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PaddleSlim
提交 339a1ef6 编写于 作者: W wanghaoshuang
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demo/auto_prune/README.md
浏览文件 @ 339a1ef6
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
## 2. 运行示例 ## 2. 运行示例
提供两种自动裁剪模式，直接以裁剪目标进行一次自动裁剪，和多次迭代的方式进行裁剪。 提供两种自动裁剪模式，直接以裁剪目标进行一次自动裁剪，和多次迭代的方式进行裁剪。
###2.1一次裁剪 ###2.1一次裁剪
......
demo/auto_prune/train_finetune.py
浏览文件 @ 339a1ef6
...@@ -34,12 +34,12 @@ add_arg('config_file', str, None, "The config file for comp ...@@ -34,12 +34,12 @@ add_arg('config_file', str, None, "The config file for comp
model_list = [m for m in dir(models) if "__" not in m] model_list = [m for m in dir(models) if "__" not in m]
ratiolist = [ ratiolist = [
# [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08], # [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08],
# [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01], # [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
] ]
def save_model(args, exe, train_prog, eval_prog,info): def save_model(args, exe, train_prog, eval_prog, info):
model_path = os.path.join(args.model_save_dir, args.model, str(info)) model_path = os.path.join(args.model_save_dir, args.model, str(info))
if not os.path.isdir(model_path): if not os.path.isdir(model_path):
os.makedirs(model_path) os.makedirs(model_path)
...@@ -58,29 +58,31 @@ def piecewise_decay(args): ...@@ -58,29 +58,31 @@ def piecewise_decay(args):
regularization=fluid.regularizer.L2Decay(args.l2_decay)) regularization=fluid.regularizer.L2Decay(args.l2_decay))
return optimizer return optimizer
def cosine_decay(args): def cosine_decay(args):
step = int(math.ceil(float(args.total_images) / args.batch_size)) step = int(math.ceil(float(args.total_images) / args.batch_size))
learning_rate = fluid.layers.cosine_decay( learning_rate = fluid.layers.cosine_decay(
learning_rate=args.lr, learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs)
step_each_epoch=step,
epochs=args.num_epochs)
optimizer = fluid.optimizer.Momentum( optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate, learning_rate=learning_rate,
momentum=args.momentum_rate, momentum=args.momentum_rate,
regularization=fluid.regularizer.L2Decay(args.l2_decay)) regularization=fluid.regularizer.L2Decay(args.l2_decay))
return optimizer return optimizer
def create_optimizer(args): def create_optimizer(args):
if args.lr_strategy == "piecewise_decay": if args.lr_strategy == "piecewise_decay":
return piecewise_decay(args) return piecewise_decay(args)
elif args.lr_strategy == "cosine_decay": elif args.lr_strategy == "cosine_decay":
return cosine_decay(args) return cosine_decay(args)
def compress(args): def compress(args):
class_dim=1000 class_dim = 1000
image_shape="3,224,224" image_shape = "3,224,224"
image_shape = [int(m) for m in image_shape.split(",")] image_shape = [int(m) for m in image_shape.split(",")]
assert args.model in model_list, "{} is not in lists: {}".format(args.model, model_list) assert args.model in model_list, "{} is not in lists: {}".format(
args.model, model_list)
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# model definition # model definition
...@@ -98,10 +100,13 @@ def compress(args): ...@@ -98,10 +100,13 @@ def compress(args):
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
if args.pretrained_model: if args.pretrained_model:
def if_exist(var): def if_exist(var):
exist = os.path.exists(os.path.join(args.pretrained_model, var.name)) exist = os.path.exists(
print("exist",exist) os.path.join(args.pretrained_model, var.name))
print("exist", exist)
return exist return exist
#fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist) #fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
val_reader = paddle.batch(reader.val(), batch_size=args.batch_size) val_reader = paddle.batch(reader.val(), batch_size=args.batch_size)
...@@ -109,7 +114,8 @@ def compress(args): ...@@ -109,7 +114,8 @@ def compress(args):
reader.train(), batch_size=args.batch_size, drop_last=True) reader.train(), batch_size=args.batch_size, drop_last=True)
train_feeder = feeder = fluid.DataFeeder([image, label], place) train_feeder = feeder = fluid.DataFeeder([image, label], place)
val_feeder = feeder = fluid.DataFeeder([image, label], place, program=val_program) val_feeder = feeder = fluid.DataFeeder(
[image, label], place, program=val_program)
def test(epoch, program): def test(epoch, program):
batch_id = 0 batch_id = 0
...@@ -117,80 +123,99 @@ def compress(args): ...@@ -117,80 +123,99 @@ def compress(args):
acc_top5_ns = [] acc_top5_ns = []
for data in val_reader(): for data in val_reader():
start_time = time.time() start_time = time.time()
acc_top1_n, acc_top5_n = exe.run(program, acc_top1_n, acc_top5_n = exe.run(
feed=train_feeder.feed(data), program,
fetch_list=[acc_top1.name, acc_top5.name]) feed=train_feeder.feed(data),
fetch_list=[acc_top1.name, acc_top5.name])
end_time = time.time() end_time = time.time()
print("Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".format(epoch, batch_id, np.mean(acc_top1_n), np.mean(acc_top5_n), end_time-start_time)) print(
"Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".
format(epoch, batch_id,
np.mean(acc_top1_n),
np.mean(acc_top5_n), end_time - start_time))
acc_top1_ns.append(np.mean(acc_top1_n)) acc_top1_ns.append(np.mean(acc_top1_n))
acc_top5_ns.append(np.mean(acc_top5_n)) acc_top5_ns.append(np.mean(acc_top5_n))
batch_id += 1 batch_id += 1
print("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(epoch, np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns)))) print("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(
epoch,
np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
def train(epoch, program): def train(epoch, program):
build_strategy = fluid.BuildStrategy() build_strategy = fluid.BuildStrategy()
exec_strategy = fluid.ExecutionStrategy() exec_strategy = fluid.ExecutionStrategy()
train_program = fluid.compiler.CompiledProgram( train_program = fluid.compiler.CompiledProgram(
program).with_data_parallel( program).with_data_parallel(
loss_name=avg_cost.name, loss_name=avg_cost.name,
build_strategy=build_strategy, build_strategy=build_strategy,
exec_strategy=exec_strategy) exec_strategy=exec_strategy)
batch_id = 0 batch_id = 0
for data in train_reader(): for data in train_reader():
start_time = time.time() start_time = time.time()
loss_n, acc_top1_n, acc_top5_n,lr_n = exe.run(train_program, loss_n, acc_top1_n, acc_top5_n, lr_n = exe.run(
feed=train_feeder.feed(data), train_program,
fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name,"learning_rate"]) feed=train_feeder.feed(data),
fetch_list=[
avg_cost.name, acc_top1.name, acc_top5.name,
"learning_rate"
])
end_time = time.time() end_time = time.time()
loss_n = np.mean(loss_n) loss_n = np.mean(loss_n)
acc_top1_n = np.mean(acc_top1_n) acc_top1_n = np.mean(acc_top1_n)
acc_top5_n = np.mean(acc_top5_n) acc_top5_n = np.mean(acc_top5_n)
lr_n = np.mean(lr_n) lr_n = np.mean(lr_n)
print("epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {};lrn: {}; time: {}".format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n, lr_n,end_time-start_time)) print(
"epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {};lrn: {}; time: {}".
