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add ignored_Id #55

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16 changes: 12 additions & 4 deletions train/loadData.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ class LoadData:
'''
Class to laod the data
'''
def __init__(self, data_dir, classes, cached_data_file, normVal=1.10):
def __init__(self, data_dir, classes, cached_data_file, ignored_Id, normVal=1.10):
'''
:param data_dir: directory where the dataset is kept
:param classes: number of classes in the dataset
Expand All @@ -26,6 +26,7 @@ def __init__(self, data_dir, classes, cached_data_file, normVal=1.10):
self.trainAnnotList = list()
self.valAnnotList = list()
self.cached_data_file = cached_data_file
self.ignored_Id = ignored_Id

def compute_class_weights(self, histogram):
'''
Expand Down Expand Up @@ -85,9 +86,16 @@ def readFile(self, fileName, trainStg=False):
self.valAnnotList.append(label_file)

if max_val > (self.classes - 1) or min_val < 0:
print('Labels can take value between 0 and number of classes.')
print('Some problem with labels. Please check.')
print('Label Image ID: ' + label_file)
if max_val == self.ignored_Id:
print('Label id: %d has been ignored' %self.ignored_Id)
print('Label Image ID: ' + label_file)

else:
print('Labels can take value between 0 and number of classes.')
print('Some problem with labels.'
'You might want to set ignored_Id = <undefined class id>'
'Please check argument ignored_Id in main.py')
print('Label Image ID: ' + label_file)
no_files += 1

if trainStg == True:
Expand Down
21 changes: 14 additions & 7 deletions train/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,14 +48,16 @@ def val(args, val_loader, model, criterion):
# compute the loss
loss = criterion(output, target_var)

epoch_loss.append(loss.data[0])
#epoch_loss.append(loss.data[0])

epoch_loss.append(loss.item())

time_taken = time.time() - start_time

# compute the confusion matrix
iouEvalVal.addBatch(output.max(1)[1].data, target_var.data)

print('[%d/%d] loss: %.3f time: %.2f' % (i, total_batches, loss.data[0], time_taken))
print('[%d/%d] loss: %.3f time: %.2f' % (i, total_batches, loss.item(), time_taken))

average_epoch_loss_val = sum(epoch_loss) / len(epoch_loss)

Expand Down Expand Up @@ -102,13 +104,13 @@ def train(args, train_loader, model, criterion, optimizer, epoch):
loss.backward()
optimizer.step()

epoch_loss.append(loss.data[0])
epoch_loss.append(loss.item())
time_taken = time.time() - start_time

#compute the confusion matrix
iouEvalTrain.addBatch(output.max(1)[1].data, target_var.data)

print('[%d/%d] loss: %.3f time:%.2f' % (i, total_batches, loss.data[0], time_taken))
print('[%d/%d] loss: %.3f time:%.2f' % (i, total_batches, loss.item(), time_taken))

average_epoch_loss_train = sum(epoch_loss) / len(epoch_loss)

Expand Down Expand Up @@ -149,7 +151,7 @@ def trainValidateSegmentation(args):
'''
# check if processed data file exists or not
if not os.path.isfile(args.cached_data_file):
dataLoad = ld.LoadData(args.data_dir, args.classes, args.cached_data_file)
dataLoad = ld.LoadData(args.data_dir, args.classes, args.cached_data_file, args.ignored_Id)
data = dataLoad.processData()
if data is None:
print('Error while pickling data. Please check.')
Expand All @@ -159,6 +161,7 @@ def trainValidateSegmentation(args):

q = args.q
p = args.p
ignored_Id = args.ignored_Id
# load the model
if not args.decoder:
model = net.ESPNet_Encoder(args.classes, p=p, q=q)
Expand Down Expand Up @@ -192,7 +195,8 @@ def trainValidateSegmentation(args):
if args.onGPU:
weight = weight.cuda()

criteria = CrossEntropyLoss2d(weight) #weight
#criteria = CrossEntropyLoss2d(weight) #weight
criteria = torch.nn.CrossEntropyLoss(weight=weight,ignore_index=ignored_Id) #ignore index -100 for default

if args.onGPU:
criteria = criteria.cuda()
Expand Down Expand Up @@ -386,7 +390,7 @@ def trainValidateSegmentation(args):
parser.add_argument('--scaleIn', type=int, default=8, help='For ESPNet-C, scaleIn=8. For ESPNet, scaleIn=1')
parser.add_argument('--max_epochs', type=int, default=300, help='Max. number of epochs')
parser.add_argument('--num_workers', type=int, default=4, help='No. of parallel threads')
parser.add_argument('--batch_size', type=int, default=12, help='Batch size. 12 for ESPNet-C and 6 for ESPNet. '
parser.add_argument('--batch_size', type=int, default=8, help='Batch size. 12 for ESPNet-C and 6 for ESPNet. '
'Change as per the GPU memory')
parser.add_argument('--step_loss', type=int, default=100, help='Decrease learning rate after how many epochs.')
parser.add_argument('--lr', type=float, default=5e-4, help='Initial learning rate')
Expand All @@ -402,6 +406,9 @@ def trainValidateSegmentation(args):
'Only used when training ESPNet')
parser.add_argument('--p', default=2, type=int, help='depth multiplier')
parser.add_argument('--q', default=8, type=int, help='depth multiplier')
parser.add_argument('--ignored_Id', default=255, type=int, help='ignoredTrainId for crossEntryLoss.'
'default 255 to ignore cityscapes background TrainId.'
'the background Id could be seen in cityscapesScripts/cityscapesscripts/helpers/label.py')

trainValidateSegmentation(parser.parse_args())