def main(args):
# print args recap
# print(args, end="\n\n")
# do not remove this line
start = time.time()
# Create the dataset object for example with the "ni, multi-task-nc, or nic
# tracks" and assuming the core50 location in ./core50/data/
dataset = CORE50(root='core50/data/',
scenario="multi-task-nc",
preload=True)
# Get the validation set
print("Recovering validation set...")
full_valdidset = dataset.get_full_valid_set()
# model
# if args.classifier == 'ResNet18':
# classifier = models.resnet18(pretrained=True)
# classifier.fc = torch.nn.Linear(512, args.n_classes)
model = DEN(FLAGS)
clf = models.resnet18(pretrained=True)
clf.fc = torch.nn.Linear(512, 512)
params = dict()
avg_perf = []
# vars to update over time
valid_acc = []
ext_mem_sz = []
ram_usage = []
heads = []
ext_mem = None
# loop over the training incremental batches (x, y, t)
for i, train_batch in enumerate(dataset):
train_x, train_y, t = train_batch
print('Begin Task ' + str(i))
train_x = train_x.reshape((len(train_x), 128 * 128 * 3))[:2000]
train_y = train_y.reshape((1, len(train_y))).astype(np.long)[:2000]
# One hot encoding
train_y = np.eye(FLAGS.n_classes)[train_y]
val_x = full_valdidset[i][0][0]
val_x = val_x.reshape((len(val_x), 128 * 128 * 3))
val_y = full_valdidset[i][0][1].astype(np.long)
val_y = val_y.reshape((1, len(val_y)))
val_y = np.eye(FLAGS.n_classes)[val_y]
data = [train_x, train_y, val_x, val_y, val_x, val_y]
model.sess = tf.Session()
print("\n\n\tTASK %d TRAINING\n" % (i + 1))
model.T = model.T + 1
model.task_indices.append(i + 1)
model.load_params(params, time=1)
perf, sparsity, expansion = model.add_task(i + 1, data)
params = model.get_params()
model.destroy_graph()
model.sess.close()
def main(args):
# print args recap
print(args, end="\n\n")
# do not remove this line
start = time.time()
# Create the dataset object for example with the "ni, multi-task-nc, or nic tracks"
# and assuming the core50 location in ./core50/data/
# ???review CORE50 to see if there is a way to shorten dataset and runtime for testing
# Original call to dataset. Takes a long time.
# dataset = CORE50(root='core50/data/', scenario=args.scenario,
# preload=args.preload_data)
#
# custom call to create CORE50 custom object
# using train=True uses training set and allows more control over batches and other stuff.
dataset = CORE50(root='core50/data/',
scenario=args.scenario,
preload=args.preload_data)
# Get the validation set
print("Recovering validation set...")
# default full validation set
# full_valdidset = dataset.get_full_valid_set()
# reduced validation set
full_valdidset = dataset.get_full_valid_set(reduced=True)
# model
if args.classifier == 'ResNet18':
classifier = models.resnet18(
pretrained=True) # classifier is a pretrained model
classifier.fc = torch.nn.Linear(
512, args.n_classes
) # in features: 512 # out features: set below -> args.n_classes = 50 # Applies a linear transformation to the incoming data
opt = torch.optim.SGD(classifier.parameters(),
lr=args.lr) # Implements stochastic gradient descent
criterion = torch.nn.CrossEntropyLoss(
) # This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class.
# vars to update over time
valid_acc = []
ext_mem_sz = []
ram_usage = []
heads = []
# Start Modification
ewc_lambda = 4 # should this be higher? closer to 0.01 or 0.4 or 0.8? that is what was used in other examples. What does a higher penalty do? what does a lower penatly do?
