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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) \n\n#### The model is slightly based on our 4th Place solution in G2Net competition, you can read more about the network from our [summary paper]  (https://www.researchgate.net/publication/359051366_GWNET_Detecting_Gravitational_Waves_using_Hierarchical_and_Residual_Learning_based_1D_CNNs). \n\n![img2](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F4712534%2F3c07868f0ac8069fee743203c9903107%2FScreenshot%202024-01-29%20at%209.49.13%20AM.png?generation=1706503747689647&alt=media)","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 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\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":"2024-01-29T04:34:28.409615Z","iopub.execute_input":"2024-01-29T04:34:28.41004Z","iopub.status.idle":"2024-01-29T04:34:28.416688Z","shell.execute_reply.started":"2024-01-29T04:34:28.41001Z","shell.execute_reply":"2024-01-29T04:34:28.41578Z"},"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    raw_eeg_path = \"/home/nischay/brain/Data/raw_eeg/eegs.npy\"    \n    \n    LR = 7e-3\n    processed_train = None\n    model_dir = '/kaggle/input/eeg-exp2-cv732-noaug'\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":"2024-01-29T04:34:43.065177Z","iopub.execute_input":"2024-01-29T04:34:43.065581Z","iopub.status.idle":"2024-01-29T04:34:43.072688Z","shell.execute_reply.started":"2024-01-29T04:34:43.065547Z","shell.execute_reply":"2024-01-29T04:34:43.07172Z"},"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":"2024-01-29T04:34:47.024918Z","iopub.execute_input":"2024-01-29T04:34:47.025614Z","iopub.status.idle":"2024-01-29T04:34:47.035433Z","shell.execute_reply.started":"2024-01-29T04:34:47.025576Z","shell.execute_reply":"2024-01-29T04:34:47.034372Z"},"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":"2024-01-29T04:34:47.230983Z","iopub.execute_input":"2024-01-29T04:34:47.231291Z","iopub.status.idle":"2024-01-29T04:34:47.243465Z","shell.execute_reply.started":"2024-01-29T04:34:47.231264Z","shell.execute_reply":"2024-01-29T04:34:47.242563Z"},"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\nFEATS = ['Fp1','T3','C3','O1','Fp2','C4','T4','O2']\n# FEATS = ['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":"2024-01-29T04:34:47.949648Z","iopub.execute_input":"2024-01-29T04:34:47.949978Z","iopub.status.idle":"2024-01-29T04:34:48.660475Z","shell.execute_reply.started":"2024-01-29T04:34:47.949953Z","shell.execute_reply":"2024-01-29T04:34:48.659517Z"},"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":"2024-01-29T04:34:48.662029Z","iopub.execute_input":"2024-01-29T04:34:48.662288Z","iopub.status.idle":"2024-01-29T04:34:48.669668Z","shell.execute_reply.started":"2024-01-29T04:34:48.662265Z","shell.execute_reply":"2024-01-29T04:34:48.668797Z"},"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":"2024-01-29T04:34:48.670592Z","iopub.execute_input":"2024-01-29T04:34:48.670886Z","iopub.status.idle":"2024-01-29T04:34:48.680565Z","shell.execute_reply.started":"2024-01-29T04:34:48.670863Z","shell.execute_reply":"2024-01-29T04:34:48.679837Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.DataFrame(EEG_IDS2, columns=['eeg_id'])","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:48.978268Z","iopub.execute_input":"2024-01-29T04:34:48.978618Z","iopub.status.idle":"2024-01-29T04:34:48.983702Z","shell.execute_reply.started":"2024-01-29T04:34:48.978589Z","shell.execute_reply":"2024-01-29T04:34:48.982542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:49.180056Z","iopub.execute_input":"2024-01-29T04:34:49.180456Z","iopub.status.idle":"2024-01-29T04:34:49.188866Z","shell.execute_reply.started":"2024-01-29T04:34:49.180423Z","shell.execute_reply":"2024-01-29T04:34:49.187781Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd, numpy as np, os\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(f'{Config.data_root}train.csv')\nprint( df.shape )\n","metadata":{"papermill":{"duration":0.186715,"end_time":"2023-03-11T22:10:21.210686","exception":false,"start_time":"2023-03-11T22:10:21.023971","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:49.36934Z","iopub.execute_input":"2024-01-29T04:34:49.369697Z","iopub.status.idle":"2024-01-29T04:34:49.534085Z","shell.execute_reply.started":"2024-01-29T04:34:49.369668Z","shell.execute_reply":"2024-01-29T04:34:49.533134Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nEEG_IDS = df.eeg_id.unique()\n\nTARGETS = df.columns[-6:]\nTARS = {'Seizure':0, 'LPD':1, 'GPD':2, 'LRDA':3, 'GRDA':4, 'Other':5}\nTARS_INV = {x:y for y,x in TARS.items()}\n\ntrain = df.groupby('eeg_id')[['patient_id']].agg('first')\n\ntmp = df.groupby('eeg_id')[TARGETS].agg('sum')\nfor t in TARGETS:\n    train[t] = tmp[t].values\n    \ny_data = train[TARGETS].values\ny_data = y_data / y_data.sum(axis=1,keepdims=True)\ntrain[TARGETS] = y_data\n\ntmp = df.groupby('eeg_id')[['expert_consensus']].agg('first')\ntrain['target'] = tmp\n\ntrain = train.reset_index()\ntrain = train.loc[train.eeg_id.isin(EEG_IDS)]\nprint('Train Data with unique eeg_id shape:', train.shape )\n","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:49.560645Z","iopub.execute_input":"2024-01-29T04:34:49.560934Z","iopub.status.idle":"2024-01-29T04:34:49.611726Z","shell.execute_reply.started":"2024-01-29T04:34:49.56091Z","shell.execute_reply":"2024-01-29T04:34:49.61084Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:49.790455Z","iopub.execute_input":"2024-01-29T04:34:49.790738Z","iopub.status.idle":"2024-01-29T04:34:49.805742Z","shell.execute_reply.started":"2024-01-29T04:34:49.790716Z","shell.execute_reply":"2024-01-29T04:34:49.804718Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Config.num_classes = len(TARS.keys())","metadata":{"papermill":{"duration":0.020696,"end_time":"2023-03-11T22:10:21.239429","exception":false,"start_time":"2023-03-11T22:10:21.218733","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:50.076887Z","iopub.execute_input":"2024-01-29T04:34:50.07721Z","iopub.status.idle":"2024-01-29T04:34:50.081719Z","shell.execute_reply.started":"2024-01-29T04:34:50.077182Z","shell.execute_reply":"2024-01-29T04:34:50.080808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.describe()","metadata":{"papermill":{"duration":0.036704,"end_time":"2023-03-11T22:10:21.283488","exception":false,"start_time":"2023-03-11T22:10:21.246784","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:50.417693Z","iopub.execute_input":"2024-01-29T04:34:50.418491Z","iopub.status.idle":"2024-01-29T04:34:50.45154Z","shell.execute_reply.started":"2024-01-29T04:34:50.418455Z","shell.execute_reply":"2024-01-29T04:34:50.450673Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{"papermill":{"duration":0.007321,"end_time":"2023-03-11T22:10:21.298932","exception":false,"start_time":"2023-03-11T22:10:21.291611","status":"completed"},"tags":[]}},{"cell_type":"code","source":"len(all_eegs2)","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:51.001909Z","iopub.execute_input":"2024-01-29T04:34:51.002682Z","iopub.status.idle":"2024-01-29T04:34:51.008387Z","shell.execute_reply.started":"2024-01-29T04:34:51.002648Z","shell.execute_reply":"2024-01-29T04:34:51.007473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"all_eegs2[test.loc[0,'eeg_id']].shape","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:51.220814Z","iopub.execute_input":"2024-01-29T04:34:51.221131Z","iopub.status.idle":"2024-01-29T04:34:51.227564Z","shell.execute_reply.started":"2024-01-29T04:34:51.221102Z","shell.execute_reply":"2024-01-29T04:34:51.226613Z"},"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":"![img1](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F4712534%2F9317ffc90ca7cf9cd990cc87250b66ab%2FScreenshot%202024-01-29%20at%209.49.18%20AM.png?generation=1706503620778537&alt=media)","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\n\n\nclass ResNet_1D_Block(nn.Module):\n\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, downsampling):\n        super(ResNet_1D_Block, self).__init__()\n        self.bn1 = nn.BatchNorm1d(num_features=in_channels)\n        self.relu = nn.ReLU(inplace=False)\n        self.dropout = nn.Dropout(p=0.0, inplace=False)\n        self.conv1 = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,\n                               stride=stride, padding=padding, bias=False)\n        self.bn2 = nn.BatchNorm1d(num_features=out_channels)\n        self.conv2 = nn.Conv1d(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size,\n                               stride=stride, padding=padding, bias=False)\n        self.maxpool = nn.MaxPool1d(kernel_size=2, stride=2, padding=0)\n        self.downsampling = downsampling\n\n    def forward(self, x):\n        identity = x\n\n        out = self.bn1(x)\n        out = self.relu(out)\n        out = self.dropout(out)\n        out = self.conv1(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n        out = self.dropout(out)\n        out = self.conv2(out)\n\n        out = self.maxpool(out)\n        identity = self.downsampling(x)\n\n        out += identity\n        return out\n\n\nclass EEGNet(nn.Module):\n\n    def __init__(self, kernels, in_channels=20, fixed_kernel_size=17, num_classes=6):\n        super(EEGNet, self).