875 lines
34 KiB
Plaintext
875 lines
34 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "59f8a415",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/nguyendc/sonnh/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2025-09-02 13:50:30.358544: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
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"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
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"E0000 00:00:1756821030.369428 3858431 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
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"E0000 00:00:1756821030.372761 3858431 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
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"W0000 00:00:1756821030.382108 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1756821030.382119 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1756821030.382121 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1756821030.382123 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"2025-09-02 13:50:30.385619: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
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"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2025-09-02 13:50:35,304] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/bin/ld: cannot find -laio: No such file or directory\n",
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"collect2: error: ld returned 1 exit status\n",
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"/usr/bin/ld: cannot find -laio: No such file or directory\n",
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"collect2: error: ld returned 1 exit status\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using device: cuda\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n",
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"- configuration_intern_vit.py\n",
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". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
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"A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n",
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"- configuration_internvl_chat.py\n",
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"- configuration_intern_vit.py\n",
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". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
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"A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n",
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"- conversation.py\n",
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". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
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"A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n",
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"- modeling_intern_vit.py\n",
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". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
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"A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n",
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"- modeling_internvl_chat.py\n",
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"- conversation.py\n",
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"- modeling_intern_vit.py\n",
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". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
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"Fetching 2 files: 100%|██████████| 2/2 [00:00<00:00, 7.56it/s]\n",
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"Loading checkpoint shards: 100%|██████████| 2/2 [00:02<00:00, 1.03s/it]\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, AutoModel, InternVLChatModel\n",
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"# from qwen_vl_utils import process_vision_info\n",
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"from PIL import Image\n",
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"import os\n",
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"import numpy as np\n",
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"from tqdm import tqdm\n",
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"import math\n",
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"import torch\n",
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"from transformers import AutoTokenizer, AutoModel\n",
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"import timm\n",
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"\n",
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"# --- Configuration ---\n",
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"# MODEL_NAME = \"OpenGVLab/InternVL3_5-4B\" # You can choose other model sizes\n",
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"MODEL_NAME = \"OpenGVLab/InternVL3_5-4B-Instruct\"\n",
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"\n",
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"\n",
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"IMAGE_DIR = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/\"\n",
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"BATCH_SIZE = 4\n",
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"# --- End Configuration ---\n",
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"\n",
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"# Check for GPU availability\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"print(f\"Using device: {device}\")\n",
