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Descripion\n\n## The notebook is based on the excellent version of the [HMS Resnet1D-GRU Train notebook by Med Ali Bouchhioua](https://www.kaggle.com/code/medali1992/hms-resnet1d-gru-train?scriptVersionId=163575181)\n\n## Changes 1 [LB:0.40]:\n\n- Convolution kernel used [3,5,7,9,11]\n- Loss functions replaced with Hardswish and SiLU\n- Adan optimizer replaced with AdamW\n- Bandpass filter with a lower limit of 0.5 Hz\n- Total Evaluators are used in the first data set from 0 to 5, the second data set from 6 to max\n- Albumentations. Random frequency cut with a bandpass filter in the range 10 - 25 Hz\n- 20 epochs with two stages\n\n## Changes 2 [LB:0.38]:\n- Order of filter changed from 6 to 2 and high cutoff frequency changed from 25 Hz to 20 Hz. An order with order 6 has a very strong effect on the signal if there are sharp jumps.\n\n## Changes 3 [LB:0.38]:\n- The total of appraisers is divided into three parts: [0..2], [3..5], [6..1000].\n\n## Changes 4 [LB:0.40]:\n- Return to total of appraisers is divided into two parts: [0..5], [6..1000].\n- Remove Regularization value 0.166666667 according to the advice of [Med Ali Bouchhioua](https://www.kaggle.com/code/konstantinboyko/hms-resnet1d-gru-v22-human-6-train/comments#2681934).\n- Added code that allows you to train the model not only in the stage/fold section, but also in the reverse fold/stage section.\n- Added filter parameters.\n\n## Changes 5 [LB:039]:\n- Albumentations. Accidentally missing an entire signal.\n\n## Changes 6 [LB:0.38]\n- The signal size is reduced by half and is selected randomly from the total signal.\n\n## Changes 7 [LB:0.36]\n- The signal size is reduced by five times and selected randomly from the total signal.\n\n## Changes 8 [LB:0.39]\n- One-stage model with the number of evaluators in the range [5..Max]\n\n## Changes 9 [LB:0.38]\n- The signal size is reduced by five times and selected randomly from the total signal.\n- Total Evaluators in the range [2..2 + 6..28]\n\n## Changes 10 [LB:0.38]\n- Random general reversal of the signal\n- Random general signal flipping upside down\n\n## Changes 11 [LB:0.37]\n- The total of appraisers is divided into two parts: [1..2 + 4..5], [6..28].\n\n## Changes 12 [LB:]\n- The total of appraisers is divided into two parts: [3 + 6..28], [1..2 + 4..5]\n\n\n## [My train notebook](https://www.kaggle.com/code/konstantinboyko/hms-resnet1d-gru-train-1-5-dataset?scriptVersionId=166379987)\n\n## [Previous Inference](https://www.kaggle.com/code/konstantinboyko/hms-resnet1d-gru-v33-human-5-stage-1-inference)\n\n# Library","metadata":{"papermill":{"duration":0.019835,"end_time":"2024-03-06T22:48:40.188331","exception":false,"start_time":"2024-03-06T22:48:40.168496","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import os\nimport gc\nimport sys\nimport math\nimport time\nimport random\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\n\nfrom glob import glob\nfrom pathlib import Path\nfrom typing import Dict, List, Union\nfrom scipy.signal import butter, lfilter, freqz\nfrom matplotlib import pyplot as plt\nfrom tqdm.auto import tqdm\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import Adam, SGD, AdamW\nfrom torch.utils.data import DataLoader, Dataset\n\nsys.path.append(\"/kaggle/input/kaggle-kl-div\")\nfrom kaggle_kl_div import score\n\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\ndevice = torch.device(\"cuda\")\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1\"\n\n!cat /etc/os-release | grep -oP \"PRETTY_NAME=\\\"\\K([^\\\"]*)\"\nprint(f\"BUILD_DATE={os.environ['BUILD_DATE']}, CONTAINER_NAME={os.environ['CONTAINER_NAME']}\")\n\ntry:\n print(\n f\"PyTorch Version:{torch.