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It scores 0.722 on GroupKfold 5 Folds split. 5 Fold submission barely took around 2 minutes to run & achieved 0.48 lb score.\n\n#### Training Notebook link: https://www.kaggle.com/code/nischaydnk/lightning-1d-eegnet-training-pipeline-hbs?scriptVersionId=160813845\n\n#### This model only uses only 8 channels from raw EEG signals as shared by [Chris Deotte](http://https://www.kaggle.com/code/cdeotte/wavenet-starter-lb-0-52?scriptVersionId=160158478) ","metadata":{}},{"cell_type":"markdown","source":"## Imports","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport os\nimport pytorch_lightning as pl\nfrom pytorch_lightning.callbacks import ModelCheckpoint, BackboneFinetuning, EarlyStopping\nfrom torch.utils.data import Dataset, DataLoader\nfrom sklearn import model_selection\nimport torchvision.transforms as transforms\nimport torchvision.io \nimport librosa\nfrom PIL import Image\nimport albumentations as alb\nimport torch.multiprocessing as mp\nimport warnings\nimport os\nfrom torch.nn.functional import cross_entropy\nimport torchmetrics\nimport sklearn.metrics\nfrom torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, ReduceLROnPlateau, OneCycleLR\nimport sys\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nwarnings.filterwarnings('ignore')","metadata":{"papermill":{"duration":0.015982,"end_time":"2023-03-11T22:10:10.234218","exception":false,"start_time":"2023-03-11T22:10:10.218236","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:36.142788Z","iopub.execute_input":"2024-02-28T11:33:36.143185Z","iopub.status.idle":"2024-02-28T11:33:45.025881Z","shell.execute_reply.started":"2024-02-28T11:33:36.143154Z","shell.execute_reply":"2024-02-28T11:33:45.024944Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Config Class","metadata":{}},{"cell_type":"code","source":"\nclass Config:\n\n    use_aug = False\n    num_classes = 6\n    batch_size = 32\n    epochs = 18\n    PRECISION = 16    \n    PATIENCE = 10    \n    seed = 2024\n    pretrained = False            \n    weight_decay = 1e-3\n    use_mixup = False\n    mixup_alpha = 0.02   \n    num_channels = 8\n    data_root = \"/kaggle/input/hms-harmful-brain-activity-classification/\"\n    \n    LR = 7e-3\n    processed_train = None\n    model_dir = '/kaggle/input/eegnet_hms_0/pytorch/copyoflightning-1d-eegnet-training-pipeline-hbs/1/'\n    trn_folds = [0,1,2,3,4]\n    ","metadata":{"papermill":{"duration":0.082277,"end_time":"2023-03-11T22:10:20.964863","exception":false,"start_time":"2023-03-11T22:10:20.882586","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:45.028097Z","iopub.execute_input":"2024-02-28T11:33:45.028924Z","iopub.status.idle":"2024-02-28T11:33:45.034579Z","shell.execute_reply.started":"2024-02-28T11:33:45.028889Z","shell.execute_reply":"2024-02-28T11:33:45.033794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Parquet to EEG Signals Numpy Processing","metadata":{}},{"cell_type":"code","source":"def eeg_from_parquet(parquet_path, display=False):\n    \n    # EXTRACT MIDDLE 50 SECONDS\n    eeg = pd.read_parquet(parquet_path, columns=FEATS)\n    rows = len(eeg)\n    offset = (rows-10_000)//2\n    eeg = eeg.iloc[offset:offset+10_000]\n    \n    if display: \n        plt.figure(figsize=(10,5))\n        offset = 0\n    \n    # CONVERT TO NUMPY\n    data = np.zeros((10_000,len(FEATS)))\n    for j,col in enumerate(FEATS):\n        \n        # FILL NAN\n        x = eeg[col].values.astype('float32')\n        m = np.nanmean(x)\n        if np.isnan(x).mean()<1: x = np.nan_to_num(x,nan=m)\n        else: x[:] = 0\n            \n        data[:,j] = x\n        \n        if display: \n            if j!