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add dygrapgh load and static load

pull/257/head
littletomatodonkey 2020-08-28 09:43:27 +00:00
parent 95ed78e2a6
commit 5a15c16581
14 changed files with 235 additions and 103 deletions
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70
configs/quick_start/CSPResNet50.yaml 100644
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@ -0,0 +1,70 @@
mode: 'train'
ARCHITECTURE:
name: 'CSPResNet50'
pretrained_model: ""
model_save_dir: "./output/"
classes_num: 1000
total_images: 1020
save_interval: 1
validate: False
valid_interval: 1
epochs: 1
topk: 5
image_shape: [3, 224, 224]
LEARNING_RATE:
function: 'Cosine'
params:
lr: 0.0125
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.00001
TRAIN:
batch_size: 32
num_workers: 4
file_list: "./dataset/flowers102/train_list.txt"
data_dir: "./dataset/flowers102/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
VALID:
batch_size: 20
num_workers: 4
file_list: "./dataset/flowers102/val_list.txt"
data_dir: "./dataset/flowers102/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:

1
configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
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@ -2,6 +2,7 @@ mode: 'train'
ARCHITECTURE:
name: 'MobileNetV3_large_x1_0'
pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained"
load_static_weights: True
model_save_dir: "./output/"
classes_num: 102
total_images: 1020

73
configs/quick_start/MobileNetV3_large_x1_0_ssld_finetune.yaml 100644
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@ -0,0 +1,73 @@
mode: 'train'
ARCHITECTURE:
name: 'MobileNetV3_large_x1_0'
params:
lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
pretrained_model: "./pretrained/MobileNetV3_large_x1_0_ssld_pretrained"
load_static_weights: True
model_save_dir: "./output/"
classes_num: 102
total_images: 1020
save_interval: 1
validate: True
valid_interval: 1
epochs: 20
topk: 5
image_shape: [3, 224, 224]
LEARNING_RATE:
function: 'Cosine'
params:
lr: 0.00375
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.000001
TRAIN:
batch_size: 32
num_workers: 4
file_list: "./dataset/flowers102/train_list.txt"
data_dir: "./dataset/flowers102/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
VALID:
batch_size: 20
num_workers: 4
file_list: "./dataset/flowers102/val_list.txt"
data_dir: "./dataset/flowers102/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:

3
configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
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@ -5,6 +5,9 @@ ARCHITECTURE:
pretrained_model:
- "./pretrained/flowers102_R50_vd_final/ppcls"
- "./pretrained/MobileNetV3_large_x1_0_pretrained/"
load_static_weights:
- False
- True
model_save_dir: "./output/"
classes_num: 102
total_images: 7169

3
configs/quick_start/ResNet50_vd.yaml
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@ -1,7 +1,10 @@
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd'
checkpoints: ""
pretrained_model: ""
load_static_weights: True
model_save_dir: "./output/"
classes_num: 102
total_images: 1020

1
configs/quick_start/ResNet50_vd_ssld_finetune.yaml
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@ -4,6 +4,7 @@ ARCHITECTURE:
params:
lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
load_static_weights: True
model_save_dir: "./output/"
classes_num: 102
total_images: 1020

1
configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
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@ -4,6 +4,7 @@ ARCHITECTURE:
params:
lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
load_static_weights: True
model_save_dir: "./output/"
classes_num: 102
total_images: 1020

2
ppcls/modeling/architectures/__init__.py
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@ -28,3 +28,5 @@ from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75
from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0

41
ppcls/modeling/architectures/distillation_models.py
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@ -1,16 +1,16 @@
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
@ -32,17 +32,20 @@ __all__ = [
]
class ResNet50_vd_distill_MobileNetV3_large_x1_0():
def net(self, input, class_dim=1000):
# student
student = MobileNetV3_large_x1_0()
out_student = student.net(input, class_dim=class_dim)
# teacher
teacher = ResNet50_vd()
out_teacher = teacher.net(input, class_dim=class_dim)
out_teacher.stop_gradient = True
class ResNet50_vd_distill_MobileNetV3_large_x1_0(fluid.dygraph.Layer):
def __init__(self, class_dim=1000, **args):
super(ResNet50_vd_distill_MobileNetV3_large_x1_0, self).__init__()
return out_teacher, out_student
self.teacher = ResNet50_vd(class_dim=class_dim, **args)
self.student = MobileNetV3_large_x1_0(class_dim=class_dim, **args)
def forward(self, input):
teacher_label = self.teacher(input)
student_label = self.student(input)
return teacher_label, student_label
class ResNeXt101_32x16d_wsl_distill_ResNet50_vd():