__version__}, CUDA is available:{torch.cuda.is_available()}, Version CUDA:{torch.version.cuda}\"\n )\n print(\n f\"Device Capability:{torch.cuda.get_device_capability()}, {torch.cuda.get_arch_list()}\"\n )\n print(\n f\"CuDNN Enabled:{torch.backends.cudnn.enabled}, Version:{torch.backends.cudnn.version()}\"\n )\nexcept Exception:\n pass","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":6.369502,"end_time":"2024-03-06T22:48:46.577112","exception":false,"start_time":"2024-03-06T22:48:40.20761","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:51.30587Z","iopub.execute_input":"2025-01-28T17:09:51.3062Z","iopub.status.idle":"2025-01-28T17:09:52.35146Z","shell.execute_reply.started":"2025-01-28T17:09:51.306178Z","shell.execute_reply":"2025-01-28T17:09:52.350354Z"}},"outputs":[{"name":"stdout","text":"Ubuntu 20.04.6 LTS\nBUILD_DATE=20240222-122512, CONTAINER_NAME=tf2-gpu/2-15+cu121\nPyTorch Version:2.1.2, CUDA is available:True, Version CUDA:12.1\nDevice Capability:(6, 0), ['sm_60', 'sm_70', 'sm_75', 'compute_70', 'compute_75']\nCuDNN Enabled:True, Version:8900\n","output_type":"stream"}],"execution_count":14},{"cell_type":"markdown","source":"# Config","metadata":{"papermill":{"duration":0.019363,"end_time":"2024-03-06T22:48:46.616399","exception":false,"start_time":"2024-03-06T22:48:46.597036","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class CFG:\n VERSION = 88\n\n model_name = \"resnet1d_gru\"\n\n seed = 2024\n batch_size = 32\n num_workers = 0\n\n fixed_kernel_size = 5\n # kernels = [3, 5, 7, 9]\n # linear_layer_features = 424\n kernels = [3, 5, 7, 9, 11]\n #linear_layer_features = 448 # Full Signal = 10_000\n #linear_layer_features = 352 # Half Signal = 5_000\n linear_layer_features = 304 # 1/5 Signal = 2_000\n\n seq_length = 50 # Second's\n sampling_rate = 200 # Hz\n nsamples = seq_length * sampling_rate # Число семплов\n out_samples = nsamples // 5\n\n # bandpass_filter = {\"low\": 0.5, \"high\": 20, \"order\": 2}\n # rand_filter = {\"probab\": 0.1, \"low\": 10, \"high\": 20, \"band\": 1.0, \"order\": 2}\n freq_channels = [] # [(8.0, 12.0)]; [(0.5, 4.5)]\n filter_order = 2\n random_close_zone = 0.0 # 0.2\n \n target_cols = [\n \"seizure_vote\",\n \"lpd_vote\",\n \"gpd_vote\",\n \"lrda_vote\",\n \"grda_vote\",\n \"other_vote\",\n ]\n\n # target_preds = [x + \"_pred\" for x in target_cols]\n # label_to_num = {\"Seizure\": 0, \"LPD\": 1, \"GPD\": 2, \"LRDA\": 3, \"GRDA\": 4, \"Other\": 5}\n # num_to_label = {v: k for k, v in label_to_num.items()}\n\n map_features = [\n (\"Fp1\", \"T3\"),\n (\"T3\", \"O1\"),\n (\"Fp1\", \"C3\"),\n (\"C3\", \"O1\"),\n (\"Fp2\", \"C4\"),\n (\"C4\", \"O2\"),\n (\"Fp2\", \"T4\"),\n (\"T4\", \"O2\"),\n #('Fz', 'Cz'), ('Cz', 'Pz'), \n ]\n\n eeg_features = [\"Fp1\", \"T3\", \"C3\", \"O1\", \"Fp2\", \"C4\", \"T4\", \"O2\"] # 'Fz', 'Cz', 'Pz']\n # 'F3', 'P3', 'F7', 'T5', 'Fz', 'Cz', 'Pz', 'F4', 'P4', 'F8', 'T6', 'EKG'] \n feature_to_index = {x: y for x, y in zip(eeg_features, range(len(eeg_features)))}\n simple_features = [] # 'Fz', 'Cz', 'Pz', 'EKG'\n\n # eeg_features = [row for row in feature_to_index]\n # eeg_feat_size = len(eeg_features)\n \n n_map_features = len(map_features)\n in_channels = n_map_features + n_map_features * len(freq_channels) + len(simple_features)\n target_size = len(target_cols)\n \n PATH = \"/kaggle/input/hms-harmful-brain-activity-classification/\"\n test_eeg = \"/kaggle/input/hms-harmful-brain-activity-classification/test_eegs/\"\n test_csv = \"/kaggle/input/hms-harmful-brain-activity-classification/test.csv\"","metadata":{"papermill":{"duration":0.032961,"end_time":"2024-03-06T22:48:46.670637","exception":false,"start_time":"2024-03-06T22:48:46.637676","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.354218Z","iopub.execute_input":"2025-01-28T17:09:52.354606Z","iopub.status.idle":"2025-01-28T17:09:52.363822Z","shell.execute_reply.started":"2025-01-28T17:09:52.354568Z","shell.execute_reply":"2025-01-28T17:09:52.362594Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"koef_1 = 1.0\nmodel_weights = [\n {\n 'bandpass_filter':{'low':0.5, 'high':20, 'order':2}, \n 'file_data': \n [\n #{'koef':koef_1, 'file_mask':\"/kaggle/input/hms-resnet1d-gru-weights-v82/pop_1_weight_oof/*_best.pth\"},\n {'koef':koef_1, 'file_mask':\"/kaggle/input/hms-resnet1d-gru-weights-v82/pop_2_weight_oof/*_best.pth\"},\n ]\n },\n]","metadata":{"papermill":{"duration":0.026662,"end_time":"2024-03-06T22:48:46.717032","exception":false,"start_time":"2024-03-06T22:48:46.69037","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.365143Z","iopub.execute_input":"2025-01-28T17:09:52.36562Z","iopub.status.idle":"2025-01-28T17:09:52.377979Z","shell.execute_reply.started":"2025-01-28T17:09:52.36559Z","shell.execute_reply":"2025-01-28T17:09:52.377103Z"}},"outputs":[],"execution_count":16},{"cell_type":"markdown","source":"# Utils","metadata":{"papermill":{"duration":0.018978,"end_time":"2024-03-06T22:48:46.7552","exception":false,"start_time":"2024-03-06T22:48:46.736222","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def init_logger(log_file=\"./test.log\"):\n from logging import getLogger, INFO, FileHandler, Formatter, StreamHandler\n\n logger = getLogger(__name__)\n logger.setLevel(INFO)\n handler1 = StreamHandler()\n handler1.setFormatter(Formatter(\"%(message)s\"))\n handler2 = FileHandler(filename=log_file)\n handler2.setFormatter(Formatter(\"%(message)s\"))\n logger.addHandler(handler1)\n logger.addHandler(handler2)\n return logger\n\n\ndef asMinutes(s):\n m = math.floor(s / 60)\n s -= m * 60\n return \"%dm %ds\" % (m, s)\n\n\ndef timeSince(since, percent):\n now = time.time()\n s = now - since\n es = s / (percent)\n rs = es - s\n return \"%s (remain %s)\" % (asMinutes(s), asMinutes(rs))\n\n\ndef quantize_data(data, classes):\n mu_x = mu_law_encoding(data, classes)\n return mu_x # quantized\n\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\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\n\ndef butter_bandpass(lowcut, highcut, fs, order=5):\n return butter(order, [lowcut, highcut], fs=fs, btype=\"band\")\n\n\ndef butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n y = lfilter(b, a, data)\n return y\n\n\ndef butter_lowpass_filter(\n data, cutoff_freq=20, sampling_rate=CFG.sampling_rate, order=4\n):\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\n\ndef denoise_filter(x):\n # Частота дискретизации и желаемые частоты среза (в Гц).