Spaces:
Runtime error
Runtime error
File size: 10,865 Bytes
e4bd7f9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 |
'''
* The Recognize Anything Model (RAM)
* Written by Xinyu Huang
'''
import json
import warnings
import numpy as np
import torch
from torch import nn
from .bert import BertConfig, BertLMHeadModel, BertModel
from .swin_transformer import SwinTransformer
from .utils import *
warnings.filterwarnings("ignore")
class RAM(nn.Module):
def __init__(self,
med_config=f'{CONFIG_PATH}/configs/med_config.json',
image_size=384,
vit='base',
vit_grad_ckpt=False,
vit_ckpt_layer=0,
prompt='a picture of ',
threshold=0.68,
delete_tag_index=[],
tag_list=f'{CONFIG_PATH}/data/ram_tag_list.txt',
tag_list_chinese=f'{CONFIG_PATH}/data/ram_tag_list_chinese.txt'):
r""" The Recognize Anything Model (RAM) inference module.
RAM is a strong image tagging model, which can recognize any common category with high accuracy.
Described in the paper " Recognize Anything: A Strong Image Tagging Model" https://recognize-anything.github.io/
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
threshold (int): tagging threshold
delete_tag_index (list): delete some tags that may disturb captioning
"""
super().__init__()
# create image encoder
if vit == 'swin_b':
if image_size == 224:
vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'
elif image_size == 384:
vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'
vision_config = read_json(vision_config_path)
assert image_size == vision_config['image_res']
# assert config['patch_size'] == 32
vision_width = vision_config['vision_width']
self.visual_encoder = SwinTransformer(
img_size=vision_config['image_res'],
patch_size=4,
in_chans=3,
embed_dim=vision_config['embed_dim'],
depths=vision_config['depths'],
num_heads=vision_config['num_heads'],
window_size=vision_config['window_size'],
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.0,
drop_path_rate=0.1,
ape=False,
patch_norm=True,
use_checkpoint=False)
elif vit == 'swin_l':
if image_size == 224:
vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json'
elif image_size == 384:
vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json'
vision_config = read_json(vision_config_path)
assert image_size == vision_config['image_res']
# assert config['patch_size'] == 32
vision_width = vision_config['vision_width']
self.visual_encoder = SwinTransformer(
img_size=vision_config['image_res'],
patch_size=4,
in_chans=3,
embed_dim=vision_config['embed_dim'],
depths=vision_config['depths'],
num_heads=vision_config['num_heads'],
window_size=vision_config['window_size'],
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.0,
drop_path_rate=0.1,
ape=False,
patch_norm=True,
use_checkpoint=False)
else:
self.visual_encoder, vision_width = create_vit(
vit, image_size, vit_grad_ckpt, vit_ckpt_layer)
# create tokenzier
self.tokenizer = init_tokenizer()
# Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder
# create image-tag interaction encoder
encoder_config = BertConfig.from_json_file(med_config)
encoder_config.encoder_width = 512
self.tag_encoder = BertModel(config=encoder_config,
add_pooling_layer=False)
# create image-tag-text decoder
decoder_config = BertConfig.from_json_file(med_config)
self.text_decoder = BertLMHeadModel(config=decoder_config)
self.delete_tag_index = delete_tag_index
self.prompt = prompt
self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1
# load tag list
self.tag_list = self.load_tag_list(tag_list)
self.tag_list_chinese = self.load_tag_list(tag_list_chinese)
# create image-tag recognition decoder
self.threshold = threshold
self.num_class = len(self.tag_list)
q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json')
q2l_config.encoder_width = 512
self.tagging_head = BertModel(config=q2l_config,
add_pooling_layer=False)
self.tagging_head.resize_token_embeddings(len(self.tokenizer))
# self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
self.label_embed = nn.Parameter(torch.zeros(self.num_class, q2l_config.encoder_width))
