{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Intro\nInference notebook for [Hotel-ID starter - similarity - training](https://www.kaggle.com/code/michaln/hotel-id-starter-similarity-training)\n\nUsing model and embeddings from the training notebook to generate embeddings for test data and find similar images.","metadata":{"id":"DAY5rHgTm7e8","papermill":{"duration":0.024896,"end_time":"2022-03-24T14:00:54.588459","exception":false,"start_time":"2022-03-24T14:00:54.563563","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# Setup","metadata":{}},{"cell_type":"code","source":"import sys\nsys.path.append('../input/timm-pytorch-image-models/pytorch-image-models-master')","metadata":{"executionInfo":{"elapsed":16271,"status":"ok","timestamp":1619310548121,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"alleged-legislation","outputId":"c6541e5f-ffb4-4609-d6c6-39784e6a07b1","papermill":{"duration":0.036572,"end_time":"2022-03-24T14:00:54.649254","exception":false,"start_time":"2022-03-24T14:00:54.612682","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:06.274773Z","iopub.execute_input":"2022-05-30T15:31:06.275131Z","iopub.status.idle":"2022-05-30T15:31:06.307005Z","shell.execute_reply.started":"2022-05-30T15:31:06.275048Z","shell.execute_reply":"2022-05-30T15:31:06.306097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Imports","metadata":{"id":"cZoSOL9Qm-Yr","papermill":{"duration":0.023644,"end_time":"2022-03-24T14:00:54.696898","exception":false,"start_time":"2022-03-24T14:00:54.673254","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport random\nimport os\nimport math","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","executionInfo":{"elapsed":14459,"status":"ok","timestamp":1619310548121,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"expired-matter","papermill":{"duration":0.030271,"end_time":"2022-03-24T14:00:54.751131","exception":false,"start_time":"2022-03-24T14:00:54.72086","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:06.309313Z","iopub.execute_input":"2022-05-30T15:31:06.309999Z","iopub.status.idle":"2022-05-30T15:31:06.314961Z","shell.execute_reply.started":"2022-05-30T15:31:06.309955Z","shell.execute_reply":"2022-05-30T15:31:06.313942Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from PIL import Image as pil_image\nfrom tqdm import tqdm","metadata":{"executionInfo":{"elapsed":16003,"status":"ok","timestamp":1619310550014,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"extreme-problem","papermill":{"duration":3.220402,"end_time":"2022-03-24T14:00:57.995239","exception":false,"start_time":"2022-03-24T14:00:54.774837","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:06.316333Z","iopub.execute_input":"2022-05-30T15:31:06.317482Z","iopub.status.idle":"2022-05-30T15:31:06.326587Z","shell.execute_reply.started":"2022-05-30T15:31:06.317438Z","shell.execute_reply":"2022-05-30T15:31:06.325629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\nimport timm\nfrom sklearn.metrics.pairwise import cosine_similarity","metadata":{"executionInfo":{"elapsed":19672,"status":"ok","timestamp":1619310554099,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"angry-domain","papermill":{"duration":2.727834,"end_time":"2022-03-24T14:01:00.766951","exception":false,"start_time":"2022-03-24T14:00:58.039117","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:06.330089Z","iopub.execute_input":"2022-05-30T15:31:06.332387Z","iopub.status.idle":"2022-05-30T15:31:11.636172Z","shell.execute_reply.started":"2022-05-30T15:31:06.332353Z","shell.execute_reply":"2022-05-30T15:31:11.635102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Global","metadata":{"id":"0B00pe7mnBTj","papermill":{"duration":0.023573,"end_time":"2022-03-24T14:01:00.814976","exception":false,"start_time":"2022-03-24T14:01:00.791403","status":"completed"},"tags":[]}},{"cell_type":"code","source":"SEED = 42\nIMG_SIZE = 512\nN_MATCHES = 5\n\nPROJECT_FOLDER = \"../input/hotel-id-to-combat-human-trafficking-2022-fgvc9/\"\n# TRAIN_DATA_FOLDER = \"../input/hotelid-2022-train-images-256x256/images/\"\nTEST_DATA_FOLDER = PROJECT_FOLDER + \"test_images/\"","metadata":{"executionInfo":{"elapsed":589,"status":"ok","timestamp":1619310979015,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"contained-brief","papermill":{"duration":0.03175,"end_time":"2022-03-24T14:01:00.871686","exception":false,"start_time":"2022-03-24T14:01:00.839936","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:11.638921Z","iopub.execute_input":"2022-05-30T15:31:11.63946Z","iopub.status.idle":"2022-05-30T15:31:11.646469Z","shell.execute_reply.started":"2022-05-30T15:31:11.639407Z","shell.execute_reply":"2022-05-30T15:31:11.644983Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(os.listdir(PROJECT_FOLDER))","metadata":{"executionInfo":{"elapsed":879,"status":"ok","timestamp":1619310979515,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"PZvmFng7ctO3","outputId":"dce0cc91-8e70-4acc-a0b8-6763ffffd5ca","papermill":{"duration":0.031651,"end_time":"2022-03-24T14:01:00.927239","exception":false,"start_time":"2022-03-24T14:01:00.895588","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:11.648178Z","iopub.execute_input":"2022-05-30T15:31:11.649124Z","iopub.status.idle":"2022-05-30T15:31:11.668833Z","shell.execute_reply.started":"2022-05-30T15:31:11.649077Z","shell.execute_reply":"2022-05-30T15:31:11.667429Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def seed_everything(seed):\n    random.seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True","metadata":{"executionInfo":{"elapsed":600,"status":"ok","timestamp":1619310981653,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"eastern-content","papermill":{"duration":0.032105,"end_time":"2022-03-24T14:01:01.031949","exception":false,"start_time":"2022-03-24T14:01:00.999844","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:11.670959Z","iopub.execute_input":"2022-05-30T15:31:11.671308Z","iopub.status.idle":"2022-05-30T15:31:11.67822Z","shell.execute_reply.started":"2022-05-30T15:31:11.671262Z","shell.execute_reply":"2022-05-30T15:31:11.676924Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset and transformations","metadata":{"id":"xaJKvvuKnW4k","papermill":{"duration":0.023988,"end_time":"2022-03-24T14:01:01.080488","exception":false,"start_time":"2022-03-24T14:01:01.0565","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import albumentations as A\nimport albumentations.pytorch as APT\nimport cv2 \n\nbase_transform = A.Compose([\n    A.ToFloat(),\n    APT.transforms.ToTensorV2(),\n])","metadata":{"executionInfo":{"elapsed":1519,"status":"ok","timestamp":1619310984075,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"revolutionary-membership","papermill":{"duration":0.747421,"end_time":"2022-03-24T14:01:01.852072","exception":false,"start_time":"2022-03-24T14:01:01.104651","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:11.682805Z","iopub.execute_input":"2022-05-30T15:31:11.683174Z","iopub.status.idle":"2022-05-30T15:31:13.34204Z","shell.execute_reply.started":"2022-05-30T15:31:11.683128Z","shell.execute_reply":"2022-05-30T15:31:13.340845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def pad_image(img):\n    w, h, c = np.shape(img)\n    if w > h:\n        pad = int((w - h) / 2)\n        img = cv2.copyMakeBorder(img, 0, 0, pad, pad, cv2.BORDER_CONSTANT, value=0)\n    else:\n        pad = int((h - w) / 2)\n        img = cv2.copyMakeBorder(img, pad, pad, 0, 0, cv2.BORDER_CONSTANT, value=0)\n        \n    return img\n\n\ndef open_and_preprocess_image(image_path):\n    img = cv2.imread(image_path)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    img = pad_image(img)\n    return cv2.resize(img, (IMG_SIZE, IMG_SIZE))","metadata":{"execution":{"iopub.status.busy":"2022-05-30T15:31:13.343914Z","iopub.execute_input":"2022-05-30T15:31:13.34422Z","iopub.status.idle":"2022-05-30T15:31:13.3604Z","shell.execute_reply.started":"2022-05-30T15:31:13.344169Z","shell.execute_reply":"2022-05-30T15:31:13.359421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class HotelImageDataset:\n    def __init__(self, data, transform=None, data_folder=\"train_images/\"):\n        self.data = data\n        self.data_folder = data_folder\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, idx):\n        record = self.data.iloc[idx]\n        image_path = self.data_folder + record[\"image_id\"]\n        \n        image = np.array(open_and_preprocess_image(image_path)).astype(np.uint8)\n\n        if self.transform:\n            transformed = self.transform(image=image)\n            image = transformed[\"image\"]\n        \n        return {\n            \"image\" : image,\n        }","metadata":{"executionInfo":{"elapsed":1058,"status":"ok","timestamp":1619310984077,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"found-mouth","papermill":{"duration":0.037507,"end_time":"2022-03-24T14:01:01.914352","exception":false,"start_time":"2022-03-24T14:01:01.876845","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:13.36208Z","iopub.execute_input":"2022-05-30T15:31:13.368081Z","iopub.status.idle":"2022-05-30T15:31:13.380208Z","shell.execute_reply.started":"2022-05-30T15:31:13.368036Z","shell.execute_reply":"2022-05-30T15:31:13.37902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model","metadata":{"id":"NMDM4PwPnced","papermill":{"duration":0.023902,"end_time":"2022-03-24T14:01:01.962307","exception":false,"start_time":"2022-03-24T14:01:01.938405","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class EmbeddingModel(nn.Module):\n    def __init__(self, n_classes=100, embedding_size=64, backbone_name=\"efficientnet_b0\"):\n        super(EmbeddingModel, self).