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It scores 0.607 on GroupKfold 5 Folds split.\n\n#### Training Notebook link: https://www.kaggle.com/nischaydnk/training-multimodal-1d-2d-approach-eegs\n\n\n#### PS: This approach is somewhat in continuation of what I have shared earlier in these notebooks .You can also refer my previous work to study more about the 1D approach I have used in the notebook. Also, I have used some of the code from [@moth](https://www.kaggle.com/alejopaullier) baseline with 2D approach [notebook](https://www.kaggle.com/code/alejopaullier/hms-efficientnetb0-pytorch-inference) & ideas shared by [@Chris](https://www.kaggle.com/cdeotte)\n\n\n\n#### My previous notebooks:\n\n##### 1D EEGNet training: https://www.kaggle.com/code/nischaydnk/lightning-1d-eegnet-training-pipeline-hbs\n##### 1D EEGNet Submission: https://www.kaggle.com/code/nischaydnk/hms-submission-1d-eegnet-pipeline-lightning\n\n\n#### Overall pipeline in this notebook is somewhat like this.\n![img2](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F4712534%2Fc4e39641fa8ec13588b62065ae1e9b58%2FScreenshot%202024-02-10%20at%2010.40.36%20PM.png?generation=1707585942607444&alt=media)","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"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\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nwarnings.filterwarnings('ignore')\n","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":"2024-02-10T13:39:16.256201Z","iopub.execute_input":"2024-02-10T13:39:16.256574Z","iopub.status.idle":"2024-02-10T13:39:26.553897Z","shell.execute_reply.started":"2024-02-10T13:39:16.256541Z","shell.execute_reply":"2024-02-10T13:39:26.552525Z"},"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    backbone_2d = 'tf_efficientnet_b0'\n    data_root = \"/kaggle/input/hms-harmful-brain-activity-classification/\"\n    kaggle_spec_path = \"/kaggle/input/hms-harmful-brain-activity-classification/test_spectrograms/\" \n    TRAIN_EEGS = \"/home/nischay//brain/Data/spec/EEG_Spectrograms/\"\n    LR = 7e-3\n    processed_train = None\n    model_dir = '/kaggle/input/mulitimod-exp1-5gkf-b08ch-cv606'\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":"2024-02-10T13:39:26.55572Z","iopub.execute_input":"2024-02-10T13:39:26.55629Z","iopub.status.idle":"2024-02-10T13:39:26.563563Z","shell.execute_reply.started":"2024-02-10T13:39:26.556256Z","shell.execute_reply":"2024-02-10T13:39:26.562576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Parquet to EEG Signals Numpy Processing","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"USE_WAVELET = None \n\nNAMES = ['LL','LP','RP','RR']\n\nFEATS2 = [['Fp1','F7','T3','T5','O1'],\n         ['Fp1','F3','C3','P3','O1'],\n         ['Fp2','F8','T4','T6','O2'],\n         ['Fp2','F4','C4','P4','O2']]\n\n\n\ndef maddest(d, axis: int = None):\n    \"\"\"\n    Denoise function.\n    \"\"\"\n    return np.mean(np.absolute(d - np.mean(d, axis)), axis)\n\ndef denoise(x: np.ndarray, wavelet: str = 'haar', level: int = 1): \n    coeff = pywt.wavedec(x, wavelet, mode=\"per\") # multilevel 1D Discrete Wavelet Transform of data.\n    sigma = (1/0.6745) * maddest(coeff[-level])\n    uthresh = sigma * np.sqrt(2*np.log(len(x)))\n    coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])\n    output = pywt.waverec(coeff, wavelet, mode='per')\n    return output\n\ndef spectrogram_from_eeg(parquet_path, display=False):\n    \n    # LOAD MIDDLE 50 SECONDS OF EEG SERIES\n    eeg = pd.read_parquet(parquet_path)\n    middle = (len(eeg)-10_000)//2\n    eeg = eeg.iloc[middle:middle+10_000]\n    \n    # VARIABLE TO HOLD SPECTROGRAM\n    img = np.zeros((128,256,4),dtype='float32')\n    \n    if display:\n        plt.figure(figsize=(10,7))\n        \n    signals = []\n    \n    for k in range(4):\n        COLS = FEATS2[k]\n        for kk in range(4):\n        \n            # COMPUTE PAIR DIFFERENCES\n            x = eeg[COLS[kk]].values - eeg[COLS[kk+1]].values\n\n            # FILL NANS\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            # DENOISE\n            if USE_WAVELET:\n                x = denoise(x, wavelet=USE_WAVELET)\n            signals.append(x)\n\n            # RAW SPECTROGRAM\n            mel_spec = librosa.feature.melspectrogram(y=x, sr=200, hop_length=len(x)//256, \n                  n_fft=1024, n_mels=128, fmin=0, fmax=20, win_length=128)\n\n            # LOG TRANSFORM\n            width = (mel_spec.shape[1]//32)*32\n            mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max).astype(np.float32)[:,:width]\n\n            # STANDARDIZE TO -1 TO 1\n            mel_spec_db = (mel_spec_db+40)/40 \n            img[:,:,k] += mel_spec_db\n                \n        # AVERAGE THE 4 MONTAGE DIFFERENCES\n        img[:,:,k] /= 4.0\n        \n        if display:\n            plt.subplot(2,2,k+1)\n            plt.imshow(img[:,:,k],aspect='auto',origin='lower')\n            plt.title(f'EEG {eeg_id} - Spectrogram {NAMES[k]}')\n            \n    if display: \n        plt.show()\n        plt.figure(figsize=(10,5))\n        offset = 0\n        for k in range(4):\n            if k>0: offset -= signals[3-k].min()\n            plt.plot(range(10_000),signals[k]+offset,label=NAMES[3-k])\n            offset += signals[3-k].max()\n        plt.legend()\n        plt.title(f'EEG {eeg_id} Signals')\n        plt.show()\n        print(); print('#'*25); print()\n        \n    return img\n\ndef plot_spectrogram(spectrogram_path: str):\n    \"\"\"\n    Source: https://www.kaggle.com/code/mvvppp/hms-eda-and-domain-journey\n    Visualize spectrogram recordings from a parquet file.\n    :param spectrogram_path: path to the spectrogram parquet.\n    \"\"\"\n    sample_spect = pd.read_parquet(spectrogram_path)\n    \n    split_spect = {\n        \"LL\": sample_spect.filter(regex='^LL', axis=1),\n        \"RL\": sample_spect.filter(regex='^RL', axis=1),\n        \"RP\": sample_spect.filter(regex='^RP', axis=1),\n        \"LP\": sample_spect.filter(regex='^LP', axis=1),\n    }\n    \n    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15, 12))\n    axes = axes.flatten()\n    label_interval = 5\n    for i, split_name in enumerate(split_spect.keys()):\n        ax = axes[i]\n        img = ax.imshow(np.log(split_spect[split_name]).T, cmap='viridis', aspect='auto', origin='lower')\n        cbar = fig.colorbar(img, ax=ax)\n        cbar.set_label('Log(Value)')\n        ax.set_title(split_name)\n        ax.set_ylabel(\"Frequency (Hz)\")\n        ax.set_xlabel(\"Time\")\n\n        ax.set_yticks(np.arange(len(split_spect[split_name].columns)))\n        ax.set_yticklabels([column_name[3:] for column_name in split_spect[split_name].columns])\n        frequencies = [column_name[3:] for column_name in split_spect[split_name].columns]\n        ax.set_yticks(np.arange(0, len(split_spect[split_name].columns), label_interval))\n        ax.set_yticklabels(frequencies[::label_interval])\n    plt.tight_layout()\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:26.56542Z","iopub.execute_input":"2024-02-10T13:39:26.565814Z","iopub.status.idle":"2024-02-10T13:39:26.593089Z","shell.execute_reply.started":"2024-02-10T13:39:26.565785Z","shell.execute_reply":"2024-02-10T13:39:26.592203Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":"2024-02-10T13:39:26.595111Z","iopub.execute_input":"2024-02-10T13:39:26.595431Z","iopub.status.idle":"2024-02-10T13:39:26.60714Z","shell.execute_reply.started":"2024-02-10T13:39:26.595396Z","shell.execute_reply":"2024-02-10T13:39:26.606361Z"},"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":"2024-02-10T13:39:26.60811Z","iopub.execute_input":"2024-02-10T13:39:26.608392Z","iopub.status.idle":"2024-02-10T13:39:26.638008Z","shell.execute_reply.started":"2024-02-10T13:39:26.608368Z","shell.execute_reply":"2024-02-10T13:39:26.637243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"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":"2024-02-10T13:39:26.639047Z","iopub.execute_input":"2024-02-10T13:39:26.639297Z","iopub.status.idle":"2024-02-10T13:39:27.496631Z","shell.execute_reply.started":"2024-02-10T13:39:26.639274Z","shell.execute_reply":"2024-02-10T13:39:27.495758Z"},"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":"2024-02-10T13:39:27.497632Z","iopub.execute_input":"2024-02-10T13:39:27.497889Z","iopub.status.idle":"2024-02-10T13:39:27.507317Z","shell.execute_reply.started":"2024-02-10T13:39:27.497867Z","shell.execute_reply":"2024-02-10T13:39:27.506482Z"},"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":"2024-02-10T13:39:27.508472Z","iopub.execute_input":"2024-02-10T13:39:27.508786Z","iopub.status.idle":"2024-02-10T13:39:27.513835Z","shell.execute_reply.started":"2024-02-10T13:39:27.508763Z","shell.execute_reply":"2024-02-10T13:39:27.512963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.DataFrame(EEG_IDS2, columns=['eeg_id'])\ntest2 = pd.read_csv('/kaggle/input/hms-harmful-brain-activity-classification/test.csv')","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.514903Z","iopub.execute_input":"2024-02-10T13:39:27.515223Z","iopub.status.idle":"2024-02-10T13:39:27.525448Z","shell.execute_reply.started":"2024-02-10T13:39:27.515199Z","shell.execute_reply":"2024-02-10T13:39:27.524642Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_spec_ids = test2[test2['eeg_id'].isin(EEG_IDS2)].spectrogram_id.unique()","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.529555Z","iopub.execute_input":"2024-02-10T13:39:27.529919Z","iopub.status.idle":"2024-02-10T13:39:27.537788Z","shell.execute_reply.started":"2024-02-10T13:39:27.529892Z","shell.execute_reply":"2024-02-10T13:39:27.537012Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test['spectrogram_id'] = test_spec_ids","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.538882Z","iopub.execute_input":"2024-02-10T13:39:27.539138Z","iopub.status.idle":"2024-02-10T13:39:27.548457Z","shell.execute_reply.started":"2024-02-10T13:39:27.539117Z","shell.execute_reply":"2024-02-10T13:39:27.547688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test2","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.549444Z","iopub.execute_input":"2024-02-10T13:39:27.549782Z","iopub.status.idle":"2024-02-10T13:39:27.56275Z","shell.execute_reply.started":"2024-02-10T13:39:27.549757Z","shell.execute_reply":"2024-02-10T13:39:27.561888Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.563943Z","iopub.execute_input":"2024-02-10T13:39:27.564278Z","iopub.status.idle":"2024-02-10T13:39:27.575202Z","shell.execute_reply.started":"2024-02-10T13:39:27.564253Z","shell.execute_reply":"2024-02-10T13:39:27.574414Z"},"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":"2024-02-10T13:39:27.57632Z","iopub.execute_input":"2024-02-10T13:39:27.576652Z","iopub.status.idle":"2024-02-10T13:39:27.83174Z","shell.execute_reply.started":"2024-02-10T13:39:27.576615Z","shell.execute_reply":"2024-02-10T13:39:27.830738Z"},"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 = df.groupby('eeg_id')[['spectrogram_id','spectrogram_label_offset_seconds']].agg({\n    'spectrogram_id':'first',\n    'spectrogram_label_offset_seconds':'min'\n})\ntrain_df.columns = ['spectogram_id','min']\n\naux = df.groupby('eeg_id')[['spectrogram_id','spectrogram_label_offset_seconds']].agg({\n    'spectrogram_label_offset_seconds':'max'\n})\ntrain_df['max'] = aux\n\naux = df.groupby('eeg_id')[['patient_id']].agg('first')\ntrain_df['patient_id'] = aux\n\naux = df.groupby('eeg_id')[TARGETS].agg('sum')\nfor label in TARGETS:\n    train_df[label] = aux[label].values\n    \ny_data = train_df[TARGETS].values\ny_data = y_data / y_data.sum(axis=1,keepdims=True)\ntrain_df[TARGETS] = y_data\n\naux = df.groupby('eeg_id')[['expert_consensus']].agg('first')\ntrain_df['target'] = aux\n\ntrain = train_df.reset_index()\nprint('Train non-overlapp eeg_id shape:', train_df.shape )\ntrain.head()\n","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.832937Z","iopub.execute_input":"2024-02-10T13:39:27.833249Z","iopub.status.idle":"2024-02-10T13:39:27.925045Z","shell.execute_reply.started":"2024-02-10T13:39:27.833222Z","shell.execute_reply":"2024-02-10T13:39:27.924161Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:27.92628Z","iopub.execute_input":"2024-02-10T13:39:27.926674Z","iopub.status.idle":"2024-02-10T13:39:27.944081Z","shell.execute_reply.started":"2024-02-10T13:39:27.92664Z","shell.execute_reply":"2024-02-10T13:39:27.943027Z"},"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":"2024-02-10T13:39:27.945307Z","iopub.execute_input":"2024-02-10T13:39:27.945614Z","iopub.status.idle":"2024-02-10T13:39:27.951256Z","shell.execute_reply.started":"2024-02-10T13:39:27.945581Z","shell.execute_reply":"2024-02-10T13:39:27.950319Z"},"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":"2024-02-10T13:39:27.9523Z","iopub.execute_input":"2024-02-10T13:39:27.952585Z","iopub.status.idle":"2024-02-10T13:39:28.00031Z","shell.execute_reply.started":"2024-02-10T13:39:27.952561Z","shell.execute_reply":"2024-02-10T13:39:27.99947Z"},"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":"from tqdm import tqdm\ntqdm.pandas()","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:28.001346Z","iopub.execute_input":"2024-02-10T13:39:28.001616Z","iopub.status.idle":"2024-02-10T13:39:28.007316Z","shell.execute_reply.started":"2024-02-10T13:39:28.001593Z","shell.execute_reply":"2024-02-10T13:39:28.006473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nfrom glob import glob\n\nREAD_SPEC_FILES = True\n\npaths_spectograms = glob(Config.kaggle_spec_path + \"*.parquet\")\nprint(f'There are {len(paths_spectograms)} spectrogram parquets')\n\nif READ_SPEC_FILES:    \n    all_spectrograms = {}\n    for file_path in tqdm(paths_spectograms):\n        aux = pd.read_parquet(file_path)\n        name = int(file_path.split(\"/\")[-1].split('.')[0])\n        all_spectrograms[name] = aux.iloc[:,1:].values\n        del aux\nelse:\n    all_spectrograms = np.load(Config.PRE_LOADED_SPECTOGRAMS, allow_pickle=True).item()\n","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:28.008457Z","iopub.execute_input":"2024-02-10T13:39:28.008753Z","iopub.status.idle":"2024-02-10T13:39:28.080402Z","shell.execute_reply.started":"2024-02-10T13:39:28.00873Z","shell.execute_reply":"2024-02-10T13:39:28.079471Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\npaths_eegs = glob(\"/kaggle/input/hms-harmful-brain-activity-classification/test_eegs/\" + \"*.parquet\")\nprint(f'There are {len(paths_eegs)} EEG spectrograms')\nall_eegs = {}\ncounter = 0\n\nfor file_path in tqdm(paths_eegs):\n    eeg_id = file_path.split(\"/\")[-1].split(\".\")[0]\n    eeg_spectrogram = spectrogram_from_eeg(file_path, counter < 1)\n    all_eegs[int(eeg_id)] = eeg_spectrogram\n    counter += 1\n    \n    \n\n    \n","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:28.08156Z","iopub.execute_input":"2024-02-10T13:39:28.081826Z","iopub.status.idle":"2024-02-10T13:39:39.149401Z","shell.execute_reply.started":"2024-02-10T13:39:28.081803Z","shell.execute_reply":"2024-02-10T13:39:39.148475Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(all_eegs2)","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:39.150681Z","iopub.execute_input":"2024-02-10T13:39:39.151131Z","iopub.status.idle":"2024-02-10T13:39:39.157064Z","shell.execute_reply.started":"2024-02-10T13:39:39.151105Z","shell.execute_reply":"2024-02-10T13:39:39.156207Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"all_eegs2[test.loc[0,'eeg_id']].shape","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:39.158077Z","iopub.execute_input":"2024-02-10T13:39:39.158373Z","iopub.status.idle":"2024-02-10T13:39:39.170891Z","shell.execute_reply.started":"2024-02-10T13:39:39.15835Z","shell.execute_reply":"2024-02-10T13:39:39.17006Z"},"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":"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","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:39:39.172053Z","iopub.execute_input":"2024-02-10T13:39:39.172332Z","iopub.status.idle":"2024-02-10T13:39:39.196231Z","shell.execute_reply.started":"2024-02-10T13:39:39.172309Z","shell.execute_reply":"2024-02-10T13:39:39.195408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import timm\n\nclass EEGMegaNet(nn.Module):\n\n    def __init__(self, backbone_2d,in_channels_2d, kernels, pretrained=False, in_channels=20, fixed_kernel_size=17, num_classes=6):\n        super(EEGMegaNet, self).