{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":2660070,"sourceType":"datasetVersion","datasetId":686792},{"sourceId":3941922,"sourceType":"datasetVersion","datasetId":2339926},{"sourceId":3951115,"sourceType":"datasetVersion","datasetId":1027206},{"sourceId":6929948,"sourceType":"datasetVersion","datasetId":3979059},{"sourceId":7001465,"sourceType":"datasetVersion","datasetId":4024908},{"sourceId":7019479,"sourceType":"datasetVersion","datasetId":4036298},{"sourceId":7047833,"sourceType":"datasetVersion","datasetId":4055669}],"dockerImageVersionId":30158,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
\n

UBC Ovariant Siamese Starter

\n
","metadata":{}},{"cell_type":"markdown","source":"In this kernel my idea is to create a Siamese type network and optimize it using CosineEmbeddingLoss\n\nThis is work in progress and will be improved over time. Current challenges are that the probability of 2 examples of same individual_id is very less","metadata":{}},{"cell_type":"markdown","source":"#

Install Required Libraries

","metadata":{}},{"cell_type":"code","source":"!pip install git+https://github.com/rwightman/pytorch-image-models\n!pip install --upgrade wandb","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-11-18T06:05:15.974833Z","iopub.execute_input":"2023-11-18T06:05:15.975037Z","iopub.status.idle":"2023-11-18T06:05:53.096948Z","shell.execute_reply.started":"2023-11-18T06:05:15.975011Z","shell.execute_reply":"2023-11-18T06:05:53.096171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n#

Import Required Libraries 📚

","metadata":{}},{"cell_type":"code","source":"import os\nimport gc\nimport cv2\nimport copy\nimport time\nimport random\n\n# For data manipulation\nimport numpy as np\nimport pandas as pd\n\n# Pytorch Imports\nimport torch\nimport torch.nn as nn\nfrom PIL import Image\n\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.cuda import amp\n\n# Utils\nimport joblib\nfrom tqdm import tqdm\nfrom collections import defaultdict\n\n# Sklearn Imports\nfrom sklearn.model_selection import GroupKFold\nfrom sklearn.model_selection import StratifiedKFold\n\n\n# For Image Models\n# import timm\nimport glob\n\n# Albumentations for augmentations\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\n\n# For colored terminal text\nfrom colorama import Fore, Back, Style\nb_ = Fore.BLUE\nsr_ = Style.RESET_ALL\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# For descriptive error messages\nos.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\nimport sys\nsys.path.append('/kaggle/input/pytorch-image-models/pytorch-image-models-master')","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:45:47.011513Z","iopub.execute_input":"2023-11-25T07:45:47.011941Z","iopub.status.idle":"2023-11-25T07:45:51.807225Z","shell.execute_reply.started":"2023-11-25T07:45:47.011833Z","shell.execute_reply":"2023-11-25T07:45:51.805932Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Training Configuration ⚙️

","metadata":{}},{"cell_type":"code","source":"CONFIG = {\"seed\": 2021,\n \"epochs\": 30,\n \"img_size\": 256,\n \"model_name\": \"tf_efficientnet_b0\",\n \"num_classes\":5,\n \"embedding_size\": 256,\n \"train_batch_size\": 32,\n \"valid_batch_size\": 64,\n \"learning_rate\": 1e-4,\n \"scheduler\": 'CosineAnnealingLR',\n \"min_lr\": 1e-6,\n \"T_max\": 500,\n \"weight_decay\": 1e-6,\n \"n_fold\": 5,\n \"margin\": 0,# 2 public_score:0.32\n \"n_accumulate\": 1,\n # ArcFace Hyperparameters\n \"s\": 30.0, \n \"m\": 0.50,\n \"ls_eps\": 0.0,\n \"device\": torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n }\ndef set_seed(seed=42):\n '''Sets the seed of the entire notebook so results are the same every time we run.\n This is for REPRODUCIBILITY.'''\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n # When running on the CuDNN backend, two further options must be set\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n # Set a fixed value for the hash seed\n os.environ['PYTHONHASHSEED'] = str(seed)\n \nset_seed(CONFIG['seed'])","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:45:51.809451Z","iopub.execute_input":"2023-11-25T07:45:51.809819Z","iopub.status.idle":"2023-11-25T07:45:51.826058Z","shell.execute_reply.started":"2023-11-25T07:45:51.809779Z","shell.execute_reply":"2023-11-25T07:45:51.824985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Set Seed for Reproducibility

","metadata":{}},{"cell_type":"code","source":"image_root='/kaggle/input/ubc-train-data/train'\ntrain_size=224\n\n# string label to numerical labels\nlabel_encode = {'HGSC': 0, 'EC': 1, 'CC':2, 'LGSC':3, 'MC': 4}\n# Convert numerical labels to original string labels\nlabel_map = {\n 0: \"HGSC\",\n 1: \"EC\",\n 2: \"CC\",\n 3: \"LGSC\",\n 4: \"MC\"\n}\n# predictions = [label_map[p] for p in predictions]\nlabels=[]\nx1=np.array(sorted(glob.glob(f'{image_root}/{label_map[0]}/*')))\nx2=np.array(sorted(glob.glob(f'{image_root}/{label_map[1]}/*')))\nx3=np.array(sorted(glob.glob(f'{image_root}/{label_map[2]}/*')))\nx4=np.array(sorted(glob.glob(f'{image_root}/{label_map[3]}/*')))\nx5=np.array(sorted(glob.glob(f'{image_root}/{label_map[4]}/*')))\ndata= np.concatenate([x1,x2,x3,x4,x5]).tolist()\nfor k in data:\n labels.append(label_encode[str(k.split(\"/\")[-2])])\ndf = pd.DataFrame({\n 'file_path': data,\n 'label': labels\n})\n\n# Create fold\n# skf = StratifiedKFold(n_splits=CONFIG['n_fold'])\n# for fold, ( _, val_) in enumerate(skf.split(X=df, y=df.label)):\n# df.loc[val_ , \"kfold\"] = int(fold)\n \ndf\n","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:45:51.827485Z","iopub.execute_input":"2023-11-25T07:45:51.828289Z","iopub.status.idle":"2023-11-25T07:45:52.452298Z","shell.execute_reply.started":"2023-11-25T07:45:51.828238Z","shell.execute_reply":"2023-11-25T07:45:52.451297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Preprocess data\nSplit data train into pair","metadata":{}},{"cell_type":"code","source":"# import pandas as pd\n# from itertools import combinations\n\n\n# # Define the classes you want to create pairs for\n# classes = df_train['label'].unique()\n\n# # Generate pairs of classes\n# class_pairs = list(combinations(classes, 2))\n\n# # Create a list to store pairs of file paths and labels\n# file_path_1 = []\n# label1=[]\n# label2=[]\n# file_path_2 = []\n\n\n# # Iterate through class pairs\n# for pair in class_pairs:\n# class1, class2 = pair\n \n# # Filter rows for each class in the pair\n# class1_rows = df_train[df_train['label'] == class1]\n# class2_rows = df_train[df_train['label'] == class2]\n \n# # Iterate through combinations of file paths for each class\n# for _, row1 in class1_rows.iterrows():\n# for _, row2 in class2_rows.iterrows():\n# file_path_1.append(row1['file_path'])\n# label1.append(row1['label'])\n# file_path_2.append(row2['file_path'])\n# label2.append(row2['label'])\n# save_data={'file_path_1':file_path_1,'label_1':label1,'file_path_2':file_path_2,'label_2':label2}\n# data=pd.DataFrame(save_data)\n# data.to_csv('siamese_training_data.csv')\n\n","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:08:11.150897Z","iopub.execute_input":"2023-11-25T07:08:11.152109Z","iopub.status.idle":"2023-11-25T07:08:11.157986Z","shell.execute_reply.started":"2023-11-25T07:08:11.152055Z","shell.execute_reply":"2023-11-25T07:08:11.156947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2023-11-21T14:26:50.919811Z","iopub.execute_input":"2023-11-21T14:26:50.920149Z","iopub.status.idle":"2023-11-21T14:26:58.72725Z","shell.execute_reply.started":"2023-11-21T14:26:50.920115Z","shell.execute_reply":"2023-11-21T14:26:58.72625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Validation data**","metadata":{}},{"cell_type":"code","source":"# df_val=df.sample(frac=0.2)\n# # Define the classes you want to create pairs for\n# classes = df_val['label'].unique()\n\n# # Generate pairs of classes\n# class_pairs = list(combinations(classes, 2))\n\n# # Create a list to store pairs of file paths and labels\n# file_path_1 = []\n# label1=[]\n# label2=[]\n# file_path_2 = []\n\n\n# # Iterate through class pairs\n# for pair in class_pairs:\n# class1, class2 = pair\n \n# # Filter rows for each class in the pair\n# class1_rows = df_val[df_val['label'] == class1]\n# class2_rows = df_val[df_val['label'] == class2]\n \n# # Iterate through combinations of file paths for each class\n# for _, row1 in class1_rows.iterrows():\n# for _, row2 in class2_rows.iterrows():\n# file_path_1.append(row1['file_path'])\n# label1.append(row1['label'])\n# file_path_2.append(row2['file_path'])\n# label2.append(row2['label'])\n \n# save_data={'file_path_1':file_path_1,'label_1':label1,'file_path_2':file_path_2,'label_2':label2}\n# data=pd.DataFrame(save_data)\n# data.to_csv('siamese_valid_data.csv')","metadata":{"execution":{"iopub.status.busy":"2023-11-21T14:37:06.521008Z","iopub.execute_input":"2023-11-21T14:37:06.52245Z","iopub.status.idle":"2023-11-21T14:37:12.094292Z","shell.execute_reply.started":"2023-11-21T14:37:06.522378Z","shell.execute_reply":"2023-11-21T14:37:12.093546Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Read the Data 📖