__init__()\n        \n        self.backbone = timm.create_model(backbone_name, num_classes=n_classes, pretrained=False)\n        in_features = self.backbone.get_classifier().in_features\n        \n        self.backbone.classifier = nn.Identity()\n        self.embedding = nn.Linear(in_features, embedding_size)\n        self.classifier = nn.Linear(embedding_size, n_classes)\n\n    def embed_and_classify(self, x):\n        x = self.forward(x)\n        return x, self.classifier(x)\n\n    def forward(self, x):\n        x = self.backbone(x)\n        x = x.view(x.size(0), -1)\n        x = self.embedding(x)\n        return x","metadata":{"papermill":{"duration":0.032166,"end_time":"2022-03-24T14:01:02.018479","exception":false,"start_time":"2022-03-24T14:01:01.986313","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:13.382154Z","iopub.execute_input":"2022-05-30T15:31:13.382588Z","iopub.status.idle":"2022-05-30T15:31:13.402445Z","shell.execute_reply.started":"2022-05-30T15:31:13.382549Z","shell.execute_reply":"2022-05-30T15:31:13.401239Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model helper functions","metadata":{"id":"YMZYKhUSneMY","papermill":{"duration":0.024153,"end_time":"2022-03-24T14:01:02.067537","exception":false,"start_time":"2022-03-24T14:01:02.043384","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def generate_embeddings(args, loader, model, bar_desc=\"Generating embeds\"):\n    outputs_all = []\n    \n    model.eval()\n    with torch.no_grad():\n        t = tqdm(loader, desc=bar_desc)\n        for i, sample in enumerate(t):\n            input = sample['image'].to(args.device)\n            output = model(input)\n            outputs_all.extend(output.detach().cpu().numpy())\n            \n    return outputs_all","metadata":{"execution":{"iopub.status.busy":"2022-05-30T15:31:13.404227Z","iopub.execute_input":"2022-05-30T15:31:13.404657Z","iopub.status.idle":"2022-05-30T15:31:13.414023Z","shell.execute_reply.started":"2022-05-30T15:31:13.404594Z","shell.execute_reply":"2022-05-30T15:31:13.41271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def find_matches(query, base_embeds, base_targets, k=N_MATCHES):\n    distance_df = pd.DataFrame(index=np.arange(len(base_targets)), data={\"hotel_id\": base_targets})\n    # calculate cosine distance of query embeds to all base embeds\n    distance_df[\"distance\"] = cosine_similarity([query], list(base_embeds))[0]\n    # sort by distance and hotel_id\n    distance_df = distance_df.sort_values(by=[\"distance\", \"hotel_id\"], ascending=False).reset_index(drop=True)\n    # return first 5 different hotel_id_codes\n    return distance_df[\"hotel_id\"].unique()[:N_MATCHES]\n\n\ndef predict(args, base_embeddings_df, test_loader, model):\n    test_embeds = generate_embeddings(args, test_loader, model, \"Generate test embeddings\")\n    \n    preds = []\n    for query_embeds in tqdm(test_embeds, desc=\"Similarity - match finding\"):\n        tmp = find_matches(query_embeds, \n                           base_embeddings_df[\"embeddings\"].values, \n                           base_embeddings_df[\"hotel_id\"].values)\n        preds.extend([tmp])\n        \n    return preds","metadata":{"papermill":{"duration":0.034692,"end_time":"2022-03-24T14:01:02.127372","exception":false,"start_time":"2022-03-24T14:01:02.09268","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:13.416578Z","iopub.execute_input":"2022-05-30T15:31:13.41702Z","iopub.status.idle":"2022-05-30T15:31:13.427791Z","shell.execute_reply.started":"2022-05-30T15:31:13.416975Z","shell.execute_reply":"2022-05-30T15:31:13.426813Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Prepare data","metadata":{"id":"AwShW1wXniD6","papermill":{"duration":0.023807,"end_time":"2022-03-24T14:01:02.175822","exception":false,"start_time":"2022-03-24T14:01:02.152015","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test_df = pd.DataFrame(data={\"image_id\": os.listdir(TEST_DATA_FOLDER), \"hotel_id\": \"\"}).sort_values(by=\"image_id\")","metadata":{"executionInfo":{"elapsed":3742,"status":"ok","timestamp":1619311036476,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"discrete-right","outputId":"c21ed589-3139-4919-b5d5-07bcf6f1df15","papermill":{"duration":0.103381,"end_time":"2022-03-24T14:01:02.453866","exception":false,"start_time":"2022-03-24T14:01:02.350485","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:13.429512Z","iopub.execute_input":"2022-05-30T15:31:13.430358Z","iopub.status.idle":"2022-05-30T15:31:13.455206Z","shell.execute_reply.started":"2022-05-30T15:31:13.4303Z","shell.execute_reply":"2022-05-30T15:31:13.454224Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Prepare model","metadata":{"id":"5JPdD2bpnniP","papermill":{"duration":0.023835,"end_time":"2022-03-24T14:01:02.502786","exception":false,"start_time":"2022-03-24T14:01:02.478951","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def get_model(backbone_name, checkpoint_path, args):\n    model = EmbeddingModel(args.n_classes, args.embedding_size, backbone_name)\n        \n    checkpoint = torch.load(checkpoint_path)\n    model.load_state_dict(checkpoint[\"model\"])\n    model = model.to(args.device)\n    \n    return model","metadata":{"papermill":{"duration":0.031082,"end_time":"2022-03-24T14:01:02.557921","exception":false,"start_time":"2022-03-24T14:01:02.526839","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:13.45656Z","iopub.execute_input":"2022-05-30T15:31:13.457001Z","iopub.status.idle":"2022-05-30T15:31:13.46339Z","shell.execute_reply.started":"2022-05-30T15:31:13.456954Z","shell.execute_reply":"2022-05-30T15:31:13.462301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class args:\n    batch_size = 64\n    num_workers = 2\n    embedding_size = 4096\n    device = ('cuda' if torch.cuda.is_available() else 'cpu')\n    \n    \nseed_everything(seed=SEED)\n\ntest_dataset = HotelImageDataset(test_df, base_transform, data_folder=TEST_DATA_FOLDER)\ntest_loader  = DataLoader(test_dataset, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=False)","metadata":{"executionInfo":{"elapsed":450,"status":"ok","timestamp":1619311064188,"user":{"displayName":"Jeom Jin-Ho","photoUrl":"","userId":"00155613517919499503"},"user_tz":-120},"id":"appointed-machinery","papermill":{"duration":0.069839,"end_time":"2022-03-24T14:01:02.65177","exception":false,"start_time":"2022-03-24T14:01:02.581931","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:13.465136Z","iopub.execute_input":"2022-05-30T15:31:13.465722Z","iopub.status.idle":"2022-05-30T15:31:13.539389Z","shell.execute_reply.started":"2022-05-30T15:31:13.465676Z","shell.execute_reply":"2022-05-30T15:31:13.538231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"base_embeddings_df = pd.read_pickle('../input/training/embedding-model-efficientnet_b1-512x512_image-embeddings.pkl')\ndisplay(base_embeddings_df.head())","metadata":{"execution":{"iopub.status.busy":"2022-05-30T15:31:13.543907Z","iopub.execute_input":"2022-05-30T15:31:13.544138Z","iopub.status.idle":"2022-05-30T15:31:20.626547Z","shell.execute_reply.started":"2022-05-30T15:31:13.544108Z","shell.execute_reply":"2022-05-30T15:31:20.62558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"args.n_classes = base_embeddings_df[\"hotel_id\"].nunique()\n\nmodel = get_model(\"efficientnet_b1\",\n                  \"../input/training/checkpoint-embedding-model-efficientnet_b1-512x512.pt\", \n                  args)","metadata":{"papermill":{"duration":5.553999,"end_time":"2022-03-24T14:01:08.229948","exception":false,"start_time":"2022-03-24T14:01:02.675949","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:20.628218Z","iopub.execute_input":"2022-05-30T15:31:20.630795Z","iopub.status.idle":"2022-05-30T15:31:26.70814Z","shell.execute_reply.started":"2022-05-30T15:31:20.630731Z","shell.execute_reply":"2022-05-30T15:31:26.707005Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{"papermill":{"duration":0.024227,"end_time":"2022-03-24T14:01:08.278753","exception":false,"start_time":"2022-03-24T14:01:08.254526","status":"completed"},"tags":[]}},{"cell_type":"code","source":"%%time\n\npreds = predict(args, base_embeddings_df, test_loader, model)\n# transform array of hotel_ids into string\ntest_df[\"hotel_id\"] = [str(list(l)).strip(\"[]\").replace(\",\", \"\") for l in preds]\n\ntest_df.to_csv(\"submission.csv\", index=False)\ntest_df.head()","metadata":{"papermill":{"duration":0.047156,"end_time":"2022-03-24T14:01:08.414557","exception":false,"start_time":"2022-03-24T14:01:08.367401","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-05-30T15:31:26.71292Z","iopub.execute_input":"2022-05-30T15:31:26.713349Z","iopub.status.idle":"2022-05-30T15:31:34.888111Z","shell.execute_reply.started":"2022-05-30T15:31:26.713301Z","shell.execute_reply":"2022-05-30T15:31:34.887025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}