__init__()\n        self.kernels = kernels\n        self.planes = 24\n        self.parallel_conv = nn.ModuleList()\n        self.in_channels = in_channels\n        \n        for i, kernel_size in enumerate(list(self.kernels)):\n            sep_conv = nn.Conv1d(in_channels=in_channels, out_channels=self.planes, kernel_size=(kernel_size),\n                               stride=1, padding=0, bias=False,)\n            self.parallel_conv.append(sep_conv)\n\n        self.bn1 = nn.BatchNorm1d(num_features=self.planes)\n        self.relu = nn.ReLU(inplace=False)\n        self.conv1 = nn.Conv1d(in_channels=self.planes, out_channels=self.planes, kernel_size=fixed_kernel_size,\n                               stride=2, padding=2, bias=False)\n        self.block = self._make_resnet_layer(kernel_size=fixed_kernel_size, stride=1, padding=fixed_kernel_size//2)\n        self.bn2 = nn.BatchNorm1d(num_features=self.planes)\n        self.avgpool = nn.AvgPool1d(kernel_size=6, stride=6, padding=2)\n        self.rnn = nn.GRU(input_size=self.in_channels, hidden_size=128, num_layers=1, bidirectional=True)\n        self.fc = nn.Linear(in_features=424, out_features=num_classes)\n        self.rnn1 = nn.GRU(input_size=156, hidden_size=156, num_layers=1, bidirectional=True)\n\n    def _make_resnet_layer(self, kernel_size, stride, blocks=9, padding=0):\n        layers = []\n        downsample = None\n        base_width = self.planes\n\n        for i in range(blocks):\n            downsampling = nn.Sequential(\n                    nn.MaxPool1d(kernel_size=2, stride=2, padding=0)\n                )\n            layers.append(ResNet_1D_Block(in_channels=self.planes, out_channels=self.planes, kernel_size=kernel_size,\n                                       stride=stride, padding=padding, downsampling=downsampling))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        out_sep = []\n\n        for i in range(len(self.kernels)):\n            sep = self.parallel_conv[i](x)\n            out_sep.append(sep)\n\n        out = torch.cat(out_sep, dim=2)\n        out = self.bn1(out)\n        out = self.relu(out)\n        out = self.conv1(out)  \n\n        out = self.block(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n        out = self.avgpool(out)  \n        \n        out = out.reshape(out.shape[0], -1)  \n\n        rnn_out, _ = self.rnn(x.permute(0,2, 1))\n        new_rnn_h = rnn_out[:, -1, :]  \n\n        new_out = torch.cat([out, new_rnn_h], dim=1)  \n        result = self.fc(new_out)  \n\n        return result\n","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:52.122757Z","iopub.execute_input":"2024-01-29T04:34:52.123089Z","iopub.status.idle":"2024-01-29T04:34:52.147116Z","shell.execute_reply.started":"2024-01-29T04:34:52.123064Z","shell.execute_reply":"2024-01-29T04:34:52.146209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import gc\niot = torch.randn(2, Config.num_channels, 10000)#.cuda()\nmodel = EEGNet(kernels=[3,5,7,9], in_channels=Config.num_channels, fixed_kernel_size=5, num_classes=6)#.cuda()\noutput = model(iot)\nprint(output.shape)\n\ndel iot, model\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:52.486303Z","iopub.execute_input":"2024-01-29T04:34:52.486683Z","iopub.status.idle":"2024-01-29T04:34:53.065193Z","shell.execute_reply.started":"2024-01-29T04:34:52.486652Z","shell.execute_reply":"2024-01-29T04:34:53.064281Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":{"execution":{"iopub.status.busy":"2024-01-29T04:34:54.75287Z","iopub.execute_input":"2024-01-29T04:34:54.753249Z","iopub.status.idle":"2024-01-29T04:34:54.765875Z","shell.execute_reply.started":"2024-01-29T04:34:54.753203Z","shell.execute_reply":"2024-01-29T04:34:54.764891Z"},"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":"2024-01-29T04:34:55.258831Z","iopub.execute_input":"2024-01-29T04:34:55.259191Z","iopub.status.idle":"2024-01-29T04:34:55.26424Z","shell.execute_reply.started":"2024-01-29T04:34:55.25916Z","shell.execute_reply":"2024-01-29T04:34:55.263361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\ndef