","metadata":{}},{"cell_type":"code","source":"# df_train = pd.read_csv(f\"{ROOT_DIR}/train.csv\")\n# df_train['file_path'] = df_train['image'].apply(get_train_file_path)\n# df_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-17T14:05:34.958382Z","iopub.execute_input":"2023-11-17T14:05:34.959441Z","iopub.status.idle":"2023-11-17T14:05:34.964251Z","shell.execute_reply.started":"2023-11-17T14:05:34.95936Z","shell.execute_reply":"2023-11-17T14:05:34.963171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2022-02-02T04:38:46.730266Z","iopub.execute_input":"2022-02-02T04:38:46.731133Z","iopub.status.idle":"2022-02-02T04:38:46.766486Z","shell.execute_reply.started":"2022-02-02T04:38:46.731086Z","shell.execute_reply":"2022-02-02T04:38:46.765561Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Create Folds

","metadata":{}},{"cell_type":"code","source":"# gkf = GroupKFold(n_splits=CONFIG['n_fold'])\n\n# for fold, ( _, val_) in enumerate(gkf.split(X=df_train, y=df_train.individual_id, groups=df_train.individual_id)):\n# df_train.loc[val_ , \"kfold\"] = fold\n# df_train.kfold.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-11-17T12:34:03.89632Z","iopub.execute_input":"2023-11-17T12:34:03.896675Z","iopub.status.idle":"2023-11-17T12:34:04.05795Z","shell.execute_reply.started":"2023-11-17T12:34:03.896628Z","shell.execute_reply":"2023-11-17T12:34:04.056877Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2023-11-18T10:25:59.610844Z","iopub.execute_input":"2023-11-18T10:25:59.611139Z","iopub.status.idle":"2023-11-18T10:25:59.625127Z","shell.execute_reply.started":"2023-11-18T10:25:59.611106Z","shell.execute_reply":"2023-11-18T10:25:59.624403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Dataset Class

","metadata":{}},{"cell_type":"code","source":"# class HappyWhaleDataset(Dataset):\n# def __init__(self, df, transforms=None):\n# self.df = df\n# self.groups = df.groupby('label').groups\n# self.keys = list(self.groups.keys())\n# self.transforms = transforms\n \n# def __len__(self):\n# return len(self.df) #\n \n# def __getitem__(self, index):\n# # Gom label thuộc cùng 1 class\n# image_indices_1 = self.groups[self.keys[index]]\n# print(len(image_indices_1))\n# image_path_1 = self.df.iloc[image_indices_1, :].sample(n=1)['file_path'].values[0]\n# print(image_path_1)\n# image_1 = cv2.cvtColor(cv2.imread(image_path_1), cv2.COLOR_BGR2RGB)\n# individual_id_1 = self.df.iloc[image_indices_1, :]['label'].values[0]\n \n# image_index_2 = self.df.sample(n=1).index\n# image_path_2 = self.df.iloc[image_index_2, :]['file_path'].values[0]\n# image_2 = cv2.cvtColor(cv2.imread(image_path_2), cv2.COLOR_BGR2RGB)\n# individual_id_2 = self.df.iloc[image_index_2, :]['label'].values[0]\n# target = 1 if individual_id_1 == individual_id_2 else -1\n \n# if self.transforms:\n# image_1 = self.transforms(image=image_1)[\"image\"]\n# image_2 = self.transforms(image=image_2)[\"image\"]\n \n# return {\n# 'image1': image_1,\n# 'image2': image_2,\n# 'target': torch.tensor(target, dtype=torch.long)\n# }\n","metadata":{"execution":{"iopub.status.busy":"2023-11-17T15:53:15.292655Z","iopub.execute_input":"2023-11-17T15:53:15.293468Z","iopub.status.idle":"2023-11-17T15:53:15.311693Z","shell.execute_reply.started":"2023-11-17T15:53:15.293395Z","shell.execute_reply":"2023-11-17T15:53:15.310463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Random pair matching dataset","metadata":{}},{"cell_type":"code","source":"class HappyWhaleDataset(Dataset):\n def __init__(self,df,transforms=None):\n self.df=df\n self.label=self.df.label\n self.transform =transforms\n def __len__(self):\n return len(self.df)\n def __getitem__(self,idx):\n sim_label=[]\n image1,label_1=Image.open(self.df.file_path.iloc[idx]).convert('RGB'),self.label.iloc[idx]\n rand_ind=random.randint(0,len(self.df)-1)\n image2,label_2=Image.open(self.df.file_path.iloc[rand_ind]).convert('RGB'),self.label.iloc[rand_ind]\n target = 1 if label_1 == label_2 else -1\n\n if self.transform:\n image1 = self.transform(image1)\n image2 = self.transform(image2)\n\n return {\n 'image1': image1,\n 'image2': image2,\n 'target': torch.tensor(target, dtype=torch.long),\n 'label': torch.tensor(label_1, dtype=torch.long)\n }","metadata":{"execution":{"iopub.status.busy":"2023-11-25T05:51:18.752989Z","iopub.execute_input":"2023-11-25T05:51:18.754078Z","iopub.status.idle":"2023-11-25T05:51:18.762881Z","shell.execute_reply.started":"2023-11-25T05:51:18.754018Z","shell.execute_reply":"2023-11-25T05:51:18.761916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# df = pd.read_csv('/kaggle/input/siamese-ubc-dataset/siamese_training_data.csv')\n# df","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:02:58.878028Z","iopub.execute_input":"2023-11-25T02:02:58.878677Z","iopub.status.idle":"2023-11-25T02:02:58.88181Z","shell.execute_reply.started":"2023-11-25T02:02:58.87864Z","shell.execute_reply":"2023-11-25T02:02:58.881048Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Pair matching data","metadata":{}},{"cell_type":"code","source":"# class HappyWhaleDataset(Dataset):\n# def __init__(self,df,transforms=None):\n# self.df=df\n# self.transform =transforms\n# def __len__(self):\n# return len(self.df)\n# def __getitem__(self,idx):\n# sim_label=[]\n# image1,label_1=Image.open(self.df.file_path_1.iloc[idx]).convert('RGB'),self.df.label_1.iloc[idx]\n# image2,label_2=Image.open(self.df.file_path_2.iloc[idx]).convert('RGB'),self.df.label_2.iloc[idx]\n# target = 1 if label_1 == label_2 else -1\n\n# if self.transform:\n# image1 = self.transform(image1)\n# image2 = self.transform(image2)\n\n# return {\n# 'image1': image1,\n# 'image2': image2,\n# 'target': torch.tensor(target, dtype=torch.long),\n# 'label': torch.tensor(label_1, dtype=torch.long)\n# }","metadata":{"execution":{"iopub.status.busy":"2023-11-25T05:42:34.916323Z","iopub.execute_input":"2023-11-25T05:42:34.916712Z","iopub.status.idle":"2023-11-25T05:42:34.928066Z","shell.execute_reply.started":"2023-11-25T05:42:34.916671Z","shell.execute_reply":"2023-11-25T05:42:34.927051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Augmentations

