File size: 22,158 Bytes
38206f4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"GAZViC5o2bya",
"QwoVd4CE2njF",
"8r0qzU2NRoIT",
"lgaEjLAo7lMd",
"RadVNaev2_mF"
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Dependencies"
],
"metadata": {
"id": "GAZViC5o2bya"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wNCZ04U82IiL",
"outputId": "dc277e76-67e7-4781-95a1-123bb139bbf3"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting onnxruntime\n",
" Downloading onnxruntime-1.17.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (6.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting coloredlogs (from onnxruntime)\n",
" Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (23.5.26)\n",
"Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (1.25.2)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (23.2)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (3.20.3)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (1.12)\n",
"Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime)\n",
" Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime) (1.3.0)\n",
"Installing collected packages: humanfriendly, coloredlogs, onnxruntime\n",
"Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.17.1\n",
"Collecting onnx\n",
" Downloading onnx-1.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from onnx) (1.25.2)\n",
"Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.10/dist-packages (from onnx) (3.20.3)\n",
"Installing collected packages: onnx\n",
"Successfully installed onnx-1.15.0\n",
"Collecting onnxruntime-extensions\n",
" Downloading onnxruntime_extensions-0.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: onnxruntime-extensions\n",
"Successfully installed onnxruntime-extensions-0.10.1\n"
]
}
],
"source": [
"!pip install onnxruntime\n",
"!pip install onnx\n",
"!pip install onnxruntime-extensions"
]
},
{
"cell_type": "markdown",
"source": [
"# Download ONNX Model\n",
"This downloads [wd-convnext-tagger-v3](https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3) created by [SmilingWolf](https://huggingface.co/SmilingWolf).\n",
"\n",
"Feel free to use SmilingWolfs other model variants instead.\n",
"\n",
"The tags and power image is also downloaded for inferencing."
],
"metadata": {
"id": "QwoVd4CE2njF"
}
},
{
"cell_type": "code",
"source": [
"!wget https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/model.onnx?download=true -O model.onnx"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AMF_IIxm2tT_",
"outputId": "b8e574b8-8276-4f74-92fa-b3a946e92655"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-03-09 05:03:49-- https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/model.onnx?download=true\n",
"Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.74, 3.163.189.37, ...\n",
"Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://cdn-lfs-us-1.huggingface.co/repos/d8/61/d8612304f05de662484c881a2ac180318d718b820314ffaaa700ef22c267e1a1/02f30d4de9bada756981a11464d13aa206f5e2d4ff6da384511beb812d58b2ca?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27model.onnx%3B+filename%3D%22model.onnx%22%3B&Expires=1710219829&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDIxOTgyOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2Q4LzYxL2Q4NjEyMzA0ZjA1ZGU2NjI0ODRjODgxYTJhYzE4MDMxOGQ3MThiODIwMzE0ZmZhYWE3MDBlZjIyYzI2N2UxYTEvMDJmMzBkNGRlOWJhZGE3NTY5ODFhMTE0NjRkMTNhYTIwNmY1ZTJkNGZmNmRhMzg0NTExYmViODEyZDU4YjJjYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=NUW35U0E0VvUCTynr4WArU1pgdg-F506HK5TiNnP7IrwbhEJfQpEcJo5CBoz1e4iUprWUCcEZJS0dRCmlGrr0PGYIjKXZ00BE4EiGZyi2vUqdP%7ExxUzWxps6XwEIVGiXc5R9yC%7EQgtd6oSJYQOH4ITBvEoNOJoQUPnjL5m1vk9T8-xHpeAxkHkHeOaF8FjlU5HKvUIc65SlUGirxOsHXl0v8o7sKmYlFs0Nmkoj9MurWKFL0sLFW5XIxkZveAGS9GB2sisitzkc4BUhICqDMSfv5CtlTEhXpgDUGbFo%7EohbeuKkQjIgnSU%7EVdFhDvY7Qew%7E5emodk-508AHvCx-UrA__&Key-Pair-Id=KCD77M1F0VK2B [following]\n",
