def train_with_correspondences(save_folder, startnet, args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
check_mkdir(save_folder)
writer = SummaryWriter(save_folder)
# Network and weight loading
model_config = model_configs.PspnetCityscapesConfig()
net = model_config.init_network().to(device)
if args['snapshot'] == 'latest':
args['snapshot'] = get_latest_network_name(save_folder)
if len(args['snapshot']) == 0: # If start from beginning
state_dict = torch.load(startnet)
# needed since we slightly changed the structure of the network in
# pspnet
state_dict = rename_keys_to_match(state_dict)
net.load_state_dict(state_dict) # load original weights
start_iter = 0
args['best_record'] = {
'iter': 0,
'val_loss': 1e10,
'acc': 0,
'acc_cls': 0,
'mean_iu': 0,
'fwavacc': 0
}
else: # If continue training
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(os.path.join(save_folder,
args['snapshot']))) # load weights
split_snapshot = args['snapshot'].split('_')
start_iter = int(split_snapshot[1])
with open(os.path.join(save_folder, 'bestval.txt')) as f:
best_val_dict_str = f.read()
args['best_record'] = eval(best_val_dict_str.rstrip())
net.train()
freeze_bn(net)
# Data loading setup
if args['corr_set'] == 'rc':
corr_set_config = data_configs.RobotcarConfig()
elif args['corr_set'] == 'cmu':
corr_set_config = data_configs.CmuConfig()
sliding_crop_im = joint_transforms.SlidingCropImageOnly(
713, args['stride_rate'])
input_transform = model_config.input_transform
pre_validation_transform = model_config.pre_validation_transform
target_transform = extended_transforms.MaskToTensor()
train_joint_transform_seg = joint_transforms.Compose([
joint_transforms.Resize(1024),
joint_transforms.RandomRotate(10),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomCrop(713)
])
train_joint_transform_corr = corr_transforms.Compose([
corr_transforms.CorrResize(1024),
corr_transforms.CorrRandomCrop(713)
])
# keep list of segmentation loaders and validators
seg_loaders = list()
validators = list()
# Correspondences
corr_set = correspondences.Correspondences(
corr_set_config.correspondence_path,
corr_set_config.correspondence_im_path,
input_size=(713, 713),
mean_std=model_config.mean_std,
input_transform=input_transform,
joint_transform=train_joint_transform_corr)
corr_loader = DataLoader(corr_set,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
# Cityscapes Training
c_config = data_configs.CityscapesConfig()
seg_set_cs = cityscapes.CityScapes(
c_config.train_im_folder,
c_config.train_seg_folder,
c_config.im_file_ending,
c_config.seg_file_ending,
id_to_trainid=c_config.id_to_trainid,
joint_transform=train_joint_transform_seg,
sliding_crop=None,
transform=input_transform,
target_transform=target_transform)
seg_loader_cs = DataLoader(seg_set_cs,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
seg_loaders.append(seg_loader_cs)
# Cityscapes Validation
val_set_cs = cityscapes.CityScapes(
c_config.val_im_folder,
c_config.val_seg_folder,
c_config.im_file_ending,
c_config.seg_file_ending,
id_to_trainid=c_config.id_to_trainid,
sliding_crop=sliding_crop_im,
transform=input_transform,
target_transform=target_transform,
transform_before_sliding=pre_validation_transform)
val_loader_cs = DataLoader(val_set_cs,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
validator_cs = Validator(val_loader_cs,
n_classes=c_config.n_classes,
save_snapshot=False,
extra_name_str='Cityscapes')
validators.append(validator_cs)
# Vistas Training and Validation
if args['include_vistas']:
v_config = data_configs.VistasConfig(
use_subsampled_validation_set=True, use_cityscapes_classes=True)
seg_set_vis = cityscapes.CityScapes(
v_config.train_im_folder,
v_config.train_seg_folder,
v_config.im_file_ending,
v_config.seg_file_ending,
id_to_trainid=v_config.id_to_trainid,
joint_transform=train_joint_transform_seg,
sliding_crop=None,
transform=input_transform,
target_transform=target_transform)
seg_loader_vis = DataLoader(seg_set_vis,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
seg_loaders.append(seg_loader_vis)
val_set_vis = cityscapes.CityScapes(
v_config.val_im_folder,
v_config.val_seg_folder,
v_config.im_file_ending,
v_config.seg_file_ending,
id_to_trainid=v_config.id_to_trainid,
sliding_crop=sliding_crop_im,
transform=input_transform,
target_transform=target_transform,
transform_before_sliding=pre_validation_transform)
val_loader_vis = DataLoader(val_set_vis,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
validator_vis = Validator(val_loader_vis,
n_classes=v_config.n_classes,
save_snapshot=False,
extra_name_str='Vistas')
validators.append(validator_vis)
else:
seg_loader_vis = None
map_validator = None
# Extra Training
extra_seg_set = cityscapes.CityScapes(
corr_set_config.train_im_folder,
corr_set_config.train_seg_folder,
corr_set_config.im_file_ending,
corr_set_config.seg_file_ending,
id_to_trainid=corr_set_config.id_to_trainid,
joint_transform=train_joint_transform_seg,
sliding_crop=None,
transform=input_transform,
target_transform=target_transform)
extra_seg_loader = DataLoader(extra_seg_set,
batch_size=args['train_batch_size'],
num_workers=args['n_workers'],
shuffle=True)
seg_loaders.append(extra_seg_loader)
# Extra Validation
extra_val_set = cityscapes.CityScapes(
corr_set_config.val_im_folder,
corr_set_config.val_seg_folder,
corr_set_config.im_file_ending,
corr_set_config.seg_file_ending,
id_to_trainid=corr_set_config.id_to_trainid,
sliding_crop=sliding_crop_im,
transform=input_transform,
target_transform=target_transform,
transform_before_sliding=pre_validation_transform)
extra_val_loader = DataLoader(extra_val_set,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
extra_validator = Validator(extra_val_loader,
n_classes=corr_set_config.n_classes,
save_snapshot=True,
extra_name_str='Extra')
validators.append(extra_validator)
# Loss setup
if args['corr_loss_type'] == 'class':
corr_loss_fct = CorrClassLoss(input_size=[713, 713])
else:
corr_loss_fct = FeatureLoss(
input_size=[713, 713],
loss_type=args['corr_loss_type'],
feat_dist_threshold_match=args['feat_dist_threshold_match'],
feat_dist_threshold_nomatch=args['feat_dist_threshold_nomatch'],
n_not_matching=0)
seg_loss_fct = torch.nn.CrossEntropyLoss(
reduction='elementwise_mean',
ignore_index=cityscapes.ignore_label).to(device)
# Optimizer setup
optimizer = optim.SGD([{
'params': [