=0: offset += x.max()\n            plt.plot(range(10_000),x-offset,label=col)\n            offset -= x.min()\n            \n    if display:\n        plt.legend()\n        name = parquet_path.split('/')[-1]\n        name = name.split('.')[0]\n        plt.title(f'EEG {name}',size=16)\n        plt.show()\n        \n    return data","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:45.035937Z","iopub.execute_input":"2024-02-28T11:33:45.036303Z","iopub.status.idle":"2024-02-28T11:33:45.048723Z","shell.execute_reply.started":"2024-02-28T11:33:45.036271Z","shell.execute_reply":"2024-02-28T11:33:45.047853Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/hms-harmful-brain-activity-classification/test.csv')\nprint('Test shape:',test.shape)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:45.049882Z","iopub.execute_input":"2024-02-28T11:33:45.050212Z","iopub.status.idle":"2024-02-28T11:33:45.078787Z","shell.execute_reply.started":"2024-02-28T11:33:45.050178Z","shell.execute_reply":"2024-02-28T11:33:45.077931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"all_eegs2 = {}\nDISPLAY = 1\nEEG_IDS2 = test.eeg_id.unique()\nPATH2 = '/kaggle/input/hms-harmful-brain-activity-classification/test_eegs/'\n\n# FEATS = ['Fp1','T3','C3','O1','Fp2','C4','T4','O2']\nFEATS = ['Fp1', 'F3', 'C3', 'P3', 'F7', 'T3', 'T5', 'O1', 'Fz', 'Cz', 'Pz', 'Fp2', 'F4', 'C4', 'P4', 'F8', 'T4', 'T6', 'O2', 'EKG']\nprint(f'There are {len(FEATS)} raw eeg features')\nprint( list(FEATS) )\n\nprint('Processing Test EEG parquets...'); print()\nfor i,eeg_id in enumerate(EEG_IDS2):\n        \n    # SAVE EEG TO PYTHON DICTIONARY OF NUMPY ARRAYS\n    data = eeg_from_parquet(f'{PATH2}{eeg_id}.parquet', i<DISPLAY)\n    all_eegs2[eeg_id] = data","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:45.081031Z","iopub.execute_input":"2024-02-28T11:33:45.081298Z","iopub.status.idle":"2024-02-28T11:33:46.843250Z","shell.execute_reply.started":"2024-02-28T11:33:45.081275Z","shell.execute_reply":"2024-02-28T11:33:46.842312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pl.seed_everything(Config.seed, workers=True)","metadata":{"papermill":{"duration":0.021264,"end_time":"2023-03-11T22:10:20.993713","exception":false,"start_time":"2023-03-11T22:10:20.972449","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:46.844391Z","iopub.execute_input":"2024-02-28T11:33:46.844697Z","iopub.status.idle":"2024-02-28T11:33:46.853939Z","shell.execute_reply.started":"2024-02-28T11:33:46.844661Z","shell.execute_reply":"2024-02-28T11:33:46.853081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def config_to_dict(cfg):\n    return dict((name, getattr(cfg, name)) for name in dir(cfg) if not name.startswith('__'))","metadata":{"papermill":{"duration":0.015742,"end_time":"2023-03-11T22:10:21.016701","exception":false,"start_time":"2023-03-11T22:10:21.000959","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:46.854986Z","iopub.execute_input":"2024-02-28T11:33:46.855253Z","iopub.status.idle":"2024-02-28T11:33:46.862038Z","shell.execute_reply.started":"2024-02-28T11:33:46.855230Z","shell.execute_reply":"2024-02-28T11:33:46.861199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.DataFrame(EEG_IDS2, columns=['eeg_id'])\ntest","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:46.863032Z","iopub.execute_input":"2024-02-28T11:33:46.863856Z","iopub.status.idle":"2024-02-28T11:33:46.876204Z","shell.execute_reply.started":"2024-02-28T11:33:46.863831Z","shell.execute_reply":"2024-02-28T11:33:46.875198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Class","metadata":{}},{"cell_type":"markdown","source":"#### Another thing I added in the model is parallel convolution Blocks which used different 1D CNN w/ different kernel size, to capture different window features. It helped a lot in improving my score. You can try out and experiment with different kernels, specified in \"kernels\" parameter in model class.