","metadata":{}},{"cell_type":"code","source":"from torchvision.transforms import transforms\ntrain_transform= transforms.Compose([\n transforms.Resize((CONFIG['img_size'], CONFIG['img_size'])),\n #================================add them ==========================#\n transforms.RandomCrop(CONFIG['img_size'], padding=4),\n transforms.ToTensor(),\n transforms.RandomHorizontalFlip(),\n transforms.RandomRotation(degrees=(-10, 10)),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])])\n\nval_transform = transforms.Compose([\n transforms.Resize((CONFIG['img_size'], CONFIG['img_size'])),\n# transforms.RandomCrop(224, padding=4),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])])","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:46:37.872013Z","iopub.execute_input":"2023-11-25T07:46:37.872407Z","iopub.status.idle":"2023-11-25T07:46:37.882266Z","shell.execute_reply.started":"2023-11-25T07:46:37.872368Z","shell.execute_reply":"2023-11-25T07:46:37.881264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# data_transforms = {\n# \"train\": A.Compose([\n# A.Resize(CONFIG['img_size'], CONFIG['img_size']),\n# A.HorizontalFlip(p=0.5),\n# A.VerticalFlip(p=0.5),\n# A.Normalize(\n# mean=[0.485, 0.456, 0.406], \n# std=[0.229, 0.224, 0.225], \n# max_pixel_value=255.0, \n# p=1.0\n# ),\n# ToTensorV2()], p=1.),\n \n# \"valid\": A.Compose([\n# A.Resize(CONFIG['img_size'], CONFIG['img_size']),\n# A.Normalize(\n# mean=[0.485, 0.456, 0.406], \n# std=[0.229, 0.224, 0.225], \n# max_pixel_value=255.0, \n# p=1.0\n# ),\n# ToTensorV2()], p=1.)\n# }","metadata":{"execution":{"iopub.status.busy":"2023-11-18T11:56:35.787021Z","iopub.execute_input":"2023-11-18T11:56:35.78777Z","iopub.status.idle":"2023-11-18T11:56:35.791893Z","shell.execute_reply.started":"2023-11-18T11:56:35.787728Z","shell.execute_reply":"2023-11-18T11:56:35.791159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Create Model

