{"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":"![image.png](attachment:d61bcba5-4d65-470f-8907-df236ac53ab2.png)\n\n**Overview** of the modelling approach:\n1. For each patient, extract 1280x1 embeddings for all slices from [[train] PyTorch-EffNetV2 baseline CV:0.49](https://www.kaggle.com/code/vslaykovsky/train-pytorch-effnetv2-baseline-cv-0-49) notebook by [\nVladimir Slaykovskiy](https://www.kaggle.com/vslaykovsky). Thus, each patient will have a sequence of 1280-dimensional vectors, i.e. a tensor of shape (num_slices, 1280). The notebook for extracting the embeddings can be found [here](https://www.kaggle.com/code/leventelippenszky/extract-effnetv2-embeddings). I used the fold 0 Effnetv2 model, so I kept folds <1,2,3,4> as the trainig folds and fold 0 as the validation fold in this notebook.\n2. An LSTM was trained to predict which vertebraes are present and which ones are fractured for all slices of a patient, similarly to [[train] PyTorch-EffNetV2 baseline CV:0.49](https://www.kaggle.com/code/vslaykovsky/train-pytorch-effnetv2-baseline-cv-0-49). A sequential model can have the additional capability to capture information from other slices of the patient, instead of simply taking into account a single slice. BCELoss was used for the vertebrae detection head and the competition loss for fracture detection (without patient_overall). The final loss was determined as the weighted sum of the two losses. \n3. After 2., we have 7+7 predictions for all slices of a patient, we need to aggregate them somehow to arrive at a 8-dimensional prediction vector for a patient. We can view this as we have a sequence of 14-dim vectors for each patient, i.e. a tensor of shape (num_slices, 14). This setup is similar to sentiment analysis, where we need to predict a 1-dim target (sentiment) for varying length sentences. GRU was trained to predict the 8-dim target for each patient with varying number of slices.\n\nThis approach was inspired by the [2nd place solution](https://github.com/darraghdog/rsna) of [RSNA 2019 Intracranial Hemorrhage Detection Challenge](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/overview) by [Darragh](https://www.kaggle.com/darraghdog).","metadata":{},"attachments":{"d61bcba5-4d65-470f-8907-df236ac53ab2.png":{"image/png":"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Libraries","metadata":{}},{"cell_type":"code","source":"import os\nimport gc\nimport re\nimport sys\nimport cv2\nimport wandb\nimport time\nimport glob\nimport random\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_theme()\nfrom sklearn.model_selection import GroupKFold\nfrom kaggle_secrets import UserSecretsClient\n\nimport torch\nfrom numba import cuda\nfrom torch import nn\nfrom torch import optim\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torch.nn.utils.rnn import pack_sequence, pad_packed_sequence\nwandb.login(key=UserSecretsClient().get_secret(\"WANDB_API_KEY\"))","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:17.940081Z","iopub.execute_input":"2022-10-09T14:24:17.940708Z","iopub.status.idle":"2022-10-09T14:24:24.844028Z","shell.execute_reply.started":"2022-10-09T14:24:17.940636Z","shell.execute_reply":"2022-10-09T14:24:24.843083Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Constants and config","metadata":{}},{"cell_type":"code","source":"DATA_DIR = \"../input/rsna-2022-cervical-spine-fracture-detection\"\nTRAIN_DIR = os.path.join(DATA_DIR, \"train_images\")\nTEST_DIR = os.path.join(DATA_DIR, \"test_images\")\nEMBS_DIR = \"../input/effnetv2-embeddings\"\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nFOLD = 0\n\nMAIN = [\"stage_1\", \"stage_2\"]\nSTAGE_1_PREDS_DIR = \"./lstm_stage_1\"\nSAVE_STAGE_1 = True\nSAVE_STAGE_2 = True\nSEED = 0\n\nconfig = {\n    \"stage_1\": {\"hidden_size\": 14, \"num_layers\": 1, \"dropout\": 0, \"batch_size\": 64, \"num_epochs\": 100, \"lr\": 0.001, \"frac_loss_weight\": 2},\n    \"stage_2\": {\"hidden_size\": 8, \"num_layers\": 1, \"dropout\": 0, \"batch_size\": 64, \"num_epochs\": 100, \"lr\": 0.001, \"factor\": 0.5, \"patience\":10},\n}","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:24.849105Z","iopub.execute_input":"2022-10-09T14:24:24.851608Z","iopub.status.idle":"2022-10-09T14:24:24.932253Z","shell.execute_reply.started":"2022-10-09T14:24:24.851562Z","shell.execute_reply":"2022-10-09T14:24:24.931162Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Setting the seed","metadata":{}},{"cell_type":"code","source":"def set_seed(seed):\n    '''\n    Sets the seed of the entire notebook for reproducibility.