def load_model():
'''
Loads a pre-trained model and settings used to generate it.
'''
try:
with open(f'{PATH_TO_MODEL}.json', 'r') as json_file:
settings = json.load(json_file)
model = NeuralNet(len(settings['all_labels']))
model.load_state_dict(
torch.load(f'{PATH_TO_MODEL}.pth',
map_location=torch.device('cpu')))
except:
print('Could not locate a trained model.')
sys.exit()
model.eval()
return model, settings
if (args.dataset == "train"):
data_x, data_y = train_x, train_y
elif (args.dataset == "test"):
data_x, data_y = test_x, test_y
elif (args.dataset == "valid"):
data_x, data_y = valid_x, valid_y
test_dataset = Dataset(root_dir, data_x, data_y, transforms=transform)
test_generator = torch.utils.data.DataLoader(test_dataset, **params)
print("Loaded dataloaders...")
criterion = torch.nn.CrossEntropyLoss()
model = NeuralNet(0.001, criterion, 64, 2)
state_dict = torch.load(model_name)
model.load_state_dict(state_dict)
for parameter in model.parameters():
parameter.requires_grad = False
if (use_cuda):
model.cuda()
model.eval()
summary(model, (1, 64, 64))
print("Loaded model...")
preds = []
labels = []
for local_batch, local_labels in tqdm(test_generator):
labels.extend(local_labels.numpy().tolist())
local_batch, local_labels = local_batch.to(device), local_labels.to(device)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
root_dir = os.path.join("data")
classes_to_idx = {
v: k
for k, v in enumerate(
open(os.path.join(root_dir, "classes.txt")).read().strip().split("\n"))
}
idx_to_classes = {v: k for k, v in classes_to_idx.items()}
criterion = nn.BCELoss()
model = NeuralNet(0.01, criterion, 256, len(classes_to_idx))
model.load_state_dict(torch.load(sys.argv[1]))
if use_cuda:
model.cuda()
model.eval()
id = random.choice(os.listdir(os.path.join(root_dir, "images"))).split(".")[0]
data = torch.load(os.path.join(root_dir, "images", id + ".pt"))
true_labels = torch.load(os.path.join(root_dir, "labels", id + ".pt"))
diseases = []
for x in range(len(true_labels)):
if (true_labels[x] == 1.0):
diseases.append(idx_to_classes[x])
print(id)
print(true_labels)
