def update(cust_id, app_id): if len(meta_data) == 0: data.initialize_data() set_meta_data() meta_data[app_id].append(cust_id) print(meta_data)
torch.manual_seed(args.seed) workers = 1 # Check if CUDA is available if torch.cuda.is_available(): using_gpu = True print("GPU enabled") workers = 4 else: using_gpu = False print("GPU not enabled") ### Data Initialization and Loading from data import initialize_data, data_transforms, data_crop, data_rot, data_rotshear,\ data_transl, data_cropshear, data_jitter1, data_grey, data_jitter3, data_jitter4 # data.py in the same folder initialize_data(args.data) # extracts the zip files, makes a validation set train_loader = torch.utils.data.DataLoader( torch.utils.data.ConcatDataset([ datasets.ImageFolder(args.data + '/train_images', transform=data_crop), datasets.ImageFolder(args.data + '/train_images', transform=data_rot), datasets.ImageFolder(args.data + '/train_images', transform=data_jitter1), # datasets.ImageFolder(args.data + '/train_images', # transform=data_flip), # datasets.ImageFolder(args.data + '/train_images', # transform=data_grey), datasets.ImageFolder(args.data + '/train_images', transform=data_rotshear), datasets.ImageFolder(args.data + '/train_images', transform=data_transl),
import os from flask import Flask, render_template, request, Response, json import pandas as pd import data import logic import validator app = Flask(__name__) app.secret_key = os.urandom(24) cdi_data = data.initialize_data() @app.route('/') def chart(): return render_template('chart.html') @app.route('/api', methods=["GET", "POST"]) def api(): try: input_data = request.json if request.method == 'POST' else request.args start, end, cdb = validator.validate_request(input_data) result = logic.calculate_cdb_for_period(cdi_data, cdb, start, end) response = result.to_json(orient='records')
edit_data_button.clicked.connect(lambda: open_edit_dialog()) actions_vbox.addWidget(edit_data_button) # Exit button exit_button = QPushButton("Exit") exit_button.clicked.connect(lambda: app.exit(0)) actions_vbox.addWidget(exit_button) return actions_vbox def get_main_layout(): layout = QHBoxLayout() layout.addLayout(get_selection_vbox()) layout.addStretch() layout.addLayout(get_actions_vbox()) return layout if __name__ == "__main__": app = QApplication(sys.argv) main_window = QWidget() main_window.setWindowTitle("Deployment Helper") data.initialize_data() edit_add_dialog.get_customer_drop_down() main_window.setLayout(get_main_layout()) main_window.show() app.exec() # sys.exit(app.exec())
import torch import matplotlib.pyplot as plt from torchvision import datasets import multiprocessing from cutout import save_image_tensor2pillow data_path = 'data/data0' save_dir = './footage/' from data import initialize_data, data_jitter_brightness initialize_data(data_path) # extracts the zip files, makes a validation set train_loader = torch.utils.data.DataLoader( torch.utils.data.ConcatDataset([ datasets.ImageFolder(data_path + '/train_images', transform=data_jitter_brightness) ]), batch_size=1, shuffle=True, num_workers=multiprocessing.cpu_count(), pin_memory=True) for batch_idx, (data, target) in enumerate(train_loader): print(type(data), target) target = target.to(torch.device('cpu')).type(torch.uint8).numpy() save_path = save_dir + format(target[0], '05d') + '.png' img = save_image_tensor2pillow(data, save_path, 'pil', True) plt.figure("img") plt.imshow(img) plt.show() break
help='SGD momentum (default: 0.5)') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--model_file', type=str, default=None, metavar='PU', help='pick up where you were (default: None)') parser.add_argument('--model_name', type=str, default='model', metavar='MN', help='name of the model file (default: model)') args = parser.parse_args() torch.manual_seed(args.seed) ### Data Initialization and Loading from data import initialize_data, data_transforms # data.py in the same folder train_images, train_labels, val_images, val_labels = initialize_data(args.data) # extracts the zip files, makes a validation set train_dataset = torch.utils.data.TensorDataset(train_images, train_labels) val_dataset = torch.utils.data.TensorDataset(val_images, val_labels) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4)
metavar='M', help="the model file to be evaluated. Usually it is of the form model_X.pth" ) parser.add_argument('--outfile', type=str, default='gtsrb_kaggle.csv', metavar='D', help="name of the output csv file") args = parser.parse_args() state_dict = torch.load(args.model) model = Net() model.load_state_dict(state_dict) model.eval() initialize_data(args.data) from data import data_transforms test_dir = args.data + '/test_images' def pil_loader(path): # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open(path, 'rb') as f: with Image.open(f) as img: return img.convert('RGB') output_file = open(args.outfile, "w") output_file.write("Filename,ClassId\n") for f in tqdm(os.listdir(test_dir)):
for i, img in enumerate(aug_imgs[class_id]): img_path = os.path.join(class_path, '{:05d}'.format(i) + '.png') io.imsave(img_path, img) # We also need to pre-process the test set def sharp_img(source_dir, dst_dir): img_paths = glob.glob(os.path.join(source_dir, '*.ppm')) if not os.path.isdir(dst_dir): print(dst_dir + ' not found, expanding it') os.mkdir(dst_dir) for img_path in img_paths: img = io.imread(img_path) img = hist_norm(img) save_path = os.path.join(dst_dir, os.path.basename(img_path)) io.imsave(save_path, img) from data import initialize_data initialize_data('data') # extracts the zip files, makes a validation set # In[5]: aug_img_set(4000, 'data/train_images', 'data/train_aug_images') # In[6]: aug_img_set(100, 'data/val_images', 'data/val_aug_images') # In[7]: sharp_img('data/test_images', 'data/test_sharp_images')