\n","metadata":{}},{"cell_type":"markdown","source":"## Define Dataset Class & Do some Visualization","metadata":{}},{"cell_type":"code","source":"from scipy.signal import butter, lfilter\n\ndef quantize_data(data, classes):\n    mu_x = mu_law_encoding(data, classes)\n    # bins = np.linspace(-1, 1, classes)\n    # quantized = np.digitize(mu_x, bins) - 1\n    return mu_x#quantized\n\ndef mu_law_encoding(data, mu):\n    mu_x = np.sign(data) * np.log(1 + mu * np.abs(data)) / np.log(mu + 1)\n    return mu_x\n\ndef mu_law_expansion(data, mu):\n    s = np.sign(data) * (np.exp(np.abs(data) * np.log(mu + 1)) - 1) / mu\n    return s\n\ndef butter_lowpass_filter(data, cutoff_freq=20, sampling_rate=200, order=4):\n    nyquist = 0.5 * sampling_rate\n    normal_cutoff = cutoff_freq / nyquist\n    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n    filtered_data = lfilter(b, a, data, axis=0)\n    return filtered_data\n\nclass EEGDataset(torch.utils.data.Dataset):\n\n    def __init__(self, data, eegs=None, augmentations = None, test = False): \n\n        self.data = data\n        self.eegs = eegs\n        self.augmentations = augmentations\n        self.test = test\n        \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, index):\n\n        row = self.data.iloc[index]      \n        data = self.eegs[row.eeg_id]\n\n#         data = np.clip(data,-1024,1024)\n#         data = np.nan_to_num(data, nan=0) / 32.0\n        \n#         data = butter_lowpass_filter(data)\n#         data = quantize_data(data,1)\n\n        samples = torch.from_numpy(data).float()    \n        samples = samples.permute(1,0)\n        if not self.test:\n            label = row[TARGETS] \n            label = torch.tensor(label).float()  \n            return samples, label\n        else:\n            return samples\n# ================================","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-02-28T11:33:46.877491Z","iopub.execute_input":"2024-02-28T11:33:46.877839Z","iopub.status.idle":"2024-02-28T11:33:46.940985Z","shell.execute_reply.started":"2024-02-28T11:33:46.877814Z","shell.execute_reply":"2024-02-28T11:33:46.940175Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_test_dls(df_test):\n    ds_test = EEGDataset(\n        df_test, \n        eegs=all_eegs2,\n        augmentations = None,\n        test = True\n    )\n    dl_test = DataLoader(ds_test, batch_size=Config.batch_size , shuffle=False, num_workers = 2)    \n    return dl_test, ds_test","metadata":{"papermill":{"duration":0.016902,"end_time":"2023-03-11T22:10:21.522443","exception":false,"start_time":"2023-03-11T22:10:21.505541","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:46.942181Z","iopub.execute_input":"2024-02-28T11:33:46.942817Z","iopub.status.idle":"2024-02-28T11:33:46.948600Z","shell.execute_reply.started":"2024-02-28T11:33:46.942783Z","shell.execute_reply":"2024-02-28T11:33:46.947528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_optimizer(lr, params):\n    model_optimizer = torch.optim.Adam(\n            filter(lambda p: p.requires_grad, params), \n            lr=lr,\n            weight_decay=Config.weight_decay\n        )\n    interval = \"epoch\"\n    \n    lr_scheduler = CosineAnnealingWarmRestarts(\n                            model_optimizer, \n                            T_0=Config.epochs, \n                            T_mult=1, \n                            eta_min=1e-5, \n                            last_epoch=-1\n                        )\n\n    return {\n        \"optimizer\": model_optimizer, \n        \"lr_scheduler\": {\n            \"scheduler\": lr_scheduler,\n            \"interval\": interval,\n            \"monitor\": \"val_loss\",\n            \"frequency\": 1\n        }\n    }","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:46.949926Z","iopub.execute_input":"2024-02-28T11:33:46.950517Z","iopub.status.idle":"2024-02-28T11:33:46.960233Z","shell.execute_reply.started":"2024-02-28T11:33:46.950485Z","shell.execute_reply":"2024-02-28T11:33:46.959332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class KLDivLossWithLogits(nn.KLDivLoss):\n\n    def __init__(self):\n        super().