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main
checkout-dice-loss
checkout-focal-loss
checkout-l1+l2-loss
checkout-l2-loss
checkout-unet
checkout-unet-l1-loss
checkout-unet-mini
checkout-unet-rmsprop
checkout-unet-sgd
testdataset
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20_Final_Project/unet_battery_test.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import os\n",
"from glob import glob\n",
"import numpy as np\n",
"import torch\n",
"from torch.utils.data import Dataset\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"from torchvision import transforms, datasets\n",
"import random\n",
"import cv2\n",
"\n",
"class CustomDataset(Dataset):\n",
" def __init__(self, list_imgs, list_masks, transform=None):\n",
" self.list_imgs = list_imgs\n",
" self.list_masks = list_masks\n",
" self.transform = transform\n",
"\n",
" def __len__(self):\n",
" return len(self.list_imgs)\n",
"\n",
" def __getitem__(self, index):\n",
" img_path = self.list_imgs[index]\n",
" mask_path = self.list_masks[index]\n",
"\n",
" img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
" mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n",
"\n",
" # 이미지 크기를 512x512로 변경\n",
" img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)\n",
" mask = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_NEAREST)\n",
"\n",
" img = img.astype(np.float32) / 255.0\n",
" mask = mask.astype(np.float32) / 255.0\n",
"\n",
" if img.ndim == 2:\n",
" img = img[:, :, np.newaxis]\n",
" if mask.ndim == 2:\n",
" mask = mask[:, :, np.newaxis]\n",
"\n",
" data = {'input': img, 'label': mask}\n",
"\n",
" if self.transform:\n",
" data = self.transform(data)\n",
" \n",
" return data\n",
"\n",
"def create_datasets(img_dir, mask_dir, train_ratio=0.7, val_ratio=0.2, transform=None):\n",
" list_imgs = sorted(glob(os.path.join(img_dir, '**', '*.bmp'), recursive=True))\n",
" list_masks = sorted(glob(os.path.join(mask_dir, '**', '*.bmp'), recursive=True))\n",
"\n",
" # combined = list(zip(list_imgs, list_masks))\n",
" # random.shuffle(combined)\n",
" # list_imgs, list_masks = zip(*combined)\n",
"\n",
" num_imgs = len(list_imgs)\n",
" num_train = int(num_imgs * train_ratio)\n",
" num_val = int(num_imgs * val_ratio)\n",
"\n",
" # train_set = CustomDataset(list_imgs[:num_train], list_masks[:num_train], transform)\n",
" # val_set = CustomDataset(list_imgs[num_train:num_train + num_val], list_masks[num_train:num_train + num_val], transform)\n",
" test_set = CustomDataset(list_imgs[num_train + num_val:], list_masks[num_train + num_val:], transform)\n",
"\n",
" # return train_set, val_set, test_set\n",
" return test_set\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Argument"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# 트렌스폼 구현하기\n",
"class ToTensor(object):\n",
" # def __call__(self, data):\n",
" # label, input = data['label'], data['input']\n",
"\n",
" # label = label.transpose((2, 0, 1)).astype(np.float32)\n",
" # input = input.transpose((2, 0, 1)).astype(np.float32)\n",
"\n",
" # data = {'label': torch.from_numpy(label), 'input': torch.from_numpy(input)}\n",
"\n",
" # return data\n",
" def __call__(self, data):\n",
" label, input = data['label'], data['input']\n",
"\n",
" # 이미지가 이미 그레이스케일이면 채널 차원 추가\n",