__init__()\n        \n        self.kernels = kernels\n        self.planes = 24\n        self.parallel_conv = nn.ModuleList()\n        self.in_channels = in_channels\n\n\n        \n        self.backbone_2d = timm.create_model(\n            Config.backbone_2d,\n            pretrained=pretrained,\n            drop_rate = 0.1,\n            drop_path_rate = 0.1\n        \n        )\n        \n        self.features_2d = nn.Sequential(*list(self.backbone_2d.children())[:-2] + [nn.AdaptiveAvgPool2d(1),nn.Flatten()])\n        \n        # nn.Sequential(\n            # nn.AdaptiveAvgPool2d(1),\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=4, stride=4, padding=2)\n        self.rnn = nn.GRU(input_size=self.in_channels, hidden_size=128, num_layers=1, bidirectional=True)\n        \n        self.fc1 = nn.Linear(in_features=1280, out_features=128)\n        self.fc2 = nn.Linear(in_features=736, out_features=128)\n        self.fc = nn.Linear(in_features=256, out_features=num_classes)\n\n        self.fc1d = nn.Linear(in_features=128, out_features=num_classes)\n        self.fc2d = nn.Linear(in_features=128, out_features=num_classes)\n        \n        \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=8, 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 _reshape_input(self, spec):\n        \"\"\"\n        Reshapes input (128, 256, 8) -> (512, 512, 3) monotone image.\n        \"\"\" \n        # === Get spectograms ===\n        spectograms = [spec[:, :, :, i:i+1] for i in range(4)]\n        spectograms = torch.cat(spectograms, dim=1)\n        \n        # === Get EEG spectograms ===\n        eegs = [spec[:, :, :, i:i+1] for i in range(4,8)]\n        eegs = torch.cat(eegs, dim=1)\n        \n        # === Reshape (512,512,3) ===\n        spec = spectograms\n            \n        spec = torch.cat([spec,spec,spec], dim=3)\n        spec = spec.permute(0, 3, 1, 2)\n        return spec\n\n    def forward(self, x, spec):\n\n        spec = self._reshape_input(spec)\n        spec = self.features_2d(spec)\n        # print(spec.shape) #2, 1280, 16, 8\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\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        new_out = self.fc2(new_out)  \n        out1d = self.fc1d(new_out)\n        \n        spec = self.fc1(spec)  \n        out2d = self.fc2d(spec)\n        \n        result = torch.cat([new_out, spec], dim=1)  \n        result = self.fc(result)\n        \n        \n        return result, new_out, spec, out1d, out2d","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:40:27.12301Z","iopub.execute_input":"2024-02-10T13:40:27.123382Z","iopub.status.idle":"2024-02-10T13:40:27.146279Z","shell.execute_reply.started":"2024-02-10T13:40:27.123357Z","shell.execute_reply":"2024-02-10T13:40:27.145142Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import gc\niot = torch.randn(2, Config.num_channels, 10000)#.cuda()\nspec = torch.randn(2, 128, 256, 8)#.cuda()\n\nmodel = EEGMegaNet(backbone_2d=Config.backbone_2d,in_channels_2d=8,kernels=[3,5,7,9],pretrained=False, in_channels=Config.num_channels, fixed_kernel_size=5, num_classes=6)#.cuda()\noutput,_,_,_,_ = model(iot, spec)\nprint(output.shape)\n\ndel iot, model\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:40:27.298991Z","iopub.execute_input":"2024-02-10T13:40:27.299699Z","iopub.status.idle":"2024-02-10T13:40:28.19901Z","shell.execute_reply.started":"2024-02-10T13:40:27.299663Z","shell.execute_reply":"2024-02-10T13:40:28.198063Z"},"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        sample = np.zeros((data.shape[0], 8))  # Assuming data.shape[1] == 8 for 8 channels\n        \n        # Mapping of channel names to indices\n        FEAT2IDX = {'Fp1': 0, 'T3': 1, 'C3': 2, 'O1': 3, 'Fp2': 4, 'C4': 5, 'T4': 6, 'O2': 7}\n        \n        # Compute differences\n        sample[:,0] = data[:,FEAT2IDX['Fp1']] - data[:,FEAT2IDX['T3']]\n        sample[:,1] = data[:,FEAT2IDX['T3']] - data[:,FEAT2IDX['O1']]\n        \n        sample[:,2] = data[:,FEAT2IDX['Fp1']] - data[:,FEAT2IDX['C3']]\n        sample[:,3] = data[:,FEAT2IDX['C3']] - data[:,FEAT2IDX['O1']]\n        \n        sample[:,4] = data[:,FEAT2IDX['Fp2']] - data[:,FEAT2IDX['C4']]\n        