def analyze_experiments(experiment_data_entries, hyperparams_selection=None, plot_seq_acc=True,
plot_seq_forgetting=False, save_img_parent_dir=None, img_extention='png', legend_location='top',
all_diff_color_force=False, ylim=None, taskcount=10):
""" Pipeline data collection and plotting/summary."""
# Collect data
experiment_data_entries, hyperparams_counts, max_task_count = collect_dataframe(experiment_data_entries,
hyperparams_selection, taskcount)
# Pad entries
pad_dataframe(experiment_data_entries, hyperparams_counts)
# Plot
if save_img_parent_dir is not None:
filename_template = save_img_parent_dir + "_TASK{}." + img_extention
filename_template = filename_template.replace(" ", "")
save_img_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'imgs',
save_img_parent_dir, img_extention)
utils.create_dir(save_img_path)
save_img_path = os.path.join(save_img_path, filename_template)
plot_multigraphs(experiment_data_entries, save_img_path, max_task_count,
plot_seq_forgetting=plot_seq_forgetting,
plot_seq_acc=plot_seq_acc,
legend_location=legend_location,
all_diff_color_force=all_diff_color_force,
ylim=ylim,
taskcount=taskcount)
# Collect some statistics
print_exp_statistics(experiment_data_entries)
def saved_for_eval(eval_loader, result, output_path, epoch=None, vs=False):
"""
save a results file in the format accepted by the submission server.
input result : [ans_idxs, acc, q_ids, att_weights, spatials, mask_ans_idxs]
"""
label2ans = eval_loader.dataset.label2ans
results, mask_results = [], []
for a, q_id in zip(result[0], result[2]):
results.append({'question_id': int(q_id), 'answer': label2ans[a]})
# mask_results.append({'question_id': int(q_id), 'answer': label2ans[ma]})
if config.mode != 'trainval':
name = 'eval_results.json'
else:
name = 'eval_results_epoch{}.json'.format(epoch)
# results_path = os.path.join(output_path, name)
# weights_path = os.path.join(output_path, 'att_weights.h5')
# mask_results_path = os.path.join(output_path, 'mask_{}_eval_results.json'.format(config.masks))
# # mask_results_path = os.path.join(output, 'att_random_eval_results.json')
# with open(mask_results_path, 'w') as fd:
# json.dump(mask_results, fd)
if vs:
path = os.path.join(output_path, "vs/")
utils.create_dir(path)
with open(os.path.join(path, name), 'w') as fd:
json.dump(results, fd)
with h5py.File(os.path.join(path, 'att_weights.h5'), 'w') as f:
f.create_dataset('weights', data=result[3])
f.create_dataset('spatials', data=result[4])
f.create_dataset('hints', data=result[6])
else:
with open(os.path.join(output_path, name), 'w') as fd:
json.dump(results, fd)
def save_hyperparams(self, output_dir, hyperparams):
""" Add extra stats (memory,...) and save to output_dir.
:param output_dir: Dir to export the dictionary to.
:param hyperparams: Dictionary with hyperparams to save
"""
utils.create_dir(output_dir)
hyperparams_outpath = os.path.join(
output_dir, utils.get_hyperparams_output_filename())
torch.save(hyperparams, hyperparams_outpath)
print("Saved hyperparams to: ", hyperparams_outpath)
def download_dset(path, location="eu"):
"""
Europe links are used, replace if Asia or North America.
Location: eu (Europe), asia,
"""
assert location in ["eu", "asia", "us"]
utils.create_dir(path)
# TRAIN/VAL IMAGES
train_link = "https://storage.googleapis.com/inat_data_2018_{}/train_val2018.tar.gz".format(
location)
train_tarname = train_link.split('/')[-1] # train_val2018.tar.gz
train_dirname = train_tarname.split('.')[0] # train_val2018
if not os.path.exists(os.path.join(path, train_tarname)):
download(path, train_link)
print("Succesfully downloaded train+val dataset iNaturalist.")
if not os.path.exists(os.path.join(path, train_dirname)):
extract(path, os.path.join(path, train_tarname))
print("Succesfully extracted train+val dataset iNaturalist.")
