{"cells":[{"metadata":{},"cell_type":"markdown","source":"Korte introductie, en doel van stage."},{"metadata":{},"cell_type":"markdown","source":"# Doelstelling\n\nAan de hand van PANDA een model of ecosysteem van modellen te ontwikkelen die de gleason score kan voorspellen adhv een digitalisering van een biopsis. (als supplement)"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"collapsed":true,"_kg_hide-output":true,"_kg_hide-input":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n# DATA visualization\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport PIL\nfrom IPython.display import Image, display\nfrom plotly import graph_objs as go\nimport plotly.express as px\nimport plotly.figure_factory as ff\n\nimport openslide","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"!pip install efficientnet_pytorch git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git\nimport time\nimport skimage.io\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport PIL.Image\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SubsetRandomSampler, RandomSampler, SequentialSampler\nfrom warmup_scheduler import GradualWarmupScheduler\nfrom efficientnet_pytorch import model as enet\nimport albumentations\nfrom sklearn.model_selection import StratifiedKFold\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import cohen_kappa_score\n#from tqdm import tqdm_notebook as tqdm\nfrom tqdm.notebook import tqdm","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Exploratory Data Analysis"},{"metadata":{},"cell_type":"markdown","source":"Wat uitleggen over de dataset en het doel ervan is. Zeggen waar het vandaag komt, welke staining, dat het wholeslide images zijn (tiff)"},{"metadata":{},"cell_type":"markdown","source":"## Inladen van data"},{"metadata":{"trusted":true},"cell_type":"code","source":"BASE_FOLDER = \"/kaggle/input/prostate-cancer-grade-assessment/\"\n\ntrain = pd.read_csv(BASE_FOLDER+\"train.csv\")\ntest = pd.read_csv(BASE_FOLDER+\"test.csv\")\nsub = pd.read_csv(BASE_FOLDER+\"sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Metadata info tonen"},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"unique ids : \", len(train.image_id.unique()))\nprint(\"unique data provider : \", len(train.data_provider.unique()))\nprint(\"unique isup_grade(target) : \", len(train.isup_grade.unique()))\nprint(\"unique gleason_score : \", len(train.gleason_score.unique()))\n\nprint(train['gleason_score'].unique())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Er zijn rond de 11k wholeslide images (.tiff) van 2 verschillende centra die kunnen gehanteerd worden voor het trainen van een neuraal netwerk."},{"metadata":{"trusted":true},"cell_type":"code","source":"print(train[train['gleason_score']=='3+4']['isup_grade'].unique())\nprint(train[train['gleason_score']=='4+3']['isup_grade'].unique())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train[(train['isup_grade'] == 2) & (train['gleason_score'] == '4+3')]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"> er is sprake van verkeerde data -> verwijderen ervan"},{"metadata":{"trusted":true},"cell_type":"code","source":"train.drop([7273],inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['gleason_score'].unique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(train[train['gleason_score']=='0+0']['isup_grade'].unique())\nprint(train[train['gleason_score']=='negative']['isup_grade'].unique())\n\nprint(len(train[train['gleason_score']=='0+0']['isup_grade']))\nprint(len(train[train['gleason_score']=='negative']['isup_grade']))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Negative ziet er uit hetzelfde te zijn als 0+0 -> tesamen voegen"},{"metadata":{"trusted":true},"cell_type":"code","source":"train['gleason_score'] = train['gleason_score'].apply(lambda x: \"0+0\" if x==\"negative\" else x)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Bekijken Data"},{"metadata":{},"cell_type":"markdown","source":"Uit leggen van ISUP en Gleason, tonen dat er research gegaan is in het domein, zelf is het maar elementair."