{"cells":[{"metadata":{"id":"tcqZTpsPdn-v","trusted":false},"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, SubsetRandomSampler\nfrom torch.utils.tensorboard import SummaryWriter\nimport pandas as pd\nimport numpy as np\nfrom tqdm.notebook import tqdm\nfrom sklearn.model_selection import train_test_split\nfrom datetime import datetime, date\n\ntorch.manual_seed(0)\nnp.random.seed(0)\n\n# TRAIN_PATH = 'train.csv'\n# TEST_PATH = 'test.csv'\n# SUBMISSION_PATH = 'submission.csv'\n# DIG_PATH = 'Dig-MNIST.csv'\nTRAIN_PATH = '../input/Kannada-MNIST/train.csv'\nTEST_PATH = '../input/Kannada-MNIST/test.csv'\nSUBMISSION_PATH = '/kaggle/working/submission.csv'\nDIG_PATH = '../input/Kannada-MNIST/Dig-MNIST.csv'","execution_count":null,"outputs":[]},{"metadata":{"id":"wbDMugN2d61R","outputId":"a870b1e1-0bba-41be-b1d4-d605318755e4","trusted":false},"cell_type":"code","source":"# !kaggle competitions download -c Kannada-MNIST\n# !unzip Kannada-MNIST.zip","execution_count":null,"outputs":[]},{"metadata":{"id":"4m9dODEzhUvK","trusted":false},"cell_type":"code","source":"class KannadaMNISTDataset(Dataset):\n    def __init__(self, csv_path, transform):\n        data = pd.read_csv(csv_path).to_numpy()\n        self.y = data[:, 0]\n        self.X = data[:, 1:].reshape(-1, 28, 28).astype(np.uint8)\n        assert self.y.shape[0] == self.X.shape[0]\n        self.transform = transform\n\n    def __len__(self):\n        return self.X.shape[0]\n\n    def __getitem__(self, idx):\n        return self.transform(self.X[idx]), self.y[idx]\n    \n    def asPIL(self, idx):\n        return transforms.ToPILImage()(self.X[idx]), self.y[idx]\n    \n    def merge(self, other):\n        self.X = np.concatenate((self.X, other.X))\n        self.y = np.concatenate((self.y, other.y))","execution_count":null,"outputs":[]},{"metadata":{"id":"gxXjWd8dk6rB","trusted":false},"cell_type":"code","source":"transform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.Resize((32, 32)),\n    transforms.RandomAffine(degrees=10, translate=(0.25, 0.25), scale=(0.75, 1.25), shear=0.1),\n    transforms.ToTensor()\n])\ndataset = KannadaMNISTDataset(TRAIN_PATH, transform)\ndataset.merge(KannadaMNISTDataset(DIG_PATH, transform))\n\ntrain_idx, test_idx = train_test_split(range(len(dataset)), train_size=0.85)\ntrain_sampler = SubsetRandomSampler(train_idx)\ntest_sampler = SubsetRandomSampler(test_idx)\n\nBATCH_SIZE = 512\ntrain_loader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=8, pin_memory=True)\ntest_loader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, sampler=test_sampler, num_workers=8, pin_memory=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"class SimpleNet(nn.Module):\n    def __init__(self):\n        super(SimpleNet, self).__init__()\n        def make_conv_group(in_, out_):\n            return nn.Sequential(\n                nn.Conv2d(in_, out_, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n                nn.BatchNorm2d(out_, momentum=0.05),\n                nn.ReLU(inplace=True)\n            )\n        \n        self.layers = nn.Sequential(\n            make_conv_group(1, 64),\n            make_conv_group(64, 128),\n            make_conv_group(128, 128),\n            make_conv_group(128, 128),\n            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),\n            nn.Dropout2d(p=0.1),\n            make_conv_group(128, 128),\n            make_conv_group(128, 128),\n            make_conv_group(128, 256),\n            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),\n            nn.Dropout2d(p=0.1),\n            make_conv_group(256, 256),\n            make_conv_group(256, 256),\n            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),\n            nn.Dropout2d(p=0.1),\n            make_conv_group(256, 