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"\n",
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"# Load the model and processor\n",
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"\n",
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"model = AutoModel.from_pretrained(\n",
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" MODEL_NAME,\n",
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" torch_dtype=torch.bfloat16,\n",
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" use_flash_attn=True,\n",
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" attn_implementation=\"flash_attention_2\",\n",
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" trust_remote_code=True,\n",
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" device_map=\"cuda\").eval()\n",
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"\n",
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"processor = AutoProcessor.from_pretrained(\n",
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" MODEL_NAME, \n",
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" trust_remote_code=True\n",
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" )\n",
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"tokenizer = AutoTokenizer.from_pretrained(\n",
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" MODEL_NAME, \n",
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" trust_remote_code=True, \n",
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" use_fast=False\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "6d826d19",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"InternVLChatModel(\n",
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" (vision_model): InternVisionModel(\n",
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" (embeddings): InternVisionEmbeddings(\n",
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" (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))\n",
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" )\n",
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" (encoder): InternVisionEncoder(\n",
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" (layers): ModuleList(\n",
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" (0-23): 24 x InternVisionEncoderLayer(\n",
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" (attn): InternAttention(\n",
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" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
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" (attn_drop): Dropout(p=0.0, inplace=False)\n",
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" (proj_drop): Dropout(p=0.0, inplace=False)\n",
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" (inner_attn): FlashAttention()\n",
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" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
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" )\n",
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" (mlp): InternMLP(\n",
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" (act): GELUActivation()\n",
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" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
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" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
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" )\n",
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" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
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" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
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" (drop_path1): Identity()\n",
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" (drop_path2): Identity()\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (language_model): Qwen3ForCausalLM(\n",
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" (model): Qwen3Model(\n",
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" (embed_tokens): Embedding(151936, 2560)\n",
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" (layers): ModuleList(\n",
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" (0-35): 36 x Qwen3DecoderLayer(\n",
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" (self_attn): Qwen3Attention(\n",
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" (q_proj): Linear(in_features=2560, out_features=4096, bias=False)\n",
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" (k_proj): Linear(in_features=2560, out_features=1024, bias=False)\n",
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" (v_proj): Linear(in_features=2560, out_features=1024, bias=False)\n",
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" (o_proj): Linear(in_features=4096, out_features=2560, bias=False)\n",
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" (q_norm): Qwen3RMSNorm((128,), eps=1e-06)\n",
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" (k_norm): Qwen3RMSNorm((128,), eps=1e-06)\n",
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" )\n",
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" (mlp): Qwen3MLP(\n",
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" (gate_proj): Linear(in_features=2560, out_features=9728, bias=False)\n",
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" (up_proj): Linear(in_features=2560, out_features=9728, bias=False)\n",
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" (down_proj): Linear(in_features=9728, out_features=2560, bias=False)\n",
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" (act_fn): SiLU()\n",
