def main():
save_path = './runs/' + strftime("%Y-%m-%d_%H-%M-%S", gmtime())
if not os.path.exists(save_path):
os.makedirs(save_path)
log_file = os.path.join(save_path, "stdout")
log = open(log_file, "a")
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
log.write(arg + ':' + str(getattr(args, arg)) + '\n')
utils.extract(args.dataset, args.raw_data)
utils.resize(args.mask_data, args.resized_size)
utils.resize(args.eval_data, args.resized_size)
utils.deskew(args.image_mask_countdata)
utils.deskew(args.eval_data)
hyperparameter_list = [args.no_scales, args.no_directions, args.sigma,\
args.frequency_max, args.frequency_factor]
image_mask_label_array = mask(args.mask_data, args.no_scales*args.no_directions, \
args.resized_size, hyperparameter_list)
eval_accuracy = eval(args.eval_data, args.mask_data, args.resized_size, args.image_mask_label_array, \
args.no_scales*args.no_directions, log, hyperparameter_list, args.match_ite, \
args.relative_weight)
print("Final Evaluation Accuracy:{eval_accuracy:.3f}".format(eval_accuracy))
log.write("Final Evaluation Accuracy:{eval_accuracy:.3f}".format(eval_accuracy))
log.close()
def main():
# Check whether GPU is available and can be used
# if CUDA is found then device is set accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_path = './runs/' + strftime("%Y-%m-%d_%H-%M-%S", gmtime())
if not os.path.exists(save_path):
os.makedirs(save_path)
log_file = os.path.join(save_path, "stdout")
log = open(log_file, "a")
# TODO: gives interrupted sys call error
# log_file = os.path.join(save_path, "stdout")
# sys.stdout = Logger(log_file)
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
log.write(arg + ':' + str(getattr(args, arg)) + '\n')
log.close()
# Initialize the weights of the model
print("Initializing network with: " + args.weight_init)
WeightInitializer = WeightInit(args.weight_init)
# Dataset loading
# TODO: hard-coded file paths
patch_size = args.patch_size
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
gen = QLearner(state_space_parameters, 1, WeightInitializer, device, args, save_path, qstore = args.qstore_path,\
replaydict = args.replay_dict_path)
if (args.continue_epsilon
not in np.array(state_space_parameters.epsilon_schedule)[:, 0]):
raise ValueError('continue-epsilon {} not in epsilon schedule!'.format(
args.continue_epsilon))
for episode in state_space_parameters.epsilon_schedule:
epsilon = episode[0]
M = episode[1]
for ite in range(1, M + 1):
if epsilon == args.continue_epsilon and args.continue_ite > M:
raise ValueError(
'continue-ite {} not within range of continue-epsilon {} in epsilon schedule!'
.format(args.continue_ite, epsilon))
if (epsilon == args.continue_epsilon and ite >= args.continue_ite
) or (epsilon < args.continue_epsilon):
print('ite:{}, epsilon:{}'.format(ite, epsilon))
gen.generate_net(epsilon, dataset)
gen.replay_dictionary.to_csv(os.path.join(save_path,
'replayDictFinal.csv'))
gen.qstore.save_to_csv(os.path.join(save_path, 'qValFinal.csv'))
def main():
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
if args.cross_dataset and not args.incremental_data:
raise ValueError(
'cross-dataset training possible only if incremental-data flag set'
)
# Check whether GPU is available and can be used
# if CUDA is found then device is set accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Launch a writer for the tensorboard summary writer instance
save_path = 'runs/' + strftime("%Y-%m-%d_%H-%M-%S", gmtime()) + '_' + args.dataset + '_' + args.architecture +\
'_variational_samples_' + str(args.var_samples) + '_latent_dim_' + str(args.var_latent_dim)
# add option specific naming to separate tensorboard log files later
if args.autoregression:
save_path += '_pixelcnn'
if args.incremental_data:
save_path += '_incremental'
if args.train_incremental_upper_bound:
save_path += '_upper_bound'
if args.generative_replay:
save_path += '_genreplay'
if args.openset_generative_replay:
save_path += '_opensetreplay'
if args.cross_dataset:
save_path += '_cross_dataset_' + args.dataset_order
# if we are resuming a previous training, note it in the name
if args.resume:
save_path = save_path + '_resumed'
writer = SummaryWriter(save_path)
# saving the parsed args to file
log_file = os.path.join(save_path, "stdout")
log = open(log_file, "a")
for arg in vars(args):
log.write(arg + ':' + str(getattr(args, arg)) + '\n')
# Dataset loading
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
# get the number of classes from the class dictionary
num_classes = dataset.num_classes
# we set an epoch multiplier to 1 for isolated training and increase it proportional to amount of tasks in CL
epoch_multiplier = 1
if args.incremental_data:
from lib.Datasets.incremental_dataset import get_incremental_dataset
# get the method to create the incremental dataste (inherits from the chosen data loader)
inc_dataset_init_method = get_incremental_dataset(
data_init_method, args)
# different options for class incremental vs. cross-dataset experiments
if args.cross_dataset:
# if a task order file is specified, load the task order from it
if args.load_task_order:
# check if file exists and if file ends with extension '.txt'
if os.path.isfile(args.load_task_order) and len(args.load_task_order) >= 4\
and args.load_task_order[-4:] == '.txt':
print("=> loading task order from '{}'".format(
args.load_task_order))
with open(args.load_task_order, 'rb') as fp:
task_order = pickle.load(fp)
# if no file is found default to cmd line task order
else:
# parse and split string at commas
task_order = args.dataset_order.split(',')
for i in range(len(task_order)):
# remove blank spaces in dataset names
task_order[i] = task_order[i].replace(" ", "")
# use task order as specified in command line
else:
# parse and split string at commas
task_order = args.dataset_order.split(',')
for i in range(len(task_order)):
# remove blank spaces in dataset names
task_order[i] = task_order[i].replace(" ", "")
# just for getting the number of classes in the first dataset
num_classes = 0
for i in range(args.num_base_tasks):
temp_dataset_init_method = getattr(datasets, task_order[i])
temp_dataset = temp_dataset_init_method(
torch.cuda.is_available(), args)
num_classes += temp_dataset.num_classes
del temp_dataset
# multiply epochs by number of tasks
if args.num_increment_tasks:
epoch_multiplier = ((len(task_order) - args.num_base_tasks) /
args.num_increment_tasks) + 1
else:
# this branch will get active if num_increment_tasks is set to zero. This is useful when training
# any isolated upper bound with all datasets present from the start.
epoch_multiplier = 1.0
else:
# class incremental
# if specified load task order from file
if args.load_task_order:
if os.path.isfile(args.load_task_order):
print("=> loading task order from '{}'".format(
args.load_task_order))
task_order = np.load(args.load_task_order).tolist()
else:
# if no file is found a random task order is created
print(
"=> no task order found. Creating randomized task order"
)
task_order = np.random.permutation(num_classes).tolist()
else:
# if randomize task order is specified create a random task order, else task order is sequential
task_order = []
for i in range(dataset.num_classes):
task_order.append(i)
if args.randomize_task_order:
task_order = np.random.permutation(num_classes).tolist()
# save the task order
np.save(os.path.join(save_path, 'task_order.npy'), task_order)
# set the number of classes to base tasks + 1 because base tasks is always one less.
# E.g. if you have 2 classes it's one task. This is a little inconsistent from the naming point of view
# but we wanted a single variable to work for both class incremental as well as cross-dataset experiments
num_classes = args.num_base_tasks + 1
# multiply epochs by number of tasks
epoch_multiplier = (
(len(task_order) -
(args.num_base_tasks + 1)) / args.num_increment_tasks) + 1
print("Task order: ", task_order)
# log the task order into the text file
log.write('task_order:' + str(task_order) + '\n')
args.task_order = task_order
# this is a little weird, but it needs to be here because the below method pops items from task_order
args_to_tensorboard(writer, args)
assert epoch_multiplier.is_integer(), print(
"uneven task division, make sure number of tasks are integers.")
