Deepseek-OCR 환경 설정
This commit is contained in:
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from transformers import AutoModel, AutoTokenizer
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import torch
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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model_name = 'deepseek-ai/DeepSeek-OCR'
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)
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model = model.eval().cuda().to(torch.bfloat16)
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# prompt = "<image>\nFree OCR. "
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prompt = "<image>\n<|grounding|>Convert the document to markdown. "
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image_file = '/workspace/2025-27484-M21472.pdf'
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output_path = '/workspace/output_hf'
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# infer(self, tokenizer, prompt='', image_file='', output_path = ' ', base_size = 1024, image_size = 640, crop_mode = True, test_compress = False, save_results = False):
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# Tiny: base_size = 512, image_size = 512, crop_mode = False
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# Small: base_size = 640, image_size = 640, crop_mode = False
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# Base: base_size = 1024, image_size = 1024, crop_mode = False
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# Large: base_size = 1280, image_size = 1280, crop_mode = False
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# Gundam: base_size = 1024, image_size = 640, crop_mode = True
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res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 640, crop_mode=True, save_results = True, test_compress = True)
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@@ -1,42 +0,0 @@
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# TODO: change modes
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# Tiny: base_size = 512, image_size = 512, crop_mode = False
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# Small: base_size = 640, image_size = 640, crop_mode = False
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# Base: base_size = 1024, image_size = 1024, crop_mode = False
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# Large: base_size = 1280, image_size = 1280, crop_mode = False
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# Gundam: base_size = 1024, image_size = 640, crop_mode = True
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BASE_SIZE = 1024
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IMAGE_SIZE = 640
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CROP_MODE = True
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MIN_CROPS= 2
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MAX_CROPS= 6 # max:9; If your GPU memory is small, it is recommended to set it to 6.
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MAX_CONCURRENCY = 100 # If you have limited GPU memory, lower the concurrency count.
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NUM_WORKERS = 64 # image pre-process (resize/padding) workers
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PRINT_NUM_VIS_TOKENS = False
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SKIP_REPEAT = True
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MODEL_PATH = 'deepseek-ai/DeepSeek-OCR' # change to your model path
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# TODO: change INPUT_PATH
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# .pdf: run_dpsk_ocr_pdf.py;
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# .jpg, .png, .jpeg: run_dpsk_ocr_image.py;
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# Omnidocbench images path: run_dpsk_ocr_eval_batch.py
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INPUT_PATH = f'/workspace/2018-0802140959-217049.pdf'
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OUTPUT_PATH = '/workspace/output/'
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PROMPT = '<image>\n<|grounding|>Convert the document to markdown.'
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# PROMPT = '<image>\nFree OCR.'
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# TODO commonly used prompts
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# document: <image>\n<|grounding|>Convert the document to markdown.
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# other image: <image>\n<|grounding|>OCR this image.
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# without layouts: <image>\nFree OCR.
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# figures in document: <image>\nParse the figure.
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# general: <image>\nDescribe this image in detail.
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# rec: <image>\nLocate <|ref|>xxxx<|/ref|> in the image.
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# '先天下之忧而忧'
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# .......
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from transformers import AutoTokenizer
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TOKENIZER = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
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@@ -1,174 +0,0 @@
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import torch.nn as nn
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import torch
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import torch.nn.functional as F
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import copy
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class MlpProjector(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.cfg = cfg
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if cfg.projector_type == "identity":
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modules = nn.Identity()
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elif cfg.projector_type == "linear":
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modules = nn.Linear(cfg.input_dim, cfg.n_embed)
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elif cfg.projector_type == "mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "normlayer_downsample_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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mlp_ratio = cfg.get("mlp_ratio", 1)
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modules = [
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nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio),
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nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
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]
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for _ in range(1, mlp_depth - 1):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "downsample_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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mlp_ratio = cfg.get("mlp_ratio", 1)
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modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)]
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for _ in range(1, mlp_depth - 1):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
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self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
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modules = []
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "hybrid_split_feature_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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channel_div = cfg.get("channel_div", 0.5)
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self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div))
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self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div))
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modules = []
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "low_high_split_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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modules = []
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2))
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modules = nn.Sequential(*modules)
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self.high_layers = nn.Sequential(*modules)
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self.low_layers = copy.deepcopy(modules)
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else:
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raise ValueError(f"Unknown projector type: {cfg.projector_type}")
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if cfg.get("token_pooling", False):
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self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)
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if cfg.get("conv_fusion_high_low_features", False):
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self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim)
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self.layers = modules
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def forward(self, x):
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if self.cfg.get("token_pooling", False):
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batch_size, wxh, channels = x.shape
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w = h = int(wxh**0.5)
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x = x.view(batch_size, w, h, channels)
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x = x.permute(0, 3, 1, 2)
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# import ipdb; ipdb.set_trace()
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patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
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batch_size, channels, h_patches, w_patches, _, _ = patches.size()
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# 在通道维度上拼接
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patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1)
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# 通过线性层
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patches = patches.permute(0, 2, 1, 3).contiguous()
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patches = patches.view(batch_size, h_patches * w_patches, channels * 4)
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x = self.token_pooling_layer(patches)
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if self.cfg.get("conv_fusion_high_low_features", False):
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x = self.fusion_layer(x[:, 0]) + x[:, 1]
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if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu':
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high_x, low_x = x[0], x[1]
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high_x = self.high_up_proj(high_x)
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low_x = self.low_up_proj(low_x)
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x = torch.concat([high_x, low_x], dim=-1)
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if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu':
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high_x = x[...,:self.cfg.input_dim[0]]
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low_x = x[...,self.cfg.input_dim[0]:]
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high_x = self.high_up_proj(high_x)
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low_x = self.low_up_proj(low_x)
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x = torch.concat([high_x, low_x], dim=-1)
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if self.cfg.projector_type == 'low_high_split_mlp_gelu':
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high_x, low_x = x[0], x[1]
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high_x = self.high_layers(high_x)
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low_x = self.low_layers(low_x)
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x = torch.concat([high_x, low_x], dim=-1)
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return x
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if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu':
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bs, hw, input_dim = x.shape
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h = w = int((hw) ** 0.5)
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"""compute padding"""
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if h % self.cfg.downsample_ratio:
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pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
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else:
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pad = 0
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x = x.reshape(bs, h, w, input_dim)
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if pad > 0:
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x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
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"""4 to 1 concat"""
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x = x.permute(0, 3, 1, 2) # B, C, H, W
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x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4
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x = x.permute(0, 2, 1)
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return self.layers(x)
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@staticmethod
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def get_flops_per_sample(cfg):
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if cfg.projector_type == "linear":
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fwd = 2 * cfg.input_dim * cfg.n_embed
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elif "mlp_gelu" in cfg.projector_type :
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mlp_depth = cfg.get("depth", 1)
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downsample_ratio = cfg.get("downsample_ratio", 1)
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input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim
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input_dim = input_dim * downsample_ratio * downsample_ratio
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fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed
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else:
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fwd = 0
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return fwd * 3
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@@ -1,504 +0,0 @@
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from contextlib import nullcontext
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import math
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from typing import Optional, Tuple
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# from megatron.model import LayerNorm
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from easydict import EasyDict as adict
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import torch
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from torch.nn import functional as F
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from torch import nn
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from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
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# from optimus import flash_attn_func
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# from megatron.core import tensor_parallel
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# from megatron.core import parallel_state as mpu
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# from megatron.core.utils import make_viewless_tensor, divide
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# from megatron.model.fused_rms_norm import RMSNorm
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# from megatron.model.transformer import (
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# FlashSelfAttention,
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# NoopTransformerLayer,
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# _cfg_to_kwargs,
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# )
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# from megatron.model.enums import AttnMaskType, AttnType
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# from megatron.model.fused_softmax import FusedScaleMaskSoftmax
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# from megatron.model.utils import attention_mask_func
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# from megatron.model.module import MegatronModule
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# try:
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# from einops import rearrange
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# except ImportError:
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# rearrange = None
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# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
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# try:
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# # flash attention 2.x
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# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
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# except ImportError:
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# try:
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# # flash attention 1.x
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# from flash_attn.flash_attn_interface import flash_attn_unpadded_func
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# except ImportError:
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# flash_attn_unpadded_func = None
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# try:
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# from flash_attn.flash_attn_interface import flash_attn_unpadded_relative_attention_bias_func
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# except ImportError:
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# flash_attn_unpadded_relative_attention_bias_func = None
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# try:
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# from flash_attn.flash_attn_interface import mask_flash_attn_unpadded_func
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# except ImportError:
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# mask_flash_attn_unpadded_func = None
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class LayerNormfp32(torch.nn.LayerNorm):
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"""Subclass torch's LayerNorm to handle fp16."""
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def forward(self, x: torch.Tensor):
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orig_type = x.dtype
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ret = super().forward(x.type(torch.float32))
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return ret.type(orig_type)
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def get_abs_pos(abs_pos, tgt_size):
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# abs_pos: L, C
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# tgt_size: M
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# return: M, C
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# print(tgt_size)
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# print(abs_pos.shape)
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# exit()
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dim = abs_pos.size(-1)
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# print(dim)
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abs_pos_new = abs_pos.squeeze(0)
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cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:]
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src_size = int(math.sqrt(abs_pos_new.shape[0] - 1))
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tgt_size = int(math.sqrt(tgt_size))
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dtype = abs_pos.dtype
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if src_size != tgt_size:
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old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1,
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2).contiguous()
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old_pos_embed = old_pos_embed.to(torch.float32)
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new_pos_embed = F.interpolate(
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old_pos_embed,
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size=(tgt_size, tgt_size),
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mode='bicubic',
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antialias=True,
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align_corners=False,
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).to(dtype)
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new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
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new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim)
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vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0)
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vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim)
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return vision_pos_embed
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else:
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return abs_pos
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@torch.jit.script
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def quick_gelu(x):
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return x * torch.sigmoid(1.702 * x)
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class CLIPVisionEmbeddings(nn.Module):
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def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3):
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super().__init__()
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self.embed_dim = hidden_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim))
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self.patch_embedding = torch.nn.Conv2d(
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in_channels=num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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bias=False,
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches + 1
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self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer(
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"position_ids", torch.arange(self.num_positions).expand((1, -1))
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)
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def forward(self, pixel_values, patch_embeds):
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batch_size = pixel_values.shape[0]
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# patch_embeds = self.patch_embedding(
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# pixel_values
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# ) # shape = [*, width, grid, grid]
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if patch_embeds is not None:
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patch_embeds = patch_embeds
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# print(patch_embeds.shape)
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else:
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patch_embeds = self.patch_embedding(pixel_values)
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# print(111111)
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# shape = [*, width, grid, grid]
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# patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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# x = torch.cat([cls_token, x], dim=1)
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embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1))
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# embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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|
||||
class NoTPFeedForward(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
cfg,
|
||||
dim: int,
|
||||
hidden_dim: int,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True)
|
||||
self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True)
|
||||
|
||||
def forward(self, x):
|
||||
output = self.fc2(quick_gelu(self.fc1(x)))
|
||||
return output
|
||||
|
||||
|
||||
# from optimus.flash_attn_interface import flash_attn_qkvpacked_func
|
||||
|
||||
|
||||
# class NoTPAttention(nn.Module):
|
||||
# def __init__(self, cfg):
|
||||
# super().__init__()
|
||||
# self.num_heads = cfg.num_attention_heads
|
||||
# self.n_local_heads = cfg.num_attention_heads
|
||||
# self.head_dim = cfg.hidden_size // cfg.num_attention_heads
|
||||
# self.max_seq_len = cfg.seq_length
|
||||
# self.use_flash_attention = cfg.use_flash_attn
|
||||
|
||||
# self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True)
|
||||
# self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True)
|
||||
|
||||
# # self.core_attention = CoreAttention(cfg, AttnType.self_attn)
|
||||
|
||||
# self.attn_drop = cfg.attention_dropout
|
||||
|
||||
# def forward(
|
||||
# self,
|
||||
# x: torch.Tensor,
|
||||
# ):
|
||||
# bsz, seqlen, _ = x.shape
|
||||
# xqkv = self.qkv_proj(x)
|
||||
# xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim)
|
||||
|
||||
# if self.use_flash_attention:
|
||||
# output = flash_attn_qkvpacked_func(xqkv)
|
||||
# output = output.view(bsz, seqlen, -1)
|
||||
# else:
|
||||
# xq, xk, xv = torch.split(xqkv, 1, dim=2)
|
||||
# xq = xq.squeeze(2)
|
||||
# xk = xk.squeeze(2)
|
||||
# xv = xv.squeeze(2)
|
||||
# # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
|
||||
|
||||
# # （B, num_head, S, head_size)
|
||||
# xq = xq.permute(0, 2, 1, 3)
|
||||
# xk = xk.permute(0, 2, 1, 3)
|
||||
# xv = xv.permute(0, 2, 1, 3)
|
||||
|
||||
# output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
|
||||
# utput = output.permute(0, 2, 1, 3).view(bsz, seqlen, -1)
|
||||
# output = self.out_proj(output)
|
||||
# return output
|
||||
|
||||
|
||||
# from optimus.flash_attn_interface import flash_attn_qkvpacked_func
|
||||
|
||||
|
||||
class NoTPAttention(torch.nn.Module):
|
||||
def __init__(self, cfg):
|
||||
super().__init__()
|
||||
self.num_heads = cfg.num_attention_heads
|
||||
self.n_local_heads = cfg.num_attention_heads
|
||||
self.head_dim = cfg.hidden_size // cfg.num_attention_heads
|
||||
self.max_seq_len = cfg.seq_length
|
||||
self.use_flash_attention = cfg.use_flash_attn
|
||||
|
||||
self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True)
|
||||
self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True)
|
||||
|
||||
# self.core_attention = CoreAttention(cfg, AttnType.self_attn)
|
||||
|
||||
self.attn_drop = cfg.attention_dropout
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
):
|
||||
bsz, seqlen, _ = x.shape
|
||||
xqkv = self.qkv_proj(x)
|
||||
xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim)
|
||||
|
||||
if self.use_flash_attention:
|
||||
output = flash_attn_qkvpacked_func(xqkv)
|
||||
output = output.view(bsz, seqlen, -1)
|
||||
# xq, xk, xv = torch.split(xqkv, 1, dim=2)
|
||||
# xq = xq.squeeze(2)
|
||||
# xk = xk.squeeze(2)
|
||||
# xv = xv.squeeze(2)
|
||||
# # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
|
||||
|
||||
# # （B, num_head, S, head_size)
|
||||
# xq = xq.permute(0, 2, 1, 3)
|
||||
# xk = xk.permute(0, 2, 1, 3)
|
||||
# xv = xv.permute(0, 2, 1, 3)
|
||||
# # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
|
||||
# output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
|
||||
# output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
|
||||
# output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
|
||||
else:
|
||||
# output = flash_attn_qkvpacked_func(xqkv)
|
||||
xq, xk, xv = torch.split(xqkv, 1, dim=2)
|
||||
xq = xq.squeeze(2)
|
||||
xk = xk.squeeze(2)
|
||||
xv = xv.squeeze(2)
|
||||
# xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
|
||||
|
||||
# （B, num_head, S, head_size)
|
||||
xq = xq.permute(0, 2, 1, 3)
|
||||
xk = xk.permute(0, 2, 1, 3)
|
||||
xv = xv.permute(0, 2, 1, 3)
|
||||
# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
|
||||
output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
|
||||
output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
|
||||
output = self.out_proj(output)
|
||||
return output
|
||||
|
||||
class NoTPTransformerBlock(nn.Module):
|
||||
def __init__(self, cfg, layer_id: int, multiple_of=256):
|
||||
super().__init__()
|
||||
|
||||
self.n_heads = cfg.num_attention_heads
|
||||
self.dim = cfg.hidden_size
|
||||
self.head_dim = cfg.hidden_size // cfg.num_attention_heads
|
||||
self.self_attn = NoTPAttention(cfg)
|
||||
self.mlp = NoTPFeedForward(
|
||||
cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size
|
||||
)
|
||||
self.layer_id = layer_id
|
||||
self.layer_norm1 = torch.nn.LayerNorm(
|
||||
cfg.hidden_size, eps=cfg.layernorm_epsilon
|
||||
)
|
||||
self.layer_norm2 = torch.nn.LayerNorm(
|
||||
cfg.hidden_size, eps=cfg.layernorm_epsilon
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
residual = self.self_attn.forward(self.layer_norm1(x))
|
||||
h = x + residual
|
||||
out = h + self.mlp.forward(self.layer_norm2(h))
|
||||
return out
|
||||
|
||||
|
||||
class NoTPTransformer(nn.Module):
|
||||
def __init__(self, cfg):
|
||||
super().__init__()
|
||||
|
||||
self.cfg = cfg
|
||||
# self.recompute_list = self.cfg.get("recompute_list", [])
|
||||
self.num_layers = cfg.num_layers # _get_num_layers(cfg)
|
||||
|
||||
self.layers = torch.nn.ModuleList()
|
||||
for layer_id in range(self.num_layers):
|
||||
self.layers.append(
|
||||
NoTPTransformerBlock(
|
||||
cfg,
|
||||
layer_id + 1,
|
||||
)
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
):
|
||||
|
||||
for lid, layer in enumerate(self.layers):
|
||||
# if lid in self.recompute_list:
|
||||
# def custom(layer_id):
|
||||
# def custom_forward(*args, **kwargs):
|
||||
# x_ = self.layers[layer_id](*args, **kwargs)
|
||||
# return x_
|
||||
|
||||
# return custom_forward
|
||||
|
||||
# assert hidden_states.requires_grad == True, logger.warning(
|
||||
# "When using recalculation, the input must have grad fn"
|
||||
# )
|
||||
# hidden_states = tensor_parallel.checkpoint(
|
||||
# custom(lid),
|
||||
# False,
|
||||
# hidden_states.contiguous()
|
||||
# )
|
||||
# else:
|
||||
hidden_states = layer(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
# from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter
|
||||
|
||||
class VitModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
cfg,
|
||||
freeze_embed=False,
|
||||
freeze_pre_norm=False
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.embeddings = CLIPVisionEmbeddings(hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size)
|
||||
|
||||
if freeze_embed:
|
||||
for name, param in self.embeddings.named_parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
self.transformer = NoTPTransformer(cfg=cfg)
|
||||
|
||||
if cfg.get("fp32norm", False):
|
||||
logger.info("Load fp32 layernorm for ViT.")