format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n, lr_n,
end_time - start_time))
batch_id += 1 batch_id += 1
params = [] params = []
for param in fluid.default_main_program().global_block().all_parameters(): for param in fluid.default_main_program().global_block().all_parameters():
#if "_weights" in param.name and "conv1_weights" not in param.name: #if "_weights" in param.name and "conv1_weights" not in param.name:
if "_sep_weights" in param.name: if "_sep_weights" in param.name:
params.append(param.name) params.append(param.name)
print("fops before pruning: {}".format(flops(fluid.default_main_program()))) print("fops before pruning: {}".format(
flops(fluid.default_main_program())))
pruned_program_iter = fluid.default_main_program() pruned_program_iter = fluid.default_main_program()
pruned_val_program_iter = val_program pruned_val_program_iter = val_program
for ratios in ratiolist: for ratios in ratiolist:
pruner = Pruner() pruner = Pruner()
pruned_val_program_iter = pruner.prune(pruned_val_program_iter, pruned_val_program_iter = pruner.prune(
fluid.global_scope(), pruned_val_program_iter,
params=params, fluid.global_scope(),
ratios=ratios, params=params,
place=place, ratios=ratios,
only_graph=True) place=place,
only_graph=True)
pruned_program_iter = pruner.prune(pruned_program_iter, pruned_program_iter = pruner.prune(
fluid.global_scope(), pruned_program_iter,
params=params, fluid.global_scope(),
ratios=ratios, params=params,
place=place) ratios=ratios,
place=place)
print("fops after pruning: {}".format(flops(pruned_program_iter))) print("fops after pruning: {}".format(flops(pruned_program_iter)))
""" do not inherit learning rate """ """ do not inherit learning rate """
if(os.path.exists(args.pretrained_model + "/learning_rate")): if (os.path.exists(args.pretrained_model + "/learning_rate")):
os.remove( args.pretrained_model + "/learning_rate") os.remove(args.pretrained_model + "/learning_rate")
if(os.path.exists(args.pretrained_model + "/@LR_DECAY_COUNTER@")): if (os.path.exists(args.pretrained_model + "/@LR_DECAY_COUNTER@")):
os.remove( args.pretrained_model + "/@LR_DECAY_COUNTER@") os.remove(args.pretrained_model + "/@LR_DECAY_COUNTER@")
fluid.io.load_vars(exe, args.pretrained_model , main_program = pruned_program_iter, predicate=if_exist) fluid.io.load_vars(
exe,
args.pretrained_model,
main_program=pruned_program_iter,
predicate=if_exist)
pruned_program = pruned_program_iter pruned_program = pruned_program_iter
pruned_val_program = pruned_val_program_iter pruned_val_program = pruned_val_program_iter
for i in range(args.num_epochs): for i in range(args.num_epochs):
train(i, pruned_program) train(i, pruned_program)
test(i, pruned_val_program) test(i, pruned_val_program)
save_model(args,exe,pruned_program,pruned_val_program,i) save_model(args, exe, pruned_program, pruned_val_program, i)
def main(): def main():
args = parser.parse_args() args = parser.parse_args()
......
demo/auto_prune/train_iterator.py
浏览文件 @ 339a1ef6
...@@ -41,9 +41,10 @@ add_arg('test_period', int, 10, "Test period in epoches.") ...@@ -41,9 +41,10 @@ add_arg('test_period', int, 10, "Test period in epoches.")