# TRAIN JSON
trainjson_link = "https://storage.googleapis.com/inat_data_2018_{}/train2018.json.tar.gz".format(
location)
trainjson_tarname = trainjson_link.split('/')[-1] # train2018.json.tar.gz
trainjson_filename = trainjson_link.split('.')[0] # train2018
if not os.path.exists(os.path.join(path, trainjson_tarname)):
download(path, train_link)
print("Succesfully downloaded train json iNaturalist.")
if not os.path.exists(os.path.join(path, trainjson_filename)):
extract(path, os.path.join(path, os.path.join(path,
trainjson_tarname)))
print("Succesfully extracted train json iNaturalist.")
# VAL JSON
trainjson_link = "https://storage.googleapis.com/inat_data_2018_{}/train2018.json.tar.gz".format(
location)
trainjson_tarname = trainjson_link.split('/')[-1] # train2018.json.tar.gz
trainjson_filename = trainjson_link.split('.')[0] # train2018
if not os.path.exists(os.path.join(path, trainjson_tarname)):
download(path, train_link)
print("Succesfully downloaded train json iNaturalist.")
if not os.path.exists(os.path.join(path, trainjson_filename)):
extract(path, os.path.join(path, os.path.join(path,
trainjson_tarname)))
print("Succesfully extracted train json iNaturalist.")
def __init__(self, crop=False, create=False):
super().__init__(crop=crop, create=create)
self.original_dataset_root = self.dataset_root
self.dataset_root = os.path.join(self.original_dataset_root, self.suffix)
utils.create_dir(self.dataset_root)
print(self.dataset_root)
# Create symbolic links if non-existing
for task in range(1, self.task_count + 1):
src_taskdir = os.path.join(self.original_dataset_root, str(self.task_ordering[task - 1]))
dst_tasklink = os.path.join(self.dataset_root, str(task))
if not os.path.exists(dst_tasklink):
os.symlink(src_taskdir, dst_tasklink)
print("CREATE LINK: {} -> {}".format(dst_tasklink, src_taskdir))
else:
print("EXISTING LINK: {} -> {}".format(dst_tasklink, src_taskdir))
def load_chkpt(self, manager):
""" Load checkpoint hyperparams from convergence. """
utils.create_dir(manager.heuristic_exp_dir)
hyperparams_path = os.path.join(
manager.heuristic_exp_dir, utils.get_hyperparams_output_filename())
try:
print("Initiating framework chkpt:{}".format(hyperparams_path))
chkpt = torch.load(hyperparams_path)
except:
print(
"CHECKPOINT LOAD FAILED: No state to restore, starting from scratch."
)
return False
self._restore_state(chkpt['state'])
print("SUCCESSFUL loading framework chkpt:{}".format(hyperparams_path))
return True
def download_dset(path):
utils.create_dir(path)
if not os.path.exists(os.path.join(path, 'tiny-imagenet-200.zip')):
subprocess.call(
"wget -P {} http://cs231n.stanford.edu/tiny-imagenet-200.zip".format(path),
shell=True)
print("Succesfully downloaded TinyImgnet dataset.")
else:
print("Already downloaded TinyImgnet dataset in {}".format(path))
if not os.path.exists(os.path.join(path, 'tiny-imagenet-200')):
subprocess.call(
"unzip {} -d {}".format(os.path.join(path, 'tiny-imagenet-200.zip'), path),
shell=True)
print("Succesfully extracted TinyImgnet dataset.")
