def runTest(self):
with fluid.unique_name.guard():
net = MulNet()
ratios = {}
ratios['conv2d_0.w_0'] = 0.5
pruners = []
pruner = L1NormFilterPruner(net, [2, 6, 3, 3], skip_leaves=False)
pruners.append(pruner)
pruner = FPGMFilterPruner(net, [2, 6, 3, 3], skip_leaves=False)
pruners.append(pruner)
pruner = L2NormFilterPruner(net, [2, 6, 3, 3], skip_leaves=False)
pruners.append(pruner)
shapes = {
'b': [3, 18],
'conv2d_0.w_0': [3, 6, 1, 1],
'conv2d_0.b_0': [3]
}
for pruner in pruners:
plan = pruner.prune_vars(ratios, 0)
for param in net.parameters():
if param.name not in shapes:
shapes[param.name] = param.shape
self.assertTrue(shapes[param.name] == param.shape)
pruner.restore()
def runTest(self):
x_shape = (1, 3, 32, 32)
net = Net()
x = np.random.uniform(-1, 1, x_shape).astype('float32')
pruner = L1NormFilterPruner(net, [paddle.to_tensor(x)])
pruner.prune_vars({"conv2d_0.w_0": 0.2}, [0])
self.assertTrue(net.linear.weight.shape == [5400, 5])
def dygraph_sen(self):
paddle.disable_static()
net = paddle.vision.models.LeNet()
optimizer = paddle.optimizer.Adam(learning_rate=0.001,
parameters=net.parameters())
inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
model = paddle.Model(net, inputs, labels)
model.prepare(optimizer, paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 5)))
model.fit(self.train_dataset, epochs=1, batch_size=128, verbose=1)
result = model.evaluate(self.val_dataset, batch_size=128, verbose=1)
pruner = None
if self._pruner == 'l1norm':
pruner = L1NormFilterPruner(net, [1, 1, 28, 28])
elif self._pruner == 'fpgm':
pruner = FPGMFilterPruner(net, [1, 1, 28, 28])
def eval_fn():
result = model.evaluate(self.val_dataset, batch_size=128)
return result['acc_top1']
sen = pruner.sensitive(
eval_func=eval_fn,
sen_file="_".join(["./dygraph_sen_",
str(time.time())]),
#sen_file="sen.pickle",
target_vars=self._param_names)
params = {}
for param in net.parameters():
params[param.name] = np.array(param.value().get_tensor())
print(f'dygraph sen: {sen}')
return sen, params
def dygraph_prune(self, net, ratios):
paddle.disable_static()
model = net(pretrained=False)
pruner = L1NormFilterPruner(model, [1, 3, 16, 16])
pruner.prune_vars(ratios, [0])
shapes = {}
for param in model.parameters():
shapes[param.name] = param.shape
return shapes
def runTest(self):
with fluid.unique_name.guard():
net = paddle.vision.models.LeNet()
optimizer = paddle.optimizer.Adam(learning_rate=0.001,
parameters=net.parameters())
inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
model = paddle.Model(net, inputs, labels)
model.prepare(optimizer, paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 5)))
model.fit(self.train_dataset, epochs=1, batch_size=128, verbose=1)
pruners = []
pruner = L1NormFilterPruner(net, [1, 1, 28, 28], opt=optimizer)
pruners.append(pruner)
pruner = FPGMFilterPruner(net, [1, 1, 28, 28], opt=optimizer)
pruners.append(pruner)
pruner = L2NormFilterPruner(net, [1, 1, 28, 28], opt=optimizer)
pruners.append(pruner)
def eval_fn():
result = model.evaluate(self.val_dataset,
batch_size=128,
verbose=1)
return result['acc_top1']
sen_file = "_".join(["./dygraph_sen_", str(time.time())])
for pruner in pruners:
sen = pruner.sensitive(eval_func=eval_fn,
sen_file=sen_file,
target_vars=self._param_names)
model.fit(self.train_dataset,
epochs=1,
batch_size=128,
verbose=1)
base_acc = eval_fn()
plan = pruner.sensitive_prune(0.01)
pruner.restore()
restore_acc = eval_fn()
self.assertTrue(restore_acc == base_acc)
plan = pruner.sensitive_prune(0.01, align=4)
for param in net.parameters():
if param.name in self._param_names:
print(f"name: {param.name}; shape: {param.shape}")
self.assertTrue(param.shape[0] % 4 == 0)
pruner.restore()
def runTest(self):
with fluid.unique_name.guard():
net = paddle.vision.models.mobilenet_v1()
ratios = {}
for param in net.parameters():
if len(param.shape) == 4:
ratios[param.name] = 0.5
pruners = []
pruner = L1NormFilterPruner(net, [1, 3, 128, 128])
pruners.append(pruner)
pruner = FPGMFilterPruner(net, [1, 3, 128, 128])
pruners.append(pruner)
pruner = L2NormFilterPruner(net, [1, 3, 128, 128])
pruners.append(pruner)
shapes = {}
for pruner in pruners:
plan = pruner.prune_vars(ratios, 0)
for param in net.parameters():
if param.name not in shapes:
shapes[param.name] = param.shape
assert (shapes[param.name] == param.shape)
pruner.restore()
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not (0.0 < args.pruning_ratio < 1.0):
raise RuntimeError(
'The model pruning rate must be in the range of (0, 1).')
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
cfg = Config(args.cfg,
iters=args.retraining_iters,
batch_size=args.batch_size,
learning_rate=args.learning_rate)
train_dataset = cfg.train_dataset
if not train_dataset:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The validation dataset is not specified in the c;onfiguration file.'
)
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
net = cfg.model
if args.model_path:
para_state_dict = paddle.load(args.model_path)
net.set_dict(para_state_dict)
logger.info('Loaded trained params of model successfully')
logger.info(
'Step 1/3: Start calculating the sensitivity of model parameters...')
sample_shape = [1] + list(train_dataset[0][0].shape)
sen_file = os.path.join(args.save_dir, 'sen.pickle')
pruner = L1NormFilterPruner(net, sample_shape)
pruner.sensitive(eval_func=partial(eval_fn, net, val_dataset,
args.num_workers),
sen_file=sen_file)
logger.info(
f'The sensitivity calculation of model parameters is complete. The result is saved in {sen_file}.'
)
flops = dygraph_flops(net, sample_shape)
logger.info(
f'Step 2/3: Start to prune the model, the ratio of pruning is {args.pruning_ratio}. FLOPs before pruning: {flops}.'
)
# Avoid the bug when pruning conv2d with small channel number.
# Remove this code after PaddleSlim 2.1 is available.
# Related issue: https://github.com/PaddlePaddle/PaddleSlim/issues/674.
skips = []
for param in net.parameters():
if param.shape[0] <= 8:
skips.append(param.name)
pruner.sensitive_prune(args.pruning_ratio, skip_vars=skips)
flops = dygraph_flops(net, sample_shape)
logger.info(f'Model pruning completed. FLOPs after pruning: {flops}.')
logger.info(f'Step 3/3: Start retraining the model.')
train(net,
train_dataset,
optimizer=cfg.optimizer,
save_dir=args.save_dir,
num_workers=args.num_workers,
iters=cfg.iters,
batch_size=cfg.batch_size,
losses=cfg.loss)
evaluate(net, val_dataset)
if paddle.distributed.get_rank() == 0:
export_model(net, cfg, args.save_dir)
ckpt = os.path.join(args.save_dir, f'iter_{args.retraining_iters}')
if os.path.exists(ckpt):
shutil.rmtree(ckpt)
logger.info(f'Model retraining finish. Model is saved in {args.save_dir}')