File size: 10,293 Bytes
002bd9b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"BASE_DIR = \"../../\"\n",
"sys.path.append(BASE_DIR)\n",
"\n",
"import gradio as gr\n",
"from src.models.sam_captioner import SAMCaptionerConfig, SAMCaptionerModel, SAMCaptionerProcessor\n",
"import torch\n",
"from PIL import Image\n",
"import requests\n",
"import numpy as np\n",
"import time\n",
"from transformers import CLIPProcessor, CLIPModel\n",
"\n",
"\n",
"import logging\n",
"import os\n",
"\n",
"import hydra\n",
"from hydra.utils import instantiate\n",
"from datasets import Dataset, load_dataset, IterableDataset, concatenate_datasets, interleave_datasets\n",
"from omegaconf import DictConfig, OmegaConf\n",
"from src.data.transforms import SamCaptionerDataTransform, SCADataTransform\n",
"from src.data.collator import SamCaptionerDataCollator, SCADataCollator\n",
"from src.arguments import (\n",
" Arguments,\n",
" global_setup,\n",
" SAMCaptionerModelArguments,\n",
" SCAModelBaseArguments,\n",
" SCAModelArguments,\n",
" SCADirectDecodingModelArguments,\n",
")\n",
"from src.models.sam_captioner import SAMCaptionerConfig, SAMCaptionerModel, SAMCaptionerProcessor\n",
"from src.sca_seq2seq_trainer import SCASeq2SeqTrainer\n",
"from src.models.sca import ScaModel, ScaConfig, ScaProcessor, ScaDirectDecodingModel\n",
"from src.integrations import CustomWandbCallBack, EvaluateFirstStepCallback\n",
"import src.models.sca\n",
"\n",
"from transformers.trainer_utils import _re_checkpoint\n",
"from transformers import set_seed\n",
"import json\n",
"from src.train import prepare_datasets, prepare_model, prepare_data_transform, prepare_processor\n",
"from hydra import initialize, compose\n",
"import subprocess\n",
"import dotenv\n",
"\n",
"logger = logging.getLogger(__name__)\n",
"\n",
"model = None\n",
"processor = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"dtype = torch.float16"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# CKPT_PATH=\n",
"# python scripts/apps/sca_app.py \\\n",
"# +model=base_sca_multitask_v2 \\\n",
"# model.model_name_or_path=$CKPT_PATH \\\n",
"# model.lm_head_model_name_or_path=$(python scripts/tools/get_sub_model_name_from_ckpt.py $CKPT_PATH \"lm\")\n",
"def get_lm_head_name(cmd_script_path, cmd_ckpt_path):\n",
"\n",
"\n",
" command = f'python {cmd_script_path} {cmd_ckpt_path} \"lm\"'\n",
" # Use subprocess to run the command and capture the output\n",
" process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
"\n",
" # Get the output\n",
" stdout, stderr = process.communicate()\n",
"\n",
" # Decode the output from bytes to string\n",
" stdout = stdout.decode('utf-8').strip()\n",
" stderr = stderr.decode('utf-8').strip()\n",
" if stderr != '':\n",
" raise Exception(stderr)\n",
"\n",
" return stdout\n",
"\n",
"cmd_script_path = \"scripts/tools/get_sub_model_name_from_ckpt.py\"\n",
"cmd_ckpt_path = \"amlt/sca-weights.111823/finetune-gpt2_large-lr_1e_4-1xlr-lsj-bs_1-pretrain_1e_4_no_lsj_bs_32.111223.rr1-4x8-v100-32g-pre/checkpoint-100000\"\n",
"cmd_model = \"base_sca_multitask_v2\"\n",
"\n",
"cmd_script_path = os.path.join(BASE_DIR, cmd_script_path)\n",
"cmd_ckpt_path = os.path.join(BASE_DIR, cmd_ckpt_path)\n",
"cmd_lm_head_model_name_or_path = get_lm_head_name(cmd_script_path, cmd_ckpt_path)\n",
"\n",
"with initialize(version_base=\"1.3\", config_path=\"../../src/conf\"):\n",
" args = compose(\n",
" config_name=\"conf\",\n",
" overrides=[\n",
" f\"+model={cmd_model}\",\n",
" f\"model.model_name_or_path={cmd_ckpt_path}\",\n",
" f\"model.lm_head_model_name_or_path={cmd_lm_head_model_name_or_path}\"\n",
" ],\n",
" )\n",
"\n",
"\n",
"args, training_args, model_args = global_setup(args)\n",
"\n",
"# Set seed before initializing model.\n",
"set_seed(args.training.seed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# NOTE(xiaoke): load sas_key from .env for huggingface model downloading.\n",