|
||||
self.pre_layrnorm = LayerNormfp32(
|
||||
cfg.hidden_size,
|
||||
eps=cfg.get("pre_layernorm_epsilon", 1e-5),
|
||||
)
|
||||
else:
|
||||
self.pre_layrnorm = torch.nn.LayerNorm(
|
||||
cfg.hidden_size,
|
||||
eps=cfg.get("pre_layernorm_epsilon", 1e-5),
|
||||
)
|
||||
|
||||
# self.pre_layrnorm = RMSNorm(
|
||||
# cfg.hidden_size,
|
||||
# eps=cfg.get("pre_layernorm_epsilon", 1e-5),
|
||||
# sequence_parallel=False,
|
||||
# use_fp32=True,
|
||||
# use_optimus=True,
|
||||
# )
|
||||
|
||||
if freeze_pre_norm:
|
||||
for name, param in self.pre_layrnorm.named_parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
for p in self.parameters():
|
||||
p.micro_dp = True
|
||||
|
||||
def set_input_tensor(self, input_tensor):
|
||||
if not isinstance(input_tensor, list):
|
||||
input_tensor = [input_tensor]
|
||||
self.transformer.set_input_tensor(input_tensor[0])
|
||||
|
||||
def __str__(self) -> str:
|
||||
return "open_clip"
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
patch_embeds
|
||||
):
|
||||
x = self.embeddings(x, patch_embeds)
|
||||
hidden_states = self.pre_layrnorm(x)
|
||||
|
||||
# hidden_states, dis = local_dp_scatter(hidden_states)
|
||||
output = self.transformer(hidden_states)
|
||||
|
||||
# output = local_dp_reduce(output, dis)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
vit_model_cfg = adict(
|
||||
num_layers=24,
|
||||
hidden_size=1024,
|
||||
num_heads = 16,
|
||||
num_attention_heads=16,
|
||||
ffn_hidden_size=4096,
|
||||
seq_length=256,
|
||||
max_position_embeddings=256,
|
||||
use_flash_attn=False,
|
||||
understand_projector_stride=2,
|
||||
hidden_dropout = 0.0,
|
||||
attention_dropout = 0.0,
|
||||
no_persist_layer_norm = False,
|
||||
layernorm_epsilon = 1e-5,
|
||||
pre_layernorm_epsilon = 1e-5,
|
||||
image_size = 224,
|
||||
patch_size = 14,
|
||||
recompute_list = []
|
||||
)
|
||||
|
||||
def build_clip_l():
|
||||
return VitModel(
|
||||
cfg=vit_model_cfg,
|
||||
freeze_embed=False,
|
||||
freeze_pre_norm=False,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
|
||||
from mmgpt.model.vision_encoder.sam_b import build_sam_vit_b
|
||||
|
||||
|
||||
|
||||
vit_model_cfg = adict(
|
||||
num_layers=24,
|
||||
hidden_size=1024,
|
||||
num_attention_heads=16,
|
||||
ffn_hidden_size=4096,
|
||||
seq_length=256,
|
||||
max_position_embeddings=256,
|
||||
use_flash_attn=False,
|
||||
understand_projector_stride=2,
|
||||
hidden_dropout = 0.0,
|
||||
attention_dropout = 0.0,
|
||||
no_persist_layer_norm = False,
|
||||
layernorm_epsilon = 1e-5,
|
||||
pre_layernorm_epsilon = 1e-5,
|
||||
image_size = 224,
|
||||
patch_size = 14,
|
||||
recompute_list = []
|
||||
)
|
||||
|
||||
sam_model = build_sam_vit_b()
|
||||
|
||||
|
||||
vision_model = VitModel(
|
||||
cfg=vit_model_cfg,
|
||||
freeze_embed=False,
|
||||
freeze_pre_norm=False,
|
||||
)
|
||||
|
||||
# model = VitModel(1344)
|
||||
# x = torch.zeros(2, 3, 224, 224)
|
||||
x = torch.zeros(2, 3, 1024, 1024)
|
||||
|
||||
|
||||
with torch.no_grad():
|
||||
# y = vision_model(x)
|
||||
patch_embed = sam_model(x)
|
||||
print(patch_embed.shape)
|
||||
y = vision_model(x, patch_embed)
|
||||
print(y.shape)
|
||||
|
||||
image_feature = torch.add(y[:, 1:], patch_embed.flatten(2).permute(0, 2, 1))
|
||||
|
||||
print(image_feature.shape)
|
||||
@@ -1,528 +0,0 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from typing import Optional, Tuple, Type
|
||||
from functools import partial
|
||||
from flash_attn import flash_attn_qkvpacked_func
|
||||
# from .common import LayerNorm2d, MLPBlock
|
||||
|
||||
# from mmgpt.model.vision_encoder.flash_4 import _attention_rel_h_rel_w
|
||||
|
||||
|
||||
def get_abs_pos(abs_pos, tgt_size):
|
||||
|
||||
dtype = abs_pos.dtype
|
||||
|
||||
src_size = abs_pos.size(1)
|
||||
|
||||
if src_size != tgt_size:
|
||||
old_pos_embed = abs_pos.permute(0, 3, 1, 2)
|
||||
old_pos_embed = old_pos_embed.to(torch.float32)
|
||||
new_pos_embed = F.interpolate(
|
||||
old_pos_embed,
|
||||
size=(tgt_size, tgt_size),
|
||||
mode='bicubic',
|
||||
antialias=True,
|
||||
align_corners=False,
|
||||
).to(dtype)
|
||||
new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
|
||||
return new_pos_embed
|
||||
else:
|
||||
return abs_pos
|
||||
|
||||
|
||||
|
||||
|
||||
class MLPBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embedding_dim: int,
|
||||
mlp_dim: int,
|
||||
act: Type[nn.Module] = nn.GELU,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
|
||||
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
|
||||
self.act = act()
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.lin2(self.act(self.lin1(x)))
|
||||
|
||||
|
||||
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
|
||||
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
|
||||
class LayerNorm2d(nn.Module):
|
||||
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(num_channels))
|
||||
self.bias = nn.Parameter(torch.zeros(num_channels))
|
||||
self.eps = eps
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
u = x.mean(1, keepdim=True)
|
||||
s = (x - u).pow(2).mean(1, keepdim=True)
|
||||
x = (x - u) / torch.sqrt(s + self.eps)
|
||||
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
||||
return x
|
||||
|
||||
|
||||
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
|
||||
class ImageEncoderViT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
img_size: int = 1024,
|
||||
patch_size: int = 16,
|
||||
in_chans: int = 3,
|
||||
embed_dim: int = 768,
|
||||
depth: int = 12,
|
||||
num_heads: int = 12,
|
||||
mlp_ratio: float = 4.0,
|
||||
out_chans: int = 256,
|
||||
qkv_bias: bool = True,
|
||||
norm_layer: Type[nn.Module] = nn.LayerNorm,
|
||||
act_layer: Type[nn.Module] = nn.GELU,
|
||||
use_abs_pos: bool = True,
|
||||
use_rel_pos: bool = False,
|
||||
rel_pos_zero_init: bool = True,
|
||||
window_size: int = 0,
|
||||
global_attn_indexes: Tuple[int, ...] = (),
|
||||
) -> None:
|
||||
"""
|
||||
Args:
|
||||
img_size (int): Input image size.
|
||||
patch_size (int): Patch size.
|
||||
in_chans (int): Number of input image channels.
|
||||
embed_dim (int): Patch embedding dimension.
|
||||
depth (int): Depth of ViT.
|
||||
num_heads (int): Number of attention heads in each ViT block.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
||||
norm_layer (nn.Module): Normalization layer.
|
||||
act_layer (nn.Module): Activation layer.
|
||||
use_abs_pos (bool): If True, use absolute positional embeddings.
|
||||
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
|
||||
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
|
||||
window_size (int): Window size for window attention blocks.
|
||||
global_attn_indexes (list): Indexes for blocks using global attention.
|
||||
"""
|
||||
super().__init__()
|
||||
self.img_size = img_size
|
||||
|
||||
self.patch_embed = PatchEmbed(
|
||||
kernel_size=(patch_size, patch_size),
|
||||
stride=(patch_size, patch_size),
|
||||
in_chans=in_chans,
|
||||
embed_dim=embed_dim,
|
||||
)
|
||||
|
||||
self.pos_embed: Optional[nn.Parameter] = None
|
||||
if use_abs_pos:
|
||||
# Initialize absolute positional embedding with pretrain image size.
|
||||
self.pos_embed = nn.Parameter(
|
||||
torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
|
||||
)
|
||||
|
||||
self.blocks = nn.ModuleList()
|
||||
for i in range(depth):
|
||||
block = Block(
|
||||
dim=embed_dim,
|
||||
num_heads=num_heads,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
norm_layer=norm_layer,
|
||||
act_layer=act_layer,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_zero_init=rel_pos_zero_init,
|
||||
window_size=window_size if i not in global_attn_indexes else 0,
|
||||
input_size=(img_size // patch_size, img_size // patch_size),
|
||||
)
|
||||
self.blocks.append(block)
|
||||
|
||||
self.neck = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
embed_dim,
|
||||
out_chans,
|
||||
kernel_size=1,
|
||||
bias=False,
|
||||
),
|
||||
LayerNorm2d(out_chans),
|
||||
nn.Conv2d(
|
||||
out_chans,
|
||||
out_chans,
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
LayerNorm2d(out_chans),
|
||||
)
|
||||
|
||||
self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
|
||||
self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.patch_embed(x)
|
||||
if self.pos_embed is not None:
|
||||
# x = x + self.pos_embed
|
||||
x = x + get_abs_pos(self.pos_embed, x.size(1))
|
||||
|
||||
for blk in self.blocks:
|
||||
x = blk(x)
|
||||
|
||||
neck_output = self.neck(x.permute(0, 3, 1, 2))
|
||||
conv2_output = self.net_2(neck_output)
|
||||
# print(f"conv2_output shape: {conv2_output.shape}")
|
||||
conv3_output = self.net_3(conv2_output)
|
||||
|
||||
return conv3_output
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
"""Transformer blocks with support of window attention and residual propagation blocks"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_heads: int,
|
||||
mlp_ratio: float = 4.0,
|
||||
qkv_bias: bool = True,
|
||||
norm_layer: Type[nn.Module] = nn.LayerNorm,
|
||||
act_layer: Type[nn.Module] = nn.GELU,
|
||||
use_rel_pos: bool = False,
|
||||
rel_pos_zero_init: bool = True,
|
||||
window_size: int = 0,
|
||||
input_size: Optional[Tuple[int, int]] = None,
|
||||
) -> None:
|
||||
"""
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads in each ViT block.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
||||
norm_layer (nn.Module): Normalization layer.
|
||||
act_layer (nn.Module): Activation layer.
|
||||
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
|
||||
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
|
||||
window_size (int): Window size for window attention blocks. If it equals 0, then
|
||||
use global attention.
|
||||
input_size (tuple(int, int) or None): Input resolution for calculating the relative
|
||||
positional parameter size.
|
||||
"""
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads,
|
||||
qkv_bias=qkv_bias,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_zero_init=rel_pos_zero_init,
|
||||
input_size=input_size if window_size == 0 else (window_size, window_size),
|
||||
)
|
||||
|
||||
self.norm2 = norm_layer(dim)
|
||||
self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
|
||||
|
||||
self.window_size = window_size
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
# Window partition
|
||||
if self.window_size > 0:
|
||||
H, W = x.shape[1], x.shape[2]
|
||||
x, pad_hw = window_partition(x, self.window_size)
|
||||
|
||||
x = self.attn(x)
|
||||
# Reverse window partition
|
||||
if self.window_size > 0:
|
||||
x = window_unpartition(x, self.window_size, pad_hw, (H, W))
|
||||
|
||||
x = shortcut + x
|
||||
x = x + self.mlp(self.norm2(x))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
"""Multi-head Attention block with relative position embeddings."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_heads: int = 8,
|
||||
qkv_bias: bool = True,
|
||||
use_rel_pos: bool = False,
|
||||
rel_pos_zero_init: bool = True,
|
||||
input_size: Optional[Tuple[int, int]] = None,
|
||||
) -> None:
|
||||
"""
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
||||
rel_pos (bool): If True, add relative positional embeddings to the attention map.