\n    '''\n    np.random.seed(seed)\n    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(SEED)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:24.934013Z","iopub.execute_input":"2022-10-09T14:24:24.934651Z","iopub.status.idle":"2022-10-09T14:24:24.944785Z","shell.execute_reply.started":"2022-10-09T14:24:24.934612Z","shell.execute_reply":"2022-10-09T14:24:24.943566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Datasets\n","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv(\"../input/rsna-effnetv2-baseline-train-csv/effnet_v2_train.csv\")\nprint(train_df.shape)\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:24.947776Z","iopub.execute_input":"2022-10-09T14:24:24.948559Z","iopub.status.idle":"2022-10-09T14:24:26.856616Z","shell.execute_reply.started":"2022-10-09T14:24:24.948522Z","shell.execute_reply":"2022-10-09T14:24:26.855635Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I dropped the first and last few slices of patients who have a lot of slices. This was important when extracting the embeddings, as the extraction was performed on all slices of a patient at once. Patients with too many slices could not have been fit into GPU memory.","metadata":{}},{"cell_type":"code","source":"def drop_first_last_slices(train_df):    \n    dfs = []\n    for patient_id, group in train_df.groupby(\"StudyInstanceUID\"):\n        num_slices = group.shape[0]\n        if num_slices >= 1000:\n            group = group.iloc[np.floor(num_slices*0.15).astype(int) : np.ceil(num_slices*0.75).astype(int),:]\n        elif num_slices >= 800:\n            group = group.iloc[np.floor(num_slices*0.15).astype(int) : np.ceil(num_slices*0.85).astype(int),:]\n        elif num_slices >= 700:\n            group = group.iloc[np.floor(num_slices*0.05).astype(int) : np.ceil(num_slices*0.9).astype(int),:]\n        dfs.append(group)\n    return pd.concat(dfs)\n\nproc_train_df = drop_first_last_slices(train_df)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:26.857968Z","iopub.execute_input":"2022-10-09T14:24:26.85966Z","iopub.status.idle":"2022-10-09T14:24:27.761172Z","shell.execute_reply.started":"2022-10-09T14:24:26.859617Z","shell.execute_reply":"2022-10-09T14:24:27.760166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Utils","metadata":{}},{"cell_type":"code","source":"# https://www.kaggle.com/code/yosukeyama/rsna2022-comp-metric?scriptVersionId=103365834\n# use if LSTM has sigmoid activation in last layer\ndef competition_loss(yhat, y):\n    \"\"\"Computes competition loss using row-wise weight normalization, takes the mean of all losses.\n    \n    Args:\n        yhat: Tensor of predicted probabilities with shape (BATCH_SIZE, 8).\n        y: Tensor of labels with the same shape.\n    \n    Returns:\n        Scalar loss value.\n    \"\"\"\n    loss_fn = nn.BCELoss(reduction=\"none\")\n    \n    if yhat.size(1) == 8:\n        competition_weights = {\n            '-' : torch.tensor([7, 1, 1, 1, 1, 1, 1, 1], dtype=torch.float, device=DEVICE),\n            '+' : torch.tensor([14, 2, 2, 2, 2, 2, 2, 2], dtype=torch.float, device=DEVICE),\n            }\n    else:\n        # give less focus on C7\n        competition_weights = {\n            '-' : torch.tensor([1, 1, 1, 1, 1, 1, 0.5], dtype=torch.float, device=DEVICE),\n            '+' : torch.tensor([2, 2, 2, 2, 2, 2, 1], dtype=torch.float, device=DEVICE),\n            }\n    \n    loss = loss_fn(yhat, y)\n    weights = y * competition_weights['+'] + (1 - y) * competition_weights['-']\n    loss = (loss * weights).sum(axis=1)\n    w_sum = weights.sum(axis=1)\n    loss = torch.div(loss, w_sum)\n    return loss.mean()\n\n\nclass AverageCalc(object):\n    '''\n    Calculates and stores the average and current value.