''' * 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