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torch\ntorch.cuda.is_available()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pathlib import Path\nimport random\n\nimport numpy as np\nimport pandas as pd\nimport time\nimport os\nimport matplotlib.pyplot as plt\n# These transformations will be passed to our model class\nimport torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport yaml\nfrom tqdm.auto import tqdm\nimport glob\nfrom torch.distributions import Beta","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:08:56.352260Z","iopub.status.busy":"2021-12-13T10:08:56.351445Z","iopub.status.idle":"2021-12-13T10:09:03.240479Z","shell.execute_reply":"2021-12-13T10:09:03.240930Z","shell.execute_reply.started":"2021-12-13T10:07:32.051211Z"},"papermill":{"duration":6.939158,"end_time":"2021-12-13T10:09:03.241098","exception":false,"start_time":"2021-12-13T10:08:56.301940","status":"completed"},"scrolled":true,"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"INPUT_DIR = '../input/unlabeled_np'","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from joblib import Parallel, delayed\nimport os\nfrom os.path import exists\n\nWAV_SIZE=2000\nSTEP_SIZE=500\nTIMES_REAL=4\nTIMES_TRAIN=8\nis_mixed_precision = True\n\nclass GaitDataset(torch.utils.data.Dataset):\n\n    def __init__(self, df, is_train=False,transforms=None):\n        self.is_train = is_train\n        self.data = df\n\n    def __len__(self):\n        if self.is_train:\n            return len(self.data)*TIMES_TRAIN\n        else:\n            return len(self.data)\n    \n    \n    def __getitem__(self, idx):\n        if self.is_train:\n            idx = np.random.randint(0,len(self.data))\n            \n        row = self.data.iloc[idx]\n\n        wav = np.load(row.file_name)['arr'][:,1:4]\n            \n        wav = wav/40.\n        \n        start_ix = np.random.randint(0, len(wav) - WAV_SIZE -1 )\n        label = wav[start_ix+WAV_SIZE+1]\n        \n        wav = wav[start_ix:start_ix+WAV_SIZE]\n        \n        sample = {\"wav\": wav, \"label\":label}\n        \n        #print('label',label.shape,tgt.shape)\n        #print('wav',wav.shape)\n\n        return sample\n        \ndef collate_wrapper(batch):\n    out = {}\n    wavs = []\n    labels = []\n    s_ix1s = []\n    e_ix1s = []\n    for item in batch:\n        wavs.append(item['wav'])\n        labels.append(item['label'])\n        \n    out['wav'] = torch.from_numpy(np.concatenate(wavs,axis=0))\n    out['label'] = torch.from_numpy(np.concatenate(labels,axis=0))\n    \n    return out\n\ndef getDataLoader(params,train_x,val_x,train_transforms=None,val_transforms=None):\n    \n    train_dataset = GaitDataset(\n            df=train_x, is_train=True, transforms=train_transforms\n        )\n    val_dataset = GaitDataset(df=val_x, transforms=val_transforms)\n    \n    trainDataLoader = torch.utils.data.DataLoader(\n                            train_dataset,\n                            batch_size=params['batch_size'],\n                            num_workers=params['num_workers'],\n                            shuffle=True,\n                            pin_memory=False,\n                            worker_init_fn=lambda id: np.random.seed(torch.initial_seed() // 2 ** 32 + id)\n                        )\n    valDataLoader = torch.utils.data.DataLoader(\n                        val_dataset,\n                        batch_size=params['batch_size'],\n                        num_workers=params['num_workers'],\n                        shuffle=False,\n                        pin_memory=False,\n                    )\n    \n    return trainDataLoader,valDataLoader","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:03.371417Z","iopub.status.busy":"2021-12-13T10:09:03.369851Z","iopub.status.idle":"2021-12-13T10:09:03.372002Z","shell.execute_reply":"2021-12-13T10:09:03.372415Z","shell.execute_reply.started":"2021-12-13T10:07:40.115136Z"},"papermill":{"duration":0.04712,"end_time":"2021-12-13T10:09:03.372538","exception":false,"start_time":"2021-12-13T10:09:03.325418","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class Mixup(nn.Module):\n    def __init__(self, mix_beta=1):\n\n        super(Mixup, self).