\n    Used to update the loss.\n    '''\n    def __init__(self):\n        self.reset()\n    \n    def reset(self):\n        self.value = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n    \n    def update(self, value, size=1):\n        self.value = value\n        self.sum += value * size\n        self.count += size\n        self.avg = self.sum/self.count\n        \n\n# https://suzyahyah.github.io/pytorch/2019/07/01/DataLoader-Pad-Pack-Sequence.html\n# https://www.codefull.org/2018/11/use-pytorchs-dataloader-with-variable-length-sequences-for-lstm-gru/\ndef pack_collate(batch):\n    \"\"\"\n    Packs variable length sequences (embeddings) using `pack_sequence`.\n    \n    Args:\n        batch: List of tuples (training) or list (inference) from __getitem__ of EffnetFeatsDatasetDisk.\n    \"\"\"\n    if len(batch[0]) == 3:\n        sorted_batch = sorted(batch, key=lambda x: x[0].size(0), reverse=True)\n        embs = [x[0] for x in sorted_batch]\n        vert_targets = [x[1] for x in sorted_batch]\n        frac_targets = [x[2] for x in sorted_batch]\n        packed_embs = pack_sequence(embs, enforce_sorted=True)\n        packed_vert_targets = pack_sequence(vert_targets, enforce_sorted=True)\n        packed_frac_targets = pack_sequence(frac_targets, enforce_sorted=True)\n        return packed_embs, packed_vert_targets, packed_frac_targets\n    elif isinstance(batch[0][1], str):\n        sorted_batch = sorted(batch, key=lambda x: x[0].size(0), reverse=True)\n        embs = [x[0] for x in sorted_batch]\n        packed_embs = pack_sequence(embs, enforce_sorted=True)\n        pat_ids = [x[1] for x in sorted_batch]\n        return packed_embs, pat_ids\n    else:\n        sorted_batch = sorted(batch, key=lambda x: x[0].size(0), reverse=True)\n        stage1_preds = [x[0] for x in sorted_batch]\n        labels = [x[1] for x in sorted_batch]\n        packed_stage1_preds = pack_sequence(stage1_preds, enforce_sorted=True)\n        labels = torch.vstack(labels)\n        return packed_stage1_preds, labels","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:27.762882Z","iopub.execute_input":"2022-10-09T14:24:27.763269Z","iopub.status.idle":"2022-10-09T14:24:27.780652Z","shell.execute_reply.started":"2022-10-09T14:24:27.76323Z","shell.execute_reply":"2022-10-09T14:24:27.779412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Stage 1 LSTM ","metadata":{}},{"cell_type":"code","source":"class EffnetFeatsDatasetDisk(Dataset):\n    def __init__(self, df, fold, mode=\"train\"):  # mode: train/infer\n        self.df = df\n        self.patients = df[\"StudyInstanceUID\"].unique()\n        self.fold = fold\n        self.mode = mode\n    \n    def __len__(self):\n        return len(self.patients)\n    \n    def __getitem__(self, idx):\n        pat_id = self.patients[idx]\n        img_feats = torch.load(os.path.join(EMBS_DIR, f\"{pat_id}_fold_{self.fold}.pt\"))\n        if self.mode == \"train\":  \n            vert_targets = torch.tensor(self.df.loc[self.df[\"StudyInstanceUID\"] == pat_id, [f\"C{i}\" for i in np.arange(1,8)]].to_numpy(), dtype=torch.float32)\n            frac_targets = torch.tensor(self.df.loc[self.df[\"StudyInstanceUID\"] == pat_id, [f\"C{i}_fracture\" for i in np.arange(1,8)]].to_numpy(), dtype=torch.float32) * vert_targets\n            return img_feats, vert_targets, frac_targets\n        elif self.mode == \"pred\":\n            return img_feats, pat_id\n\n\n# https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/1%20-%20Simple%20Sentiment%20Analysis.ipynb\nclass LSTM1(nn.Module):\n    def __init__(self, embed_size, hidden_size, num_layers, dropout, bidirectional):\n        super(LSTM1, self).