","metadata":{}},{"cell_type":"code","source":"!pip install huggingface_hub","metadata":{"execution":{"iopub.status.busy":"2023-11-17T14:11:10.43136Z","iopub.execute_input":"2023-11-17T14:11:10.431783Z","iopub.status.idle":"2023-11-17T14:11:21.916202Z","shell.execute_reply.started":"2023-11-17T14:11:10.431743Z","shell.execute_reply":"2023-11-17T14:11:21.914619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.nn as nn\nimport math\nimport torch.utils.model_zoo as model_zoo\nimport torch\nimport torch.nn.functional as F\n\n__all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b', 'res2net50_v1b_26w_4s']\n\nmodel_urls = {\n 'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth',\n 'res2net101_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth',\n}\n\n\n# https://github.com/DengPingFan/PraNet/blob/master/lib/Res2Net_v1b.py \n\nclass Bottle2neck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale=4, stype='normal'):\n \"\"\" Constructor\n Args:\n inplanes: input channel dimensionality\n planes: output channel dimensionality\n stride: conv stride. Replaces pooling layer.\n downsample: None when stride = 1\n baseWidth: basic width of conv3x3\n scale: number of scale.\n type: 'normal': normal set. 'stage': first block of a new stage.\n \"\"\"\n super(Bottle2neck, self).__init__()\n\n width = int(math.floor(planes * (baseWidth / 64.0)))\n self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width * scale)\n\n if scale == 1:\n self.nums = 1\n else:\n self.nums = scale - 1\n if stype == 'stage':\n self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)\n convs = []\n bns = []\n for i in range(self.nums):\n convs.append(nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, bias=False))\n bns.append(nn.BatchNorm2d(width))\n self.convs = nn.ModuleList(convs)\n self.bns = nn.ModuleList(bns)\n\n self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes * self.expansion)\n\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stype = stype\n self.scale = scale\n self.width = width\n\n def forward(self, x):\n residual = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n spx = torch.split(out, self.width, 1)\n for i in range(self.nums):\n if i == 0 or self.stype == 'stage':\n sp = spx[i]\n else:\n sp = sp + spx[i]\n sp = self.convs[i](sp)\n sp = self.relu(self.bns[i](sp))\n if i == 0:\n out = sp\n else:\n out = torch.cat((out, sp), 1)\n if self.scale != 1 and self.stype == 'normal':\n out = torch.cat((out, spx[self.nums]), 1)\n elif self.scale != 1 and self.stype == 'stage':\n out = torch.cat((out, self.pool(spx[self.nums])), 1)\n\n out = self.conv3(out)\n out = self.bn3(out)\n\n if self.downsample is not None:\n residual = self.downsample(x)\n\n out += residual\n out = self.relu(out)\n\n return out\n\n\nclass Res2Net(nn.Module):\n\n def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):\n self.inplanes = 64\n super(Res2Net, self).__init__()\n self.baseWidth = baseWidth\n self.scale = scale\n self.conv1 = nn.Sequential(\n nn.Conv2d(3, 32, 3, 2, 1, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n nn.Conv2d(32, 32, 3, 1, 1, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n nn.Conv2d(32, 64, 3, 1, 1, bias=False)\n )\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU()\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0])\n self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n self.avgpool = nn.AdaptiveAvgPool2d(1)\n self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n\n def _make_layer(self, block, planes, blocks, stride=1):\n downsample = None\n if stride != 1 or self.inplanes != planes * block.expansion:\n downsample = nn.Sequential(\n nn.AvgPool2d(kernel_size=stride, stride=stride,\n ceil_mode=True, count_include_pad=False),\n nn.Conv2d(self.inplanes, planes * block.expansion,\n kernel_size=1, stride=1, bias=False),\n nn.BatchNorm2d(planes * block.expansion),\n )\n\n layers = []\n layers.append(block(self.inplanes, planes, stride, downsample=downsample,\n stype='stage', baseWidth=self.baseWidth, scale=self.scale))\n self.inplanes = planes * block.expansion\n for i in range(1, blocks):\n layers.append(block(self.inplanes, planes, baseWidth=self.baseWidth, scale=self.scale))\n\n return nn.Sequential(*layers)\n\n def forward(self, x):\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n x = self.maxpool(x)\n\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n\n x = self.avgpool(x)\n x = x.view(x.size(0), -1)\n x = self.fc(x)\n\n return x\n\n\ndef res2net50_v1b(pretrained=False, **kwargs):\n \"\"\"Constructs a Res2Net-50_v1b lib.\n Res2Net-50 refers to the Res2Net-50_v1b_26w_4s.\n Args:\n pretrained (bool): If True, returns a lib pre-trained on ImageNet\n \"\"\"\n model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))\n return model\n\n\ndef res2net101_v1b(pretrained=False, **kwargs):\n \"\"\"Constructs a Res2Net-50_v1b_26w_4s lib.\n Args:\n pretrained (bool): If True, returns a lib pre-trained on ImageNet\n \"\"\"\n model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))\n return model\n\n\ndef res2net50_v1b_26w_4s(pretrained=False, **kwargs):\n \"\"\"Constructs a Res2Net-50_v1b_26w_4s lib.\n Args:\n pretrained (bool): If True, returns a lib pre-trained on ImageNet\n \"\"\"\n model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)\n if pretrained:\n model_state = torch.load('/kaggle/input/res2net50-v1b-26w-4s3cf99910/res2net50_v1b_26w_4s-3cf99910.pth',map_location=torch.device('cpu'))\n model.load_state_dict(model_state)\n # lib.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))\n return model\n\n\ndef res2net101_v1b_26w_4s(pretrained=False, **kwargs):\n \"\"\"Constructs a Res2Net-50_v1b_26w_4s lib.\n Args:\n pretrained (bool): If True, returns a lib pre-trained on ImageNet\n \"\"\"\n model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))\n return model\n\n\ndef res2net152_v1b_26w_4s(pretrained=False, **kwargs):\n \"\"\"Constructs a Res2Net-50_v1b_26w_4s lib.\n Args:\n pretrained (bool): If True, returns a lib pre-trained on ImageNet\n \"\"\"\n model = Res2Net(Bottle2neck, [3, 8, 36, 3], baseWidth=26, scale=4, **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['res2net152_v1b_26w_4s']),map_location=torch.device('cpu'))\n return model\n\n# backbone_model = res2net50_v1b_26w_4s(pretrained=True).to(CONFIG['device'])\nbackbone_model = res2net50_v1b_26w_4s(pretrained=True).to(CONFIG['device'])","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:03:13.155097Z","iopub.execute_input":"2023-11-25T02:03:13.155387Z","iopub.status.idle":"2023-11-25T02:03:18.452791Z","shell.execute_reply.started":"2023-11-25T02:03:13.155352Z","shell.execute_reply":"2023-11-25T02:03:18.452082Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# class HappyWhaleModel(nn.Module):\n# def __init__(self, model, pretrained=True):\n# super(HappyWhaleModel, self).__init__()\n# self.model = model\n# num_ftrs = self.model.fc.in_features\n# self.dropout= nn.Dropout(0.1)\n# self.model.fc = nn.Linear(num_ftrs, 5)\n\n# def forward(self, images):\n# output = self.model(images)\n# # features = self.dropout(features)\n# # output = self.fc(features)\n# return output\n \n# model = HappyWhaleModel(backbone_model)\n# model.to(CONFIG['device'])\n# print('ok')","metadata":{"execution":{"iopub.status.busy":"2023-11-18T10:29:35.432319Z","iopub.execute_input":"2023-11-18T10:29:35.432598Z","iopub.status.idle":"2023-11-18T10:29:35.436664Z","shell.execute_reply.started":"2023-11-18T10:29:35.432567Z","shell.execute_reply":"2023-11-18T10:29:35.435755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# class HappyWhaleModel(nn.Module):\n# def __init__(self, model_name, pretrained=True):\n# super(HappyWhaleModel, self).__init__()\n# self.model = timm.create_model(model_name, pretrained=pretrained, num_classes=0)\n# self.fc = nn.LazyLinear(CONFIG['embedding_size'])\n# self.dropout = nn.Dropout(p=0.3)\n\n# def forward(self, images):\n# features = self.model(images)\n# features = self.dropout(features)\n# output = self.fc(features)\n# return output\n \n# model = HappyWhaleModel(CONFIG['model_name'])\n# model.to(CONFIG['device'])","metadata":{"execution":{"iopub.status.busy":"2023-11-18T10:29:37.688632Z","iopub.execute_input":"2023-11-18T10:29:37.688917Z","iopub.status.idle":"2023-11-18T10:29:37.693226Z","shell.execute_reply.started":"2023-11-18T10:29:37.688883Z","shell.execute_reply":"2023-11-18T10:29:37.692351Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Resnet50 \n public score: 0.32","metadata":{}},{"cell_type":"code","source":"class HappyWhaleModel(nn.Module):\n def __init__(self, model, pretrained=True):\n super(HappyWhaleModel, self).__init__()\n self.model = model\n num_ftrs = self.model.fc.in_features\n self.fc1 = nn.Sequential(nn.ReLU(),nn.Linear(1000, 512))\n self.fc2 = nn.Sequential(nn.ReLU(),nn.Linear(512, 5))\n\n\n def forward(self, images):\n output = self.model(images)\n# features = self.dropout(features)\n output = self.fc1(output)\n prediction = self.fc2(output)\n return output,prediction\n \nmodel = HappyWhaleModel(backbone_model)\nmodel.to(CONFIG['device'])\nprint('ok')","metadata":{"execution":{"iopub.status.busy":"2023-11-21T15:16:08.712616Z","iopub.execute_input":"2023-11-21T15:16:08.713372Z","iopub.status.idle":"2023-11-21T15:16:08.732973Z","shell.execute_reply.started":"2023-11-21T15:16:08.713333Z","shell.execute_reply":"2023-11-21T15:16:08.732141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Resnet50 + Gem pooling + Arcface","metadata":{}},{"cell_type":"code","source":"class GeM(nn.Module):\n def __init__(self, p=3, eps=1e-6):\n super(GeM, self).__init__()\n self.p = nn.Parameter(torch.ones(1)*p)\n self.eps = eps\n\n def forward(self, x):\n return self.gem(x, p=self.p, eps=self.eps)\n \n def gem(self, x, p=3, eps=1e-6):\n return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(1./p)\n \n def __repr__(self):\n return self.__class__.__name__ + \\\n '(' + 'p=' + '{:.4f}'.format(self.p.data.tolist()[0]) + \\\n ', ' + 'eps=' + str(self.eps) + ')'","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:46:48.116623Z","iopub.execute_input":"2023-11-25T07:46:48.117073Z","iopub.status.idle":"2023-11-25T07:46:48.128532Z","shell.execute_reply.started":"2023-11-25T07:46:48.117026Z","shell.execute_reply":"2023-11-25T07:46:48.12745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class ArcMarginProduct(nn.Module):\n r\"\"\"Implement of large margin arc distance: :\n Args:\n in_features: size of each input sample\n out_features: size of each output sample\n s: norm of input feature\n m: margin\n cos(theta + m)\n \"\"\"\n def __init__(self, in_features, out_features, s=30.0, \n m=0.50, easy_margin=False, ls_eps=0.0):\n super(ArcMarginProduct, self).__init__()\n self.in_features = in_features\n self.out_features = out_features\n self.s = s\n self.m = m\n self.ls_eps = ls_eps # label smoothing\n self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))\n nn.init.xavier_uniform_(self.weight)\n\n self.easy_margin = easy_margin\n self.cos_m = math.cos(m)\n self.sin_m = math.sin(m)\n self.th = math.cos(math.pi - m)\n self.mm = math.sin(math.pi - m) * m\n\n def forward(self, input, label):\n # --------------------------- cos(theta) & phi(theta) ---------------------\n cosine = F.linear(F.normalize(input), F.normalize(self.weight))\n sine = torch.sqrt(1.0 - torch.pow(cosine, 2))\n phi = cosine * self.cos_m - sine * self.sin_m\n if self.easy_margin:\n phi = torch.where(cosine > 0, phi, cosine)\n else:\n phi = torch.where(cosine > self.th, phi, cosine - self.mm)\n # --------------------------- convert label to one-hot ---------------------\n # one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')\n one_hot = torch.zeros(cosine.size(), device=CONFIG['device'])\n one_hot.scatter_(1, label.view(-1, 1).long(), 1)\n if self.ls_eps > 0:\n one_hot = (1 - self.ls_eps) * one_hot + self.ls_eps / self.out_features\n # -------------torch.where(out_i = {x_i if condition_i else y_i) ------------\n output = (one_hot * phi) + ((1.0 - one_hot) * cosine)\n output *= self.s\n\n return output","metadata":{"execution":{"iopub.status.busy":"2023-11-25T05:43:26.563015Z","iopub.execute_input":"2023-11-25T05:43:26.563425Z","iopub.status.idle":"2023-11-25T05:43:26.582762Z","shell.execute_reply.started":"2023-11-25T05:43:26.56338Z","shell.execute_reply":"2023-11-25T05:43:26.581933Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Efficientnet","metadata":{}},{"cell_type":"code","source":"import timm\nclass HappyWhaleModel(nn.Module):\n def __init__(self, model_name, embedding_size, pretrained=True):\n super(HappyWhaleModel, self).__init__()\n self.model = timm.create_model(model_name, pretrained=pretrained)\n in_features = self.model.classifier.in_features\n self.model.classifier = nn.Identity()\n self.model.global_pool = nn.Identity()\n self.pooling = GeM()\n self.embedding = nn.Linear(in_features, embedding_size)\n self.fc = nn.Sequential(nn.ReLU(),nn.Linear(embedding_size,CONFIG[\"num_classes\"]))\n# self.fc = ArcMarginProduct(embedding_size, \n# CONFIG[\"num_classes\"],\n# s=CONFIG[\"s\"], \n# m=CONFIG[\"m\"], \n# easy_margin=CONFIG[\"ls_eps\"], \n# ls_eps=CONFIG[\"ls_eps\"])\n\n def forward(self, images):\n features = self.model(images)\n pooled_features = self.pooling(features).flatten(1)\n embedding = self.embedding(pooled_features)\n output = self.fc(embedding)\n return embedding,output\n \n def extract(self, images):\n features = self.model(images)\n pooled_features = self.pooling(features).flatten(1)\n embedding = self.embedding(pooled_features)\n return embedding\n\n \nmodel =HappyWhaleModel(CONFIG['model_name'], CONFIG['embedding_size'])\nmodel.to(CONFIG['device'])\nprint('ok')","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:20.051818Z","iopub.execute_input":"2023-11-25T02:25:20.05216Z","iopub.status.idle":"2023-11-25T02:25:20.261794Z","shell.execute_reply.started":"2023-11-25T02:25:20.052114Z","shell.execute_reply":"2023-11-25T02:25:20.260991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Loss Function