param for name, param in net.named_parameters()
if name[-4:] == 'bias' and param.requires_grad
],
'lr':
2 * args['lr']
}, {
'params': [
param for name, param in net.named_parameters()
if name[-4:] != 'bias' and param.requires_grad
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'],
nesterov=True)
if len(args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(os.path.join(save_folder, 'opt_' + args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
open(os.path.join(save_folder,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
if len(args['snapshot']) == 0:
f_handle = open(os.path.join(save_folder, 'log.log'), 'w', buffering=1)
else:
clean_log_before_continuing(os.path.join(save_folder, 'log.log'),
start_iter)
f_handle = open(os.path.join(save_folder, 'log.log'), 'a', buffering=1)
##########################################################################
#
# MAIN TRAINING CONSISTS OF ALL SEGMENTATION LOSSES AND A CORRESPONDENCE LOSS
#
##########################################################################
softm = torch.nn.Softmax2d()
val_iter = 0
train_corr_loss = AverageMeter()
train_seg_cs_loss = AverageMeter()
train_seg_extra_loss = AverageMeter()
train_seg_vis_loss = AverageMeter()
seg_loss_meters = list()
seg_loss_meters.append(train_seg_cs_loss)
if args['include_vistas']:
seg_loss_meters.append(train_seg_vis_loss)
seg_loss_meters.append(train_seg_extra_loss)
curr_iter = start_iter
for i in range(args['max_iter']):
optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
optimizer.param_groups[1]['lr'] = args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
#######################################################################
# SEGMENTATION UPDATE STEP
#######################################################################
#
for si, seg_loader in enumerate(seg_loaders):
# get segmentation training sample
inputs, gts = next(iter(seg_loader))
slice_batch_pixel_size = inputs.size(0) * inputs.size(
2) * inputs.size(3)
inputs = inputs.to(device)
gts = gts.to(device)
optimizer.zero_grad()
outputs, aux = net(inputs)
main_loss = args['seg_loss_weight'] * seg_loss_fct(outputs, gts)
aux_loss = args['seg_loss_weight'] * seg_loss_fct(aux, gts)
loss = main_loss + 0.4 * aux_loss
loss.backward()
optimizer.step()
seg_loss_meters[si].update(main_loss.item(),
slice_batch_pixel_size)
#######################################################################
# CORRESPONDENCE UPDATE STEP
#######################################################################
if args['corr_loss_weight'] > 0 and args[
'n_iterations_before_corr_loss'] < curr_iter:
img_ref, img_other, pts_ref, pts_other, weights = next(
iter(corr_loader))
# Transfer data to device
# img_ref is from the "good" sequence with generally better
# segmentation results
img_ref = img_ref.to(device)
img_other = img_other.to(device)
pts_ref = [p.to(device) for p in pts_ref]
pts_other = [p.to(device) for p in pts_other]
weights = [w.to(device) for w in weights]
# Forward pass
if args['corr_loss_type'] == 'hingeF': # Works on features
net.output_all = True
with torch.no_grad():
output_feat_ref, aux_feat_ref, output_ref, aux_ref = net(
img_ref)
output_feat_other, aux_feat_other, output_other, aux_other = net(
img_other
) # output1 must be last to backpropagate derivative correctly
net.output_all = False
else: # Works on class probs
with torch.no_grad():
output_ref, aux_ref = net(img_ref)
if args['corr_loss_type'] != 'hingeF' and args[
'corr_loss_type'] != 'hingeC':
output_ref = softm(output_ref)
aux_ref = softm(aux_ref)
# output1 must be last to backpropagate derivative correctly
output_other, aux_other = net(img_other)
if args['corr_loss_type'] != 'hingeF' and args[
'corr_loss_type'] != 'hingeC':
output_other = softm(output_other)
aux_other = softm(aux_other)
# Correspondence filtering
pts_ref_orig, pts_other_orig, weights_orig, batch_inds_to_keep_orig = correspondences.refine_correspondence_sample(
output_ref,
output_other,
pts_ref,
pts_other,
weights,
remove_same_class=args['remove_same_class'],
remove_classes=args['classes_to_ignore'])
pts_ref_orig = [
p for b, p in zip(batch_inds_to_keep_orig, pts_ref_orig)
if b.item() > 0
]
pts_other_orig = [
p for b, p in zip(batch_inds_to_keep_orig, pts_other_orig)
if b.item() > 0
]
weights_orig = [
p for b, p in zip(batch_inds_to_keep_orig, weights_orig)
if b.item() > 0
]
if args['corr_loss_type'] == 'hingeF':
# remove entire samples if needed
output_vals_ref = output_feat_ref[batch_inds_to_keep_orig]
output_vals_other = output_feat_other[batch_inds_to_keep_orig]
else:
# remove entire samples if needed
output_vals_ref = output_ref[batch_inds_to_keep_orig]
output_vals_other = output_other[batch_inds_to_keep_orig]
pts_ref_aux, pts_other_aux, weights_aux, batch_inds_to_keep_aux = correspondences.refine_correspondence_sample(
aux_ref,
aux_other,
pts_ref,
pts_other,
weights,
remove_same_class=args['remove_same_class'],
remove_classes=args['classes_to_ignore'])
pts_ref_aux = [
p for b, p in zip(batch_inds_to_keep_aux, pts_ref_aux)
if b.item() > 0
]
pts_other_aux = [
p for b, p in zip(batch_inds_to_keep_aux, pts_other_aux)
if b.item() > 0
]
weights_aux = [
p for b, p in zip(batch_inds_to_keep_aux, weights_aux)
if b.item() > 0
]
if args['corr_loss_type'] == 'hingeF':
# remove entire samples if needed
aux_vals_ref = aux_feat_ref[batch_inds_to_keep_orig]
aux_vals_other = aux_feat_other[batch_inds_to_keep_orig]
else:
# remove entire samples if needed
aux_vals_ref = aux_ref[batch_inds_to_keep_aux]
aux_vals_other = aux_other[batch_inds_to_keep_aux]
optimizer.zero_grad()
# correspondence loss
if output_vals_ref.size(0) > 0:
loss_corr_hr = corr_loss_fct(output_vals_ref,
output_vals_other, pts_ref_orig,
pts_other_orig, weights_orig)
else:
loss_corr_hr = 0 * output_vals_other.sum()
if aux_vals_ref.size(0) > 0:
loss_corr_aux = corr_loss_fct(
aux_vals_ref, aux_vals_other, pts_ref_aux, pts_other_aux,
weights_aux) # use output from img1 as "reference"
else:
loss_corr_aux = 0 * aux_vals_other.sum()
loss_corr = args['corr_loss_weight'] * \
(loss_corr_hr + 0.4 * loss_corr_aux)
loss_corr.backward()
optimizer.step()
train_corr_loss.update(loss_corr.item())
#######################################################################
# LOGGING ETC
#######################################################################
curr_iter += 1
val_iter += 1