model_list = [m for m in dir(models) if "__" not in m] model_list = [m for m in dir(models) if "__" not in m]
ratiolist = [ ratiolist = [
# [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08], # [0.06, 0.0, 0.09, 0.03, 0.09, 0.02, 0.05, 0.03, 0.0, 0.07, 0.07, 0.05, 0.08],
# [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01], # [0.08, 0.02, 0.03, 0.13, 0.1, 0.06, 0.03, 0.04, 0.14, 0.02, 0.03, 0.02, 0.01],
] ]
def piecewise_decay(args): def piecewise_decay(args):
step = int(math.ceil(float(args.total_images) / args.batch_size)) step = int(math.ceil(float(args.total_images) / args.batch_size))
...@@ -194,21 +195,26 @@ def compress(args): ...@@ -194,21 +195,26 @@ def compress(args):
for ratios in ratiolist: for ratios in ratiolist:
pruner = Pruner() pruner = Pruner()
pruned_val_program_iter = pruner.prune(pruned_val_program_iter, pruned_val_program_iter = pruner.prune(
fluid.global_scope(), pruned_val_program_iter,
params=params, fluid.global_scope(),
ratios=ratios, params=params,
place=place, ratios=ratios,
only_graph=True) place=place,
only_graph=True)
pruned_program_iter = pruner.prune(pruned_program_iter, pruned_program_iter = pruner.prune(
fluid.global_scope(), pruned_program_iter,
params=params, fluid.global_scope(),
ratios=ratios, params=params,
place=place) ratios=ratios,
place=place)
print("fops after pruning: {}".format(flops(pruned_program_iter))) print("fops after pruning: {}".format(flops(pruned_program_iter)))
fluid.io.load_vars(exe, args.pretrained_model , main_program = pruned_program_iter, predicate=if_exist) fluid.io.load_vars(
exe,
args.pretrained_model,
main_program=pruned_program_iter,
predicate=if_exist)
pruner = AutoPruner( pruner = AutoPruner(
pruned_val_program_iter, pruned_val_program_iter,
...@@ -238,8 +244,6 @@ def compress(args): ...@@ -238,8 +244,6 @@ def compress(args):
pruner.reward(score) pruner.reward(score)
def main(): def main():
args = parser.parse_args() args = parser.parse_args()
print_arguments(args) print_arguments(args)
......
demo/nas/search_space_doc.md
浏览文件 @ 339a1ef6
...@@ -3,22 +3,22 @@ ...@@ -3,22 +3,22 @@
1. 根据原本模型结构构造搜索空间： 1. 根据原本模型结构构造搜索空间：
1.1 MobileNetV2Space 1.1 MobileNetV2Space
1.2 MobileNetV1Space 1.2 MobileNetV1Space
1.3 ResNetSpace 1.3 ResNetSpace
2. 根据相应模型的block构造搜索空间 2. 根据相应模型的block构造搜索空间
2.1 MobileNetV1BlockSpace 2.1 MobileNetV1BlockSpace
2.2 MobileNetV2BlockSpace 2.2 MobileNetV2BlockSpace
2.3 ResNetBlockSpace 2.3 ResNetBlockSpace
2.4 InceptionABlockSpace 2.4 InceptionABlockSpace
2.5 InceptionCBlockSpace 2.5 InceptionCBlockSpace
...@@ -29,7 +29,7 @@ ...@@ -29,7 +29,7 @@
**block_num(int|None)**：`block_num`表示搜索空间中block的数量。 **block_num(int|None)**：`block_num`表示搜索空间中block的数量。
**block_mask(list|None)**：`block_mask`表示当前的block是一个reduction block还是一个normal block，是一组由0、1组成的列表，0表示当前block是normal block，1表示当前block是reduction block。如果设置了`block_mask`，则主要以`block_mask`为主要配置，`input_size`，`output_size`和`block_num`三种配置是无效的。 **block_mask(list|None)**：`block_mask`表示当前的block是一个reduction block还是一个normal block，是一组由0、1组成的列表，0表示当前block是normal block，1表示当前block是reduction block。如果设置了`block_mask`，则主要以`block_mask`为主要配置，`input_size`，`output_size`和`block_num`三种配置是无效的。
**Note:** **Note:**
1. reduction block表示经过这个block之后的feature map大小下降为之前的一半，normal block表示经过这个block之后feature map大小不变。 1. reduction block表示经过这个block之后的feature map大小下降为之前的一半，normal block表示经过这个block之后feature map大小不变。
2. `input_size`和`output_size`用来计算整个模型结构中reduction block数量。 2. `input_size`和`output_size`用来计算整个模型结构中reduction block数量。