"--2024-03-09 05:03:49-- https://cdn-lfs-us-1.huggingface.co/repos/d8/61/d8612304f05de662484c881a2ac180318d718b820314ffaaa700ef22c267e1a1/02f30d4de9bada756981a11464d13aa206f5e2d4ff6da384511beb812d58b2ca?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27model.onnx%3B+filename%3D%22model.onnx%22%3B&Expires=1710219829&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDIxOTgyOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2Q4LzYxL2Q4NjEyMzA0ZjA1ZGU2NjI0ODRjODgxYTJhYzE4MDMxOGQ3MThiODIwMzE0ZmZhYWE3MDBlZjIyYzI2N2UxYTEvMDJmMzBkNGRlOWJhZGE3NTY5ODFhMTE0NjRkMTNhYTIwNmY1ZTJkNGZmNmRhMzg0NTExYmViODEyZDU4YjJjYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=NUW35U0E0VvUCTynr4WArU1pgdg-F506HK5TiNnP7IrwbhEJfQpEcJo5CBoz1e4iUprWUCcEZJS0dRCmlGrr0PGYIjKXZ00BE4EiGZyi2vUqdP%7ExxUzWxps6XwEIVGiXc5R9yC%7EQgtd6oSJYQOH4ITBvEoNOJoQUPnjL5m1vk9T8-xHpeAxkHkHeOaF8FjlU5HKvUIc65SlUGirxOsHXl0v8o7sKmYlFs0Nmkoj9MurWKFL0sLFW5XIxkZveAGS9GB2sisitzkc4BUhICqDMSfv5CtlTEhXpgDUGbFo%7EohbeuKkQjIgnSU%7EVdFhDvY7Qew%7E5emodk-508AHvCx-UrA__&Key-Pair-Id=KCD77M1F0VK2B\n",
"Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 3.163.189.20, 3.163.189.28, 3.163.189.91, ...\n",
"Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|3.163.189.20|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 394990732 (377M) [application/octet-stream]\n",
"Saving to: ‘model.onnx’\n",
"\n",
"model.onnx 100%[===================>] 376.69M 31.6MB/s in 5.2s \n",
"\n",
"2024-03-09 05:03:54 (72.7 MB/s) - ‘model.onnx’ saved [394990732/394990732]\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Download Tags / Test Image"
],
"metadata": {
"id": "8r0qzU2NRoIT"
}
},
{
"cell_type": "code",
"source": [
"!wget https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/selected_tags.csv?download=true -O tags.csv\n",
"!wget https://huggingface.co/spaces/SmilingWolf/wd-tagger/resolve/main/power.jpg?download=true -O power.jpg"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WPrRzNP-RqKs",
"outputId": "a4a5af15-3bf2-4383-d4de-616e85485c20"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-03-09 05:03:54-- https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/selected_tags.csv?download=true\n",
"Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.74, 3.163.189.37, ...\n",
"Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 308468 (301K) [text/plain]\n",
"Saving to: ‘tags.csv’\n",
"\n",
"\rtags.csv 0%[ ] 0 --.-KB/s \rtags.csv 100%[===================>] 301.24K --.-KB/s in 0.03s \n",
"\n",
"2024-03-09 05:03:54 (11.1 MB/s) - ‘tags.csv’ saved [308468/308468]\n",
"\n",
"--2024-03-09 05:03:55-- https://huggingface.co/spaces/SmilingWolf/wd-tagger/resolve/main/power.jpg?download=true\n",
"Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.74, 3.163.189.37, ...\n",
"Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 91159 (89K) [image/jpeg]\n",
"Saving to: ‘power.jpg’\n",
"\n",
"power.jpg 100%[===================>] 89.02K --.-KB/s in 0.01s \n",
"\n",
"2024-03-09 05:03:55 (8.09 MB/s) - ‘power.jpg’ saved [91159/91159]\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# ONNX QUANT\n",
"To cut down on model size and have it work on mobile devices, quantization is needed (i think).\n",
"\n",
"First preprocess model for quantization - then quantize.\n",
"\n",
"The quant model name will be **model.quant.onnx**\n",
"\n",
"The convnext model went from ~377 MB down to 105 MB!"
