def main():
"""
:param n_model: number of models for the comittee
:param n_train: number of training data to be used, this decides how long the training process will be
:param batch_train_size: batch size for training process, keep it under 20
:param idx_ratio: ratio of high entropy:ratio of random
:return:
"""
# paths
save_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'results',
've_test')
csv_name_train = 'train.csv'
csv_name_test = 'test.csv'
csv_name_index = 'index.csv'
dir_name = 'vote_90_5_70_'
save_weights_flag = True
cityscape_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data',
'cityscapes')
cityscape_loss_weight_path = os.path.join(dr(dr(dr(abspath(__file__)))),
'data', 'cityscapes',
'class_weights.pkl')
cityscape_pretrain_path = os.path.join(dr(dr(dr(abspath(__file__)))),
'data', 'cityscape_pretrain')
inference_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data',
'cityscapes', 'inference')
color_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data',
'cityscapes', 'color')
print('cityscape_path: ' + cityscape_path)
print(dir_name)
# arguments
n_train = 2880 # divisible by 8: batch size and 10: 10% increment of training data increase
n_pretrain = 0
n_test = 500
n_epoch = 40
n_model = 10
test_factor = 3 # committee only tested every test_factor-th batch
batch_train_size = 3 * max(torch.cuda.device_count(), 1)
batch_test_size = 25 * max(torch.cuda.device_count(), 1)
lr = 0.0001
loss_print = 2
continue_flag = False
poly_exp = 1.0
feature_extract = True
dropout_rate = 0.9
idx_ratio = [1.0, 0.0]
data_limit = 0.7
manual_seed = 1
# report qbc semseg to user in terminal
text = (('n_model(dropout): ' + str(n_model)) +
(', n_train: ' + str(n_train)) +
(', batch_train_size: ' + str(batch_train_size)) +
(', idx_ratio: ' + str(idx_ratio)) +
(', test_factor: ' + str(test_factor)))
print(text)
# CUDA
cuda_flag = torch.cuda.is_available()
device = torch.device("cuda" if cuda_flag else "cpu")
device_cpu = torch.device("cpu")
dataloader_kwargs = {'pin_memory': True} if cuda_flag else {}
print(torch.cuda.device_count(), "GPUs detected")
torch.manual_seed(manual_seed)
# print("Max memory allocated:" + str(np.round(torch.cuda.max_memory_allocated(device) / 1e9, 3)) + ' Gb')
# get data and index library
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
transform = T.Compose([
T.Resize((800, 800), Image.BICUBIC),
T.ToTensor(),
T.Normalize(*mean_std)
])
train_dataset = dataset_preset.Dataset_Cityscapes_n(
root=cityscape_path,
split='train',
mode='fine',
target_type='semantic',
transform=transform,
target_transform=segmen_preset.label_id2label,
n=n_train)
train_dataset_idx = dataset_preset.Dataset_Cityscapes_n_i(
root=cityscape_path,
split='train',
mode='fine',
target_type='semantic',
transform=transform,
target_transform=segmen_preset.label_id2label,
n=n_train) # also get index of data
test_dataset = dataset_preset.Dataset_Cityscapes_n_i(
root=cityscape_path,
split='val',
mode='fine',
target_type='semantic',
transform=transform,
target_transform=segmen_preset.label_id2label,
n=n_test)
test_dataloader = DataLoader(test_dataset,
batch_size=batch_test_size,
shuffle=True,
num_workers=3 *
max(torch.cuda.device_count(), 1),
drop_last=False)
print("Datasets loaded!")
