BlockAI 베타서비스가 오픈되었습니다.
logo
BlockAI

DarkNet53

BlockAI
BlockAI
Copied 0Updated on 2024.10.05
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 ''' ---BlockAI/DarkNet53 Auto Generate Code--- Author : BlockAI Project Name: DarkNet53 Project Link: https://blockai.kr/BlockAI/DarkNet53 (BlockAI) Create Date : 2024-10-05 ---Requirements--- # 사용자의 환경(OS, CUDA 등)에 따라 라이브러리 버전을 맞춰주세요 pip install torch==2.0 torchvision==0.15.2 torchtext==0.15.2 torchaudio==2.0.2 pip install pytorch-lightning==2.0.4 pip install tqdm pip install pandas pip install scikit-learn pip install transformers pip install timm ---Folder Structure--- --📂 data |--📂 train |--📁 LABEL_NAME_1 |--🖼️ IMAGE_FILE |--🖼️ IMAGE_FILE |--🖼️ ... |--📁 LABEL_NAME_2 |--🖼️ IMAGE_FILE |--🖼️ IMAGE_FILE |--🖼️ ... |--📁 ... |--📂 test |--📁 LABEL_NAME_1 |--🖼️ IMAGE_FILE |--🖼️ IMAGE_FILE |--🖼️ ... |--📁 LABEL_NAME_2 |--🖼️ IMAGE_FILE |--🖼️ IMAGE_FILE |--🖼️ ... |--📁 ... --📄 DarkNet53.py --📄 DarkNet53.ipynb --📄 requirements.txt ''' import os import argparse import copy from glob import glob from tqdm import tqdm import numpy as np import pandas as pd from sklearn import preprocessing from sklearn.model_selection import train_test_split from PIL import Image import torch import pytorch_lightning as pl import torchvision path_sep = os.sep # https://pytorch.org/tutorials/beginner/basics/data_tutorial.html#creating-a-custom-dataset-for-your-files class Dataset(torch.utils.data.Dataset): def __init__(self, inputs, targets=[], input_transform=None): self.inputs = inputs self.targets = targets self.input_transform = input_transform # 학습 및 추론 과정에서 데이터를 1개씩 꺼내오는 곳 def __getitem__(self, idx): # 정답이 있다면 if문을, 없다면 else문을 수행합니다 if len(self.targets) == 0: return self.input_transform(Image.open(self.inputs[idx])) else: return self.input_transform(Image.open(self.inputs[idx])), torch.tensor(self.targets[idx]) # 입력하는 개수만큼 데이터를 사용합니다 # 'return 100'이면 1에폭에 100개의 데이터만 사용합니다 def __len__(self): return len(self.inputs) # https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html class Dataloader(pl.LightningDataModule): # 데이터의 종류에 따라 코드 수정이 필요할 수 있습니다 def __init__(self, data_folder, batch_size, train_ratio, shuffle): super().__init__() self.data_folder = data_folder self.batch_size = batch_size self.train_ratio = train_ratio self.shuffle = shuffle self.train_dataset = None self.test_dataset = None self.predict_dataset = None def get_inputs_targets(self, data): inputs = [] targets = [] for data_path in data: # 이미지 경로를 저장합니다 inputs.append(data_path) # 이미지 경로에서 타겟 값을 분리하여 저장합니다 targets.append(data_path.split(path_sep)[-2]) return inputs, targets def setup(self, stage='fit'): # train 폴더에 있는 모든 이미지 경로를 받아와서 데이터셋을 만듭니다 train_data = sorted(glob(os.path.join(self.data_folder, 'train', '*', '*'))) train_inputs, train_targets = self.get_inputs_targets(train_data) # test 폴더에 있는 모든 이미지 경로를 받아와서 데이터셋을 만듭니다 test_data = sorted(glob(os.path.join(self.data_folder, 'test', '*', '*'))) test_inputs, test_targets = self.get_inputs_targets(test_data) # target 값을 encoding 해줍니다 self.target_encoder = preprocessing.LabelEncoder() train_targets = self.target_encoder.fit_transform(train_targets) test_targets = self.target_encoder.transform(test_targets) train_transform = torchvision.transforms.Compose([ ]) test_transform = torchvision.transforms.Compose([ ]) self.train_dataset = Dataset(train_inputs, train_targets, train_transform) self.test_dataset = Dataset(test_inputs, test_targets, test_transform) self.predict_dataset = Dataset(test_inputs, [], test_transform) def train_dataloader(self): return torch.utils.data.DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=args.shuffle) def test_dataloader(self): return torch.utils.data.DataLoader(self.test_dataset, batch_size=self.batch_size) def predict_dataloader(self): return torch.utils.data.DataLoader(self.predict_dataset, batch_size=self.batch_size) # https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html class Model(pl.LightningModule): def __init__(self): super().