\n\n![](https://i.imgur.com/Qxd5t7Y.jpg)\n\nCheck the official documentation here","metadata":{}},{"cell_type":"code","source":"# class CrossEn(nn.Module):\n# def __init__(self, config=None):\n# super(CrossEn, self).__init__()\n\n# def forward(self, sim_matrix):\n# logpt = F.log_softmax(sim_matrix, dim=-1)\n# logpt = torch.diag(logpt)\n# nce_loss = -logpt\n# sim_loss = nce_loss.mean()\n# return sim_loss","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.nn.functional as F\n\nclass infoNCE(nn.Module):\n\n def __init__(self,):\n super(infoNCE, self).__init__()\n\n def forward(self, sim_i_2_t, batch_num):\n \"\"\"\n Args:\n visual_embeds: \n lang_embeds: \n logit_scale:\n \"\"\"\n loss_i_2_t = F.cross_entropy(sim_i_2_t, torch.arange(batch_num).to(CONFIG['device']))\n# loss_t_2_i = F.cross_entropy(sim_t_2_i, torch.arange(batch_num).to(CONFIG['device']))\n# loss = (loss_t_2_i+loss_i_2_t)/2\n return loss_i_2_t\n# feat_1=nn.functional.normalize(feat1)\n# feat_2=nn.functional.normalize(feat2)\n# similarity_matrix1=feat_1 @ feat_2.transpose(1, 0)\n# similarity_matrix2=feat_2 @ feat_1.transpose(1, 0)\n\n# NCE_criterion = infoNCE()\n\n# loss=NCE_criterion(similarity_matrix1,similarity_matrix2,len(similarity_matrix1))\n# print(loss)","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:24.152478Z","iopub.execute_input":"2023-11-25T02:25:24.153279Z","iopub.status.idle":"2023-11-25T02:25:24.159746Z","shell.execute_reply.started":"2023-11-25T02:25:24.15324Z","shell.execute_reply":"2023-11-25T02:25:24.15895Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def criterion(outputs1, outputs2, targets):\n return nn.CosineEmbeddingLoss(margin=CONFIG['margin'])(outputs1, outputs2, targets)\nCE_criterion=nn.CrossEntropyLoss()\nNCE_criterion = infoNCE()\n","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:17:34.740999Z","iopub.execute_input":"2023-11-25T02:17:34.741786Z","iopub.status.idle":"2023-11-25T02:17:34.747011Z","shell.execute_reply.started":"2023-11-25T02:17:34.741739Z","shell.execute_reply":"2023-11-25T02:17:34.746034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Training Function