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" )\n",
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" (input_layernorm): Qwen3RMSNorm((2560,), eps=1e-06)\n",
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" (post_attention_layernorm): Qwen3RMSNorm((2560,), eps=1e-06)\n",
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" )\n",
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" )\n",
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" (norm): Qwen3RMSNorm((2560,), eps=1e-06)\n",
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" (rotary_emb): Qwen3RotaryEmbedding()\n",
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" )\n",
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" (lm_head): Linear(in_features=2560, out_features=151936, bias=False)\n",
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" )\n",
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" (mlp1): Sequential(\n",
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" (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n",
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" (1): Linear(in_features=4096, out_features=2560, bias=True)\n",
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" (2): GELU(approximate='none')\n",
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" (3): Linear(in_features=2560, out_features=2560, bias=True)\n",
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" )\n",
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")"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "7bbfcf47",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"InternVisionModel(\n",
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" (embeddings): InternVisionEmbeddings(\n",
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" (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))\n",
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" )\n",
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" (encoder): InternVisionEncoder(\n",
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" (layers): ModuleList(\n",
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" (0-23): 24 x InternVisionEncoderLayer(\n",
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" (attn): InternAttention(\n",
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" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
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" (attn_drop): Dropout(p=0.0, inplace=False)\n",
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" (proj_drop): Dropout(p=0.0, inplace=False)\n",
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" (inner_attn): FlashAttention()\n",
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" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
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" )\n",
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" (mlp): InternMLP(\n",
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" (act): GELUActivation()\n",
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" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
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" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
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" )\n",
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" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
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" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
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" (drop_path1): Identity()\n",
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" (drop_path2): Identity()\n",
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" )\n",
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" )\n",
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" )\n",
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")"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.vision_model"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ae26d6cf",
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"metadata": {},
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"source": [
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"# demo ?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "817d3ccb",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d41f94bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import math\n",
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"import numpy as np\n",
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"import torch\n",
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"import torchvision.transforms as T\n",
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"# from decord import VideoReader, cpu\n",
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"from PIL import Image\n",
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"from torchvision.transforms.functional import InterpolationMode\n",
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"# from modelscope import AutoModel, AutoTokenizer\n",
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"\n",
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"IMAGENET_MEAN = (0.485, 0.456, 0.406)\n",
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"IMAGENET_STD = (0.229, 0.224, 0.225)\n",
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"\n",
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"def build_transform(input_size):\n",
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" MEAN, STD = IMAGENET_MEAN, IMAGENET_STD\n",
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" transform = T.Compose([\n",
