{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(os.listdir('../input/generative-dog-images'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!unzip ../input/generative-dog-images/all-dogs.zip -d /kaggle/working/ > /dev/null","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"PATH = '/kaggle/working/all-dogs/'\nimages = os.listdir(PATH)\ndef plotDog():\n    fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12,10))\n\n    for indx, axis in enumerate(axes.flatten()):\n        rnd_indx = np.random.randint(0, len(os.listdir(PATH)))\n        img = plt.imread(PATH + images[rnd_indx])\n        imgplot = axis.imshow(img)\n        axis.set_title(images[rnd_indx])\n        axis.set_axis_off()\n    # plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n    return plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plotDog()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(f'There are {len(images)} pictures of dogs.')\n\nfig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12,10))\n\nfor indx, axis in enumerate(axes.flatten()):\n    rnd_indx = np.random.randint(0, len(os.listdir(PATH)))\n    img = plt.imread(PATH + images[rnd_indx])\n    imgplot = axis.imshow(img)\n    axis.set_title(images[rnd_indx])\n    axis.set_axis_off()\nplt.tight_layout(rect=[0, 0.03, 1, 0.95])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size = 32\nimage_size = 64\n\nrandom_transforms = [transforms.ColorJitter(), transforms.RandomRotation(degrees=20)]\ntransform = transforms.Compose([transforms.Resize(64),\n                                transforms.CenterCrop(64),\n                                transforms.RandomHorizontalFlip(p=0.5),\n                                transforms.RandomApply(random_transforms, p=0.2),\n                                transforms.ToTensor(),\n                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\ntrain_data = dset.ImageFolder('/kaggle/working/', transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=batch_size)\n                                           \nimgs, label = next(iter(train_loader))\nimgs = imgs.numpy().transpose(0, 2, 3, 1)","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":1}