def run_inference(model_architecture='resnet7_2_1',
checkpoint_path='./data/pytorch_model_epoch300.bin',
image_path='./data/52658141.png'):
device = 'cpu'
'''
Create an instance of a resnet model and load a checkpoint
'''
output_channels = 4
if model_architecture == 'resnet7_2_1':
resnet_model = resnet7_2_1(pretrained=True,
pretrained_model_path=checkpoint_path,
output_channels=output_channels)
resnet_model = resnet_model.to(device)
'''
Load the input image
'''
image = load_image(image_path)
'''
Run model inference on the image
'''
pred = main_utils.inference(resnet_model, image)
pred = pred[0]
severity = sum([i * pred[i] for i in range(len(pred))])
print(f"{image_path} has severity of {severity}")
def run_inference_gradcam(
image_path,
model_architecture='resnet7_2_1',
checkpoint_path='/opt/mlmodel/data/pytorch_model_epoch300.bin'):
device = 'cpu'
'''
Create an instance of a resnet model and load a checkpoint
'''
output_channels = 4
if model_architecture == 'resnet7_2_1':
resnet_model = resnet7_2_1(pretrained=True,
pretrained_model_path=checkpoint_path,
output_channels=output_channels)
resnet_model = resnet_model.to(device)
'''
Create an instance of model with Grad-CAM
'''
model_gcam = GradCAM(model=resnet_model)
'''
Load the input image
'''
image = load_image(image_path)
'''
Run model inference on the image with Grad-CAM
'''
pred, gcam_img, input_img = main_utils.inference_gradcam(
model_gcam, image, 'layer7.1.conv2')
pred = pred[0]
severity = sum([i * pred[i] for i in range(len(pred))])
return severity, gcam_img, input_img
def __call__(self, study_name):
png_path = Path.png_path(study_name)
image = load_image(png_path)
xray_transform = CenterCrop(2048)
image = xray_transform(image)
image = 65535 * image
image = image.astype(np.uint16)
return run_inference(png_path), image
def run_inference_gradcam(model_architecture='resnet7_2_1',
checkpoint_path='./data/pytorch_model_epoch300.bin',
image_path='./data/52658141.png',
gcam_path='/mnt/images/52658141_gcam.png'):
device = 'cpu'
'''
Create an instance of a resnet model and load a checkpoint
'''
output_channels = 4
if model_architecture == 'resnet7_2_1':
resnet_model = resnet7_2_1(pretrained=True,
pretrained_model_path=checkpoint_path,
output_channels=output_channels)
resnet_model = resnet_model.to(device)
'''
Create an instance of model with Grad-CAM
'''
model_gcam = GradCAM(model=resnet_model)
'''
Load the input image
'''
image = load_image(image_path)
'''
Run model inference on the image with Grad-CAM
'''
pred, gcam_img, input_img = main_utils.inference_gradcam(
model_gcam, image, 'layer7.1.conv2')
pred = pred[0]
severity = sum([i * pred[i] for i in range(len(pred))])
print(f"{image_path} has severity of {severity}")
save_gradcam_overlay(gcam_path, gcam_img[0], input_img[0])
print(f"Grad-CAM overlay saved at {gcam_path}")
return
def main():
args = parser.get_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# assert torch.cuda.is_available(), "No GPU/CUDA is detected!"
assert args.do_train or args.do_eval, \
"Either do_train or do_eval has to be True!"
assert not(args.do_train and args.do_eval), \
"do_train and do_eval cannot be both True!"