else:
print("Already extracted TinyImgnet dataset in {}".format(os.path.join(path, 'tiny-imagenet-200')))
def main():
args = parse_args()
seed_torch(args.seed)
print("epochs", args.epochs)
print("lr", args.lr)
print("optimizer", args.optimizer)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
train_loader = data.get_loader('train', args.batch_size)
val_loader = data.get_loader('test', args.batch_size)
output_path = 'saved_models/{}/{}'.format(config.dataset, args.output)
utils.create_dir(output_path)
torch.backends.cudnn.benchmark = True
embeddings = np.load(os.path.join(config.dataroot, 'glove6b_init_300d.npy'))
constructor = 'build_baseline_with_dl'
model = getattr(base_model, constructor)(embeddings).cuda()
model = nn.DataParallel(model).cuda()
if args.optimizer == 'adadelta':
optimizer = optim.Adadelta(model.parameters(), rho=0.95, eps=1e-6, weight_decay=args.weight_decay)
elif args.optimizer == 'rmsprop':
optimizer = optim.RMSprop(model.parameters(), lr=0.01, alpha=0.99, eps=1e-08, weight_decay=args.weight_decay, momentum=0,
centered=False)
elif args.optimizer == 'adam':
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=args.weight_decay)
elif args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
else:
optimizer = optim.Adamax(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
start_epoch = 0
f1_val_best = 0
tracker = utils.Tracker()
model_path = os.path.join(output_path, 'model.pth')
for epoch in range(start_epoch, args.epochs):
run(model, train_loader, optimizer, tracker, train=True, prefix='train', epoch=epoch)
r = run(model, val_loader, optimizer, tracker, train=False, prefix='test', epoch=epoch)
if r[4].mean() > f1_val_best:
f1_val_best = r[4].mean()
best_results = r
utils.print_results(best_results[2], best_results[3])
def compute_target(answers_dset, ans2label, name):
"""Augment answers_dset with soft score as label
***answers_dset should be preprocessed***
Write result into a cache file
"""
target = []
for ans_entry in answers_dset:
answers = ans_entry['answers']
answer_count = {}
for answer in answers:
answer_ = preprocess_answer(answer['answer'])
answer_count[answer_] = answer_count.get(answer_, 0) + 1
labels = []
scores = []
for answer in answer_count:
if answer not in ans2label:
continue
labels.append(ans2label[answer])
score = get_score(answer_count[answer])
scores.append(score)
label_counts = {}
for k, v in answer_count.items():
if k in ans2label:
label_counts[ans2label[k]] = v
# when all the answers are removed in training mode, skip this entrance
target.append({
'question_id': ans_entry['question_id'],
'question_type': ans_entry['question_type'],
'image_id': ans_entry['image_id'],
'labels': labels,
'label_counts': label_counts,
'scores': scores
})
utils.create_dir(config.cache_root)
cache_file = os.path.join(config.cache_root, name + '_target.json')
json.dump(target, open(cache_file, 'w'))
return target
def create_ans2label(occurence, name):
"""Note that this will also create label2ans.pkl at the same time
occurence: dict {answer -> whatever}
name: prefix of the output file
cache_root: str
"""
ans2label = {}
label2ans = []
label = 0
for answer in occurence:
label2ans.append(answer)
ans2label[answer] = label
label += 1
utils.create_dir(config.cache_root)
cache_file = os.path.join(config.cache_root, name + '_ans2label.json')
json.dump(ans2label, open(cache_file, 'w'))
cache_file = os.path.join(config.cache_root, name + '_label2ans.json')
json.dump(label2ans, open(cache_file, 'w'))
return ans2label
def create_train_val_test_imagefolder_dict_joint(dataset_root, img_paths, outfile, no_crop=True):
"""
For JOINT training: All 10 tasks in 1 data folder.
Makes specific wrapper dictionary with the 3 ImageFolder objects we will use for training, validation and evaluation.
"""
# Data loading code
if no_crop:
out_dir = os.path.join(dataset_root, "no_crop")
else:
out_dir = dataset_root
# Tiny Imgnet total values from pytorch
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
dsets = create_train_test_val_imagefolders(img_paths[1], dataset_root, normalize, True, no_crop=no_crop)
################ SAVE ##################
utils.create_dir(out_dir)
torch.save(dsets, os.path.join(out_dir, outfile))
print("JOINT SIZES: train={}, val={}, test={}".format(len(dsets['train']), len(dsets['val']),
len(dsets['test'])))
print("JOINT: Saved dictionary format of train/val/test dataset Imagefolders.")
def create_train_val_test_imagefolder_dict(dataset_root, img_paths, task_count, outfile, no_crop=True, transform=False):
"""
Makes specific wrapper dictionary with the 3 ImageFolder objects we will use for training, validation and evaluation.
