def model_accuracy(models, image_paths, target_paths, results_dir):
accList = []
for model in models:
print('Path to model {}'.format(model))
base_model = os.path.basename(model)
batch_size = get_batch(base_model)
train_loader, valid_loader, test_loader = ConvNet.train_valid_test_split(
image_paths, target_paths, batch_size)
print('Model: {}'.format(base_model))
match = re.search('arch(\d+)', base_model)
net_size = match.group(1)
net_size = int(net_size)
lr = re.search('lr(\d+\.\d+)', base_model)
lr = float(lr.group(1))
batch = get_batch(base_model)
net = ConvNet.Net(net_size)
net = nn.DataParallel(net)
net.load_state_dict(
torch.load(model, map_location=lambda storage, loc: storage))
#net.cuda()
accuracy = ConvNet.model_eval(test_loader, net, batch, lr, net_size,
results_dir)
accList.append(accuracy)
maxInd = accList.index(max(accList))
print('Highest accuracy: {} for model: {}'.format(accList[maxInd],
models[maxInd]))
models[maxInd]
return maxInd
def main():
startTime = time.time()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ConvNet.Net().to(device)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
epochs = 90
for epoch in range(1, epochs + 1):
ConvNet.train(model, device, train_data, criterion, optimizer, epoch)
ConvNet.test(model, device, criterion, test_data)
print(50 * "-")
print('Finished Training')
endTime = time.time()
elapsedTime = int(endTime - startTime)
showTime(elapsedTime)
final_prediction = b[:, :, 0]
labels = (final_prediction > 0.5).astype(np.int)
return labels
if __name__ == '__main__':
args = parser.parse_args()
rasterfn = args.inpfile
base = os.path.basename(rasterfn)
fname = os.path.splitext(base)[0]
newRasterfn = '/exports/csce/eddie/geos/groups/geos_cnn_imgclass/data/AerialImageDataset/predict_raster/predict_{}.tif'.format(
fname)
base_model = os.path.basename(args.model)
match = re.search('arch(\d+)', base_model)
net_size = match.group(1)
net_size = int(net_size)
net = ConvNet.Net(net_size)
net = nn.DataParallel(net)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
net.to(device)
if torch.cuda.is_available():
net.load_state_dict(torch.load(args.model))
else:
net.load_state_dict(
torch.load(args.model, map_location=lambda storage, loc: storage))
toTensor = transforms.ToTensor()
array = image_loader(rasterfn)
array2raster(rasterfn, newRasterfn, array)