__init__(reduction=\"batchmean\")\n\n    def forward(self, y, t):\n        y = nn.functional.log_softmax(y,  dim=1)\n        loss = super().forward(y, t)\n\n        return loss\n","metadata":{"papermill":{"duration":0.238854,"end_time":"2023-03-11T22:10:24.816235","exception":false,"start_time":"2023-03-11T22:10:24.577381","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:46.961230Z","iopub.execute_input":"2024-02-28T11:33:46.961533Z","iopub.status.idle":"2024-02-28T11:33:46.973763Z","shell.execute_reply.started":"2024-02-28T11:33:46.961509Z","shell.execute_reply":"2024-02-28T11:33:46.972913Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Define Pytorch Lightning Module","metadata":{}},{"cell_type":"code","source":"# stat = torch.load(\"/kaggle/input/classifier_transformer/pytorch/0/1/epoch36-valid_loss0.3950.ckpt\")\n# print(stat['state_dict'])","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:46.974880Z","iopub.execute_input":"2024-02-28T11:33:46.976930Z","iopub.status.idle":"2024-02-28T11:33:46.983025Z","shell.execute_reply.started":"2024-02-28T11:33:46.976904Z","shell.execute_reply":"2024-02-28T11:33:46.982145Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import sys\nsys.path.append(\"/kaggle/input/eegpt_mcae_58chs_4s_large4e/pytorch/1/1/\")\n\nfrom functools import partial\nfrom EEGPT_mcae import EEGTransformer\n\nclass LinearWithConstraint(nn.Linear):\n    def __init__(self, *args, doWeightNorm = True, max_norm=1, **kwargs):\n        self.max_norm = max_norm\n        self.doWeightNorm = doWeightNorm\n        super(LinearWithConstraint, self).__init__(*args, **kwargs)\n\n    def forward(self, x):\n        if self.doWeightNorm: \n            self.weight.data = torch.renorm(\n                self.weight.data, p=2, dim=0, maxnorm=self.max_norm\n            )\n        return super(LinearWithConstraint, self).forward(x)\n\nclass Classifier(torch.nn.Module):\n    def __init__(self):\n        super().__init__()    \n        \n        self.drop            = torch.nn.Dropout(p=0.50)\n        self.linear_probe1   =   LinearWithConstraint(2048, 64, max_norm=1)\n        self.norm0           =   torch.nn.LayerNorm(512)\n        self.norm            =   torch.nn.LayerNorm(64)\n        self.transformer     =   torch.nn.TransformerEncoder(\n            torch.nn.TransformerEncoderLayer(64,4,64*4,dropout=0.4, activation=\"gelu\", batch_first=True),\n            num_layers=4,\n        )\n        self.pos_emb = torch.nn.Parameter(torch.randn(1,157,64)*0.001, requires_grad=True)\n        self.cls_tok = torch.nn.Parameter(torch.randn(1,1,64)*0.001, requires_grad=True)\n        \n        \n        self.fc              = LinearWithConstraint(64, 6, max_norm=0.25)\n        \n        self.loss_fn          = KLDivLossWithLogits()\n\n    \n    def forward(self, x):\n        # print(x.shape) # B, C, T\n        \n        x = self.norm0(x)\n        h = x.flatten(2)\n        \n        h = self.linear_probe1(self.drop(h))\n        h = torch.cat([self.cls_tok.repeat((h.shape[0],1,1)), h], dim=1) \n        h = h + self.pos_emb\n        h = self.norm(h)\n        \n        h = self.transformer(h)\n        h = h[:,0,:]\n        h = self.fc(h)\n        return x, h\n    \nclass EEGModel(pl.LightningModule):\n    def __init__(self, num_classes = Config.num_classes, pretrained = Config.pretrained, fold = -1):\n        super().