sample[:,5] = data[:,FEAT2IDX['C4']] - data[:,FEAT2IDX['O2']]\n        \n        sample[:,6] = data[:,FEAT2IDX['Fp2']] - data[:,FEAT2IDX['T4']]\n        sample[:,7] = data[:,FEAT2IDX['T4']] - data[:,FEAT2IDX['O2']]\n\n        # sample = np.concatenate([sample,data],1)\n        \n        # Feature Engineering on sample instead of data\n        \n        sample = (sample - np.mean(sample, axis=0)) / np.std(sample, axis=0)\n\n        sample = np.clip(sample, -1024, 1024)\n        sample = np.nan_to_num(sample, nan=0)# / 32.0\n        \n        sample = butter_lowpass_filter(sample)\n        sample = quantize_data(sample, 1)\n\n\n        samples = torch.from_numpy(sample).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":"2024-02-10T13:40:30.382498Z","iopub.execute_input":"2024-02-10T13:40:30.383389Z","iopub.status.idle":"2024-02-10T13:40:30.408723Z","shell.execute_reply.started":"2024-02-10T13:40:30.383355Z","shell.execute_reply":"2024-02-10T13:40:30.40738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport numpy as np\nimport pandas as pd\nfrom scipy.signal import butter, lfilter\n\nclass EEGDataset(torch.utils.data.Dataset):\n    def __init__(self, data, eegs=None, specs=None, eeg_specs=None, spec_aug = False, augmentations=None, test=False): \n        self.data = data\n        self.eegs = eegs\n        self.specs = specs  # Spectrograms for each ID\n        self.eeg_specs = eeg_specs  # EEG spectrograms for each ID\n        self.augmentations = augmentations\n        self.test = test\n        self.spec_aug = spec_aug\n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, index):\n        row = self.data.iloc[index]\n        \n        # Processing EEG signal data\n        eeg_data = self.eegs[row.eeg_id]\n        sample = np.zeros((eeg_data.shape[0], 8))  # Assuming eeg_data.shape[1] == 8 for 8 channels\n        FEAT2IDX = {'Fp1': 0, 'T3': 1, 'C3': 2, 'O1': 3, 'Fp2': 4, 'C4': 5, 'T4': 6, 'O2': 7}\n        \n        # Compute differences\n        for i, (start, end) in enumerate([('Fp1', 'T3'), ('T3', 'O1'), ('Fp1', 'C3'), ('C3', 'O1'), \n                                          ('Fp2', 'C4'), ('C4', 'O2'), ('Fp2', 'T4'), ('T4', 'O2')]):\n            sample[:, i] = eeg_data[:, FEAT2IDX[start]] - eeg_data[:, FEAT2IDX[end]]\n        \n        sample = self.process_sample(sample)\n        \n        samples = torch.from_numpy(sample).float()\n        samples = samples.permute(1, 0)\n        \n        # Processing spectrogram data\n        spec = self.__spec_data_generation(row)\n        if self.spec_aug:\n            spec = self.__transform(spec) \n        \n        if not self.test:\n            label = row[TARGETS]  # Assuming 'TARGETS' is defined somewhere as the label column name\n            label = torch.tensor(label).float()  \n            return samples, spec, label\n        else:\n            return samples, spec\n    \n    def process_sample(self, sample):\n        # Normalize the sample data\n        sample = (sample - np.mean(sample, axis=0)) / np.std(sample, axis=0)\n        sample = np.clip(sample, -1024, 1024)\n        sample = np.nan_to_num(sample, nan=0)\n        sample = butter_lowpass_filter(sample)\n        sample = quantize_data(sample, 1)\n        return sample\n\n    def __spec_data_generation(self, row):\n        \"\"\"\n        Generates data containing batch_size samples. This method directly\n        uses class attributes for spectrograms and EEG spectrograms.\n        \"\"\"\n        X = np.zeros((128, 256, 8), dtype='float32')   \n        \n        if not self.test:\n            # Assuming your DataFrame has a column that combines min and max values for slicing\n            r = int((row['min'] + row['max']) // 4)\n        else:\n            r = 0  # Adjust as necessary for test mode\n        \n        for region in range(4):\n            img = self.specs[row.spectrogram_id][r:r+300, region*100:(region+1)*100].T\n            \n            # Log transform spectogram\n            img = np.clip(img, np.exp(-4), np.exp(8))\n            img = np.log(img)\n\n            # Standarize per image\n            ep = 1e-6\n            mu = np.nanmean(img.flatten())\n            