writer.add_scalar('train_seg_loss_cs', train_seg_cs_loss.avg,
curr_iter)
writer.add_scalar('train_seg_loss_extra', train_seg_extra_loss.avg,
curr_iter)
writer.add_scalar('train_seg_loss_vis', train_seg_vis_loss.avg,
curr_iter)
writer.add_scalar('train_corr_loss', train_corr_loss.avg, curr_iter)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'], curr_iter)
if (i + 1) % args['print_freq'] == 0:
str2write = '[iter %d / %d], [train corr loss %.5f] , [seg cs loss %.5f], [seg vis loss %.5f], [seg extra loss %.5f]. [lr %.10f]' % (
curr_iter, len(corr_loader), train_corr_loss.avg,
train_seg_cs_loss.avg, train_seg_vis_loss.avg,
train_seg_extra_loss.avg, optimizer.param_groups[1]['lr'])
print(str2write)
f_handle.write(str2write + "\n")
if val_iter >= args['val_interval']:
val_iter = 0
for validator in validators:
validator.run(net,
optimizer,
args,
curr_iter,
save_folder,
f_handle,
writer=writer)
# Post training
f_handle.close()
writer.close()
def train_with_clustering(save_folder, tmp_seg_folder, startnet, args):
print(save_folder.split('/')[-1])
skip_clustering = False
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
check_mkdir(save_folder)
writer = SummaryWriter(save_folder)
check_mkdir(tmp_seg_folder)
# Network and weight loading
model_config = model_configs.PspnetCityscapesConfig()
net = model_config.init_network(
n_classes=args['n_clusters'],
for_clustering=True,
output_features=True,
use_original_base=args['use_original_base']).to(device)
state_dict = torch.load(startnet)
if 'resnet101' in startnet:
load_resnet101_weights(net, state_dict)
else:
# needed since we slightly changed the structure of the network in pspnet
state_dict = rename_keys_to_match(state_dict)
# different amount of classes
init_last_layers(state_dict, args['n_clusters'])
net.load_state_dict(state_dict) # load original weights
start_iter = 0
args['best_record'] = {
'iter': 0,
'val_loss_feat': 1e10,
'val_loss_out': 1e10,
'val_loss_cluster': 1e10
}
# Data loading setup
if args['corr_set'] == 'rc':
corr_set_config = data_configs.RobotcarConfig()
elif args['corr_set'] == 'pola':
corr_set_config = data_configs.PolaConfig()
elif args['corr_set'] == 'cmu':
corr_set_config = data_configs.CmuConfig()
elif args['corr_set'] == 'both':
corr_set_config1 = data_configs.CmuConfig()
corr_set_config2 = data_configs.RobotcarConfig()
ref_image_lists = corr_set_config.reference_image_list
# ref_image_lists = glob.glob("/media/HDD1/datasets/Creusot_Jan15/Creusot_3/*.jpg", recursive=True)
# print(f'ici on print ref image list ---------------------------------------------------- {ref_image_lists}')
# print(corr_set_config)
# corr_im_paths = [corr_set_config.correspondence_im_path]
# ref_featurs_pos = [corr_set_config.reference_feature_poitions]
input_transform = model_config.input_transform
#corr_set_train = correspondences.Correspondences(corr_set_config.correspondence_path,
# corr_set_config.correspondence_im_path,
# input_size=(713, 713),
# input_transform=input_transform,
# joint_transform=train_joint_transform_corr,
# listfile=corr_set_config.correspondence_train_list_file)
scales = [0, 1, 2, 3]
# corr_set_train = Poladata.MonoDataset(corr_set_config,
# seg_folder = "media/HDD1/NsemSEG/Result_fold/" ,
# im_file_ending = ".jpg" )
train_joint_transform = joint_transforms.Compose([
# train_joint_transform_corr = corr_transforms.Compose([
# corr_transforms.CorrResize(1024),
# corr_transforms.CorrRandomCrop(713)
joint_transforms.Resize(1024),
joint_transforms.RandomCrop(713)
])
sliding_crop = joint_transforms.SlidingCrop(713, 2 / 3., 255)
# corr_set_train = correspondences.Correspondences(corr_set_config.train_im_folder,
# corr_set_config.train_im_folder,
# input_size=(713, 713),
# input_transform=input_transform,
# joint_transform=train_joint_transform,
# listfile=None)
corr_set_train = Poladata.MonoDataset(
corr_set_config.train_im_folder,
corr_set_config.train_seg_folder,
im_file_ending=".jpg",
id_to_trainid=None,
joint_transform=train_joint_transform,
sliding_crop=sliding_crop,
transform=input_transform,
target_transform=None, #train_joint_transform,
transform_before_sliding=None #sliding_crop
)
#print (corr_set_train)
# print(corr_set_train.mask)
corr_loader_train = DataLoader(corr_set_train,
batch_size=1,
num_workers=args['n_workers'],
shuffle=True)
# corr_loader_train = input_transform(corr_loader_train)
# print(corr_loader_train)
seg_loss_fct = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
# Optimizer setup
optimizer = optim.SGD([{
'params': [
param for name, param in net.named_parameters()
if name[-4:] == 'bias' and param.requires_grad
],
'lr':
2 * args['lr']
}, {
'params': [
param for name, param in net.named_parameters()
if name[-4:] != 'bias' and param.requires_grad
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'],
nesterov=True)
# Clustering
deepcluster = clustering.Kmeans(args['n_clusters'])
if skip_clustering:
deepcluster.set_index(cluster_centroids)
open(os.path.join(save_folder,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
f_handle = open(os.path.join(save_folder, 'log.log'), 'w', buffering=1)
# clean_log_before_continuing(os.path.join(save_folder, 'log.log'), start_iter)
# f_handle = open(os.path.join(save_folder, 'log.log'), 'a', buffering=1)
val_iter = 0
curr_iter = start_iter
while curr_iter <= args['max_iter']:
net.eval()
net.output_features = True
# max_num_features_per_image = args['max_features_per_image']
# print('-----------------------------------------------------------------')
# print (f'ref_image_lists est: {ref_image_lists},model_config es : {model_config} , net es: {net} , max feature par image es : {max_num_features_per_image} ')
# print('-----------------------------------------------------------------')
# print('le next du loader es : ---------------')
# print(next(iter(corr_loader_train)))
# features, _ = extract_features_for_reference(net, model_config, ref_image_lists,
# corr_im_paths, ref_featurs_pos,
# max_num_features_per_image=args['max_features_per_image'],
# fraction_correspondeces=0.5)
print(
'ici on a la len de la ref im list --------------------------------------------------------'
)
print(len(ref_image_lists))
features = extract_features_for_reference_nocorr(
net,
model_config,
corr_set_train,
10,
max_num_features_per_image=args['max_features_per_image'])
cluster_features = np.vstack(features)