__init__()\n        self.num_classes = num_classes\n        self.fold = fold\n        \n        self.classifier = Classifier()\n        \n        stat = torch.load(\"/kaggle/input/classifier_transformer/pytorch/for_eegpt_origin_1_layernorm/1/epoch12-train_loss0.3072.ckpt\")\n        self.classifier.load_state_dict(stat['state_dict'])\n#         new_stat1 = {}\n#         new_stat2 = {}\n#         for k,v in stat['state_dict'].items():\n#             if k.startswith(\"linear_probe1.\"):\n#                 new_stat1[k[len(\"linear_probe1.\"):]]=v\n#             if k.startswith(\"linear_probe2.\"):\n#                 new_stat2[k[len(\"linear_probe2.\"):]]=v\n#         self.linear_probe1.load_state_dict(new_stat1)\n#         self.linear_probe2.load_state_dict(new_stat2)\n        \n        self.target_encoder = EEGTransformer(\n                img_size=[19, 10000],\n                patch_size=32*2,\n                # patch_stride = 32,\n                embed_num=4,\n                embed_dim=512,\n                depth=8,\n                num_heads=8,\n                mlp_ratio=4.0,\n                drop_rate=0.0,\n                attn_drop_rate=0.0,\n                drop_path_rate=0.0,\n                init_std=0.02,\n                qkv_bias=True, \n                norm_layer=partial(nn.LayerNorm, eps=1e-6))\n        target_encoder_stat = torch.load(\"/kaggle/input/eegpt_mcae_58chs_4s_large4e/pytorch/1/1/eegpt_tencoder_large4E.ckpt\")\n\n        self.target_encoder.load_state_dict(target_encoder_stat)\n    \n        use_channels_names = [ 'FP1', 'F3', 'C3', 'P3', 'F7', 'T7', 'P7', 'O1', 'FZ', 'CZ', 'PZ', 'FP2', 'F4', 'C4', 'P4', 'F8', 'T8', 'P8', 'O2' ]\n        self.chans_id        =   self.target_encoder.prepare_chan_ids(use_channels_names)\n        \n        self.loss_function = KLDivLossWithLogits() #nn.KLDivLoss() #nn.BCEWithLogitsLoss() \n        self.validation_step_outputs = []\n        self.lin = nn.Softmax(dim=1)\n        self.best_score = 1000.0\n    def forward(self,x):\n        B, C, T = x.shape\n        x = x[:,:-1,:] / 1000\n        x = x - x.mean(dim=1, keepdim=True)\n        \n        self.target_encoder.eval()\n        z = self.target_encoder(x, self.chans_id.to(x))\n        \n        x, h = self.classifier(z)\n        \n        return h\n\n    def configure_optimizers(self):\n        return get_optimizer(lr=Config.LR, params=self.parameters())\n\n    def training_step(self, batch, batch_idx):\n        image, target = batch        \n\n        y_pred = self(image)\n        loss = self.loss_function(y_pred,target)\n\n        self.log(\"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True)\n        return loss        \n\n    def validation_step(self, batch, batch_idx):\n        image, target = batch \n        # print(target)\n        y_pred = self(image)\n        val_loss = self.loss_function(y_pred, target)\n        self.log(\"val_loss\", val_loss, on_step=True, on_epoch=True, logger=True, prog_bar=True)\n        self.validation_step_outputs.append({\"val_loss\": val_loss, \"logits\": y_pred, \"targets\": target})\n\n        return {\"val_loss\": val_loss, \"logits\": y_pred, \"targets\": target}\n    \n    def train_dataloader(self):\n        return self._train_dataloader \n    \n    def validation_dataloader(self):\n        return self._validation_dataloader\n    \n    def on_validation_epoch_end(self):\n        outputs = self.validation_step_outputs\n        # print(len(outputs))\n        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()\n        output_val = nn.Softmax(dim=1)(torch.cat([x['logits'] for x in outputs],dim=0)).cpu().detach().numpy()\n        target_val = torch.cat([x['targets'] for x