__init__() self.save_hyperparameters() self.conv2d_1 = torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1) self.batchnorm2d_1 = torch.nn.BatchNorm2d(num_features=32) self.conv2d_2 = torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1) self.batchnorm2d_2 = torch.nn.BatchNorm2d(num_features=64) self.conv2d_3 = torch.nn.Conv2d(in_channels=64, out_channels=32, kernel_size=1, padding=0) self.batchnorm2d_3 = torch.nn.BatchNorm2d(num_features=32) self.conv2d_4 = torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1) self.batchnorm2d_4 = torch.nn.BatchNorm2d(num_features=64) self.conv2d_5 = torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1) self.batchnorm2d_5 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_6 = torch.nn.Conv2d(in_channels=128, out_channels=64, kernel_size=1, padding=0) self.batchnorm2d_6 = torch.nn.BatchNorm2d(num_features=64) self.conv2d_7 = torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1) self.batchnorm2d_7 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_8 = torch.nn.Conv2d(in_channels=128, out_channels=64, kernel_size=1, padding=0) self.batchnorm2d_8 = torch.nn.BatchNorm2d(num_features=64) self.conv2d_9 = torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1) self.batchnorm2d_9 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_10 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1) self.batchnorm2d_10 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_11 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_11 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_12 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_12 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_13 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_13 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_14 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_14 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_15 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_15 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_16 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_16 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_17 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_17 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_18 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_18 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_19 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_19 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_20 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_20 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_21 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_21 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_22 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_22 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_23 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_23 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_24 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_24 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_25 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, padding=0) self.batchnorm2d_25 = torch.nn.BatchNorm2d(num_features=128) self.conv2d_26 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.batchnorm2d_26 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_27 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1) self.batchnorm2d_27 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_28 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_28 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_29 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_29 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_30 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_30 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_31 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_31 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_32 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_32 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_33 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_33 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_34 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_34 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_35 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_35 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_36 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_36 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_37 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_37 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_38 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_38 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_39 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_39 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_40 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_40 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_41 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_41 