def main():
global args
args = parser.parse_args()
print(args)
if args.saveTest == 'True':
args.saveTest = True
elif args.saveTest == 'False':
args.saveTest = False
# Check if the save directory exists or not
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
cudnn.benchmark = True
data_transform = transforms.Compose([
transforms.Resize((args.imageSize, args.imageSize),
interpolation=Image.NEAREST),
transforms.ToTensor(),
])
# Data Loading
data_dir = '/home/salman/pytorch/segmentationNetworks/datasets/miccaiSegOrgans'
# json path for class definitions
json_path = '/home/salman/pytorch/segmentationNetworks/datasets/miccaiSegOrganClasses.json'
image_dataset = miccaiSegDataset(os.path.join(data_dir, 'test'),
data_transform, json_path)
dataloader = torch.utils.data.DataLoader(image_dataset,
batch_size=args.batchSize,
shuffle=True,
num_workers=args.workers)
# Get the dictionary for the id and RGB value pairs for the dataset
classes = image_dataset.classes
key = utils.disentangleKey(classes)
num_classes = len(key)
# Initialize the model
model = SegNet(args.bnMomentum, num_classes)
# Load the saved model
if os.path.isfile(args.model):
print("=> loading checkpoint '{}'".format(args.model))
checkpoint = torch.load(args.model)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint (epoch {})".format(checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.model))
print(model)
# Define loss function (criterion)
criterion = nn.CrossEntropyLoss()
if use_gpu:
model.cuda()
criterion.cuda()
# Initialize an evaluation Object
evaluator = utils.Evaluate(key, use_gpu)
# Evaulate on validation/test set
print('>>>>>>>>>>>>>>>>>>>>>>>Testing<<<<<<<<<<<<<<<<<<<<<<<')
validate(dataloader, model, criterion, key, evaluator)
# Calculate the metrics
print('>>>>>>>>>>>>>>>>>> Evaluating the Metrics <<<<<<<<<<<<<<<<<')
IoU = evaluator.getIoU()
print('Mean IoU: {}, Class-wise IoU: {}'.format(torch.mean(IoU), IoU))
PRF1 = evaluator.getPRF1()
precision, recall, F1 = PRF1[0], PRF1[1], PRF1[2]
print('Mean Precision: {}, Class-wise Precision: {}'.format(
torch.mean(precision), precision))
print('Mean Recall: {}, Class-wise Recall: {}'.format(
torch.mean(recall), recall))
print('Mean F1: {}, Class-wise F1: {}'.format(torch.mean(F1), F1))
def main():
global args, best_prec1
args = parser.parse_args()
print(args)
if args.saveTest == 'True':
args.saveTest = True
elif args.saveTest == 'False':
args.saveTest = False
# Check if the save directory exists or not
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
cudnn.benchmark = True
data_transforms = {
'train': transforms.Compose([
transforms.Resize((args.imageSize, args.imageSize), interpolation=Image.NEAREST),
transforms.TenCrop(args.resizedImageSize),
transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
#transforms.Lambda(lambda normalized: torch.stack([transforms.Normalize([0.295, 0.204, 0.197], [0.221, 0.188, 0.182])(crop) for crop in normalized]))
#transforms.RandomResizedCrop(224, interpolation=Image.NEAREST),
#transforms.RandomHorizontalFlip(),
#transforms.RandomVerticalFlip(),
#transforms.ToTensor(),
]),
'test': transforms.Compose([
transforms.Resize((args.imageSize, args.imageSize), interpolation=Image.NEAREST),
transforms.ToTensor(),
#transforms.Normalize([0.295, 0.204, 0.197], [0.221, 0.188, 0.182])
]),
}
# Data Loading
data_dir = 'datasets/miccaiSegRefined'
# json path for class definitions
json_path = 'datasets/miccaiSegClasses.json'
image_datasets = {x: miccaiSegDataset(os.path.join(data_dir, x), data_transforms[x],
json_path) for x in ['train', 'test']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=args.batchSize,