in outputs],dim=0).cpu().detach().numpy()\n        self.validation_step_outputs = []\n\n        val_df = pd.DataFrame(target_val, columns = list(TARGETS))\n        pred_df = pd.DataFrame(output_val, columns = list(TARGETS))\n\n        val_df['id'] = [f'id_{i}' for i in range(len(val_df))] \n        pred_df['id'] = [f'id_{i}' for i in range(len(pred_df))] \n\n\n        avg_score = score(val_df, pred_df, row_id_column_name = 'id')\n\n        if avg_score < self.best_score:\n            print(f'Fold {self.fold}: Epoch {self.current_epoch} validation loss {avg_loss}')\n            print(f'Fold {self.fold}: Epoch {self.current_epoch} validation KDL score {avg_score}')\n            self.best_score = avg_score\n            # val_df.to_csv(f'{Config.output_dir}/val_df_f{self.fold}.csv',index=False)\n            # pred_df.to_csv(f'{Config.output_dir}/pred_df_f{self.fold}.csv',index=False)\n        \n        return {'val_loss': avg_loss,'val_cmap':avg_score}\n    ","metadata":{"papermill":{"duration":0.032359,"end_time":"2023-03-11T22:10:24.859359","exception":false,"start_time":"2023-03-11T22:10:24.827","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:46.986626Z","iopub.execute_input":"2024-02-28T11:33:46.987007Z","iopub.status.idle":"2024-02-28T11:33:47.036249Z","shell.execute_reply.started":"2024-02-28T11:33:46.986982Z","shell.execute_reply":"2024-02-28T11:33:47.035446Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tqdm import tqdm\ntqdm.pandas()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:47.037273Z","iopub.execute_input":"2024-02-28T11:33:47.037529Z","iopub.status.idle":"2024-02-28T11:33:47.042500Z","shell.execute_reply.started":"2024-02-28T11:33:47.037508Z","shell.execute_reply":"2024-02-28T11:33:47.041521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def predict(data_loader, model):\n        \n    model.to('cuda')\n    model.eval()    \n    predictions = []\n    for batch in tqdm(data_loader):\n\n        with torch.no_grad():\n            x = batch\n            x = x.cuda()\n            # inputs = {key:val.reshape(val.shape[0], -1).to(config.device) for key,val in batch.items()}\n            outputs = model(x)\n            outputs = nn.Softmax(dim=1)(outputs)\n        predictions.extend(outputs.detach().cpu().numpy())\n    predictions = np.vstack(predictions)\n    return predictions\n\ndef predict_nobatch(ds_test, model):\n    \n    model.to('cuda')\n    model.eval()    \n    predictions = []\n    for en in tqdm(range(len(ds_test))):\n        # print(en)\n        x = ds_test[en]\n        x = x.unsqueeze(0).cuda()\n        # print(images.shape)\n        with torch.no_grad():\n            outputs = model(x)\n            outputs = nn.Softmax(dim=1)(outputs)\n            outputs = outputs.detach().cpu().numpy()\n\n        predictions.append(outputs)\n        \n    return predictions","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:47.043748Z","iopub.execute_input":"2024-02-28T11:33:47.044081Z","iopub.status.idle":"2024-02-28T11:33:47.053113Z","shell.execute_reply.started":"2024-02-28T11:33:47.044050Z","shell.execute_reply":"2024-02-28T11:33:47.052267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pytorch_lightning.loggers import WandbLogger\nimport gc\ntorch.set_float32_matmul_precision('high')\ndef run_inference(fold_id, Config):\n    print(f\"Running training for fold {fold_id}...\")\n    logger = None\n    pred_cols = [f'pred_{t}' for t in TARGETS]\n    df_test = test.copy()\n    dl_test, ds_test = get_test_dls(df_test)\n    \n\n    print(f\"Running inference model '{Config.model_dir}/model.ckpt'..