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_42 = torch.nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, padding=0) self.batchnorm2d_42 = torch.nn.BatchNorm2d(num_features=256) self.conv2d_43 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1) self.batchnorm2d_43 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_44 = torch.nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=2, padding=1) self.batchnorm2d_44 = torch.nn.BatchNorm2d(num_features=1024) self.conv2d_45 = torch.nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, padding=0) self.batchnorm2d_45 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_46 = torch.nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=1) self.batchnorm2d_46 = torch.nn.BatchNorm2d(num_features=1024) self.conv2d_47 = torch.nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, padding=0) self.batchnorm2d_47 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_48 = torch.nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=1) self.batchnorm2d_48 = torch.nn.BatchNorm2d(num_features=1024) self.conv2d_49 = torch.nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, padding=0) self.batchnorm2d_49 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_50 = torch.nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=1) self.batchnorm2d_50 = torch.nn.BatchNorm2d(num_features=1024) self.conv2d_51 = torch.nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, padding=0) self.batchnorm2d_51 = torch.nn.BatchNorm2d(num_features=512) self.conv2d_52 = torch.nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=1) self.batchnorm2d_52 = torch.nn.BatchNorm2d(num_features=1024) self.avgpool2d_1 = torch.nn.AvgPool2d(kernel_size=8, stride=1) self.linear_1 = torch.nn.Linear(in_features=1024, out_features=1000) self.leakyrelu_1 = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True) self.softmax_1 = torch.nn.Softmax() def forward(self, x_emptyimage): x_0 = self.conv2d_1(x_emptyimage) x_0 = self.batchnorm2d_1(x_0) x_0 = self.leakyrelu_1(x_0) x_0 = self.conv2d_2(x_0) x_0 = self.batchnorm2d_2(x_0) x_0 = self.leakyrelu_1(x_0) x_0 = self.conv2d_3(x_0) x_0 = self.batchnorm2d_3(x_0) x_0 = self.leakyrelu_1(x_0) x_0 = self.conv2d_4(x_0) x_0 = self.batchnorm2d_4(x_0) x_0 = self.leakyrelu_1(x_0) x_1 = torch.add((x_0, x_0), other=0) x_1 = self.conv2d_5(x_1) x_1 = self.batchnorm2d_5(x_1) x_1 = self.leakyrelu_1(x_1) x_1 = self.conv2d_6(x_1) x_1 = self.batchnorm2d_6(x_1) x_1 = self.leakyrelu_1(x_1) x_1 = self.conv2d_7(x_1) x_1 = self.batchnorm2d_7(x_1) x_1 = self.leakyrelu_1(x_1) x_2 = torch.add((x_1, x_1)) x_2 = self.conv2d_8(x_2) x_2 = self.batchnorm2d_8(x_2) x_2 = self.leakyrelu_1(x_2) x_2 = self.conv2d_9(x_2) x_2 = self.batchnorm2d_9(x_2) x_2 = self.leakyrelu_1(x_2) x_3 = torch.add((x_2, x_2)) x_3 = self.conv2d_10(x_3) x_3 = self.batchnorm2d_10(x_3) x_3 = self.leakyrelu_1(x_3) x_3 = self.conv2d_11(x_3) x_3 = self.batchnorm2d_11(x_3) x_3 = self.leakyrelu_1(x_3) x_3 = self.conv2d_12(x_3) x_3 = self.batchnorm2d_12(x_3) x_3 = self.leakyrelu_1(x_3) x_3 = torch.add(x_3) x_3 = self.conv2d_13(x_3) x_3 = self.batchnorm2d_13(x_3) x_3 = self.leakyrelu_1(x_3) x_3 = self.conv2d_14(x_3) x_3 = self.batchnorm2d_14(x_3) x_3 = self.leakyrelu_1(x_3) x_4 = torch.add((x_3, x_3)) x_4 = self.conv2d_15(x_4) x_4 = self.batchnorm2d_15(x_4) x_4 = self.leakyrelu_1(x_4) x_4 = self.conv2d_16(x_4) x_4 = self.batchnorm2d_16(x_4) x_4 = self.leakyrelu_1(x_4) x_5 = torch.add((x_4, x_4)) x_5 = self.conv2d_17(x_5) x_5 = self.batchnorm2d_17(x_5) x_5 = self.leakyrelu_1(x_5) x_5 = self.conv2d_18(x_5) x_5 = self.batchnorm2d_18(x_5) x_5 = self.leakyrelu_1(x_5) x_6 = torch.add((x_5, x_5)) x_6 = self.conv2d_19(x_6) x_6 = self.batchnorm2d_19(x_6) x_6 = self.leakyrelu_1(x_6) x_6 = self.conv2d_20(x_6) x_6 = self.batchnorm2d_20(x_6) x_6 = self.leakyrelu_1(x_6) x_7 = torch.add((x_6, x_6)) x_7 = self.conv2d_21(x_7) x_7 = self.batchnorm2d_21(x_7) x_7 = self.leakyrelu_1(x_7) x_7 = self.conv2d_22(x_7) x_7 = self.batchnorm2d_22(x_7) x_7 = self.leakyrelu_1(x_7) x_8 = torch.add((x_7, x_7)) x_8 = self.conv2d_23(x_8) x_8 = self.batchnorm2d_23(x_8) x_8 = self.leakyrelu_1(x_8) x_8 = self.conv2d_24(x_8) x_8 = self.batchnorm2d_24(x_8) x_8 = self.leakyrelu_1(x_8) x_9 = torch.add((x_8, x_8)) x_9 = self.conv2d_25(x_9) x_9 = self.batchnorm2d_25(x_9) x_9 = self.leakyrelu_1(x_9) x_9 = self.conv2d_26(x_9) x_9 = self.batchnorm2d_26(x_9) x_9 = self.leakyrelu_1(x_9) x_10 = torch.add((x_9, x_9)) x_10 = self.conv2d_27(x_10) x_10 = self.batchnorm2d_27(x_10) x_10 = self.leakyrelu_1(x_10) x_10 = self.conv2d_28(x_10) x_10 = self.batchnorm2d_28(x_10) x_10 = self.leakyrelu_1(x_10) x_10 = self.conv2d_29(x_10) x_10 = self.batchnorm2d_29(x_10) x_10 = self.leakyrelu_1(x_10) x_11 = torch.add((x_10, x_10)) x_11 = self.conv2d_30(x_11) x_11 = self.batchnorm2d_30(x_11) x_11 = self.leakyrelu_1(x_11) x_11 = self.conv2d_31(x_11) x_11 = self.batchnorm2d_31(x_11) x_11 = self.leakyrelu_1(x_11) x_12 = torch.add((x_11, x_11)) x_12 = self.conv2d_32(x_12) x_12 = self.batchnorm2d_32(x_12) x_12 = self.leakyrelu_1(x_12) x_12 = self.conv2d_33(x_12) x_12 = self.batchnorm2d_33(x_12) x_12 = self.leakyrelu_1(x_12) x_13 = torch.add((x_12, x_12)) x_13 = self.conv2d_34(x_13) x_13 = self.batchnorm2d_34(x_13) x_13 = self.leakyrelu_1(x_13) x_13 = self.conv2d_35(x_13) x_13 = self.batchnorm2d_35(x_13) x_13 = self.leakyrelu_1(x_13) x_14 = torch.add((x_13, x_13)) x_14 = self.conv2d_36(x_14) x_14 = self.batchnorm2d_36(x_14) x_14 = self.leakyrelu_1(x_14) x_14 = self.conv2d_37(x_14) x_14 = self.batchnorm2d_37(x_14) x_14 = self.leakyrelu_1(x_14) x_15 = torch.add((x_14, x_14)) x_15 = self.conv2d_38(x_15) x_15 = self.batchnorm2d_38(x_15) x_15 = self.leakyrelu_1(x_15) x_15 = self.conv2d_39(x_15) x_15 = self.batchnorm2d_39(x_15) x_15 = self.leakyrelu_1(x_15) x_16 = torch.add((x_15, x_15)) x_16 = self.conv2d_40(x_16) x_16 = self.batchnorm2d_40(x_16) x_16 = self.leakyrelu_1(x_16) x_16 = self.conv2d_41(x_16) x_16 = self.batchnorm2d_41(x_16) x_16 = self.leakyrelu_1(x_16) x_17 = torch.add((x_16, x_16)) x_17 = self.conv2d_42(x_17) x_17 = self.batchnorm2d_42(x_17) x_17 = self.leakyrelu_1(x_17) x_17 = self.conv2d_43(x_17) x_17 = self.batchnorm2d_43(x_17) x_17 = self.leakyrelu_1(x_17) x_18 = torch.add((x_17, x_17)) x_18 = self.conv2d_44(x_18) x_18 = self.batchnorm2d_44(x_18) x_18 = self.leakyrelu_1(x_18) x_18 = self.conv2d_45(x_18) x_18 = self.batchnorm2d_45(x_18) x_18 = self.leakyrelu_1(x_18) x_18 = self.conv2d_46(x_18) x_18 = self.batchnorm2d_46(x_18) x_18 = self.leakyrelu_1(x_18) x_18 = torch.add(x_18) x_18 = self.conv2d_47(x_18) x_18 = self.batchnorm2d_47(x_18) x_18 = self.leakyrelu_1(x_18) x_18 = self.conv2d_48(x_18) x_18 = self.batchnorm2d_48(x_18) x_18 = self.leakyrelu_1(x_18) x_19 = torch.add((x_18, x_18)) x_19 = self.conv2d_49(x_19) x_19 = self.batchnorm2d_49(x_19) x_19 = self.leakyrelu_1(x_19) x_19 = self.conv2d_50(x_19) x_19 = self.batchnorm2d_50(x_19) x_19 = self.leakyrelu_1(x_19) x_20 = torch.add((x_19, x_19)) x_20 = self.conv2d_51(x_20) x_20 = self.batchnorm2d_51(x_20) x_20 = self.leakyrelu_1(x_20) x_20 = self.conv2d_52(x_20) x_20 = self.batchnorm2d_52(x_20) x_20 = self.leakyrelu_1(x_20) x_21 = torch.add((x_20, x_20)) x_21 = self.avgpool2d_1(x_21) x_21 = torch.flatten(x_21) x_21 = self.linear_1(x_21) x_21 = self.softmax_1(x_21) return def training_step(self, batch, batch_idx): x_emptyimage, y = batch logits = self(x_emptyimage) self.log("train_loss", loss) return loss def validation_step(self, batch, batch_idx): x_emptyimage, y = batch logits = self(x_emptyimage) self.log("val_loss", loss) return loss def test_step(self, batch, batch_idx): x_emptyimage, y = batch logits = self(x_emptyimage) self.log("test_loss", loss) return loss def predict_step(self, batch, batch_idx): x_emptyimage = batch logits = self(x_emptyimage) return logits def configure_optimizers(self): pass if __name__ == '__main__': # https://docs.python.org/ko/3/library/argparse.html # 하이퍼 파라미터 등 각종 설정값을 입력받습니다 # 터미널 실행 예시 : python3 run.py --batch_size=64 ... # 실행 시 '--batch_size=64' 같은 인자를 입력하지 않으면 default 값이 기본으로 실행됩니다 parser = argparse.ArgumentParser() parser.add_argument('--data_folder', default='./data') parser.add_argument('--batch_size', default=0) parser.add_argument('--max_epoch', default=0) parser.add_argument('--shuffle', default=False) parser.add_argument('--train_ratio', default=1.0) args = parser.parse_args() dataloader = Dataloader(args.data_folder, args.batch_size, args.train_ratio, args.shuffle) model = Model() # https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html # 학습 및 추론을 위한 Trainer 설정 trainer = pl.Trainer(accelerator='gpu', devices=1, max_epochs=args.max_epoch) trainer.fit(model=model, datamodule=dataloader) # trainer.test(model=model, datamodule=dataloader) # predictions = trainer.predict(model=model, datamodule=dataloader)
logo
BlockAI
  • 대표: 황태욱
  • 본사: 대전광역시 유성구 대학로 27, 유성현대리조텔 608호
  • 전화번호: 050-6890-5295
  • 사업자 번호: 534-47-00677
Copyright @ BlockAI 2022. All Rights Reserved.