# fisher_max = 0.0001
# variable dictionary to hold fisher values
fisher_dict = {}
# variable dictionary to hold previous optimized weight values
optpar_dict = {}
# End Modification
# loop over the training incremental batches (x, y, t)
for i, train_batch in enumerate(dataset):
train_x, train_y, t = train_batch
# Start Modifiction
# Make train_x and train_y smaller for testing here
limit_size = False # make true to limit training size # make false to allow full training set
if limit_size:
train_size = 3200
# train_size = 11900
train_x = train_x[0:train_size]
train_y = train_y[0:train_size]
# End Modification
# Print current batch number
print("----------- batch {0} -------------".format(i))
# Print current batch shape
print("x shape: {0}, y shape: {1}".format(train_x.shape,
train_y.shape))
# print task label type
print("Task Label: ", t)
# utils.train_net: a custom function to train neural network. returns stats.
_, _, stats = train_net_ewc(opt,
classifier,
criterion,
args.batch_size,
train_x,
train_y,
t,
fisher_dict,
optpar_dict,
ewc_lambda,
args.epochs,
preproc=preprocess_imgs)
# if multi-task-nc: make deep copy in list heads (aka nn brains)
if args.scenario == "multi-task-nc":
heads.append(copy.deepcopy(classifier.fc))
ext_mem_sz += stats['disk']
ram_usage += stats['ram']
# Start Modifiction
# Calculate the Fisher matrix values given new completed task
on_task_update(t,
train_x,
train_y,
fisher_dict,
optpar_dict,
classifier,
opt,
criterion,
args.batch_size,
preproc=preprocess_imgs
) # training complete # compute fisher matrix values
# End Modification
# test all nn models in list heads for performance. return stats for each.
stats, _ = test_multitask(classifier,
full_valdidset,
args.batch_size,
preproc=preprocess_imgs,
multi_heads=heads)
# print new stats on performance
valid_acc += stats['acc']
print("------------------------------------------")
print("Avg. acc: {}".format(stats['acc']))
print("------------------------------------------")
# Generate submission.zip
# directory with the code snapshot to generate the results
sub_dir = 'submissions/' + args.sub_dir
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
# copy code
# custom function in utils folder to deal with possible file path issues
create_code_snapshot(".", sub_dir + "/code_snapshot")
# generating metadata.txt: with all the data used for the CLScore
elapsed = (time.time() - start) / 60
print("Training Time: {}m".format(elapsed))
with open(sub_dir + "/metadata.txt", "w") as wf:
for obj in [
np.average(valid_acc), elapsed,
np.average(ram_usage),
np.max(ram_usage),
np.average(ext_mem_sz),
np.max(ext_mem_sz)
]:
wf.write(str(obj) + "\n")
# run final full test
# test_preds.txt: with a list of labels separated by "\n"
print("Final inference on test set...")
full_testset = dataset.get_full_test_set()
stats, preds = test_multitask(classifier,
full_testset,
args.batch_size,
preproc=preprocess_imgs)
with open(sub_dir + "/test_preds.txt", "w") as wf:
for pred in preds:
wf.write(str(pred) + "\n")
print("Experiment completed.")
def main(args):
# print args recap
print(args, end="\n\n")
# do not remove this line
start = time.time()
# Create the dataset object for example with the "ni, multi-task-nc, or nic
# tracks" and assuming the core50 location in ./core50/data/
dataset = CORE50(root='core50/data/',
scenario=args.scenario,
preload=args.preload_data)
# Get the validation set
print("Recovering validation set...")