del features
# cluster the features
cluster_indices, clustering_loss, cluster_centroids, pca_info = deepcluster.cluster_imfeatures(
cluster_features, verbose=True, use_gpu=False)
# save cluster centroids
h5f = h5py.File(
os.path.join(save_folder, 'centroids_%d.h5' % curr_iter), 'w')
h5f.create_dataset('cluster_centroids', data=cluster_centroids)
h5f.create_dataset('pca_transform_Amat', data=pca_info[0])
h5f.create_dataset('pca_transform_bvec', data=pca_info[1])
h5f.close()
# Print distribution of clusters
cluster_distribution, _ = np.histogram(
cluster_indices,
bins=np.arange(args['n_clusters'] + 1),
density=True)
str2write = 'cluster distribution ' + \
np.array2string(cluster_distribution, formatter={
'float_kind': '{0:.8f}'.format}).replace('\n', ' ')
print(str2write)
f_handle.write(str2write + "\n")
# set last layer weight to a normal distribution
reinit_last_layers(net)
# make a copy of current network state to do cluster assignment
net_for_clustering = copy.deepcopy(net)
optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
optimizer.param_groups[1]['lr'] = args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
net.train()
freeze_bn(net)
net.output_features = False
cluster_training_count = 0
# Train using the training correspondence set
corr_train_loss = AverageMeter()
seg_train_loss = AverageMeter()
feature_train_loss = AverageMeter()
while cluster_training_count < args[
'cluster_interval'] and curr_iter <= args['max_iter']:
# First extract cluster labels using saved network checkpoint
print(
'on rentre dans la boucle extract cluster_______________________________________________'
)
net.to("cpu")
net_for_clustering.to(device)
net_for_clustering.eval()
net_for_clustering.output_features = True
data_samples = []
extract_label_count = 0
while (extract_label_count < args['chunk_size']) and (
cluster_training_count + extract_label_count <
args['cluster_interval']
) and (val_iter + extract_label_count < args['val_interval']) and (
extract_label_count + curr_iter <= args['max_iter']):
# img_ref, img_other, pts_ref, pts_other, _ = next(iter(corr_set_train))
corr_loader_train = input_transform(corr_loader_train)
print(
f'la valeur de corr loader train es de {corr_loader_train} lors de l iteration : {curr_iter}'
)
img_ref, img_other, pts_ref, pts_other, _ = next(
iter(corr_loader_train))
# print('le next du loader es : ---------------')
# print(next(iter(corr_loader_train)))
# print(img_ref)
# Transfer data to device
img_ref = img_ref.to(device)
with torch.no_grad():
features = net_for_clustering(img_ref)
# assign feature to clusters for entire patch
output = features.cpu().numpy()
output_flat = output.reshape(
(output.shape[0], output.shape[1], -1))
cluster_image = np.zeros(
(output.shape[0], output.shape[2], output.shape[3]),
dtype=np.int64)
for b in range(output_flat.shape[0]):
out_f = output_flat[b]
out_f2, _ = preprocess_features(np.swapaxes(out_f, 0, 1),
pca_info=pca_info)
cluster_labels = deepcluster.assign(out_f2)
cluster_image[b] = cluster_labels.reshape(
(output.shape[2], output.shape[3]))
cluster_image = torch.from_numpy(cluster_image).to(device)
# assign cluster to correspondence positions
cluster_labels = assign_cluster_ids_to_correspondence_points(
features,
pts_ref, (deepcluster, pca_info),
inds_other=pts_other,
orig_im_size=(713, 713))
# Transfer data to cpu
img_ref = img_ref.cpu()
cluster_labels = [p.cpu() for p in cluster_labels]
cluster_image = cluster_image.cpu()
data_samples.append((img_ref, cluster_labels, cluster_image))
extract_label_count += 1
net_for_clustering.to("cpu")
net.to(device)
for data_sample in data_samples:
img_ref, cluster_labels, cluster_image = data_sample
# Transfer data to device
img_ref = img_ref.to(device)
cluster_labels = [p.to(device) for p in cluster_labels]
cluster_image = cluster_image.to(device)
optimizer.zero_grad()
outputs_ref, aux_ref = net(img_ref)
seg_main_loss = seg_loss_fct(outputs_ref, cluster_image)
seg_aux_loss = seg_loss_fct(aux_ref, cluster_image)
loss = args['seg_loss_weight'] * \
(seg_main_loss + 0.4 * seg_aux_loss)
loss.backward()
optimizer.step()
cluster_training_count += 1
if type(seg_main_loss) == torch.Tensor:
seg_train_loss.update(seg_main_loss.item(), 1)
####################################################################################################
# LOGGING ETC
####################################################################################################
curr_iter += 1
val_iter += 1
writer.add_scalar('train_seg_loss', seg_train_loss.avg,
curr_iter)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'],
curr_iter)
if (curr_iter + 1) % args['print_freq'] == 0:
str2write = '[iter %d / %d], [train seg loss %.5f], [train corr loss %.5f], [train feature loss %.5f]. [lr %.10f]' % (
curr_iter + 1, args['max_iter'], seg_train_loss.avg,
optimizer.param_groups[1]['lr'])
print(str2write)
f_handle.write(str2write + "\n")
if curr_iter > args['max_iter']:
break
# Post training
f_handle.close()
writer.close()
def train_with_clustering(save_folder, tmp_seg_folder, startnet, args):
print(save_folder.split('/')[-1])
skip_clustering = False
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
check_mkdir(save_folder)
writer = SummaryWriter(save_folder)
check_mkdir(tmp_seg_folder)
# Network and weight loading
model_config = model_configs.PspnetCityscapesConfig()
net = model_config.init_network(
n_classes=args['n_clusters'],
for_clustering=True,
output_features=True,
use_original_base=args['use_original_base']).to(device)
if args['snapshot'] == 'latest':
args['snapshot'] = get_latest_network_name(save_folder)
if len(args['snapshot']) == 0: # If start from beginning
state_dict = torch.load(startnet)
if 'resnet101' in startnet:
load_resnet101_weights(net, state_dict)
else:
# needed since we slightly changed the structure of the network in pspnet
state_dict = rename_keys_to_match(state_dict)
init_last_layers(state_dict,
args['n_clusters']) # different amount of classes
net.load_state_dict(state_dict) # load original weights
start_iter = 0
args['best_record'] = {
'iter': 0,
'val_loss_feat': 1e10,
'val_loss_out': 1e10,
'val_loss_cluster': 1e10
}
else: # If continue training
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(os.path.join(save_folder,
args['snapshot']))) # load weights
split_snapshot = args['snapshot'].split('_')
start_iter = int(split_snapshot[1])