},{"metadata":{},"cell_type":"markdown","source":"### ISUP\nIn 2014, the International Society of Urological Pathology released supplementary guidance and a revised prostate cancer grading system called the ISUP Grade Groups. The ISUP Grade Group system is simpler, with just five grades, 1 to 5.\n\nUit https://tackleprostate.org/the-gleason-score.php\n\n![afbeelding.png](attachment:afbeelding.png)","attachments":{"afbeelding.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"temp = train.groupby('isup_grade').count()['image_id'].reset_index().sort_values(by='image_id',ascending=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.bar(temp, x='isup_grade', y='image_id',\n             hover_data=['image_id', 'isup_grade'], color='image_id',\n             labels={'image_id':'Amount of images', 'isup_grade':'ISUP Grade'}, height=400)\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Praten over de verdeeldheid in de code, de imbalans van de data. Vermelden dat het model hier rekening mee zal houden en misschien andere oplossingen aankaarten (zoals gewoon opsporen kanker of niet)"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.pie(temp, names='isup_grade', values='image_id', title='Pie-Chart of ISUP_grade Distribution',\n             labels={'image_id':'Amount of images', 'isup_grade':'ISUP Grade'}, color='isup_grade')\nfig.update_traces(textposition='inside', textinfo='percent+label')\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Er zal een bias zijn naar 0 en 1 -> balanceren of weigthen van data?\nMisschien beter kijken naar Gleason score?"},{"metadata":{},"cell_type":"markdown","source":">Data providers"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize=(10,6))\nax = sns.countplot(x=\"isup_grade\", hue=\"data_provider\", data=train)\nfor p in ax.patches:\n    '''\n    Courtesy of Rohit Singh for teaching me this\n    https://www.kaggle.com/rohitsingh9990/panda-eda-better-visualization-simple-baseline\n    '''\n    height = p.get_height()\n    ax.text(p.get_x()+p.get_width()/2,\n                height +3,\n                '{:1.2f}%'.format(100*height/10616),\n                ha=\"center\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Tonen waar de data vandaan komt."},{"metadata":{},"cell_type":"markdown","source":"### Gleason Score\nThis is given after a pathologist has examined under a microscope cancerous tissue obtained from the needle biopsy. The cells identified are given a grade number from 1 to 5, depending on the abnormality of the cells, 1 being the lowest, 5 the highest. The grades of the two most common patterns are added together to give a score from 2 to 10. The higher the score, the more aggressive and fast-growing the cancer. Scores totalling 5 or less are insignificant and are not reported.\n\n\n    A Gleason score of 6 (cells are well differentiated) is ‘favourable’\n    A Gleason score of 7 (cells are moderately differentiated) is ‘average’\n    A Gleason Score of 8–10 (cells are poorly differentiated) is ‘adverse’\n\n\nUit https://tackleprostate.org/the-gleason-score.php"},{"metadata":{"trusted":true},"cell_type":"code","source":"temp = train.groupby('gleason_score').count()['image_id'].reset_index().sort_values(by='image_id',ascending=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.bar(temp, x='gleason_score', y='image_id',\n             hover_data=['image_id', 'gleason_score'], color='image_id',\n             labels={'image_id':'Amount of images', 'gleason_score':'Gleason Score'}, height=400)\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Zorgen dat in het trainen de verhouding behouden wordt zonder bias te introduceren. Stratified K-fold."