"""
# Data loading code
if no_crop:
out_dir = os.path.join(dataset_root, "no_crop", "{}tasks".format(task_count))
else:
out_dir = os.path.join(dataset_root, "{}tasks".format(task_count))
for task in range(1, task_count + 1):
print("\nTASK ", task)
# Tiny Imgnet total values from pytorch
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
dsets = create_train_test_val_imagefolders(img_paths[task], dataset_root, normalize, transform, no_crop)
utils.create_dir(os.path.join(out_dir, str(task)))
torch.save(dsets, os.path.join(out_dir, str(task), outfile))
print("SIZES: train={}, val={}, test={}".format(len(dsets['train']), len(dsets['val']),
len(dsets['test'])))
print("Saved dictionary format of train/val/test dataset Imagefolders.")
def lr_grid_single_task(args, manager, save_models_mode='keep_none'):
"""
Finetunes from starting model, acquire best lr and acc. LR gridsearch, with #finetune_iterations per LR.
Makes symbolic link to the overall best iteration, corresponding with the obtained best lr.
"""
# Init
manager.store_policy = StoragePolicy(save_models_mode)
args.task_name = manager.dataset.get_taskname(args.task_counter)
manager.ft_parent_exp_dir = os.path.join(manager.parent_exp_dir,
'task_' + str(args.task_counter),
'FT_LR_GRIDSEARCH')
utils.create_dir(manager.ft_parent_exp_dir)
print("FINETUNE LR GRIDSEARCH: Task ", args.task_name)
# Logfile
logfile_parent_dir = os.path.join(manager.ft_parent_exp_dir, 'log')
utils.create_dir(logfile_parent_dir)
logfile = os.path.join(logfile_parent_dir,
utils.get_now() + '_finetune_grid.log')
utils.append_to_file(logfile, "FINETUNE GRIDSEARCH LOG: Processed LRs")
# Load Checkpoint
processed_lrs = {}
grid_checkpoint_file = os.path.join(manager.ft_parent_exp_dir,
'grid_checkpoint.pth')
if os.path.exists(grid_checkpoint_file):
checkpoint = torch.load(grid_checkpoint_file)
processed_lrs = checkpoint['processed_lrs']
print("STARTING FROM CHECKPOINT: ", checkpoint)
utils.append_to_file(logfile, "STARTING FROM CHECKPOINT")
########################################################
# PRESTEPS
args.presteps_elapsed_time = 0
if hasattr(manager.method, 'grid_prestep'):
manager.method.grid_prestep(args, manager)
########################################################
# LR GRIDSEARCH
best_acc = 0
best_lr = None
manager.best_exp_grid_node_dirname = None
best_iteration_batch_dirs = []
for lr in args.lrs:
print("\n", "<" * 20, "LR ", lr, ">" * 20)
accum_acc = 0
best_iteration_dir = None
best_iteration_acc = 0
iteration_batch_dirs = []
if lr not in processed_lrs:
processed_lrs[lr] = {'acc': []}
for finetune_iteration in range(0, args.finetune_iterations):
print("\n", "-" * 20, "FT ITERATION ", finetune_iteration,
"-" * 20)
start_time = time.time()
# Paths
exp_grid_node_dirname = "lr=" + str(
utils.float_to_scientific_str(lr))
if args.finetune_iterations > 1:
exp_grid_node_dirname += "_it" + str(finetune_iteration)
manager.gridsearch_exp_dir = os.path.join(
manager.ft_parent_exp_dir, exp_grid_node_dirname)
iteration_batch_dirs.append(manager.gridsearch_exp_dir)
if finetune_iteration < len(processed_lrs[lr]['acc']):
acc = processed_lrs[lr]['acc'][finetune_iteration]
utils.set_random(finetune_iteration)
print("RESTORING FROM CHECKPOINT: ITERATION = ",
finetune_iteration, "ACC = ", acc)
else:
# Set new seed for reproducability
utils.set_random(finetune_iteration)
# Only actually saved when in save_model mode
utils.create_dir(manager.gridsearch_exp_dir)
# TRAIN
model, acc = manager.method.grid_train(args, manager, lr)
# Append results
processed_lrs[lr]['acc'].append(acc)
msg = "LR = {}, FT Iteration {}/{}, Acc = {}".format(
