure_divide(width, grid_x)
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refine_height = ensure_divide(height, grid_y)
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grid_width = refine_width / grid_x
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grid_height = refine_height / grid_y
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best_grid_size = find_best_resize(
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(grid_width, grid_height),
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scale_resolution,
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patch_size,
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allow_upscale=allow_upscale,
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)
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refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)
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return refine_size
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def split_to_patches(image, grid):
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patches = []
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width, height = image.size
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grid_x = int(width / grid[0])
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grid_y = int(height / grid[1])
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for i in range(0, height, grid_y):
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images = []
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for j in range(0, width, grid_x):
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box = (j, i, j + grid_x, i + grid_y)
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patch = image.crop(box)
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images.append(patch)
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patches.append(images)
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return patches
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def get_grid_placeholder(tokenizer, grid, query_num):
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image_placeholder = (
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tokenizer.im_start + tokenizer.unk_token * query_num + tokenizer.im_end
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)
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cols = grid[0]
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rows = grid[1]
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slices = []
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for i in range(rows):
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lines = []
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for j in range(cols):
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lines.append(image_placeholder)
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slices.append("".join(lines))
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slice_placeholder = tokenizer.slice_start + "\n".join(slices) + tokenizer.slice_end
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return slice_placeholder
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