" if label.ndim == 2:\n",
" label = label[:, :, np.newaxis]\n",
" if input.ndim == 2:\n",
" input = input[:, :, np.newaxis]\n",
"\n",
" # 채널을 첫 번째 차원으로 이동\n",
" label = label.transpose((2, 0, 1)).astype(np.float32)\n",
" input = input.transpose((2, 0, 1)).astype(np.float32)\n",
"\n",
" data = {'label': torch.from_numpy(label), 'input': torch.from_numpy(input)}\n",
"\n",
" return data\n",
"\n",
"class Normalization(object):\n",
" def __init__(self, mean=0.5, std=0.5):\n",
" self.mean = mean\n",
" self.std = std\n",
"\n",
" def __call__(self, data):\n",
" label, input = data['label'], data['input']\n",
"\n",
" input = (input - self.mean) / self.std\n",
"\n",
" data = {'label': label, 'input': input}\n",
"\n",
" return data\n",
"\n",
"class RandomFlip(object):\n",
" def __call__(self, data):\n",
" label, input = data['label'], data['input']\n",
"\n",
" if np.random.rand() > 0.5:\n",
" label = np.fliplr(label)\n",
" input = np.fliplr(input)\n",
"\n",
" if np.random.rand() > 0.5:\n",
" label = np.flipud(label)\n",
" input = np.flipud(input)\n",
"\n",
" data = {'label': label, 'input': input}\n",
"\n",
" return data\n",
" \n",
"# class Resize(object):\n",
"# def __init__(self, output_size):\n",
"# assert isinstance(output_size, (int, tuple))\n",
"# self.output_size = output_size\n",
"\n",
"# def __call__(self, data):\n",
"# label, input = data['label'], data['input']\n",
"\n",
"# h, w = input.shape[:2]\n",
"# if isinstance(self.output_size, int):\n",
"# if h > w:\n",
"# new_h, new_w = self.output_size * h / w, self.output_size\n",
"# else:\n",
"# new_h, new_w = self.output_size, self.output_size * w / h\n",
"# else:\n",
"# new_h, new_w = self.output_size\n",
"\n",
"# new_h, new_w = int(new_h), int(new_w)\n",
"\n",
"# input = cv2.resize(input, (new_w, new_h))\n",
"# label = cv2.resize(label, (new_w, new_h))\n",
"\n",
"# return {'label': label, 'input': input}\n",
"\n",
"class Rotate(object):\n",
" def __init__(self, angle_range):\n",
" assert isinstance(angle_range, (tuple, list)) and len(angle_range) == 2\n",
" self.angle_min, self.angle_max = angle_range\n",
"\n",
" def __call__(self, data):\n",
" label, input = data['label'], data['input']\n",
"\n",
" # NumPy 배열로 변환 (필요한 경우)\n",
" if not isinstance(input, np.ndarray):\n",
" input = np.array(input)\n",
" if not isinstance(label, np.ndarray):\n",
" label = np.array(label)\n",
"\n",
" # (H, W, C) 형태를 (H, W)로 변경 (필요한 경우)\n",
" if input.ndim == 3 and input.shape[2] == 1:\n",
" input = input.squeeze(2)\n",
" if label.ndim == 3 and label.shape[2] == 1:\n",
" label = label.squeeze(2)\n",
"\n",
" # 랜덤 각도 선택 및 회전 적용\n",
" angle = np.random.uniform(self.angle_min, self.angle_max)\n",
" h, w = input.shape[:2]\n",
" center = (w / 2, h / 2)\n",
" rot_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
" input = cv2.warpAffine(input, rot_matrix, (w, h))\n",
" label = cv2.warpAffine(label, rot_matrix, (w, h))\n",
"\n",
" return {'label': label, 'input': input}\n",
" \n",
"# class Crop(object):\n",
"# def __init__(self, output_size):\n",
"# assert isinstance(output_size, (int, tuple))\n",
"# if isinstance(output_size, int):\n",
"# self.output_size = (output_size, output_size)\n",
"# else:\n",
"# assert len(output_size) == 2\n",