512),\n            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),\n            nn.Dropout2d(p=0.1),\n            make_conv_group(512, 2048),\n            make_conv_group(2048, 256),\n            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),\n            nn.Dropout2d(p=0.1),\n            make_conv_group(256, 256)\n        )\n        self.head = nn.Linear(256, 10)\n        \n        for layer in self.layers.modules():\n          if isinstance(layer, nn.Conv2d):\n            nn.init.xavier_uniform_(layer.weight.data, gain=nn.init.calculate_gain('relu'))\n            \n    def forward(self, x):\n        x = self.layers(x)\n        x = x.view(x.size(0), -1)\n        x = self.head(x)\n        x = nn.Softmax(dim=1)(x)\n        return x","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"net = SimpleNet().cuda()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(net.parameters())\nscheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', threshold=1e-5, verbose=True)\nEPOCH = 0","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"for epoch in tqdm(range(EPOCH, EPOCH + 100), leave=False):\n    net.train()\n    for batch_x, batch_y in tqdm(train_loader, leave=False):\n        optimizer.zero_grad()\n\n        outputs = net(batch_x.cuda())\n        loss = criterion(outputs, batch_y.cuda())\n        loss.backward()\n        optimizer.step()\n  \n    test_loss = 0.0\n    net.eval()\n    for batch_x, batch_y in tqdm(test_loader, leave=False):\n        outputs = net(batch_x.cuda())\n        loss = criterion(outputs, batch_y.cuda())\n\n        test_loss += loss.item()\n    test_loss /= len(test_idx)\n    \n    scheduler.step(test_loss)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"test_dataset = KannadaMNISTDataset(TEST_PATH, transform)\nloader = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE, num_workers=8, pin_memory=True)\ntest_answers = None\nnet.eval()\nfor batch_x, _ in tqdm(loader):\n    outputs = net(batch_x.cuda()).cpu().detach().numpy()\n    if test_answers is not None:\n        test_answers = np.concatenate((test_answers, outputs))\n    else:\n        test_answers = outputs\ngood_ind = test_answers.max(axis=1) >= 0.95\ngood_labels = test_answers[good_ind].argmax(axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"pseudo_dataset = KannadaMNISTDataset(TEST_PATH, transform)\npseudo_dataset.X = pseudo_dataset.X[good_ind]\npseudo_dataset.y = good_labels\npseudo_loader = torch.utils.data.DataLoader(pseudo_dataset, batch_size=BATCH_SIZE, num_workers=8, pin_memory=True)\n\nfor lr in [1e-4, 1e-5, 1e-6]:\n    for g in optimizer.param_groups:\n        g['lr'] = lr\n    for epoch in tqdm(range(EPOCH, EPOCH + 100), leave=False):\n        net.train()\n        for batch_x, batch_y in tqdm(pseudo_loader, leave=False):\n            optimizer.zero_grad()\n\n            outputs = net(batch_x.cuda())\n            loss = criterion(outputs, batch_y.cuda())\n            loss.backward()\n            optimizer.step()\n\n        EPOCH += 1","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"test_transform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.Resize((32, 32)),\n    transforms.ToTensor()\n])\ntest_dataset = KannadaMNISTDataset(TEST_PATH, test_transform)\nloader = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE)\n\nresults = []\nnet.eval()\nfor batch_x, _ in tqdm(loader):\n    outputs = net(batch_x.cuda())\n    results.extend(map(lambda x: x.item(), outputs.argmax(dim=1)))\n\nsubmission = pd.DataFrame({\n    'id': range(len(test_dataset)),\n    'label': results\n})\nsubmission.to_csv(SUBMISSION_PATH, index=False)","execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"name":"Untitled7.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 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