__init__()\n        self.beta_distribution = Beta(mix_beta, mix_beta)\n\n    def forward(self, X, Y, weight=None):\n\n        bs = X.shape[0]\n        n_dims = len(X.shape)\n        perm = torch.randperm(bs)\n        coeffs = self.beta_distribution.rsample(torch.Size((bs,))).to(X.device)\n\n        if n_dims == 2:\n            X = coeffs.view(-1, 1) * X + (1 - coeffs.view(-1, 1)) * X[perm]\n        elif n_dims == 3:\n            X = coeffs.view(-1, 1, 1) * X + (1 - coeffs.view(-1, 1, 1)) * X[perm]\n        else:\n            X = coeffs.view(-1, 1, 1, 1) * X + (1 - coeffs.view(-1, 1, 1, 1)) * X[perm]\n\n        Y = coeffs.view(-1, 1,1) * Y + (1 - coeffs.view(-1, 1, 1)) * Y[perm]\n\n        if weight is None:\n            return X, Y\n        else:\n            weight = coeffs.view(-1) * weight + (1 - coeffs.view(-1)) * weight[perm]\n            return X, Y, weight","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class Wave_Block(nn.Module):\n\n    def __init__(self, in_channels, out_channels, dilation_rates, kernel_size):\n        super(Wave_Block, self).__init__()\n        self.num_rates = dilation_rates\n        self.convs = nn.ModuleList()\n        self.filter_convs = nn.ModuleList()\n        self.gate_convs = nn.ModuleList()\n\n        self.convs.append(nn.Conv1d(in_channels, out_channels, kernel_size=1))\n        dilation_rates = [2 ** i for i in range(dilation_rates)]\n        for dilation_rate in dilation_rates:\n            self.filter_convs.append(\n                nn.Conv1d(out_channels, out_channels, kernel_size=kernel_size, padding=int((dilation_rate*(kernel_size-1))/2), dilation=dilation_rate))\n            self.gate_convs.append(\n                nn.Conv1d(out_channels, out_channels, kernel_size=kernel_size, padding=int((dilation_rate*(kernel_size-1))/2), dilation=dilation_rate))\n            self.convs.append(nn.Conv1d(out_channels, out_channels, kernel_size=1))\n\n    def forward(self, x):\n        x = self.convs[0](x)\n        res = x\n        for i in range(self.num_rates):\n            x = torch.tanh(self.filter_convs[i](x)) * torch.sigmoid(self.gate_convs[i](x))\n            x = self.convs[i + 1](x)\n            res = res + x\n        return res\n# detail \nclass Classifier(nn.Module):\n    def __init__(self, inch=3, kernel_size=3):\n        super().__init__()\n        self.LSTM = nn.GRU(input_size=128, hidden_size=128, num_layers=2, \n                           batch_first=True, bidirectional=True, dropout = 0.1)\n        \n        #self.wave_block1 = Wave_Block(inch, 16, 12, kernel_size)\n        self.wave_block2 = Wave_Block(inch, 32, 8, kernel_size)\n        self.wave_block3 = Wave_Block(32, 64, 4, kernel_size)\n        self.wave_block4 = Wave_Block(64, 128, 1, kernel_size)\n        self.fc = nn.Linear(256, 3)\n    \n\n    def forward(self, x):\n        x = x.permute(0, 2, 1)\n        #x = self.wave_block1(x)\n        x = self.wave_block2(x)\n        x = self.wave_block3(x)\n\n        x = self.wave_block4(x)\n        x = x.permute(0, 2, 1)\n        x, h = self.LSTM(x)\n        \n        x = x[:,-1,:]\n        x = self.fc(x)\n    \n        \n        return x","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.cuda.amp as amp\nclass AmpNet(Classifier):\n    \n    def __init__(self,params):\n        super(AmpNet, self).