full_valdidset = dataset.get_full_valid_set()
# model
if args.classifier == 'ResNet18':
classifier = models.resnet18(pretrained=True)
classifier.fc = torch.nn.Linear(512, args.n_classes)
opt = torch.optim.SGD(classifier.parameters(), lr=args.lr)
criterion = torch.nn.CrossEntropyLoss()
# vars to update over time
valid_acc = []
ext_mem_sz = []
ram_usage = []
heads = []
ext_mem = None
# loop over the training incremental batches (x, y, t)
for i, train_batch in enumerate(dataset):
train_x, train_y, t = train_batch
# adding eventual replay patterns to the current batch
idxs_cur = np.random.choice(train_x.shape[0],
args.replay_examples,
replace=False)
if i == 0:
ext_mem = [train_x[idxs_cur], train_y[idxs_cur]]
else:
ext_mem = [
np.concatenate((train_x[idxs_cur], ext_mem[0])),
np.concatenate((train_y[idxs_cur], ext_mem[1]))
]
train_x = np.concatenate((train_x, ext_mem[0]))
train_y = np.concatenate((train_y, ext_mem[1]))
print("----------- batch {0} -------------".format(i))
print("x shape: {0}, y shape: {1}".format(train_x.shape,
train_y.shape))
print("Task Label: ", t)
# train the classifier on the current batch/task
_, _, stats = train_net(opt,
classifier,
criterion,
args.batch_size,
train_x,
train_y,
t,
args.epochs,
preproc=preprocess_imgs)
if args.scenario == "multi-task-nc":
heads.append(copy.deepcopy(classifier.fc))
# collect statistics
ext_mem_sz += stats['disk']
ram_usage += stats['ram']
# test on the validation set
stats, _ = test_multitask(classifier,
full_valdidset,
args.batch_size,
preproc=preprocess_imgs,
multi_heads=heads,
last_layer_name="fc",
verbose=False)
valid_acc += stats['acc']
print("------------------------------------------")
print("Avg. acc: {}".format(stats['acc']))
print("------------------------------------------")
# Generate submission.zip
# directory with the code snapshot to generate the results
sub_dir = 'submissions/' + args.sub_dir
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
# copy code
create_code_snapshot(".", sub_dir + "/code_snapshot")
# generating metadata.txt: with all the data used for the CLScore
elapsed = (time.time() - start) / 60
print("Training Time: {}m".format(elapsed))
with open(sub_dir + "/metadata.txt", "w") as wf:
for obj in [
np.average(valid_acc), elapsed,
np.average(ram_usage),
np.max(ram_usage),
np.average(ext_mem_sz),
np.max(ext_mem_sz)
]:
wf.write(str(obj) + "\n")
# test_preds.txt: with a list of labels separated by "\n"
print("Final inference on test set...")
full_testset = dataset.get_full_test_set()
stats, preds = test_multitask(classifier,
full_testset,
args.batch_size,
preproc=preprocess_imgs,
multi_heads=heads,
last_layer_name="fc",
verbose=False)
with open(sub_dir + "/test_preds.txt", "w") as wf:
for pred in preds:
wf.write(str(pred) + "\n")
print("Experiment completed.")
def main(args):
# print args recap
print(args, end="\n\n")
# do not remove this line
start = time.time()
# Create the dataset object
dataset = CORE50(
root='/home/jbonato/Documents/cvpr_clvision_challenge/core50/data/',
scenario=args.scenario,
preload=True)
################################# Get the validation set
print("Recovering validation set...")
full_valdidset = dataset.get_full_valid_set()
device0 = torch.device('cuda:0')
################################ # code for training
if args.scenario == 'ni':
NI = NI_wrap(dataset,
full_valdidset,
device=device0,
path='/home/jbonato/Documents/cvpr_clvision_challenge/',
load=args.load)
elif args.scenario == 'multi-task-nc':
NI = NC_wrap(dataset,
full_valdidset,
device=device0,
path='/home/jbonato/Documents/cvpr_clvision_challenge/',
load=args.load)
elif args.scenario == 'nic':
NI = NIC_wrap(dataset,
full_valdidset,
device=device0,
path='/home/jbonato/Documents/cvpr_clvision_challenge/',
load=args.load)
stats, valid_acc = NI.train()
ram_usage = np.asarray(stats['ram'])
ext_mem_sz = np.asarray(stats['disk'])
################################# Generate submission.zip
################################# directory with the code snapshot to generate the results
sub_dir = 'submissions/' + args.sub_dir
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
################################# copy code
create_code_snapshot(".", sub_dir + "/code_snapshot")
################################## generating metadata.txt: with all the data used for the CLScore
elapsed = (time.time() - start) / 60
print("Training Time: {}m".format(elapsed))
with open(sub_dir + "/metadata.txt", "w") as wf:
for obj in [
np.average(valid_acc), elapsed,
np.average(ram_usage),
np.max(ram_usage),
np.average(ext_mem_sz),
np.max(ext_mem_sz)
]:
wf.write(str(obj) + "\n")
with open(sub_dir + "/valid_hist.txt", "w") as wf:
for obj in [valid_acc]:
wf.write(str(obj) + "\n")
# test_preds.txt: with a list of labels separated by "\n"
print("Final inference on test set...")