# Get the incremental dataset
dataset = inc_dataset_init_method(torch.cuda.is_available(), device,
task_order, args)
else:
# add command line options to TensorBoard
args_to_tensorboard(writer, args)
log.close()
# Get a sample input from the data loader to infer color channels/size
net_input, _ = next(iter(dataset.train_loader))
# get the amount of color channels in the input images
num_colors = net_input.size(1)
# import model from architectures class
net_init_method = getattr(architectures, args.architecture)
# if we are not building an autoregressive model the number of output channels of the model is equivalent to
# the amount of input channels. For an autoregressive models we set the number of output channels of the
# non-autoregressive decoder portion according to the command line option below
if not args.autoregression:
args.out_channels = num_colors
# build the model
model = net_init_method(device, num_classes, num_colors, args)
# optionally add the autoregressive decoder
if args.autoregression:
model.pixelcnn = PixelCNN(device,
num_colors,
args.out_channels,
args.pixel_cnn_channels,
num_layers=args.pixel_cnn_layers,
k=args.pixel_cnn_kernel_size,
padding=args.pixel_cnn_kernel_size // 2)
# Parallel container for multi GPU use and cast to available device
model = torch.nn.DataParallel(model).to(device)
print(model)
# Initialize the weights of the model, by default according to He et al.
print("Initializing network with: " + args.weight_init)
WeightInitializer = WeightInit(args.weight_init)
WeightInitializer.init_model(model)
# Define optimizer and loss function (criterion)
optimizer = torch.optim.Adam(model.parameters(), args.learning_rate)
epoch = 0
best_prec = 0
best_loss = random.getrandbits(128)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
epoch = checkpoint['epoch']
best_prec = checkpoint['best_prec']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# optimize until final amount of epochs is reached. Final amount of epochs is determined through the
while epoch < (args.epochs * epoch_multiplier):
# visualize the latent space before each task increment and at the end of training if it is 2-D
if epoch % args.epochs == 0 and epoch > 0 or (epoch + 1) % (
args.epochs * epoch_multiplier) == 0:
if model.module.latent_dim == 2:
print("Calculating and visualizing dataset embedding")
# infer the number of current tasks to plot the different classes in the embedding
if args.incremental_data:
if args.cross_dataset:
num_tasks = sum(
dataset.num_classes_per_task[:len(dataset.
seen_tasks)])
else:
num_tasks = len(dataset.seen_tasks)
else:
num_tasks = num_classes
zs = get_latent_embedding(model, dataset.train_loader,
num_tasks, device)
visualize_dataset_in_2d_embedding(writer,
zs,
args.dataset,
save_path,
task=num_tasks)
# continual learning specific part
if args.incremental_data:
# at the end of each task increment
if epoch % args.epochs == 0 and epoch > 0:
print('Saving the last checkpoint from the previous task ...')
save_task_checkpoint(save_path, epoch // args.epochs)
print("Incrementing dataset ...")
dataset.increment_tasks(
model,
args.batch_size,
args.workers,
writer,
save_path,
is_gpu=torch.cuda.is_available(),
upper_bound_baseline=args.train_incremental_upper_bound,
generative_replay=args.generative_replay,
openset_generative_replay=args.openset_generative_replay,
openset_threshold=args.openset_generative_replay_threshold,
openset_tailsize=args.openset_weibull_tailsize,
autoregression=args.autoregression)
# grow the classifier and increment the variable for number of overall classes so we can use it later
if args.cross_dataset:
grow_classifier(
model.module.classifier,
sum(dataset.num_classes_per_task[:len(dataset.
seen_tasks)]) -
model.module.num_classes, WeightInitializer)
model.module.num_classes = sum(
dataset.num_classes_per_task[:len(dataset.seen_tasks)])
else:
model.module.num_classes += args.num_increment_tasks
grow_classifier(model.module.classifier,
args.num_increment_tasks,
WeightInitializer)
# reset moving averages etc. of the optimizer
optimizer = torch.optim.Adam(model.parameters(),
args.learning_rate)
# change the number of seen classes
if epoch % args.epochs == 0:
model.module.seen_tasks = dataset.seen_tasks
# train
train(dataset, model, criterion, epoch, optimizer, writer, device,
args)
# evaluate on validation set
prec, loss = validate(dataset, model, criterion, epoch, writer, device,
save_path, args)
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
best_prec = max(prec, best_prec)
save_checkpoint(
{
'epoch': epoch,
'arch': args.architecture,
'state_dict': model.state_dict(),
'best_prec': best_prec,
'best_loss': best_loss,
'optimizer': optimizer.state_dict()
}, is_best, save_path)
# increment epoch counters
epoch += 1
# if a new task begins reset the best prec so that new best model can be stored.
if args.incremental_data and epoch % args.epochs == 0:
best_prec = 0
best_loss = random.getrandbits(128)
writer.close()
def main():
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# choose dataset evaluation function to import (e.g. variational will operate on z values)
if args.train_var:
from lib.Training.evaluate import eval_var_dataset as eval_dataset
from lib.Training.evaluate import eval_var_openset_dataset as eval_openset_dataset
else:
from lib.Training.evaluate import eval_dataset as eval_dataset
from lib.Training.evaluate import eval_openset_dataset as eval_openset_dataset
# Get the dataset which has been trained and the corresponding number of classes
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
num_classes = dataset.num_classes
net_input, _ = next(iter(dataset.train_loader))
num_colors = net_input.size(1)
# Split a part of the non-used dataset to use as validation set for determining open set (e.g entropy)
# rejection thresholds
split_perc = 0.5
split_sets = torch.utils.data.random_split(dataset.valset,
[int((1 - split_perc) * len(dataset.valset)),
int(split_perc * len(dataset.valset))])
# overwrite old set and create new split set to determine thresholds/priors
dataset.valset = split_sets[0]
dataset.threshset = split_sets[1]
# overwrite old data loader and create new loader for thresh set
is_gpu = torch.cuda.is_available()
dataset.val_loader = torch.utils.data.DataLoader(dataset.valset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=is_gpu, sampler=None)
dataset.threshset_loader = torch.utils.data.DataLoader(dataset.threshset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=is_gpu, sampler=None)
# Load open set datasets
openset_datasets_names = args.openset_datasets.strip().split(',')
openset_datasets = []
for openset_dataset in openset_datasets_names:
openset_data_init_method = getattr(datasets, openset_dataset)
openset_datasets.append(openset_data_init_method(torch.cuda.is_available(), args))
# Initialize empty model
net_init_method = getattr(architectures, args.architecture)
model = net_init_method(device, num_classes, num_colors, args).to(device)
model = torch.nn.DataParallel(model).to(device)
print(model)
# load model (using the resume functionality)
assert(os.path.isfile(args.resume)), "=> no model checkpoint found at '{}'".format(args.resume)
# Fill the random model with the parameters of the checkpoint
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
best_prec = checkpoint['best_prec']
best_loss = checkpoint['best_loss']
print("Saved model's validation accuracy: ", best_prec)
print("Saved model's validation loss: ", best_loss)
model.load_state_dict(checkpoint['state_dict'])
model.to(device)
model.eval()
# set the save path to the directory from which the model has been loaded
save_path = os.path.dirname(args.resume)
# start of the model evaluation on the training dataset and fitting
print("Evaluating original train dataset: " + args.dataset + ". This may take a while...")
dataset_eval_dict_train = eval_dataset(model, dataset.train_loader, num_classes, device,
latent_var_samples=args.var_samples, model_var_samples=args.model_samples)
print("Training accuracy: ", dataset_eval_dict_train["accuracy"])
# Get the mean of z for correctly classified data inputs
mean_zs = get_means(dataset_eval_dict_train["zs_correct"])
# visualize the mean z vectors
mean_zs_tensor = torch.stack(mean_zs, dim=0)
visualize_means(mean_zs_tensor, num_classes, args.dataset, save_path, "z")
# calculate each correctly classified example's distance to the mean z
distances_to_z_means_correct_train = calc_distances_to_means(mean_zs, dataset_eval_dict_train["zs_correct"],
args.distance_function)
# Weibull fitting
# set tailsize according to command line parameters (according to percentage of dataset size)
tailsize = int(len(dataset.trainset) * args.openset_weibull_tailsize / num_classes)
print("Fitting Weibull models with tailsize: " + str(tailsize))
tailsizes = [tailsize] * num_classes
weibull_models, valid_weibull = fit_weibull_models(distances_to_z_means_correct_train, tailsizes)
assert valid_weibull, "Weibull fit is not valid"
# ------------------------------------------------------------------------------------------
# Fitting on train dataset complete. Determine rejection thresholds/priors on the created split set
# ------------------------------------------------------------------------------------------
print("Evaluating original threshold split dataset: " + args.dataset + ". This may take a while...")