__init__()\n    @torch.cuda.amp.autocast()\n    def forward(self,*args):\n        return super(AmpNet, self).forward(*args)\n\nis_mixed_precision = True  #True #False","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:03.911096Z","iopub.status.busy":"2021-12-13T10:09:03.910123Z","iopub.status.idle":"2021-12-13T10:09:03.957868Z","shell.execute_reply":"2021-12-13T10:09:03.957316Z","shell.execute_reply.started":"2021-12-13T10:07:40.273717Z"},"papermill":{"duration":0.080072,"end_time":"2021-12-13T10:09:03.957995","exception":false,"start_time":"2021-12-13T10:09:03.877923","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def getOptimzersScheduler(model,params,steps_in_epoch=25,pct_start=0.1):\n    \n    \n    mdl_parameters = [\n                {'params': model.parameters(), 'lr': 1e-4},\n                #{'params': model.fc.parameters(), 'lr': 1e-4},\n                #{'params': model.attention.parameters(), 'lr': 1e-5},\n            ]\n    \n    optimizer = torch.optim.Adam(mdl_parameters, lr=params['learning_rate'][0])\n    \n    scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer,steps_per_epoch=1,\n                                                    pct_start=pct_start,\n                                                    max_lr=params['learning_rate'],\n                                                    epochs  = params['max_epochs'], \n                                                    div_factor = params['div_factor'], \n                                                    final_div_factor=params['final_div_factor'],\n                                                    verbose=True)\n    \n    return optimizer,scheduler,False","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.013462Z","iopub.status.busy":"2021-12-13T10:09:04.011182Z","iopub.status.idle":"2021-12-13T10:09:04.015765Z","shell.execute_reply":"2021-12-13T10:09:04.015309Z","shell.execute_reply.started":"2021-12-13T10:07:40.336045Z"},"papermill":{"duration":0.032959,"end_time":"2021-12-13T10:09:04.015870","exception":false,"start_time":"2021-12-13T10:09:03.982911","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def save_model(epoch,model,ckpt_path='./',name='',val_rmse=0):\n    path = os.path.join(ckpt_path, '{}_{}.pth'.format(name, epoch))\n    torch.save(model.state_dict(), path, _use_new_zipfile_serialization=False)\n    \ndef load_model(model,ckpt_path):\n    state = torch.load(ckpt_path)\n    model.load_state_dict(state,strict=False)\n    return model","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.070210Z","iopub.status.busy":"2021-12-13T10:09:04.069584Z","iopub.status.idle":"2021-12-13T10:09:04.072171Z","shell.execute_reply":"2021-12-13T10:09:04.071787Z","shell.execute_reply.started":"2021-12-13T10:07:40.351127Z"},"papermill":{"duration":0.032012,"end_time":"2021-12-13T10:09:04.072285","exception":false,"start_time":"2021-12-13T10:09:04.040273","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"CLASS_WT = torch.tensor([0.4,1,1,1]).float().cuda()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def training_step(model, batch, batch_idx,optimizer,scheduler,isStepScheduler=False):\n    # Load images and labels\n    x = batch[\"wav\"].float()\n    y = batch[\"label\"].float()\n    \n    #print('x',x.shape,y.shape)\n    \n    if GPU:\n        x, y  = x.cuda(non_blocking=True), y.cuda(non_blocking=True)\n\n    criterion = torch.nn.L1Loss(reduction=\"sum\") \n\n    #criterion = FocalLoss()\n    \n    #optimizer.zero_grad()\n    iters_to_accumulate=2\n    # Forward \n\n    if is_mixed_precision:\n        with amp.autocast():\n            preds = model(x)\n            loss = criterion(preds,y)/ iters_to_accumulate\n            \n            scaler.scale(loss).backward()\n            \n            if (batch_idx + 1) % iters_to_accumulate == 0:\n                #print('accumulating')\n            # may unscale_ here if desired (e.g., to allow clipping unscaled gradients)\n                scaler.unscale_(optimizer)\n                scaler.step(optimizer)\n                scaler.update()\n                optimizer.zero_grad()\n\n            \n            loss = loss.item()\n    else:\n        preds = model(x,att_mask)\n        loss = criterion(preds.flatten(), y.flatten())\n        loss.backward()\n        optimizer.step()\n        loss = loss.item()\n        \n    if isStepScheduler:\n        scheduler.step()\n\n    # Calculate validation IOU (global)\n    #preds = preds.detach()\n    #y = y.detach().cpu()\n    return loss\n\ndef validation_step(model, batch, batch_idx):\n    # Load images and labels\n    x = batch[\"wav\"].float()\n    y = batch[\"label\"].float()\n    \n    #print('x',x.shape,y.shape)\n    \n    iters_to_accumulate= 2\n    \n    if GPU:\n        x, y = x.cuda(non_blocking=True), y.cuda(non_blocking=True)\n\n    criterion = torch.nn.L1Loss(reduction=\"sum\") \n    #criterion = FocalLoss()\n    \n\n    # Forward pass & softmax\n    with torch.no_grad():\n        if is_mixed_precision:\n            with amp.autocast():\n                preds = model(x)\n                loss = criterion(preds, y)/ iters_to_accumulate        \n    \n    loss = loss.item()\n    return loss,preds.detach().cpu().numpy(),y.long().detach().cpu().numpy()\n","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.191528Z","iopub.status.busy":"2021-12-13T10:09:04.190722Z","iopub.status.idle":"2021-12-13T10:09:04.193071Z","shell.execute_reply":"2021-12-13T10:09:04.192683Z","shell.execute_reply.started":"2021-12-13T10:07:40.375185Z"},"papermill":{"duration":0.040406,"end_time":"2021-12-13T10:09:04.193173","exception":false,"start_time":"2021-12-13T10:09:04.152767","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import roc_auc_score,f1_score,precision_score,average_precision_score\ndef train_epoch(model,trainDataLoader,optimizer,scheduler,isStepScheduler=True):\n    total_intersection=0\n    total_union=0\n    total_loss=0\n    model.train()\n    torch.set_grad_enabled(True)\n    total_step=0\n    ious = []\n    \n    \n    pbar = tqdm(enumerate(trainDataLoader),total=len(trainDataLoader))\n    for bi,data in pbar:\n        loss= training_step(model,data,bi,optimizer,scheduler)\n        total_loss+=loss\n        total_step+=1\n        pbar.set_postfix({'loss':total_loss/total_step})\n        \n    if not isStepScheduler: #in case epoch based scheduler\n        scheduler.step()\n            \n    total_loss /= total_step\n    return total_loss\n        \n\ndef val_epoch(model,valDataLoader):\n    total_intersection=0\n    total_union=0\n    total_loss=0\n    \n    total_step=0\n    model.eval()\n    preds = []\n    targets = []\n    pbar=tqdm(enumerate(valDataLoader),total=len(valDataLoader))\n    for bi,data in pbar :\n        loss, pred ,tgt = validation_step(model,data,bi)\n        total_loss+=loss\n        total_step+=1\n        preds.extend(pred)\n        targets.extend(tgt)\n        \n        pbar.set_postfix({'loss':total_loss/total_step})\n        \n\n    \n    del targets,preds\n    gc.collect()\n\n    total_loss /= total_step\n    return total_loss","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.253299Z","iopub.status.busy":"2021-12-13T10:09:04.251736Z","iopub.status.idle":"2021-12-13T10:09:04.253898Z","shell.execute_reply":"2021-12-13T10:09:04.254284Z","shell.execute_reply.started":"2021-12-13T10:07:40.398637Z"},"papermill":{"duration":0.036917,"end_time":"2021-12-13T10:09:04.254423","exception":false,"start_time":"2021-12-13T10:09:04.217506","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GPU=True\ndef training_loop(params,train_x,val_x,savedir='./',mdl_name='resnet34'):\n    \n    #create model\n    model = AmpNet(params).cuda()\n    #load model\n    #get loaders\n    train_transforms=None\n    val_transforms = None\n    trainDataLoader,valDataLoader = getDataLoader(params,train_x,val_x,train_transforms,val_transforms)\n    \n    optimizer,scheduler,isStepScheduler = getOptimzersScheduler(model,params,\n                                                                steps_in_epoch=len(trainDataLoader),\n                                                                