lr, finetune_iteration + 1, args.finetune_iterations, acc)
print(msg)
utils.append_to_file(logfile, msg)
# New best
if acc > best_iteration_acc:
if args.finetune_iterations > 1:
msg = "=> NEW BEST FT ITERATION {}/{}: (Attempt '{}': Acc '{}' > best attempt Acc '{}')" \
.format(finetune_iteration + 1,
args.finetune_iterations,
finetune_iteration,
acc,
best_iteration_acc)
print(msg)
utils.append_to_file(logfile, msg)
best_iteration_acc = acc
best_iteration_dir = manager.gridsearch_exp_dir
accum_acc = accum_acc + acc
# update logfile/checkpoint
torch.save({'processed_lrs': processed_lrs}, grid_checkpoint_file)
# Save iteration hyperparams
if hasattr(manager.method,
"grid_chkpt") and manager.method.grid_chkpt:
it_elapsed_time = time.time() - start_time
hyperparams = {
'val_acc': acc,
'lr': lr,
'iteration_elapsed_time': it_elapsed_time,
'args': vars(args),
'manager': vars(manager)
}
utils.print_timing(it_elapsed_time, 'TRAIN')
manager.save_hyperparams(manager.gridsearch_exp_dir,
hyperparams)
avg_acc = accum_acc / args.finetune_iterations
print("Done FT iterations\n")
print("LR AVG ACC = ", avg_acc, ", BEST OF LRs ACC = ", best_acc)
# New it-avg best
if avg_acc > best_acc:
best_lr = lr
best_acc = avg_acc
manager.best_exp_grid_node_dirname = best_iteration_dir # Keep ref to best in all attempts
print("UPDATE best lr = {}".format(best_lr))
print("UPDATE best lr acc= {}".format(best_acc))
utils.append_to_file(logfile,
"UPDATE best lr = {}".format(best_lr))
utils.append_to_file(logfile,
"UPDATE best lr acc= {}\n".format(best_acc))
# Clean all from previous best
if manager.store_policy.only_keep_best:
for out_dir in best_iteration_batch_dirs:
if os.path.exists(out_dir):
shutil.rmtree(out_dir, ignore_errors=True)
print("[CLEANUP] removing {}".format(out_dir))
best_iteration_batch_dirs = iteration_batch_dirs
else:
if manager.store_policy.only_keep_best:
for out_dir in iteration_batch_dirs:
if os.path.exists(out_dir):
shutil.rmtree(out_dir, ignore_errors=True)
print("[CLEANUP] removing {}".format(out_dir))
if manager.store_policy.keep_none:
for out_dir in iteration_batch_dirs:
if os.path.exists(out_dir):
shutil.rmtree(out_dir, ignore_errors=True)
print("[CLEANUP] removing {}".format(out_dir))
print("FINETUNE DONE: best_lr={}, best_acc={}".format(best_lr, best_acc))
########################################################
# POSTPROCESS
if hasattr(manager.method, 'grid_poststep'):
manager.method.grid_poststep(args, manager)
return best_lr, best_acc
name = 'eval_results.json'
else:
name = 'eval_results_epoch{}.json'.format(epoch)
results_path = os.path.join(output_path, name)
with open(results_path, 'w') as fd:
json.dump(results, fd)
if __name__ == '__main__':
args = parse_args()
seed_torch(args.seed)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.test_only:
args.resume = True
output_path = 'saved_models/{}/{}'.format(config.type + config.version, args.output)
utils.create_dir(output_path)
torch.backends.cudnn.benchmark = True
######################################### DATASET PREPARATION #######################################
if config.mode == 'train':
train_loader = data.get_loader('train')
val_loader = data.get_loader('val')
elif args.test_only:
train_loader = None
val_loader = data.get_loader('test')
else:
train_loader = data.get_loader('trainval')
val_loader = data.get_loader('test')
######################################### MODEL PREPARATION #######################################
embeddings = np.load(os.path.join(config.cache_root, 'glove6b_init_300d.npy'))
def main(overwrite_args, nc_per_task):
"""
For quick implementation: args are overwritten by overwrite_args (if specified).