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" T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),\n",
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" T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),\n",
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" T.ToTensor(),\n",
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" T.Normalize(mean=MEAN, std=STD)\n",
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" ])\n",
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" return transform\n",
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"\n",
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"def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):\n",
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" best_ratio_diff = float('inf')\n",
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" best_ratio = (1, 1)\n",
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" area = width * height\n",
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" for ratio in target_ratios:\n",
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" target_aspect_ratio = ratio[0] / ratio[1]\n",
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" ratio_diff = abs(aspect_ratio - target_aspect_ratio)\n",
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" if ratio_diff < best_ratio_diff:\n",
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" best_ratio_diff = ratio_diff\n",
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" best_ratio = ratio\n",
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" elif ratio_diff == best_ratio_diff:\n",
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" if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:\n",
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" best_ratio = ratio\n",
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" return best_ratio\n",
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"\n",
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"def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):\n",
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" orig_width, orig_height = image.size\n",
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" aspect_ratio = orig_width / orig_height\n",
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"\n",
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" # calculate the existing image aspect ratio\n",
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" target_ratios = set(\n",
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" (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\n",
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" i * j <= max_num and i * j >= min_num)\n",
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" target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])\n",
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"\n",
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" # find the closest aspect ratio to the target\n",
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" target_aspect_ratio = find_closest_aspect_ratio(\n",
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" aspect_ratio, target_ratios, orig_width, orig_height, image_size)\n",
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"\n",
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" # calculate the target width and height\n",
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" target_width = image_size * target_aspect_ratio[0]\n",
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" target_height = image_size * target_aspect_ratio[1]\n",
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" blocks = target_aspect_ratio[0] * target_aspect_ratio[1]\n",
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"\n",
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" # resize the image\n",
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" resized_img = image.resize((target_width, target_height))\n",
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" processed_images = []\n",
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" for i in range(blocks):\n",
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" box = (\n",
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" (i % (target_width // image_size)) * image_size,\n",
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" (i // (target_width // image_size)) * image_size,\n",
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" ((i % (target_width // image_size)) + 1) * image_size,\n",
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" ((i // (target_width // image_size)) + 1) * image_size\n",
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" )\n",
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" # split the image\n",
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" split_img = resized_img.crop(box)\n",
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" processed_images.append(split_img)\n",
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" assert len(processed_images) == blocks\n",
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" if use_thumbnail and len(processed_images) != 1:\n",
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" thumbnail_img = image.resize((image_size, image_size))\n",
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" processed_images.append(thumbnail_img)\n",
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" return processed_images\n",
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"\n",