...@@ -80,7 +80,7 @@ class ResNetBlockSpace2(SearchSpaceBase): ...@@ -80,7 +80,7 @@ class ResNetBlockSpace2(SearchSpaceBase):
self.bottleneck_params_list = [] self.bottleneck_params_list = []
for i in range(len(self.block_mask)): for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(self.filter_num[tokens[i * 3 + 0]], self.bottleneck_params_list.append(self.filter_num[tokens[i * 3 + 0]],
self.filter_num[tokens[i * 3 + 1]], self.filter_num[tokens[i * 3 + 1]],
self.filter_num[tokens[i * 3 + 2]], self.filter_num[tokens[i * 3 + 2]],
2 if self.block_mask[i] == 1 else 1) 2 if self.block_mask[i] == 1 else 1)
...@@ -113,4 +113,4 @@ class ResNetBlockSpace2(SearchSpaceBase): ...@@ -113,4 +113,4 @@ class ResNetBlockSpace2(SearchSpaceBase):
conv = fluid.layers.conv2d(input, num_filters, filter_size, stride, name=name+'_conv') conv = fluid.layers.conv2d(input, num_filters, filter_size, stride, name=name+'_conv')
bn = fluid.layers.batch_norm(conv, act=act, name=name+'_bn') bn = fluid.layers.batch_norm(conv, act=act, name=name+'_bn')
return bn return bn
``` ```
demo/optimizer.py
浏览文件 @ 339a1ef6
...@@ -99,8 +99,8 @@ def exponential_decay_with_warmup(learning_rate, ...@@ -99,8 +99,8 @@ def exponential_decay_with_warmup(learning_rate,
(step_each_epoch * warmup_epoch)) (step_each_epoch * warmup_epoch))
fluid.layers.assign(input=decayed_lr, output=lr) fluid.layers.assign(input=decayed_lr, output=lr)
with switch.default(): with switch.default():
div_res = (global_step - warmup_epoch * step_each_epoch div_res = (
) / decay_epochs global_step - warmup_epoch * step_each_epoch) / decay_epochs
div_res = ops.floor(div_res) div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res) decayed_lr = learning_rate * (decay_rate**div_res)
fluid.layers.assign(input=decayed_lr, output=lr) fluid.layers.assign(input=decayed_lr, output=lr)
......
demo/utility.py
浏览文件 @ 339a1ef6
...@@ -36,7 +36,8 @@ logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') ...@@ -36,7 +36,8 @@ logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__) _logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO) _logger.setLevel(logging.INFO)
DOWNLOAD_RETRY_LIMIT=3 DOWNLOAD_RETRY_LIMIT = 3
def print_arguments(args): def print_arguments(args):
"""Print argparse's arguments. """Print argparse's arguments.
...@@ -211,6 +212,7 @@ def _download(url, path, md5sum=None): ...@@ -211,6 +212,7 @@ def _download(url, path, md5sum=None):
return fullname return fullname
def _md5check(fullname, md5sum=None): def _md5check(fullname, md5sum=None):
if md5sum is None: if md5sum is None:
return True return True
...@@ -224,10 +226,11 @@ def _md5check(fullname, md5sum=None): ...@@ -224,10 +226,11 @@ def _md5check(fullname, md5sum=None):
if calc_md5sum != md5sum: if calc_md5sum != md5sum:
_logger.info("File {} md5 check failed, {}(calc) != " _logger.info("File {} md5 check failed, {}(calc) != "
"{}(base)".format(fullname, calc_md5sum, md5sum)) "{}(base)".format(fullname, calc_md5sum, md5sum))
return False return False
return True return True
def _decompress(fname): def _decompress(fname):
""" """
Decompress for zip and tar file Decompress for zip and tar file
...@@ -261,6 +264,7 @@ def _decompress(fname): ...@@ -261,6 +264,7 @@ def _decompress(fname):
shutil.rmtree(fpath_tmp) shutil.rmtree(fpath_tmp)
os.remove(fname) os.remove(fname)
def _move_and_merge_tree(src, dst): def _move_and_merge_tree(src, dst):
""" """
Move src directory to dst, if dst is already exists, Move src directory to dst, if dst is already exists,
......