"logger.info(f\"Try to load sas_key from .env file: {dotenv.load_dotenv('.env')}.\")\n",
"use_auth_token = os.getenv(\"USE_AUTH_TOKEN\", False)\n",
"\n",
"processor = prepare_processor(model_args, use_auth_token)\n",
"\n",
"image_mean, image_std = (\n",
" processor.sam_processor.image_processor.image_mean,\n",
" processor.sam_processor.image_processor.image_std,\n",
")\n",
"\n",
"model = prepare_model(model_args, use_auth_token)\n",
"model = model.to(device, dtype)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_url = \"https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\"\n",
"input_image = Image.open(requests.get(img_url, stream=True).raw)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"input_points = [[[[0, 0]], [[0, 200]], [[200, 200]], [[200, 0]]]]\n",
"input_boxes = None\n",
"\n",
"inputs = processor(input_image, input_points=input_points, input_boxes=input_boxes, return_tensors=\"pt\")\n",
"for k, v in inputs.items():\n",
" if isinstance(v, torch.Tensor):\n",
" # NOTE(xiaoke): in original clip, dtype is float16\n",
" inputs[k] = v.to(device, dtype if v.dtype == torch.float32 else v.dtype)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"multimask_output = False\n",
"tic = time.perf_counter()\n",
"with torch.inference_mode():\n",
" model_outputs = model.generate(\n",
" **inputs,\n",
" multimask_output=multimask_output,\n",
" pad_token_id=processor.tokenizer.eos_token_id,\n",
" num_beams=3,\n",
" # max_new_tokens=20,\n",
" # return_patches=return_patches,\n",
" # return_dict_in_generate=True,\n",
" )\n",
"toc = time.perf_counter()\n",
"print(f\"Time taken: {(toc - tic)*1000:0.4f} ms\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"batch_size, num_masks, num_text_heads, num_tokens = model_outputs.sequences.shape\n",
"batch_size_, num_masks, num_mask_heads, *_ = model_outputs.pred_masks.shape\n",
"\n",
"masks = processor.post_process_masks(\n",
" model_outputs.pred_masks, inputs[\"original_sizes\"], inputs[\"reshaped_input_sizes\"]\n",
" ) # List[(num_masks, num_heads, H, W)]\n",
"iou_scores = model_outputs.iou_scores # (batch_size, num_masks, num_heads)\n",
"captions = processor.tokenizer.batch_decode(\n",
" model_outputs.sequences.reshape(-1, num_tokens), skip_special_tokens=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import amcg\n",
"\n",
"generator = amcg.ScaAutomaticMaskCaptionGenerator(model, processor)\n",
"np_input_image = np.array(input_image)\n",
"outputs = generator.generate(np_input_image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"\n",
"\n",
"def show_anns(anns):\n",
" if len(anns) == 0:\n",
" return\n",
" sorted_anns = sorted(anns, key=(lambda x: x[\"area\"]), reverse=True)\n",
" ax = plt.gca()\n",
" ax.set_autoscale_on(False)\n",
"\n",
" img = np.ones((sorted_anns[0][\"segmentation\"].shape[0], sorted_anns[0][\"segmentation\"].shape[1], 4))\n",
" img[:, :, 3] = 0\n",
" for ann in sorted_anns:\n",
" m = ann[\"segmentation\"]\n",
" color_mask = np.concatenate([np.random.random(3), [0.35]])\n",
" img[m] = color_mask\n",
" if \"caption\" in ann:\n",
" captions: str = ann[\"caption\"]\n",
" # calculate the centroid of the mask\n",
" y, x = np.where(m)\n",
" random_index = np.random.choice(range(len(x)))\n",
" random_position = (x[random_index], y[random_index])\n",
" # display the caption at the centroid of the mask\n",
" ax.text(*random_position, captions, color=\"white\", fontsize=12, ha=\"center\", va=\"center\")\n",
" ax.imshow(img)\n",
"\n",
"\n",
"plt.figure(figsize=(20, 20))\n",
"plt.imshow(input_image)\n",
"show_anns(outputs)\n",
"plt.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(20,20))\n",
"plt.imshow(input_image)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"input_image.size"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sca-v2",
"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": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|