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.pie(temp, names='gleason_score', values='image_id', title='Pie-Chart of Gleason Grade Distribution',\n             labels={'image_id':'Amount of images', 'gleason_score':'Gleason Score'}, color='gleason_score')\nfig.update_traces(textposition='inside', textinfo='percent+label')\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":">Data providers"},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\n'''\nVisualizing the GLEASON_SCORE distribution wrt Data_providers\n'''\n\nfig = plt.figure(figsize=(10,6))\nax = sns.countplot(x=\"gleason_score\", hue=\"data_provider\", data=train)\nfor p in ax.patches:\n    height = p.get_height()\n    ax.text(p.get_x()+p.get_width()/2.,\n                height + 3,\n                '{:1.2f}%'.format(100*height/10616),\n                ha=\"center\")\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Werken met OpenSlide"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(train.loc[lambda train: train['image_id'] == '3752b697cae9f81a9d5ffe44dac58e7a', 'gleason_score'].item())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def show_images(image_batch, max_size=(600,400)):\n    f,axs =  plt.subplots(3,3,figsize=(16,16))\n    counter = {'0': 0, '1': 0}\n    for afbeelding in image_batch:\n        slide = openslide.OpenSlide(os.path.join(BASE_FOLDER+\"train_images\", f'{afbeelding}.tiff'))\n        #print(counter)\n        axs[counter['0'], counter['1']].imshow(slide.get_thumbnail(size=max_size)) #UNZOOMED FIGURE\n        axs[counter['0'], counter['1']].set_title(\n            f\"Gleason grade: {train.loc[lambda train: train['image_id'] == afbeelding, 'gleason_score'].item()}\\nISUP score: {train.loc[lambda train: train['image_id'] == afbeelding, 'isup_grade'].item()}\")\n        \n        if counter['0'] == 2:\n            counter['1'] += 1\n            counter['0'] = 0\n        else:\n            counter['0'] += 1\n    \n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Wat foto's van de verschillende centra tonen. Polsen of de coupes in Az Delta er gelijkaardig uitzien."},{"metadata":{},"cell_type":"markdown","source":"## Radboud"},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_radboud = train[train['data_provider']=='radboud']['image_id'].sample(9)\nshow_images(batch_radboud)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Karolinska"},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_karolinska = train[train['data_provider']=='karolinska']['image_id'].sample(9)\nshow_images(batch_karolinska)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Image to tiles"},{"metadata":{"trusted":true},"cell_type":"code","source":"DEBUG = True\n\ndata_dir = '../input/prostate-cancer-grade-assessment'\ntrain = pd.read_csv(os.path.join(data_dir, 'train.csv'))\nimage_folder = os.path.join(data_dir, 'train_images')\n\nkernel_type = 'how_to_train_effnet_b0_to_get_LB_0.86'\n\nenet_type = 'efficientnet-b0'\nfold = 0\ntile_size = 128\nimage_size = 128\nn_tiles = 16\nbatch_size = 1\nnum_workers = 0\nout_dim = 5\ninit_lr = 3e-4\nwarmup_factor = 10\n\nwarmup_epo = 1\nn_epochs = 1 if DEBUG else 30\ntrain = train.sample(100).reset_index(drop=True) if DEBUG else train\n\n#device = torch.device('cuda')\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nprint(image_folder)\nprint(device)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!nvidia-smi","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Van https://www.kaggle.com/iafoss/panda-16x128x128-tiles"},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_tiles(img, sz=128, N=16):\n    result = []\n    shape = img.shape\n    pad0,pad1 = (sz - shape[0]%sz)%sz, (sz - shape[1]%sz)%sz\n    img = np.pad(img,[[pad0//2,pad0-pad0//2],[pad1//2,pad1-pad1//2],[0,0]],\n                constant_values=255)\n    img = img.reshape(img.shape[0]//sz,sz,img.shape[1]//sz,sz,3)\n    img = img.transpose(0,2,1,3,4).reshape(-1,sz,sz,3)\n    # Padden als er nog niet genoeg tiles aantwezig zijn (met volledig witte tiles)\n    if len(img) < N:\n        