"# self.output_size = output_size\n",
"\n",
"# def __call__(self, data):\n",
"# label, input = data['label'], data['input']\n",
"\n",
"# h, w = input.shape[:2]\n",
"# new_h, new_w = self.output_size\n",
"\n",
"# top = np.random.randint(0, h - new_h)\n",
"# left = np.random.randint(0, w - new_w)\n",
"\n",
"# input = input[top: top + new_h, left: left + new_w]\n",
"# label = label[top: top + new_h, left: left + new_w]\n",
"\n",
"# return {'label': label, 'input': input}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# UNet Model (Origin)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"## 라이브러리 불러오기\n",
"import os\n",
"import numpy as np\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import DataLoader\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"## 네트워크 구축하기\n",
"class UNet(nn.Module):\n",
" def __init__(self):\n",
" super(UNet, self).__init__()\n",
"\n",
" # Convolution + BatchNormalization + Relu 정의하기\n",
" def CBR2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True): \n",
" layers = []\n",
" layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
" kernel_size=kernel_size, stride=stride, padding=padding,\n",
" bias=bias)]\n",
" layers += [nn.BatchNorm2d(num_features=out_channels)]\n",
" layers += [nn.ReLU()]\n",
"\n",
" cbr = nn.Sequential(*layers)\n",
"\n",
" return cbr\n",
"\n",
" # 수축 경로(Contracting path)\n",
" self.enc1_1 = CBR2d(in_channels=1, out_channels=64)\n",
" self.enc1_2 = CBR2d(in_channels=64, out_channels=64)\n",
"\n",
" self.pool1 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc2_1 = CBR2d(in_channels=64, out_channels=128)\n",
" self.enc2_2 = CBR2d(in_channels=128, out_channels=128)\n",
"\n",
" self.pool2 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc3_1 = CBR2d(in_channels=128, out_channels=256)\n",
" self.enc3_2 = CBR2d(in_channels=256, out_channels=256)\n",
"\n",
" self.pool3 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc4_1 = CBR2d(in_channels=256, out_channels=512)\n",
" self.enc4_2 = CBR2d(in_channels=512, out_channels=512)\n",
"\n",
" self.pool4 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc5_1 = CBR2d(in_channels=512, out_channels=1024)\n",
"\n",
" # 확장 경로(Expansive path)\n",
" self.dec5_1 = CBR2d(in_channels=1024, out_channels=512)\n",
"\n",
" self.unpool4 = nn.ConvTranspose2d(in_channels=512, out_channels=512,\n",
" kernel_size=2, stride=2, padding=0, bias=True)\n",
"\n",
" self.dec4_2 = CBR2d(in_channels=2 * 512, out_channels=512)\n",
" self.dec4_1 = CBR2d(in_channels=512, out_channels=256)\n",
"\n",
" self.unpool3 = nn.ConvTranspose2d(in_channels=256, out_channels=256,\n",
" kernel_size=2, stride=2, padding=0, bias=True)\n",
"\n",
" self.dec3_2 = CBR2d(in_channels=2 * 256, out_channels=256)\n",
" self.dec3_1 = CBR2d(in_channels=256, out_channels=128)\n",
"\n",
" self.unpool2 = nn.ConvTranspose2d(in_channels=128, out_channels=128,\n",
" kernel_size=2, stride=2, padding=0, bias=True)\n",
"\n",
" self.dec2_2 = CBR2d(in_channels=2 * 128, out_channels=128)\n",
" self.dec2_1 = CBR2d(in_channels=128, out_channels=64)\n",
"\n",
" self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64,\n",
" kernel_size=2, stride=2, padding=0, bias=True)\n",
"\n",
" self.dec1_2 = CBR2d(in_channels=2 * 64, out_channels=64)\n",