","metadata":{}},{"cell_type":"code","source":"def train_one_epoch(model, optimizer, scheduler, dataloader, device, epoch):\n model.train()\n \n dataset_size = 0\n running_loss = 0.0\n running_acc = 0.0\n arcface= False\n\n \n bar = tqdm(enumerate(dataloader), total=len(dataloader))\n for step, data in bar:\n images1 = data['image1'].to(device, dtype=torch.float)\n# print(type(images1),images1.shape)\n images2 = data['image2'].to(device, dtype=torch.float)\n# print(type(images2),images2.shape)\n targets = data['target'].to(device, dtype=torch.long)\n labels = data['label'].to(device,dtype=torch.long)\n batch_size = images1.size(0)\n # Extract embedding for first branch image and their logits\n #=========================== Arcface ===========================#\n if arcface:\n outputs1,logits = model(images1,labels)\n # The same embedding for second branch image but without logit\n outputs2,_ = model(images2,labels)\n else:\n outputs1,logits = model(images1)\n # The same embedding for second branch image but without logit\n outputs2,_ = model(images2)\n #============================= InfoNCE loss ===========================#\n # Normalize features of two embedding vectors\n feat_1=nn.functional.normalize(outputs1)\n feat_2=nn.functional.normalize(outputs2)\n # Caculate similarity between 2 embedding vectors\n similarity_matrix1=feat_1 @ feat_2.transpose(1, 0)\n# similarity_matrix2= similarity_matrix1.t()\n # Softmax 2 embedding \n similarity_matrix1= F.softmax(similarity_matrix1, dim=-1)\n# similarity_matrix2= F.softmax(similarity_matrix2, dim=-1)\n \n # Compute loss for two embedding\n nce_loss = NCE_criterion(similarity_matrix1,len(similarity_matrix1))\n \n loss = criterion(outputs1, outputs2, targets) + CE_criterion(logits,labels) + nce_loss\n loss = loss / CONFIG['n_accumulate']\n loss.backward()\n \n if (step + 1) % CONFIG['n_accumulate'] == 0:\n optimizer.step()\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n if scheduler is not None:\n scheduler.step()\n prediction= torch.sigmoid(logits)\n _, predicted = torch.max(prediction, 1) # max index\n acc= torch.sum(predicted==labels)\n\n running_loss += (loss.item() * batch_size)\n dataset_size += batch_size\n running_acc += acc.item()\n epoch_acc = running_acc / dataset_size \n epoch_loss = running_loss / dataset_size\n bar.set_postfix(Epoch=epoch, Train_Loss=epoch_loss,Train_Acc=epoch_acc,\n LR=optimizer.param_groups[0]['lr'])\n gc.collect()\n return epoch_loss,epoch_acc","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:37.768387Z","iopub.execute_input":"2023-11-25T02:25:37.769047Z","iopub.status.idle":"2023-11-25T02:25:37.785242Z","shell.execute_reply.started":"2023-11-25T02:25:37.769007Z","shell.execute_reply":"2023-11-25T02:25:37.78444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Validation Function

","metadata":{}},{"cell_type":"code","source":"@torch.inference_mode()\ndef valid_one_epoch(model, dataloader, device, epoch):\n model.eval()\n dataset_size = 0\n running_loss = 0.0\n running_acc = 0.0\n arcface=False\n bar = tqdm(enumerate(dataloader), total=len(dataloader))\n for step, data in bar: \n images1 = data['image1'].to(device, dtype=torch.float)\n# print(type(images1),images1.shape)\n images2 = data['image2'].to(device, dtype=torch.float)\n# print(type(images2),images2.shape)\n targets = data['target'].to(device, dtype=torch.long)\n labels = data['label'].to(device, dtype=torch.long)\n batch_size = images1.size(0)\n #=========================== Arcface ===========================#\n if arcface:\n outputs1,logits = model(images1,labels)\n # The same embedding for second branch image but without logit\n outputs2,_ = model(images2,labels)\n else:\n outputs1,logits = model(images1)\n # The same embedding for second branch image but without logit\n outputs2,_ = model(images2)\n #============================= InfoNCE loss ===========================#\n # Normalize features of two embedding vectors\n feat_1=nn.functional.normalize(outputs1)\n feat_2=nn.functional.normalize(outputs2)\n # Caculate similarity between 2 embedding vectors\n similarity_matrix1=feat_1 @ feat_2.transpose(1, 0)\n# similarity_matrix2= similarity_matrix1.t()\n # Softmax 2 embedding \n similarity_matrix1= F.softmax(similarity_matrix1, dim=-1)\n# similarity_matrix2= F.softmax(similarity_matrix2, dim=-1)\n \n # Compute loss for two embedding\n nce_loss = NCE_criterion(similarity_matrix1,len(similarity_matrix1))\n loss = criterion(outputs1, outputs2, targets) + CE_criterion(logits,labels) + nce_loss\n prediction= torch.sigmoid(logits)\n _, predicted = torch.max(prediction, 1)\n acc = torch.sum( predicted==labels)\n running_acc += acc.item()\n running_loss += (loss.item() * batch_size)\n \n dataset_size += batch_size\n epoch_acc = running_acc / dataset_size\n \n epoch_loss = running_loss / dataset_size\n \n bar.set_postfix(Epoch=epoch, Valid_Loss=epoch_loss,Valid_Acc=epoch_acc,\n LR=optimizer.param_groups[0]['lr']) \n gc.collect()\n return epoch_loss,epoch_acc","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:40.410561Z","iopub.execute_input":"2023-11-25T02:25:40.410857Z","iopub.status.idle":"2023-11-25T02:25:40.426276Z","shell.execute_reply.started":"2023-11-25T02:25:40.410824Z","shell.execute_reply":"2023-11-25T02:25:40.425398Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Run Training