show_batch(img_ds, num_items, num_rows, num_cols, EEG_IDS, predict_arr=None):\n    fig = plt.figure(figsize=(12, 6))    \n    img_index = [0,0]\n    for index, img_index in enumerate(img_index):  # list first items\n        img = img_ds[img_index]        \n        ax = fig.add_subplot(num_rows, num_cols, index + 1, xticks=[], yticks=[])\n        if isinstance(img, torch.Tensor):\n            img = img.detach().numpy()\n            img = img.transpose(1,0)\n        offset = 0\n        for j in range(img.shape[-1]):\n            if j != 0: offset -= img[:, j].min()\n            ax.plot(img[:, j] + offset, label=f'feature {j+1}')\n            offset += img[:, j].max() + 1  # Adding 1 for visual separation\n\n        ax.legend()\n        ax.set_title(f'EEG_Id = {EEG_IDS[img_index]}', size=14)\n\n    plt.tight_layout()\n    plt.show()\n","metadata":{"papermill":{"duration":0.017145,"end_time":"2023-03-11T22:10:21.547015","exception":false,"start_time":"2023-03-11T22:10:21.52987","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:55.448276Z","iopub.execute_input":"2024-01-29T04:34:55.449026Z","iopub.status.idle":"2024-01-29T04:34:55.456973Z","shell.execute_reply.started":"2024-01-29T04:34:55.448996Z","shell.execute_reply":"2024-01-29T04:34:55.456076Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dummy_test = test.copy()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:55.788605Z","iopub.execute_input":"2024-01-29T04:34:55.789362Z","iopub.status.idle":"2024-01-29T04:34:55.79365Z","shell.execute_reply.started":"2024-01-29T04:34:55.789333Z","shell.execute_reply":"2024-01-29T04:34:55.792596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dl_test, ds_test = get_test_dls(dummy_test)\nshow_batch(ds_test, 8, 2, 4, EEG_IDS)","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:34:56.001076Z","iopub.execute_input":"2024-01-29T04:34:56.001725Z","iopub.status.idle":"2024-01-29T04:34:57.008556Z","shell.execute_reply.started":"2024-01-29T04:34:56.001692Z","shell.execute_reply":"2024-01-29T04:34:57.007644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, ReduceLROnPlateau, OneCycleLR\n\ndef 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":{"papermill":{"duration":0.01903,"end_time":"2023-03-11T22:10:22.312531","exception":false,"start_time":"2023-03-11T22:10:22.293501","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:57.010025Z","iopub.execute_input":"2024-01-29T04:34:57.010318Z","iopub.status.idle":"2024-01-29T04:34:57.017302Z","shell.execute_reply.started":"2024-01-29T04:34:57.010292Z","shell.execute_reply":"2024-01-29T04:34:57.01644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.nn as nn\nfrom torch.nn.functional import cross_entropy\nimport torchmetrics\n","metadata":{"papermill":{"duration":0.28664,"end_time":"2023-03-11T22:10:22.608728","exception":false,"start_time":"2023-03-11T22:10:22.322088","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:57.190562Z","iopub.execute_input":"2024-01-29T04:34:57.190844Z","iopub.status.idle":"2024-01-29T04:34:57.195187Z","shell.execute_reply.started":"2024-01-29T04:34:57.19082Z","shell.execute_reply":"2024-01-29T04:34:57.194266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import sklearn.metrics\nimport sys","metadata":{"papermill":{"duration":0.039494,"end_time":"2023-03-11T22:10:22.663948","exception":false,"start_time":"2023-03-11T22:10:22.624454","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-01-29T04:34:57.418971Z","iopub.execute_input":"2024-01-29T04:34:57.419265Z","iopub.status.idle":"2024-01-29T04:34:57.423363Z","shell.execute_reply.started":"2024-01-29T04:34:57.419242Z","shell.execute_reply":"2024-01-29T04:34:57.42245Z"},"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":"2024-01-29T04:34:57.623721Z","iopub.execute_input":"2024-01-29T04:34:57.624025Z","iopub.status.idle":"2024-01-29T04:34:57.629436Z","shell.execute_reply.started":"2024-01-29T04:34:57.624001Z","shell.execute_reply":"2024-01-29T04:34:57.62843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Define