docs/zh_cn/model_zoo.md
浏览文件 @ 339a1ef6
...@@ -16,7 +16,7 @@ ...@@ -16,7 +16,7 @@
| MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar) | | MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar) |
|ResNet50|-|76.50%/93.00%| 99 | 2.71 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | |ResNet50|-|76.50%/93.00%| 99 | 2.71 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) |
|ResNet50|quant_post|76.33%/93.02% (-0.17%/+0.02%)| 25.1| 1.19 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_post.tar) | |ResNet50|quant_post|76.33%/93.02% (-0.17%/+0.02%)| 25.1| 1.19 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_post.tar) |
|ResNet50|quant_aware| 76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) | |ResNet50|quant_aware| 76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) |
<table border=0 cellpadding=0 cellspacing=0 width=861 style='border-collapse: <table border=0 cellpadding=0 cellspacing=0 width=861 style='border-collapse:
collapse;table-layout:fixed;width:644pt'> collapse;table-layout:fixed;width:644pt'>
...@@ -357,8 +357,8 @@ ...@@ -357,8 +357,8 @@
| 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） | TensorRT时延(V100, ms) | 下载 | | 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） | TensorRT时延(V100, ms) | 下载 |
| :----------------------------: | :---------: | :----: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |:----------: | | :----------------------------: | :---------: | :----: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |:----------: |
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | 95 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | | MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | 95 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |
| MobileNet-V1-YOLOv3 | quant_post | COCO | 8 | 27.9 (-1.4)| 28.0 (-1.3) | 26.0 (-1.0) | 25 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) | | MobileNet-V1-YOLOv3 | quant_post | COCO | 8 | 27.9 (-1.4)| 28.0 (-1.3) | 26.0 (-1.0) | 25 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
| MobileNet-V1-YOLOv3 | quant_aware | COCO | 8 | 28.1 (-1.2)| 28.2 (-1.1) | 25.8 (-1.2) | 26.3 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) | | MobileNet-V1-YOLOv3 | quant_aware | COCO | 8 | 28.1 (-1.2)| 28.2 (-1.1) | 25.8 (-1.2) | 26.3 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) |
| R34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 162 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | | R34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 162 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
| R34-YOLOv3 | quant_post | COCO | 8 | 35.7 (-0.5) | - | - | 42.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) | | R34-YOLOv3 | quant_post | COCO | 8 | 35.7 (-0.5) | - | - | 42.7 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) |
| R34-YOLOv3 | quant_aware | COCO | 8 | 35.2 (-1.0) | 33.3 (-1.0) | 30.3 (-1.1)| 44 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) | | R34-YOLOv3 | quant_aware | COCO | 8 | 35.2 (-1.0) | 33.3 (-1.0) | 30.3 (-1.1)| 44 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
......
tests/test_sa_nas.py
浏览文件 @ 339a1ef6
...@@ -19,24 +19,27 @@ from paddleslim.nas import SANAS ...@@ -19,24 +19,27 @@ from paddleslim.nas import SANAS
from paddleslim.analysis import flops from paddleslim.analysis import flops
import numpy as np import numpy as np
def compute_op_num(program): def compute_op_num(program):
params = {} params = {}
ch_list = [] ch_list = []
for block in program.blocks: for block in program.blocks:
for param in block.all_parameters(): for param in block.all_parameters():
if len(param.shape) == 4: if len(param.shape) == 4:
params[param.name] = param.shape params[param.name] = param.shape
ch_list.append(int(param.shape[0])) ch_list.append(int(param.shape[0]))
return params, ch_list return params, ch_list
class TestSANAS(unittest.TestCase): class TestSANAS(unittest.TestCase):
def setUp(self): def setUp(self):
self.init_test_case() self.init_test_case()
port = np.random.randint(8337, 8773) port = np.random.randint(8337, 8773)
self.sanas = SANAS(configs=self.configs, server_addr=("", port), save_checkpoint=None) self.sanas = SANAS(
configs=self.configs, server_addr=("", port), save_checkpoint=None)
def init_test_case(self): def init_test_case(self):
self.configs=[('MobileNetV2BlockSpace', {'block_mask':[0]})] self.configs = [('MobileNetV2BlockSpace', {'block_mask': [0]})]
self.filter_num = np.array([ self.filter_num = np.array([
3, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 128, 144, 160, 192, 224, 3, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 128, 144, 160, 192, 224,
256, 320, 384, 512 256, 320, 384, 512
...@@ -53,7 +56,10 @@ class TestSANAS(unittest.TestCase): ...@@ -53,7 +56,10 @@ class TestSANAS(unittest.TestCase):
conv_list, ch_pro = compute_op_num(program) conv_list, ch_pro = compute_op_num(program)
### assert conv number ### assert conv number
self.assertTrue((repeat_num * 3) == len(conv_list), "the number of conv is NOT match, the number compute from token: {}, actual conv number: {}".format(repeat_num * 3, len(conv_list))) self.assertTrue((repeat_num * 3) == len(
conv_list
), "the number of conv is NOT match, the number compute from token: {}, actual conv number: {}".