full_testset = dataset.get_full_test_set()
pred = NI.test(full_testset, standalone=False)
with open(sub_dir + "/test_preds.txt", "w") as wf:
for jj in range(pred.shape[0]):
wf.write(str(pred[jj]) + "\n")
print("Experiment completed.")
def main(args):
# print args recap
print(args, end="\n\n")
# do not remove this line
start = time.time()
# Create the dataset object for example with the "ni, multi-task-nc, or nic tracks"
# and assuming the core50 location in ./core50/data/
# ???review CORE50 to see if there is a way to shorten dataset and runtime for testing
# Original call to dataset. Takes a long time.
# dataset = CORE50(root='core50/data/', scenario=args.scenario,
# preload=args.preload_data)
#
# custom call to create CORE50 custom object
# using train=True uses training set and allows more control over batches and other stuff.
dataset = CORE50(root='core50/data/',
scenario=args.scenario,
preload=args.preload_data)
# Get the validation set
print("Recovering validation set...")
# default full validation set
# full_valdidset = dataset.get_full_valid_set()
# reduced validation set
full_valdidset = dataset.get_full_valid_set(reduced=True)
# model
if args.classifier == 'ResNet18':
classifier = models.resnet18(
pretrained=True) # classifier is a pretrained model
classifier.fc = torch.nn.Linear(
512, args.n_classes
) # in features: 512 # out features: set below -> args.n_classes = 50 # Applies a linear transformation to the incoming data
opt = torch.optim.SGD(classifier.parameters(),
lr=args.lr) # Implements stochastic gradient descent
criterion = torch.nn.CrossEntropyLoss(
) # This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class.
# vars to update over time
valid_acc = []
ext_mem_sz = []
ram_usage = []
heads = []
# enumerate(dataset) provides itirator over all training sets and test sets
# loop over the training incremental batches (x, y, t)
for i, train_batch in enumerate(dataset):
train_x, train_y, t = train_batch
# Start modification
# run batch 0 and 1. Then break.
# if i == 2: break
# shuffle new data
train_x, train_y = shuffle_in_unison((train_x, train_y), seed=0)
if i == 0:
# this is the first round
# store data for later
all_x = train_x[0:train_x.shape[0] // 2]
all_y = train_y[0:train_y.shape[0] // 2]
else:
# this is not the first round
# create hybrid training set old and new data
# shuffle old data
all_x, all_y = shuffle_in_unison((all_x, all_y), seed=0)
# create temp holder
temp_x = train_x
temp_y = train_y
# set current variables to be used for training
train_x = np.append(all_x, train_x, axis=0)
train_y = np.append(all_y, train_y)
train_x, train_y = shuffle_in_unison((train_x, train_y), seed=0)
# append half of old and all of new data
temp_x, temp_y = shuffle_in_unison((temp_x, temp_y), seed=0)
keep_old = (all_x.shape[0] // (i + 1)) * i
keep_new = temp_x.shape[0] // (i + 1)
all_x = np.append(all_x[0:keep_old], temp_x[0:keep_new], axis=0)
all_y = np.append(all_y[0:keep_old], temp_y[0:keep_new])
del temp_x
del temp_y
# rest of code after this should be the same
# End modification
# Print current batch number
print("----------- batch {0} -------------".format(i))
# Print current batch shape
print("x shape: {0}, y shape: {1}".format(train_x.shape,
train_y.shape))
# print task label type
print("Task Label: ", t)