threshset_eval_dict = eval_dataset(model, dataset.threshset_loader, num_classes, device,
latent_var_samples=args.var_samples, model_var_samples=args.model_samples)
# Again calculate distances to mean z
print("Split set accuracy: ", threshset_eval_dict["accuracy"])
distances_to_z_means_threshset = calc_distances_to_means(mean_zs, threshset_eval_dict["zs_correct"],
args.distance_function)
# get Weibull outlier probabilities for thresh set
outlier_probs_threshset = calc_outlier_probs(weibull_models, distances_to_z_means_threshset)
threshset_classification = calc_openset_classification(outlier_probs_threshset, num_classes,
num_outlier_threshs=100)
# also check outlier detection based on entropy
max_entropy = np.max(threshset_eval_dict["out_entropy"])
threshset_entropy_classification = calc_entropy_classification(threshset_eval_dict["out_entropy"],
max_entropy,
num_outlier_threshs=100)
# determine rejection priors based on 5% of the split data considered as inlying
if (np.array(threshset_classification["outlier_percentage"]) <= 0.05).any() == True:
EVT_prior_index = np.argwhere(np.array(threshset_classification["outlier_percentage"])
<= 0.05)[0][0]
EVT_prior = threshset_classification["thresholds"][EVT_prior_index]
else:
EVT_prior = 0.5
EVT_prior_index = 50
if (np.array(threshset_entropy_classification["entropy_outlier_percentage"]) <= 0.05).any() == True:
entropy_threshold_index = np.argwhere(np.array(threshset_entropy_classification["entropy_outlier_percentage"])
<= 0.05)[0][0]
entropy_threshold = threshset_entropy_classification["entropy_thresholds"][entropy_threshold_index]
else:
# this should never actually happen
entropy_threshold = np.median(threshset_entropy_classification["entropy_thresholds"])
entropy_threshold_index = 50
print("EVT prior: " + str(EVT_prior) + "; Entropy threshold: " + str(entropy_threshold))
# ------------------------------------------------------------------------------------------
# Beginning of all testing/open set recognition on test and unknown sets.
# ------------------------------------------------------------------------------------------
# We evaluate the validation set to later evaluate trained dataset's statistical inlier/outlier estimates.
print("Evaluating original validation dataset: " + args.dataset + ". This may take a while...")
dataset_eval_dict = eval_dataset(model, dataset.val_loader, num_classes, device,
latent_var_samples=args.var_samples, model_var_samples=args.model_samples)
# Again calculate distances to mean z
print("Validation accuracy: ", dataset_eval_dict["accuracy"])
distances_to_z_means_correct = calc_distances_to_means(mean_zs, dataset_eval_dict["zs_correct"],
args.distance_function)
# Evaluate outlier probability of trained dataset's validation set
outlier_probs_correct = calc_outlier_probs(weibull_models, distances_to_z_means_correct)
dataset_classification_correct = calc_openset_classification(outlier_probs_correct, num_classes,
num_outlier_threshs=100)
dataset_entropy_classification_correct = calc_entropy_classification(dataset_eval_dict["out_entropy"],
max_entropy,
num_outlier_threshs=100)
print(args.dataset + '(trained) EVT outlier percentage: ' +
str(dataset_classification_correct["outlier_percentage"][EVT_prior_index]))
print(args.dataset + '(trained) entropy outlier percentage: ' +
str(dataset_entropy_classification_correct["entropy_outlier_percentage"][entropy_threshold_index]))
# Repeat process for open set recognition on unseen datasets (
openset_dataset_eval_dicts = collections.OrderedDict()
openset_outlier_probs_dict = collections.OrderedDict()
openset_classification_dict = collections.OrderedDict()
openset_entropy_classification_dict = collections.OrderedDict()
for od, openset_dataset in enumerate(openset_datasets):
print("Evaluating openset dataset: " + openset_datasets_names[od] + ". This may take a while...")
openset_dataset_eval_dict = eval_openset_dataset(model, openset_dataset.val_loader, num_classes,
device, latent_var_samples=args.var_samples,
model_var_samples=args.model_samples)
openset_distances_to_z_means = calc_distances_to_means(mean_zs, openset_dataset_eval_dict["zs"],
args.distance_function)
openset_outlier_probs = calc_outlier_probs(weibull_models, openset_distances_to_z_means)
# getting outlier classification accuracies across the entire datasets
openset_classification = calc_openset_classification(openset_outlier_probs, num_classes,
num_outlier_threshs=100)
openset_entropy_classification = calc_entropy_classification(openset_dataset_eval_dict["out_entropy"],
max_entropy, num_outlier_threshs=100)
# dictionary of dictionaries: per datasetname one dictionary with respective values
openset_dataset_eval_dicts[openset_datasets_names[od]] = openset_dataset_eval_dict
openset_outlier_probs_dict[openset_datasets_names[od]] = openset_outlier_probs
openset_classification_dict[openset_datasets_names[od]] = openset_classification
openset_entropy_classification_dict[openset_datasets_names[od]] = openset_entropy_classification
# print outlier rejection values for all unseen unknown datasets
for other_data_name, other_data_dict in openset_classification_dict.items():
print(other_data_name + ' EVT outlier percentage: ' +
str(other_data_dict["outlier_percentage"][entropy_threshold_index]))
for other_data_name, other_data_dict in openset_entropy_classification_dict.items():
print(other_data_name + ' entropy outlier percentage: ' +
str(other_data_dict["entropy_outlier_percentage"][entropy_threshold_index]))
# joint prediction uncertainty plot for all datasets
if (args.train_var and args.var_samples > 1) or args.model_samples > 1:
visualize_classification_uncertainty(dataset_eval_dict["out_mus_correct"],
dataset_eval_dict["out_sigmas_correct"],
openset_dataset_eval_dicts,
"out_mus", "out_sigmas",
args.dataset + ' (trained)',
args.var_samples, save_path)
# visualize the outlier probabilities
visualize_weibull_outlier_probabilities(outlier_probs_correct, openset_outlier_probs_dict,
args.dataset + ' (trained)', save_path, tailsize)
visualize_classification_scores(dataset_eval_dict["out_mus_correct"], openset_dataset_eval_dicts, 'out_mus',
args.dataset + ' (trained)', save_path)
visualize_entropy_histogram(dataset_eval_dict["out_entropy"], openset_dataset_eval_dicts,
dataset_entropy_classification_correct["entropy_thresholds"][-1], "out_entropy",
args.dataset + ' (trained)', save_path)
# joint plot for outlier detection accuracy for seen and both unseen datasets
visualize_openset_classification(dataset_classification_correct["outlier_percentage"],
openset_classification_dict, "outlier_percentage",
args.dataset + ' (trained)',
dataset_classification_correct["thresholds"], save_path, tailsize)
visualize_entropy_classification(dataset_entropy_classification_correct["entropy_outlier_percentage"],
openset_entropy_classification_dict, "entropy_outlier_percentage",
args.dataset + ' (trained)',
dataset_entropy_classification_correct["entropy_thresholds"], save_path)
def main():
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# Get the dataset which has been trained and the corresponding number of classes
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
num_classes = dataset.num_classes
net_input, _ = next(iter(dataset.train_loader))
num_colors = net_input.size(1)
# Load open set dataset 1
openset_data_init_method = getattr(datasets, args.openset_dataset)
openset_dataset = openset_data_init_method(torch.cuda.is_available(), args)