Additional params are passed in the main function.
Do this task and return acc (task contains multiple classes = class-incremental in this setup)
:param overwrite_args:
:param nc_per_task: array with the amount of classes for each task
:return:
"""
parser = argparse.ArgumentParser(description='Continuum learning')
parser.add_argument('--task_name', type=str, help='name of the task')
parser.add_argument('--task_count',
type=int,
help='count of the task, STARTING FROM 1')
parser.add_argument('--prev_model_path',
type=str,
help='path to prev model where to start from')
parser.add_argument('--save_path',
type=str,
default='results/',
help='save models during and at the end of training')
parser.add_argument('--n_outputs',
type=int,
default=200,
help='total number of outputs for ALL tasks')
parser.add_argument('--method',
choices=[
'gem', 'baseline_rehearsal_full_mem', 'icarl',
'baseline_rehearsal_partial_mem'
],
type=str,
default='gem',
help='method to use for train')
parser.add_argument(
'--postprocess',
action="store_true",
help='Do datamanagement (e.g. update buffers) after task is learned')
# implemented in separate step to only perform once on THE best model in pipeline
parser.add_argument('--debug', action="store_true", help='Debug mode')
# model parameters
parser.add_argument('--n_hiddens',
type=int,
default=100,
help='number of hidden neurons at each layer')
parser.add_argument('--n_layers',
type=int,
default=2,
help='number of hidden layers')
parser.add_argument('--n_inputs',
type=int,
default=-1,
help='number of hidden layers')
parser.add_argument('--weight_decay',
type=float,
default=0,
help='weight_decay')
parser.add_argument('--is_scratch_model',
action='store_true',
help='is this the first task you train for?')
# memory parameters
parser.add_argument('--n_memories',
type=int,
default=0,
help='number of memories per task')
parser.add_argument('--memory_strength',
default=0,
type=float,
help='memory strength (meaning depends on memory)')
parser.add_argument('--finetune',
action="store_true",
help='whether to initialize nets in indep. nets')
# optimizer parameters
parser.add_argument('--n_epochs',
type=int,
default=1,
help='Number of epochs per task')
parser.add_argument('--batch_size',
type=int,
default=70,
help='batch size')
parser.add_argument('--lr',
type=float,
default=1e-3,
help='SGD learning rate')
# experiment parameters
parser.add_argument('--cuda', action="store_true", help='Use GPU?')