# utils.train_net: a custom function to train neural network. returns stats.
_, _, stats = train_net(opt,
classifier,
criterion,
args.batch_size,
train_x,
train_y,
t,
args.epochs,
preproc=preprocess_imgs)
# if multi-task-nc: make deep copy in list heads (aka nn brains)
if args.scenario == "multi-task-nc":
heads.append(copy.deepcopy(classifier.fc))
ext_mem_sz += stats['disk']
ram_usage += stats['ram']
# test all nn models in list heads for performance. return stats for each.
stats, _ = test_multitask(classifier,
full_valdidset,
args.batch_size,
preproc=preprocess_imgs,
multi_heads=heads)
# print new stats on performance
valid_acc += stats['acc']
print("------------------------------------------")
print("Avg. acc: {}".format(stats['acc']))
print("------------------------------------------")
# Generate submission.zip
# directory with the code snapshot to generate the results
sub_dir = 'submissions/' + args.sub_dir
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
# copy code
# custom function in utils folder to deal with possible file path issues
create_code_snapshot(".", sub_dir + "/code_snapshot")
# generating metadata.txt: with all the data used for the CLScore
elapsed = (time.time() - start) / 60
print("Training Time: {}m".format(elapsed))
with open(sub_dir + "/metadata.txt", "w") as wf:
for obj in [
np.average(valid_acc), elapsed,
np.average(ram_usage),
np.max(ram_usage),
np.average(ext_mem_sz),
np.max(ext_mem_sz)
]:
wf.write(str(obj) + "\n")
# run final full test
# test_preds.txt: with a list of labels separated by "\n"
print("Final inference on test set...")
full_testset = dataset.get_full_test_set()
stats, preds = test_multitask(classifier,
full_testset,
args.batch_size,
preproc=preprocess_imgs)
with open(sub_dir + "/test_preds.txt", "w") as wf:
for pred in preds:
wf.write(str(pred) + "\n")
print("Experiment completed.")
writer = SummaryWriter(log_dir)
# Saving params
hyper = json.dumps(dict(exp_config))
writer.add_text("parameters", hyper, 0)
# Other variables init
tot_it_step = 0
rm = None
# do not remove this line
start_time = time.time()
# Create the dataset object
dataset = CORE50(
root='/home/admin/ssd_data/cvpr_competition/cvpr_competition_data/',
scenario=scenario,
preload=False)
preproc = preprocess_imgs
# Get the fixed test set
full_valdidset = dataset.get_full_valid_set()
# Model setup
model = MyMobilenetV1(pretrained=True, latent_layer_num=latent_layer_num)
replace_bn_with_brn(model,
momentum=init_update_rate,
r_d_max_inc_step=inc_step,
max_r_max=max_r_max,
max_d_max=max_d_max)
model.saved_weights = {}
model.past_j = {i: 0 for i in range(50)}
def main(args):
# print args recap
print(args, end="\n\n")
# do not remove this line
start = time.time()
# Create the dataset object for example with the "ni, multi-task-nc, or nic
# tracks" and assuming the core50 location in ./core50/data/
dataset = CORE50(root='core50/data/',
scenario=args.scenario,
preload=args.preload_data)
# Get the validation set
print("Recovering validation set...")
full_valdidset = dataset.get_full_valid_set()
# model
if args.classifier == 'ResNet18':
# classifier = models.resnet18(pretrained=True)
classifier = resnet18(pretrained=True)
classifier.fc = torch.nn.Linear(512, args.n_classes)
if args.classifier == 'ResNet34':
# classifier = models.resnet34(pretrained=True)
classifier = resnet34(pretrained=True)
classifier.fc = torch.nn.Linear(512, args.n_classes)
if args.classifier == 'ResNet50':
classifier = resnet50(pretrained=True)
classifier.fc = torch.nn.Linear(2048, args.n_classes)
if args.classifier == 'MobileNetV2':
classifier = mobilenet_v2(pretrained=True)
classifier.classifier = torch.nn.Sequential(
torch.nn.Dropout(0.2),
torch.nn.Linear(1280, args.n_classes),
)
if args.classifier == 'mnasnet':
classifier = mnasnet1_0(pretrained=True)
classifier.classifier = torch.nn.Sequential(
torch.nn.Dropout(p=0.2, inplace=True),
torch.nn.Linear(1280, args.n_classes))
if args.classifier == 'siamese':
classifier = SiameseNetwork(n_class=args.n_classes)
if args.optimizer == 'sgd':
opt = torch.optim.SGD(classifier.parameters(), lr=args.lr)
if args.optimizer == 'adam':
opt = torch.optim.Adam(classifier.parameters(), lr=args.lr)
if args.optimizer == 'radam':
opt = RAdam(classifier.parameters(), lr=args.lr)
# for param in classifier.conv1.parameters() or param in classifier.layer1.parameters() or param in classifier.relu.parameters():
# param.requires_grad = False
# opt = torch.optim.SGD(filter(lambda p: p.requires_grad, classifier.parameters()), lr=args.lr)
regularization_terms = {}
task_count = 0
criterion = torch.nn.CrossEntropyLoss()
if args.classifier == 'siamese':