img = np.pad(img,[[0,N-len(img)],[0,0],[0,0],[0,0]],constant_values=255)\n    # Reshapen naar M x (3 x sz x sz) dan laatste dimensie sommeren = M values -> sorteren en de N laagste pakken \n    # (die hebben het minste aantal witte pixels = value 255)\n    idxs = np.argsort(img.reshape(img.shape[0],-1).sum(-1))[:N]\n    img = img[idxs]\n    for i in range(len(img)):\n        result.append({'img':img[i], 'idx':i})\n    return result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"skf = StratifiedKFold(5, shuffle=True, random_state=42)\ntrain['fold'] = -1\nfor i, (train_idx, valid_idx) in enumerate(skf.split(train, train['isup_grade'])):\n    try:\n        train.loc[valid_idx, 'fold'] = i\n    except Exception as exc:\n        print(exc)\n        continue\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class PANDADataset(Dataset):\n    def __init__(self,\n                df,\n                image_size,\n                n_tiles=16,\n                tile_mode=0,\n                rand=False,\n                transform=None):\n        \n        self.df = df.reset_index(drop=True)\n        self.image_size = image_size\n        self.n_tiles = n_tiles\n        self.tile_mode = tile_mode\n        self.rand = rand\n        self.transform = transform\n    \n    def __len__(self):\n        return self.df.shape[0]\n\n    def __getitem__(self, index):\n        row = self.df.iloc[index]\n        img_id = row.image_id\n        \n        tiff_file = os.path.join(image_folder, f'{img_id}.tiff')\n        image = skimage.io.MultiImage(tiff_file)[0]\n        tiles = get_tiles(image, sz=self.image_size, N=self.n_tiles)\n\n        if self.rand:\n            idxes = np.random.choice(list(range(self.n_tiles)), self.n_tiles, replace=False)\n        else:\n            idxes = list(range(self.n_tiles))\n\n        n_row_tiles = int(np.sqrt(self.n_tiles))\n        images = np.zeros((image_size * n_row_tiles, image_size * n_row_tiles, 3))\n        for h in range(n_row_tiles):\n            for w in range(n_row_tiles):\n                i = h * n_row_tiles + w\n    \n                if len(tiles) > idxes[i]:\n                    this_img = tiles[idxes[i]]['img']\n                else:\n                    this_img = np.ones((self.image_size, self.image_size, 3)).astype(np.uint8) * 255\n                this_img = 255 - this_img\n                if self.transform is not None:\n                    this_img = self.transform(image=this_img)['image']\n                h1 = h * image_size\n                w1 = w * image_size\n                images[h1:h1+image_size, w1:w1+image_size] = this_img\n\n        if self.transform is not None:\n            images = self.transform(image=images)['image']\n        images = images.astype(np.float32)\n        images /= 255\n        images = images.transpose(2, 0, 1)\n\n        label = np.zeros(5).astype(np.float32)\n        label[:row.isup_grade] = 1.\n        return torch.tensor(images), torch.tensor(label)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pretrained_model = {\n    'efficientnet-b0': '../input/efficientnet-pytorch/efficientnet-b0-08094119.pth'\n}\n\n\n\nclass enetv2(nn.Module):\n    def __init__(self, backbone, out_dim):\n        super(enetv2, self).__init__()\n        self.enet = enet.EfficientNet.from_name(backbone)\n        self.enet.load_state_dict(torch.load(pretrained_model[backbone]))\n\n        self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)\n        self.enet._fc = nn.Identity()\n\n    def extract(self, x):\n        return self.enet(x)\n\n    def forward(self, x):\n        x = self.extract(x)\n        x = self.myfc(x)\n        return x\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"transforms_train = albumentations.Compose([\n    albumentations.Transpose(p=0.5),\n    albumentations.VerticalFlip(p=0.5),\n    albumentations.HorizontalFlip(p=0.5),\n])\ntransforms_val = albumentations.Compose([])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Tiling tonen"},{"metadata":{},"cell_type":"markdown","source":"Een methode om de overbodige data te verwijderen en alle foto's naar dezelfde dimensie te krijgen.