parser.add_argument('--log_every',
type=int,
default=100,
help='frequency of logs, in minibatches')
# data parameters
parser.add_argument('dataset_path',
type=str,
help='path to imgfolders of datasets')
parser.add_argument('--shuffle_tasks',
type=str,
default='no',
help='present tasks in order')
parser.add_argument('--n_tasks',
type=int,
default=10,
help='number of hidden layers')
#####################################
# ARGS PARSING
#####################################
args = parser.parse_known_args()[0]
# Overwrite with specified method args
args.nc_per_task = nc_per_task # Array with nr outputs per class
for key_arg, val_arg in overwrite_args.items():
setattr(args, key_arg, val_arg)
# Index starting from 0 (for array)
args.task_idx = args.task_count - 1
args.n_exemplars_to_append_per_batch = 0
if 'baseline_rehearsal' in args.method:
args.finetune = True
if args.method == 'baseline_rehearsal_full_mem':
args.full_mem_mode = True
elif args.method == 'baseline_rehearsal_partial_mem':
args.full_mem_mode = False
else:
raise Exception("UNKNOWN BASELINE METHOD:", args.method)
# Input checks
assert args.n_outputs == sum(args.nc_per_task)
assert args.n_tasks == len(nc_per_task)
# Baselines from scratch, others from SI model
if args.task_count == 1 and 'baseline' not in args.method:
assert 'SI' in args.prev_model_path, "FIRST TASK NOT STARTING FROM SCRATCH, BUT FROM SI: " \
"ONLY STORING WRAPPER WITH EXEMPLARS, path = {}" \
.format(args.prev_model_path)
assert args.postprocess, "FIRST TASK WE DO ONLY POSTPROCESSING"
assert os.path.isfile(
args.prev_model_path
), "Must specify existing prev_model_path, got: " + args.prev_model_path
print("RUNNING WITH ARGS: ", overwrite_args)
#####################################
# DATASET
#####################################
# load data: We consider 1 task, not class-incremental
dsets = torch.load(args.dataset_path)
args.task_imgfolders = dsets
args.dset_loaders = {
x:
torch.utils.data.DataLoader(ImageFolder_Subset_PathRetriever(dsets[x]),
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
for x in ['train', 'val']
}
dset_sizes = {x: len(dsets[x]) for x in ['train', 'val']}
# Assign random part of batch to exemplars
if 'baseline' in args.method or args.method == 'icarl':
if args.method == 'baseline_rehearsal_partial_mem': # Only trains on exemplar sets, no validation
# Based on lengths of tr datasets: random sampling, but guaranteed at each batch from all exemplar sets
n_mem_samples = args.n_memories * args.task_idx
elif args.method == 'baseline_rehearsal_full_mem' or args.method == 'icarl':
n_mem_samples = args.n_memories * args.n_tasks
n_total_samples = float(dset_sizes['train']) + n_mem_samples
ratio = float(n_mem_samples) / n_total_samples
if not args.debug:
args.n_exemplars_to_append_per_batch = int(
np.ceil(args.batch_size * ratio)) # Ceil: at least 1 per task
else:
args.n_exemplars_to_append_per_batch = int(args.batch_size / 2 +
17)
args.total_batch_size = args.batch_size
args.batch_size = args.batch_size - args.n_exemplars_to_append_per_batch
print("BATCH CONSISTS OF: {} new samples, {} exemplars".format(
args.batch_size, args.n_exemplars_to_append_per_batch))
print("mem length = {}, tr_dset_size={}, ratio={}".format(
float(n_mem_samples), float(dset_sizes['train']), ratio))
#####################################
# LOADING MODEL
#####################################
if args.is_scratch_model: # make model with self.net
assert args.task_idx == 0 # Has to start from 0
Model = importlib.import_module('model.' + args.method)
model = Model.Net(args.n_inputs, args.n_outputs, args.n_tasks, args)
else:
# load prev saved model
print("Loading prev model from path: ", args.prev_model_path)
model = torch.load(args.prev_model_path)
print("MODEL LOADED")
model.init_setup(args)
# Checks
assert model.n_tasks == args.n_tasks, "model tasks={}, args tasks={}".format(
model.n_tasks, args.n_tasks)
assert model.n_outputs == args.n_outputs
print("MODEL SETUP FOR CURRENT TASK")
if args.postprocess:
#####################################
# POSTPROCESS
#####################################
"""
iCARL each task,
GEM only first task model SI (during training no exemplars where gathered)
"""
print("POSTPROCESSING")
model.manage_memory(args.task_idx, args)
# if args.debug:
# model.check_exemplars(args.task_idx)
utils.create_dir(os.path.dirname(args.save_path),
"Postprocessed model dir")
torch.save(model, args.save_path)
print("SAVED POSTPROCESSED MODEL TO: {}".format(args.save_path))
return None, None
else:
#####################################
# TRAIN
#####################################
# if there is a checkpoint to be resumed, in case where the training has stopped before on a given task
resume = os.path.join(args.save_path, 'epoch.pth.tar')
model, best_val_acc = methods.rehearsal.train_rehearsal.train_model(
model, args, dset_sizes, resume=resume)
print("FINISHED TRAINING")
return model, best_val_acc