def slim_speedup(masks_file, model_checkpoint):
device = torch.device('cuda')
model = VGG(depth=19)
model.to(device)
model.eval()
dummy_input = torch.randn(64, 3, 32, 32)
if use_mask:
apply_compression_results(model, masks_file)
dummy_input = dummy_input.to(device)
start = time.time()
for _ in range(32):
out = model(dummy_input)
#print(out.size(), out)
print('mask elapsed time: ', time.time() - start)
return
else:
#print("model before: ", model)
m_speedup = ModelSpeedup(model, dummy_input.to(device), masks_file)
m_speedup.speedup_model()
#print("model after: ", model)
dummy_input = dummy_input.to(device)
start = time.time()
for _ in range(32):
out = model(dummy_input)
#print(out.size(), out)
print('speedup elapsed time: ', time.time() - start)
return
def model_inference(config):
masks_file = config['masks_file']
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
# device = torch.device(config['device'])
if config['model_name'] == 'vgg16':
model = VGG(depth=16)
elif config['model_name'] == 'vgg19':
model = VGG(depth=19)
elif config['model_name'] == 'lenet':
model = LeNet()
model.to(device)
model.eval()
dummy_input = torch.randn(config['input_shape']).to(device)
use_mask_out = use_speedup_out = None
# must run use_mask before use_speedup because use_speedup modify the model
if use_mask:
apply_compression_results(model, masks_file, device)
start = time.time()
for _ in range(32):
use_mask_out = model(dummy_input)
print('elapsed time when use mask: ', time.time() - start)
if use_speedup:
m_speedup = ModelSpeedup(model, dummy_input, masks_file, device)
m_speedup.speedup_model()
start = time.time()
for _ in range(32):
use_speedup_out = model(dummy_input)
print('elapsed time when use speedup: ', time.time() - start)
if compare_results:
if torch.allclose(use_mask_out, use_speedup_out, atol=1e-07):
print('the outputs from use_mask and use_speedup are the same')
else:
raise RuntimeError(
'the outputs from use_mask and use_speedup are different')