# Load open set dataset 2
# Note: This could be easily refactored to one or flexible amount of datasets, we have kept this hard-coded
# to reproduce the plots of the paper. Please feel free to refactor this.
openset_data_init_method2 = getattr(datasets, args.openset_dataset2)
openset_dataset2 = openset_data_init_method2(torch.cuda.is_available(), args)
if not args.autoregression:
args.out_channels = num_colors
# Initialize empty model
net_init_method = getattr(architectures, args.architecture)
model = net_init_method(device, num_classes, num_colors, args)
# Optional addition of autoregressive decoder portion
if args.autoregression:
model.pixelcnn = PixelCNN(device, num_colors, args.out_channels, args.pixel_cnn_channels,
num_layers=args.pixel_cnn_layers, k=args.pixel_cnn_kernel_size,
padding=args.pixel_cnn_kernel_size // 2)
model = torch.nn.DataParallel(model).to(device)
# load model (using the resume functionality)
assert(os.path.isfile(args.resume)), "=> no model checkpoint found at '{}'".format(args.resume)
# Fill the random model with the parameters of the checkpoint
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
best_prec = checkpoint['best_prec']
best_loss = checkpoint['best_loss']
# print the saved model's validation accuracy (as a check to see if the loaded model has really been trained)
print("Saved model's validation accuracy: ", best_prec)
print("Saved model's validation loss: ", best_loss)
model.load_state_dict(checkpoint['state_dict'])
model.to(device)
model.eval()
# set the save path to the directory from which the model has been loaded
save_path = os.path.dirname(args.resume)
# start of the model evaluation on the training dataset and fitting
print("Evaluating original train dataset: " + args.dataset + ". This may take a while...")
dataset_eval_dict_train = eval_dataset(model, dataset.train_loader, dataset.num_classes, device,
samples=args.var_samples)
print("Training accuracy: ", dataset_eval_dict_train["accuracy"])
# Get the mean of z for correctly classified data inputs
mean_zs = get_means(dataset_eval_dict_train["zs_correct"])
# visualize the mean z vectors
mean_zs_tensor = torch.stack(mean_zs, dim=0)
visualize_means(mean_zs_tensor, dataset.class_to_idx, args.dataset, save_path, "z")
# calculate each correctly classified example's distance to the mean z
distances_to_z_means_correct_train = calc_distances_to_means(mean_zs, dataset_eval_dict_train["zs_correct"],
args.distance_function)
# Weibull fitting
# set tailsize according to command line parameters (according to percentage of dataset size)
tailsize = int(len(dataset.trainset) * args.openset_weibull_tailsize / num_classes)
print("Fitting Weibull models with tailsize: " + str(tailsize))
tailsizes = [tailsize] * num_classes
weibull_models, valid_weibull = fit_weibull_models(distances_to_z_means_correct_train, tailsizes)
assert valid_weibull, "Weibull fit is not valid"
# ------------------------------------------------------------------------------------------
# Fitting on train dataset complete. Beginning of all testing/open set recognition on validation and unknown sets.
# ------------------------------------------------------------------------------------------
# We evaluate the validation set to later evaluate trained dataset's statistical inlier/outlier estimates.
print("Evaluating original validation dataset: " + args.dataset + ". This may take a while...")
dataset_eval_dict = eval_dataset(model, dataset.val_loader, dataset.num_classes, device, samples=args.var_samples)
# Again calculate distances to mean z
print("Validation accuracy: ", dataset_eval_dict["accuracy"])
distances_to_z_means_correct = calc_distances_to_means(mean_zs, dataset_eval_dict["zs_correct"],
args.distance_function)
# Evaluate outlier probability of trained dataset's validation set
outlier_probs_correct = calc_outlier_probs(weibull_models, distances_to_z_means_correct)
# Repeat process for open set recognition on unseen dataset 1 (
print("Evaluating openset dataset: " + args.openset_dataset + ". This may take a while...")
openset_dataset_eval_dict = eval_openset_dataset(model, openset_dataset.val_loader, openset_dataset.num_classes,
device, samples=args.var_samples)
openset_distances_to_z_means = calc_distances_to_means(mean_zs, openset_dataset_eval_dict["zs"],
args.distance_function)
openset_outlier_probs = calc_outlier_probs(weibull_models, openset_distances_to_z_means)
# visualize the outlier probabilities
visualize_weibull_outlier_probabilities(outlier_probs_correct, openset_outlier_probs,
args.dataset, args.openset_dataset, save_path, tailsize)
# getting outlier classification accuracies across the entire datasets
dataset_classification_correct = calc_openset_classification(outlier_probs_correct, dataset.num_classes,
num_outlier_threshs=100)
openset_classification = calc_openset_classification(openset_outlier_probs, dataset.num_classes,
num_outlier_threshs=100)
# open set recognition on unseen dataset 2 (Lots of redundant code copy pasting, could be refactored)
print("Evaluating openset dataset 2: " + args.openset_dataset2 + ". This may take a while...")
openset_dataset_eval_dict2 = eval_openset_dataset(model, openset_dataset2.val_loader, openset_dataset2.num_classes,
device, samples=args.var_samples)
# joint prediction uncertainty plot for all datasets
visualize_classification_uncertainty(dataset_eval_dict["out_mus_correct"],
dataset_eval_dict["out_sigmas_correct"],
openset_dataset_eval_dict["out_mus"],
openset_dataset_eval_dict["out_sigmas"],
openset_dataset_eval_dict2["out_mus"],
openset_dataset_eval_dict2["out_sigmas"],
args.dataset + ' (trained)', args.openset_dataset, args.openset_dataset2,
args.var_samples, save_path)
# get outlier probabilities of open set dataset 2
openset_distances_to_z_means2 = calc_distances_to_means(mean_zs, openset_dataset_eval_dict2["zs"],
args.distance_function)
openset_outlier_probs2 = calc_outlier_probs(weibull_models, openset_distances_to_z_means2)
visualize_weibull_outlier_probabilities(outlier_probs_correct, openset_outlier_probs2,
args.dataset, args.openset_dataset2, save_path, tailsize)
# getting outlier classification accuracy for open set dataset 2
openset_classification2 = calc_openset_classification(openset_outlier_probs2, dataset.num_classes,
num_outlier_threshs=100)
# joint plot for outlier detection accuracy for seen and both unseen datasets
visualize_openset_classification(dataset_classification_correct["outlier_percentage"],
openset_classification["outlier_percentage"],
openset_classification2["outlier_percentage"],
args.dataset + ' (trained)', args.openset_dataset, args.openset_dataset2,
dataset_classification_correct["thresholds"], save_path, tailsize)
def main():
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
if args.debug:
pdb.set_trace()
# Check whether GPU is available and can be used
# if CUDA is found then device is set accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Launch a writer for the tensorboard summary writer instance
save_path = 'runs/' + strftime(
"%Y-%m-%d_%H-%M-%S",
gmtime()) + '_' + args.dataset + '_' + args.architecture
# if we are resuming a previous training, note it in the name
if args.resume:
save_path = save_path + '_resumed'
writer = SummaryWriter(save_path)
# saving the parsed args to file
log_file = os.path.join(save_path, "stdout")
log = open(log_file, "a")
for arg in vars(args):
log.write(arg + ':' + str(getattr(args, arg)) + '\n')
# Dataset loading
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
# get the number of classes from the class dictionary
num_classes = dataset.num_classes
# we set an epoch multiplier to 1 for isolated training and increase it proportional to amount of tasks in CL
epoch_multiplier = 1
# add command line options to TensorBoard
args_to_tensorboard(writer, args)
log.close()
# build the model
model = architectures.Inos_model(args.num_class, args)
# Parallel container for multi GPU use and cast to available device
model = torch.nn.DataParallel(model).to(device)
print(model)
if not args.pretrained:
# Initialize the weights of the model, by default according to He et al.
print("Initializing network with: " + args.weight_init)
WeightInitializer = WeightInit(args.weight_init)
WeightInitializer.init_model(model)
# Define optimizer and loss function (criterion)
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=0.9,
weight_decay=2e-4)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[30, 60, 80, 100], gamma=0.5)
epoch = 0
best_prec = 0
best_loss = random.getrandbits(128)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
epoch = checkpoint['epoch']
best_prec = checkpoint['best_prec']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# optimize until final amount of epochs is reached. Final amount of epochs is determined through the
while epoch < (args.epochs * epoch_multiplier):
if epoch + 2 == epoch % args.epochs:
print("debug perpose")
# train
train(dataset, model, criterion, epoch, optimizer, writer, device,
args)
# evaluate on validation set
prec, loss = validate(dataset, model, criterion, epoch, writer, device,
save_path, args)
# evaluate on test set
prec_t, loss_t = test(dataset, model, criterion, epoch, writer, device,
save_path, args)
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
best_prec = max(prec, best_prec)
save_checkpoint(
{
'epoch': epoch,
'arch': args.architecture,
'state_dict': model.state_dict(),
'best_prec': best_prec,
'best_loss': best_loss,
'optimizer': optimizer.state_dict()
}, is_best, save_path)
# increment epoch counters
epoch += 1
scheduler.step()
writer.close()
def main():
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# Get the dataset which has been trained and the corresponding number of classes
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
num_classes = dataset.num_classes
net_input, _ = next(iter(dataset.train_loader))
num_colors = net_input.size(1)
# Split a part of the non-used dataset to use as validation set for determining open set (e.g entropy)
# rejection thresholds
split_perc = 0.5
split_sets = torch.utils.data.random_split(dataset.valset,
[int((1 - split_perc) * len(dataset.valset)),
int(split_perc * len(dataset.valset))])
# overwrite old set and create new split set to determine thresholds/priors
dataset.valset = split_sets[0]
dataset.threshset = split_sets[1]
# overwrite old data loader and create new loader for thresh set
is_gpu = torch.cuda.is_available()
dataset.val_loader = torch.utils.data.DataLoader(dataset.valset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=is_gpu, sampler=None)
dataset.threshset_loader = torch.utils.data.DataLoader(dataset.threshset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=is_gpu, sampler=None)
# Load open set datasets
openset_datasets_names = args.openset_datasets.strip().split(',')
openset_datasets = []
for openset_dataset in openset_datasets_names:
openset_data_init_method = getattr(datasets, openset_dataset)
openset_datasets.append(openset_data_init_method(torch.cuda.is_available(), args))
if not args.autoregression:
args.out_channels = num_colors
# Initialize empty model
net_init_method = getattr(architectures, args.architecture)
model = net_init_method(device, num_classes, num_colors, args)
# Optional addition of autoregressive decoder portion
if args.autoregression:
model.pixelcnn = PixelCNN(device, num_colors, args.out_channels, args.pixel_cnn_channels,
num_layers=args.pixel_cnn_layers, k=args.pixel_cnn_kernel_size,
padding=args.pixel_cnn_kernel_size // 2)
model = torch.nn.DataParallel(model).to(device)
# load model (using the resume functionality)
assert(os.path.isfile(args.resume)), "=> no model checkpoint found at '{}'".format(args.resume)
# Fill the random model with the parameters of the checkpoint
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
best_prec = checkpoint['best_prec']
best_loss = checkpoint['best_loss']
# print the saved model's validation accuracy (as a check to see if the loaded model has really been trained)
print("Saved model's validation accuracy: ", best_prec)
print("Saved model's validation loss: ", best_loss)
model.load_state_dict(checkpoint['state_dict'])
model.to(device)
model.eval()
# set the save path to the directory from which the model has been loaded
save_path = os.path.dirname(args.resume)
# start of the model evaluation on the training dataset and fitting
print("Evaluating original train dataset: " + args.dataset + ". This may take a while...")
dataset_eval_dict_train = eval_dataset(model, dataset.train_loader, dataset.num_classes, device,
samples=args.var_samples, calc_reconstruction=args.calc_reconstruction,
autoregression=args.autoregression)
print("Training accuracy: ", dataset_eval_dict_train["accuracy"])
# Get the mean of z for correctly classified data inputs
mean_zs = get_means(dataset_eval_dict_train["zs_correct"])
# visualize the mean z vectors
mean_zs_tensor = torch.stack(mean_zs, dim=0)
visualize_means(mean_zs_tensor, dataset.class_to_idx, args.dataset, save_path, "z")
# calculate each correctly classified example's distance to the mean z
distances_to_z_means_correct_train = calc_distances_to_means(mean_zs, dataset_eval_dict_train["zs_correct"],
args.distance_function)
# Weibull fitting
# set tailsize according to command line parameters (according to percentage of dataset size)
tailsize = int(len(dataset.trainset) * args.openset_weibull_tailsize / num_classes)
print("Fitting Weibull models with tailsize: " + str(tailsize))
tailsizes = [tailsize] * num_classes
weibull_models, valid_weibull = fit_weibull_models(distances_to_z_means_correct_train, tailsizes)
assert valid_weibull, "Weibull fit is not valid"
# Determine rejection thresholds/priors on the created split set
print("Evaluating original threshold split dataset: " + args.dataset + ". This may take a while...")
threshset_eval_dict = eval_dataset(model, dataset.threshset_loader, num_classes, device, samples=args.var_samples,
calc_reconstruction=args.calc_reconstruction, autoregression=args.autoregression)
# Again calculate distances to mean z
print("Split set accuracy: ", threshset_eval_dict["accuracy"])
distances_to_z_means_threshset = calc_distances_to_means(mean_zs, threshset_eval_dict["zs_correct"],
args.distance_function)
outlier_probs_threshset = calc_outlier_probs(weibull_models, distances_to_z_means_threshset)
threshset_classification = calc_openset_classification(outlier_probs_threshset, num_classes,
num_outlier_threshs=100)
max_entropy = np.max(threshset_eval_dict["out_entropy"])
threshset_entropy_classification = calc_entropy_classification(threshset_eval_dict["out_entropy"],
max_entropy,
num_outlier_threshs=100)
# We have added a flag to turn off calculation of the decoder because it is computationally heavy for many samples
# (repeated calculation of the decoder), whereas latent space sampling and repeated calculation of our latent based
# EVT approach and even the single layer classifier is cheap.
if args.calc_reconstruction:
max_recon_loss = np.max(threshset_eval_dict["recon_loss_mus"])
threshset_recon_classification = calc_reconstruction_classification(threshset_eval_dict["recon_loss_mus"],
max_recon_loss,
num_outlier_threshs=1000)
# determine the index for the corresponding rejection priors/thresholds. Although this should never happen,
# we also set a default if no threshold satisfies the 95% inlier condition.
if (np.array(threshset_classification["outlier_percentage"]) <= args.percent_validation_outliers).any() == True:
EVT_prior_index = np.argwhere(np.array(threshset_classification["outlier_percentage"])
<= 0.05)[0][0]
EVT_prior = threshset_classification["thresholds"][EVT_prior_index]
else:
EVT_prior = 0.5
EVT_prior_index = 50
if (np.array(threshset_entropy_classification["entropy_outlier_percentage"]) <=
args.percent_validation_outliers).any() == True:
entropy_threshold_index = np.argwhere(np.array(threshset_entropy_classification["entropy_outlier_percentage"])
<= 0.05)[0][0]
entropy_threshold = threshset_entropy_classification["entropy_thresholds"][entropy_threshold_index]
else:
entropy_threshold = np.median(threshset_entropy_classification["entropy_thresholds"])
entropy_threshold_index = 50
if args.calc_reconstruction:
if (np.array(threshset_recon_classification["reconstruction_outlier_percentage"]) <=
args.percent_validation_outliers).any() == True:
recon_threshold_index = np.argwhere(
np.array(threshset_recon_classification["reconstruction_outlier_percentage"]) <= 0.05)[0][0]
recon_threshold = threshset_recon_classification["reconstruction_thresholds"][recon_threshold_index]
else:
recon_threshold = np.median(threshset_recon_classification["reconstruction_thresholds"])
recon_threshold_index = 500
print("EVT prior: " + str(EVT_prior) + "; Entropy threshold: " + str(entropy_threshold))
if args.calc_reconstruction:
print("Reconstruction loss threshold: " + str(recon_threshold))
# ------------------------------------------------------------------------------------------
# Fitting on train dataset complete. Beginning of all testing/open set recognition on validation and unknown sets.
# ------------------------------------------------------------------------------------------
# We evaluate the validation set to later evaluate trained dataset's statistical inlier/outlier estimates.
print("Evaluating original validation dataset: " + args.dataset + ". This may take a while...")