\n # Отфильтруйте шумный сигнал\n y = butter_bandpass_filter(x, CFG.lowcut, CFG.highcut, CFG.sampling_rate, order=6)\n y = (y + np.roll(y, -1) + np.roll(y, -2) + np.roll(y, -3)) / 4\n y = y[0:-1:4]\n return y","metadata":{"papermill":{"duration":0.037455,"end_time":"2024-03-06T22:48:46.811987","exception":false,"start_time":"2024-03-06T22:48:46.774532","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.380186Z","iopub.execute_input":"2025-01-28T17:09:52.380476Z","iopub.status.idle":"2025-01-28T17:09:52.392687Z","shell.execute_reply.started":"2025-01-28T17:09:52.380445Z","shell.execute_reply":"2025-01-28T17:09:52.391554Z"}},"outputs":[],"execution_count":17},{"cell_type":"markdown","source":"# Parquet to EEG Signals Numpy Processing","metadata":{"papermill":{"duration":0.018905,"end_time":"2024-03-06T22:48:46.850077","exception":false,"start_time":"2024-03-06T22:48:46.831172","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def eeg_from_parquet(\n parquet_path: str, display: bool = False, seq_length=CFG.seq_length\n) -> np.ndarray:\n \"\"\"\n Эта функция читает файл паркета и извлекает средние 50 секунд показаний. Затем он заполняет значения NaN\n со средним значением (игнорируя NaN).\n :param parquet_path: путь к файлу паркета.\n :param display: отображать графики ЭЭГ или нет.\n :return data: np.array формы (time_steps, eeg_features) -> (10_000, 8)\n \"\"\"\n\n # Вырезаем среднюю 50 секундную часть\n eeg = pd.read_parquet(parquet_path, columns=CFG.eeg_features)\n rows = len(eeg)\n\n # начало смещения данных, чтобы забрать середину\n offset = (rows - CFG.nsamples) // 2\n\n # средние 50 секунд, имеет одинаковое количество показаний слева и справа\n eeg = eeg.iloc[offset : offset + CFG.nsamples]\n\n if display:\n plt.figure(figsize=(10, 5))\n offset = 0\n\n # Конвертировать в numpy\n\n # создать заполнитель той же формы с нулями\n data = np.zeros((CFG.nsamples, len(CFG.eeg_features)))\n\n for index, feature in enumerate(CFG.eeg_features):\n x = eeg[feature].values.astype(\"float32\") # конвертировать в float32\n\n # Вычисляет среднее арифметическое вдоль указанной оси, игнорируя NaN.\n mean = np.nanmean(x)\n nan_percentage = np.isnan(x).mean() # percentage of NaN values in feature\n\n # Заполнение значения Nan\n # Поэлементная проверка на NaN и возврат результата в виде логического массива.\n if nan_percentage < 1: # если некоторые значения равны Nan, но не все\n x = np.nan_to_num(x, nan=mean)\n else: # если все значения — Nan\n x[:] = 0\n data[:, index] = x\n\n if display:\n if index != 0:\n offset += x.max()\n plt.plot(range(CFG.nsamples), x - offset, label=feature)\n offset -= x.min()\n\n if display:\n plt.legend()\n name = parquet_path.split(\"/\")[-1].split(\".\")[0]\n plt.yticks([])\n plt.title(f\"EEG {name}\", size=16)\n plt.show()\n return data","metadata":{"papermill":{"duration":0.033092,"end_time":"2024-03-06T22:48:46.90245","exception":false,"start_time":"2024-03-06T22:48:46.869358","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.394142Z","iopub.execute_input":"2025-01-28T17:09:52.394463Z","iopub.status.idle":"2025-01-28T17:09:52.410188Z","shell.execute_reply.started":"2025-01-28T17:09:52.394434Z","shell.execute_reply":"2025-01-28T17:09:52.40933Z"}},"outputs":[],"execution_count":18},{"cell_type":"markdown","source":"# Dataset","metadata":{"papermill":{"duration":0.018935,"end_time":"2024-03-06T22:48:46.940444","exception":false,"start_time":"2024-03-06T22:48:46.921509","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class EEGDataset(Dataset):\n def __init__(\n self,\n df: pd.DataFrame,\n batch_size: int,\n eegs: Dict[int, np.ndarray],\n mode: str = \"train\",\n downsample: int = None,\n bandpass_filter: Dict[str, Union[int, float]] = None,\n rand_filter: Dict[str, Union[int, float]] = None,\n ):\n self.df = df\n self.batch_size = batch_size\n self.mode = mode\n self.eegs = eegs\n self.downsample = downsample\n self.bandpass_filter = bandpass_filter\n self.rand_filter = rand_filter\n \n def __len__(self):\n \"\"\"\n Length of dataset.