def main():
cudnn.benchmark = True
model = DenseNet121(N_CLASSES).cuda()
## getting the images list to be used for heatmap generation
# image_names = []
# with open(TEST_IMAGE_LIST, 'r') as f:
# for line in f:
# items = line.split()
# image_names.append(items[0])
##############################################################
if os.path.isfile(CKPT_PATH):
print("=> loading checkpoint")
checkpoint = torch.load(CKPT_PATH)
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$'
)
state_dict = checkpoint['state_dict']
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
for key in list(state_dict.keys()):
split_key = key.split('.')
new_key = '.'.join(split_key[1:])
state_dict[new_key] = state_dict[key]
del state_dict[key]
model.load_state_dict(state_dict)
print("=> loaded checkpoint")
else:
print("=> no checkpoint found")
normalize = transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
test_dataset = ChestXrayDataSet(
data_dir=DATA_DIR,
image_list_file=TEST_IMAGE_LIST,
transform=transforms.Compose([
transforms.Resize(256),
transforms.TenCrop(224),
transforms.Lambda(lambda crops: torch.stack(
[transforms.ToTensor()(crop) for crop in crops])),
transforms.Lambda(
lambda crops: torch.stack([normalize(crop) for crop in crops]))
]))
test_loader = sy.FederatedDataLoader(
federated_dataset=test_dataset.federate(tuple(workers)),
batch_size=BATCH_SIZE,
shuffle=False)
# initialize the ground truth and output tensor
gt = torch.FloatTensor()
gt = gt.cuda()
pred = torch.FloatTensor()
pred = pred.cuda()
# switch to evaluate mode
model.eval()
model_nosyft = model
for i, (inp, target) in enumerate(test_loader):
location = inp.location
target = target.get().cuda()
gt = torch.cat((gt, target), 0)
inp = inp.get()
bs, n_crops, c, h, w = inp.size()
input_var = inp.view(-1, c, h, w).cuda().send(location)
model.send(location)
output = model(input_var)
output_mean = output.view(bs, n_crops, -1).mean(1)
output_mean_data = output_mean.get()
model.get()
pred = torch.cat((pred, output_mean_data.data), 0)
pred_np = pred.cpu().numpy()
pred_np[pred_np < THRESHOLD] = 0
pred_np[pred_np >= THRESHOLD] = 1
indexes = []
AUROCs, specificity, sensitivity, F1score = compute_AUCs(gt, pred)
AUROC_avg = np.array(AUROCs).mean()
print('The average AUROC is {AUROC_avg:.3f}'.format(AUROC_avg=AUROC_avg))
for i in range(N_CLASSES):
print('The AUROC of {} is {}'.format(CLASS_NAMES[i], AUROCs[i]))
print('The specificity of {} is {}'.format(CLASS_NAMES[i],
specificity[i]))
print('The sensitivity of {} is {}'.format(CLASS_NAMES[i],
sensitivity[i]))
print('The F1 score of {} is {}'.format(CLASS_NAMES[i], F1score[i]))
for arr in pred_np:
indexes.append([index for index, val in enumerate(arr) if val == 1])
for index, disease_array in enumerate(indexes):
if len(disease_array) > 0:
print(
f'XRAY :: {index + 1} :: {[CLASS_NAMES[i] for i in disease_array]}'
)
else:
print(f'XRAY :: {index + 1} :: No Disease detected')
# print("Confidence of the 14 classes are {}".format(pred[i] * 100))
print("Confidence of the 14 classes are {}".format({
CLASS_NAMES[j]: pred[index][j] * 100
for j in range(len(pred[index]))
}))
get_heat_map(model_nosyft, image_names[index], DATA_DIR)
def main():
cudnn.benchmark = True
# initialize and load the model
model = DenseNet121(N_CLASSES).cuda()
# model = torch.nn.DataParallel(model).cuda()
## getting the images list to be used for heatmap generation
image_names = []
with open(TEST_IMAGE_LIST, 'r') as f:
for line in f:
items = line.split()
image_names.append(items[0])
##############################################################
if os.path.isfile(CKPT_PATH):
print("=> loading checkpoint")
checkpoint = torch.load(CKPT_PATH)
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$'
)
state_dict = checkpoint['state_dict']
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
for key in list(state_dict.keys()):
split_key = key.split('.')
new_key = '.'.join(split_key[1:])
state_dict[new_key] = state_dict[key]
del state_dict[key]
model.load_state_dict(state_dict)
print("=> loaded checkpoint")
else:
print("=> no checkpoint found")
normalize = transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
test_dataset = ChestXrayDataSet(
data_dir=DATA_DIR,
image_list_file=TEST_IMAGE_LIST,
transform=transforms.Compose([
transforms.Resize(256),
transforms.TenCrop(224),
transforms.Lambda(lambda crops: torch.stack(
[transforms.ToTensor()(crop) for crop in crops])),
transforms.Lambda(
lambda crops: torch.stack([normalize(crop) for crop in crops]))
]))
test_loader = sy.FederatedDataLoader(
federated_dataset=test_dataset.federate((bob, alice)),
batch_size=BATCH_SIZE,
shuffle=False)
# initialize the ground truth and output tensor
gt = torch.FloatTensor()
gt = gt.cuda()
pred = torch.FloatTensor()
pred = pred.cuda()
# switch to evaluate mode
model.eval()
model_nosyft = model
for i, (inp, target) in enumerate(test_loader):
location = inp.location
inp = inp.get()
bs, n_crops, c, h, w = inp.size()
# print(inp.size())
# input_var = torch.autograd.Variable(torch.FloatTensor(inp.view(-1, c, h, w)).cuda(), volatile=True).send(location)
input_var = inp.view(-1, c, h, w).cuda().send(location)
model.send(location)
output = model(input_var)
# print(f'output of the inference is {output.shape}')
output_mean = output.view(bs, n_crops, -1).mean(1)
output_mean_data = output_mean.get()
model.get()
pred = torch.cat((pred, output_mean_data.data), 0)
pred_np = pred.cpu().numpy()
pred_np[pred_np < THRESHOLD] = 0
pred_np[pred_np >= THRESHOLD] = 1
indexes = []
for arr in pred_np:
indexes.append([index for index, val in enumerate(arr) if val == 1])
for index, disease_array in enumerate(indexes):
if len(disease_array) > 0:
print(
f'XRAY :: {index + 1} :: {[CLASS_NAMES[i] for i in disease_array]}'
)
else:
print(f'XRAY :: {index + 1} :: No Disease detected')
get_heat_map(model_nosyft, image_names[index], DATA_DIR)