# create models, optimizers, scheduler, criterion, the model
fcn_model = torchvision.models.segmentation.deeplabv3_resnet101(
pretrained=False,
progress=True,
num_classes=segmen_preset.n_labels_valid,
aux_loss=True)
fcn_model = fcn_model.cuda()
fcn_model = nn.DataParallel(fcn_model)
# the optimizers
optimizer = torch.optim.Adam(
[{
'params': fcn_model.module.classifier.parameters()
}, {
'params':
list(fcn_model.module.backbone.parameters()) +
list(fcn_model.module.aux_classifier.parameters())
}],
lr=lr)
lambda1 = lambda epoch: math.pow(1 - (epoch / n_epoch), poly_exp)
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
with open(cityscape_loss_weight_path,
"rb") as file: # (needed for python3)
class_weights = np.array(pickle.load(file))
class_weights = torch.from_numpy(class_weights)
class_weights = Variable(class_weights.type(torch.FloatTensor)).cuda()
criterion = torch.nn.CrossEntropyLoss(weight=class_weights).cuda()
# report everything
print(str(n_model) + " fcn models created")
text = ('n_model: ' + str(n_model)) + (', n_train: ' + str(n_train)) + (', n_epoch: ' + str(n_epoch)) +\
(', batch_train_size: ' + str(batch_train_size)) + (', idx_ratio: ' + str(idx_ratio))
print(text)
# to document training process, create directory, etc
train_text = [str(x) for x in range(1, n_epoch + 1)]
test_text = [str(x) for x in range(1, n_epoch + 1)]
train_index_text = [str(x) for x in range(1, 8)]
train_index_docu = 0
train_index = []
test_text_index = 0
# write text to csv
dir_number = 1
while os.path.exists(
os.path.join(save_path, (dir_name + '{:03d}'.format(dir_number)))):
dir_number += 1
run_path = os.path.join(save_path,
(dir_name + '{:03d}'.format(dir_number)))
os.makedirs(run_path) # make run_* dir
f = open(os.path.join(run_path, 'info.txt'), 'w+') # write .txt file
f.write(text)
f.close()
copy(__file__, os.path.join(run_path, os.path.basename(__file__)))
# write training progress
csv_path_train = os.path.join(run_path, csv_name_train)
title = [
"Training progress for n_model = " + str(n_model) + ", idx_ratio: " +
str(idx_ratio) + ', for multiple epoch'
]
with open(csv_path_train, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=',')
test_writer.writerow(title)
# write test progress
csv_path_test = os.path.join(run_path, csv_name_test)
title = [
"Test progress for n_model = " + str(1) + ", idx_ratio: " +
str(idx_ratio) + ', for multiple epoch, torch seed: ' +
str(manual_seed) + 'run_path: ' + run_path
]
with open(csv_path_test, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=',')
test_writer.writerow(title)
# write index and train progress
csv_path_index = os.path.join(run_path, csv_name_index)
title = [
"Index progress for n_model = " + str(n_model) + ", idx_ratio: " +
str(idx_ratio) + ', for multiple epoch'
]
with open(csv_path_train, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=',')
test_writer.writerow(title)
# training start
for i_epoch in range(n_epoch):
# initialize with random
if len(train_index) == 0:
train_index = np.array(
random.sample(range(n_train), k=int(n_train / 10)))
train_index_text[train_index_docu] = train_index_text[train_index_docu] + ': ' \
+ str([x for x in train_index]).strip('[]')
# update train and index documentation
text = train_index_text[train_index_docu].split(";")
with open(csv_path_index, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=';')
test_writer.writerow(text)
print(train_index_text)
train_index_docu = train_index_docu + 1
# append with vote entropy
elif (len(train_index) < int(0.7 * n_train)) and (i_epoch % 5 == 0):
t = Timer()
t.start()
# perform vote entropy on entire dataset
indices, fcn_model = vote_entropy_dropout(fcn_model,
train_dataset_idx,
train_index,
idx_ratio,
batch_test_size,
device,
n_model,
dropout_rate,
i_epoch,
n_data=int(n_train / 10))
train_index = np.append(train_index, indices)
train_index_text[train_index_docu] = train_index_text[train_index_docu] + ': ' + \
str([x for x in train_index]).strip('[]') +\