with open(os.path.join(save_folder, 'bestval.txt')) as f:
best_val_dict_str = f.read()
args['best_record'] = eval(best_val_dict_str.rstrip())
if start_iter >= args['max_iter']:
return
if (start_iter % args['cluster_interval']) == 0:
skip_clustering = False
else:
skip_clustering = True
last_cluster_network_snapshot_iter = (
start_iter //
args['cluster_interval']) * args['cluster_interval']
# load cluster info
cluster_info = {}
f = h5py.File(
os.path.join(
save_folder, 'centroids_{}.h5'.format(
last_cluster_network_snapshot_iter)), 'r')
for k, v in f.items():
cluster_info[k] = np.array(v)
cluster_centroids = cluster_info['cluster_centroids']
pca_info = [
cluster_info['pca_transform_Amat'],
cluster_info['pca_transform_bvec']
]
# load network that was used for last clustering
net_for_clustering = model_config.init_network(
n_classes=args['n_clusters'],
for_clustering=True,
output_features=True,
use_original_base=args['use_original_base'])
if last_cluster_network_snapshot_iter == 0:
state_dict = torch.load(
startnet, map_location=lambda storage, loc: storage)
if 'resnet101' in startnet:
load_resnet101_weights(net_for_clustering, state_dict)
else:
# needed since we slightly changed the structure of the network in pspnet
state_dict = rename_keys_to_match(state_dict)
init_last_layers(
state_dict,
args['n_clusters']) # different amount of classes
net_for_clustering.load_state_dict(
state_dict) # load original weights
else:
cluster_network_weights = get_network_name_from_iteration(
save_folder, last_cluster_network_snapshot_iter)
net_for_clustering.load_state_dict(
torch.load(os.path.join(save_folder,
cluster_network_weights),
map_location=lambda storage, loc: storage)
) # load weights
# Data loading setup
if args['corr_set'] == 'rc':
corr_set_config = data_configs.RobotcarConfig()
elif args['corr_set'] == 'cmu':
corr_set_config = data_configs.CmuConfig()
elif args['corr_set'] == 'both':
corr_set_config1 = data_configs.CmuConfig()
corr_set_config2 = data_configs.RobotcarConfig()
if args['corr_set'] == 'both':
ref_image_lists = [
corr_set_config1.reference_image_list,
corr_set_config2.reference_image_list
]
corr_im_paths = [
corr_set_config1.correspondence_im_path,
corr_set_config2.correspondence_im_path
]
ref_featurs_pos = [
corr_set_config1.reference_feature_poitions,
corr_set_config2.reference_feature_poitions
]
else:
ref_image_lists = [corr_set_config.reference_image_list]
corr_im_paths = [corr_set_config.correspondence_im_path]
ref_featurs_pos = [corr_set_config.reference_feature_poitions]
input_transform = model_config.input_transform
train_joint_transform_corr = corr_transforms.Compose([
corr_transforms.CorrResize(1024),
corr_transforms.CorrRandomCrop(713)
])
# Correspondences for training
if args['corr_set'] == 'both':
corr_set_train1 = correspondences.Correspondences(
corr_set_config1.correspondence_path,
corr_set_config1.correspondence_im_path,
input_size=(713, 713),
input_transform=input_transform,
joint_transform=train_joint_transform_corr,
listfile=corr_set_config1.correspondence_train_list_file)
corr_set_train2 = correspondences.Correspondences(
corr_set_config2.correspondence_path,
corr_set_config2.correspondence_im_path,
input_size=(713, 713),
input_transform=input_transform,
joint_transform=train_joint_transform_corr,
listfile=corr_set_config2.correspondence_train_list_file)
corr_set_train = merged.Merged([corr_set_train1, corr_set_train2])
else:
corr_set_train = correspondences.Correspondences(
corr_set_config.correspondence_path,
corr_set_config.correspondence_im_path,
input_size=(713, 713),
input_transform=input_transform,
joint_transform=train_joint_transform_corr,
listfile=corr_set_config.correspondence_train_list_file)
corr_loader_train = DataLoader(corr_set_train,
batch_size=1,
num_workers=args['n_workers'],
shuffle=True)
# Correspondences for validation
if args['corr_set'] == 'both':
corr_set_val1 = correspondences.Correspondences(
corr_set_config1.correspondence_path,
corr_set_config1.correspondence_im_path,
input_size=(713, 713),
input_transform=input_transform,
joint_transform=train_joint_transform_corr,
listfile=corr_set_config1.correspondence_val_list_file)
corr_set_val2 = correspondences.Correspondences(
corr_set_config2.correspondence_path,
corr_set_config2.correspondence_im_path,
input_size=(713, 713),
input_transform=input_transform,
joint_transform=train_joint_transform_corr,
listfile=corr_set_config2.correspondence_val_list_file)
corr_set_val = merged.Merged([corr_set_val1, corr_set_val2])
else:
corr_set_val = correspondences.Correspondences(
corr_set_config.correspondence_path,
corr_set_config.correspondence_im_path,
input_size=(713, 713),
input_transform=input_transform,
joint_transform=train_joint_transform_corr,
listfile=corr_set_config.correspondence_val_list_file)
corr_loader_val = DataLoader(corr_set_val,
batch_size=1,
num_workers=args['n_workers'],
shuffle=False)
# Loss setup
val_corr_loss_fct_feat = FeatureLoss(
input_size=[713, 713],
loss_type=args['feature_distance_measure'],
feat_dist_threshold_match=0.8,
feat_dist_threshold_nomatch=0.2,
n_not_matching=0)
val_corr_loss_fct_out = FeatureLoss(input_size=[713, 713],
loss_type='KL',
feat_dist_threshold_match=0.8,
feat_dist_threshold_nomatch=0.2,
n_not_matching=0)
loss_fct = ClusterCorrespondenceLoss(input_size=[713, 713],
size_average=True).to(device)
seg_loss_fct = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
if args['feature_hinge_loss_weight'] > 0:
feature_loss_fct = FeatureLoss(input_size=[713, 713],
loss_type='hingeF',
feat_dist_threshold_match=0.8,
feat_dist_threshold_nomatch=0.2,
n_not_matching=0)
# Validator
corr_validator = CorrValidator(corr_loader_val,
val_corr_loss_fct_feat,
val_corr_loss_fct_out,
loss_fct,
save_snapshot=True,
extra_name_str='Corr')
# Optimizer setup
optimizer = optim.SGD([{
'params': [
param for name, param in net.named_parameters()
if name[-4:] == 'bias' and param.requires_grad
],
'lr':
2 * args['lr']
}, {
'params': [
param for name, param in net.named_parameters()
if name[-4:] != 'bias' and param.requires_grad
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'],
nesterov=True)
# Clustering
deepcluster = clustering.Kmeans(args['n_clusters'])
if skip_clustering:
deepcluster.set_index(cluster_centroids)
if len(args['snapshot']) > 0:
optimizer.load_state_dict(