|
||||
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
|
||||
input_size (tuple(int, int) or None): Input resolution for calculating the relative
|
||||
positional parameter size.
|
||||
"""
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = head_dim**-0.5
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
|
||||
self.use_rel_pos = use_rel_pos
|
||||
if self.use_rel_pos:
|
||||
assert (
|
||||
input_size is not None
|
||||
), "Input size must be provided if using relative positional encoding."
|
||||
# initialize relative positional embeddings
|
||||
self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
|
||||
self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
B, H, W, _ = x.shape
|
||||
# qkv with shape (3, B, nHead, H * W, C)
|
||||
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
|
||||
# q, k, v with shape (B * nHead, H * W, C)
|
||||
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
|
||||
|
||||
rel_h, rel_w = None, None
|
||||
if self.use_rel_pos:
|
||||
rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
|
||||
|
||||
q = q.view(B, self.num_heads, H * W, -1)
|
||||
k = k.view(B, self.num_heads, H * W, -1)
|
||||
v = v.view(B, self.num_heads, H * W, -1)
|
||||
|
||||
if self.use_rel_pos:
|
||||
rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
|
||||
rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
|
||||
attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4))
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
|
||||
# x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w)
|
||||
else:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
# qkv = torch.stack([q, k, v], dim=1).transpose(1, 3).reshape(B, H * W, 3, self.num_heads, -1)
|
||||
# x = flash_attn_qkvpacked_func(qkv, dropout_p=0.0, causal=False).transpose(1, 2)
|
||||
|
||||
|
||||
|
||||
x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
|
||||
|
||||
x = self.proj(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
|
||||
"""
|
||||
Partition into non-overlapping windows with padding if needed.
|
||||
Args:
|
||||
x (tensor): input tokens with [B, H, W, C].
|
||||
window_size (int): window size.
|
||||
|
||||
Returns:
|
||||
windows: windows after partition with [B * num_windows, window_size, window_size, C].
|
||||
(Hp, Wp): padded height and width before partition
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
|
||||
pad_h = (window_size - H % window_size) % window_size
|
||||
pad_w = (window_size - W % window_size) % window_size
|
||||
if pad_h > 0 or pad_w > 0:
|
||||
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
|
||||
Hp, Wp = H + pad_h, W + pad_w
|
||||
|
||||
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows, (Hp, Wp)
|
||||
|
||||
|
||||
def window_unpartition(
|
||||
windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Window unpartition into original sequences and removing padding.
|
||||
Args:
|
||||
windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
|
||||
window_size (int): window size.
|
||||
pad_hw (Tuple): padded height and width (Hp, Wp).
|
||||
hw (Tuple): original height and width (H, W) before padding.
|
||||
|
||||
Returns:
|
||||
x: unpartitioned sequences with [B, H, W, C].
|
||||
"""
|
||||
Hp, Wp = pad_hw
|
||||
H, W = hw
|
||||
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
|
||||
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
|
||||
|
||||
if Hp > H or Wp > W:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
return x
|
||||
|
||||
|
||||
def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Get relative positional embeddings according to the relative positions of
|
||||
query and key sizes.
|
||||
Args:
|
||||
q_size (int): size of query q.
|
||||
k_size (int): size of key k.
|
||||
rel_pos (Tensor): relative position embeddings (L, C).
|
||||
|
||||
Returns:
|
||||
Extracted positional embeddings according to relative positions.
|
||||
"""
|
||||
max_rel_dist = int(2 * max(q_size, k_size) - 1)
|
||||
# Interpolate rel pos if needed.
|
||||
if rel_pos.shape[0] != max_rel_dist:
|
||||
# Interpolate rel pos.
|
||||
dtype = rel_pos.dtype
|
||||
rel_pos = rel_pos.to(torch.float32)
|
||||
rel_pos_resized = F.interpolate(
|
||||
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
|
||||
size=max_rel_dist,
|
||||
mode="linear",
|
||||
).to(dtype)
|
||||
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
|
||||
else:
|
||||
rel_pos_resized = rel_pos
|
||||
|
||||
# Scale the coords with short length if shapes for q and k are different.
|
||||
q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0)
|
||||
k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0)
|
||||
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
|
||||
|
||||
return rel_pos_resized[relative_coords.long()]
|
||||
|
||||
|
||||
def add_decomposed_rel_pos(
|
||||
q: torch.Tensor,
|
||||
rel_pos_h: torch.Tensor,
|
||||
rel_pos_w: torch.Tensor,
|
||||
q_size: Tuple[int, int],
|
||||
k_size: Tuple[int, int],
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
|
||||
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
|
||||
Args:
|
||||
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
|
||||
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
|
||||
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
|
||||
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
|
||||
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
|
||||
|
||||
Returns:
|
||||
attn (Tensor): attention map with added relative positional embeddings.
|
||||
"""
|
||||
q_h, q_w = q_size
|
||||
k_h, k_w = k_size
|
||||
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
|
||||
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
|
||||
|
||||
B, _, dim = q.shape
|
||||
r_q = q.reshape(B, q_h, q_w, dim)
|
||||
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
|
||||
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
|
||||
rel_h = rel_h.unsqueeze(-1)
|
||||
rel_w = rel_w.unsqueeze(-2)
|
||||
rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1)
|
||||
rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w)
|
||||
|
||||
return rel_h, rel_w
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
"""
|
||||
Image to Patch Embedding.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
kernel_size: Tuple[int, int] = (16, 16),
|
||||
stride: Tuple[int, int] = (16, 16),
|
||||
padding: Tuple[int, int] = (0, 0),
|
||||
in_chans: int = 3,
|
||||
embed_dim: int = 768,
|
||||
) -> None:
|
||||
"""
|
||||
Args:
|
||||
kernel_size (Tuple): kernel size of the projection layer.
|
||||
stride (Tuple): stride of the projection layer.
|
||||
padding (Tuple): padding size of the projection layer.
|
||||
in_chans (int): Number of input image channels.
|
||||
embed_dim (int): Patch embedding dimension.
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.proj = nn.Conv2d(
|
||||
in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.proj(x)
|
||||
# B C H W -> B H W C
|
||||
x = x.permute(0, 2, 3, 1)
|
||||
return x
|
||||
|
||||
|
||||
def build_sam_vit_b(checkpoint=None):
|
||||
return _build_sam(
|
||||
encoder_embed_dim=768,
|
||||
encoder_depth=12,
|
||||
encoder_num_heads=12,
|
||||
encoder_global_attn_indexes=[2, 5, 8, 11],
|
||||
checkpoint=checkpoint,
|
||||
)
|
||||
|
||||
|
||||
def _build_sam(
|
||||
encoder_embed_dim,
|
||||
encoder_depth,
|
||||
encoder_num_heads,
|
||||
encoder_global_attn_indexes,
|
||||
checkpoint=None,
|
||||
):
|
||||
prompt_embed_dim = 256
|
||||
image_size = 1024
|
||||
vit_patch_size = 16
|
||||
image_embedding_size = image_size // vit_patch_size
|
||||
image_encoder=ImageEncoderViT(
|
||||
depth=encoder_depth,
|
||||
embed_dim=encoder_embed_dim,
|
||||
img_size=image_size,
|
||||
mlp_ratio=4,
|
||||
norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
|
||||
num_heads=encoder_num_heads,
|
||||
patch_size=vit_patch_size,
|
||||
qkv_bias=True,
|
||||
use_rel_pos=True,
|
||||
global_attn_indexes=encoder_global_attn_indexes,
|
||||
window_size=14,
|
||||
out_chans=prompt_embed_dim,
|
||||
)
|
||||
|
||||
if checkpoint is not None:
|
||||
# with open(checkpoint, "rb") as f:
|
||||
state_dict = torch.load(checkpoint)
|
||||
# print(state_dict.keys())
|
||||
# for key in state_dict:
|
||||
# image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False)
|
||||
# ocr-anyting
|
||||
# image_encoder.load_state_dict(state_dict, strict=True)
|
||||
# tob
|
||||
image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True)
|
||||
print(checkpoint)
|
||||
return image_encoder
|
||||
@@ -1,582 +0,0 @@
|
||||
|
||||
"""Inference-only Deepseek-OCR model compatible with HuggingFace weights."""
|
||||
import math
|
||||
from collections.abc import Iterable, Mapping, Sequence
|
||||
from typing import List, Literal, Optional, Set, Tuple, TypedDict, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange, repeat
|
||||
from transformers import BatchFeature
|
||||
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.model_executor import SamplingMetadata
|
||||
from vllm.model_executor.layers.quantization import QuantizationConfig
|
||||
from vllm.model_executor.model_loader.utils import set_default_torch_dtype
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY
|
||||
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
|
||||
MultiModalKwargs, NestedTensors)
|
||||
from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
|
||||
ImageSize, MultiModalDataItems)
|
||||
from vllm.multimodal.processing import (BaseMultiModalProcessor,
|
||||
BaseProcessingInfo, PromptReplacement,
|
||||
PromptUpdate)
|
||||
from vllm.multimodal.profiling import BaseDummyInputsBuilder
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
|
||||
MlpProjectorConfig,
|
||||
VisionEncoderConfig)
|
||||
from process.image_process import (
|
||||
DeepseekOCRProcessor, count_tiles)
|
||||
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
|
||||
# from vllm.utils import is_list_of
|
||||
|
||||
from vllm.model_executor.models.interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
|
||||
from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
|
||||
init_vllm_registered_model, maybe_prefix,
|
||||
merge_multimodal_embeddings)
|
||||
|
||||
from deepencoder.sam_vary_sdpa import build_sam_vit_b
|
||||
from deepencoder.clip_sdpa import build_clip_l
|
||||
from deepencoder.build_linear import MlpProjector
|
||||
from addict import Dict
|
||||
# import time
|
||||
from config import IMAGE_SIZE, BASE_SIZE, CROP_MODE, PRINT_NUM_VIS_TOKENS, PROMPT
|
||||
# The image token id may be various
|
||||
_IMAGE_TOKEN = "<image>"
|
||||
|
||||
|
||||
class DeepseekOCRProcessingInfo(BaseProcessingInfo):
|
||||
|
||||
def get_hf_config(self):
|
||||
return self.ctx.get_hf_config(DeepseekVLV2Config)
|
||||
|
||||
def get_hf_processor(self, **kwargs: object):
|
||||
return self.ctx.get_hf_processor(DeepseekOCRProcessor, **kwargs)
|
||||
|
||||
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
|
||||
return {"image": None}
|
||||
|
||||
def get_num_image_tokens(self,
|
||||
*,
|
||||
image_width: int,
|
||||
image_height: int,
|
||||
cropping: bool = True) -> int:
|
||||
hf_processor = self.get_hf_processor()
|
||||
|
||||
|
||||
# image_size = hf_processor.image_size
|
||||
# patch_size = hf_processor.patch_size
|
||||
# downsample_ratio = hf_processor.downsample_ratio
|
||||
|
||||
image_size = IMAGE_SIZE
|
||||
base_size = BASE_SIZE
|
||||
patch_size = 16
|
||||
downsample_ratio = 4
|
||||
|
||||
if CROP_MODE:
|
||||
if image_width <= 640 and image_height <= 640:
|
||||
crop_ratio = [1, 1]
|
||||
else:
|
||||
# images_crop_raw, crop_ratio = hf_processor.dynamic_preprocess(image)
|
||||
|
||||
# find the closest aspect ratio to the target
|
||||
crop_ratio = count_tiles(image_width, image_height, image_size=IMAGE_SIZE)
|
||||
|
||||
# print('===========')
|
||||
# print('crop_ratio ', crop_ratio)
|
||||
# print('============')
|
||||
|
||||
num_width_tiles, num_height_tiles = crop_ratio
|
||||
else:
|
||||
num_width_tiles = num_height_tiles = 1
|
||||
|
||||
h = w = math.ceil((base_size // patch_size) / downsample_ratio)
|
||||
|
||||
h2 = w2 = math.ceil((image_size // patch_size) / downsample_ratio)
|
||||
|
||||
global_views_tokens = h * (w + 1)
|
||||
if num_width_tiles >1 or num_height_tiles>1:
|
||||
local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2 + 1)
|
||||
else:
|
||||
local_views_tokens = 0
|
||||
|
||||
|
||||
return global_views_tokens + local_views_tokens + 1
|
||||
|
||||
def get_image_size_with_most_features(self) -> ImageSize:
|
||||
|
||||
if IMAGE_SIZE == 1024 and BASE_SIZE == 1280:
|
||||
return ImageSize(width=1024*2, height=1024*2)
|
||||
return ImageSize(width=640*2, height=640*2)
|
||||
|
||||
|
||||
class DeepseekOCRDummyInputsBuilder(
|
||||
BaseDummyInputsBuilder[DeepseekOCRProcessingInfo]):
|
||||
|
||||
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
||||
num_images = mm_counts.get("image", 0)
|
||||
|
||||
processor = self.info.get_hf_processor()
|
||||
image_token = processor.image_token
|
||||
|
||||
return image_token * num_images
|
||||
|
||||
def get_dummy_mm_data(
|
||||
self,
|
||||
seq_len: int,
|
||||
mm_counts: Mapping[str, int],
|
||||
) -> MultiModalDataDict:
|
||||
num_images = mm_counts.get("image", 0)
|
||||
|
||||
max_image_size = self.info.get_image_size_with_most_features()
|
||||
|
||||
if '<image>' in PROMPT:
|
||||
return {
|
||||
"image":
|
||||
DeepseekOCRProcessor().tokenize_with_images(images = self._get_dummy_images(width=max_image_size.width,
|
||||
height=max_image_size.height,
|
||||
num_images=num_images), bos=True, eos=True, cropping=CROP_MODE)
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"image": []
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
class DeepseekOCRMultiModalProcessor(
|
||||
BaseMultiModalProcessor[DeepseekOCRProcessingInfo]):
|
||||
|
||||
|
||||
def _call_hf_processor(
|
||||
self,
|
||||
prompt: str,
|
||||
mm_data: Mapping[str, object],
|
||||
mm_kwargs: Mapping[str, object],
|
||||
) -> BatchFeature:
|
||||
|
||||
|
||||
# print(mm_data)
|
||||
if mm_data:
|
||||