";{:.4f}".format(np.array(t.stop()).mean())
# update train and index documentation
text = train_index_text[train_index_docu].split(";")
with open(csv_path_index, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=';')
test_writer.writerow(text)
print(train_index_text)
train_index_docu = train_index_docu + 1
# retrain with selected data
print(train_index)
print('length: ' + str(len(train_index)))
train_subset = Subset(train_dataset_idx, train_index)
train_dataloader = DataLoader(train_subset,
batch_size=batch_train_size,
shuffle=True)
loss_epoch = []
time_epoch = []
for i_batch, (data_train, target_train, index,
_) in enumerate(train_dataloader):
# train batch
t = Timer()
t.start()
output, loss, iou, fcn_model, optimizer = train_batch(
fcn_model, data_train, target_train, optimizer, device,
criterion)
print('Epoch: ' + str(i_epoch) + '\t Batch: ' + str(i_batch) +
'/' + str(len(train_dataloader)) + '; model ' + str(0) +
'; train loss avg: ' + "{:.3f}".format(loss) +
'; train iou avg: ' + "{:.3f}".format(iou.mean()))
for param_group in optimizer.param_groups:
print(param_group['lr'])
loss_epoch.append(loss)
time_epoch.append(t.stop())
# document train result
train_text[i_epoch] = train_text[i_epoch] + ";{:.4f}".format(np.array(loss_epoch).mean()) + \
";{:.7f}".format(np.array(optimizer.param_groups[0]['lr']))\
+ ';' + str(len(train_index)) + ";{:.4f}".format(np.array(time_epoch).mean())
# update train documentation
text = train_text[i_epoch].split(";")
with open(csv_path_train, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=';')
test_writer.writerow(text)
# save temporary model and perform test
if i_epoch % 10 == 0 or (i_epoch + 1) == n_epoch:
print('Save and Test Model')
fcn_model.train()
torch.save(
fcn_model.state_dict(),
os.path.join(run_path, ('model_weight_epoch_train' +
'{:03d}'.format(i_epoch) + '.pt')))
fcn_model.eval()
torch.save(
fcn_model.state_dict(),
os.path.join(run_path, ('model_weight_epoch_' +
'{:03d}'.format(i_epoch) + '.pt')))
# perform test
test_idx = test_text_index
create_pred_img(fcn_model, test_dataloader, inference_path, color_path)
all_result_dict = cityscapes_eval()
# document test result
test_text[test_idx] = test_text[test_idx] + ";{:.4f}".format(all_result_dict['averageScoreClasses']) +\
";{:.7f}".format(np.array(optimizer.param_groups[0]['lr']))\
+ ';' + str(len(train_index))
# update test documentation
text = test_text[test_idx].split(";")
with open(csv_path_test, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=';')
test_writer.writerow(text)
test_text_index = test_text_index + 1
# one epoch ends here
scheduler.step()
print(optimizer)
def main(n_train, batch_train_size, n_test, batch_test_size):
"""
:param n_model: number of models for the comittee
:param n_train: number of training data to be used, this decides how long the training process will be
:param batch_train_size: batch size for training process, keep it under 20
:param idx_ratio: ratio of high entropy:ratio of random
:return:
"""
# paths
img_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data', 've_test',
'example.png')
save_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'results',
've_test')
csv_name_train = 'train.csv'
csv_name_test = 'test.csv'
csv_name_index = 'index.csv'
dir_name = 'vote_bulk_40_from_90_005_'
index_path_name = 'vote_90_5_005'
save_weights_flag = True
cityscape_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data',
'cityscapes')
cityscape_loss_weight_path = os.path.join(dr(dr(dr(abspath(__file__)))),
'data', 'cityscapes',
'class_weights.pkl')
cityscape_pretrain_path = os.path.join(dr(dr(dr(abspath(__file__)))),
'data', 'cityscape_pretrain')
inference_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data',
'cityscapes', 'inference')
color_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'data',
'cityscapes', 'color')
print('cityscape_path: ' + cityscape_path)
print(dir_name)
print(index_path_name)
# arguments
n_train = 2880
n_pretrain = 0
n_test = 500