" self.dec1_1 = CBR2d(in_channels=64, out_channels=64)\n",
"\n",
" self.fc = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0, bias=True)\n",
" \n",
" # forward 함수 정의하기\n",
" def forward(self, x):\n",
" enc1_1 = self.enc1_1(x)\n",
" enc1_2 = self.enc1_2(enc1_1)\n",
" pool1 = self.pool1(enc1_2)\n",
"\n",
" enc2_1 = self.enc2_1(pool1)\n",
" enc2_2 = self.enc2_2(enc2_1)\n",
" pool2 = self.pool2(enc2_2)\n",
"\n",
" enc3_1 = self.enc3_1(pool2)\n",
" enc3_2 = self.enc3_2(enc3_1)\n",
" pool3 = self.pool3(enc3_2)\n",
"\n",
" enc4_1 = self.enc4_1(pool3)\n",
" enc4_2 = self.enc4_2(enc4_1)\n",
" pool4 = self.pool4(enc4_2)\n",
"\n",
" enc5_1 = self.enc5_1(pool4)\n",
"\n",
" dec5_1 = self.dec5_1(enc5_1)\n",
"\n",
" unpool4 = self.unpool4(dec5_1)\n",
" cat4 = torch.cat((unpool4, enc4_2), dim=1)\n",
" dec4_2 = self.dec4_2(cat4)\n",
" dec4_1 = self.dec4_1(dec4_2)\n",
"\n",
" unpool3 = self.unpool3(dec4_1)\n",
" cat3 = torch.cat((unpool3, enc3_2), dim=1)\n",
" dec3_2 = self.dec3_2(cat3)\n",
" dec3_1 = self.dec3_1(dec3_2)\n",
"\n",
" unpool2 = self.unpool2(dec3_1)\n",
" cat2 = torch.cat((unpool2, enc2_2), dim=1)\n",
" dec2_2 = self.dec2_2(cat2)\n",
" dec2_1 = self.dec2_1(dec2_2)\n",
"\n",
" unpool1 = self.unpool1(dec2_1)\n",
" cat1 = torch.cat((unpool1, enc1_2), dim=1)\n",
" dec1_2 = self.dec1_2(cat1)\n",
" dec1_1 = self.dec1_1(dec1_2)\n",
"\n",
" x = self.fc(dec1_1)\n",
"\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# UNet Model (Mini)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"## 라이브러리 불러오기\n",
"import os\n",
"import numpy as np\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import DataLoader\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"## 네트워크 구축하기\n",
"class UNet(nn.Module):\n",
" def __init__(self):\n",
" super(UNet, self).__init__()\n",
"\n",
" # Convolution + BatchNormalization + Relu 정의하기\n",
" def CBR2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True): \n",
" layers = []\n",
" layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
" kernel_size=kernel_size, stride=stride, padding=padding,\n",
" bias=bias)]\n",
" layers += [nn.BatchNorm2d(num_features=out_channels)]\n",
" layers += [nn.ReLU()]\n",
"\n",
" cbr = nn.Sequential(*layers)\n",
"\n",
" return cbr\n",
"\n",
" # 수축 경로(Contracting path)\n",
" self.enc1_1 = CBR2d(in_channels=1, out_channels=64)\n",
" self.pool1 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc2_1 = CBR2d(in_channels=64, out_channels=128)\n",
" self.pool2 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc3_1 = CBR2d(in_channels=128, out_channels=256)\n",
" self.pool3 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc4_1 = CBR2d(in_channels=256, out_channels=512)\n",
" self.pool4 = nn.MaxPool2d(kernel_size=2)\n",
"\n",
" self.enc5_1 = CBR2d(in_channels=512, out_channels=1024)\n",
"\n",
" # 확장 경로(Expansive path)의 깊이 감소\n",
" self.dec5_1 = CBR2d(in_channels=1024, out_channels=512)\n",
" self.unpool4 = nn.ConvTranspose2d(in_channels=512, out_channels=512, kernel_size=2, stride=2)\n",
"\n",
" self.dec4_1 = CBR2d(in_channels=512 + 512, out_channels=256)\n",
" self.unpool3 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=2, stride=2)\n",
"\n",