processed_outputs = self.info.ctx.call_hf_processor(
|
||||
self.info.get_hf_processor(**mm_kwargs),
|
||||
dict(prompt=prompt, **mm_data),
|
||||
mm_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
tokenizer = self.info.get_tokenizer()
|
||||
processed_outputs = tokenizer(prompt,
|
||||
add_special_tokens=True,
|
||||
return_tensors="pt")
|
||||
|
||||
return processed_outputs
|
||||
|
||||
def _get_mm_fields_config(
|
||||
self,
|
||||
hf_inputs: BatchFeature,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
) -> Mapping[str, MultiModalFieldConfig]:
|
||||
return dict(
|
||||
pixel_values=MultiModalFieldConfig.batched("image"),
|
||||
images_spatial_crop=MultiModalFieldConfig.batched("image"),
|
||||
# image_embeds=MultiModalFieldConfig.batched("image2"),
|
||||
images_crop=MultiModalFieldConfig.batched("image"),
|
||||
)
|
||||
|
||||
def _get_prompt_updates(
|
||||
self,
|
||||
mm_items: MultiModalDataItems,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
out_mm_kwargs: MultiModalKwargs,
|
||||
) -> Sequence[PromptUpdate]:
|
||||
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
|
||||
|
||||
image_token_id = hf_processor.image_token_id
|
||||
assert isinstance(image_token_id, int)
|
||||
|
||||
def get_replacement_deepseek_vl2(item_idx: int):
|
||||
images = mm_items.get_items(
|
||||
"image", (ImageEmbeddingItems, ImageProcessorItems))
|
||||
|
||||
|
||||
|
||||
if isinstance(images, ImageEmbeddingItems):
|
||||
num_image_tokens = images.get_feature_size(item_idx)
|
||||
else:
|
||||
|
||||
|
||||
width = images[0][-1][0][0]
|
||||
height = images[0][-1][0][1]
|
||||
|
||||
num_image_tokens = self.info.get_num_image_tokens(
|
||||
image_width=width,
|
||||
image_height=height,
|
||||
# flag = True,
|
||||
cropping=CROP_MODE,
|
||||
)
|
||||
return [image_token_id] * num_image_tokens
|
||||
|
||||
return [
|
||||
PromptReplacement(
|
||||
modality="image",
|
||||
target=[image_token_id],
|
||||
replacement=get_replacement_deepseek_vl2,
|
||||
)
|
||||
]
|
||||
|
||||
def _cached_apply_hf_processor(
|
||||
self,
|
||||
prompt: Union[str, list[int]],
|
||||
mm_data_items: MultiModalDataItems,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
) -> tuple[list[int], MultiModalKwargs, bool]:
|
||||
# The processor logic is different for len(images) <= 2 vs > 2
|
||||
# Since the processing cache assumes that the processor output is
|
||||
# invariant of how many images are passed per prompt, we only
|
||||
# perform caching for the most common case
|
||||
if mm_data_items.get_count("image", strict=False) > 2:
|
||||
# This code path corresponds to the cache being disabled
|
||||
return self._apply_hf_processor_main(
|
||||
prompt=prompt,
|
||||
mm_items=mm_data_items,
|
||||
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
|
||||
enable_hf_prompt_update=True,
|
||||
)
|
||||
|
||||
return super()._cached_apply_hf_processor(
|
||||
prompt=prompt,
|
||||
mm_data_items=mm_data_items,
|
||||
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
|
||||
)
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
DeepseekOCRMultiModalProcessor,
|
||||
info=DeepseekOCRProcessingInfo,
|
||||
dummy_inputs=DeepseekOCRDummyInputsBuilder)
|
||||
class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
|
||||
|
||||
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
|
||||
"language.": "language_model.",
|
||||
})
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
|
||||
config: DeepseekVLV2Config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
multimodal_config = vllm_config.model_config.multimodal_config
|
||||
|
||||
# config.model_type ='deepseek_vl_v2'
|
||||
|
||||
self.config = config
|
||||
self.multimodal_config = multimodal_config
|
||||
|
||||
|
||||
self.vision_config = config.vision_config
|
||||
self.projector_config = config.projector_config
|
||||
self.text_config = config.text_config
|
||||
|
||||
model_config = vllm_config.model_config
|
||||
tokenizer = cached_tokenizer_from_config(model_config)
|
||||
self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
|
||||
|
||||
self.sam_model = build_sam_vit_b()
|
||||
self.vision_model = build_clip_l()
|
||||
|
||||
n_embed = 1280
|
||||
self.projector = MlpProjector(Dict(projector_type="linear", input_dim=2048, n_embed=n_embed))
|
||||
self.tile_tag = config.tile_tag
|
||||
self.global_view_pos = config.global_view_pos
|
||||
|
||||
# self.sam_model = torch.compile(self.sam_model, mode="reduce-overhead")
|
||||
# self.vision_model = torch.compile(self.vision_model, mode="reduce-overhead")
|
||||
# self.projector = torch.compile(self.projector, mode="max-autotune")
|
||||
|
||||
|
||||
|
||||
|
||||
# special token for image token sequence format
|
||||
embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
|
||||
if self.tile_tag == "2D":
|
||||
# <|view_separator|>, <|\n|>
|
||||
self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
|
||||
self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
|
||||
)
|
||||
|
||||
if self.text_config.topk_method == "noaux_tc":
|
||||
architectures = ["DeepseekV3ForCausalLM"]
|
||||
elif not self.text_config.use_mla:
|
||||
architectures = ["DeepseekForCausalLM"]
|
||||
else:
|
||||
architectures = ["DeepseekV2ForCausalLM"]
|
||||
|
||||
self.language_model = init_vllm_registered_model(
|
||||
vllm_config=vllm_config,
|
||||
hf_config=self.text_config,
|
||||
prefix=maybe_prefix(prefix, "language"),
|
||||
architectures=architectures,
|
||||
)
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.make_empty_intermediate_tensors)
|
||||
|
||||
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object):
|
||||
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
images_spatial_crop = kwargs.pop("images_spatial_crop", None)
|
||||
images_crop = kwargs.pop("images_crop", None)
|
||||
|
||||
|
||||
if pixel_values is None or torch.sum(pixel_values).item() == 0:
|
||||
return None
|
||||
|
||||
if pixel_values is not None:
|
||||
if not isinstance(pixel_values, (torch.Tensor, list)):
|
||||
raise ValueError("Incorrect type of pixel values. "
|
||||
f"Got type: {type(pixel_values)}")
|
||||
|
||||
if not isinstance(images_spatial_crop, (torch.Tensor, list)):
|
||||
raise ValueError("Incorrect type of image sizes. "
|
||||
f"Got type: {type(images_spatial_crop)}")
|
||||
|
||||
if not isinstance(images_crop, (torch.Tensor, list)):
|
||||
raise ValueError("Incorrect type of image crop. "
|
||||
f"Got type: {type(images_crop)}")
|
||||
|
||||
return [pixel_values, images_crop, images_spatial_crop]
|
||||
|
||||
|
||||
raise AssertionError("This line should be unreachable.")
|
||||
|
||||
|
||||
|
||||
def _pixel_values_to_embedding(
|
||||
self,
|
||||
pixel_values: torch.Tensor,
|
||||
images_crop: torch.Tensor,
|
||||
images_spatial_crop: torch.Tensor,
|
||||
) -> NestedTensors:
|
||||
|
||||
# Pixel_values (global view): [n_image, batch_size, 3, height, width]
|
||||
# images_spatial_crop: [n_image, batch_size, [num_tiles_w, num_tiles_h]]
|
||||
# images_crop (local view): [n_image, batch_size, num_pathes, 3, h, w]
|
||||
# split the pixel and image_crop, all batch_size = 1
|
||||
|
||||
images_in_this_batch = []
|
||||
|
||||
|
||||
# print(type(images_crop))
|
||||
|
||||
# print(pixel_values.shape)
|
||||
|
||||
|
||||
with torch.no_grad():
|
||||
for jdx in range(images_spatial_crop.size(0)):
|
||||
# with torch.set_grad_enabled(False):
|
||||
patches = images_crop[jdx][0].to(torch.bfloat16) # batch_size = 1
|
||||
image_ori = pixel_values[jdx]
|
||||
crop_shape = images_spatial_crop[jdx][0]
|
||||
|
||||
if torch.sum(patches).item() != 0: # if all values = 0, no crop
|
||||
# P, C, H, W = patches.shape
|
||||
# crop_flag = 1
|
||||
local_features_1 = self.sam_model(patches)
|
||||
#TODO del patches
|
||||
# torch.compiler.cudagraph_mark_step_begin()
|
||||
local_features_2 = self.vision_model(patches, local_features_1)
|
||||
|
||||
|
||||
local_features = torch.cat((local_features_2[:, 1:], local_features_1.flatten(2).permute(0, 2, 1)), dim=-1)
|
||||
local_features = self.projector(local_features)
|
||||
|
||||
|
||||
global_features_1 = self.sam_model(image_ori)
|
||||
global_features_2 = self.vision_model(image_ori, global_features_1)
|
||||
global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1)
|
||||
global_features = self.projector(global_features)
|
||||
|
||||
if PRINT_NUM_VIS_TOKENS:
|
||||
print('=====================')
|
||||
print('BASE: ', global_features.shape)
|
||||
print('PATCHES: ', local_features.shape)
|
||||
print('=====================')
|
||||
|
||||
_, hw, n_dim = global_features.shape
|
||||
h = w = int(hw ** 0.5)
|
||||
|
||||
_2, hw2, n_dim2 = local_features.shape
|
||||
h2 = w2 = int(hw2 ** 0.5)
|
||||
|
||||
width_crop_num, height_crop_num = crop_shape[0], crop_shape[1]
|
||||
|
||||
global_features = global_features.view(h, w, n_dim)
|
||||
|
||||
global_features = torch.cat(
|
||||
[global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
|
||||
)
|
||||
|
||||
global_features = global_features.view(-1, n_dim)
|
||||
|
||||
|
||||
local_features = local_features.view(height_crop_num, width_crop_num, h2, w2, n_dim2).permute(0, 2, 1, 3, 4).reshape(height_crop_num*h2, width_crop_num*w2, n_dim2)
|
||||
local_features = torch.cat(
|
||||
[local_features, self.image_newline[None, None, :].expand(height_crop_num * h2, 1, n_dim2)], dim=1
|
||||
)
|
||||
local_features = local_features.view(-1, n_dim2)
|
||||
|
||||
global_local_features = torch.cat([local_features, global_features, self.view_seperator[None, :]], dim=0)
|
||||
|
||||
else:
|
||||
global_features_1 = self.sam_model(image_ori)
|
||||
global_features_2 = self.vision_model(image_ori, global_features_1)
|
||||
global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1)
|
||||
global_features = self.projector(global_features)
|
||||
|
||||
if PRINT_NUM_VIS_TOKENS:
|
||||
print('=====================')
|
||||
print('BASE: ', global_features.shape)
|
||||
print('NO PATCHES')
|
||||
print('=====================')
|
||||
|
||||
_, hw, n_dim = global_features.shape
|
||||
h = w = int(hw ** 0.5)
|
||||
|
||||
global_features = global_features.view(h, w, n_dim)
|
||||
|
||||
global_features = torch.cat(
|
||||
[global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
|
||||
)
|
||||
|
||||
global_features = global_features.view(-1, n_dim)
|
||||
|
||||
global_local_features = torch.cat([global_features, self.view_seperator[None, :]], dim=0)
|
||||
|
||||
images_in_this_batch.append(global_local_features)
|
||||
|
||||
return images_in_this_batch
|
||||
|
||||
def _process_image_input(
|
||||
self, image_input) -> torch.Tensor:
|
||||
|
||||
|
||||
# image_input: [pixel_values, images_crop, images_spatial_crop]
|
||||
|
||||
pixel_values = image_input[0].to(torch.bfloat16)
|
||||
# print(image_input[1][0].shape)
|
||||
# print(type(image_input[1]))
|
||||
# exit()
|
||||
|
||||
# images_crop = image_input[1].to(torch.bfloat16)
|
||||
images_crop = image_input[1]
|
||||
# images_crop = image_input[1]
|
||||
images_spatial_crop = image_input[2].to(dtype=torch.long)
|
||||
|
||||
# local_start = time.time()
|
||||
vision_features = self._pixel_values_to_embedding(
|
||||
pixel_values=pixel_values, images_crop = images_crop, images_spatial_crop=images_spatial_crop)
|
||||
|
||||
# local_total_time = time.time() - local_start
|
||||
|
||||
# print('encoder_time: ', local_total_time)
|
||||
# exit()
|
||||
return vision_features
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.language_model
|
||||
|
||||
def get_multimodal_embeddings(
|
||||
self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return None
|
||||
vision_embeddings = self._process_image_input(image_input)
|
||||
return vision_embeddings
|
||||
|
||||
|
||||
|
||||
def get_input_embeddings(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
||||
) -> torch.Tensor:
|
||||
|
||||
|
||||
|
||||
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
||||
|
||||
|
||||
if multimodal_embeddings is not None:
|
||||
inputs_embeds = merge_multimodal_embeddings(
|
||||
input_ids, inputs_embeds, multimodal_embeddings,
|
||||
self.image_token_id)
|
||||
# print(len(multimodal_embeddings))
|
||||
# print(input_ids.shape)
|
||||
# print(type(inputs_embeds))
|
||||
# print(inputs_embeds.shape)
|
||||
|
||||
return inputs_embeds
|
||||
|
||||
def forward(self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
**kwargs: object):
|
||||
|
||||
if intermediate_tensors is not None:
|
||||
inputs_embeds = None
|
||||
|
||||
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
||||
# condition is for v0 compatibility
|
||||
elif inputs_embeds is None:
|
||||
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
||||
inputs_embeds = self.get_input_embeddings(input_ids,
|
||||
vision_embeddings)
|
||||
input_ids = None
|
||||
|
||||
hidden_states = self.language_model(input_ids,
|
||||
positions,
|
||||
intermediate_tensors,
|
||||
inputs_embeds=inputs_embeds)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[torch.Tensor]:
|
||||
return self.language_model.compute_logits(hidden_states,
|
||||
sampling_metadata)
|
||||
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
|
||||
processed_weights = []
|
||||
|
||||
for name, tensor in weights:
|
||||
if 'sam_model' in name or 'vision_model' in name or 'projector' in name or 'image_newline' in name or 'view_seperator' in name:
|
||||
new_name = name.replace('model.', '', 1)
|
||||
else:
|
||||
new_name = 'language.' + name
|
||||
|
||||
processed_weights.append((new_name, tensor))
|
||||
|
||||
loader = AutoWeightsLoader(self)
|
||||
autoloaded_weights = loader.load_weights(processed_weights, mapper=self.hf_to_vllm_mapper)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
return autoloaded_weights
|
||||
@@ -1,502 +0,0 @@
|
||||
import math
|
||||
from typing import List, Tuple
|
||||
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
from PIL import Image, ImageOps
|
||||
from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast
|
||||
from transformers.processing_utils import ProcessorMixin
|
||||