__init__()\n        self.hidden_size = hidden_size\n        self.lstm = nn.LSTM(input_size=embed_size, \n                            hidden_size=hidden_size,\n                            num_layers=num_layers,\n                            dropout=dropout,\n                            bidirectional=bidirectional,\n                            batch_first=True)\n        \n        # use sigmoid and binary crossentropy\n        # https://stats.stackexchange.com/questions/272862/which-deep-learning-model-can-classify-categories-which-are-not-mutually-exclusi\n        self.vert_cls = nn.Linear(hidden_size * 2, 7)\n        self.frac_cls = nn.Linear(hidden_size * 2, 7)\n        \n    def forward(self, x):\n        # h_0, c_0 defaults to zeros if not provided\n        # x: (BATCH_SIZE, SEQ_LEN, 1280)\n        output_packed, _ = self.lstm(x)\n        # pad_packed_sequence re-orders the batch to orig\n        # output: (BATCH_SIZE, SEQ_LEN, 2*HIDDEN_SIZE), lens: (BATCH_SIZE)\n        output, lens = pad_packed_sequence(output_packed, batch_first=True)\n        \n        # logits: (BATCH_SIZE, SEQ_LEN, 7)\n        vert_logits = self.vert_cls(output)\n        frac_logits = self.frac_cls(output)\n        return torch.sigmoid(vert_logits), torch.sigmoid(frac_logits), lens\n            \n\nclass Stage1:\n    def __init__(self, df, fold, config):\n        self.df = df\n        self.fold = fold\n        self.config = config\n        self.criterion = nn.BCELoss(reduction=\"mean\")\n\n    def train_one_epoch(self, train_loader, model, optimizer):\n        run_vert_loss = AverageCalc()\n        run_frac_loss = AverageCalc()\n        run_loss = AverageCalc()\n\n        model.train()\n        for packed_embs, packed_vert, packed_frac in train_loader:\n            packed_embs = packed_embs.to(DEVICE)\n            vert, lens_vert = pad_packed_sequence(packed_vert, batch_first=True)\n            vert_cat = torch.cat([vert[i, :l, :] for i, l in enumerate(lens_vert)], dim=0).to(DEVICE)\n\n            frac, lens_frac = pad_packed_sequence(packed_frac, batch_first=True)\n            frac_cat = torch.cat([frac[i, :l, :] for i, l in enumerate(lens_frac)], dim=0).to(DEVICE)\n            assert torch.all(lens_vert == lens_frac)\n\n            optimizer.zero_grad()\n\n            vert_preds, frac_preds, lens = model(packed_embs)\n            vert_preds_cat = torch.cat([vert_preds[i, :l, :] for i, l in enumerate(lens)], dim=0)\n            frac_preds_cat = torch.cat([frac_preds[i, :l, :] for i, l in enumerate(lens)], dim=0)\n            assert torch.all(lens_vert == lens)\n\n            vert_loss = self.criterion(vert_preds_cat, vert_cat)\n            frac_loss = competition_loss(frac_preds_cat, frac_cat)\n\n            loss = vert_loss + self.config[\"frac_loss_weight\"] * frac_loss\n            loss.backward()\n\n            run_vert_loss.update(vert_loss.item(), self.config[\"batch_size\"])\n            run_frac_loss.update(frac_loss.item(), self.config[\"batch_size\"])\n            run_loss.update(loss.item(), self.config[\"batch_size\"])\n\n            optimizer.step()\n        return run_vert_loss.avg, run_frac_loss.avg, run_loss.avg\n\n    \n    @torch.no_grad()\n    def val_one_epoch(self, val_loader, model):\n        model.eval()\n        run_vert_loss = AverageCalc()\n        run_frac_loss = AverageCalc()\n        run_loss = AverageCalc()\n\n        for packed_embs, packed_vert, packed_frac in val_loader:\n            packed_embs = packed_embs.to(DEVICE)\n            vert, lens_vert = pad_packed_sequence(packed_vert, batch_first=True)\n            vert_cat = torch.cat([vert[i, :l, :] for i, l in enumerate(lens_vert)], dim=0).to(DEVICE)\n\n            frac, lens_frac = pad_packed_sequence(packed_frac, batch_first=True)\n            frac_cat = torch.cat([frac[i, :l, :] for i, l in enumerate(lens_frac)], dim=0).to(DEVICE)\n            assert torch.all(lens_vert == lens_frac)\n\n            vert_preds, frac_preds, lens = model(packed_embs)\n            vert_preds_cat = torch.cat([vert_preds[i, :l, :] for i, l in enumerate(lens)], dim=0)\n            frac_preds_cat = torch.cat([frac_preds[i, :l, :] for i, l in enumerate(lens)], dim=0)\n            assert torch.all(lens_vert == lens)\n\n            vert_loss = self.criterion(vert_preds_cat, vert_cat)\n            frac_loss = competition_loss(frac_preds_cat, frac_cat)   \n            loss = vert_loss + self.config[\"frac_loss_weight\"] * frac_loss\n\n            run_vert_loss.update(vert_loss.item(), self.config[\"batch_size\"])\n            run_frac_loss.update(frac_loss.item(), self.config[\"batch_size\"])\n            run_loss.update(loss.item(), self.config[\"batch_size\"])        \n        return run_vert_loss.avg, run_frac_loss.avg, run_loss.avg\n\n    \n    def run(self):\n        wandb.init(project=\"RSNA-2022\",\n                   name=f\"lstm_stage_1_fold_{self.fold}\",\n                   config=self.config)\n\n        train_df = self.df[self.df[\"split\"] != self.fold].reset_index(drop=True)\n        val_df = self.df[self.df[\"split\"] == self.fold].reset_index(drop=True)\n\n        train_ds = EffnetFeatsDatasetDisk(train_df, self.fold)\n        val_ds = EffnetFeatsDatasetDisk(val_df, self.fold)\n\n        train_loader = DataLoader(train_ds, batch_size=self.config[\"batch_size\"], shuffle=True, num_workers=os.cpu_count(), collate_fn=pack_collate)\n        val_loader = DataLoader(val_ds, batch_size=self.config[\"batch_size\"], shuffle=False, num_workers=os.cpu_count(), collate_fn=pack_collate)\n        \n        model = LSTM1(1280, self.config[\"hidden_size\"],\n                     num_layers = self.config[\"num_layers\"],\n                     dropout = self.config[\"dropout\"],\n                     bidirectional=True).to(DEVICE)\n        optimizer = optim.Adam(model.parameters(), lr=self.config[\"lr\"])\n\n        for epoch in range(self.config[\"num_epochs\"]):\n            train_vert_loss, train_frac_loss, train_loss = self.train_one_epoch(train_loader, model, optimizer)\n            wandb.log({\"train_vert_loss\": train_vert_loss, \"train_frac_loss\": train_frac_loss, \"train_loss\": train_loss})\n            \n            val_vert_loss, val_frac_loss, val_loss = self.val_one_epoch(val_loader, model)\n            wandb.log({\"val_vert_loss\": val_vert_loss, \"val_frac_loss\": val_frac_loss, \"val_loss\": val_loss})\n            \n            if (epoch+1) % 10 == 0:\n                print(f\"epoch {epoch+1} train vert loss: {train_vert_loss}\")\n                print(f\"epoch {epoch+1} train frac loss: {train_frac_loss}\")\n                print(f\"epoch {epoch+1} train loss: {train_loss}\")\n                print(f\"epoch {epoch+1} val vert loss: {val_vert_loss}\")\n                print(f\"epoch {epoch+1} val frac loss: {val_frac_loss}\")\n                print(f\"epoch {epoch+1} val loss: {val_loss}\")\n\n        wandb.finish()\n        torch.save(model.state_dict(), f\"lstm_stage_1_fold_{self.fold}.pt\")\n\n        \n    @torch.no_grad()\n    def save_preds(self):\n        os.makedirs(STAGE_1_PREDS_DIR, exist_ok=True)\n        model = LSTM1(1280, self.config[\"hidden_size\"],\n                     num_layers = self.config[\"num_layers\"],\n                     dropout = self.config[\"dropout\"],\n                     bidirectional=True).to(DEVICE)\n        model.load_state_dict(torch.load(f\"lstm_stage_1_fold_{self.fold}.pt\", map_location=DEVICE))\n        model.eval()\n\n        ds = EffnetFeatsDatasetDisk(self.df, self.fold, mode=\"pred\")\n        loader = DataLoader(ds, batch_size=self.config[\"batch_size\"], shuffle=False, num_workers=os.cpu_count(), collate_fn=pack_collate)\n\n        for packed_embs, pat_ids in loader:\n            packed_embs = packed_embs.to(DEVICE)                       \n            vert_preds, frac_preds, lens = model(packed_embs)\n            for b in range(vert_preds.size(0)):\n                pat_vert_preds = vert_preds[b, :lens[b], :]\n                pat_frac_preds = frac_preds[b, :lens[b], :]\n\n                pat_preds = torch.cat([pat_vert_preds, pat_frac_preds], dim=1)\n                torch.save(pat_preds, os.path.join(STAGE_1_PREDS_DIR, f\"{pat_ids[b]}_fold_{self.fold}.pt\"))\n                \n                \ndef plot_stage_1_preds(pat_id, df, fold):\n    pat_preds = torch.load(os.path.join(STAGE_1_PREDS_DIR, f\"{pat_id}_fold_{fold}.pt\"), map_location=torch.device(\"cpu\"))\n    fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(25, 10))\n    axs[0].plot(pat_preds[:,:7], label=[f\"C{i}\" for i in np.arange(1,8)])\n    axs[0].legend()\n    axs[0].set_title(f\"Vertebrae predictions\")\n    axs[1].plot(pat_preds[:,7:], label=[f\"C{i}_fracture\" for i in np.arange(1,8)])\n    axs[1].legend()\n    axs[1].set_title(f\"Fracture predictions\")\n    labels = df.loc[df[\"StudyInstanceUID\"]==pat_id, [f\"C{i}_fracture\" for i in np.arange(1,8)]].drop_duplicates().to_numpy()\n    fig.suptitle(f\"Labels for {pat_id}: {labels}\")","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:27.782235Z","iopub.execute_input":"2022-10-09T14:24:27.782706Z","iopub.status.idle":"2022-10-09T14:24:27.826631Z","shell.execute_reply.started":"2022-10-09T14:24:27.782649Z","shell.execute_reply":"2022-10-09T14:24:27.825685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%wandb\nif \"stage_1\" in MAIN:\n    stage1 = Stage1(proc_train_df, FOLD, config[\"stage_1\"])\n    stage1.run()\n    if SAVE_STAGE_1:\n        stage1.save_preds()","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:27.828077Z","iopub.execute_input":"2022-10-09T14:24:27.828535Z","iopub.status.idle":"2022-10-09T14:24:27.845123Z","shell.execute_reply.started":"2022-10-09T14:24:27.828495Z","shell.execute_reply":"2022-10-09T14:24:27.844123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_stage_1_preds(\"1.2.826.0.1.3680043.10051\", proc_train_df, 0)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:27.846687Z","iopub.execute_input":"2022-10-09T14:24:27.847015Z","iopub.status.idle":"2022-10-09T14:24:28.037288Z","shell.execute_reply.started":"2022-10-09T14:24:27.846982Z","shell.execute_reply":"2022-10-09T14:24:28.033857Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_stage_1_preds(\"1.2.826.0.1.3680043.19381\", proc_train_df, 0)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.038778Z","iopub.status.idle":"2022-10-09T14:24:28.039686Z","shell.execute_reply.started":"2022-10-09T14:24:28.039353Z","shell.execute_reply":"2022-10-09T14:24:28.039399Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_stage_1_preds(\"1.2.826.0.1.3680043.32480\", proc_train_df, 0)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.041145Z","iopub.status.idle":"2022-10-09T14:24:28.041869Z","shell.execute_reply.started":"2022-10-09T14:24:28.041612Z","shell.execute_reply":"2022-10-09T14:24:28.041636Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_stage_1_preds(\"1.2.826.0.1.3680043.6200\", proc_train_df, 0)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.043149Z","iopub.status.idle":"2022-10-09T14:24:28.043874Z","shell.execute_reply.started":"2022-10-09T14:24:28.043614Z","shell.execute_reply":"2022-10-09T14:24:28.043638Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_stage_1_preds(\"1.2.826.0.1.3680043.27262\", proc_train_df, 0)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.04514Z","iopub.status.idle":"2022-10-09T14:24:28.045864Z","shell.execute_reply.started":"2022-10-09T14:24:28.045606Z","shell.execute_reply":"2022-10-09T14:24:28.045631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Stage 2 GRU","metadata":{}},{"cell_type":"code","source":"class Stage1PredsfdDataset(Dataset):\n    def __init__(self, df, fold, mode=\"train\"):\n        self.df = df\n        self.patients = df[\"StudyInstanceUID\"].unique()\n        self.fold = fold\n        self.mode = mode\n    \n    def __len__(self):\n        return len(self.patients)\n    \n    def __getitem__(self, idx):\n        pat_id = self.patients[idx]\n        stage1_preds = torch.load(os.path.join(STAGE_1_PREDS_DIR, f\"{pat_id}_fold_{self.fold}.pt\"))\n        labels = torch.tensor(self.df.loc[self.df[\"StudyInstanceUID\"] == pat_id, [\"patient_overall\"] + [f\"C{i}_fracture\" for i in np.arange(1,8)]].iloc[0,:], \n                              dtype=torch.float32)\n        if self.mode == \"train\":\n            return stage1_preds, labels\n        elif self.mode == \"pred\":\n            return stage1_preds, pat_id\n    \n\n# https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/1%20-%20Simple%20Sentiment%20Analysis.ipynb\nclass GRU2(nn.Module):\n    def __init__(self, embed_size, hidden_size, num_layers, dropout, bidirectional):\n        super(GRU2, self).__init__()\n        self.hidden_size = hidden_size\n        self.gru = nn.GRU(input_size=embed_size, \n                            hidden_size=hidden_size,\n                            num_layers=num_layers,\n                            dropout=dropout,\n                            bidirectional=bidirectional,\n                            batch_first=True)\n        \n        # use sigmoid and binary crossentropy\n        # https://stats.stackexchange.com/questions/272862/which-deep-learning-model-can-classify-categories-which-are-not-mutually-exclusi\n        self.linear = nn.Linear(hidden_size * 2, 8)\n        \n    def forward(self, x):\n        # h_0, c_0 defaults to zeros if not provided\n        # x: (BATCH_SIZE, SEQ_LEN, 14)\n        output_packed, h_n = self.gru(x)\n        output, lens = pad_packed_sequence(output_packed, batch_first=True)\n        # output tensor: (BATCH_SIZE, SEQ_LEN, 2*HIDDEN_SIZE)     \n        # h_n: (2*NUM_LAYERS, BATCH_SIZE, HIDDEN_SIZE)\n        assert torch.equal(output[3, lens[3]-1, :self.hidden_size], h_n[0, 3, :])\n        \n        hidden = torch.cat((h_n[-2,:,:], h_n[-1,:,:]), dim=1)\n        # logits: (BATCH_SIZE, 8)\n        logits = self.linear(hidden)\n        return torch.sigmoid(logits)\n    \n\nclass Stage2:\n    def __init__(self, df, fold, config):\n        self.df = df\n        self.fold = fold\n        self.config = config\n        \n        \n    def train_one_epoch(self, train_loader, model, optimizer):\n        run_loss = AverageCalc()\n        model.train()\n        for x, y in train_loader:\n            x = x.to(DEVICE)\n            y = y.to(DEVICE)\n\n            yhat = model(x)\n\n            loss = competition_loss(yhat, y)\n            loss.backward()\n            run_loss.update(loss.item(), self.config[\"batch_size\"])\n\n            optimizer.step()\n            optimizer.zero_grad()\n        return run_loss.avg\n    \n    \n    @torch.no_grad()\n    def val_one_epoch(self, val_loader, model):\n        run_loss = AverageCalc()\n        model.eval()\n        for x, y in val_loader:\n            x = x.to(DEVICE)\n            y = y.to(DEVICE)\n            \n            yhat = model(x)\n\n            loss = competition_loss(yhat, y)\n            run_loss.update(loss.item(), self.config[\"batch_size\"])\n        return run_loss.avg\n        \n        \n    def run(self):\n        wandb.init(project=\"RSNA-2022\",\n                   name=f\"gru_stage_2_fold_{self.fold}\",\n                   config=self.config)\n\n        train_df = self.df[self.df[\"split\"] != self.fold].reset_index(drop=True)\n        val_df = self.df[self.df[\"split\"] == self.fold].reset_index(drop=True)\n\n        train_ds = Stage1PredsfdDataset(train_df, self.fold)\n        val_ds = Stage1PredsfdDataset(val_df, self.fold)\n\n        train_loader = DataLoader(train_ds, batch_size=self.config[\"batch_size\"], shuffle=True, collate_fn=pack_collate)\n        val_loader = DataLoader(val_ds, batch_size=self.config[\"batch_size\"], shuffle=False, collate_fn=pack_collate)\n               \n        model = GRU2(14, self.config[\"hidden_size\"],\n                     num_layers = self.config[\"num_layers\"],\n                     