type=int, default=128) parser.add_argument( '--numberOfTrainingImages', help= 'The maximum number of training images (Default: 0, which means no limit)', type=int, default=0) args = parser.parse_args() args.cuda = not args.disable_cuda and torch.cuda.is_available() imageSize = ast.literal_eval(args.imageSize) loader = Loader.Importer( args.trainDirectory, args.numberOfTrainingImages + args.numberOfValidationImages) trainFilepathToClassDic, validationFilepathToClassDic = loader.SplitForTrainAndValidation( args.numberOfTrainingImages, args.numberOfValidationImages) trainFilepaths = [*trainFilepathToClassDic] validationFilepaths = [*validationFilepathToClassDic] print("len(trainFilepaths) = {}; len(validationFilepaths) = {}".format( len(trainFilepaths), len(validationFilepaths))) # Create a neural network and an optimizer if args.architecture == 'ConvStack_3_3_32_7_2_32_7_2_32_7_2_12_256_0.5': structureElements = ConvStackClassifier.ExtractStructureFromFilename( args.architecture) neuralNet = ConvStackClassifier.NeuralNet(structureElements[2], structureElements[0], structureElements[3],
help='The learning rate (Default: 0.001)', type=float, default=0.001) parser.add_argument('--momentum', help='The learning momentum (Default: 0.9)', type=float, default=0.9) parser.add_argument('--minibatchSize', help='The minibatch size (Default: 32)', type=int, default=32) args = parser.parse_args() args.cuda = not args.disable_cuda and torch.cuda.is_available() loader = Loader.Importer(os.path.join(args.baseDirectory, 'train')) testDirectory = os.path.join(args.baseDirectory, 'test') imageFilepaths = [ os.path.join(testDirectory, f) for f in os.listdir(testDirectory) if os.path.isfile(os.path.join(testDirectory, f)) ] #print ("imageFilepaths =", imageFilepaths) # Create a neural network and an optimizer if args.architecture == 'ConvStack_3_3_32_7_2_32_7_2_32_7_2_12_256_0.5': structureElements = ConvStackClassifier.ExtractStructureFromFilename( args.architecture) neuralNet = ConvStackClassifier.NeuralNet(structureElements[2], structureElements[0], structureElements[3], structureElements[4],