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"def load_image(image_file, input_size=448, max_num=12):\n",
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" image = Image.open(image_file).convert('RGB')\n",
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" transform = build_transform(input_size=input_size)\n",
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" images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)\n",
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" pixel_values = [transform(image) for image in images]\n",
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" pixel_values = torch.stack(pixel_values)\n",
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" return pixel_values"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2ec71a4",
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"metadata": {},
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"source": [
|
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"# Attention pooling\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a404fa19",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn.functional as F\n",
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"\n",
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"def gem_pool(x, p: float = 3.0, eps: float = 1e-6):\n",
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" # x: [B, N, D]\n",
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" return (x.clamp(min=eps).pow(p).mean(dim=1)).pow(1.0/p)\n",
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"\n",
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"@torch.no_grad()\n",
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"def image_embedding(pixel_values, model, use_tiling=True):\n",
|
||
" # pixel_values: nếu dùng processor của InternVL, có thể là [T,3,H,W]; nếu bạn tự resize = [1,3,H,W]\n",
|
||
" out = model.vision_model(pixel_values=pixel_values, output_hidden_states=True)\n",
|
||
" tok = out.last_hidden_state # [T, N, 1024] hoặc [1, N, 1024]\n",
|
||
" if tok.dim() == 2: # phòng trường hợp model trả [N, D]\n",
|
||
" tok = tok.unsqueeze(0)\n",
|
||
"\n",
|
||
" # 1) Attention pooling theo token, trong từng tile\n",
|
||
" w_tok = torch.softmax(tok.norm(dim=-1), dim=1).unsqueeze(-1) # [T,N,1]\n",
|
||
" attn_tile = (tok * w_tok).sum(dim=1) # [T,1024]\n",
|
||
"\n",
|
||
" # 2) Các pooling khác theo token\n",
|
||
" mean_tile = tok.mean(dim=1) # [T,1024]\n",
|
||
" max_tile = tok.max(dim=1).values # [T,1024]\n",
|
||
" gem_tile = gem_pool(tok, p=3.0) # [T,1024]\n",
|
||
"\n",
|
||
" # 3) Attention across-tiles (giữ multi-scale nhưng gọn)\n",
|
||
" tile_scores = attn_tile.norm(dim=-1) # [T]\n",
|
||
" w_tile = torch.softmax(tile_scores, dim=0).unsqueeze(-1) # [T,1]\n",
|
||
"\n",
|
||
" mean_vec = (mean_tile * w_tile).sum(dim=0)\n",
|
||
" max_vec = (max_tile * w_tile).sum(dim=0)\n",
|
||
" gem_vec = (gem_tile * w_tile).sum(dim=0)\n",
|
||
" attn_vec = (attn_tile * w_tile).sum(dim=0)\n",
|
||
"\n",
|
||
" # 4) Hợp nhất nhiều “góc nhìn” → 1 vector giàu thông tin\n",
|
||
" one_vec = torch.cat([mean_vec, max_vec, gem_vec, attn_vec], dim=0) # [4*1024]\n",
|
||
" one_vec = F.normalize(one_vec, dim=-1).unsqueeze(0) # [1, 4096]\n",
|
||
" return one_vec.half() # FP16 để tiết kiệm bộ nhớ"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ed35a4ce",
|
||
"metadata": {},
|
||
"source": [
|
||
"# pool"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "3edf8b67",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"import torch.nn.functional as F\n",
|
||
"\n",
|
||
"# --- Pooling theo token (trong 1 tile) ---\n",
|
||
"def _pool_tokens(tokens: torch.Tensor, how: str = \"mean\") -> torch.Tensor:\n",
|
||
" \"\"\"\n",
|
||
" tokens: [1, N, D] hoặc [N, D]\n",
|
||
" return: [D]\n",
|
||
" \"\"\"\n",
|
||
" if tokens.dim() == 3: # [1, N, D] -> [N, D]\n",
|
||
" tokens = tokens.squeeze(0)\n",
|
||
"\n",
|
||
" if how == \"mean\":\n",
|
||
" v = tokens.mean(dim=0)\n",
|
||
" elif how == \"max\":\n",
|
||
" v = tokens.max(dim=0).values\n",
|
||
" elif how == \"gem\":\n",
|
||
" p = 3.0\n",
|
||
" v = (tokens.clamp(min=1e-6).pow(p).mean(dim=0)).pow(1.0/p)\n",
|
||
" elif how == \"cls\":\n",
|
||
" # chỉ dùng nếu backbone có CLS token ở vị trí đầu\n",
|
||
" v = tokens[0]\n",
|
||
" else:\n",
|
||
" raise ValueError(f\"Unknown pooling: {how}\")\n",
|
||
"\n",
|
||
" return v\n",
|
||
"\n",
|
||
"\n",
|
||
"@torch.no_grad()\n",
|
||
"def image_embedding_global(model, pixel_values: torch.Tensor,\n",
|
||
" pool: str = \"mean\",\n",
|
||
" normalize: bool = False,\n",
|
||
" global_index: int = 0,\n",
|
||
" use_projector: bool = False) -> torch.Tensor:\n",
|
||
" \"\"\"\n",
|
||
" Trả về 1 vector [1, D] mô tả toàn ảnh, chỉ dùng GLOBAL tile.\n",
|
||
" - pixel_values: [T,3,H,W] (ví dụ T=7) hoặc [1,3,H,W]\n",
|
||
" - global_index: thường = 0 (tile toàn ảnh nằm đầu)\n",
|
||
" - use_projector: CHỈ bật nếu bạn chắc chắn chiều khớp với projector (mlp1)\n",
|
||
" \"\"\"\n",
|
||
" model.eval()\n",
|
||
" device = next(model.parameters()).device\n",
|
||
" x = pixel_values.to(device)\n",
|
||
"\n",
|
||
" out = model.vision_model(pixel_values=x) # last_hidden_state: [T, N, D] hoặc [1, N, D]\n",
|
||
" tok = out.last_hidden_state\n",
|
||
"\n",
|
||
" # chọn global tile\n",
|
||
" if tok.size(0) > 1:\n",
|
||
" tok = tok[global_index:global_index+1] # [1, N, D]\n",
|
||
"\n",
|
||
" # (tuỳ chọn) projector sang không gian khác - cẩn thận mismatch chiều!\n",
|
||
" if use_projector:\n",
|
||
" # CHỈ nên bật khi biết chắc input dim của mlp1 khớp với tok.size(-1)\n",
|
||