" self.dec3_1 = CBR2d(in_channels=256 + 256, out_channels=128)\n",
" self.unpool2 = nn.ConvTranspose2d(in_channels=128, out_channels=128, kernel_size=2, stride=2)\n",
"\n",
" self.dec2_1 = CBR2d(in_channels=128 + 128, out_channels=64)\n",
" self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, stride=2)\n",
"\n",
" self.dec1_1 = CBR2d(in_channels=64 + 64, out_channels=64)\n",
" self.fc = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0, bias=True)\n",
" \n",
" # forward 함수 정의하기\n",
" def forward(self, x):\n",
" enc1_1 = self.enc1_1(x)\n",
" pool1 = self.pool1(enc1_1)\n",
"\n",
" enc2_1 = self.enc2_1(pool1)\n",
" pool2 = self.pool2(enc2_1)\n",
"\n",
" enc3_1 = self.enc3_1(pool2)\n",
" pool3 = self.pool3(enc3_1)\n",
"\n",
" enc4_1 = self.enc4_1(pool3)\n",
" pool4 = self.pool4(enc4_1)\n",
"\n",
" enc5_1 = self.enc5_1(pool4)\n",
"\n",
" dec5_1 = self.dec5_1(enc5_1)\n",
"\n",
" unpool4 = self.unpool4(dec5_1)\n",
" cat4 = torch.cat((unpool4, enc4_1), dim=1)\n",
" dec4_1 = self.dec4_1(cat4)\n",
"\n",
" unpool3 = self.unpool3(dec4_1)\n",
" cat3 = torch.cat((unpool3, enc3_1), dim=1)\n",
" dec3_1 = self.dec3_1(cat3)\n",
"\n",
" unpool2 = self.unpool2(dec3_1)\n",
" cat2 = torch.cat((unpool2, enc2_1), dim=1)\n",
" dec2_1 = self.dec2_1(cat2)\n",
"\n",
" unpool1 = self.unpool1(dec2_1)\n",
" cat1 = torch.cat((unpool1, enc1_1), dim=1)\n",
" dec1_1 = self.dec1_1(cat1)\n",
"\n",
" x = self.fc(dec1_1)\n",
"\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model - Load, Save"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"## 네트워크 저장하기\n",
"def save(ckpt_dir, net, optim, epoch):\n",
" if not os.path.exists(ckpt_dir):\n",
" os.makedirs(ckpt_dir)\n",
"\n",
" torch.save({'net': net.state_dict(), 'optim': optim.state_dict()},\n",
" \"%s/model_epoch%d.pth\" % (ckpt_dir, epoch))\n",
"\n",
"## 네트워크 불러오기\n",
"def load(ckpt_dir, net, optim):\n",
" if not os.path.exists(ckpt_dir):\n",
" epoch = 0\n",
" return net, optim, epoch\n",
"\n",
" ckpt_lst = os.listdir(ckpt_dir)\n",
" ckpt_lst.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))\n",
"\n",
" dict_model = torch.load('%s/%s' % (ckpt_dir, ckpt_lst[-1]))\n",
"\n",
" net.load_state_dict(dict_model['net'])\n",
" optim.load_state_dict(dict_model['optim'])\n",
" epoch = int(ckpt_lst[-1].split('epoch')[1].split('.pth')[0])\n",
"\n",
" return net, optim, epoch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hyper Parameters"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# 훈련 파라미터 설정하기\n",
"lr = 1e-3\n",
"batch_size = 4\n",
"num_epoch = 10\n",
"\n",
"# base_dir = './2nd_Battery/unet'\n",
"# base_dir = './2nd_Battery/unet-mini'\n",
"base_dir = './2nd_Battery/unet-dice-loss'\n",
"# base_dir = './2nd_Battery/unet-focal-loss'\n",
"# base_dir = './2nd_Battery/unet-sgd'\n",
"# base_dir = './2nd_Battery/unet-rmsprop'\n",
"# base_dir = './2nd_Battery/unet-l1'\n",
"# base_dir = './2nd_Battery/unet-l2'\n",
"ckpt_dir = os.path.join(base_dir, \"checkpoint\")\n",
"log_dir = os.path.join(base_dir, \"log\")\n",
"\n",
"# 네트워크 생성하기\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"net = UNet().to(device)\n",
"\n",
"# 손실함수 정의하기\n",
"fn_loss = nn.BCEWithLogitsLoss().to(device)\n",
"\n",
"# Optimizer 설정하기\n",
"optim = torch.optim.Adam(net.parameters(), lr=lr)\n",
"\n",
