model.eval()
correct = 0
with torch.no_grad():
for data, target in tqdm(iterable=test_loader, desc='Test'):
data, target = data.to(device), target.to(device)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
acc = 100 * correct / len(test_loader.dataset)
print('Accuracy: {}%\n'.format(acc))
return acc
if __name__ == '__main__':
model = VGG().to(device)
optimizer = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=5e-4)
criterion = torch.nn.CrossEntropyLoss()
print('\nPre-train the model:')
for i in range(5):
trainer(model, optimizer, criterion, i)
evaluator(model)
config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
pruner = L1NormPruner(model, config_list)
_, masks = pruner.compress()
print('\nThe accuracy with masks:')
model.load_state_dict(best_state_dict)
pre_flops, pre_params, _ = count_flops_params(
model,
torch.randn([128, 3, 32, 32]).to(device))
g_epoch = 0
# Start to prune and speedup
print('\n' + '=' * 50 +
' START TO PRUNE THE BEST ACCURACY PRETRAINED MODEL ' + '=' * 50)
config_list = [{
'total_sparsity': 0.5,
'op_types': ['Conv2d'],
}]
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.SGD)(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
if 'apoz' in args.pruner:
pruner = ActivationAPoZRankPruner(model,
config_list,
trainer,
traced_optimizer,
criterion,
training_batches=20)
else:
pruner = ActivationMeanRankPruner(model,
config_list,
trainer,
traced_optimizer,
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch Iterative Example for model comporession')
parser.add_argument('--pretrain-epochs', type=int, default=10,
help='number of epochs to pretrain the model')
parser.add_argument('--pruning-algo', type=str, default='l1',
choices=['level', 'l1', 'l2', 'fpgm', 'slim', 'apoz',
'mean_activation', 'taylorfo', 'admm'],
help='algorithm to evaluate weights to prune')
parser.add_argument('--cool-down-rate', type=float, default=0.9,
help='Cool down rate of the temperature.')
args = parser.parse_args()
model = VGG().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
criterion = torch.nn.CrossEntropyLoss()
# pre-train the model
for i in range(args.pretrain_epochs):
trainer(model, optimizer, criterion, i)
evaluator(model)
config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.8}]
# evaluator in 'SimulatedAnnealingPruner' could not be None.
pruner = SimulatedAnnealingPruner(model, config_list, pruning_algorithm=args.pruning_algo,
evaluator=evaluator, cool_down_rate=args.cool_down_rate, finetuner=finetuner)
pruner.compress()
_, model, masks, _, _ = pruner.get_best_result()
evaluator(model)
pre_best_acc = acc
best_state_dict = model.state_dict()
print("Best accuracy: {}".format(pre_best_acc))
model.load_state_dict(best_state_dict)
pre_flops, pre_params, _ = count_flops_params(model, torch.randn([128, 3, 32, 32]).to(device))
g_epoch = 0
# Start to prune and speedup
print('\n' + '=' * 50 + ' START TO PRUNE THE BEST ACCURACY PRETRAINED MODEL ' + '=' * 50)
config_list = [{
'sparsity': 0.8,
'op_types': ['Conv2d'],
}]
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.SGD)(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
pruner = ADMMPruner(model, config_list, trainer, traced_optimizer, criterion, iterations=10, training_epochs=1, granularity='coarse-grained')
_, masks = pruner.compress()
pruner.show_pruned_weights()
pruner._unwrap_model()
ModelSpeedup(model, torch.randn([128, 3, 32, 32]).to(device), masks).speedup_model()
print('\n' + '=' * 50 + ' EVALUATE THE MODEL AFTER PRUNING ' + '=' * 50)
evaluator(model)
# Optimizer used in the pruner might be patched, so recommend to new an optimizer for fine-tuning stage.
print('\n' + '=' * 50 + ' START TO FINE TUNE THE MODEL ' + '=' * 50)
optimizer, scheduler = optimizer_scheduler_generator(model, _lr=0.01, total_epoch=args.fine_tune_epochs)
best_acc = 0.0