" in_feat = getattr(model.mlp1[1], \"in_features\", None)\n",
|
||
" if in_feat is not None and tok.size(-1) == in_feat:\n",
|
||
" tok = model.mlp1(tok) # [1, N, D’]\n",
|
||
" else:\n",
|
||
" raise ValueError(f\"Projector input dim mismatch: tokens={tok.size(-1)} vs mlp1.in={in_feat}\")\n",
|
||
"\n",
|
||
" v = _pool_tokens(tok, how=pool) # [D]\n",
|
||
" if normalize:\n",
|
||
" v = F.normalize(v, dim=-1)\n",
|
||
" return v.unsqueeze(0) # [1, D]\n",
|
||
"\n",
|
||
"\n",
|
||
"@torch.no_grad()\n",
|
||
"def image_embedding_mean(model, pixel_values: torch.Tensor,\n",
|
||
" pool: str = \"mean\",\n",
|
||
" normalize: bool = True,\n",
|
||
" use_projector: bool = False) -> torch.Tensor:\n",
|
||
" \"\"\"\n",
|
||
" Trả về 1 vector [1, D] mô tả toàn ảnh, bằng cách:\n",
|
||
" (1) pool theo token trong từng tile → [T, D]\n",
|
||
" (2) lấy mean across-tiles → [D]\n",
|
||
" \"\"\"\n",
|
||
" model.eval()\n",
|
||
" device = next(model.parameters()).device\n",
|
||
" x = pixel_values.to(device)\n",
|
||
"\n",
|
||
" out = model.vision_model(pixel_values=x)\n",
|
||
" tok = out.last_hidden_state # [T, N, D] hoặc [1, N, D]\n",
|
||
"\n",
|
||
" if use_projector:\n",
|
||
" in_feat = getattr(model.mlp1[1], \"in_features\", None)\n",
|
||
" if in_feat is not None and tok.size(-1) == in_feat:\n",
|
||
" tok = model.mlp1(tok)\n",
|
||
" else:\n",
|
||
" raise ValueError(f\"Projector input dim mismatch: tokens={tok.size(-1)} vs mlp1.in={in_feat}\")\n",
|
||
"\n",
|
||
" # pool theo token trong từng tile\n",
|
||
" T = tok.size(0)\n",
|
||
" per_tile = [ _pool_tokens(tok[t:t+1], how=pool) for t in range(T) ] # list of [D]\n",
|
||
" per_tile = torch.stack(per_tile, dim=0) # [T, D]\n",
|
||
"\n",
|
||
" # mean across-tiles\n",
|
||
" v = per_tile.mean(dim=0) # [D]\n",
|
||
" if normalize:\n",
|
||
" v = F.normalize(v, dim=-1)\n",
|
||
" return v.unsqueeze(0) # [1, D]\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "613cf001",
|
||
"metadata": {},
|
||
"source": [
|
||
"# infer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "cdfdab0e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_image_embedding(path):\n",
|
||
" \"\"\"\n",
|
||
" Processes a batch of images and extracts their embeddings.\n",
|
||
" \"\"\"\n",
|
||
" images_pil = []\n",
|
||
" valid_paths = []\n",
|
||
" if path.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
|
||
" try:\n",
|
||
" # The processor expects PIL images in RGB format\n",
|
||
" # images_pil.append(Image.open(path).convert(\"RGB\"))\n",
|
||
" # print(path)\n",
|
||
" valid_paths.append(path)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not load image {path}. Skipping. Error: {e}\")\n",
|
||
"\n",
|
||
" if not valid_paths:\n",
|
||
" return np.array([]), []\n",
|
||
"\n",
|
||
" all_pixel_values = []\n",
|
||
" for valid_path in valid_paths:\n",
|
||
" pixel_values = load_image(valid_path, max_num=12).to(torch.bfloat16).cuda()\n",
|
||
" # print(pixel_values.shape)\n",
|
||
" all_pixel_values.append(pixel_values)\n",
|
||
" # For pure vision feature extraction, we can provide an empty text prompt.\n",
|
||
" # The processor handles tokenizing text and preparing images.\n",
|
||
" inputs = torch.cat(all_pixel_values, dim=0).to(device)\n",
|
||
" \n",
|
||
" # embeddings = image_embedding(inputs, model, use_tiling=True)\n",
|
||
" embeddings = image_embedding_mean(model, inputs)\n",
|
||
" \n",
|
||
" return embeddings.to(torch.float16).cpu().numpy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "cdaebb7b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 2800/2800 [20:51<00:00, 2.24it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Embeddings extracted and saved.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import json\n",
|
||
"\n",
|
||
"# --- Process all images in the directory ---\n",
|
||
"image_files = [os.path.join(IMAGE_DIR, f) for f in os.listdir(IMAGE_DIR) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
|
||
"all_embeddings = []\n",
|
||
"filepaths = []\n",
|
||
"BATCH_SIZE = 1\n",
|
||
"\n",
|
||
"with open(\"embeddings_factures_osteopathie_1k_InternVL3_5-4B-Instruct.json\", \"w\") as f:\n",
|
||
"\n",
|
||
" f.write(\"[\\n\")\n",
|
||
" first = True\n",
|
||
" for i in tqdm(range(0, len(image_files), BATCH_SIZE)):\n",
|
||
" batch_paths = image_files[i]\n",
|
||
" batch_embeddings = get_image_embedding(batch_paths)\n",
|
||
" embeddings_list = [emb.tolist() for emb in batch_embeddings]\n",
|
||
" for path, emb in zip(batch_paths, embeddings_list):\n",
|
||
" if not first:\n",
|
||
" f.write(\",\\n\")\n",
|
||
" json.dump({\"filepath\": path, \"embedding\": emb}, f)\n",
|
||
" first = False\n",
|
||
" f.write(\"\\n]\\n\")\n",
|
||
"\n",
|
||
"print(\"Embeddings extracted and saved.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f0d0bf0a",
|
||
"metadata": {},
|
||
"source": [
|
||
"# check"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "0772fc89",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loaded 2800 samples with embedding dimension 1024\n",
|
||
"Applied L2 normalization to embeddings\n",
|
||
"(2800, 1024)\n",
|
||
"(3918600,)\n",
|
||
"mean sim: 0.9939966 std: 0.0073577887\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.cluster import DBSCAN, MeanShift, AffinityPropagation\n",
|
||
"from sklearn.preprocessing import normalize\n",
|
||
"from sklearn.metrics import silhouette_score\n",
|
||
"from sklearn.neighbors import NearestNeighbors\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"import argparse\n",
|
||
"import warnings\n",
|
||
"warnings.filterwarnings('ignore')\n",
|
||