\n \"\"\"\n # Обозначает количество пакетов за эпоху\n return len(self.df)\n\n def __getitem__(self, index):\n \"\"\"\n Get one item.\n \"\"\"\n # Сгенерировать один пакет данных\n X, y_prob = self.__data_generation(index)\n if self.downsample is not None:\n X = X[:: self.downsample, :]\n output = {\n \"eeg\": torch.tensor(X, dtype=torch.float32),\n \"labels\": torch.tensor(y_prob, dtype=torch.float32),\n }\n return output\n\n def __data_generation(self, index):\n # Генерирует данные, содержащие образцы размера партии\n X = np.zeros(\n (CFG.out_samples, CFG.in_channels), dtype=\"float32\"\n ) # Size=(10000, 14)\n\n row = self.df.iloc[index] # Строка Pandas\n data = self.eegs[row.eeg_id] # Size=(10000, 8)\n if CFG.nsamples != CFG.out_samples:\n if self.mode != \"train\":\n offset = (CFG.nsamples - CFG.out_samples) // 2\n else:\n #offset = random.randint(0, CFG.nsamples - CFG.out_samples) \n offset = ((CFG.nsamples - CFG.out_samples) * random.randint(0, 1000)) // 1000\n data = data[offset:offset+CFG.out_samples,:]\n\n for i, (feat_a, feat_b) in enumerate(CFG.map_features):\n if self.mode == \"train\" and CFG.random_close_zone > 0 and random.uniform(0.0, 1.0) <= CFG.random_close_zone:\n continue\n \n diff_feat = (\n data[:, CFG.feature_to_index[feat_a]]\n - data[:, CFG.feature_to_index[feat_b]]\n ) # Size=(10000,)\n\n if not self.bandpass_filter is None:\n diff_feat = butter_bandpass_filter(\n diff_feat,\n self.bandpass_filter[\"low\"],\n self.bandpass_filter[\"high\"],\n CFG.sampling_rate,\n order=self.bandpass_filter[\"order\"],\n )\n \n if (\n self.mode == \"train\"\n and not self.rand_filter is None\n and random.uniform(0.0, 1.0) <= self.rand_filter[\"probab\"]\n ):\n lowcut = random.randint(\n self.rand_filter[\"low\"], self.rand_filter[\"high\"]\n )\n highcut = lowcut + self.rand_filter[\"band\"]\n diff_feat = butter_bandpass_filter(\n diff_feat,\n lowcut,\n highcut,\n CFG.sampling_rate,\n order=self.rand_filter[\"order\"],\n )\n\n X[:, i] = diff_feat\n\n n = CFG.n_map_features\n if len(CFG.freq_channels) > 0:\n for i in range(CFG.n_map_features):\n diff_feat = X[:, i]\n for j, (lowcut, highcut) in enumerate(CFG.freq_channels):\n band_feat = butter_bandpass_filter(\n diff_feat, lowcut, highcut, CFG.sampling_rate, order=CFG.filter_order, # 6\n )\n X[:, n] = band_feat\n n += 1\n\n for spml_feat in CFG.simple_features:\n feat_val = data[:, CFG.feature_to_index[spml_feat]]\n \n if not self.bandpass_filter is None:\n feat_val = butter_bandpass_filter(\n feat_val,\n self.bandpass_filter[\"low\"],\n self.bandpass_filter[\"high\"],\n CFG.sampling_rate,\n order=self.bandpass_filter[\"order\"],\n )\n\n if (\n self.mode == \"train\"\n and not self.rand_filter is None\n and random.uniform(0.0, 1.0) <= self.rand_filter[\"probab\"]\n ):\n lowcut = random.randint(\n self.rand_filter[\"low\"], self.rand_filter[\"high\"]\n )\n highcut = lowcut + self.rand_filter[\"band\"]\n feat_val = butter_bandpass_filter(\n feat_val,\n lowcut,\n highcut,\n CFG.sampling_rate,\n order=self.rand_filter[\"order\"],\n )\n\n X[:, n] = feat_val\n n += 1\n \n # Обрезать края превышающие значения [-1024, 1024]\n X = np.clip(X, -1024, 1024)\n\n # Замените NaN нулем и разделить все на 32\n X = np.nan_to_num(X, nan=0) / 32.0\n\n # обрезать полосовым фильтром верхнюю границу в 20 Hz.