n_epoch = 40
test_factor = 3 # committee only tested every test_factor-th batch
batch_train_size = 3 * max(torch.cuda.device_count(), 1)
batch_train_size_pretrain = 4
batch_test_size = 25 * max(torch.cuda.device_count(), 1)
lr = 0.0001
loss_print = 2
idx_ratio = [0.0, 1.0] # proportion to qbc:random
continue_flag = False
poly_exp = 1.0
feature_extract = True
manual_seed = 2
np.random.seed(manual_seed)
# CUDA
cuda_flag = torch.cuda.is_available()
device = torch.device("cuda" if cuda_flag else "cpu")
device_cpu = torch.device("cpu")
dataloader_kwargs = {'pin_memory': True} if cuda_flag else {}
print(torch.cuda.device_count(), "GPUs detected")
torch.manual_seed(manual_seed)
# print("Max memory allocated:" + str(np.round(torch.cuda.max_memory_allocated(device) / 1e9, 3)) + ' Gb')
# get data and index library
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
transform = T.Compose([
T.Resize((800, 800), Image.BICUBIC),
T.ToTensor(),
T.Normalize(*mean_std)
])
train_dataset = dataset_preset.Dataset_Cityscapes_n(
root=cityscape_path,
split='train',
mode='fine',
target_type='semantic',
transform=transform,
target_transform=segmen_preset.label_id2label,
n=n_train)
# read used index
csv_path_index_source = os.path.join(save_path, index_path_name,
csv_name_index)
with open(csv_path_index_source) as csv_file:
data = csv_file.readlines()
train_index = np.array(
list(
map(
int, data[-1][3:data[-1].find(';', (len(data[-1]) -
20))].split(','))))
print(len(train_index))
# np.random.shuffle(train_index)
train_index = train_index[int(n_train * 0.1):int(n_train * 0.5)]
print(len(train_index))
train_dataset = Subset(train_dataset, indices=train_index)
test_dataset = dataset_preset.Dataset_Cityscapes_n_i(
root=cityscape_path,
split='val',
mode='fine',
target_type='semantic',
transform=transform,
target_transform=segmen_preset.label_id2label,
n=n_test)
# only test on part of data
train_dataloader = DataLoader(train_dataset,
batch_size=batch_train_size,
shuffle=True,
num_workers=3 *
max(torch.cuda.device_count(), 1),
drop_last=True)
test_dataloader = DataLoader(test_dataset,
batch_size=batch_test_size,
shuffle=True,
num_workers=3 *
max(torch.cuda.device_count(), 1),
drop_last=True)
print("Datasets loaded!")
# create models, optimizers, scheduler, criterion
# the models
fcn_model = torchvision.models.segmentation.deeplabv3_resnet101(
pretrained=False,
progress=True,
num_classes=segmen_preset.n_labels_valid,
aux_loss=True)
fcn_model = fcn_model.cuda()
fcn_model = nn.DataParallel(fcn_model)
# the optimizers
params_to_update = fcn_model.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name, param in fcn_model.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t", name)
else:
for name, param in fcn_model.named_parameters():
if param.requires_grad == True:
print("\t", name)
params = add_weight_decay(fcn_model, l2_value=0.0001)
'''optimizer = torch.optim.SGD([{'params': fcn_model.module.classifier.parameters()},
{'params': list(fcn_model.module.backbone.parameters()) +
list(fcn_model.module.aux_classifier.parameters())}
], lr=lr, momentum=0.9)'''
optimizer = torch.optim.Adam(
[{
'params': fcn_model.module.classifier.parameters()
}, {
'params':
list(fcn_model.module.backbone.parameters()) +
list(fcn_model.module.aux_classifier.parameters())
}],
lr=lr,
weight_decay=0.0001)
lambda1 = lambda epoch: math.pow(1 - (epoch / n_epoch), poly_exp)
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
with open(cityscape_loss_weight_path,
"rb") as file: # (needed for python3)
class_weights = np.array(pickle.load(file))
class_weights = torch.from_numpy(class_weights)
class_weights = Variable(class_weights.type(torch.FloatTensor)).cuda()
criterion = torch.nn.CrossEntropyLoss(weight=class_weights).cuda()
# report everything
text = ('Model created' + (', n_train: ' + str(n_train)) +
(', n_epoch: ' + str(n_epoch)) +
(', batch_train_size: ' + str(batch_train_size)) +
(', idx_ratio: ' + str(idx_ratio)) + (', n_test: ' + str(n_test)) +