torch.load(os.path.join(save_folder, 'opt_' + args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
open(os.path.join(save_folder,
str(datetime.datetime.now()) + '.txt'),
'w').write(str(args) + '\n\n')
if len(args['snapshot']) == 0:
f_handle = open(os.path.join(save_folder, 'log.log'), 'w', buffering=1)
else:
clean_log_before_continuing(os.path.join(save_folder, 'log.log'),
start_iter)
f_handle = open(os.path.join(save_folder, 'log.log'), 'a', buffering=1)
val_iter = 0
curr_iter = start_iter
while curr_iter <= args['max_iter']:
if not skip_clustering:
# Extract image features from reference images
net.eval()
net.output_features = True
features, _ = extract_features_for_reference(
net,
model_config,
ref_image_lists,
corr_im_paths,
ref_featurs_pos,
max_num_features_per_image=args['max_features_per_image'],
fraction_correspondeces=0.5)
cluster_features = np.vstack(features)
del features
# cluster the features
cluster_indices, clustering_loss, cluster_centroids, pca_info = deepcluster.cluster_imfeatures(
cluster_features, verbose=True, use_gpu=False)
# save cluster centroids
h5f = h5py.File(
os.path.join(save_folder, 'centroids_%d.h5' % curr_iter), 'w')
h5f.create_dataset('cluster_centroids', data=cluster_centroids)
h5f.create_dataset('pca_transform_Amat', data=pca_info[0])
h5f.create_dataset('pca_transform_bvec', data=pca_info[1])
h5f.close()
# Print distribution of clusters
cluster_distribution, _ = np.histogram(
cluster_indices,
bins=np.arange(args['n_clusters'] + 1),
density=True)
str2write = 'cluster distribution ' + \
np.array2string(cluster_distribution, formatter={'float_kind': '{0:.8f}'.format}).replace('\n', ' ')
print(str2write)
f_handle.write(str2write + "\n")
reinit_last_layers(
net) # set last layer weight to a normal distribution
# make a copy of current network state to do cluster assignment
net_for_clustering = copy.deepcopy(net)
else:
skip_clustering = False
optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
optimizer.param_groups[1]['lr'] = args['lr'] * (
1 - float(curr_iter) / args['max_iter'])**args['lr_decay']
net.train()
freeze_bn(net)
net.output_features = False
cluster_training_count = 0
# Train using the training correspondence set
corr_train_loss = AverageMeter()
seg_train_loss = AverageMeter()
feature_train_loss = AverageMeter()
while cluster_training_count < args[
'cluster_interval'] and curr_iter <= args['max_iter']:
# First extract cluster labels using saved network checkpoint
net.to("cpu")
net_for_clustering.to(device)
net_for_clustering.eval()
net_for_clustering.output_features = True
if args['feature_hinge_loss_weight'] > 0:
net_for_clustering.output_all = False
data_samples = []
extract_label_count = 0
while (extract_label_count < args['chunk_size']) and (
cluster_training_count + extract_label_count <
args['cluster_interval']
) and (val_iter + extract_label_count < args['val_interval']) and (
extract_label_count + curr_iter <= args['max_iter']):
img_ref, img_other, pts_ref, pts_other, _ = next(
iter(corr_loader_train))
# Transfer data to device
img_ref = img_ref.to(device)
img_other = img_other.to(device)
pts_ref = [p.to(device) for p in pts_ref]
pts_other = [p.to(device) for p in pts_other]
with torch.no_grad():
features = net_for_clustering(img_ref)
# assign feature to clusters for entire patch
output = features.cpu().numpy()
output_flat = output.reshape(
(output.shape[0], output.shape[1], -1))
cluster_image = np.zeros(
(output.shape[0], output.shape[2], output.shape[3]),
dtype=np.int64)
for b in range(output_flat.shape[0]):
out_f = output_flat[b]
out_f2, _ = preprocess_features(np.swapaxes(out_f, 0, 1),
pca_info=pca_info)
cluster_labels = deepcluster.assign(out_f2)
cluster_image[b] = cluster_labels.reshape(
(output.shape[2], output.shape[3]))
cluster_image = torch.from_numpy(cluster_image).to(device)
# assign cluster to correspondence positions
cluster_labels = assign_cluster_ids_to_correspondence_points(
features,
pts_ref, (deepcluster, pca_info),
inds_other=pts_other,
orig_im_size=(713, 713))
# Transfer data to cpu
img_ref = img_ref.cpu()
img_other = img_other.cpu()
pts_ref = [p.cpu() for p in pts_ref]
pts_other = [p.cpu() for p in pts_other]
cluster_labels = [p.cpu() for p in cluster_labels]
cluster_image = cluster_image.cpu()
data_samples.append((img_ref, img_other, pts_ref, pts_other,
cluster_labels, cluster_image))
extract_label_count += 1
net_for_clustering.to("cpu")
net.to(device)
for data_sample in data_samples:
img_ref, img_other, pts_ref, pts_other, cluster_labels, cluster_image = data_sample
# Transfer data to device
img_ref = img_ref.to(device)
img_other = img_other.to(device)
pts_ref = [p.to(device) for p in pts_ref]
pts_other = [p.to(device) for p in pts_other]
cluster_labels = [p.to(device) for p in cluster_labels]
cluster_image = cluster_image.to(device)
optimizer.zero_grad()
if args['feature_hinge_loss_weight'] > 0:
net.output_all = True
# Randomization to decide if reference or target image should be used for training
if args['fraction_reference_bp'] is None: # use both
if args['feature_hinge_loss_weight'] > 0:
out_feat_ref, aux_feat_ref, outputs_ref, aux_ref = net(
img_ref)
else:
outputs_ref, aux_ref = net(img_ref)
seg_main_loss = seg_loss_fct(outputs_ref, cluster_image)
seg_aux_loss = seg_loss_fct(aux_ref, cluster_image)
if args['feature_hinge_loss_weight'] > 0:
out_feat_other, aux_feat_other, outputs_other, aux_other = net(
img_other)
else:
outputs_other, aux_other = net(img_other)
elif np.random.rand(
1)[0] < args['fraction_reference_bp']: # use reference
if args['feature_hinge_loss_weight'] > 0:
out_feat_ref, aux_feat_ref, outputs_ref, aux_ref = net(
img_ref)
else:
outputs_ref, aux_ref = net(img_ref)
seg_main_loss = seg_loss_fct(outputs_ref, cluster_image)
seg_aux_loss = seg_loss_fct(aux_ref, cluster_image)
with torch.no_grad():
if args['feature_hinge_loss_weight'] > 0:
out_feat_other, aux_feat_other, outputs_other, aux_other = net(
img_other)
else:
outputs_other, aux_other = net(img_other)
else: # use target
with torch.no_grad():
if args['feature_hinge_loss_weight'] > 0:
out_feat_ref, aux_feat_ref, outputs_ref, aux_ref = net(
img_ref)
else:
outputs_ref, aux_ref = net(img_ref)
if args['feature_hinge_loss_weight'] > 0:
out_feat_other, aux_feat_other, outputs_other, aux_other = net(
img_other)
else:
outputs_other, aux_other = net(img_other)
seg_main_loss = 0.