# criterion = ContrastiveLoss(margin=2.)
criterion = ContrastiveAndCELoss(margin=10.)
# vars to update over time
valid_acc = []
ext_mem_sz = []
ram_usage = []
heads = []
ext_mem = None
# loop over the training incremental batches (x, y, t)
for i, train_batch in enumerate(dataset):
train_x, train_y, t = train_batch
# adding eventual replay patterns to the current batch
idxs_cur = np.random.choice(train_x.shape[0],
args.replay_examples,
replace=False)
if i == 0:
ext_mem = [train_x[idxs_cur], train_y[idxs_cur]]
else:
ext_mem = [
np.concatenate((train_x[idxs_cur], ext_mem[0])),
np.concatenate((train_y[idxs_cur], ext_mem[1]))
]
train_x = np.concatenate((train_x, ext_mem[0]))
train_y = np.concatenate((train_y, ext_mem[1]))
print("----------- batch {0} -------------".format(i))
print(
"x shape: {0}, y shape: {1}, ext_mem_x shape: {2}, ext_mem_y shape: {3}"
.format(train_x.shape, train_y.shape, ext_mem[0].shape,
ext_mem[1].shape))
print("Task Label: ", t)
# train the classifier on the current batch/task
# _, _, stats, regularization_terms, classifier = train_net_mrcl(
# opt, classifier, criterion, args.batch_size, train_x, train_y, i,
# regularization_terms, task_count, args.regularize_mode, args.icarl,
# args.epochs, preproc=preprocess_imgs
# )
i_es = 0
lr = args.lr
valid_acc2 = []
for iepoch in range(args.epochs):
if args.classifier != 'siamese':
_, _, stats, regularization_terms = train_net(
opt,
classifier,
criterion,
args.batch_size,
train_x,
train_y,
i,
regularization_terms,
task_count,
args.regularize_mode,
args.icarl,
args.aug,
args.resize_shape,
train_ep=1,
preproc=preprocess_imgs)
else:
_, _, stats, regularization_terms = train_net_siamese(
opt,
classifier,
criterion,
args.batch_size,
train_x,
train_y,
i,
regularization_terms,
task_count,
args.regularize_mode,
args.icarl,
args.aug,
args.resize_shape,
train_ep=1,
preproc=preprocess_imgs)
# stats2, _ = test_multitask(
# classifier, full_valdidset, args.batch_size, args.aug, args.resize_shape,
# preproc=preprocess_imgs, multi_heads=heads, verbose=False
# )
# valid_acc2 += stats2['acc']
# print("{}th epoch, Avg. acc: {}".format(iepoch+1, stats2['acc']))
# if stats2['acc']==max(valid_acc2):
# classifier_max = copy.deepcopy(classifier)
# print('update classifier using {}th'.format(iepoch+1))
# else:
# i_es +=1 # counter for early stop
# if i_es>2:
# if args.optimizer =='sgd' and lr>=0.0001:
# lr = 0.1*lr
# opt = torch.optim.SGD(classifier.parameters(), lr=lr)
# i_es = 0
# print('learning rate reduced to {}'.format(lr))
# else:
# print('early stop at epoch {}'.format(iepoch+1))
# classifier = copy.deepcopy(classifier_max)
# break
# classifier = copy.deepcopy(classifier_max)
if args.scenario == "multi-task-nc":
if args.classifier == 'mnasnet':
heads.append(copy.deepcopy(classifier.classifier[1]))
elif args.classifier == 'siamese':
heads.append(copy.deepcopy(classifier.cnn1.classifier[1]))
else:
heads.append(copy.deepcopy(classifier.fc))
