"enc3": torch.ones(128, 128), "enc4": torch.ones(8, 128), "dec1": torch.ones(128, 8), "dec2": torch.ones(128, 128), "dec3": torch.ones(128, 128), "dec4": torch.ones(128, 128), "dout": torch.ones(640, 128), } # Setup cuda use_cuda = False # torch.cuda.is_available() #Don't really need CUDA here at the moment, so just set false device = torch.device("cuda:0" if use_cuda else "cpu") torch.backends.cudnn.benchmark = True # Load datasets #yrdy_dataset = jet_dataset.ParticleJetDataset('train_data/train/', yamlConfig) test_dataset = jet_dataset.ParticleJetDataset(options.test, yamlConfig) test_labels = test_dataset.labels_list #train_loader = torch.utils.data.DataLoader(full_dataset, batch_size=10000, # shuffle=True, num_workers=0) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=25000, shuffle=False, num_workers=0) dir = "model_files/" dir = options.model_files # # try: # if options.batnorm: # loadmodel = models.three_layer_model_batnorm_masked(prune_mask_set, bn_affine=options.bn_affine,
import hls4ml ## Load yaml config def parse_config(config_file): print("Loading configuration from", config_file) config = open(config_file, 'r') return yaml.load(config, Loader=yaml.FullLoader) yamlConfig = parse_config("yamlConfig.yml") # Setup test data set test_dataset = jet_dataset.ParticleJetDataset("./train_data/test/", yamlConfig) test_size = len(test_dataset) print("test dataset size: " + str(len(test_dataset))) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_size, shuffle=False, num_workers=10, pin_memory=True) X_test = None y_test = None for i, data in enumerate(test_loader, 0): X_test, y_test = data[0].numpy(), data[1].numpy()
# Setup cuda use_cuda = torch.cuda.is_available() device = torch.device("cuda:0" if use_cuda else "cpu") print("Using Device: {}".format(device)) torch.backends.cudnn.benchmark = True torch.backends.cudnn.fastest = True if use_cuda: print("cuda:0 device type: {}".format(torch.cuda.get_device_name(0))) # Set Batch size and split value batch_size = 1024 train_split = 0.75 # Setup and split dataset full_dataset = jet_dataset.ParticleJetDataset(options.inputFile, yamlConfig) test_dataset = jet_dataset.ParticleJetDataset(options.test, yamlConfig) train_size = int( train_split * len(full_dataset)) # 25% for Validation set, 75% for train set val_size = len(full_dataset) - train_size test_size = len(test_dataset) num_val_batches = math.ceil(val_size / batch_size) num_train_batches = math.ceil(train_size / batch_size) print("train_batches " + str(num_train_batches)) print("val_batches " + str(num_val_batches)) train_dataset, val_dataset = torch.utils.data.random_split( full_dataset, [train_size, val_size])
torch.backends.cudnn.enabled = True torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False else: torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.fastest = True # Set Batch size and split value batch_size = 1024 if options.fold is None: #No fold passed, just load a whole folder and randomly split train/test train_split = 0.75 # Setup and split dataset full_dataset = jet_dataset.ParticleJetDataset(options.inputFile,yamlConfig) train_size = int(train_split * len(full_dataset)) # 25% for Validation set, 75% for train set val_size = len(full_dataset) - train_size num_val_batches = math.ceil(val_size/batch_size) num_train_batches = math.ceil(train_size/batch_size) print("train_batches " + str(num_train_batches)) print("val_batches " + str(num_val_batches)) train_dataset, val_dataset = torch.utils.data.random_split(full_dataset,[train_size,val_size]) else: train_filenames = [] val_filename = ""