std = np.nanstd(img.flatten())\n            img = (img-mu)/(std+ep)\n            img = np.nan_to_num(img, nan=0.0)\n            X[14:-14, :, region] = img[:, 22:-22] / 2.0\n        \n        # Process EEG spectrogram - assuming a single channel example\n        img = self.eeg_specs[row.eeg_id]\n        X[:, :, 4:] = img                \n        return X\n\n    def __transform(self, img):\n        transforms = A.Compose([\n            A.HorizontalFlip(p=0.5),\n        ])\n        return transforms(image=img)['image']\n\n","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:40:30.598434Z","iopub.execute_input":"2024-02-10T13:40:30.599269Z","iopub.status.idle":"2024-02-10T13:40:30.619462Z","shell.execute_reply.started":"2024-02-10T13:40:30.599235Z","shell.execute_reply":"2024-02-10T13:40:30.618563Z"},"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        specs=all_spectrograms,\n        eeg_specs=all_eegs,\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":"2024-02-10T13:40:31.099481Z","iopub.execute_input":"2024-02-10T13:40:31.100375Z","iopub.status.idle":"2024-02-10T13:40:31.105371Z","shell.execute_reply.started":"2024-02-10T13:40:31.100345Z","shell.execute_reply":"2024-02-10T13:40:31.104471Z"},"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, spec = 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":"2024-02-10T13:40:31.180209Z","iopub.execute_input":"2024-02-10T13:40:31.180498Z","iopub.status.idle":"2024-02-10T13:40:31.189032Z","shell.execute_reply.started":"2024-02-10T13:40:31.180474Z","shell.execute_reply":"2024-02-10T13:40:31.188135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dummy_test = test.copy()\ndummy_test","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:40:31.263218Z","iopub.execute_input":"2024-02-10T13:40:31.263528Z","iopub.status.idle":"2024-02-10T13:40:31.272426Z","shell.execute_reply.started":"2024-02-10T13:40:31.263487Z","shell.execute_reply":"2024-02-10T13:40:31.27148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"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":"2024-02-10T13:40:31.606317Z","iopub.execute_input":"2024-02-10T13:40:31.607061Z","iopub.status.idle":"2024-02-10T13:40:32.567375Z","shell.execute_reply.started":"2024-02-10T13:40:31.607026Z","shell.execute_reply":"2024-02-10T13:40:32.566556Z"},"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":"2024-02-10T13:40:32.569482Z","iopub.execute_input":"2024-02-10T13:40:32.569911Z","iopub.status.idle":"2024-02-10T13:40:32.577537Z","shell.execute_reply.started":"2024-02-10T13:40:32.56988Z","shell.execute_reply":"2024-02-10T13:40:32.576562Z"},"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":"2024-02-10T13:40:32.578992Z","iopub.execute_input":"2024-02-10T13:40:32.579329Z","iopub.status.idle":"2024-02-10T13:40:32.588029Z","shell.execute_reply.started":"2024-02-10T13:40:32.579299Z","shell.execute_reply":"2024-02-10T13:40:32.587236Z"},"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":"2024-02-10T13:40:32.589718Z","iopub.execute_input":"2024-02-10T13:40:32.590033Z","iopub.status.idle":"2024-02-10T13:40:32.597478Z","shell.execute_reply.started":"2024-02-10T13:40:32.590005Z","shell.execute_reply":"2024-02-10T13:40:32.59662Z"},"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        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":"2024-02-10T13:40:32.598724Z","iopub.execute_input":"2024-02-10T13:40:32.599039Z","iopub.status.idle":"2024-02-10T13:40:32.607034Z","shell.execute_reply.started":"2024-02-10T13:40:32.599009Z","shell.execute_reply":"2024-02-10T13:40:32.606152Z"},"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):\n        super().