dataset_eval_dict = eval_dataset(model, dataset.val_loader, num_classes, device, samples=args.var_samples,
calc_reconstruction=args.calc_reconstruction, autoregression=args.autoregression)
# Again calculate distances to mean z
print("Validation accuracy: ", dataset_eval_dict["accuracy"])
distances_to_z_means_correct = calc_distances_to_means(mean_zs, dataset_eval_dict["zs_correct"],
args.distance_function)
# Evaluate outlier probability of trained dataset's validation set
outlier_probs_correct = calc_outlier_probs(weibull_models, distances_to_z_means_correct)
dataset_classification_correct = calc_openset_classification(outlier_probs_correct, num_classes,
num_outlier_threshs=100)
dataset_entropy_classification_correct = calc_entropy_classification(dataset_eval_dict["out_entropy"],
max_entropy,
num_outlier_threshs=100)
if args.calc_reconstruction:
dataset_recon_classification_correct = calc_reconstruction_classification(dataset_eval_dict["recon_loss_mus"],
max_recon_loss,
num_outlier_threshs=1000)
print(args.dataset + '(trained) EVT outlier percentage: ' +
str(dataset_classification_correct["outlier_percentage"][EVT_prior_index]))
print(args.dataset + '(trained) entropy outlier percentage: ' +
str(dataset_entropy_classification_correct["entropy_outlier_percentage"][entropy_threshold_index]))
if args.calc_reconstruction:
print(args.dataset + '(trained) reconstruction loss outlier percentage: ' +
str(dataset_recon_classification_correct["reconstruction_outlier_percentage"][recon_threshold_index]))
# ------------------------------------------------------------------------------------------
# Repeat process for open set recognition (no fitting, just testing) on all unseen datasets
# ------------------------------------------------------------------------------------------
# dicitionaries to hold results
openset_dataset_eval_dicts = collections.OrderedDict()
openset_outlier_probs_dict = collections.OrderedDict()
openset_classification_dict = collections.OrderedDict()
openset_entropy_classification_dict = collections.OrderedDict()
if args.calc_reconstruction:
openset_recon_classification_dict = collections.OrderedDict()
for od, openset_dataset in enumerate(openset_datasets):
print("Evaluating openset dataset: " + openset_datasets_names[od] + ". This may take a while...")
openset_dataset_eval_dict = eval_openset_dataset(model, openset_dataset.val_loader, num_classes, device,
samples=args.var_samples, autoregression=args.autoregression,
calc_reconstruction=args.calc_reconstruction)
openset_distances_to_z_means = calc_distances_to_means(mean_zs, openset_dataset_eval_dict["zs"],
args.distance_function)
openset_outlier_probs = calc_outlier_probs(weibull_models, openset_distances_to_z_means)
# getting outlier classification accuracies across the entire datasets
openset_classification = calc_openset_classification(openset_outlier_probs, num_classes,
num_outlier_threshs=100)
openset_entropy_classification = calc_entropy_classification(openset_dataset_eval_dict["out_entropy"],
max_entropy,
num_outlier_threshs=100)
if args.calc_reconstruction:
openset_recon_classification_correct = calc_reconstruction_classification(
openset_dataset_eval_dict["recon_loss_mus"], max_recon_loss, num_outlier_threshs=1000)
openset_dataset_eval_dicts[openset_datasets_names[od]] = openset_dataset_eval_dict
openset_outlier_probs_dict[openset_datasets_names[od]] = openset_outlier_probs
openset_classification_dict[openset_datasets_names[od]] = openset_classification
openset_entropy_classification_dict[openset_datasets_names[od]] = openset_entropy_classification
if args.calc_reconstruction:
openset_recon_classification_dict[openset_datasets_names[od]] = openset_recon_classification_correct
# Print the results
# TODO: maybe log this to file also
for other_data_name, other_data_dict in openset_classification_dict.items():
print(other_data_name + ' EVT outlier percentage: ' +
str(other_data_dict["outlier_percentage"][entropy_threshold_index]))
for other_data_name, other_data_dict in openset_entropy_classification_dict.items():
print(other_data_name + ' entropy outlier percentage: ' +
str(other_data_dict["entropy_outlier_percentage"][entropy_threshold_index]))
if args.calc_reconstruction:
for other_data_name, other_data_dict in openset_recon_classification_dict.items():
print(other_data_name + ' reconstruction loss outlier percentage: ' +
str(other_data_dict["reconstruction_outlier_percentage"][recon_threshold_index]))
# joint prediction uncertainty plot for all datasets
if args.var_samples > 1:
visualize_classification_uncertainty(dataset_eval_dict["out_mus_correct"],
dataset_eval_dict["out_sigmas_correct"],
openset_dataset_eval_dicts,
"out_mus", "out_sigmas",
args.dataset + ' (trained)',
args.var_samples, save_path)
# visualize the outlier probabilities
visualize_weibull_outlier_probabilities(outlier_probs_correct, openset_outlier_probs_dict,
args.dataset + ' (trained)', save_path, tailsize)
# histograms
visualize_classification_scores(dataset_eval_dict["out_mus_correct"], openset_dataset_eval_dicts, 'out_mus',
args.dataset + ' (trained)', save_path)
visualize_entropy_histogram(dataset_eval_dict["out_entropy"], openset_dataset_eval_dicts,
dataset_entropy_classification_correct["entropy_thresholds"][-1], "out_entropy",
args.dataset + ' (trained)', save_path)
if args.calc_reconstruction:
visualize_recon_loss_histogram(dataset_eval_dict["recon_loss_mus"], openset_dataset_eval_dicts,
dataset_recon_classification_correct["reconstruction_thresholds"][-1],
"recon_loss_mus", args.dataset + ' (trained)', save_path)
# joint plot for outlier detection accuracy for both seen and unseen datasets
visualize_openset_classification(dataset_classification_correct["outlier_percentage"],
openset_classification_dict, "outlier_percentage",
args.dataset + ' (trained)',
dataset_classification_correct["thresholds"], save_path, tailsize)
visualize_entropy_classification(dataset_entropy_classification_correct["entropy_outlier_percentage"],
openset_entropy_classification_dict, "entropy_outlier_percentage",
args.dataset + ' (trained)',
dataset_entropy_classification_correct["entropy_thresholds"], save_path)
if args.calc_reconstruction:
visualize_reconstruction_classification(dataset_recon_classification_correct["reconstruction_outlier_percentage"],
openset_recon_classification_dict, "reconstruction_outlier_percentage",
args.dataset + ' (trained)',
dataset_recon_classification_correct["reconstruction_thresholds"],
save_path, autoregression=args.autoregression)
def main():
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# Check whether GPU is available and can be used
# if CUDA is found then device is set accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cudnn.benchmark = True
num_GPUs = torch.cuda.device_count()
# If save directory for runs doesn't exist then create it
if not os.path.exists('runs'):
os.mkdir('runs')
# Create a time-stamped save path for individual experiment
save_path = 'runs/' + strftime("%Y-%m-%d_%H-%M-%S", gmtime()) + \
';' + args.dataset + ';' + args.architecture
os.mkdir(save_path)
# List of values to log to csv
columns_list = [
'Filters', 'Parameters', 'Mean', 'Variance', 'Skew', 'BestVal',
'BestValsTrain', 'BestEpoch', 'LastValPrec', 'LastTrainPrec',
'AllTrain', 'AllVal'
]
df = pd.DataFrame(columns=columns_list)
# Dataset loading
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
# get the amount of color channels in the input images
net_input, _ = next(iter(dataset.train_loader))
num_colors = net_input.size(1)
# import model from architectures class
net_init_method = getattr(architectures, args.architecture)
# Get the parameters for all valid skewed models
SNModels = SkewNormalModels(depth=args.vgg_depth,
num_classes=dataset.num_classes,
patch_size=args.patch_size)
skew_model_params = SNModels.get_valid_models()
print("Total number of models: ", len(skew_model_params["filters"]))
# Weight-init method
WeightInitializer = WeightInit(args.weight_init)
# Optionally resume a previous experiment
current_id = args.resume_model_id
for i in range(len(skew_model_params["filters"]) - current_id):
print("Model filters: ", skew_model_params["filters"][i + current_id])
print("Model parameters: ",
skew_model_params["total_params"][i + current_id], " mean: ",
skew_model_params["means"][i + current_id], " var: ",
skew_model_params["vars"][i + current_id], " skew: ",
skew_model_params["skews"][i + current_id])
model = net_init_method(device,
dataset.num_classes,
num_colors,
args,
skew_model_params["filters"][i + current_id],
custom_filters=True)
# Parallel container for multi GPU use and cast to available device
model = torch.nn.DataParallel(model).to(device)
print(model)
# Initialize the weights of the model
print("Initializing networks with: " + args.weight_init)
WeightInitializer.init_model(model)
# Define criterion and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=args.nesterov)
# Initialize SGDWR learning rate scheduler
lr_scheduler = LearningRateScheduler(args.lr_wr_epochs,
len(dataset.train_loader.dataset),
args.batch_size,
args.learning_rate,
args.lr_wr_mul, args.lr_wr_min)
# Get estimated GPU memory usage of the model and split batch if too little memory is available
if torch.cuda.is_available():
GPUMemory = GPUMem(torch.cuda.is_available())
print('available:{}'.format(
(GPUMemory.total_mem -
GPUMemory.total_mem * GPUMemory.get_mem_util()) / 1024.))