from config import IMAGE_SIZE, BASE_SIZE, CROP_MODE, MIN_CROPS, MAX_CROPS, PROMPT, TOKENIZER
|
||||
|
||||
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 count_tiles(orig_width, orig_height, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False):
|
||||
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)
|
||||
# print(target_ratios)
|
||||
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)
|
||||
|
||||
return target_aspect_ratio
|
||||
|
||||
|
||||
def dynamic_preprocess(image, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, 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)
|
||||
# print(target_ratios)
|
||||
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)
|
||||
|
||||
# print(target_aspect_ratio)
|
||||
# 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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class ImageTransform:
|
||||
|
||||
def __init__(self,
|
||||
mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
|
||||
std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
|
||||
normalize: bool = True):
|
||||
self.mean = mean
|
||||
self.std = std
|
||||
self.normalize = normalize
|
||||
|
||||
transform_pipelines = [T.ToTensor()]
|
||||
|
||||
if normalize:
|
||||
transform_pipelines.append(T.Normalize(mean, std))
|
||||
|
||||
self.transform = T.Compose(transform_pipelines)
|
||||
|
||||
def __call__(self, pil_img: Image.Image):
|
||||
x = self.transform(pil_img)
|
||||
return x
|
||||
|
||||
|
||||
class DeepseekOCRProcessor(ProcessorMixin):
|
||||
tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
|
||||
attributes = ["tokenizer"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
tokenizer: LlamaTokenizerFast = TOKENIZER,
|
||||
candidate_resolutions: Tuple[Tuple[int, int]] = [[1024, 1024]],
|
||||
patch_size: int = 16,
|
||||
downsample_ratio: int = 4,
|
||||
image_mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
|
||||
image_std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
|
||||
normalize: bool = True,
|
||||
image_token: str = "<image>",
|
||||
pad_token: str = "<｜▁pad▁｜>",
|
||||
add_special_token: bool = False,
|
||||
sft_format: str = "deepseek",
|
||||
mask_prompt: bool = True,
|
||||
ignore_id: int = -100,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
# self.candidate_resolutions = candidate_resolutions # placeholder no use
|
||||
self.image_size = IMAGE_SIZE
|
||||
self.base_size = BASE_SIZE
|
||||
# self.patch_size = patch_size
|
||||
self.patch_size = 16
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.normalize = normalize
|
||||
# self.downsample_ratio = downsample_ratio
|
||||
self.downsample_ratio = 4
|
||||
|
||||
self.image_transform = ImageTransform(mean=image_mean, std=image_std, normalize=normalize)
|
||||
|
||||
|
||||
self.tokenizer = tokenizer
|
||||
# self.tokenizer = add_special_token(tokenizer)
|
||||
self.tokenizer.padding_side = 'left' # must set this，padding side with make a difference in batch inference
|
||||
|
||||
# add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
|
||||
if self.tokenizer.pad_token is None:
|
||||
self.tokenizer.add_special_tokens({'pad_token': pad_token})
|
||||
|
||||
# add image token
|
||||
# image_token_id = self.tokenizer.vocab.get(image_token)
|
||||
# if image_token_id is None:
|
||||
# special_tokens = [image_token]
|
||||
# special_tokens_dict = {"additional_special_tokens": special_tokens}
|
||||
# self.tokenizer.add_special_tokens(special_tokens_dict)
|
||||
self.image_token_id = self.tokenizer.vocab.get(image_token)
|
||||
|
||||
# add five special tokens for grounding-related tasks
|
||||
# <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
|
||||
# special_tokens = ['<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>']
|
||||
# special_tokens_dict = {"additional_special_tokens": special_tokens}
|
||||
|
||||
# special_tokens = ['<image>','<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>', '<td>', '</td>', '<tr>', '</tr>']
|
||||
# special_tokens_dict = {"additional_special_tokens": special_tokens}
|
||||
# self.tokenizer.add_special_tokens(special_tokens_dict)
|
||||
|
||||
# # add special tokens for SFT data
|
||||
# special_tokens = ["<|User|>", "<|Assistant|>"]
|
||||
# special_tokens_dict = {"additional_special_tokens": special_tokens}
|
||||
# self.tokenizer.add_special_tokens(special_tokens_dict)
|
||||
|
||||
self.image_token = image_token
|
||||
self.pad_token = pad_token
|
||||
self.add_special_token = add_special_token
|
||||
self.sft_format = sft_format
|
||||
self.mask_prompt = mask_prompt
|
||||
self.ignore_id = ignore_id
|
||||
|
||||
super().__init__(
|
||||
tokenizer,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
# def select_best_resolution(self, image_size):
|
||||
# # used for cropping
|
||||
# original_width, original_height = image_size
|
||||
# best_fit = None
|
||||
# max_effective_resolution = 0
|
||||
# min_wasted_resolution = float("inf")
|
||||
|
||||
# for width, height in self.candidate_resolutions:
|
||||
# scale = min(width / original_width, height / original_height)
|
||||
# downscaled_width, downscaled_height = int(
|
||||
# original_width * scale), int(original_height * scale)
|
||||
# effective_resolution = min(downscaled_width * downscaled_height,
|
||||
# original_width * original_height)
|
||||
# wasted_resolution = (width * height) - effective_resolution
|
||||
|
||||
# if effective_resolution > max_effective_resolution or (
|
||||
# effective_resolution == max_effective_resolution
|
||||
# and wasted_resolution < min_wasted_resolution):
|
||||
# max_effective_resolution = effective_resolution
|
||||
# min_wasted_resolution = wasted_resolution
|
||||
# best_fit = (width, height)
|
||||
|
||||
# return best_fit
|
||||
|
||||
@property
|
||||
def bos_id(self):
|
||||
return self.tokenizer.bos_token_id
|
||||
|
||||
@property
|
||||
def eos_id(self):
|
||||
return self.tokenizer.eos_token_id
|
||||
|
||||
@property
|
||||
def pad_id(self):
|
||||
return self.tokenizer.pad_token_id
|
||||
|
||||
def encode(self, text: str, bos: bool = True, eos: bool = False):
|
||||
t = self.tokenizer.encode(text, add_special_tokens=False)
|
||||
|
||||
if bos:
|
||||
t = [self.bos_id] + t
|
||||
if eos:
|
||||
t = t + [self.eos_id]
|
||||
|
||||
return t
|
||||
|
||||
def decode(self, t: List[int], **kwargs) -> str:
|
||||
return self.tokenizer.decode(t, **kwargs)
|
||||
|
||||
def process_one(
|
||||
self,
|
||||
prompt: str,
|
||||
images: List,
|
||||
inference_mode: bool = True,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
|
||||
Args:
|
||||
prompt (str): the formatted prompt;
|
||||
conversations (List[Dict]): conversations with a list of messages;
|
||||
images (List[ImageType]): the list of images;
|
||||
inference_mode (bool): if True, then remove the last eos token;
|
||||
system_prompt (str): the system prompt;
|
||||
**kwargs:
|
||||
|
||||
Returns:
|
||||
outputs (BaseProcessorOutput): the output of the processor,
|
||||
- input_ids (torch.LongTensor): [N + image tokens]
|
||||
- target_ids (torch.LongTensor): [N + image tokens]
|
||||
- pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
|
||||
- image_id (int): the id of the image token
|
||||
- num_image_tokens (List[int]): the number of image tokens
|
||||
"""
|
||||
|
||||
assert (prompt is not None and images is not None
|
||||
), "prompt and images must be used at the same time."
|
||||
|
||||
sft_format = prompt
|
||||
|
||||
input_ids, pixel_values, images_crop, images_seq_mask, images_spatial_crop, num_image_tokens, _ = images[0]
|
||||
|
||||
|
||||
return {
|
||||
"input_ids": input_ids,
|
||||
"pixel_values": pixel_values,
|
||||
"images_crop": images_crop,
|
||||
"images_seq_mask": images_seq_mask,
|
||||
"images_spatial_crop": images_spatial_crop,
|
||||
"num_image_tokens": num_image_tokens,
|
||||
}
|
||||
|
||||
|
||||
# prepare = BatchFeature(
|
||||
# data=dict(
|
||||
# input_ids=input_ids,
|
||||
# pixel_values=pixel_values,
|
||||
# images_crop = images_crop,
|
||||
# images_seq_mask=images_seq_mask,
|
||||
# images_spatial_crop=images_spatial_crop,
|
||||
# num_image_tokens=num_image_tokens,
|
||||
# ),
|
||||
# tensor_type="pt",
|
||||
# )
|
||||
# return prepare
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
*,
|
||||
prompt: str,
|
||||
images: List,
|
||||
inference_mode: bool = True,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
|
||||
Args:
|
||||
prompt (str): the formatted prompt;
|
||||
images (List[ImageType]): the list of images;
|
||||
inference_mode (bool): if True, then remove the last eos token;
|
||||
**kwargs:
|
||||
|
||||
Returns:
|
||||
outputs (BaseProcessorOutput): the output of the processor,
|
||||
- input_ids (torch.LongTensor): [N + image tokens]
|
||||
- images (torch.FloatTensor): [n_images, 3, H, W]
|
||||
- image_id (int): the id of the image token
|
||||
- num_image_tokens (List[int]): the number of image tokens
|
||||
"""
|
||||
|
||||
prepare = self.process_one(
|
||||
prompt=prompt,
|
||||
images=images,
|
||||
inference_mode=inference_mode,
|
||||
)
|
||||
|
||||
return prepare
|
||||
|
||||
def tokenize_with_images(
|
||||
self,
|
||||
# conversation: str,
|
||||
images: List[Image.Image],
|
||||
bos: bool = True,
|
||||
eos: bool = True,
|
||||
cropping: bool = True,
|
||||
):
|
||||
"""Tokenize text with <image> tags."""
|
||||
|
||||
# print(conversation)
|
||||
conversation = PROMPT
|
||||
assert conversation.count(self.image_token) == len(images)
|
||||
text_splits = conversation.split(self.image_token)
|
||||
images_list, images_crop_list, images_seq_mask, images_spatial_crop = [], [], [], []
|
||||
image_shapes = []
|
||||
num_image_tokens = []
|
||||
tokenized_str = []
|
||||
# print('image: ', len(images))
|
||||
for text_sep, image in zip(text_splits, images):
|
||||
"""encode text_sep"""
|
||||
tokenized_sep = self.encode(text_sep, bos=False, eos=False)
|
||||
tokenized_str += tokenized_sep
|
||||
images_seq_mask += [False] * len(tokenized_sep)
|
||||
|
||||
"""select best resolution for anyres"""
|
||||
# if cropping:
|
||||
# best_width, best_height = self.select_best_resolution(image.size)
|
||||
# else:
|
||||
# best_width, best_height = self.image_size, self.image_size
|
||||
|
||||
image_shapes.append(image.size)
|
||||
|
||||
if image.size[0] <= 640 and image.size[1] <= 640:
|
||||
crop_ratio = [1, 1]
|
||||
else:
|
||||
if cropping:
|
||||
# print('image-size: ', image.size)
|
||||
# best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
|
||||
# print('image ', image.size)
|
||||
# print('open_size:', image.size)
|
||||
images_crop_raw, crop_ratio = dynamic_preprocess(image, image_size=IMAGE_SIZE)
|
||||
# print('crop_ratio: ', crop_ratio)
|
||||
else:
|
||||
# best_width, best_height = self.image_size, self.image_size
|
||||
crop_ratio = [1, 1]
|
||||
# print(image.size, (best_width, best_height)) # check the select_best_resolutions func
|
||||
|
||||
# print(crop_ratio)
|
||||
"""process the global view"""
|
||||
|
||||
# if cropping
|
||||
if self.image_size <= 640 and not cropping:
|
||||
# print('directly resize')
|
||||
image = image.resize((self.image_size, self.image_size))
|
||||
|
||||
global_view = ImageOps.pad(image, (self.base_size, self.base_size),
|
||||
color=tuple(int(x * 255) for x in self.image_transform.mean))
|
||||
images_list.append(self.image_transform(global_view))
|
||||
|
||||
"""record height / width crop num"""
|
||||
# width_crop_num, height_crop_num = best_width // self.image_size, best_height // self.image_size
|
||||
num_width_tiles, num_height_tiles = crop_ratio
|
||||
images_spatial_crop.append([num_width_tiles, num_height_tiles])
|
||||
|
||||
|
||||
|
||||
|
||||
if num_width_tiles > 1 or num_height_tiles > 1:
|
||||
"""process the local views"""
|
||||
# local_view = ImageOps.pad(image, (best_width, best_height),
|
||||
# color=tuple(int(x * 255) for x in self.image_transform.mean))
|
||||
# for i in range(0, best_height, self.image_size):
|
||||
# for j in range(0, best_width, self.image_size):
|
||||
# images_crop_list.append(
|
||||
# self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size))))
|
||||
for i in range(len(images_crop_raw)):
|
||||
images_crop_list.append(self.image_transform(images_crop_raw[i]))
|
||||
|
||||
# """process the global view"""
|
||||
# global_view = ImageOps.pad(image, (self.image_size, self.image_size),
|
||||
# color=tuple(int(x * 255) for x in self.image_transform.mean))
|
||||
# images_list.append(self.image_transform(global_view))
|
||||
|
||||
# """process the local views"""
|
||||
# local_view = ImageOps.pad(image, (best_width, best_height),
|
||||
# color=tuple(int(x * 255) for x in self.image_transform.mean))
|
||||
# for i in range(0, best_height, self.image_size):
|
||||
# for j in range(0, best_width, self.image_size):
|
||||
# images_list.append(
|
||||
# self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size))))
|
||||
|
||||
# """add image tokens"""
|
||||
"""add image tokens"""
|
||||
num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)
|
||||
num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)
|
||||
|
||||
|
||||
tokenized_image = ([self.image_token_id] * num_queries_base + [self.image_token_id]) * num_queries_base
|
||||
tokenized_image += [self.image_token_id]
|
||||
if num_width_tiles > 1 or num_height_tiles > 1:
|
||||
tokenized_image += ([self.image_token_id] * (num_queries * num_width_tiles) + [self.image_token_id]) * (
|
||||
num_queries * num_height_tiles)
|
||||
tokenized_str += tokenized_image
|
||||
images_seq_mask += [True] * len(tokenized_image)
|
||||
num_image_tokens.append(len(tokenized_image))
|
||||
|
||||
"""process the last text split"""
|
||||
tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
|
||||
tokenized_str += tokenized_sep
|
||||
images_seq_mask += [False] * len(tokenized_sep)
|
||||
|
||||
"""add the bos and eos tokens"""
|
||||
if bos:
|
||||
tokenized_str = [self.bos_id] + tokenized_str
|
||||
images_seq_mask = [False] + images_seq_mask
|
||||
if eos:
|
||||
tokenized_str = tokenized_str + [self.eos_id]
|
||||
images_seq_mask = images_seq_mask + [False]
|
||||