format(repeat_num * 3, len(conv_list)))
### assert number of channels ### assert number of channels
ch_token = [] ch_token = []
...@@ -64,7 +70,10 @@ class TestSANAS(unittest.TestCase): ...@@ -64,7 +70,10 @@ class TestSANAS(unittest.TestCase):
ch_token.append(filter_num) ch_token.append(filter_num)
init_ch_num = filter_num init_ch_num = filter_num
self.assertTrue(str(ch_token) == str(ch_pro), "channel num is WRONG, channel num from token is {}, channel num come fom program is {}".format(str(ch_token), str(ch_pro))) self.assertTrue(
str(ch_token) == str(ch_pro),
"channel num is WRONG, channel num from token is {}, channel num come fom program is {}".
format(str(ch_token), str(ch_pro)))
def test_all_function(self): def test_all_function(self):
### unittest for next_archs ### unittest for next_archs
...@@ -73,7 +82,8 @@ class TestSANAS(unittest.TestCase): ...@@ -73,7 +82,8 @@ class TestSANAS(unittest.TestCase):
token2arch_program = fluid.Program() token2arch_program = fluid.Program()
with fluid.program_guard(next_program, startup_program): with fluid.program_guard(next_program, startup_program):
inputs = fluid.data(name='input', shape=[None, 3, 32, 32], dtype='float32') inputs = fluid.data(
name='input', shape=[None, 3, 32, 32], dtype='float32')
archs = self.sanas.next_archs() archs = self.sanas.next_archs()
for arch in archs: for arch in archs:
output = arch(inputs) output = arch(inputs)
...@@ -85,8 +95,10 @@ class TestSANAS(unittest.TestCase): ...@@ -85,8 +95,10 @@ class TestSANAS(unittest.TestCase):
### uniitest for tokens2arch ### uniitest for tokens2arch
with fluid.program_guard(token2arch_program, startup_program): with fluid.program_guard(token2arch_program, startup_program):
inputs = fluid.data(name='input', shape=[None, 3, 32, 32], dtype='float32') inputs = fluid.data(
arch = self.sanas.tokens2arch(self.sanas.current_info()['current_tokens']) name='input', shape=[None, 3, 32, 32], dtype='float32')
arch = self.sanas.tokens2arch(self.sanas.current_info()[
'current_tokens'])
for arch in archs: for arch in archs:
output = arch(inputs) output = arch(inputs)
inputs = output inputs = output
...@@ -94,7 +106,11 @@ class TestSANAS(unittest.TestCase): ...@@ -94,7 +106,11 @@ class TestSANAS(unittest.TestCase):
### unittest for current_info ### unittest for current_info
current_info = self.sanas.current_info() current_info = self.sanas.current_info()
self.assertTrue(isinstance(current_info, dict), "the type of current info must be dict, but now is {}".format(type(current_info))) self.assertTrue(
isinstance(current_info, dict),
"the type of current info must be dict, but now is {}".format(
type(current_info)))
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
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