Pytorch Lightning Module","metadata":{}},{"cell_type":"code","source":"class 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        self.backbone = EEGNet(kernels=[3,5,7,9], in_channels=Config.num_channels, fixed_kernel_size=5, num_classes=Config.num_classes)\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,images):\n        logits = self.backbone(images)\n        return logits\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    \n    \n    \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":"2024-01-29T04:34:59.695132Z","iopub.execute_input":"2024-01-29T04:34:59.695901Z","iopub.status.idle":"2024-01-29T04:34:59.712127Z","shell.execute_reply.started":"2024-01-29T04:34:59.695867Z","shell.execute_reply":"2024-01-29T04:34:59.711175Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tqdm import tqdm\ntqdm.pandas()","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:35:01.129163Z","iopub.execute_input":"2024-01-29T04:35:01.129596Z","iopub.status.idle":"2024-01-29T04:35:01.135342Z","shell.execute_reply.started":"2024-01-29T04:35:01.129559Z","shell.execute_reply":"2024-01-29T04:35:01.134108Z"},"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":"2024-01-29T04:35:01.72761Z","iopub.execute_input":"2024-01-29T04:35:01.727988Z","iopub.status.idle":"2024-01-29T04:35:01.737468Z","shell.execute_reply.started":"2024-01-29T04:35:01.727955Z","shell.execute_reply":"2024-01-29T04:35:01.736328Z"},"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}/eegnet_best_loss_fold{fold_id}.ckpt'..\")\n    \n    model = EEGModel.load_from_checkpoint(f'{Config.model_dir}/eegnet_best_loss_fold{fold_id}.ckpt',map_location='cuda:0',\n                                          train_dataloader=None,validation_dataloader=None,config=Config)\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":"2024-01-29T04:36:15.341617Z","iopub.execute_input":"2024-01-29T04:36:15.34225Z","iopub.status.idle":"2024-01-29T04:36:15.349512Z","shell.execute_reply.started":"2024-01-29T04:36:15.342212Z","shell.execute_reply":"2024-01-29T04:36:15.348356Z"},"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()\nsub_df[TARGETS] = 0.0","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:36:15.985163Z","iopub.execute_input":"2024-01-29T04:36:15.986036Z","iopub.status.idle":"2024-01-29T04:36:15.993851Z","shell.execute_reply.started":"2024-01-29T04:36:15.985998Z","shell.execute_reply":"2024-01-29T04:36:15.992847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:36:16.348986Z","iopub.execute_input":"2024-01-29T04:36:16.349349Z","iopub.status.idle":"2024-01-29T04:36:16.361446Z","shell.execute_reply.started":"2024-01-29T04:36:16.349317Z","shell.execute_reply":"2024-01-29T04:36:16.360594Z"},"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":"2024-01-29T04:36:16.532219Z","iopub.execute_input":"2024-01-29T04:36:16.532542Z","iopub.status.idle":"2024-01-29T04:36:20.22529Z","shell.execute_reply.started":"2024-01-29T04:36:16.532517Z","shell.execute_reply":"2024-01-29T04:36:20.224233Z"},"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":"2024-01-29T04:36:22.612118Z","iopub.execute_input":"2024-01-29T04:36:22.612943Z","iopub.status.idle":"2024-01-29T04:36:22.619951Z","shell.execute_reply.started":"2024-01-29T04:36:22.612907Z","shell.execute_reply":"2024-01-29T04:36:22.618984Z"},"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":"2024-01-29T04:36:22.851006Z","iopub.execute_input":"2024-01-29T04:36:22.851717Z","iopub.status.idle":"2024-01-29T04:36:22.860376Z","shell.execute_reply.started":"2024-01-29T04:36:22.851682Z","shell.execute_reply":"2024-01-29T04:36:22.859553Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df","metadata":{"execution":{"iopub.status.busy":"2024-01-29T04:36:24.518235Z","iopub.execute_input":"2024-01-29T04:36:24.518592Z","iopub.status.idle":"2024-01-29T04:36:24.530435Z","shell.execute_reply.started":"2024-01-29T04:36:24.518562Z","shell.execute_reply":"2024-01-29T04:36:24.529526Z"},"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":"2024-01-29T04:36:25.837302Z","iopub.execute_input":"2024-01-29T04:36:25.837712Z","iopub.status.idle":"2024-01-29T04:36:25.844274Z","shell.execute_reply.started":"2024-01-29T04:36:25.837681Z","shell.execute_reply":"2024-01-29T04:36:25.843162Z"},"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":[]}]}