","metadata":{}},{"cell_type":"code","source":"def run_training(model, optimizer, scheduler, device, num_epochs):\n # To automatically log gradients\n# wandb.watch(model, log_freq=100)\n if torch.cuda.is_available():\n print(\"[INFO] Using GPU: {}\\n\".format(torch.cuda.get_device_name()))\n \n start = time.time()\n best_model_wts = copy.deepcopy(model.state_dict())\n best_epoch_loss = np.inf\n best_epoch_acc=np.inf\n history = defaultdict(list)\n \n for epoch in range(1, num_epochs + 1): \n gc.collect()\n train_epoch_loss,train_epoch_acc = train_one_epoch(model, optimizer, scheduler, \n dataloader=train_loader, \n device=CONFIG['device'], epoch=epoch)\n \n val_epoch_loss,val_epoch_acc = valid_one_epoch(model, valid_loader, device=CONFIG['device'], \n epoch=epoch)\n \n history['Train Loss'].append(train_epoch_loss)\n history['Valid Loss'].append(val_epoch_loss)\n history['lr'].append( scheduler.get_lr()[0] )\n history['Train Accuracy'].append(train_epoch_acc)\n history['Valid Accuracy'].append(val_epoch_acc)\n \n # Log the metrics\n# wandb.log({\"Train Loss\": train_epoch_loss})\n# wandb.log({\"Valid Loss\": val_epoch_loss})\n# if best_epoch_acc <= val_epoch_acc:\n# print(f\"{b_}Validation Accuracy Improved ({best_epoch_acc} ---> {val_epoch_acc})\")\n# best_epoch_acc = val_epoch_acc\n# best_model_wts = copy.deepcopy(model.state_dict())\n# PATH = \"Acc{:.2f}_Loss{:.4f}_epoch{:.0f}.bin\".format(best_epoch_acc, val_epoch_loss, epoch)\n# torch.save(model.state_dict(), PATH)\n# print(f\"Model Saved{sr_}\")\n\n \n # deep copy the model\n if val_epoch_loss <= best_epoch_loss:\n print(f\"{b_}Validation Loss Improved ({best_epoch_loss} ---> {val_epoch_loss})\")\n best_epoch_loss = val_epoch_loss\n# run.summary[\"Best Loss\"] = best_epoch_loss\n best_model_wts = copy.deepcopy(model.state_dict())\n PATH = \"Loss{:.4f}_epoch{:.0f}.bin\".format(best_epoch_loss, epoch)\n torch.save(model.state_dict(), PATH)\n# Save a model file from the current directory\n \n end = time.time()\n time_elapsed = end - start\n print('Training complete in {:.0f}h {:.0f}m {:.0f}s'.format(\n time_elapsed // 3600, (time_elapsed % 3600) // 60, (time_elapsed % 3600) % 60))\n print(\"Best Loss: {:.4f}\".format(best_epoch_loss))\n \n # load best model weights\n model.load_state_dict(best_model_wts)\n \n return model, history","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:42.896762Z","iopub.execute_input":"2023-11-25T02:25:42.897061Z","iopub.status.idle":"2023-11-25T02:25:42.910384Z","shell.execute_reply.started":"2023-11-25T02:25:42.897026Z","shell.execute_reply":"2023-11-25T02:25:42.909477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def fetch_scheduler(optimizer):\n if CONFIG['scheduler'] == 'CosineAnnealingLR':\n scheduler = lr_scheduler.CosineAnnealingLR(optimizer,T_max=CONFIG['T_max'], \n eta_min=CONFIG['min_lr'])\n elif CONFIG['scheduler'] == 'CosineAnnealingWarmRestarts':\n scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer,T_0=CONFIG['T_0'], \n eta_min=CONFIG['min_lr'])\n elif CONFIG['scheduler'] == None:\n return None\n \n return scheduler","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:44.998429Z","iopub.execute_input":"2023-11-25T02:25:44.999239Z","iopub.status.idle":"2023-11-25T02:25:45.005608Z","shell.execute_reply.started":"2023-11-25T02:25:44.999203Z","shell.execute_reply":"2023-11-25T02:25:45.004608Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def prepare_loaders(df, fold):\n# df_train = df[df.kfold != fold].reset_index(drop=True)\n# df_valid = df[df.kfold == fold].reset_index(drop=True)\n df_train = df.sample(frac=0.8)\n df_valid = df.sample(frac=0.2)\n# print(df_train)\n# print(df_valid)\n train_dataset = HappyWhaleDataset(df_train, transforms=train_transform)#data_transforms[\"train\"])\n valid_dataset = HappyWhaleDataset(df_valid, transforms=val_transform)#data_transforms[\"valid\"])\n\n train_loader = DataLoader(train_dataset, batch_size=CONFIG['train_batch_size'], \n num_workers=2, shuffle=True, pin_memory=True, drop_last=True) #\n valid_loader = DataLoader(valid_dataset, batch_size=CONFIG['valid_batch_size'], \n num_workers=2, shuffle=False, pin_memory=True)\n \n return train_loader, valid_loader","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:47.540426Z","iopub.execute_input":"2023-11-25T02:25:47.541193Z","iopub.status.idle":"2023-11-25T02:25:47.547859Z","shell.execute_reply.started":"2023-11-25T02:25:47.541159Z","shell.execute_reply":"2023-11-25T02:25:47.547026Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Prepare Dataloaders","metadata":{}},{"cell_type":"code","source":"fold=1\ntrain_loader, valid_loader = prepare_loaders(df, fold=fold) \n","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:50.904261Z","iopub.execute_input":"2023-11-25T02:25:50.905105Z","iopub.status.idle":"2023-11-25T02:25:50.911867Z","shell.execute_reply.started":"2023-11-25T02:25:50.905066Z","shell.execute_reply":"2023-11-25T02:25:50.911143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Define Optimizer and Scheduler","metadata":{}},{"cell_type":"code","source":"optimizer = optim.Adam(model.parameters(), lr=CONFIG['learning_rate'], \n weight_decay=CONFIG['weight_decay'])\nscheduler = fetch_scheduler(optimizer)","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:25:55.040892Z","iopub.execute_input":"2023-11-25T02:25:55.041187Z","iopub.status.idle":"2023-11-25T02:25:55.049215Z","shell.execute_reply.started":"2023-11-25T02:25:55.041152Z","shell.execute_reply":"2023-11-25T02:25:55.048364Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Start Training","metadata":{}},{"cell_type":"code","source":"model, history = run_training(model, optimizer, scheduler,device=CONFIG['device'],\n num_epochs=CONFIG['epochs']) #CONFIG['epochs']","metadata":{"execution":{"iopub.status.busy":"2023-11-25T02:29:20.690265Z","iopub.execute_input":"2023-11-25T02:29:20.691117Z","iopub.status.idle":"2023-11-25T03:38:43.666535Z","shell.execute_reply.started":"2023-11-25T02:29:20.691077Z","shell.execute_reply":"2023-11-25T03:38:43.665753Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Upvote!](https://img.shields.io/badge/Upvote-If%20you%20like%20my%20work-07b3c8?style=for-the-badge&logo=kaggle)","metadata":{}},{"cell_type":"markdown","source":"# Resnet for infer","metadata":{}},{"cell_type":"code","source":"class HappyWhaleModel(nn.Module):\n def __init__(self, model, pretrained=True):\n super(HappyWhaleModel, self).__init__()\n self.model = model\n num_ftrs = self.model.fc.in_features\n self.dropout= nn.Dropout(0.1)\n self.fc1 = nn.Sequential(nn.ReLU(),nn.Linear(1000, 512))\n self.fc2 = nn.Sequential(nn.ReLU(),nn.Linear(512, 5))\n\n\n def forward(self, images):\n output = self.model(images)\n# features = self.dropout(features)\n output = self.fc1(output)\n prediction = self.fc2(output)\n return output,prediction\n\nbackbone_model = res2net50_v1b_26w_4s(pretrained=False).to(CONFIG['device']) \nmodel = HappyWhaleModel(backbone_model)\nmodel.load_state_dict(torch.load('/kaggle/input/infonce-loss-best-weight/resnetinfonce.bin'))\nmodel.to(CONFIG['device'])\nprint('ok')","metadata":{"execution":{"iopub.status.busy":"2023-11-19T09:37:05.449871Z","iopub.execute_input":"2023-11-19T09:37:05.450897Z","iopub.status.idle":"2023-11-19T09:37:07.180599Z","shell.execute_reply.started":"2023-11-19T09:37:05.450839Z","shell.execute_reply":"2023-11-19T09:37:07.179733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Efficient net for infer","metadata":{}},{"cell_type":"code","source":"import timm\nclass HappyWhaleModel(nn.Module):\n def __init__(self, model_name, embedding_size, pretrained=False):\n super(HappyWhaleModel, self).