\n\nVerlies aan data? Grootte van windows aanpassen? Zijn er gehele structuren die verloren gaan?"},{"metadata":{"trusted":true},"cell_type":"code","source":"dataset_show = PANDADataset(train, image_size, n_tiles, 0, transform=transforms_train)\nfrom pylab import rcParams\nrcParams['figure.figsize'] = 20,10\nfor i in range(2):\n    f, axarr = plt.subplots(1,5)\n    for p in range(5):\n        idx = np.random.randint(0, len(dataset_show))\n        img, label = dataset_show[idx]\n        axarr[p].imshow(1. - img.transpose(0, 1).transpose(1,2).squeeze())\n        axarr[p].set_title(str(sum(label)))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Planning voor later uitleggen.\n\n* Modellen van Kaggle hanteren en \"hertrainen\" op de data van Az Delta.\n* Zelf modellen maken hier een aanpassen voor Az Delta\n* Hier technieken leren en deze dan combineren met de expertise van doktoren om in Az Delta een model te bouwen \n\nVragen:\n\n* Grootte van data\n* Consistentie van data (dezelfde dimensies?)\n* Geen te groot verlies aan data door tiling? Stretchen van afbeelding zeker geen goed idee?\n* Opslag, transfer van data voorzien? Waarmee trainen?"},{"metadata":{},"cell_type":"markdown","source":"### Model maken"},{"metadata":{"trusted":true},"cell_type":"code","source":"criterion = nn.BCEWithLogitsLoss()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train = train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def train_epoch(loader, optimizer):\n\n    model.train()\n    train_loss = []\n    bar = tqdm(loader)\n    for (data, target) in bar:\n        \n        data, target = data.to(device), target.to(device)\n        loss_func = criterion\n        optimizer.zero_grad()\n        logits = model(data)\n        loss = loss_func(logits, target)\n        loss.backward()\n        optimizer.step()\n\n        loss_np = loss.detach().cpu().numpy()\n        train_loss.append(loss_np)\n        smooth_loss = sum(train_loss[-100:]) / min(len(train_loss), 100)\n        bar.set_description('loss: %.5f, smth: %.5f' % (loss_np, smooth_loss))\n    return train_loss\n\n\ndef val_epoch(loader, get_output=False):\n\n    model.eval()\n    val_loss = []\n    LOGITS = []\n    PREDS = []\n    TARGETS = []\n\n    with torch.no_grad():\n        for (data, target) in tqdm(loader):\n            data, target = data.to(device), target.to(device)\n            logits = model(data)\n\n            loss = criterion(logits, target)\n\n            pred = logits.sigmoid().sum(1).detach().round()\n            LOGITS.append(logits)\n            PREDS.append(pred)\n            TARGETS.append(target.sum(1))\n\n            val_loss.append(loss.detach().cpu().numpy())\n        val_loss = np.mean(val_loss)\n\n    LOGITS = torch.cat(LOGITS).cpu().numpy()\n    PREDS = torch.cat(PREDS).cpu().numpy()\n    TARGETS = torch.cat(TARGETS).cpu().numpy()\n    acc = (PREDS == TARGETS).mean() * 100.\n    \n    qwk = cohen_kappa_score(PREDS, TARGETS, weights='quadratic')\n    qwk_k = cohen_kappa_score(PREDS[df_valid['data_provider'] == 'karolinska'], df_valid[df_valid['data_provider'] == 'karolinska'].isup_grade.values, weights='quadratic')\n    qwk_r = cohen_kappa_score(PREDS[df_valid['data_provider'] == 'radboud'], df_valid[df_valid['data_provider'] == 'radboud'].isup_grade.values, weights='quadratic')\n    print('qwk', qwk, 'qwk_k', qwk_k, 'qwk_r', qwk_r)\n\n    if get_output:\n        return LOGITS\n    else:\n        return val_loss, acc, qwk","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_idx = np.where((df_train['fold'] != fold))[0]\nvalid_idx = np.where((df_train['fold'] == fold))[0]\n\ndf_this  = df_train.loc[train_idx]\ndf_valid = df_train.loc[valid_idx]\n\ndataset_train = PANDADataset(df_this , image_size, n_tiles, transform=transforms_train)\ndataset_valid = PANDADataset(df_valid, image_size, n_tiles, transform=transforms_val)\n\ntrain_loader = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, sampler=RandomSampler(dataset_train), num_workers=num_workers)\nvalid_loader = torch.utils.data.DataLoader(dataset_valid, batch_size=batch_size, sampler=SequentialSampler(dataset_valid), num_workers=num_workers)\n\nmodel = enetv2(enet_type, out_dim=out_dim)\nmodel = model.to(device)\n\noptimizer = optim.Adam(model.parameters(), lr=init_lr/warmup_factor)\nscheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, n_epochs-warmup_epo)\nscheduler = GradualWarmupScheduler(optimizer, multiplier=warmup_factor, total_epoch=warmup_epo, after_scheduler=scheduler_cosine)\n\nprint(len(dataset_train), len(dataset_valid))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"qwk_max = 0.\nbest_file = f'{kernel_type}_best_fold{fold}.pth'\nfor epoch in range(1, n_epochs+1):\n    print(time.ctime(), 'Epoch:', epoch)\n    scheduler.step(epoch-1)\n\n    train_loss = train_epoch(train_loader, optimizer)\n    val_loss, acc, qwk = val_epoch(valid_loader)\n\n    content = time.ctime() + ' ' + f'Epoch {epoch}, lr: {optimizer.param_groups[0][\"lr\"]:.7f}, train loss: {np.mean(train_loss):.5f}, val loss: {np.mean(val_loss):.5f}, acc: {(acc):.5f}, qwk: {(qwk):.5f}'\n    print(content)\n    #with open(f'log_{kernel_type}.txt', 'a') as appender:\n    #    appender.write(content + '\\n')\n\n    if qwk > qwk_max:\n        print('score2 ({:.6f} --> {:.6f}).  Saving model ...'.format(qwk_max, qwk))\n        #torch.save(model.state_dict(), best_file)\n        qwk_max = qwk\n\n#torch.save(model.state_dict(), os.path.join(f'{kernel_type}_final_fold{fold}.pth'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"qwk_max = 0.\nbest_file = f'{kernel_type}_best_fold{fold}.pth'\nfor epoch in range(1, n_epochs+1):\n    print(time.ctime(), 'Epoch:', epoch)\n    scheduler.step(epoch-1)\n\n    train_loss = train_epoch(train_loader, optimizer)\n    val_loss, acc, qwk = val_epoch(valid_loader)\n\n    content = time.ctime() + ' ' + f'Epoch {epoch}, lr: {optimizer.param_groups[0][\"lr\"]:.7f}, train loss: {np.mean(train_loss):.5f}, val loss: {np.mean(val_loss):.5f}, acc: {(acc):.5f}, qwk: {(qwk):.5f}'\n    print(content)\n    #with open(f'log_{kernel_type}.txt', 'a') as appender:\n    #    appender.write(content + '\\n')\n\n    if qwk > qwk_max:\n        print('score2 ({:.6f} --> {:.6f}).  Saving model ...'.format(qwk_max, qwk))\n        #torch.save(model.state_dict(), best_file)\n        qwk_max = qwk\n\n#torch.save(model.state_dict(), os.path.join(f'{kernel_type}_final_fold{fold}.pth'))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# JJShadow emsemble"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Modellen inladen en predicten op "},{"metadata":{"trusted":true},"cell_type":"code","source":"Shujun_PRED_LIST=[]\nfor pred_list_index in range(len(ensemble_shujun_list[:-2])):\n    Shujun_PRED_LIST.append([])\n\n\nfor phase_index, ensemble_recipe in enumerate(ensemble_shujun_list[:-2]):\n    print(\"shujun_ensemble_phase\",phase_index)\n\n    MODELS=[]\n    top=3\n    num_classes=[6,4,4,]\n\n    if ensemble_recipe['tileImageSize'] == 288:\n        print(\"top_fold is 3\")\n        top_fold = 3 ## just 2 phase.. 4th ensemble model's top fold is 3(20x288x288)\n    else:\n        top_fold = 2\n    for i in range(top_fold,top_fold+1):\n        for j in range(top):\n            \n            if ensemble_recipe['TILE_SIZE'] == 64: ## janey network.