"from sklearn.metrics.pairwise import cosine_similarity\n",
|
||
"from datetime import datetime\n",
|
||
"\n",
|
||
"\n",
|
||
"embeddings_path = \"/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_InternVL3_5-4B-Instruct.json\"\n",
|
||
"with open(embeddings_path, 'r') as f:\n",
|
||
" data = json.load(f)\n",
|
||
"\n",
|
||
"file_paths = []\n",
|
||
"embeddings_list = []\n",
|
||
"\n",
|
||
"for item in data:\n",
|
||
" file_paths.append(item['filepath'])\n",
|
||
" embeddings_list.append(item['embedding'])\n",
|
||
"\n",
|
||
"embeddings = np.array(embeddings_list, dtype=np.float32)\n",
|
||
"print(f\"Loaded {len(file_paths)} samples with embedding dimension {embeddings.shape[1]}\")\n",
|
||
"\n",
|
||
"# Normalize embeddings using L2 normalization for cosine distance\n",
|
||
"embeddings_normalized = normalize(embeddings, norm='l2', axis=1)\n",
|
||
"print(\"Applied L2 normalization to embeddings\")\n",
|
||
"\n",
|
||
"sims = cosine_similarity(embeddings)\n",
|
||
"print(embeddings.shape)\n",
|
||
"# lấy upper triangle exclude diagonal để inspect\n",
|
||
"triu_idxs = np.triu_indices_from(sims, k=1)\n",
|
||
"dist_vals = sims[triu_idxs]\n",
|
||
"print(dist_vals.shape)\n",
|
||
"print(\"mean sim:\", dist_vals.mean(), \"std:\", dist_vals.std())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cb4ea42b",
|
||
"metadata": {},
|
||
"source": [
|
||
"# temp"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "2c3e6dd0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"image_path = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/c363e486-5d45-425e-aef9-4791cad120f7_20250213_120759_1_scale_1.0.jpg\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "29620d93",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n",
|
||
"Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"User: <image>\n",
|
||
"Please describe the image shortly.\n",
|
||
"Assistant: The image shows a receipt for a consultation with Noël Breignaud, an osteopath. It includes his contact information, with the address \"104, cours des fossés, 33210 Langon\" and his phone number. The receipt details a payment of 55€ for a consultation dated 15/06/2020. The receipt number is 1750401922774. There are handwritten details and signatures, with the amount and date written in ink. Noël Breignaud's signature and a circular stamp are also present.\n",
|
||
"User: <image>\n",
|
||
"Please describe the image in detail.\n",
|
||
"Assistant: The image is a handwritten receipt or invoice from a practitioner named Noël Breign aud, who is an osteopath. The text on the left side of the document includes the following details:\n",
|
||
"\n",
|
||
"- **Name:** Noël Breignaud\n",
|
||
"- **Profession:** Ouestopathe (Osteopath)\n",
|
||
"- **Address:** 104, cours des fossés, 33210 Lagnon\n",
|
||
"- **Phone Number:** Tel. 06 88 70 66 43\n",
|
||
"\n",
|
||
"On the right side, there are registration and identification numbers:\n",
|
||
"\n",
|
||
"- **Nº SIRET:** 510 123 631 00010\n",
|
||
"- **Nº ADELI:** 330001108\n",
|
||
"- **Code APE:** 8690E\n",
|
||
"\n",
|
||
"The handwritten section of the document is in French and reads:\n",
|
||
"\n",
|
||
"- \"Déclaire avoir reçu de M. M. (fils) G[obon], Acquitté la somme de 55 €\n",
|
||
"Pour 1 consultation en date du 05/04/2024\n",
|
||
"N°: 1750460-19212774\"\n",
|
||
"\n",
|
||
"At the bottom right, there is a signature that appears to be of Noël Breignaud, with a red stamp partially visible, which seems to contain the text \"Noël BREIGNAUD\" and other markings.\n",
|
||
"\n",
|
||
"The date in the handwritten section is \"05/04/2024,\" indicating the receipt or service provided on that date. The amount mentioned is 55 euros for one consultation.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"pixel_values = load_image(image_path, max_num=12).to(torch.bfloat16).cuda()\n",
|
||
"generation_config = dict(max_new_tokens=1024, do_sample=True)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"question = '<image>\\nPlease describe the image shortly.'\n",
|
||
"response = model.chat(tokenizer, pixel_values, question, generation_config)\n",
|
||
"print(f'User: {question}\\nAssistant: {response}')\n",
|
||
"\n",
|
||
"# single-image multi-round conversation (单图多轮对话)\n",
|
||
"question = '<image>\\nPlease describe the image in detail.'\n",
|
||
"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)\n",
|
||
"print(f'User: {question}\\nAssistant: {response}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "35dc90e0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"vout = model.vision_model(pixel_values=pixel_values, output_hidden_states=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "77f3720a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"torch.Size([7, 1025, 1024])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"patch_feats = vout.last_hidden_state # [B, N_patches, Dv], Dv ~ 1024 theo kiến trúc của bạn\n",
|
||
"print(patch_feats.shape)\n",
|
||
"# Nếu backbone có CLS token, bạn có thể dùng patch_feats[:,0]\n",
|
||
"# Cách an toàn chung: mean-pool\n",
|
||
"# img_vec = patch_feats.mean(dim=1) # [B, Dv]\n",
|
||
"# img_vec = torch.nn.functional.normalize(img_vec, dim=-1) # L2 normalize cho retrieval"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "0043634c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"torch.Size([7, 1024])"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# img_vec.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "92032162",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.12"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|