|
||||
assert len(tokenized_str) == len(
|
||||
images_seq_mask), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
|
||||
|
||||
|
||||
|
||||
masked_tokenized_str = []
|
||||
for token_index in tokenized_str:
|
||||
if token_index != self.image_token_id:
|
||||
masked_tokenized_str.append(token_index)
|
||||
else:
|
||||
masked_tokenized_str.append(self.ignore_id)
|
||||
|
||||
assert len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str), \
|
||||
(f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
|
||||
f"imags_seq_mask's length {len(images_seq_mask)}, are not equal")
|
||||
|
||||
input_ids = torch.LongTensor(tokenized_str)
|
||||
target_ids = torch.LongTensor(masked_tokenized_str)
|
||||
images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
|
||||
|
||||
# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
|
||||
target_ids[(input_ids < 0) |
|
||||
(input_ids == self.image_token_id)] = self.ignore_id
|
||||
input_ids[input_ids < 0] = self.pad_id
|
||||
|
||||
inference_mode = True
|
||||
|
||||
if inference_mode:
|
||||
# Remove the ending eos token
|
||||
assert input_ids[-1] == self.eos_id
|
||||
input_ids = input_ids[:-1]
|
||||
target_ids = target_ids[:-1]
|
||||
images_seq_mask = images_seq_mask[:-1]
|
||||
|
||||
if len(images_list) == 0:
|
||||
pixel_values = torch.zeros((1, 3, self.base_size, self.base_size))
|
||||
images_spatial_crop = torch.zeros((1, 1), dtype=torch.long)
|
||||
images_crop = torch.zeros((1, 3, self.image_size, self.image_size)).unsqueeze(0)
|
||||
else:
|
||||
pixel_values = torch.stack(images_list, dim=0)
|
||||
images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
|
||||
if images_crop_list:
|
||||
images_crop = torch.stack(images_crop_list, dim=0).unsqueeze(0)
|
||||
else:
|
||||
images_crop = torch.zeros((1, 3, self.image_size, self.image_size)).unsqueeze(0)
|
||||
|
||||
input_ids = input_ids.unsqueeze(0)
|
||||
|
||||
|
||||
return [[input_ids, pixel_values, images_crop, images_seq_mask, images_spatial_crop, num_image_tokens, image_shapes]]
|
||||
|
||||
|
||||
AutoProcessor.register("DeepseekVLV2Processor", DeepseekOCRProcessor)
|
||||
@@ -1,40 +0,0 @@
|
||||
import torch
|
||||
from transformers import LogitsProcessor
|
||||
from transformers.generation.logits_process import _calc_banned_ngram_tokens
|
||||
from typing import List, Set
|
||||
|
||||
|
||||
class NoRepeatNGramLogitsProcessor(LogitsProcessor):
|
||||
|
||||
def __init__(self, ngram_size: int, window_size: int = 100, whitelist_token_ids: set = None):
|
||||
if not isinstance(ngram_size, int) or ngram_size <= 0:
|
||||
raise ValueError(f"`ngram_size` has to be a strictly positive integer, but is {ngram_size}")
|
||||
if not isinstance(window_size, int) or window_size <= 0:
|
||||
raise ValueError(f"`window_size` has to be a strictly positive integer, but is {window_size}")
|
||||
self.ngram_size = ngram_size
|
||||
self.window_size = window_size
|
||||
self.whitelist_token_ids = whitelist_token_ids or set()
|
||||
|
||||
def __call__(self, input_ids: List[int], scores: torch.FloatTensor) -> torch.FloatTensor:
|
||||
if len(input_ids) < self.ngram_size:
|
||||
return scores
|
||||
|
||||
current_prefix = tuple(input_ids[-(self.ngram_size - 1):])
|
||||
|
||||
search_start = max(0, len(input_ids) - self.window_size)
|
||||
search_end = len(input_ids) - self.ngram_size + 1
|
||||
|
||||
banned_tokens = set()
|
||||
for i in range(search_start, search_end):
|
||||
ngram = tuple(input_ids[i:i + self.ngram_size])
|
||||
if ngram[:-1] == current_prefix:
|
||||
banned_tokens.add(ngram[-1])
|
||||
|
||||
banned_tokens = banned_tokens - self.whitelist_token_ids
|
||||
|
||||
if banned_tokens:
|
||||
scores = scores.clone()
|
||||
for token in banned_tokens:
|
||||
scores[token] = -float("inf")
|
||||
|
||||
return scores
|
||||
@@ -1,10 +0,0 @@
|
||||
transformers
|
||||
tokenizers
|
||||
PyMuPDF
|
||||
img2pdf
|
||||
einops
|
||||
easydict
|
||||
addict
|
||||
Pillow
|
||||
numpy
|
||||
matplotlib
|
||||
@@ -1,161 +0,0 @@
|
||||
import os
|
||||
import re
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
if torch.version.cuda == '11.8':
|
||||
os.environ["TRITON_PTXAS_PATH"] = "/usr/local/cuda-11.8/bin/ptxas"
|
||||
os.environ['VLLM_USE_V1'] = '0'
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
|
||||
|
||||
from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, MAX_CONCURRENCY, CROP_MODE, NUM_WORKERS
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
import glob
|
||||
from PIL import Image
|
||||
from deepseek_ocr import DeepseekOCRForCausalLM
|
||||
|
||||
from vllm.model_executor.models.registry import ModelRegistry
|
||||
|
||||
from vllm import LLM, SamplingParams
|
||||
from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
|
||||
from process.image_process import DeepseekOCRProcessor
|
||||
ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
|
||||
|
||||
|
||||
llm = LLM(
|
||||
model=MODEL_PATH,
|
||||
hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
|
||||
block_size=256,
|
||||
enforce_eager=False,
|
||||
trust_remote_code=True,
|
||||
max_model_len=8192,
|
||||
swap_space=0,
|
||||
max_num_seqs = MAX_CONCURRENCY,
|
||||
tensor_parallel_size=1,
|
||||
gpu_memory_utilization=0.9,
|
||||
)
|
||||
|
||||
logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=40, window_size=90, whitelist_token_ids= {128821, 128822})] #window for fast；whitelist_token_ids: <td>,</td>
|
||||
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.0,
|
||||
max_tokens=8192,
|
||||
logits_processors=logits_processors,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
|
||||
class Colors:
|
||||
RED = '\033[31m'
|
||||
GREEN = '\033[32m'
|
||||
YELLOW = '\033[33m'
|
||||
BLUE = '\033[34m'
|
||||
RESET = '\033[0m'
|
||||
|
||||
def clean_formula(text):
|
||||
|
||||
formula_pattern = r'\\\[(.*?)\\\]'
|
||||
|
||||
def process_formula(match):
|
||||
formula = match.group(1)
|
||||
|
||||
formula = re.sub(r'\\quad\s*\([^)]*\)', '', formula)
|
||||
|
||||
formula = formula.strip()
|
||||
|
||||
return r'\[' + formula + r'\]'
|
||||
|
||||
cleaned_text = re.sub(formula_pattern, process_formula, text)
|
||||
|
||||
return cleaned_text
|
||||
|
||||
def re_match(text):
|
||||
pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
|
||||
matches = re.findall(pattern, text, re.DOTALL)
|
||||
|
||||
|
||||
# mathes_image = []
|
||||
mathes_other = []
|
||||
for a_match in matches:
|
||||
mathes_other.append(a_match[0])
|
||||
return matches, mathes_other
|
||||
|
||||
def process_single_image(image):
|
||||
"""single image"""
|
||||
prompt_in = prompt
|
||||
cache_item = {
|
||||
"prompt": prompt_in,
|
||||
"multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
|
||||
}
|
||||
return cache_item
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# INPUT_PATH = OmniDocBench images path
|
||||
|
||||
os.makedirs(OUTPUT_PATH, exist_ok=True)
|
||||
|
||||
# print('image processing until processing prompts.....')
|
||||
|
||||
print(f'{Colors.RED}glob images.....{Colors.RESET}')
|
||||
|
||||
images_path = glob.glob(f'{INPUT_PATH}/*')
|
||||
|
||||
images = []
|
||||
|
||||
for image_path in images_path:
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
images.append(image)
|
||||
|
||||
prompt = PROMPT
|
||||
|
||||
# batch_inputs = []
|
||||
|
||||
|
||||
# for image in tqdm(images):
|
||||
|
||||
# prompt_in = prompt
|
||||
# cache_list = [
|
||||
# {
|
||||
# "prompt": prompt_in,
|
||||
# "multi_modal_data": {"image": Image.open(image).convert('RGB')},
|
||||
# }
|
||||
# ]
|
||||
# batch_inputs.extend(cache_list)
|
||||
|
||||
with ThreadPoolExecutor(max_workers=NUM_WORKERS) as executor:
|
||||
batch_inputs = list(tqdm(
|
||||
executor.map(process_single_image, images),
|
||||
total=len(images),
|
||||
desc="Pre-processed images"
|
||||
))
|
||||
|
||||
|
||||
|
||||
|
||||
outputs_list = llm.generate(
|
||||
batch_inputs,
|
||||
sampling_params=sampling_params
|
||||
)
|
||||
|
||||
|
||||
output_path = OUTPUT_PATH
|
||||
|
||||
os.makedirs(output_path, exist_ok=True)
|
||||
|
||||
for output, image in zip(outputs_list, images_path):
|
||||
|
||||
content = output.outputs[0].text
|
||||
mmd_det_path = output_path + image.split('/')[-1].replace('.jpg', '_det.md')
|
||||
|
||||
with open(mmd_det_path, 'w', encoding='utf-8') as afile:
|
||||
afile.write(content)
|
||||
|
||||
content = clean_formula(content)
|
||||
matches_ref, mathes_other = re_match(content)
|
||||
for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
|
||||
content = content.replace(a_match_other, '').replace('\n\n\n\n', '\n\n').replace('\n\n\n', '\n\n').replace('<center>', '').replace('</center>', '')
|
||||
|
||||
mmd_path = output_path + image.split('/')[-1].replace('.jpg', '.md')
|
||||
|
||||
with open(mmd_path, 'w', encoding='utf-8') as afile:
|
||||
afile.write(content)
|
||||
@@ -1,303 +0,0 @@
|
||||
import asyncio
|
||||
import re
|
||||
import os
|
||||
|
||||
import torch
|
||||
if torch.version.cuda == '11.8':
|
||||
os.environ["TRITON_PTXAS_PATH"] = "/usr/local/cuda-11.8/bin/ptxas"
|
||||
|
||||
os.environ['VLLM_USE_V1'] = '0'
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
|
||||
|
||||
from vllm import AsyncLLMEngine, SamplingParams
|
||||
from vllm.engine.arg_utils import AsyncEngineArgs
|
||||
from vllm.model_executor.models.registry import ModelRegistry
|
||||
import time
|
||||
from deepseek_ocr import DeepseekOCRForCausalLM
|
||||
from PIL import Image, ImageDraw, ImageFont, ImageOps
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
|
||||
from process.image_process import DeepseekOCRProcessor
|
||||
from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, CROP_MODE
|
||||
|
||||
|
||||
|
||||
ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
|
||||
|
||||
def load_image(image_path):
|
||||
|
||||
try:
|
||||
image = Image.open(image_path)
|
||||
|
||||
corrected_image = ImageOps.exif_transpose(image)
|
||||
|
||||
return corrected_image
|
||||
|
||||
except Exception as e:
|
||||
print(f"error: {e}")
|
||||
try:
|
||||
return Image.open(image_path)
|
||||
except:
|
||||
return None
|
||||
|
||||
|
||||
def re_match(text):
|
||||
pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
|
||||
matches = re.findall(pattern, text, re.DOTALL)
|
||||
|
||||
|
||||
mathes_image = []
|
||||
mathes_other = []
|
||||
for a_match in matches:
|
||||
if '<|ref|>image<|/ref|>' in a_match[0]:
|
||||
mathes_image.append(a_match[0])
|
||||
else:
|
||||
mathes_other.append(a_match[0])
|
||||
return matches, mathes_image, mathes_other
|
||||
|
||||
|
||||
def extract_coordinates_and_label(ref_text, image_width, image_height):
|
||||
|
||||
|
||||
try:
|
||||
label_type = ref_text[1]
|
||||
cor_list = eval(ref_text[2])
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return None
|
||||
|
||||
return (label_type, cor_list)
|
||||
|
||||
|
||||
def draw_bounding_boxes(image, refs):
|
||||
|
||||
image_width, image_height = image.size
|
||||
img_draw = image.copy()
|
||||
draw = ImageDraw.Draw(img_draw)
|
||||
|
||||
overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
|
||||
draw2 = ImageDraw.Draw(overlay)
|
||||
|
||||
# except IOError:
|
||||
font = ImageFont.load_default()
|
||||
|
||||
img_idx = 0
|
||||
|
||||
for i, ref in enumerate(refs):
|
||||
try:
|
||||
result = extract_coordinates_and_label(ref, image_width, image_height)
|
||||
if result:
|
||||
label_type, points_list = result
|
||||
|
||||
color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
|
||||
|
||||
color_a = color + (20, )
|
||||
for points in points_list:
|
||||
x1, y1, x2, y2 = points
|
||||
|
||||
x1 = int(x1 / 999 * image_width)
|
||||
y1 = int(y1 / 999 * image_height)
|
||||
|
||||
x2 = int(x2 / 999 * image_width)
|
||||
y2 = int(y2 / 999 * image_height)
|
||||
|
||||
if label_type == 'image':
|
||||
try:
|
||||
cropped = image.crop((x1, y1, x2, y2))
|
||||
cropped.save(f"{OUTPUT_PATH}/images/{img_idx}.jpg")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
img_idx += 1
|
||||
|
||||
try:
|
||||
if label_type == 'title':
|
||||
draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
|
||||
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
|
||||
else:
|
||||
draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
|
||||
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
|
||||
|
||||
text_x = x1
|
||||
text_y = max(0, y1 - 15)
|
||||
|
||||
text_bbox = draw.textbbox((0, 0), label_type, font=font)
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height],
|
||||
fill=(255, 255, 255, 30))
|
||||
|
||||
draw.text((text_x, text_y), label_type, font=font, fill=color)
|
||||
except:
|
||||
pass
|
||||
except:
|
||||
continue
|
||||
img_draw.paste(overlay, (0, 0), overlay)
|
||||
return img_draw
|
||||
|
||||
|
||||
def process_image_with_refs(image, ref_texts):
|
||||
result_image = draw_bounding_boxes(image, ref_texts)
|
||||
return result_image
|
||||
|
||||
|
||||
|
||||
|
||||
async def stream_generate(image=None, prompt=''):
|
||||
|
||||
|
||||
engine_args = AsyncEngineArgs(
|
||||
model=MODEL_PATH,
|
||||
hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
|
||||
block_size=256,
|
||||
max_model_len=8192,
|
||||
enforce_eager=False,
|
||||
trust_remote_code=True,
|
||||
tensor_parallel_size=1,
|
||||
gpu_memory_utilization=0.75,
|
||||
)
|
||||
engine = AsyncLLMEngine.from_engine_args(engine_args)
|
||||
|
||||
logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=30, window_size=90, whitelist_token_ids= {128821, 128822})] #whitelist: <td>, </td>
|
||||
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.0,
|
||||
max_tokens=8192,
|
||||
logits_processors=logits_processors,
|
||||
skip_special_tokens=False,
|
||||
# ignore_eos=False,
|
||||
|
||||
)
|
||||
|
||||
request_id = f"request-{int(time.time())}"
|
||||
|
||||
printed_length = 0
|
||||
|
||||
if image and '<image>' in prompt:
|
||||
request = {
|
||||
"prompt": prompt,
|
||||
"multi_modal_data": {"image": image}
|
||||
}
|
||||
elif prompt:
|
||||
request = {
|
||||
"prompt": prompt
|
||||
}
|
||||
else:
|
||||
assert False, f'prompt is none!!!'