"# 그 밖에 부수적인 functions 설정하기\n",
"fn_tonumpy = lambda x: x.to('cpu').detach().numpy().transpose(0, 2, 3, 1)\n",
"fn_denorm = lambda x, mean, std: (x * std) + mean\n",
"fn_class = lambda x: 1.0 * (x > 0.95)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TC - Dice Loss"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class DiceLoss(nn.Module):\n",
" def __init__(self, smooth=1e-6):\n",
" super(DiceLoss, self).__init__()\n",
" self.smooth = smooth\n",
"\n",
" def forward(self, preds, targets):\n",
" preds = torch.sigmoid(preds)\n",
" intersection = (preds * targets).sum()\n",
" dice = (2. * intersection + self.smooth) / (preds.sum() + targets.sum() + self.smooth)\n",
" return 1 - dice\n",
"\n",
"fn_loss = DiceLoss().to(device)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TC - Focal Loss"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"class FocalLoss(nn.Module):\n",
" def __init__(self, alpha=0.8, gamma=2.0):\n",
" super(FocalLoss, self).__init__()\n",
" self.alpha = alpha\n",
" self.gamma = gamma\n",
"\n",
" def forward(self, preds, targets):\n",
" BCE = nn.functional.binary_cross_entropy_with_logits(preds, targets, reduction='none')\n",
" BCE_exp = torch.exp(-BCE)\n",
" focal_loss = self.alpha * (1 - BCE_exp) ** self.gamma * BCE\n",
" return focal_loss.mean()\n",
"\n",
"fn_loss = FocalLoss().to(device)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TC - SGD"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"optim = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TC - RMSProp"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"optim = torch.optim.RMSprop(net.parameters(), lr=lr, alpha=0.9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TC - L1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"class L1Loss(nn.Module):\n",
" def __init__(self):\n",
" super(L1Loss, self).__init__()\n",
"\n",
" def forward(self, preds, targets):\n",
" return torch.mean(torch.abs(preds - targets))\n",
" \n",
"fn_loss = L1Loss().to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TC - L2"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"class L2Loss(nn.Module):\n",
" def __init__(self):\n",
" super(L2Loss, self).__init__()\n",
"\n",
" def forward(self, preds, targets):\n",
" return torch.mean((preds - targets) ** 2)\n",
" \n",
"fn_loss = L2Loss().to(device)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dir_testset = 'C:/Users/pinb/Desktop/testimages/testset'\n",
"dir_groundtruth = 'C:/Users/pinb/Desktop/testimages/maskset'\n",
"# transform = transforms.Compose([Normalization(mean=0.5, std=0.5), RandomFlip(), Rotate(angle_range=(-90, 90)), ToTensor()])\n",
"transform = transforms.Compose([Normalization(mean=0.5, std=0.5), ToTensor()])\n",
"test_set = create_datasets(dir_testset, dir_groundtruth, 0, 0, transform=transform)\n",
"\n",
"# data = test_set.__getitem__(0) # 이미지 불러오기\n",
"\n",
"# input_img = data['input']\n",
"# label = data['label']\n",
"\n",
"# # 이미지 시각화\n",
"# plt.subplot(121)\n",
"# plt.imshow(input_img.reshape(input_img.shape[0], input_img.shape[1]), cmap='gray')\n",
"# plt.title('Input Image')\n",
"\n",
"# plt.subplot(122)\n",
"# plt.imshow(label.reshape(label.shape[0], label.shape[1]), cmap='gray')\n",
"# plt.title('Label')\n",