__init__()\n        self.num_classes = num_classes\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.backbone = EEGMegaNet(backbone_2d=Config.backbone_2d,\n                                   in_channels_2d=8,\n                                   kernels=[3,5,7,9],pretrained=Config.pretrained,\n                                   in_channels=Config.num_channels,\n                                   fixed_kernel_size=5, num_classes=6)\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,eeg, spec):\n        logits = self.backbone(eeg, spec)\n        # logits = self.lin(logits)\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        eeg, spec, target = batch        \n\n        y_pred = self(eeg, spec)\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        eeg, spec, target = batch \n        # print(target)\n        y_pred = self(eeg, spec)\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        avg_score = avg_loss\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","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":"2024-02-10T13:40:32.702789Z","iopub.execute_input":"2024-02-10T13:40:32.703463Z","iopub.status.idle":"2024-02-10T13:40:32.721217Z","shell.execute_reply.started":"2024-02-10T13:40:32.70343Z","shell.execute_reply":"2024-02-10T13:40:32.720298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"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, spec = batch\n            x = x.cuda()\n            spec = spec.cuda()\n            \n            # inputs = {key:val.reshape(val.shape[0], -1).to(config.device) for key,val in batch.items()}\n            outputs,_,_,out1d,out2d = model(x, spec)\n            outputs = outputs*0.5 + out1d*0.25 + out2d*0.25\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":"2024-02-10T13:40:33.185763Z","iopub.execute_input":"2024-02-10T13:40:33.186635Z","iopub.status.idle":"2024-02-10T13:40:33.195526Z","shell.execute_reply.started":"2024-02-10T13:40:33.1866Z","shell.execute_reply":"2024-02-10T13:40:33.194492Z"},"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":"2024-02-10T13:40:33.690851Z","iopub.execute_input":"2024-02-10T13:40:33.691243Z","iopub.status.idle":"2024-02-10T13:40:33.698792Z","shell.execute_reply.started":"2024-02-10T13:40:33.691215Z","shell.execute_reply":"2024-02-10T13:40:33.697709Z"},"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":"2024-02-10T13:40:33.768686Z","iopub.execute_input":"2024-02-10T13:40:33.769055Z","iopub.status.idle":"2024-02-10T13:40:33.777462Z","shell.execute_reply.started":"2024-02-10T13:40:33.769026Z","shell.execute_reply":"2024-02-10T13:40:33.776474Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:40:33.854388Z","iopub.execute_input":"2024-02-10T13:40:33.855204Z","iopub.status.idle":"2024-02-10T13:40:33.867493Z","shell.execute_reply.started":"2024-02-10T13:40:33.855169Z","shell.execute_reply":"2024-02-10T13:40:33.86644Z"},"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)","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":"2024-02-10T13:40:34.080812Z","iopub.execute_input":"2024-02-10T13:40:34.081431Z","iopub.status.idle":"2024-02-10T13:40:44.418337Z","shell.execute_reply.started":"2024-02-10T13:40:34.0814Z","shell.execute_reply":"2024-02-10T13:40:44.41726Z"},"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":"2024-02-10T13:40:44.420004Z","iopub.execute_input":"2024-02-10T13:40:44.420291Z","iopub.status.idle":"2024-02-10T13:40:44.426724Z","shell.execute_reply.started":"2024-02-10T13:40:44.420268Z","shell.execute_reply":"2024-02-10T13:40:44.42581Z"},"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":"2024-02-10T13:40:44.427878Z","iopub.execute_input":"2024-02-10T13:40:44.428165Z","iopub.status.idle":"2024-02-10T13:40:44.446112Z","shell.execute_reply.started":"2024-02-10T13:40:44.428142Z","shell.execute_reply":"2024-02-10T13:40:44.445345Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_df","metadata":{"execution":{"iopub.status.busy":"2024-02-10T13:40:44.447974Z","iopub.execute_input":"2024-02-10T13:40:44.448254Z","iopub.status.idle":"2024-02-10T13:40:44.461688Z","shell.execute_reply.started":"2024-02-10T13:40:44.44823Z","shell.execute_reply":"2024-02-10T13:40:44.46073Z"},"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":"2024-02-10T13:40:44.462894Z","iopub.execute_input":"2024-02-10T13:40:44.463228Z","iopub.status.idle":"2024-02-10T13:40:44.472071Z","shell.execute_reply.started":"2024-02-10T13:40:44.463198Z","shell.execute_reply":"2024-02-10T13:40:44.471285Z"},"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":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}