# To track results from different commits (temporary)
if args.commit_sha == None:
args.run_id = args.run_id + '_' + str(sha)
else:
args.run_id = args.run_id + '_' + args.commit_sha
if not args.run_id == None:
args.output_dir = os.path.join(args.output_dir, args.run_id)
if not (os.path.exists(args.output_dir)) and args.do_train:
os.makedirs(args.output_dir)
if args.do_eval:
# output_dir has to exist if doing evaluation
assert os.path.exists(args.output_dir), \
"Output directory {} doesn't exist!".format(args.output_dir)
# if args.data_split_mode=='testing':
# # Checkpoint has to exist if doing evaluation with testing split
# assert os.path.exists(args.checkpoint_path), \
# "Checkpoint doesn't exist!"
'''
Configure a log file
'''
if args.do_train:
log_path = os.path.join(args.output_dir, 'training.log')
if args.do_eval:
log_path = os.path.join(args.output_dir, 'evaluation.log')
logging.basicConfig(filename=log_path,
level=logging.INFO,
filemode='w',
format='%(asctime)s - %(name)s %(message)s',
datefmt='%m-%d %H:%M')
'''
Log important info
'''
logger = logging.getLogger(__name__)
logger.info("***** Code info *****")
logger.info(" Git commit sha: %s", sha)
'''
Print important info
'''
print('Model architecture:', args.model_architecture)
print('Training folds:', args.training_folds)
print('Evaluation folds:', args.evaluation_folds)
print('Device being used:', device)
print('Output directory:', args.output_dir)
print('Logging in:\t {}'.format(log_path))
print('Input image formet:', args.image_format)
print('Loss function: {}'.format(args.loss))
if args.do_inference:
'''
Create an instance of a resnet model and load a checkpoint
'''
output_channels = 4
if args.model_architecture == 'resnet7_2_1':
resnet_model = resnet7_2_1(
pretrained=True,
pretrained_model_path=args.checkpoint_path,
output_channels=output_channels)
resnet_model = resnet_model.to(device)
'''
Load the input image
'''
image = load_image(args.image_path)
'''
Run model inference on the image
'''
pred = main_utils.inference(resnet_model, image)
pred = pred[0]
severity = sum([i * pred[i] for i in range(len(pred))])
print(f"{args.image_path} has severity of {severity}")
return
if args.do_train:
'''
Create tensorboard and checkpoint directories if they don't exist
'''
args.tsbd_dir = os.path.join(args.output_dir, 'tsbd')
args.checkpoints_dir = os.path.join(args.output_dir, 'checkpoints')
directories = [args.tsbd_dir, args.checkpoints_dir]
for directory in directories:
if not (os.path.exists(directory)):
os.makedirs(directory)
# Avoid overwriting previous tensorboard and checkpoint data
args.tsbd_dir = os.path.join(
args.tsbd_dir, 'tsbd_{}'.format(len(os.listdir(args.tsbd_dir))))
if not os.path.exists(args.tsbd_dir):
os.makedirs(args.tsbd_dir)
args.checkpoints_dir = os.path.join(
args.checkpoints_dir,
'checkpoints_{}'.format(len(os.listdir(args.checkpoints_dir))))
if not os.path.exists(args.checkpoints_dir):
os.makedirs(args.checkpoints_dir)
'''
Create an instance of a resnet model
'''
output_channels = 4
if args.model_architecture == 'resnet7_2_1':
resnet_model = resnet7_2_1(output_channels=output_channels)
resnet_model = resnet_model.to(device)
'''
Train the model
'''
print("***** Training the model *****")
main_utils.train(args, device, resnet_model)
print("***** Finished training *****")
if args.do_eval:
def run_eval_on_checkpoint(checkpoint_path):
'''
Create an instance of a resnet model and load a checkpoint
'''
output_channels = 4
if args.model_architecture == 'resnet7_2_1':
resnet_model = resnet7_2_1(
pretrained=True,
pretrained_model_path=checkpoint_path,
output_channels=output_channels)
resnet_model = resnet_model.to(device)
'''
Evaluate the model
'''
print("***** Evaluating the model *****")
eval_results, embeddings, labels_raw = main_utils.evaluate(
args, device, resnet_model)
print("***** Finished evaluation *****")
return eval_results, embeddings, labels_raw
eval_results, _, _ = run_eval_on_checkpoint(
checkpoint_path=args.checkpoint_path)
results_path = os.path.join(args.output_dir, 'eval_results.json')
with open(results_path, 'w') as fp:
json.dump(eval_results, fp)