print('required per gpu with buffer: {}'.format(
(4. / float(num_GPUs) * model.module.gpu_usage) + 1.))
# calculate smaller chunk size to split batch into sequential computations
mem_scale_factor = 4.0 # TODO: WEIRD factor... why is this necessary and where does it come from?
# TODO: the + 1 Gb should be taken from the cache allocator
if ((GPUMemory.total_mem -
GPUMemory.total_mem * GPUMemory.get_mem_util()) / 1024.) < (
(mem_scale_factor / float(num_GPUs) *
model.module.gpu_usage) + 1.):
# code for variable batch size implementation as per gpu constraint; remove for old code
approx_small_batch_size = (((GPUMemory.total_mem - GPUMemory.total_mem * GPUMemory.get_mem_util()) / 1024.
- 1.) * float(num_GPUs) / mem_scale_factor) //\
(model.module.gpu_usage / float(args.batch_size))
diff = float('inf')
temp_small_batch_size = approx_small_batch_size
for j in range(1, (args.batch_size // 2) + 1):
if args.batch_size % j == 0 and abs(
j - approx_small_batch_size) < diff:
diff = abs(j - approx_small_batch_size)
temp_small_batch_size = j
batch_seq_split_size = temp_small_batch_size
else:
batch_seq_split_size = args.batch_size
else:
batch_seq_split_size = args.batch_size
# Get training and validation dataset loaders
dataset.train_loader, dataset.val_loader = dataset.get_dataset_loader(
batch_seq_split_size, args.workers, device)
print(
'sequential batch size split size:{}'.format(batch_seq_split_size))
epoch = 0
best_epoch = 0
best_prec = 0
best_val_train_prec = 0
all_train = []
all_val = []
while epoch < args.epochs:
# train for one epoch
train_prec = train(dataset.train_loader, model, criterion, epoch,
optimizer, lr_scheduler, device,
batch_seq_split_size, args)
# evaluate on validation set
prec = validate(dataset.val_loader, model, criterion, epoch,
device, args)
all_train.append(train_prec)
all_val.append(prec)
# remember best prec@1 and save checkpoint
is_best = prec > best_prec
if is_best:
best_epoch = epoch
best_val_train_prec = train_prec
best_prec = prec
# if architecture doesn't train at all skip it
if epoch == args.lr_wr_epochs - 1 and train_prec < (
2 * 100.0 / dataset.num_classes):
break
# increment epoch counters
epoch += 1
lr_scheduler.scheduler_epoch += 1
# append architecture results to csv
df = df.append(pd.DataFrame([[
skew_model_params["filters"][i + current_id],
skew_model_params["total_params"][i + current_id],
skew_model_params["means"][i + current_id],
skew_model_params["vars"][i + current_id],
skew_model_params["skews"][i + current_id], best_prec,
best_val_train_prec, best_epoch, prec, train_prec, all_train,
all_val
]],
columns=columns_list),
ignore_index=True)
df.to_csv(save_path + '/model_%03d' % (i + 1 + current_id) + '.csv')
del model
del optimizer
def main():
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# Check whether GPU is available and can be used
# if CUDA is found then set flag to True
is_gpu = torch.cuda.is_available()
# Dataset loading
# There's only one dataset loader right now, but it can be extended
data_init_method = getattr(datasets, 'CUSTOM')
dataset = data_init_method(is_gpu, args)
# Construct the network
net_init_method = getattr(architectures, args.architecture)
model = net_init_method(args.batch_norm)
# Initialize the weights of the model
print("Initializing network with: " + args.weight_init)
WeightInitializer = WeightInit(args.weight_init)
WeightInitializer.init_model(model)
if is_gpu:
# CUDNN
import torch.backends.cudnn as cudnn
model = model.cuda()
cudnn.benchmark = True
print(model)
# Define optimizer and loss function (criterion)
criterion = nn.BCELoss()
if is_gpu:
criterion = criterion.cuda()
# autoencoders also work with SGD but it is much harder to find the correct parameters
# TODO: expose this to cmd line eventually
if args.optimizer == 'ADAM':
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
betas=(args.momentum, 0.999))
else:
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
save_path = 'runs/' + strftime("%Y-%m-%d_%H:%M:%S", gmtime()) + \
';' + args.architecture
os.mkdir(save_path)
epoch = 1
best_loss = 100000000 # some arbitrarily large initial number
while epoch <= args.epochs:
model.train()
loss = train_unsup(dataset.train_loader, model, criterion, epoch,
optimizer, is_gpu, args)
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
save_checkpoint(
{
'epoch': epoch,
'arch': args.architecture,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
}, is_best, save_path)
# increment epoch counter
epoch += 1
def main():
# set device for torch computations
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_path = './runs/' + strftime("%Y-%m-%d_%H-%M-%S", gmtime())
if not os.path.exists(save_path):
os.makedirs(save_path)
# parse command line arguments
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# create log file
log_file = os.path.join(save_path, "stdout")
# write parsed args to log file
log = open(log_file, "a")
for arg in vars(args):
print(arg, getattr(args, arg))
log.write(arg + ':' + str(getattr(args, arg)) + '\n')
log.close()
# instantiate the weight initializer
print("Initializing network with: " + args.weight_init)
weight_initializer = WeightInit(args.weight_init)
# instantiate dataset object
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
# instantiate a tabular Q-learner
q_learner = QLearner(args, dataset.num_classes, save_path)
# start new architecture search
if int(args.task) == 1:
if args.continue_search is True:
# raise exceptions if requirements to start new search not met
if args.continue_epsilon not in np.array(
state_space_parameters.epsilon_schedule)[:, 0]:
raise ValueError(
'continue-epsilon {} not in epsilon schedule!'.format(
args.continue_epsilon))
if (args.replay_buffer_csv_path is None) or (not os.path.exists(
args.replay_buffer_csv_path)):
raise ValueError(
'specify correct path to replay buffer to continue ')
if (args.q_values_csv_path is None) or (not os.path.exists(
args.q_values_csv_path)):
raise ValueError('wrong path is specified for Q-values')
# iterate as per the epsilon-greedy schedule
for episode in state_space_parameters.epsilon_schedule:
epsilon = episode[0]
m = episode[1]
# raise exception if net number to continue from greater than number of nets for the continue_epsilon
if epsilon == args.continue_epsilon and args.continue_ite > m:
raise ValueError(
'continue-ite {} not within range of continue-epsilon {} in epsilon schedule!'