shuffle=True,
num_workers=args.workers)
for x in ['train', 'test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}
# Get the dictionary for the id and RGB value pairs for the dataset
classes = image_datasets['train'].classes
key = utils.disentangleKey(classes)
num_classes = len(key)
# Initialize the model
model = UNet(num_classes)
# # Optionally resume from a checkpoint
# if args.resume:
# if os.path.isfile(args.resume):
# print("=> loading checkpoint '{}'".format(args.resume))
# checkpoint = torch.load(args.resume)
# #args.start_epoch = checkpoint['epoch']
# pretrained_dict = checkpoint['state_dict']
# pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model.state_dict()}
# model.state_dict().update(pretrained_dict)
# model.load_state_dict(model.state_dict())
# print("=> loaded checkpoint (epoch {})".format(checkpoint['epoch']))
# else:
# print("=> no checkpoint found at '{}'".format(args.resume))
#
# # # Freeze the encoder weights
# # for param in model.encoder.parameters():
# # param.requires_grad = False
#
# optimizer = optim.Adam(model.parameters(), lr = args.lr, weight_decay = args.wd)
# else:
optimizer = optim.Adam(model.parameters(), lr = args.lr, weight_decay = args.wd)
# Load the saved model
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint (epoch {})".format(checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
print(model)
# Define loss function (criterion)
criterion = nn.CrossEntropyLoss()
# Use a learning rate scheduler
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
if use_gpu:
model.cuda()
criterion.cuda()
# Initialize an evaluation Object
evaluator = utils.Evaluate(key, use_gpu)
for epoch in range(args.start_epoch, args.epochs):
#adjust_learning_rate(optimizer, epoch)
# Train for one epoch
print('>>>>>>>>>>>>>>>>>>>>>>>Training<<<<<<<<<<<<<<<<<<<<<<<')
train(dataloaders['train'], model, criterion, optimizer, scheduler, epoch, key)
# Evaulate on validation set
print('>>>>>>>>>>>>>>>>>>>>>>>Testing<<<<<<<<<<<<<<<<<<<<<<<')
validate(dataloaders['test'], model, criterion, epoch, key, evaluator)
# Calculate the metrics
print('>>>>>>>>>>>>>>>>>> Evaluating the Metrics <<<<<<<<<<<<<<<<<')
IoU = evaluator.getIoU()
print('Mean IoU: {}, Class-wise IoU: {}'.format(torch.mean(IoU), IoU))
PRF1 = evaluator.getPRF1()
precision, recall, F1 = PRF1[0], PRF1[1], PRF1[2]
print('Mean Precision: {}, Class-wise Precision: {}'.format(torch.mean(precision), precision))
print('Mean Recall: {}, Class-wise Recall: {}'.format(torch.mean(recall), recall))
print('Mean F1: {}, Class-wise F1: {}'.format(torch.mean(F1), F1))
evaluator.reset()
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, filename=os.path.join(args.save_dir, 'checkpoint_{}.tar'.format(epoch)))
import os
import numpy as np
import tensorflow as tf
import utils
from preprocess import PreProcess
from topic_model import GraphAnchorLDA
from topic_gcn import StructuralTopicGCN
if __name__ == "__main__":
params = utils.load_json_file("../config.json")
preprocesser = PreProcess(params)
preprocesser.generate_topic_concepts()
graph_topic_model = GraphAnchorLDA(params)
graph_topic_model.learn_topic_distribution()
graph_topic_model.generate_topic_features()
topic_gcn_model = StructuralTopicGCN(params)
topic_gcn_model.train()
# evaluate
evaluator = utils.Evaluate(params)
if params["dataset"] != "ppi":
evaluator.ten_times_node_classification(flag_multi_label=False)
else:
# multi_label node classification
evaluator.ten_times_node_classification(flag_multi_label=True)
# evaluator.five_times_link_prediction()