\n X = butter_lowpass_filter(X, order=CFG.filter_order) # 4\n\n y_prob = np.zeros(CFG.target_size, dtype=\"float32\") # Size=(6,)\n if self.mode != \"test\":\n y_prob = row[CFG.target_cols].values.astype(np.float32)\n\n return X, y_prob","metadata":{"papermill":{"duration":0.039699,"end_time":"2024-03-06T22:48:46.999292","exception":false,"start_time":"2024-03-06T22:48:46.959593","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.411047Z","iopub.execute_input":"2025-01-28T17:09:52.411549Z","iopub.status.idle":"2025-01-28T17:09:52.429018Z","shell.execute_reply.started":"2025-01-28T17:09:52.41152Z","shell.execute_reply":"2025-01-28T17:09:52.428137Z"}},"outputs":[],"execution_count":19},{"cell_type":"markdown","source":"# Model","metadata":{"papermill":{"duration":0.019359,"end_time":"2024-03-06T22:48:47.037758","exception":false,"start_time":"2024-03-06T22:48:47.018399","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class ResNet_1D_Block(nn.Module):\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size,\n stride,\n padding,\n downsampling,\n dilation=1,\n groups=1,\n dropout=0.0,\n ):\n super(ResNet_1D_Block, self).__init__()\n\n self.bn1 = nn.BatchNorm1d(num_features=in_channels)\n # self.relu = nn.ReLU(inplace=False)\n # self.relu_1 = nn.PReLU()\n # self.relu_2 = nn.PReLU()\n self.relu_1 = nn.Hardswish()\n self.relu_2 = nn.Hardswish()\n\n self.dropout = nn.Dropout(p=dropout, inplace=False)\n self.conv1 = nn.Conv1d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=False,\n )\n\n self.bn2 = nn.BatchNorm1d(num_features=out_channels)\n self.conv2 = nn.Conv1d(\n in_channels=out_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=False,\n )\n\n self.maxpool = nn.MaxPool1d(\n kernel_size=2,\n stride=2,\n padding=0,\n dilation=dilation,\n )\n self.downsampling = downsampling\n\n def forward(self, x):\n identity = x\n\n out = self.bn1(x)\n out = self.relu_1(out)\n out = self.dropout(out)\n out = self.conv1(out)\n out = self.bn2(out)\n out = self.relu_2(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 def __init__(\n self,\n kernels,\n in_channels,\n fixed_kernel_size,\n num_classes,\n linear_layer_features,\n dilation=1,\n groups=1,\n ):\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(\n in_channels=in_channels,\n out_channels=self.planes,\n kernel_size=(kernel_size),\n stride=1,\n padding=0,\n dilation=dilation,\n groups=groups,\n bias=False,\n )\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.relu_1 = nn.ReLU()\n # self.relu_2 = nn.ReLU()\n self.relu_1 = nn.SiLU()\n self.relu_2 = nn.SiLU()\n\n self.conv1 = nn.Conv1d(\n in_channels=self.planes,\n out_channels=self.planes,\n kernel_size=fixed_kernel_size,\n stride=2,\n padding=2,\n dilation=dilation,\n groups=groups,\n bias=False,\n )\n\n self.block = self._make_resnet_layer(\n kernel_size=fixed_kernel_size,\n stride=1,\n dilation=dilation,\n groups=groups,\n padding=fixed_kernel_size // 2,\n )\n self.bn2 = nn.BatchNorm1d(num_features=self.planes)\n self.avgpool = nn.AvgPool1d(kernel_size=6, stride=6, padding=2)\n\n self.rnn = nn.GRU(\n input_size=self.in_channels,\n hidden_size=128,\n num_layers=1,\n bidirectional=True,\n # dropout=0.2,\n )\n\n self.fc = nn.Linear(in_features=linear_layer_features, out_features=num_classes)\n\n def _make_resnet_layer(\n self,\n kernel_size,\n stride,\n dilation=1,\n groups=1,\n blocks=9,\n padding=0,\n dropout=0.0,\n ):\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(\n ResNet_1D_Block(\n in_channels=self.planes,\n out_channels=self.planes,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n downsampling=downsampling,\n dilation=dilation,\n groups=groups,\n dropout=dropout,\n )\n )\n return nn.Sequential(*layers)\n\n def extract_features(self, x):\n x = x.permute(0, 2, 1)\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_1(out)\n out = self.conv1(out)\n\n out = self.block(out)\n out = self.bn2(out)\n out = self.relu_2(out)\n out = self.avgpool(out)\n\n out = out.reshape(out.shape[0], -1)\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 return new_out\n\n def forward(self, x):\n new_out = self.extract_features(x)\n result = self.fc(new_out)\n return result","metadata":{"papermill":{"duration":0.047393,"end_time":"2024-03-06T22:48:47.104227","exception":false,"start_time":"2024-03-06T22:48:47.056834","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.430243Z","iopub.execute_input":"2025-01-28T17:09:52.430552Z","iopub.status.idle":"2025-01-28T17:09:52.452877Z","shell.execute_reply.started":"2025-01-28T17:09:52.430525Z","shell.execute_reply":"2025-01-28T17:09:52.451984Z"}},"outputs":[],"execution_count":20},{"cell_type":"markdown","source":"# Inference Function","metadata":{"papermill":{"duration":0.018741,"end_time":"2024-03-06T22:48:47.142333","exception":false,"start_time":"2024-03-06T22:48:47.123592","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def inference_function(test_loader, model, device):\n model.eval() # set model in evaluation mode\n softmax = nn.Softmax(dim=1)\n prediction_dict = {}\n preds = []\n with tqdm(test_loader, unit=\"test_batch\", desc=\"Inference\") as tqdm_test_loader:\n for step, batch in enumerate(tqdm_test_loader):\n X = batch.pop(\"eeg\").to(device) # send inputs to `device`\n batch_size = X.size(0)\n with torch.no_grad():\n y_preds = model(X) # forward propagation pass\n y_preds = softmax(y_preds)\n preds.append(y_preds.to(\"cpu\").numpy()) # save predictions\n\n prediction_dict[\"predictions\"] = np.concatenate(\n preds\n ) # np.array() of shape (fold_size, target_cols)\n return prediction_dict","metadata":{"papermill":{"duration":0.028725,"end_time":"2024-03-06T22:48:47.19001","exception":false,"start_time":"2024-03-06T22:48:47.161285","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.453961Z","iopub.execute_input":"2025-01-28T17:09:52.454202Z","iopub.status.idle":"2025-01-28T17:09:52.476911Z","shell.execute_reply.started":"2025-01-28T17:09:52.454183Z","shell.execute_reply":"2025-01-28T17:09:52.476001Z"}},"outputs":[],"execution_count":21},{"cell_type":"markdown","source":"# Load data","metadata":{"papermill":{"duration":0.018869,"end_time":"2024-03-06T22:48:47.22791","exception":false,"start_time":"2024-03-06T22:48:47.209041","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test_df = pd.read_csv(CFG.test_csv)\nprint(f\"Test dataframe shape is: {test_df.shape}\")\ntest_df.head()","metadata":{"papermill":{"duration":0.047928,"end_time":"2024-03-06T22:48:47.295126","exception":false,"start_time":"2024-03-06T22:48:47.247198","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.478061Z","iopub.execute_input":"2025-01-28T17:09:52.47839Z","iopub.status.idle":"2025-01-28T17:09:52.49892Z","shell.execute_reply.started":"2025-01-28T17:09:52.478363Z","shell.execute_reply":"2025-01-28T17:09:52.497981Z"}},"outputs":[{"name":"stdout","text":"Test dataframe shape is: (1, 3)\n","output_type":"stream"},{"execution_count":22,"output_type":"execute_result","data":{"text/plain":" spectrogram_id eeg_id patient_id\n0 853520 3911565283 6885","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
spectrogram_ideeg_idpatient_id
085352039115652836885
\n