### not using the nearest neighbour classifier in icarl
# if args.icarl:
# exem_class = []
# classifier = maybe_cuda(classifier)
# classifier.eval()
# # update exemplar features
# if args.classifier=='mnasnet':
# for i_class in range(args.n_classes):
# exemplar_features = []
# # nb_iters = (i_class==ext_mem[1]).sum()//args.batch_size + 1
# # x_i = torch.from_numpy(preprocess_imgs(ext_mem[0][i_class==ext_mem[1]])).type(torch.FloatTensor)
# nb_iters = (i_class==train_y).sum()//args.batch_size + 1
# x_i = torch.from_numpy(preprocess_imgs(train_x[i_class==train_y])).type(torch.FloatTensor)
# if len(x_i)==0:
# print('no exemplars to be updated for class {}...'.format(i_class))
# break
# with torch.no_grad():
# for it in range(nb_iters):
# start = it * args.batch_size
# end = (it + 1) * args.batch_size
# x_i_mb = maybe_cuda(x_i[start:end])
# if x_i_mb.shape[0]==0:
# break
# feat_exem = classifier.layers(x_i_mb).mean([2, 3]) # mnasnet
# exemplar_features.extend(np.array(feat_exem.cpu()))
# mean_exem_feats = np.mean(exemplar_features, axis=0)
# # mean_exem_feats = mean_exem_feats / np.linalg.norm(mean_exem_feats) # Normalize
# exem_class.append(torch.from_numpy(mean_exem_feats).type(torch.FloatTensor))
# # classify with nearest
# stats_icarl, _ = test_multitask_icarl(
# classifier, full_valdidset, exem_class, args.batch_size,
# preproc=preprocess_imgs, multi_heads=heads, verbose=False
# )
# print("icarl avg. acc: {}".format(stats_icarl['acc']))
# else:
# for i_class in range(args.n_classes):
# nb_iters = (i_class==ext_mem[1]).sum()//args.batch_size if (i_class==ext_mem[1]).sum()//args.batch_size >0 else (i_class==ext_mem[1]).sum()
# x_i = torch.from_numpy(preprocess_imgs(ext_mem[0][i_class==ext_mem[1]])).type(torch.FloatTensor)
# if len(x_i)==0:
# break
# with torch.no_grad():
# for it in range(nb_iters):
# start = it * args.batch_size
# end = (it + 1) * args.batch_size
# x_i_mb = maybe_cuda(x_i[start:end])
# feat_exem = classifier.conv1(x_i_mb)
# feat_exem = classifier.bn1(feat_exem)
# feat_exem = classifier.relu(feat_exem)
# print(feat_exem.shape)
# feat_exem = classifier.maxpool(feat_exem)
# feat_exem = classifier.layer1(feat_exem)
# feat_exem = classifier.layer2(feat_exem)
# feat_exem = classifier.layer3(feat_exem)
# feat_exem = classifier.layer4(feat_exem)
# feat_exem = classifier.avgpool(feat_exem)
# feat_exem = feat_exem.view(feat_exem.size(0), -1)
# exemplar_features.append(np.array(feat_exem.cpu()))
# mean_exem_feats = np.mean(np.mean(exemplar_features, axis=0), axis=0)
# mean_exem_feats = mean_exem_feats / np.linalg.norm(mean_exem_feats) # Normalize
# exem_class.append(torch.from_numpy(mean_exem_feats).type(torch.FloatTensor))
# # classify with nearest
# stats_icarl, _ = test_multitask_icarl(
# classifier, full_valdidset, exem_class, args.batch_size,
# preproc=preprocess_imgs, multi_heads=heads, verbose=False
# )
# print("icarl avg. acc: {}".format(stats_icarl['acc']))
###
task_count += 1
# collect statistics
ext_mem_sz += stats['disk']
ram_usage += stats['ram']
# test on the validation set
if args.classifier != 'siamese':
stats, _ = test_multitask(classifier,
full_valdidset,
args.batch_size,
args.aug,
args.resize_shape,
preproc=preprocess_imgs,
multi_heads=heads,
verbose=False)
else:
exem_class = []