"\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"loader_test = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0)\n",
"\n",
"# 그밖에 부수적인 variables 설정하기\n",
"num_data_test = len(test_set)\n",
"num_batch_test = np.ceil(num_data_test / batch_size)\n",
"\n",
"# 결과 디렉토리 생성하기\n",
"result_dir = os.path.join(base_dir, 'result')\n",
"if not os.path.exists(result_dir):\n",
" os.makedirs(os.path.join(result_dir, 'gt'))\n",
" os.makedirs(os.path.join(result_dir, 'img'))\n",
" os.makedirs(os.path.join(result_dir, 'pr'))\n",
" os.makedirs(os.path.join(result_dir, 'numpy'))\n",
"\n",
"net, optim, st_epoch = load(ckpt_dir=ckpt_dir, net=net, optim=optim)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"with torch.no_grad():\n",
" net.eval()\n",
" loss_arr = []\n",
"\n",
" for batch, data in enumerate(loader_test, 1):\n",
" # forward pass\n",
" label = data['label'].to(device)\n",
" input = data['input'].to(device)\n",
"\n",
" output = net(input)\n",
"\n",
" # 손실함수 계산하기\n",
" loss = fn_loss(output, label)\n",
"\n",
" loss_arr += [loss.item()]\n",
"\n",
" print(\"TEST: BATCH %04d / %04d | LOSS %.4f\" %\n",
" (batch, num_batch_test, np.mean(loss_arr)))\n",
"\n",
" # Tensorboard 저장하기\n",
" label = fn_tonumpy(label)\n",
" input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))\n",
" output = fn_tonumpy(fn_class(output))\n",
"\n",
" # 테스트 결과 저장하기\n",
" for j in range(label.shape[0]):\n",
" id = num_batch_test * (batch - 1) + j\n",
"\n",
" gt = label[j].squeeze()\n",
" img = input[j].squeeze()\n",
" pr = output[j].squeeze()\n",
"\n",
" plt.imsave(os.path.join(result_dir, 'gt', 'gt_%04d.png' % id), gt, cmap='gray')\n",
" plt.imsave(os.path.join(result_dir, 'img', 'img_%04d.png' % id), img, cmap='gray')\n",
" plt.imsave(os.path.join(result_dir, 'pr', 'pr_%04d.png' % id), pr, cmap='gray')\n",
" np.save(os.path.join(result_dir, 'numpy', 'gt_%04d.npy' % id), gt)\n",
" np.save(os.path.join(result_dir, 'numpy', 'img_%04d.npy' % id), img)\n",
" np.save(os.path.join(result_dir, 'numpy', 'pr_%04d.npy' % id), pr)\n",
"\n",
"print(\"AVERAGE TEST: BATCH %04d / %04d | LOSS %.4f\" %\n",
" (batch, num_batch_test, np.mean(loss_arr)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\pinb\\AppData\\Local\\Temp\\ipykernel_19912\\3510449017.py:45: RuntimeWarning: invalid value encountered in divide\n",
" precision = tp / (tp + fp) # precision = TP / (TP + FP)\n",
"C:\\Users\\pinb\\AppData\\Local\\Temp\\ipykernel_19912\\3510449017.py:46: RuntimeWarning: invalid value encountered in divide\n",
" recall = tp / (tp + fn) # recall = TP / (TP + FN)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"precision: 0.7764027600652067\n",
"recall: 0.7843549615385272\n",
"accuracy: 0.9770164763057941\n",
"f1: 0.7741721124958945\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 800x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# base_dir = './2nd_Battery/unet'\n",
"# base_dir = './2nd_Battery/unet-mini'\n",
"base_dir = './2nd_Battery/unet-dice-loss'\n",
"# base_dir = './2nd_Battery/unet-focal-loss'\n",
"# base_dir = './2nd_Battery/unet-sgd'\n",
"# base_dir = './2nd_Battery/unet-rmsprop'\n",
"# base_dir = './2nd_Battery/unet-l1'\n",
"# base_dir = './2nd_Battery/unet-l2'\n",