__init__()\n self.model = timm.create_model(model_name, pretrained=pretrained)\n in_features = self.model.classifier.in_features\n self.model.classifier = nn.Identity()\n self.model.global_pool = nn.Identity()\n self.pooling = GeM()\n self.embedding = nn.Linear(in_features, embedding_size)\n self.fc = nn.Sequential(nn.ReLU(),nn.Linear(embedding_size,CONFIG[\"num_classes\"]))\n# self.fc = ArcMarginProduct(embedding_size, \n# CONFIG[\"num_classes\"],\n# s=CONFIG[\"s\"], \n# m=CONFIG[\"m\"], \n# easy_margin=CONFIG[\"ls_eps\"], \n# ls_eps=CONFIG[\"ls_eps\"])\n\n def forward(self, images):\n features = self.model(images)\n pooled_features = self.pooling(features).flatten(1)\n embedding = self.embedding(pooled_features)\n output = self.fc(embedding)\n return embedding,output\n \n def extract(self, images):\n features = self.model(images)\n pooled_features = self.pooling(features).flatten(1)\n embedding = self.embedding(pooled_features)\n return embedding\n\nmodel =HappyWhaleModel(CONFIG['model_name'], CONFIG['embedding_size'])\nmodel.load_state_dict(torch.load('/kaggle/input/siamese-efficientnet-gem/Loss4.2528_epoch26.bin',map_location=CONFIG['device']))\nmodel.to(CONFIG['device'])\nprint('ok')","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:47:12.516969Z","iopub.execute_input":"2023-11-25T07:47:12.518733Z","iopub.status.idle":"2023-11-25T07:47:15.576912Z","shell.execute_reply.started":"2023-11-25T07:47:12.518645Z","shell.execute_reply":"2023-11-25T07:47:15.575798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Infer valid","metadata":{}},{"cell_type":"code","source":"import torch.nn.functional as F\n\nclass HappyWhaleDataset(Dataset):\n def __init__(self,df,transforms=None):\n self.df=df\n self.label=self.df.label\n self.transform =transforms\n def __len__(self):\n return len(self.df)\n def __getitem__(self,idx):\n image1,label_1=Image.open(self.df.file_path.iloc[idx]).convert('RGB'),self.label.iloc[idx]\n# rand_ind=random.randint(0,len(self.df)-1)\n# image2,label_2=Image.open(self.df.file_path.iloc[rand_ind]).convert('RGB'),self.label.iloc[rand_ind]\n# target = 1 if label_1 == label_2 else -1\n\n if self.transform:\n image1 = self.transform(image1)\n# image2 = self.transform(image2)\n\n return {\n 'image1': image1,\n# 'image2': image2,\n# 'target': torch.tensor(target, dtype=torch.long),\n 'label': torch.tensor(label_1, dtype=torch.long)\n }","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:47:21.989986Z","iopub.execute_input":"2023-11-25T07:47:21.991316Z","iopub.status.idle":"2023-11-25T07:47:22.003252Z","shell.execute_reply.started":"2023-11-25T07:47:21.99124Z","shell.execute_reply":"2023-11-25T07:47:22.002101Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_valid = df.sample(frac=0.2)\n","metadata":{"execution":{"iopub.status.busy":"2023-11-25T07:47:39.447161Z","iopub.execute_input":"2023-11-25T07:47:39.447689Z","iopub.status.idle":"2023-11-25T07:47:39.459771Z","shell.execute_reply.started":"2023-11-25T07:47:39.447651Z","shell.execute_reply":"2023-11-25T07:47:39.458952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_df=df_valid.iloc[:400]","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:09:25.44755Z","iopub.execute_input":"2023-11-25T08:09:25.447941Z","iopub.status.idle":"2023-11-25T08:09:25.454182Z","shell.execute_reply.started":"2023-11-25T08:09:25.447904Z","shell.execute_reply":"2023-11-25T08:09:25.452783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"valid_dataset = HappyWhaleDataset(pred_df, transforms=val_transform)\ndataloader = DataLoader(valid_dataset, batch_size=16, num_workers=1, shuffle=False, pin_memory=True)","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:09:28.826994Z","iopub.execute_input":"2023-11-25T08:09:28.827446Z","iopub.status.idle":"2023-11-25T08:09:28.83465Z","shell.execute_reply.started":"2023-11-25T08:09:28.827395Z","shell.execute_reply":"2023-11-25T08:09:28.83384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"bar = tqdm(enumerate(dataloader), total=len(dataloader))\npred=[]\nfor step, data in bar: \n images1 = data['image1'].to(CONFIG['device'], dtype=torch.float)\n batch_size = images1.size(0)\n outputs1,logits = model(images1)\n prediction= torch.sigmoid(logits).tolist()\n pred.append(prediction)","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:09:30.077022Z","iopub.execute_input":"2023-11-25T08:09:30.077366Z","iopub.status.idle":"2023-11-25T08:10:18.878599Z","shell.execute_reply.started":"2023-11-25T08:09:30.077331Z","shell.execute_reply":"2023-11-25T08:10:18.877507Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predict=[]\npred_logits=[]\nfor i in pred:\n for k in i:\n m = k[np.argmax(k)]\n pred_logits.append(m)\n predict.append(np.argmax(k))","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:10:30.822961Z","iopub.execute_input":"2023-11-25T08:10:30.823701Z","iopub.status.idle":"2023-11-25T08:10:30.847297Z","shell.execute_reply.started":"2023-11-25T08:10:30.823629Z","shell.execute_reply":"2023-11-25T08:10:30.845597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_df['pred'] = predict\npred_df['probalities'] = pred_logits","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:10:38.292132Z","iopub.execute_input":"2023-11-25T08:10:38.292551Z","iopub.status.idle":"2023-11-25T08:10:38.306076Z","shell.execute_reply.started":"2023-11-25T08:10:38.292507Z","shell.execute_reply":"2023-11-25T08:10:38.304798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_df","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:10:41.048358Z","iopub.execute_input":"2023-11-25T08:10:41.048725Z","iopub.status.idle":"2023-11-25T08:10:41.071537Z","shell.execute_reply.started":"2023-11-25T08:10:41.048668Z","shell.execute_reply":"2023-11-25T08:10:41.069975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result = pred_df.groupby(['label','pred'])['probalities'].aggregate(['min','max','count'])","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:11:04.961924Z","iopub.execute_input":"2023-11-25T08:11:04.962328Z","iopub.status.idle":"2023-11-25T08:11:04.975258Z","shell.execute_reply.started":"2023-11-25T08:11:04.962286Z","shell.execute_reply":"2023-11-25T08:11:04.973941Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result","metadata":{"execution":{"iopub.status.busy":"2023-11-25T08:11:07.279594Z","iopub.execute_input":"2023-11-25T08:11:07.280014Z","iopub.status.idle":"2023-11-25T08:11:07.29828Z","shell.execute_reply.started":"2023-11-25T08:11:07.279976Z","shell.execute_reply":"2023-11-25T08:11:07.296805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result1 = 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