.format(args.continue_ite, epsilon))
# iterate through number of nets for an epsilon
for ite in range(1, m + 1):
# check conditions to generate and train arc
if (epsilon == args.continue_epsilon
and ite >= args.continue_ite) or (
epsilon < args.continue_epsilon):
print('ite:{}, epsilon:{}'.format(ite, epsilon))
# generate net states for search
q_learner.generate_search_net_states(epsilon)
# check if net already trained before
search_net_in_replay_dict = q_learner.check_search_net_in_replay_buffer(
)
# add to the end of the replay buffer if net already trained before
if search_net_in_replay_dict:
q_learner.add_search_net_to_replay_buffer(
search_net_in_replay_dict, verbose=True)
# train net if net not trained before
else:
# train/val search net
mem_fit, spp_size, hard_best_val, hard_val_all_epochs, soft_best_val, soft_val_all_epochs,\
train_flag, hard_best_background, hard_best_crack, hard_best_spallation,\
hard_best_exposed_bars, hard_best_efflorescence, hard_best_corrosion_stain =\
train_val_net(q_learner.state_list, dataset, weight_initializer, device, args, save_path)
# check if net fits memory
while mem_fit is False:
print(
"net failed mem check even with batch splitting, sampling again!"
)
q_learner.generate_search_net_states(epsilon)
net_in_replay_dict = q_learner.check_search_net_in_replay_buffer(
)
if search_net_in_replay_dict:
q_learner.add_search_net_to_replay_buffer(
net_in_replay_dict)
break
else:
mem_fit, spp_size, hard_best_val, hard_val_all_epochs, soft_best_val, \
soft_val_all_epochs, train_flag, hard_best_background, hard_best_crack,\
hard_best_spallation, hard_best_exposed_bars, hard_best_efflorescence,\
hard_best_corrosion_stain =\
train_val_net(q_learner.state_list, dataset, weight_initializer, device, args,
save_path)
# add new net and performance measures to replay buffer if it fits in memory after splitting
# batch
if mem_fit:
reward = q_learner.accuracies_to_reward(
hard_val_all_epochs)
q_learner.add_search_net_to_replay_buffer(
search_net_in_replay_dict,
spp_size=spp_size,
reward=reward,
hard_best_val=hard_best_val,
hard_val_all_epochs=hard_val_all_epochs,
soft_best_val=soft_best_val,
soft_val_all_epochs=soft_val_all_epochs,
train_flag=train_flag,
hard_best_background=hard_best_background,
hard_best_crack=hard_best_crack,
hard_best_spallation=hard_best_spallation,
hard_best_exposed_bars=hard_best_exposed_bars,
hard_best_efflorescence=hard_best_efflorescence,
hard_best_corrosion_stain=
hard_best_corrosion_stain,
verbose=True)
# sample nets from replay buffer, update Q-values and save partially filled replay buffer and
# Q-values
q_learner.update_q_values_and_save_partial()
# save fully filled replay buffer and final Q-values
q_learner.save_final()
# load single architecture config from replay buffer and train till convergence
elif int(args.task) == 2:
# raise exceptions if requirements to continue incomplete search not met
if (args.replay_buffer_csv_path is None) or (not os.path.exists(
args.replay_buffer_csv_path)):
raise ValueError('wrong path specified for replay buffer')
if int(args.fixed_net_index_no) < 0:
raise ValueError(
'specify a non negative integer for fixed net index')
# generate states for fixed net from a complete search
q_learner.generate_fixed_net_states()
# train/val fixed net exhaustively
mem_fit, spp_size, hard_best_val, hard_val_all_epochs, soft_best_val, soft_val_all_epochs, train_flag,\
hard_best_background, hard_best_crack, hard_best_spallation, hard_best_exposed_bars, hard_best_efflorescence, \
hard_best_corrosion_stain = train_val_net(q_learner.state_list, dataset, weight_initializer, device, args,
save_path)
# add fixed net and performance measures to a data frame and save it
q_learner.add_fixed_net_to_fixed_net_buffer(
spp_size=spp_size,
hard_best_val=hard_best_val,
hard_val_all_epochs=hard_val_all_epochs,
soft_best_val=soft_best_val,
soft_val_all_epochs=soft_val_all_epochs,
hard_best_background=hard_best_background,
hard_best_crack=hard_best_crack,
hard_best_spallation=hard_best_spallation,
hard_best_exposed_bars=hard_best_exposed_bars,
hard_best_efflorescence=hard_best_efflorescence,
hard_best_corrosion_stain=hard_best_corrosion_stain)
# save fixed net buffer
q_learner.save_final()
# raise exception if no matching task
else:
raise NotImplementedError('Given task no. not implemented.')
def main():
# Command line options
args = parser.parse_args()
print("Command line options:")
for arg in vars(args):
print(arg, getattr(args, arg))
# import the correct loss and training functions depending which model to optimize
# TODO: these could easily be refactored into one function, but we kept it this way for modularity
if args.train_var:
if args.joint:
from lib.Training.train import train_var_joint as train
from lib.Training.validate import validate_var_joint as validate
from lib.Training.loss_functions import var_loss_function_joint as criterion
else:
from lib.Training.train import train_var as train
from lib.Training.validate import validate_var as validate
from lib.Training.loss_functions import var_loss_function as criterion
else:
if args.joint:
from lib.Training.train import train_joint as train
from lib.Training.validate import validate_joint as validate
from lib.Training.loss_functions import loss_function_joint as criterion
else:
from lib.Training.train import train as train
from lib.Training.validate import validate as validate
from lib.Training.loss_functions import loss_function as criterion
# Check whether GPU is available and can be used
# if CUDA is found then device is set accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Launch a writer for the tensorboard summary writer instance
save_path = 'runs/' + strftime("%Y-%m-%d_%H-%M-%S", gmtime()) + '_' + args.dataset + '_' + args.architecture +\
'_dropout_' + str(args.dropout)
if args.train_var:
save_path += '_variational_samples_' + str(
args.var_samples) + '_latent_dim_' + str(args.var_latent_dim)
if args.joint:
save_path += '_joint'
# if we are resuming a previous training, note it in the name
if args.resume:
save_path = save_path + '_resumed'
writer = SummaryWriter(save_path)
# saving the parsed args to file
log_file = os.path.join(save_path, "stdout")
log = open(log_file, "a")
for arg in vars(args):
log.write(arg + ':' + str(getattr(args, arg)) + '\n')
# Dataset loading
data_init_method = getattr(datasets, args.dataset)
dataset = data_init_method(torch.cuda.is_available(), args)
# get the number of classes from the class dictionary
num_classes = dataset.num_classes
# add command line options to TensorBoard
args_to_tensorboard(writer, args)
log.close()
# Get a sample input from the data loader to infer color channels/size
net_input, _ = next(iter(dataset.train_loader))
# get the amount of color channels in the input images
num_colors = net_input.size(1)
# import model from architectures class
net_init_method = getattr(architectures, args.architecture)
# build the model
model = net_init_method(device, num_classes, num_colors, args)
# Parallel container for multi GPU use and cast to available device
model = torch.nn.DataParallel(model).to(device)
print(model)
# Initialize the weights of the model, by default according to He et al.
print("Initializing network with: " + args.weight_init)
WeightInitializer = WeightInit(args.weight_init)
WeightInitializer.init_model(model)
# Define optimizer and loss function (criterion)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
epoch = 0
best_prec = 0
best_loss = random.getrandbits(128)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
epoch = checkpoint['epoch']
best_prec = checkpoint['best_prec']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# optimize until final amount of epochs is reached.
while epoch < args.epochs:
# train
train(dataset, model, criterion, epoch, optimizer, writer, device,
args)
# evaluate on validation set
prec, loss = validate(dataset, model, criterion, epoch, writer, device,
args)
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
best_prec = max(prec, best_prec)
save_checkpoint(
{
'epoch': epoch,
'arch': args.architecture,
'state_dict': model.state_dict(),
'best_prec': best_prec,
'best_loss': best_loss,
'optimizer': optimizer.state_dict()
}, is_best, save_path)
# increment epoch counters
epoch += 1
writer.close()