\n                if debug:\n                    print(\"Janey Network\")\n                model = JaneyNetwork(num_classes).to(device)\n            else:\n                model = Network(num_classes,arch = ensemble_recipe['arch']).to(device)\n            model = model.to(device)\n            model = nn.DataParallel(model)\n            weights_paths=ensemble_recipe['path'].format(i,j+1)\n            if debug:\n                print(weights_paths)\n            model.load_state_dict(torch.load(weights_paths))\n            model.eval()\n            MODELS.append(model) \n\n    ####shujun code output ###\n\n    total_images=0\n    ## janey model's batch is 8 for OOM\n    if ensemble_recipe['TILE_SIZE'] == 64 or ensemble_recipe['tileImageSize'] == 288: ## janey network.\n        batch_size=8\n    else:\n        batch_size=16\n    if debug:\n            print(\"batch_size\",batch_size)\n    if len(df)%batch_size==0:\n        batches=int(len(df)/batch_size)\n    else:\n        batches=int(len(df)/batch_size)+1\n    model.eval()\n    predictions=[]\n    mean=torch.Tensor(ensemble_recipe['mean']).to(device).reshape(1,1,3,1,1)\n    std=torch.Tensor(ensemble_recipe['std']).to(device).reshape(1,1,3,1,1)\n    if debug:\n        print(mean)\n        print(std)\n    with torch.no_grad():\n        for i in tqdm(range(batches)):\n            paths=df.image_id[i*batch_size:(i+1)*batch_size].to_list()\n            x=get_batch_of_image(paths, sz = ensemble_recipe['tileImageSize'], N = ensemble_recipe['TILE_SIZE'], phase=phase_index, index_df = df)\n            x0=torch.Tensor(x).to(device)\n            x_h=torch.Tensor(x).to(device).flip(-1)\n            x_v=torch.Tensor(x).to(device).flip(-2)\n            x_vh=torch.Tensor(x).to(device).flip(-1,-2)\n            x0t=torch.Tensor(x).to(device).transpose(-1,-2)\n            x_ht=torch.Tensor(x).to(device).flip(-1).transpose(-1,-2)\n            x_vt=torch.Tensor(x).to(device).flip(-2).transpose(-1,-2)\n            x_vht=torch.Tensor(x).to(device).flip(-1,-2).transpose(-1,-2)\n            outputs=[]\n            isCutThresholdTile = ensemble_recipe['isCutThreshold']\n            if debug:\n                print('isCutThresholdTile',isCutThresholdTile)\n            for model in MODELS:\n                output = model(standardize_batch(x0,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x_h,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x_v,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x_vh,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x0t,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x_ht,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x_vt,mean,std,isCutThresholdTile=isCutThresholdTile))[0]+\\\n                         model(standardize_batch(x_vht,mean,std,isCutThresholdTile=isCutThresholdTile))[0]\n\n\n                output=output.cpu().numpy()/8\n                outputs.append(output)\n            outputs=np.asarray(outputs)\n            outputs=np.mean(outputs,axis=0)\n\n            predictions.append(output)\n    for pred in predictions:\n        Shujun_PRED_LIST[phase_index].extend(pred)\n    \n    del output, predictions ### fix OOM memory issue.. hope..\n    torch.cuda.empty_cache()\n\n\n#### here shujun ensemble.. ####\nif debug:\n    for check_p_index in range(len(Shujun_PRED_LIST)):\n        print(len(Shujun_PRED_LIST[check_p_index]))\n    print(\"at last ensemble phase\")\n\nshujun_pred_list = np.asarray(Shujun_PRED_LIST)\n\nprint(\"shape\",shujun_pred_list.shape)\nif debug:\n    print(Aksell_PRED_LIST) \n    print(Shujun_PRED_LIST) \ntotal_list = np.vstack((np.asarray(Aksell_PRED_LIST),np.asarray(Shujun_PRED_LIST))).transpose()\n#shujun_ensemble_preds = np.asarray(PRED_LIST).mean(axis=0) ## shujun ensemble here","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Werken met TPU\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Zelf proberen\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data_dir = '../input/prostate-cancer-grade-assessment'\ntrain = pd.read_csv(os.path.join(data_dir, 'train.csv'))\nimage_folder = os.path.join(data_dir, 'train_images')\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nprint(image_folder)\nprint(device)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}