dropout = self.config[\"dropout\"],\n                     bidirectional=True).to(DEVICE)\n        optimizer = optim.Adam(model.parameters(), lr=self.config[\"lr\"])\n        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=self.config[\"factor\"], patience=self.config[\"patience\"])\n        \n        for epoch in range(self.config[\"num_epochs\"]):\n            train_loss = self.train_one_epoch(train_loader, model, optimizer)\n            wandb.log({\"train_loss\": train_loss})\n            \n            val_loss = self.val_one_epoch(val_loader, model)\n            wandb.log({\"val_loss\": val_loss})\n            scheduler.step(val_loss)\n            \n            if (epoch+1) % 10 == 0:\n                print(f\"epoch {epoch+1} train loss: {train_loss}\")\n                print(f\"epoch {epoch+1} val loss: {val_loss}\")\n             \n        wandb.finish()\n        torch.save(model.state_dict(), f\"gru_stage_2_fold_{self.fold}.pt\")\n        \n        \n    @torch.no_grad()\n    def save_preds_df(self):\n        model = GRU2(14, self.config[\"hidden_size\"],\n                     num_layers = self.config[\"num_layers\"],\n                     dropout = self.config[\"dropout\"],\n                     bidirectional=True).to(DEVICE)\n        model.load_state_dict(torch.load(f\"gru_stage_2_fold_{self.fold}.pt\", map_location=DEVICE))\n        model.eval()\n\n        ds = Stage1PredsfdDataset(self.df, self.fold, mode=\"pred\")\n        loader = DataLoader(ds, batch_size=self.config[\"batch_size\"], shuffle=False, collate_fn=pack_collate)\n\n        pat_dfs = []\n        for packed_stage_1_preds, pat_ids in loader:\n            packed_stage_1_preds = packed_stage_1_preds.to(DEVICE)                       \n            preds = model(packed_stage_1_preds)\n            for b in range(preds.size(0)):\n                pat_preds = preds[b, :]\n                pat_df = pd.DataFrame(columns=[\"StudyInstanceUID\", \"patient_overall_pred\"] + [f\"C{i}_frac_pred\" for i in np.arange(1,8)],\n                                      data=[[pat_ids[b]] +  pat_preds.detach().cpu().numpy().tolist()])\n                pat_dfs.append(pat_df)\n        return pd.concat(pat_dfs).reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.04735Z","iopub.status.idle":"2022-10-09T14:24:28.048192Z","shell.execute_reply.started":"2022-10-09T14:24:28.047895Z","shell.execute_reply":"2022-10-09T14:24:28.047921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%wandb\nif \"stage_2\" in MAIN:\n    stage2 = Stage2(proc_train_df, FOLD, config[\"stage_2\"])\n    stage2.run()\n    if SAVE_STAGE_2:\n        stage2_preds_df = stage2.save_preds_df()\n        stage2_preds_df = stage2_preds_df.merge(proc_train_df[[\"StudyInstanceUID\", \"patient_overall\"] + [f\"C{i}_fracture\" for i in np.arange(1,8)] + [\"split\"]].drop_duplicates(),\n                                        how=\"left\", on=\"StudyInstanceUID\")","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.049954Z","iopub.status.idle":"2022-10-09T14:24:28.05073Z","shell.execute_reply.started":"2022-10-09T14:24:28.050443Z","shell.execute_reply":"2022-10-09T14:24:28.050469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"stage2_preds_df[stage2_preds_df[\"StudyInstanceUID\"].isin([\"1.2.826.0.1.3680043.27262\", \"1.2.826.0.1.3680043.19381\", \"1.2.826.0.1.3680043.32480\"])]","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.052113Z","iopub.status.idle":"2022-10-09T14:24:28.052956Z","shell.execute_reply.started":"2022-10-09T14:24:28.052654Z","shell.execute_reply":"2022-10-09T14:24:28.052683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"stage2_preds_df[stage2_preds_df[\"split\"]==0]","metadata":{"execution":{"iopub.status.busy":"2022-10-09T14:24:28.054275Z","iopub.status.idle":"2022-10-09T14:24:28.055023Z","shell.execute_reply.started":"2022-10-09T14:24:28.054766Z","shell.execute_reply":"2022-10-09T14:24:28.05479Z"},"trusted":true},"execution_count":null,"outputs":[]}]}