"result_dir = os.path.join(base_dir, 'result')\n",
"\n",
"##\n",
"lst_data = os.listdir(os.path.join(result_dir, 'numpy'))\n",
"\n",
"lst_img = [f for f in lst_data if f.startswith('img')]\n",
"lst_gt = [f for f in lst_data if f.startswith('gt')]\n",
"lst_pr = [f for f in lst_data if f.startswith('pr')]\n",
"\n",
"lst_img.sort()\n",
"lst_gt.sort()\n",
"lst_pr.sort()\n",
"\n",
"avg_precision = 0\n",
"avg_recall = 0\n",
"avg_accuracy = 0\n",
"avg_f1 = 0\n",
"\n",
"##\n",
"id = 0\n",
"length = len(lst_img)\n",
"\n",
"for id in range(0, length):\n",
" img = np.load(os.path.join(result_dir,\"numpy\", lst_img[id]))\n",
" gt = np.load(os.path.join(result_dir,\"numpy\", lst_gt[id]))\n",
" pr = np.load(os.path.join(result_dir,\"numpy\", lst_pr[id]))\n",
"\n",
" img = np.uint8(img * 255)\n",
" gt = np.uint8(gt * 255)\n",
" pr = np.uint8(pr * 255)\n",
"\n",
" tp = gt & pr # True Positive: gt와 pr이 모두 1인 경우\n",
" fp = pr & ~gt # False Positive: pr은 1이지만 gt은 0인 경우\n",
" tn = ~gt & ~pr # True Negative: gt와 pr이 모두 0인 경우\n",
" fn = ~pr & gt # False Negative: pr은 0이지만 gt은 1인 경우\n",
"\n",
" precision = tp / (tp + fp) # precision = TP / (TP + FP)\n",
" recall = tp / (tp + fn) # recall = TP / (TP + FN)\n",
" accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
" f1 = 2 * precision * recall / (precision + recall)\n",
"\n",
" min_value = np.min(gt)\n",
" max_value = np.max(gt)\n",
" normalized_f1 = ((f1 - min_value) / (max_value - min_value))\n",
"\n",
" s_tp = np.sum(tp) / len(tp.flatten())\n",
" s_fp = np.sum(fp) / len(fp.flatten())\n",
" s_tn = np.sum(tn) / len(tn.flatten())\n",
" s_fn = np.sum(fn) / len(fn.flatten())\n",
" s_precision = s_tp / (s_tp + s_fp)\n",
" s_recall = s_tp / (s_tp + s_fn)\n",
" s_accuracy = (s_tp + s_tn) / (s_tp + s_tn + s_fp + s_fn)\n",
" s_f1 = 2 * s_precision * s_recall / (s_precision + s_recall)\n",
"\n",
" avg_precision += s_precision\n",
" avg_recall += s_recall\n",
" avg_accuracy += s_accuracy\n",
" avg_f1 += s_f1\n",
"\n",
"\n",
"print(f\"precision: {avg_precision / length}\")\n",
"print(f\"recall: {avg_recall / length}\")\n",
"print(f\"accuracy: {avg_accuracy / length}\")\n",
"print(f\"f1: {avg_f1 / length}\")\n",
"\n",
"## 플롯 그리기\n",
"plt.figure(figsize=(8,6))\n",
"plt.subplot(161)\n",
"plt.imshow(img, cmap='gray')\n",
"plt.title('img')\n",
"\n",
"plt.subplot(162)\n",
"plt.imshow(gt, cmap='gray')\n",
"plt.title('gt')\n",
"\n",
"plt.subplot(163)\n",
"plt.imshow(pr, cmap='gray')\n",
"plt.title('pr')\n",
"\n",
"plt.subplot(164)\n",
"plt.imshow(precision, cmap='gray')\n",
"plt.title('precision')\n",
"\n",
"plt.subplot(165)\n",
"plt.imshow(recall, cmap='gray')\n",
"plt.title('recall')\n",
"\n",
"plt.subplot(166)\n",
"plt.imshow(accuracy, cmap='gray')\n",
"plt.title('accuracy')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# UNet\n",
"LOSS 0.2072\n",
"\n",
"# UNet - Mini\n",
"LOSS 0.1324\n",
"\n",
"# UNet - Dice Loss\n",
"LOSS 0.3879\n",
"\n",
"# UNet - Focal Loss\n",
"LOSS 0.0112\n",
"\n",
"# UNet - SGD Opt\n",
"LOSS 0.1787\n",
"\n",
"# UNEt - RMSProp Opt\n",
"LOSS 0.1666\n",
"\n",
"# UNet - L1 Loss\n",
"LOSS 0.0357\n",
"\n",
"# UNet - L2 Loss\n",
"LOSS 0.0241\n",
"\n",
"\n",
"# UNet - L1 + L2 Loss\n",
"LOSS 0.0550\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}