classifier = maybe_cuda(classifier)
classifier.eval()
# update exemplar features
for i_class in range(args.n_classes):
exemplar_features = []
nb_iters = (i_class == train_y).sum() // args.batch_size + 1
x_i = preprocess_imgs(train_x[i_class == train_y],
aug=False,
resize_shape=args.resize_shape).type(
torch.FloatTensor)
if len(x_i) == 0:
print('no exemplars to be updated for class {}...'.format(
i_class))
break
with torch.no_grad():
for it in range(nb_iters):
start = it * args.batch_size
end = (it + 1) * args.batch_size
x_i_mb = maybe_cuda(x_i[start:end])
if x_i_mb.shape[0] == 0:
break
feat_exem = classifier.forward_once(x_i_mb)
i = random.choice(
range(x_i_mb.shape[0])
) # sample one exemplar for each iteration per class
# exemplar_features.extend(np.array(feat_exem.cpu()))
exemplar_features.append(np.array(feat_exem[i].cpu()))
# mean_exem_feats = np.mean(exemplar_features, axis=0)
# # mean_exem_feats = mean_exem_feats / np.linalg.norm(mean_exem_feats) # Normalize
# exem_class.append(torch.from_numpy(mean_exem_feats).type(torch.FloatTensor))
exem_class.append(
torch.from_numpy(np.array(exemplar_features)).type(
torch.FloatTensor))
# classify with nearest
stats, _ = test_multitask_siamese(classifier,
full_valdidset,
exem_class,
args.batch_size,
args.aug,
args.resize_shape,
preproc=preprocess_imgs,
multi_heads=heads,
verbose=False)
valid_acc += stats['acc']
print("------------------------------------------")
print("Avg. acc: {}".format(stats['acc']))
print("------------------------------------------")
# Generate submission.zip
# directory with the code snapshot to generate the results
sub_dir = 'submissions/' + args.sub_dir
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
# copy code
create_code_snapshot(".", sub_dir + "/code_snapshot")
# generating metadata.txt: with all the data used for the CLScore
elapsed = (time.time() - start) / 60
print("Training Time: {}m".format(elapsed))
print("Final average valid acc: {}".format(np.average(valid_acc)))
with open(sub_dir + "/metadata.txt", "w") as wf:
for obj in [
np.average(valid_acc), elapsed,
np.average(ram_usage),
np.max(ram_usage),
np.average(ext_mem_sz),
np.max(ext_mem_sz)
]:
wf.write(str(obj) + "\n")
# test_preds.txt: with a list of labels separated by "\n"
print("Final inference on test set...")
full_testset = dataset.get_full_test_set()
if args.classifier != 'siamese':
stats, preds = test_multitask(classifier,
full_testset,
args.batch_size,
args.aug,
args.resize_shape,
preproc=preprocess_imgs,
multi_heads=heads,
verbose=False)
else:
stats, _ = test_multitask_siamese(classifier,
full_testset,
exem_class,
args.batch_size,
args.aug,
args.resize_shape,
preproc=preprocess_imgs,
multi_heads=heads,
verbose=False)
with open(sub_dir + "/test_preds.txt", "w") as wf:
for pred in preds:
wf.write(str(pred) + "\n")
print("Experiment completed.")
def setup_dataset(scenario, preload=True):
dataset = CORE50(root='core50/data/', scenario=scenario,
preload=preload)
full_valdidset = dataset.get_full_valid_set()
num_tasks = dataset.nbatch[dataset.scenario]
return dataset, full_valdidset, num_tasks