\")\n    \n#     model = EEGModel.load_from_checkpoint(f'{Config.model_dir}/model.ckpt',map_location='cuda:0',\n#                                           train_dataloader=None,validation_dataloader=None,config=Config)\n    model = EEGModel().to('cuda:0')\n    \n    preds = predict(dl_test, model)  \n    print(preds.shape)\n    gc.collect()\n    # torch.cuda.empty_cache()\n    return preds\n    ","metadata":{"papermill":{"duration":0.025305,"end_time":"2023-03-11T22:10:24.896057","exception":false,"start_time":"2023-03-11T22:10:24.870752","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:47.054252Z","iopub.execute_input":"2024-02-28T11:33:47.054528Z","iopub.status.idle":"2024-02-28T11:33:47.066286Z","shell.execute_reply.started":"2024-02-28T11:33:47.054505Z","shell.execute_reply":"2024-02-28T11:33:47.065549Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# list(train[train['fold']==0].index)\ntest.head(3)\nsub_df = test[['eeg_id']].copy()\nTARGETS = ['seizure_vote', 'lpd_vote','gpd_vote','lrda_vote','grda_vote','other_vote']\nsub_df[TARGETS] = 0.0","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:47.067304Z","iopub.execute_input":"2024-02-28T11:33:47.067553Z","iopub.status.idle":"2024-02-28T11:33:47.081222Z","shell.execute_reply.started":"2024-02-28T11:33:47.067532Z","shell.execute_reply":"2024-02-28T11:33:47.080509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:47.082358Z","iopub.execute_input":"2024-02-28T11:33:47.082629Z","iopub.status.idle":"2024-02-28T11:33:47.100306Z","shell.execute_reply.started":"2024-02-28T11:33:47.082607Z","shell.execute_reply":"2024-02-28T11:33:47.099405Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds = []\nfor en,f in enumerate(Config.trn_folds):\n    preds = run_inference(f, Config)\n    test_preds.append(preds)\n \n","metadata":{"papermill":{"duration":655.665744,"end_time":"2023-03-11T22:21:20.57329","exception":false,"start_time":"2023-03-11T22:10:24.907546","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:47.101445Z","iopub.execute_input":"2024-02-28T11:33:47.101804Z","iopub.status.idle":"2024-02-28T11:33:54.794181Z","shell.execute_reply.started":"2024-02-28T11:33:47.101774Z","shell.execute_reply":"2024-02-28T11:33:54.793171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds = np.mean(test_preds, 0)\ntest_preds.shape","metadata":{"papermill":{"duration":0.012965,"end_time":"2023-03-11T22:21:21.693668","exception":false,"start_time":"2023-03-11T22:21:21.680703","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-28T11:33:54.795629Z","iopub.execute_input":"2024-02-28T11:33:54.795955Z","iopub.status.idle":"2024-02-28T11:33:54.802616Z","shell.execute_reply.started":"2024-02-28T11:33:54.795930Z","shell.execute_reply":"2024-02-28T11:33:54.801778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df[TARGETS] = test_preds\nsub_df.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:54.803885Z","iopub.execute_input":"2024-02-28T11:33:54.804198Z","iopub.status.idle":"2024-02-28T11:33:54.820682Z","shell.execute_reply.started":"2024-02-28T11:33:54.804174Z","shell.execute_reply":"2024-02-28T11:33:54.819658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:54.821881Z","iopub.execute_input":"2024-02-28T11:33:54.822158Z","iopub.status.idle":"2024-02-28T11:33:54.834021Z","shell.execute_reply.started":"2024-02-28T11:33:54.822135Z","shell.execute_reply":"2024-02-28T11:33:54.833152Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Sub row 0 sums to:',sub_df.iloc[0,-6:].sum())\n","metadata":{"execution":{"iopub.status.busy":"2024-02-28T11:33:54.835181Z","iopub.execute_input":"2024-02-28T11:33:54.835474Z","iopub.status.idle":"2024-02-28T11:33:54.843811Z","shell.execute_reply.started":"2024-02-28T11:33:54.835443Z","shell.execute_reply":"2024-02-28T11:33:54.842724Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Here we go, submission looks good :D ","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}