pct_start=0.1)\n    best_loss= 999\n    #control loop\n    for e in range(params['max_epochs']):\n        train_loss = train_epoch(model,trainDataLoader,optimizer,scheduler,isStepScheduler)\n        loss = val_epoch(model,valDataLoader)\n        #logging here\n        #print(e,'Train Result',f'loss={train_loss}')\n        print(e,'Val Result',f'loss={best_loss} ')\n        if loss < best_loss :\n            print(f'Saving for loss {best_loss}')\n            save_model(e,model,ckpt_path=savedir,name=mdl_name,val_rmse=best_loss)\n            best_loss=loss\n        else:\n            print(f'Not Saving for AP {loss}')\n            \n        if e>17:\n            break\n        ","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.366733Z","iopub.status.busy":"2021-12-13T10:09:04.366045Z","iopub.status.idle":"2021-12-13T10:09:04.368153Z","shell.execute_reply":"2021-12-13T10:09:04.368549Z","shell.execute_reply.started":"2021-12-13T10:07:40.433804Z"},"papermill":{"duration":0.033772,"end_time":"2021-12-13T10:09:04.368672","exception":false,"start_time":"2021-12-13T10:09:04.334900","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hparams = {\n    # Optional hparams\n    \"backbone\": 'wavenet_4096', #'', #'tf_efficientnetv2_b2',\n    \"learning_rate\": [5e-4],\n    \"max_epochs\": 21,\n    \"batch_size\": 64,\n    \"num_workers\": 0,\n    \"val_sanity_checks\": 0,\n    \"fast_dev_run\": False,\n    \"output_path\": f\"\",\n    \"gpu\": torch.cuda.is_available(),\n    'div_factor':1,\n    'final_div_factor':20,\n}","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.419756Z","iopub.status.busy":"2021-12-13T10:09:04.418956Z","iopub.status.idle":"2021-12-13T10:09:04.424162Z","shell.execute_reply":"2021-12-13T10:09:04.423750Z","shell.execute_reply.started":"2021-12-13T10:07:40.448083Z"},"papermill":{"duration":0.031307,"end_time":"2021-12-13T10:09:04.424259","exception":false,"start_time":"2021-12-13T10:09:04.392952","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"scaler = amp.GradScaler()","metadata":{"execution":{"iopub.execute_input":"2021-12-13T10:09:04.579019Z","iopub.status.busy":"2021-12-13T10:09:04.578065Z","iopub.status.idle":"2021-12-13T10:09:04.580331Z","shell.execute_reply":"2021-12-13T10:09:04.580784Z","shell.execute_reply.started":"2021-12-13T10:07:40.520165Z"},"papermill":{"duration":0.034696,"end_time":"2021-12-13T10:09:04.580936","exception":false,"start_time":"2021-12-13T10:09:04.546240","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\nimport random\nseed=42\ndef set_seed(seed=42):\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = False\n    torch.use_deterministic_algorithms = True\n    random.seed(0)\n    np.random.seed(0)\nset_seed(seed)\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(f'{INPUT_DIR}/unlabeled_metadata.csv')","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import KFold, StratifiedKFold, StratifiedGroupKFold,GroupKFold\n#kf = GroupKFold(n_splits=5,random_state=42,shuffle=True)\nkf = GroupKFold(n_splits=5)\nfor i, (train_index, test_index) in enumerate(kf.split(train.file_name,train.file_name,groups=train.base_name)):\n    train.loc[test_index,'fold'] =i\n    \n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import gc\nversion='1_pretrain'\nfn=0\nfor fn in [0]:  \n    set_seed()\n\n    mdl_name=hparams['backbone']\n    savedir = f'trained-models-{mdl_name}-v{version}'\n    Path(savedir).mkdir(exist_ok=True, parents=True)\n    \n    val = train[train.fold==fn]\n    tr = train[train.fold!=fn]\n    \n    print('Train',tr.shape,'Val',val.shape)\n    \n   \n    training_loop(hparams,tr,val,savedir=savedir,mdl_name=f'{mdl_name}-fold{fn}')\n    gc.collect()\n\n    #break","metadata":{"papermill":{"duration":0.04818,"end_time":"2021-12-13T13:17:52.100451","exception":false,"start_time":"2021-12-13T13:17:52.052271","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}