{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch\nimport pytorch_lightning as pl\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader, random_split\nimport torchvision\nfrom torchvision.datasets import ImageFolder\nimport torchvision.transforms as transforms","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"TRAIN_ROOT = '/kaggle/input/acc-german-traffic-sign-classification/GTSRB_Challenge/train/'\n\nTRAIN_TRANSFORMS = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n    transforms.RandomRotation(30),\n    torchvision.transforms.Resize((64, 64), interpolation=2)\n])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = ImageFolder(root=TRAIN_ROOT, transform=TRAIN_TRANSFORMS)\ntrain_d, val_d = random_split(data, [36000, 3209])\ntrain_loader = DataLoader(train_d, shuffle=True, batch_size=128, num_workers=8)\nval_loader = DataLoader(val_d, batch_size=128, num_workers=8)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class Net(pl.LightningModule):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.fc = nn.Sequential(\n            nn.Flatten(),\n            nn.Linear(64*64*3, 64*3*8),\n            nn.ReLU(),\n            nn.Linear(64*3*8, 64*2),\n            nn.ReLU(),\n            nn.Linear(64*2, 43)\n        )\n    \n    def forward(self, x):\n        return self.fc(x)\n    \n    def configure_optimizers(self):\n        opt = torch.optim.Adam(self.parameters())\n        return opt\n    \n    def training_step(self, batch, idx):\n        x, y = batch\n        pred = self(x)\n        loss = F.cross_entropy(pred, y)\n        return loss\n    \n    def validation_step(self, batch, idx):\n        x, y = batch\n        pred = self(x)\n        loss = F.cross_entropy(pred, y)\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Net()\ntrainer = pl.Trainer(gpus=1, max_epochs=3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"trainer.fit(model, train_loader, val_loader)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!nvidia-smi","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"correct = 0\ntotal = 0\nmodel.cuda()\nwith torch.no_grad():\n    for data in val_loader:\n        images, labels = data\n        images = images.cuda()\n        labels = labels.cuda()\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\nprint('Accuracy of the network on the 10000 test images: %d %%' % (\n    100 * correct / total))","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}