def main(args):
print('[*] Arguments: %s' % args)
print('[*] Read MNIST...')
num_test_images = args.num_test_images
images, labels = load_mnist('test', path='./data', max_ind=num_test_images)
images, labels = images[:num_test_images, :, :], labels[:num_test_images]
images = images.astype(np.float32)
images = images / 255.
print('[*] The shape of image: %s' % str(images.shape))
print('[*] Load the network...')
if args.network == 'mlp': # Lab 2
if args.quantized:
print('[!] MLP does not support quantization')
return
model_path = os.path.join('./pretrained_weights',
'mlp_iter_10000.caffemodel')
net = MLP(model_path, args)
elif args.network == 'cnn':
if args.quantized: # Lab 14
model_path = os.path.join('./pretrained_weights',
'quantized_cnn_weights.txt')
else: # Lab 11
model_path = os.path.join('./pretrained_weights',
'cnn_weights.txt')
net = CNN(model_path, args)
else:
raise
print('[*] Run tests...')
test_images = [images[i, :, :].copy() for i in xrange(num_test_images)]
n_correct = 0
start_time = time.time()
for i in xrange(num_test_images):
X = test_images[i]
X = X.reshape((28 * 28)) # 28x28->784
logit = net.inference(X)
prediction = np.argmax(logit)
label = labels[i, ]
n_correct += (label == prediction)
print('[*] Statistics...')
model_stats = {
'total_time': time.time() - start_time,
'total_image': num_test_images,
'accuracy': float(n_correct) / num_test_images,
'avg_num_call': net.total_num_call[0] / num_test_images,
'm_size': net.m_size,
'v_size': net.v_size,
}
pp.pprint(model_stats)
self.total_num_call = np.zeros(1, dtype=np.int)
self.output = np.zeros(10, dtype=np.float32)
def __del__(self):
self.lib.delNet(self.net)
def inference(self, input):
self.lib.inference(self.net, input.ctypes.data,
self.output.ctypes.data,
self.total_num_call.ctypes.data)
return self.output
if __name__ == "__main__":
print("read dataset...")
images, labels = load_mnist("test", path="./data")
images, labels = images[:NUM_TEST_IMAGES, :, :], labels[:NUM_TEST_IMAGES]
images = images.astype(np.float32)
images = images / 255.
print("images", images.shape)
print("create network...")
model_path = os.path.join("./pretrained_weights",
"mlp_iter_10000.caffemodel")
net = MLP(model_path, m_size=M_SIZE, v_size=V_SIZE)
test_images = [images[i, :, :].copy() for i in xrange(NUM_TEST_IMAGES)]
print("run test...")
n_correct = 0
start_time = time.time()
for i in xrange(NUM_TEST_IMAGES):