"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"test_eeg_parquet_paths = glob(CFG.test_eeg + \"*.parquet\")\ntest_eeg_df = pd.read_parquet(test_eeg_parquet_paths[0])\ntest_eeg_features = test_eeg_df.columns\nprint(f\"There are {len(test_eeg_features)} raw eeg features\")\nprint(list(test_eeg_features))\ndel test_eeg_df\n_ = gc.collect()\n\n# %%time\nall_eegs = {}\neeg_ids = test_df.eeg_id.unique()\nfor i, eeg_id in tqdm(enumerate(eeg_ids)):\n # Save EEG to Python dictionary of numpy arrays\n eeg_path = CFG.test_eeg + str(eeg_id) + \".parquet\"\n data = eeg_from_parquet(eeg_path)\n all_eegs[eeg_id] = data","metadata":{"papermill":{"duration":0.358056,"end_time":"2024-03-06T22:48:47.673994","exception":false,"start_time":"2024-03-06T22:48:47.315938","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.501639Z","iopub.execute_input":"2025-01-28T17:09:52.501978Z","iopub.status.idle":"2025-01-28T17:09:52.688561Z","shell.execute_reply.started":"2025-01-28T17:09:52.50195Z","shell.execute_reply":"2025-01-28T17:09:52.687852Z"}},"outputs":[{"name":"stdout","text":"There are 20 raw eeg features\n['Fp1', 'F3', 'C3', 'P3', 'F7', 'T3', 'T5', 'O1', 'Fz', 'Cz', 'Pz', 'Fp2', 'F4', 'C4', 'P4', 'F8', 'T4', 'T6', 'O2', 'EKG']\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"527632c2051b47e2b08fe89796e67074"}},"metadata":{}}],"execution_count":23},{"cell_type":"markdown","source":"# Inference ","metadata":{"papermill":{"duration":0.019353,"end_time":"2024-03-06T22:48:47.713723","exception":false,"start_time":"2024-03-06T22:48:47.69437","status":"completed"},"tags":[]}},{"cell_type":"code","source":"koef_sum = 0\nkoef_count = 0\npredictions = []\nfiles = []\n \nfor model_block in model_weights:\n test_dataset = EEGDataset(\n df=test_df,\n batch_size=CFG.batch_size,\n mode=\"test\",\n eegs=all_eegs,\n bandpass_filter=model_block['bandpass_filter']\n )\n\n if len(predictions) == 0:\n output = test_dataset[0]\n X = output[\"eeg\"]\n print(f\"X shape: {X.shape}\")\n \n test_loader = DataLoader(\n test_dataset,\n batch_size=CFG.batch_size,\n shuffle=False,\n num_workers=CFG.num_workers,\n pin_memory=True,\n drop_last=False,\n )\n\n model = EEGNet(\n kernels=CFG.kernels,\n in_channels=CFG.in_channels,\n fixed_kernel_size=CFG.fixed_kernel_size,\n num_classes=CFG.target_size,\n linear_layer_features=CFG.linear_layer_features,\n )\n\n for file_line in model_block['file_data']:\n koef = file_line['koef']\n for weight_model_file in glob(file_line['file_mask']):\n files.append(weight_model_file)\n checkpoint = torch.load(weight_model_file, map_location=device)\n model.load_state_dict(checkpoint[\"model\"])\n model.to(device)\n prediction_dict = inference_function(test_loader, model, device)\n predict = prediction_dict[\"predictions\"]\n predict *= koef\n koef_sum += koef\n koef_count += 1\n predictions.append(predict)\n torch.cuda.empty_cache()\n gc.collect()\n\npredictions = np.array(predictions)\nkoef_sum /= koef_count\npredictions /= koef_sum\npredictions = np.mean(predictions, axis=0)","metadata":{"papermill":{"duration":2.029447,"end_time":"2024-03-06T22:48:49.76278","exception":false,"start_time":"2024-03-06T22:48:47.733333","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-01-28T17:09:52.689509Z","iopub.execute_input":"2025-01-28T17:09:52.689859Z","iopub.status.idle":"2025-01-28T17:09:53.636101Z","shell.execute_reply.started":"2025-01-28T17:09:52.689829Z","shell.execute_reply":"2025-01-28T17:09:53.635022Z"}},"outputs":[{"name":"stdout","text":"X shape: torch.Size([2000, 8])\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Inference: 0%| | 0/1 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
eeg_idseizure_votelpd_votegpd_votelrda_votegrda_voteother_vote
039115652830.0201950.0284990.0089360.0786580.1008360.762876
\n"},"metadata":{}}],"execution_count":26}]}