if q2l_config.hidden_size != 512:
self.wordvec_proj = nn.Linear(512, q2l_config.hidden_size)
else:
self.wordvec_proj = nn.Identity()
self.fc = nn.Linear(q2l_config.hidden_size, 1)
self.del_selfattention()
# share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder"
tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '',
' ')
self.image_proj = nn.Linear(vision_width, 512)
# self.label_embed = nn.Parameter(torch.load(f'{CONFIG_PATH}/data/textual_label_embedding.pth',map_location='cpu').float())
# adjust thresholds for some tags
self.class_threshold = torch.ones(self.num_class) * self.threshold
ram_class_threshold_path = f'{CONFIG_PATH}/data/ram_tag_list_threshold.txt'
with open(ram_class_threshold_path, 'r', encoding='utf-8') as f:
ram_class_threshold = [float(s.strip()) for s in f]
for key,value in enumerate(ram_class_threshold):
self.class_threshold[key] = value
def load_tag_list(self, tag_list_file):
with open(tag_list_file, 'r', encoding="utf-8") as f:
tag_list = f.read().splitlines()
tag_list = np.array(tag_list)
return tag_list
# delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label
def del_selfattention(self):
del self.tagging_head.embeddings
for layer in self.tagging_head.encoder.layer:
del layer.attention
def generate_tag(self,
image,
threshold=0.68,
tag_input=None,
):
label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))
image_embeds = self.image_proj(self.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1],
dtype=torch.long).to(image.device)
# recognized image tags using image-tag recogntiion decoder
image_cls_embeds = image_embeds[:, 0, :]
image_spatial_embeds = image_embeds[:, 1:, :]
bs = image_spatial_embeds.shape[0]
label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1)
tagging_embed = self.tagging_head(
encoder_embeds=label_embed,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False,
mode='tagging',
)
logits = self.fc(tagging_embed[0]).squeeze(-1)
targets = torch.where(
torch.sigmoid(logits) > self.class_threshold.to(image.device),
torch.tensor(1.0).to(image.device),
torch.zeros(self.num_class).to(image.device))
tag = targets.cpu().numpy()
tag[:,self.delete_tag_index] = 0
tag_output = []
tag_output_chinese = []
for b in range(bs):
index = np.argwhere(tag[b] == 1)
token = self.tag_list[index].squeeze(axis=1)
tag_output.append(' | '.join(token))
token_chinese = self.tag_list_chinese[index].squeeze(axis=1)
tag_output_chinese.append(' | '.join(token_chinese))
return tag_output, tag_output_chinese
def generate_tag_openset(self,
image,
threshold=0.68,
tag_input=None,
):
label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))
image_embeds = self.image_proj(self.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1],
dtype=torch.long).to(image.device)
# recognized image tags using image-tag recogntiion decoder
image_cls_embeds = image_embeds[:, 0, :]
image_spatial_embeds = image_embeds[:, 1:, :]
bs = image_spatial_embeds.shape[0]
label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1)
tagging_embed = self.tagging_head(
encoder_embeds=label_embed,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False,
mode='tagging',
)
logits = self.fc(tagging_embed[0]).squeeze(-1)
targets = torch.where(
torch.sigmoid(logits) > self.class_threshold.to(image.device),
torch.tensor(1.0).to(image.device),
torch.zeros(self.num_class).to(image.device))
tag = targets.cpu().numpy()
tag[:,self.delete_tag_index] = 0
tag_output = []
for b in range(bs):
index = np.argwhere(tag[b] == 1)
token = self.tag_list[index].squeeze(axis=1)
tag_output.append(' | '.join(token))
return tag_output
# load RAM pretrained model parameters
def ram(pretrained='', **kwargs):
model = RAM(**kwargs)
if pretrained:
if kwargs['vit'] == 'swin_b':
model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)
elif kwargs['vit'] == 'swin_l':
model, msg = load_checkpoint_swinlarge(model, pretrained, kwargs)
else:
model, msg = load_checkpoint(model, pretrained)
print('vit:', kwargs['vit'])
# print('msg', msg)
return model
|