(', batch_test_size: ' + str(batch_test_size)) +
(', test_factor: ' + str(test_factor)) +
(', optimizer: ' + str(optimizer)) +
(', model: ' + str(fcn_model)))
print(text)
# for documentation
train_text = [str(x) for x in range(1, n_epoch + 1)]
test_text = [str(x) for x in range(1, n_epoch + 1)]
test_text_index = 0
# write text to csv
dir_number = 1
while os.path.exists(
os.path.join(save_path, (dir_name + '{:03d}'.format(dir_number)))):
dir_number += 1
run_path = os.path.join(save_path,
(dir_name + '{:03d}'.format(dir_number)))
os.makedirs(run_path) # make run_* dir
f = open(os.path.join(run_path, 'info.txt'), 'w+') # write .txt file
f.write(text)
f.close()
copy(__file__, os.path.join(run_path, os.path.basename(__file__)))
# write training progress
csv_path_train = os.path.join(run_path, csv_name_train)
title = [
"Training progress for n_model = " + str(1) + ", idx_ratio: " +
str(idx_ratio) + ', for multiple epoch, torch seed: ' +
str(manual_seed)
]
with open(csv_path_train, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=',')
test_writer.writerow(title)
# write test progress
csv_path_test = os.path.join(run_path, csv_name_test)
title = [
"Test progress for n_model = " + str(1) + ", idx_ratio: " +
str(idx_ratio) + ', for multiple epoch, torch seed: ' +
str(manual_seed) + 'run_path: ' + run_path + 'index_from: ' +
index_path_name
]
with open(csv_path_test, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=',')
test_writer.writerow(title)
# load from previous run if requested
if continue_flag:
fcn_model.load_state_dict(
torch.load(
'C:\\Users\\steve\\Desktop\\projects\\al_kitti\\results\\first_test\\adam_run_005\\model_weight_epoch_10.pt'
))
print('weight loaded')
# training process, n-th batch
for i_epoch in range(n_epoch):
loss_epoch = []
iou_epoch = []
time_epoch = []
for i_batch, (data_train, target_train) in enumerate(train_dataloader):
t = Timer()
t.start()
# train batch
output, loss, iou, fcn_model, optimizer = train_batch(
fcn_model, data_train, target_train, optimizer, device,
criterion)
print('Epoch: ' + str(i_epoch) + '\t Batch: ' + str(i_batch) +
'/' + str(len(train_dataloader)) + '; model ' + str(0) +
'; train loss avg: ' + "{:.3f}".format(loss) +
'; train iou avg: ' + "{:.3f}".format(iou.mean()))
for param_group in optimizer.param_groups:
print(param_group['lr'])
loss_epoch.append(loss)
iou_epoch.append(iou.mean())
time_epoch.append(t.stop())
# document train result
train_text[i_epoch] = train_text[i_epoch] + ";{:.4f}".format(np.array(loss_epoch).mean()) + \
";{:.4f}".format(np.array(iou_epoch).mean()) + \
";{:.7f}".format(np.array(optimizer.param_groups[0]['lr'])) + ';' + str(len(train_index))
# update train documentation
text = train_text[i_epoch].split(";")
with open(csv_path_train, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=';')
test_writer.writerow(text)
# one epoch ends here
scheduler.step()
print(optimizer)
# save temporary model
if i_epoch % 10 == 0 or (i_epoch + 1) == n_epoch:
fcn_model.train()
torch.save(
fcn_model.state_dict(),
os.path.join(run_path, ('model_weight_epoch_train' +
'{:03d}'.format(i_epoch) + '.pt')))
fcn_model.eval()
torch.save(
fcn_model.state_dict(),
os.path.join(run_path, ('model_weight_epoch_' +
'{:03d}'.format(i_epoch) + '.pt')))
# perform test
create_pred_img(fcn_model, test_dataloader, inference_path, color_path)
all_result_dict = cityscapes_eval()
# average training time
mean_time = np.array(time_epoch).mean()
# document test result
test_text[test_text_index] = test_text[test_text_index] + \
";{:.4f}".format(all_result_dict['averageScoreClasses']) + \
";{:.7f}".format(np.array(optimizer.param_groups[0]['lr'])) \
+ ";{:.4f}".format(mean_time) + ';' + str(len(train_index))
# update test documentation
text = test_text[test_text_index].split(";")
with open(csv_path_test, mode='a+', newline='') as test_file:
test_writer = csv.writer(test_file, delimiter=';')
test_writer.writerow(text)
test_text_index = test_text_index + 1