seg_aux_loss = 0.
if args['feature_hinge_loss_weight'] > 0:
net.output_all = False
main_loss, _ = loss_fct(outputs_ref,
outputs_other,
None,
pts_ref,
pts_other,
cluster_labels=cluster_labels)
aux_loss, _ = loss_fct(aux_ref,
aux_other,
None,
pts_ref,
pts_other,
cluster_labels=cluster_labels)
if args['feature_hinge_loss_weight'] > 0:
feature_loss = feature_loss_fct(out_feat_ref,
out_feat_other, pts_ref,
pts_other, None)
feature_loss_aux = feature_loss_fct(
aux_feat_ref, aux_feat_other, pts_ref, pts_other, None)
loss = args['corr_loss_weight']*(main_loss + 0.4 * aux_loss) + args['seg_loss_weight']*(seg_main_loss + 0.4 * seg_aux_loss) \
+ args['feature_hinge_loss_weight']*(feature_loss + 0.4 * feature_loss_aux)
else:
feature_loss = 0.
loss = args['corr_loss_weight']*(main_loss + 0.4 * aux_loss) + \
args['seg_loss_weight']*(seg_main_loss + 0.4 * seg_aux_loss)
loss.backward()
optimizer.step()
cluster_training_count += 1
corr_train_loss.update(main_loss.item(), 1)
if type(seg_main_loss) == torch.Tensor:
seg_train_loss.update(seg_main_loss.item(), 1)
if type(feature_loss) == torch.Tensor:
feature_train_loss.update(feature_loss.item(), 1)
####################################################################################################
# LOGGING ETC
####################################################################################################
curr_iter += 1
val_iter += 1
writer.add_scalar('train_corr_loss', corr_train_loss.avg,
curr_iter)
writer.add_scalar('train_seg_loss', seg_train_loss.avg,
curr_iter)
writer.add_scalar('train_feature_loss', feature_train_loss.avg,
curr_iter)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'],
curr_iter)
if (curr_iter + 1) % args['print_freq'] == 0:
str2write = '[iter %d / %d], [train seg loss %.5f], [train corr loss %.5f], [train feature loss %.5f]. [lr %.10f]' % (
curr_iter + 1, args['max_iter'], seg_train_loss.avg,
corr_train_loss.avg, feature_train_loss.avg,
optimizer.param_groups[1]['lr'])
print(str2write)
f_handle.write(str2write + "\n")
if val_iter >= args['val_interval']:
val_iter = 0
net_for_clustering.to(device)
corr_validator.run(net,
net_for_clustering,
(deepcluster, pca_info),
optimizer,
args,
curr_iter,
save_folder,
f_handle,
writer=writer)
net_for_clustering.to("cpu")
if curr_iter > args['max_iter']:
break
# Post training
f_handle.close()
writer.close()
def run(self, net, net_for_clustering, cluster_info, optimizer, args, curr_iter, save_dir, f_handle, writer=None):
# the following code is written assuming that batch size is 1
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.eval()
net_for_clustering.eval()
ps_output_features1 = net_for_clustering.output_features
ps_output_features2 = net.output_features
net_for_clustering.output_features = True
net.output_features = False
feat_ext_for_cluster = FeatureExtractor(net_for_clustering, n_slices_per_pass=5)
feat_ext = FeatureExtractor(net, n_slices_per_pass=5)
confmat = np.zeros((self.n_clusters, self.n_clusters))
for vi, data in enumerate(self.data_loader):
img_slices, _, slices_info = data
output = feat_ext_for_cluster.run_on_slices(img_slices, slices_info)
seg_out = feat_ext.run_on_slices(img_slices, slices_info)
seg = np.argmax(seg_out, 0)
output_flat = output.reshape((output.shape[0], -1))
out_f2, _ = preprocess_features(np.swapaxes(output_flat, 0, 1), pca_info=cluster_info[1])
cluster_labels = cluster_info[0].assign(out_f2)
cluster_image = cluster_labels.reshape((output.shape[1], output.shape[2]))
acc, acc_cls, mean_iu, fwavacc, confmat, _ = evaluate_incremental(
confmat, seg, cluster_image, self.n_clusters)
if (vi % 100) == 0:
str2write = 'validating: %d / %d' % (
vi + 1, len(self.data_loader))
print(str2write)
net_for_clustering.output_features = ps_output_features1
net.output_features = ps_output_features2
net.train()
if 'freeze_bn' not in args or args['freeze_bn']:
freeze_bn(net)
if self.save_snapshot:
snapshot_name = 'iter_%d_acc_%.5f_acc-cls_%.5f_mean-iu_%.5f_fwavacc_%.5f_lr_%.10f' % (
curr_iter, acc, acc_cls, mean_iu, fwavacc, optimizer.param_groups[1]['lr'])
torch.save(net.state_dict(), os.path.join(save_dir, snapshot_name + '.pth'))
torch.save(optimizer.state_dict(), os.path.join(save_dir, 'opt_' + snapshot_name + '.pth'))
if args['best_record']['mean_iu'] < mean_iu:
args['best_record']['iter'] = curr_iter
args['best_record']['acc'] = acc
args['best_record']['acc_cls'] = acc_cls
args['best_record']['mean_iu'] = mean_iu
args['best_record']['fwavacc'] = fwavacc
args['best_record']['snapshot'] = snapshot_name
open(os.path.join(save_dir, 'bestval.txt'), 'w').write(
str(args['best_record']) + '\n\n')
str2write = '%s best record: [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]' % (self.extra_name_str,
args['best_record']['acc'], args['best_record']['acc_cls'], args['best_record']['mean_iu'], args['best_record']['fwavacc'])
print(str2write)
f_handle.write(str2write + "\n")
str2write = '%s [iter %d], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]' % (self.extra_name_str,
curr_iter, acc, acc_cls, mean_iu, fwavacc)
print(str2write)
f_handle.write(str2write + "\n")
if writer is not None:
writer.add_scalar(self.extra_name_str + ': acc', acc, curr_iter)
writer.add_scalar(self.extra_name_str +
': acc_cls', acc_cls, curr_iter)
writer.add_scalar(self.extra_name_str +
': mean_iu', mean_iu, curr_iter)
writer.add_scalar(self.extra_name_str +
': fwavacc', fwavacc, curr_iter)
return mean_iu
def run(self, net, optimizer, args, curr_iter, save_dir, f_handle, writer=None):
# the following code is written assuming that batch size is 1
net.eval()
segmentor = Segmentor(net, self.n_classes, colorize_fcn=None, n_slices_per_pass=10)
confmat = np.zeros((self.n_classes, self.n_classes))
for vi, data in enumerate(self.data_loader):