],
"metadata": {
"id": "lgaEjLAo7lMd"
}
},
{
"cell_type": "code",
"source": [
"!python -m onnxruntime.quantization.preprocess --input model.onnx --output model_pre_quant.onnx"
],
"metadata": {
"id": "sdk95gWw7Imp"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import onnx\n",
"from onnxruntime.quantization import quantize_dynamic, QuantType\n",
"\n",
"model_fp32 = 'model_pre_quant.onnx'\n",
"model_quant = 'model.quant.onnx'\n",
"# quantized_model = quantize_dynamic(model_fp32, model_quant, nodes_to_exclude=[\"Conv\", \"/core_model/stem/stem.0/Conv\", \"/core_model/stages/stages.0/blocks/blocks.0/conv_dw/Conv\", \"/core_model/stages/stages.0/blocks/blocks.1/conv_dw/Conv\", \"/core_model/stages/stages.0/blocks/blocks.2/conv_dw/Conv\"])\n",
"quantized_model = quantize_dynamic(model_fp32, model_quant, op_types_to_quantize=['MatMul', 'Transpose', 'Gemm', 'LayerNormalization'])\n",
"\n",
"# remove unneeded model\n",
"%rm model_pre_quant.onnx"
],
"metadata": {
"id": "E7M68khX7H93"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Add Preprocessing / Postprocessing\n",
"\n",
"To make mobile inference easier, we will add preprocessing to the model.\n",
"\n",
"Instead of resizing, adding padding, converting image to float32 array, and converting to BGR before inferencing - we can add these steps to the model so that only a uint8 tensor is needed.\n",
"\n",
"The model will be named **model.quant.preproc.onnx**\n",
"\n",
"**WARNING** \n",
"It's very possible that I could be doing this wrong or that it could have some improvements. I'm not really sure what I'm doing but I found that these settings have given me the closest results to the base quant model."
],
"metadata": {
"id": "TmCdMPTwb6Mc"
}
},
{
"cell_type": "code",
"source": [
"import onnx\n",
"from onnxruntime_extensions.tools.pre_post_processing import create_named_value, Normalize, Transpose, Debug, ReverseAxis, PixelsToYCbCr, PrePostProcessor, Unsqueeze, LetterBox, ConvertImageToBGR, Resize, CenterCrop, ImageBytesToFloat, ChannelsLastToChannelsFirst\n",
"\n",
"image_mean = [0.5,0.5,0.5]\n",
"image_std = [0.5,0.5,0.5]\n",
"\n",
"img_size = 448\n",
"mean_std = list(zip(image_mean, image_std))\n",
"new_input = create_named_value('image', onnx.TensorProto.UINT8, [\"num_bytes\"])\n",
"pipeline = PrePostProcessor([new_input], onnx_opset=18)\n",
"pipeline.add_pre_processing(\n",
" [\n",
"\n",
" ConvertImageToBGR(),\n",
" Resize((img_size, img_size), policy=\"not_larger\"),\n",
" LetterBox(target_shape=(img_size, img_size)), # adds padding\n",
" ImageBytesToFloat((255/2) / 255), # NO IDEA WHAT IM DOING. all i know is that the default value gives bad results\n",
" Normalize(mean_std, layout='HWC'), # copied values from the config on HF. seems to help results match closer to non-preprocessed model.\n",
" Unsqueeze(axes=[0]), # add batch dim so shape is {1, 448, 448, channels}.\n",
" ]\n",
")"
],
"metadata": {
"id": "HzJjPcSrb-DL"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Save Model\n",
"model = onnx.load('model.quant.onnx')\n",
"new_model = pipeline.run(model)\n",
"onnx.save_model(new_model, 'model.quant.preproc.onnx')"
],
"metadata": {
"id": "_zjoi_AWhIZN"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Test Model\n",
"Most of the inference code is directly from SmilingWolf's wd tagger space: https://huggingface.co/spaces/SmilingWolf/wd-tagger/blob/main/app.py"
],
"metadata": {
"id": "nXk6AZM0kfL4"
}
},
{
"cell_type": "code",
"source": [
"import onnxruntime as _ort\n",
"from onnxruntime_extensions import get_library_path as _lib_path\n",
"from PIL import Image\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Step 1: setup session options\n",
"so = _ort.SessionOptions()\n",
"so.register_custom_ops_library(_lib_path())\n",
"\n",
"# Step 2: create session\n",
"sess = _ort.InferenceSession(\"/content/model.quant.preproc.onnx\",so) # Don't forget to add session options (so)\n",
"\n",
"# Step 3: load image (no preprocessing needed!)\n",
"image = np.frombuffer(open('/content/power.jpg', 'rb').read(), dtype=np.uint8)\n",