|
||||
async for request_output in engine.generate(
|
||||
request, sampling_params, request_id
|
||||
):
|
||||
if request_output.outputs:
|
||||
full_text = request_output.outputs[0].text
|
||||
new_text = full_text[printed_length:]
|
||||
print(new_text, end='', flush=True)
|
||||
printed_length = len(full_text)
|
||||
final_output = full_text
|
||||
print('\n')
|
||||
|
||||
return final_output
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
os.makedirs(OUTPUT_PATH, exist_ok=True)
|
||||
os.makedirs(f'{OUTPUT_PATH}/images', exist_ok=True)
|
||||
|
||||
image = load_image(INPUT_PATH).convert('RGB')
|
||||
|
||||
|
||||
if '<image>' in PROMPT:
|
||||
|
||||
image_features = DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)
|
||||
else:
|
||||
image_features = ''
|
||||
|
||||
prompt = PROMPT
|
||||
|
||||
result_out = asyncio.run(stream_generate(image_features, prompt))
|
||||
|
||||
|
||||
save_results = 1
|
||||
|
||||
if save_results and '<image>' in prompt:
|
||||
print('='*15 + 'save results:' + '='*15)
|
||||
|
||||
image_draw = image.copy()
|
||||
|
||||
outputs = result_out
|
||||
|
||||
with open(f'{OUTPUT_PATH}/result_ori.mmd', 'w', encoding = 'utf-8') as afile:
|
||||
afile.write(outputs)
|
||||
|
||||
matches_ref, matches_images, mathes_other = re_match(outputs)
|
||||
# print(matches_ref)
|
||||
result = process_image_with_refs(image_draw, matches_ref)
|
||||
|
||||
|
||||
for idx, a_match_image in enumerate(tqdm(matches_images, desc="image")):
|
||||
outputs = outputs.replace(a_match_image, f' + '.jpg)\n')
|
||||
|
||||
for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
|
||||
outputs = outputs.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
|
||||
|
||||
# if 'structural formula' in conversation[0]['content']:
|
||||
# outputs = '<smiles>' + outputs + '</smiles>'
|
||||
with open(f'{OUTPUT_PATH}/result.mmd', 'w', encoding = 'utf-8') as afile:
|
||||
afile.write(outputs)
|
||||
|
||||
if 'line_type' in outputs:
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.patches import Circle
|
||||
lines = eval(outputs)['Line']['line']
|
||||
|
||||
line_type = eval(outputs)['Line']['line_type']
|
||||
# print(lines)
|
||||
|
||||
endpoints = eval(outputs)['Line']['line_endpoint']
|
||||
|
||||
fig, ax = plt.subplots(figsize=(3,3), dpi=200)
|
||||
ax.set_xlim(-15, 15)
|
||||
ax.set_ylim(-15, 15)
|
||||
|
||||
for idx, line in enumerate(lines):
|
||||
try:
|
||||
p0 = eval(line.split(' -- ')[0])
|
||||
p1 = eval(line.split(' -- ')[-1])
|
||||
|
||||
if line_type[idx] == '--':
|
||||
ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth=0.8, color='k')
|
||||
else:
|
||||
ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth = 0.8, color = 'k')
|
||||
|
||||
ax.scatter(p0[0], p0[1], s=5, color = 'k')
|
||||
ax.scatter(p1[0], p1[1], s=5, color = 'k')
|
||||
except:
|
||||
pass
|
||||
|
||||
for endpoint in endpoints:
|
||||
|
||||
label = endpoint.split(': ')[0]
|
||||
(x, y) = eval(endpoint.split(': ')[1])
|
||||
ax.annotate(label, (x, y), xytext=(1, 1), textcoords='offset points',
|
||||
fontsize=5, fontweight='light')
|
||||
|
||||
try:
|
||||
if 'Circle' in eval(outputs).keys():
|
||||
circle_centers = eval(outputs)['Circle']['circle_center']
|
||||
radius = eval(outputs)['Circle']['radius']
|
||||
|
||||
for center, r in zip(circle_centers, radius):
|
||||
center = eval(center.split(': ')[1])
|
||||
circle = Circle(center, radius=r, fill=False, edgecolor='black', linewidth=0.8)
|
||||
ax.add_patch(circle)
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
plt.savefig(f'{OUTPUT_PATH}/geo.jpg')
|
||||
plt.close()
|
||||
|
||||
result.save(f'{OUTPUT_PATH}/result_with_boxes.jpg')
|
||||
@@ -1,330 +0,0 @@
|
||||
import os
|
||||
import fitz
|
||||
import img2pdf
|
||||
import io
|
||||
import re
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
|
||||
if torch.version.cuda == '11.8':
|
||||
os.environ["TRITON_PTXAS_PATH"] = "/usr/local/cuda-11.8/bin/ptxas"
|
||||
os.environ['VLLM_USE_V1'] = '0'
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
|
||||
|
||||
|
||||
from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, SKIP_REPEAT, MAX_CONCURRENCY, NUM_WORKERS, CROP_MODE
|
||||
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import numpy as np
|
||||
from deepseek_ocr import DeepseekOCRForCausalLM
|
||||
|
||||
from vllm.model_executor.models.registry import ModelRegistry
|
||||
|
||||
from vllm import LLM, SamplingParams
|
||||
from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
|
||||
from process.image_process import DeepseekOCRProcessor
|
||||
|
||||
ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
|
||||
|
||||
|
||||
llm = LLM(
|
||||
model=MODEL_PATH,
|
||||
hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
|
||||
block_size=256,
|
||||
enforce_eager=False,
|
||||
trust_remote_code=True,
|
||||
max_model_len=8192,
|
||||
swap_space=0,
|
||||
max_num_seqs=MAX_CONCURRENCY,
|
||||
tensor_parallel_size=1,
|
||||
gpu_memory_utilization=0.9,
|
||||
disable_mm_preprocessor_cache=True
|
||||
)
|
||||
|
||||
logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=20, window_size=50, whitelist_token_ids= {128821, 128822})] #window for fast；whitelist_token_ids: <td>,</td>
|
||||
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.0,
|
||||
max_tokens=8192,
|
||||
logits_processors=logits_processors,
|
||||
skip_special_tokens=False,
|
||||
include_stop_str_in_output=True,
|
||||
)
|
||||
|
||||
|
||||
class Colors:
|
||||
RED = '\033[31m'
|
||||
GREEN = '\033[32m'
|
||||
YELLOW = '\033[33m'
|
||||
BLUE = '\033[34m'
|
||||
RESET = '\033[0m'
|
||||
|
||||
def pdf_to_images_high_quality(pdf_path, dpi=144, image_format="PNG"):
|
||||
"""
|
||||
pdf2images
|
||||
"""
|
||||
images = []
|
||||
|
||||
pdf_document = fitz.open(pdf_path)
|
||||
|
||||
zoom = dpi / 72.0
|
||||
matrix = fitz.Matrix(zoom, zoom)
|
||||
|
||||
for page_num in range(pdf_document.page_count):
|
||||
page = pdf_document[page_num]
|
||||
|
||||
pixmap = page.get_pixmap(matrix=matrix, alpha=False)
|
||||
Image.MAX_IMAGE_PIXELS = None
|
||||
|
||||
if image_format.upper() == "PNG":
|
||||
img_data = pixmap.tobytes("png")
|
||||
img = Image.open(io.BytesIO(img_data))
|
||||
else:
|
||||
img_data = pixmap.tobytes("png")
|
||||
img = Image.open(io.BytesIO(img_data))
|
||||
if img.mode in ('RGBA', 'LA'):
|
||||
background = Image.new('RGB', img.size, (255, 255, 255))
|
||||
background.paste(img, mask=img.split()[-1] if img.mode == 'RGBA' else None)
|
||||
img = background
|
||||
|
||||
images.append(img)
|
||||
|
||||
pdf_document.close()
|
||||
return images
|
||||
|
||||
def pil_to_pdf_img2pdf(pil_images, output_path):
|
||||
|
||||
if not pil_images:
|
||||
return
|
||||
|
||||
image_bytes_list = []
|
||||
|
||||
for img in pil_images:
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
img_buffer = io.BytesIO()
|
||||
img.save(img_buffer, format='JPEG', quality=95)
|
||||
img_bytes = img_buffer.getvalue()
|
||||
image_bytes_list.append(img_bytes)
|
||||
|
||||
try:
|
||||
pdf_bytes = img2pdf.convert(image_bytes_list)
|
||||
with open(output_path, "wb") as f:
|
||||
f.write(pdf_bytes)
|
||||
|
||||
except Exception as e:
|
||||
print(f"error: {e}")
|
||||
|
||||
|
||||
|
||||
def re_match(text):
|
||||
pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
|
||||
matches = re.findall(pattern, text, re.DOTALL)
|
||||
|
||||
|
||||
mathes_image = []
|
||||
mathes_other = []
|
||||
for a_match in matches:
|
||||
if '<|ref|>image<|/ref|>' in a_match[0]:
|
||||
mathes_image.append(a_match[0])
|
||||
else:
|
||||
mathes_other.append(a_match[0])
|
||||
return matches, mathes_image, mathes_other
|
||||
|
||||
|
||||
def extract_coordinates_and_label(ref_text, image_width, image_height):
|
||||
|
||||
|
||||
try:
|
||||
label_type = ref_text[1]
|
||||
cor_list = eval(ref_text[2])
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return None
|
||||
|
||||
return (label_type, cor_list)
|
||||
|
||||
|
||||
def draw_bounding_boxes(image, refs, jdx):
|
||||
|
||||
image_width, image_height = image.size
|
||||
img_draw = image.copy()
|
||||
draw = ImageDraw.Draw(img_draw)
|
||||
|
||||
overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
|
||||
draw2 = ImageDraw.Draw(overlay)
|
||||
|
||||
# except IOError:
|
||||
font = ImageFont.load_default()
|
||||
|
||||
img_idx = 0
|
||||
|
||||
for i, ref in enumerate(refs):
|
||||
try:
|
||||
result = extract_coordinates_and_label(ref, image_width, image_height)
|
||||
if result:
|
||||
label_type, points_list = result
|
||||
|
||||
color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
|
||||
|
||||
color_a = color + (20, )
|
||||
for points in points_list:
|
||||
x1, y1, x2, y2 = points
|
||||
|
||||
x1 = int(x1 / 999 * image_width)
|
||||
y1 = int(y1 / 999 * image_height)
|
||||
|
||||
x2 = int(x2 / 999 * image_width)
|
||||
y2 = int(y2 / 999 * image_height)
|
||||
|
||||
if label_type == 'image':
|
||||
try:
|
||||
cropped = image.crop((x1, y1, x2, y2))
|
||||
cropped.save(f"{OUTPUT_PATH}/images/{jdx}_{img_idx}.jpg")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
img_idx += 1
|
||||
|
||||
try:
|
||||
if label_type == 'title':
|
||||
draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
|
||||
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
|
||||
else:
|
||||
draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
|
||||
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
|
||||
|
||||
text_x = x1
|
||||
text_y = max(0, y1 - 15)
|
||||
|
||||
text_bbox = draw.textbbox((0, 0), label_type, font=font)
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height],
|
||||
fill=(255, 255, 255, 30))
|
||||
|
||||
draw.text((text_x, text_y), label_type, font=font, fill=color)
|
||||
except:
|
||||
pass
|
||||
except:
|
||||
continue
|
||||
img_draw.paste(overlay, (0, 0), overlay)
|
||||
return img_draw
|
||||
|
||||
|
||||
def process_image_with_refs(image, ref_texts, jdx):
|
||||
result_image = draw_bounding_boxes(image, ref_texts, jdx)
|
||||
return result_image
|
||||
|
||||
|
||||
def process_single_image(image):
|
||||
"""single image"""
|
||||
prompt_in = prompt
|
||||
cache_item = {
|
||||
"prompt": prompt_in,
|
||||
"multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
|
||||
}
|
||||
return cache_item
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
os.makedirs(OUTPUT_PATH, exist_ok=True)
|
||||
os.makedirs(f'{OUTPUT_PATH}/images', exist_ok=True)
|
||||
|
||||
print(f'{Colors.RED}PDF loading .....{Colors.RESET}')
|
||||
|
||||
|
||||
images = pdf_to_images_high_quality(INPUT_PATH)
|
||||
|
||||
|
||||
prompt = PROMPT
|
||||
|
||||
# batch_inputs = []
|
||||
|
||||
with ThreadPoolExecutor(max_workers=NUM_WORKERS) as executor:
|
||||
batch_inputs = list(tqdm(
|
||||
executor.map(process_single_image, images),
|
||||
total=len(images),
|
||||
desc="Pre-processed images"
|
||||
))
|
||||
|
||||
|
||||
# for image in tqdm(images):
|
||||
|
||||
# prompt_in = prompt
|
||||
# cache_list = [
|
||||
# {
|
||||
# "prompt": prompt_in,
|
||||
# "multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
|
||||
# }
|
||||
# ]
|
||||
# batch_inputs.extend(cache_list)
|
||||
|
||||
|
||||
outputs_list = llm.generate(
|
||||
batch_inputs,
|
||||
sampling_params=sampling_params
|
||||
)
|
||||
|
||||
|
||||
output_path = OUTPUT_PATH
|
||||
|
||||
os.makedirs(output_path, exist_ok=True)
|
||||
|
||||
|
||||
mmd_det_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('.pdf', '_det.mmd')
|
||||
mmd_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('pdf', 'mmd')
|
||||
pdf_out_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('.pdf', '_layouts.pdf')
|
||||
contents_det = ''
|
||||
contents = ''
|
||||
draw_images = []
|
||||
jdx = 0
|
||||
for output, img in zip(outputs_list, images):
|
||||
content = output.outputs[0].text
|
||||
|
||||
if '<｜end▁of▁sentence｜>' in content: # repeat no eos
|
||||
content = content.replace('<｜end▁of▁sentence｜>', '')
|
||||
else:
|
||||
if SKIP_REPEAT:
|
||||
continue
|
||||
|
||||
|
||||
page_num = f'\n<--- Page Split --->'
|
||||
|
||||
contents_det += content + f'\n{page_num}\n'
|
||||
|
||||
image_draw = img.copy()
|
||||
|
||||
matches_ref, matches_images, mathes_other = re_match(content)
|
||||
# print(matches_ref)
|
||||
result_image = process_image_with_refs(image_draw, matches_ref, jdx)
|
||||
|
||||
|
||||
draw_images.append(result_image)
|
||||
|
||||
|
||||
for idx, a_match_image in enumerate(matches_images):
|
||||
content = content.replace(a_match_image, f' + '_' + str(idx) + '.jpg)\n')
|
||||
|
||||
for idx, a_match_other in enumerate(mathes_other):
|
||||
content = content.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:').replace('\n\n\n\n', '\n\n').replace('\n\n\n', '\n\n')
|
||||
|
||||
|
||||
contents += content + f'\n{page_num}\n'
|
||||
|
||||
|
||||
jdx += 1
|
||||
|
||||
with open(mmd_det_path, 'w', encoding='utf-8') as afile:
|
||||
afile.write(contents_det)
|
||||
|
||||
with open(mmd_path, 'w', encoding='utf-8') as afile:
|
||||
afile.write(contents)
|
||||
|
||||
|
||||
pil_to_pdf_img2pdf(draw_images, pdf_out_path)
|
||||
|
||||
@@ -1,48 +0,0 @@
|
||||
# PyTorch 2.6.0 + CUDA 12.6 + cuDNN9
|
||||
FROM pytorch/pytorch:2.6.0-cuda12.6-cudnn9-devel
|
||||
|
||||
ENV DEBIAN_FRONTEND=noninteractive \
|
||||
HF_HOME=/workspace/.cache/huggingface \
|
||||
CUDA_HOME=/usr/local/cuda \
|
||||
LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} \
|
||||
PIP_DISABLE_PIP_VERSION_CHECK=1 \
|
||||
PYTHONUNBUFFERED=1 \
|
||||
HF_HUB_DISABLE_TELEMETRY=1
|
||||
|
||||
ARG TORCH_CUDA_ARCH_LIST=80
|
||||
ENV TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}
|
||||
ENV TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
# 빌드 도구
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
git build-essential ninja-build cmake \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN python -m pip install -U pip setuptools wheel packaging ninja
|
||||
|
||||
# 명시 재설치(일관성)
|
||||
RUN pip install --no-cache-dir \
|
||||
torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0
|
||||
|
||||
# DeepSeek-OCR(HF) 호환 스택
|
||||
RUN pip install --no-cache-dir \
|
||||
"transformers==4.43.3" \
|
||||
"accelerate==0.33.0" \
|
||||
"tokenizers==0.19.1" \
|
||||
"numpy==1.26.4" \
|
||||
"safetensors>=0.4.2" \
|
||||
"einops" "timm>=0.9"
|
||||
|
||||
# flash-attn 2.7.3 (CUDA 12.6에서 빌드)
|
||||
RUN pip install --no-cache-dir --no-build-isolation --no-binary=flash-attn flash-attn==2.7.3
|
||||
|
||||
# vLLM 제거(혹시 들어오더라도 충돌 방지)
|
||||
RUN pip uninstall -y vllm || true
|
||||
|
||||
# OpenCV ↔ numpy 1.26 호환 버전 고정
|
||||
RUN pip install --no-cache-dir "opencv-python-headless==4.8.1.78"
|
||||
|
||||
# 앱 소스
|
||||
COPY DeepSeek-OCR-master/DeepSeek-OCR-hf/ /workspace/DeepSeek-OCR-hf/
|
||||
@@ -1,39 +0,0 @@
|
||||
# PyTorch 2.6.0 + CUDA 12.6 + cuDNN9
|
||||
FROM pytorch/pytorch:2.6.0-cuda12.6-cudnn9-devel
|
||||
|
||||
# 기본 환경 변수 설정
|
||||
ENV DEBIAN_FRONTEND=noninteractive \
|
||||
HF_HOME=/workspace/.cache/huggingface \
|
||||
CUDA_HOME=/usr/local/cuda \
|
||||
LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} \
|
||||
PIP_DISABLE_PIP_VERSION_CHECK=1 \
|
||||
PYTHONUNBUFFERED=1 \
|
||||
TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas \
|
||||
TORCH_CUDA_ARCH_LIST="8.0"
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
# 필수 빌드 도구 설치
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
git build-essential ninja-build \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# pip 업그레이드
|
||||
RUN python -m pip install -U pip setuptools wheel
|
||||
|
||||
# 기존 라이브러리 제거 및 특정 버전 재설치
|
||||
RUN pip uninstall -y vllm torch torchvision torchaudio triton flash-attn || true
|
||||
RUN pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0
|
||||
|
||||
# 프로젝트 의존성 설치
|
||||
COPY requirements.txt /tmp/requirements.txt
|
||||
RUN pip install -r /tmp/requirements.txt
|
||||
|
||||
# vLLM 특정 버전 설치
|
||||
RUN pip install vllm==0.8.5
|
||||
|
||||
# FlashAttention 소스에서 빌드하여 설치
|
||||
RUN pip cache purge && \
|
||||
pip install --no-cache-dir --no-build-isolation --no-binary=flash-attn flash-attn==2.7.3
|
||||
|
||||
WORKDIR /workspace
|
||||
@@ -1,37 +0,0 @@
|
||||
1version: '3.8'
|
||||
|
||||
services:
|
||||
api_gateway:
|
||||
build:
|
||||
context: ./api_gateway
|
||||
ports:
|
||||
- "80:8000" # 호스트의 80번 포트를 게이트웨이의 8000번 포트로 연결
|
||||
networks:
|
||||
- vlm_network
|
||||
depends_on:
|
||||
- deepseek_ocr # deepseek_ocr 서비스가 시작된 후에 게이트웨이를 시작
|
||||
restart: always
|
||||
|
||||
deepseek_ocr:
|
||||
build:
|
||||
context: ./model_services/deepseek_ocr
|
||||
# deploy 키를 사용하여 GPU 리소스를 요청합니다.
|
||||
# 이 설정은 docker-compose up --build 대신 docker stack deploy 또는 docker compose up --deploy와 함께 사용할 때 공식적으로 지원됩니다.
|
||||
# 일반적인 docker-compose up에서도 동작하는 경우가 많습니다.
|
||||
deploy:
|
||||
resources:
|
||||
reservations:
|
||||
devices:
|
||||
- driver: nvidia
|
||||
count: 1 # 1개의 GPU를 할당
|
||||
capabilities: [gpu]
|
||||
environment:
|
||||
- NVIDIA_VISIBLE_DEVICES=all # 컨테이너가 모든 GPU를 볼 수 있도록 설정
|
||||
- MODEL_PATH=deepseek-ai/deepseek-vl-7b-base # 사용할 모델 지정 (필요시 수정)
|
||||
networks:
|
||||
- vlm_network
|
||||
restart: on-failure
|
||||
|
||||
networks:
|
||||
vlm_network:
|
||||
driver: bridge
|
||||
@@ -1,32 +1,39 @@
|
||||
# 1. vLLM 호환을 위해 NVIDIA CUDA 베이스 이미지 선택
|
||||
# 참고: vLLM 버전에 따라 적절한 CUDA 버전을 선택해야 할 수 있습니다.
|
||||
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04
|
||||
# PyTorch 2.6.0 + CUDA 12.6 + cuDNN9
|
||||
FROM pytorch/pytorch:2.6.0-cuda12.6-cudnn9-devel
|
||||
|
||||
# 2. 환경 변수 설정 및 기본 패키지 설치
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
RUN apt-get update && apt-get install -y \
|
||||
python3.9 \
|
||||
python3.9-pip \
|
||||
git \
|
||||
# 기본 환경 변수 설정
|
||||
ENV DEBIAN_FRONTEND=noninteractive \
|
||||
HF_HOME=/workspace/.cache/huggingface \
|
||||
CUDA_HOME=/usr/local/cuda \
|
||||
LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} \
|
||||
PIP_DISABLE_PIP_VERSION_CHECK=1 \
|
||||
PYTHONUNBUFFERED=1 \
|
||||
TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas \
|
||||
TORCH_CUDA_ARCH_LIST="8.0"
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
# 필수 빌드 도구 설치
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
git build-essential ninja-build \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# python3.9을 기본 python/pip으로 설정
|
||||
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1 && \
|
||||
update-alternatives --install /usr/bin/pip pip /usr/bin/pip 1
|
||||
# pip 업그레이드
|
||||
RUN python -m pip install -U pip setuptools wheel
|
||||
|
||||
# 3. 작업 디렉토리 설정
|
||||
WORKDIR /app
|
||||
# 기존 라이브러리 제거 및 특정 버전 재설치
|
||||
RUN pip uninstall -y vllm torch torchvision torchaudio triton flash-attn || true
|
||||
RUN pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0
|
||||
|
||||
# 4. 소스 코드 및 의존성 파일 복사
|
||||
# (main.py, requirements.txt, deepseek_ocr.py, process/, config.py 등 모든 파일)
|
||||
COPY . .
|
||||
# 프로젝트 의존성 설치
|
||||
COPY requirements.txt /tmp/requirements.txt
|
||||
RUN pip install -r /tmp/requirements.txt
|
||||
|
||||
# 5. Python 의존성 설치
|
||||
# vLLM은 torch를 필요로 하므로 함께 설치합니다.
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
# vLLM 특정 버전 설치
|
||||
RUN pip install vllm==0.8.5
|
||||
|
||||
# 6. 서비스 포트 노출
|
||||
EXPOSE 8000
|
||||
# FlashAttention 소스에서 빌드하여 설치
|
||||
RUN pip cache purge && \
|
||||
pip install --no-cache-dir --no-build-isolation --no-binary=flash-attn flash-attn==2.7.3
|
||||
|
||||
# 7. FastAPI 서버 실행
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
|
||||
WORKDIR /workspace
|
||||
@@ -8,25 +8,24 @@
|
||||
BASE_SIZE = 1024
|
||||
IMAGE_SIZE = 640
|
||||
CROP_MODE = True
|
||||
MIN_CROPS= 2
|
||||
MAX_CROPS= 6 # max:9; If your GPU memory is small, it is recommended to set it to 6.
|
||||
MAX_CONCURRENCY = 100 # If you have limited GPU memory, lower the concurrency count.
|
||||
NUM_WORKERS = 64 # image pre-process (resize/padding) workers
|
||||
MIN_CROPS = 2
|
||||
MAX_CROPS = 6 # max:9; If your GPU memory is small, it is recommended to set it to 6.
|
||||
MAX_CONCURRENCY = 100 # If you have limited GPU memory, lower the concurrency count.
|
||||
NUM_WORKERS = 64 # image pre-process (resize/padding) workers
|
||||
PRINT_NUM_VIS_TOKENS = False
|
||||
SKIP_REPEAT = True
|
||||
MODEL_PATH = 'deepseek-ai/DeepSeek-OCR' # change to your model path
|
||||
MODEL_PATH = "deepseek-ai/DeepSeek-OCR" # change to your model path
|
||||
|
||||
# TODO: change INPUT_PATH
|
||||
# .pdf: run_dpsk_ocr_pdf.py;
|
||||
# .jpg, .png, .jpeg: run_dpsk_ocr_image.py;
|
||||
# Omnidocbench images path: run_dpsk_ocr_eval_batch.py
|
||||
|
||||
FILE_NAME='2025-27484-M21472.pdf'
|
||||
|
||||
INPUT_PATH = f'/workspace/2025-27484-M21472.pdf'
|
||||
OUTPUT_PATH = '/workspace/output/'
|
||||
INPUT_PATH = "/workspace/2018-0802140959-217049.pdf"
|
||||
OUTPUT_PATH = "/workspace/output/"
|
||||
|
||||
PROMPT = '<image>\n<|grounding|>Convert the document to markdown.'
|
||||
PROMPT = "<image>\n<|grounding|>Convert the document to markdown."
|
||||
# PROMPT = '<image>\nFree OCR.'
|
||||
# TODO commonly used prompts
|
||||
# document: <image>\n<|grounding|>Convert the document to markdown.
|
||||
|
||||
21
model_services/deepseek_ocr/docker-compose.yml
Normal file
21
model_services/deepseek_ocr/docker-compose.yml
Normal file
@@ -0,0 +1,21 @@
|
||||
services:
|
||||
deepseek_ocr_vllm:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
image: deepseek-ocr-vllm:cu126
|
||||
container_name: deepseek_ocr_vllm
|
||||
working_dir: /workspace
|
||||
volumes:
|
||||
- ./:/workspace
|
||||
gpus: all
|
||||
shm_size: "8g"
|
||||
ipc: "host"
|
||||
environment:
|
||||
- HF_HOME=/workspace/.cache/huggingface
|
||||
- CUDA_HOME=/usr/local/cuda
|
||||
- LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
|
||||
- PIP_DISABLE_PIP_VERSION_CHECK=1
|
||||
- PYTHONUNBUFFERED=1
|
||||
tty: true
|
||||
entrypoint: ["/bin/bash"]
|
||||
@@ -1,112 +0,0 @@
|
||||
import os
|
||||
import base64
|
||||
import io
|
||||
import time
|
||||
|
||||
from fastapi import FastAPI
|
||||
from pydantic import BaseModel
|
||||
from PIL import Image
|
||||
|
||||
# vLLM 및 모델 관련 import
|
||||
from vllm import AsyncLLMEngine, SamplingParams
|
||||
from vllm.engine.arg_utils import AsyncEngineArgs
|
||||
from vllm.model_executor.models.registry import ModelRegistry
|
||||
|
||||
# DeepSeek-OCR 관련 로컬 import
|
||||
from deepseek_ocr import DeepseekOCRForCausalLM
|
||||
from process.image_process import DeepseekOCRProcessor
|
||||
from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
|
||||
|
||||
# --- Configuration ---
|
||||
# Docker 환경에서는 환경 변수를 사용하거나, Dockerfile에서 모델을 다운로드하는 것이 좋습니다.
|
||||
# 여기서는 config.py의 기본값을 사용하되, 환경 변수로 재정의할 수 있도록 합니다.
|
||||
MODEL_PATH = os.environ.get("MODEL_PATH", "deepseek-ai/deepseek-vl-7b-base")
|
||||
# 참고: 실제 `config.py`는 로컬 경로를 사용하므로, 허깅페이스 모델 ID로 대체합니다.
|
||||
# 이 모델을 사용하려면 인터넷 연결이 필요하며, 처음 실행 시 다운로드됩니다.
|
||||
|
||||
# --- Model Initialization ---
|
||||
|
||||
# 1. 커스텀 모델 등록
|
||||
ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
|
||||
|
||||
# 2. vLLM 엔진 설정
|
||||
engine_args = AsyncEngineArgs(
|
||||
model=MODEL_PATH,
|
||||
hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
|
||||
max_model_len=8192,
|
||||
enforce_eager=False,
|
||||
trust_remote_code=True,
|
||||
tensor_parallel_size=1, # 단일 GPU 사용
|
||||
gpu_memory_utilization=0.90, # GPU 메모리 사용률
|
||||
)
|
||||
engine = AsyncLLMEngine.from_engine_args(engine_args)
|
||||
|
||||
# 3. Deepseek OCR 프로세서 초기화
|
||||
processor = DeepseekOCRProcessor()
|
||||
|
||||
# 4. FastAPI 앱 초기화
|
||||
app = FastAPI()
|
||||
|
||||
# --- Pydantic Models ---
|
||||
class InferenceRequest(BaseModel):
|
||||
# Base64로 인코딩된 이미지 문자열
|
||||
base64_image: str
|
||||
|
||||
class InferenceResponse(BaseModel):
|
||||
text: str
|
||||
|
||||
# --- API Endpoints ---
|
||||
|
||||
@app.get("/")
|
||||
def health_check():
|
||||
return {"status": "DeepSeek-OCR service is running"}
|
||||
|
||||
@app.post("/process", response_model=InferenceResponse)
|
||||
async def process_image(request: InferenceRequest):
|
||||
"""
|
||||
Base64 인코딩된 이미지를 받아 OCR 추론을 수행합니다.
|
||||
"""
|
||||
try:
|
||||
# 1. Base64 이미지 디코딩
|
||||
image_data = base64.b64decode(request.base64_image)
|
||||
image = Image.open(io.BytesIO(image_data)).convert('RGB')
|
||||
|
||||
# 2. 이미지 전처리
|
||||
prompt = "<image>"
|
||||
image_features = processor.tokenize_with_images(
|
||||
images=[image],
|
||||
bos=True,
|
||||
eos=True,
|
||||
cropping=False # CROP_MODE 기본값 사용
|
||||
)
|
||||
|
||||
# 3. 샘플링 파라미터 설정 (기존 스크립트 참조)
|
||||
logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=30, window_size=90, whitelist_token_ids={128821, 128822})]
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.0,
|
||||
max_tokens=8192,
|
||||
logits_processors=logits_processors,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
|
||||
# 4. vLLM으로 추론 실행
|
||||
request_id = f"dpsk-request-{int(time.time())}"
|
||||
vllm_request = {
|
||||
"prompt": prompt,
|
||||
"multi_modal_data": {"image": image_features}
|
||||
}
|
||||
|
||||
final_output = None
|
||||
async for request_output in engine.generate(vllm_request, sampling_params, request_id):
|
||||
# 스트리밍 결과의 마지막 최종본을 사용
|
||||
final_output = request_output
|
||||
|
||||
if final_output and final_output.outputs:
|
||||
generated_text = final_output.outputs[0].text
|
||||
return InferenceResponse(text=generated_text)
|
||||
else:
|
||||
raise Exception("Model generated no output.")
|
||||
|
||||
except Exception as e:
|
||||
# 실제 운영 환경에서는 로깅을 추가하는 것이 좋습니다.
|
||||
return {"error": f"An error occurred: {str(e)}"}, 500
|
||||
Reference in New Issue
Block a user