img_slices, gt, slices_info = data
gt.squeeze_(0)
prediction_tmp = segmentor.run_on_slices(img_slices.squeeze_(0), slices_info.squeeze_(0))
if prediction_tmp.shape != gt.size():
prediction_tmp = Image.fromarray(prediction_tmp.astype(np.uint8)).convert('P')
prediction_tmp = F.resize(prediction_tmp, gt.size(), interpolation=Image.NEAREST)
acc, acc_cls, mean_iu, fwavacc, confmat, _ = evaluate_incremental(
confmat, np.asarray(prediction_tmp), gt.numpy(), self.n_classes)
str2write = 'validating: %d / %d' % (vi + 1, len(self.data_loader))
print(str2write)
# f_handle.write(str2write + "\n")
# Store confusion matrix
confmatdir = os.path.join(save_dir, 'confmat')
os.makedirs(confmatdir, exist_ok=True)
with open(os.path.join(confmatdir, self.extra_name_str + str(curr_iter) + '_confmat.pkl'), 'wb') as confmat_file:
pickle.dump(confmat, confmat_file)
if self.save_snapshot:
snapshot_name = 'iter_%d_acc_%.5f_acc-cls_%.5f_mean-iu_%.5f_fwavacc_%.5f_lr_%.10f' % (
curr_iter, acc, acc_cls, mean_iu, fwavacc, optimizer.param_groups[1]['lr'])
torch.save(net.state_dict(), os.path.join(
save_dir, snapshot_name + '.pth'))
torch.save(optimizer.state_dict(), os.path.join(
save_dir, 'opt_' + snapshot_name + '.pth'))
if args['best_record']['mean_iu'] < mean_iu:
args['best_record']['iter'] = curr_iter
args['best_record']['acc'] = acc
args['best_record']['acc_cls'] = acc_cls
args['best_record']['mean_iu'] = mean_iu
args['best_record']['fwavacc'] = fwavacc
args['best_record']['snapshot'] = snapshot_name
open(os.path.join(save_dir, 'bestval.txt'), 'w').write(
str(args['best_record']) + '\n\n')
str2write = '%s best record: [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]' % (self.extra_name_str,
args['best_record']['acc'], args['best_record']['acc_cls'], args['best_record']['mean_iu'], args['best_record']['fwavacc'])
print(str2write)
f_handle.write(str2write + "\n")
str2write = '%s [iter %d], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]' % (self.extra_name_str,
curr_iter, acc, acc_cls, mean_iu, fwavacc)
print(str2write)
f_handle.write(str2write + "\n")
if writer is not None:
writer.add_scalar(self.extra_name_str + ': acc', acc, curr_iter)
writer.add_scalar(self.extra_name_str +
': acc_cls', acc_cls, curr_iter)
writer.add_scalar(self.extra_name_str +
': mean_iu', mean_iu, curr_iter)
writer.add_scalar(self.extra_name_str +
': fwavacc', fwavacc, curr_iter)
net.train()
if 'freeze_bn' not in args or args['freeze_bn']:
freeze_bn(net)
return mean_iu
def run(self, net, net_for_clustering, cluster_info, optimizer, args, curr_iter, save_dir, f_handle, writer=None):
# the following code is written assuming that batch size is 1
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.eval()
previous_setting_output_all = net.output_all
net.output_all = True
val_loss_feat = AverageMeter()
val_loss_out = AverageMeter()
val_loss_cluster = AverageMeter()
for vi, data in enumerate(self.data_loader):
img_ref, img_other, pts_ref, pts_other, weights = data
img_ref = img_ref.to(device)
img_other = img_other.to(device)
pts_ref = [p.to(device) for p in pts_ref]
pts_other = [p.to(device) for p in pts_other]
weights = [w.to(device) for w in weights]
with torch.no_grad():
feats_ref, out_ref = net(img_ref)
feats_other, out_other = net(img_other)
features = net_for_clustering(img_ref)
loss_feat = self.loss_fct_feat(
feats_ref, feats_other, pts_ref, pts_other, weights)
loss_out = self.loss_fct_out(
out_ref, out_other, pts_ref, pts_other, weights)
cluster_labels = assign_cluster_ids_to_correspondence_points(
features, pts_ref, cluster_info, inds_other=pts_other, orig_im_size=self.loss_fct_cluster.input_size)
loss_cluster, _ = self.loss_fct_cluster(
out_ref, out_other, feats_ref, pts_ref, pts_other, cluster_labels=cluster_labels)
val_loss_feat.update(loss_feat.item())
val_loss_out.update(loss_out.item())
val_loss_cluster.update(loss_cluster.item())
if (vi % 100) == 0:
str2write = 'validating: %d / %d' % (
vi + 1, len(self.data_loader))
print(str2write)
net.output_all = previous_setting_output_all
net.train()
if 'freeze_bn' not in args or args['freeze_bn']:
freeze_bn(net)
if self.save_snapshot:
snapshot_name = 'iter_%d_lossfeat_%.5f_lossout_%.5f_lr_%.10f' % (
curr_iter, val_loss_feat.avg, val_loss_out.avg, optimizer.param_groups[1]['lr'])
torch.save(net.state_dict(), os.path.join(
save_dir, snapshot_name + '.pth'))
torch.save(optimizer.state_dict(), os.path.join(
save_dir, 'opt_' + snapshot_name + '.pth'))
if args['best_record']['val_loss_cluster'] > val_loss_cluster.avg:
args['best_record']['val_loss_cluster'] = val_loss_cluster.avg
args['best_record']['val_loss_feat'] = val_loss_feat.avg
args['best_record']['val_loss_out'] = val_loss_out.avg
args['best_record']['iter'] = curr_iter
args['best_record']['snapshot'] = snapshot_name
open(os.path.join(save_dir, 'bestval.txt'), 'w').write(
str(args['best_record']) + '\n\n')
str2write = '%s best record: [val loss feat %.5f], [val loss out %.5f], [val loss cluster %.5f]' % (
self.extra_name_str, args['best_record']['val_loss_feat'], args['best_record']['val_loss_out'], args['best_record']['val_loss_cluster'])
print(str2write)
f_handle.write(str2write + "\n")
str2write = '%s [iter %d], [val loss feat %.5f], [val loss out %.5f], [val loss cluster %.5f]' % (
self.extra_name_str, curr_iter, val_loss_feat.avg, val_loss_out.avg, val_loss_cluster.avg)
print(str2write)
f_handle.write(str2write + "\n")
if writer is not None:
writer.add_scalar(self.extra_name_str +
': val_loss_feat', val_loss_feat.avg, curr_iter)
writer.add_scalar(self.extra_name_str +
': val_loss_out', val_loss_out.avg, curr_iter)
writer.add_scalar(
self.extra_name_str + ': val_loss_cluster', val_loss_cluster.avg, curr_iter)
return val_loss_feat.avg