"\n",
"# Step 4: run cell!\n",
"\n",
"\n",
"###### Inference Code ######\n",
"kaomojis = [\n",
" \"0_0\",\n",
" \"(o)_(o)\",\n",
" \"+_+\",\n",
" \"+_-\",\n",
" \"._.\",\n",
" \"<o>_<o>\",\n",
" \"<|>_<|>\",\n",
" \"=_=\",\n",
" \">_<\",\n",
" \"3_3\",\n",
" \"6_9\",\n",
" \">_o\",\n",
" \"@_@\",\n",
" \"^_^\",\n",
" \"o_o\",\n",
" \"u_u\",\n",
" \"x_x\",\n",
" \"|_|\",\n",
" \"||_||\",\n",
"]\n",
"\n",
"\n",
"def load_labels(dataframe) -> list[str]:\n",
" name_series = dataframe[\"name\"]\n",
" name_series = name_series.map(\n",
" lambda x: x.replace(\"_\", \" \") if x not in kaomojis else x\n",
" )\n",
" tag_names = name_series.tolist()\n",
"\n",
" rating_indexes = list(np.where(dataframe[\"category\"] == 9)[0])\n",
" general_indexes = list(np.where(dataframe[\"category\"] == 0)[0])\n",
" character_indexes = list(np.where(dataframe[\"category\"] == 4)[0])\n",
" return tag_names, rating_indexes, general_indexes, character_indexes\n",
"\n",
"csv_path = \"/content/tags.csv\"\n",
"\n",
"tags_df = pd.read_csv(csv_path)\n",
"sep_tags = load_labels(tags_df)\n",
"\n",
"tag_names = sep_tags[0]\n",
"rating_indexes = sep_tags[1]\n",
"general_indexes = sep_tags[2]\n",
"character_indexes = sep_tags[3]\n",
"\n",
"input_name = sess.get_inputs()[0].name\n",
"label_name = sess.get_outputs()[0].name\n",
"\n",
"preds = sess.run([label_name], {input_name: image})[0]\n",
"\n",
"\n",
"labels = list(zip(tag_names, preds[0].astype(float)))\n",
"ratings_names = [labels[i] for i in rating_indexes]\n",
"rating = dict(ratings_names)\n",
"\n",
"character_names = [labels[i] for i in character_indexes]\n",
"\n",
"character_res = [x for x in character_names if x[1] > 0.85]\n",
"character_res = dict(character_res)\n",
"\n",
"general_names = [labels[i] for i in general_indexes]\n",
"general_res = [x for x in general_names if x[1] > 0.35]\n",
"general_res = dict(general_res)\n",
"\n",
"sorted_general_strings = sorted(\n",
" general_res.items(),\n",
" key=lambda x: x[1],\n",
" reverse=True,\n",
")\n",
"sorted_general_strings = [x[0] for x in sorted_general_strings]\n",
"sorted_general_strings = (\n",
" \", \".join(sorted_general_strings).replace(\"(\", \"\\(\").replace(\")\", \"\\)\")\n",
")\n",
"\n",
"print(rating)\n",
"print(character_res)\n",
"print(general_res)\n",
"print(sorted_general_strings)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m5B0Wj4NkhMt",
"outputId": "6b551906-9b9e-4db9-f2bc-5ae5427381a3"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'general': 0.9169240593910217, 'sensitive': 0.0812525749206543, 'questionable': 0.0006865859031677246, 'explicit': 0.0002942383289337158}\n",
"{'power (chainsaw man)': 0.9924684762954712}\n",
"{'1girl': 0.9980734586715698, 'solo': 0.967477560043335, 'long hair': 0.8743129968643188, 'looking at viewer': 0.8921941518783569, 'smile': 0.7079806327819824, 'open mouth': 0.8572969436645508, 'simple background': 0.6686466336250305, 'shirt': 0.9388805627822876, 'blonde hair': 0.647895336151123, 'white background': 0.5928694009780884, 'red eyes': 0.4210684299468994, 'hair between eyes': 0.8992906212806702, 'jacket': 0.5598545074462891, 'white shirt': 0.8964416980743408, 'upper body': 0.666782557964325, 'horns': 0.9738106727600098, 'teeth': 0.9321538209915161, 'necktie': 0.9494357109069824, 'collared shirt': 0.8381757736206055, 'orange eyes': 0.4594384431838989, 'symbol-shaped pupils': 0.8655499219894409, 'fangs': 0.3685188889503479, 'demon horns': 0.5966249704360962, 'sharp teeth': 0.8942122459411621, 'black necktie': 0.8483953475952148, 'claw pose': 0.5946617722511292, 'red horns': 0.9497503042221069, 'cross-shaped pupils': 0.9292328357696533, 'pillarboxed': 0.766990065574646}\n",
"1girl, horns, solo, red horns, necktie, shirt, teeth, cross-shaped pupils, hair between eyes, white shirt, sharp teeth, looking at viewer, long hair, symbol-shaped pupils, open mouth, black necktie, collared shirt, pillarboxed, smile, simple background, upper body, blonde hair, demon horns, claw pose, white background, jacket, orange eyes, red eyes, fangs\n"
]
}
]
}
]
} |