File size: 7,017 Bytes
a197764 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
import re
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(is_train, input_size, pad2square=False, normalize_type='imagenet'):
if normalize_type == 'imagenet':
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
else:
raise NotImplementedError
if is_train: # use data augumentation
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.RandomResizedCrop(input_size, scale=(0.8, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
else:
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
# print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images, target_aspect_ratio
def image_process(image_path, config):
image = Image.open(image_path).convert('RGB')
transform = build_transform(is_train=False, input_size=config.vision_config.image_size,
pad2square=config.pad2square, normalize_type='imagenet')
if config.dynamic_image_size:
images, target_aspect_ratio = dynamic_preprocess(image, min_num=config.min_dynamic_patch, max_num=config.max_dynamic_patch,
image_size=config.vision_config.image_size, use_thumbnail=config.use_thumbnail)
else:
images = [image]
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values.to(torch.bfloat16).cuda(), torch.tensor([[target_aspect_ratio[0], target_aspect_ratio[1]]], dtype=torch.long)
def parse_block_pos(str_, target_aspect_ratio):
block_num_w, block_num_h = target_aspect_ratio[0][0], target_aspect_ratio[0][1]
action, location, direction, location_or_text = None, None, None, None
str_ = str_.strip()
match = re.match(r'^(.*?)\((.*?)\)$', str_)
if match:
action, location_or_text = match.groups()
if action == 'CLICK':
match = re.match(r'^\[(\d{1}), (\d{3}), (\d{3})\].*?$', location_or_text)
if match:
block_idx, cx, cy = match.groups()
block_idx = int(block_idx)
cx = int(cx)
cy = int(cy)
cx += (block_idx % block_num_w) * 1000
cy += (block_idx // block_num_w) * 1000
cx /= block_num_w * 1000
cy /= block_num_h * 1000
location = [cx, cy]
else:
print(location_or_text)
elif action.startswith('SWIPE_'):
action, direction = action.split('_', 1)
return {
'action': action,
'location': location,
'direction': direction,
'content': location_or_text
}
question_template = '''## Task: {task}
## History Actions:
{history}
## Action Space
1. CLICK([block_index, cx, cy], "text")
2. TYPE("text")
3. PRESS_BACK()
4. PRESS_HOME()
5. PRESS_ENTER()
6. SWIPE_UP()
7. SWIPE_DOWN()
8. SWIPE_LEFT()
9. SWIPE_RIGHT()
10. COMPLETED()
## Requirements: Please infer the next action according to the Task and History Actions. Think step by step. Return with Image Description, Next Action Description and Action Code. The Action Code should follow the definition in the Action Space.'''
path = './SpiritSight-Agent-2B-base'
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
# use_flash_attn=False,
trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
task = "Go to search bar in Google Chrome then search for walmart."
history = ""
question = question_template.format(task=task, history=history)
image_path = './image.png'
pixel_values, target_aspect_ratio = image_process(image_path, model.config)
generation_config = dict(max_new_tokens=1024, do_sample=True)
response = model.chat(
tokenizer=tokenizer,
pixel_values=pixel_values,
question=question,
target_aspect_ratio=target_aspect_ratio,
generation_config=generation_config
)
print(response)
action_code_str = response.split()[-1]
action_code = parse_block_pos(action_code_str, target_aspect_ratio.cpu().numpy())
print(action_code)
|