3
ppcls/utils/logger.py
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@ -16,9 +16,6 @@ import logging
import os
import datetime
from imp import reload
reload(logging)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)s: %(message)s",

106
ppcls/utils/save_load.py
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@ -26,7 +26,7 @@ import paddle.fluid as fluid
from ppcls.utils import logger
__all__ = ['init_model', 'save_model']
__all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']
def _mkdir_if_not_exist(path):
@ -45,71 +45,35 @@ def _mkdir_if_not_exist(path):
raise OSError('Failed to mkdir {}'.format(path))
def _load_state(path):
if os.path.exists(path + '.pdopt'):
# XXX another hack to ignore the optimizer state
tmp = tempfile.mkdtemp()
dst = os.path.join(tmp, os.path.basename(os.path.normpath(path)))
shutil.copy(path + '.pdparams', dst + '.pdparams')
state = fluid.io.load_program_state(dst)
shutil.rmtree(tmp)
else:
state = fluid.io.load_program_state(path)
return state
def load_params(exe, prog, path, ignore_params=None):
"""
Load model from the given path.
Args:
exe (fluid.Executor): The fluid.Executor object.
prog (fluid.Program): load weight to which Program object.
path (string): URL string or loca model path.
ignore_params (list): ignore variable to load when finetuning.
It can be specified by finetune_exclude_pretrained_params
and the usage can refer to the document
docs/advanced_tutorials/TRANSFER_LEARNING.md
"""
def load_dygraph_pretrain(
model,
path=None,
load_static_weights=False, ):
if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
if load_static_weights:
pre_state_dict = fluid.load_program_state(path)
param_state_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
weight_name = model_dict[key].name
print("dyg key: {}, weight_name: {}".format(key, weight_name))
if weight_name in pre_state_dict.keys():
print('Load weight: {}, shape: {}'.format(
weight_name, pre_state_dict[weight_name].shape))
param_state_dict[key] = pre_state_dict[weight_name]
else:
param_state_dict[key] = model_dict[key]
model.set_dict(param_state_dict)
return
logger.info(
logger.coloring('Loading parameters from {}...'.format(path),
'HEADER'))
ignore_set = set()
state = _load_state(path)
# ignore the parameter which mismatch the shape
# between the model and pretrain weight.
all_var_shape = {}
for block in prog.blocks:
for param in block.all_parameters():
all_var_shape[param.name] = param.shape
ignore_set.update([
name for name, shape in all_var_shape.items()
if name in state and shape != state[name].shape
])
if ignore_params:
all_var_names = [var.name for var in prog.list_vars()]
ignore_list = filter(
lambda var: any([re.match(name, var) for name in ignore_params]),
all_var_names)
ignore_set.update(list(ignore_list))
if len(ignore_set) > 0:
for k in ignore_set:
if k in state:
logger.warning(
'variable {} is already excluded automatically'.format(k))
del state[k]
fluid.io.set_program_state(prog, state)
param_state_dict, optim_state_dict = fluid.load_dygraph(path)
model.set_dict(param_state_dict)
return
def init_model(config, net, optimizer):
def init_model(config, net, optimizer=None):
"""
load model from checkpoint or pretrained_model
"""
@ -128,16 +92,24 @@ def init_model(config, net, optimizer):
return
pretrained_model = config.get('pretrained_model')
load_static_weights = config.get('load_static_weights', False)
use_distillation = config.get('use_distillation', False)
if pretrained_model:
if not isinstance(pretrained_model, list):
pretrained_model = [pretrained_model]
# TODO: load pretrained_model
raise NotImplementedError
for pretrain in pretrained_model:
load_params(exe, program, pretrain)
logger.info(
logger.coloring("Finish initing model from {}".format(
pretrained_model), "HEADER"))
if not isinstance(load_static_weights, list):
load_static_weights = [load_static_weights] * len(pretrained_model)
for idx, pretrained in enumerate(pretrained_model):
load_static = load_static_weights[idx]
model = net
if use_distillation and not load_static:
model = net.teacher
load_dygraph_pretrain(
model, path=pretrained, load_static_weights=load_static)
logger.info(
logger.coloring("Finish initing model from {}".format(
pretrained_model), "HEADER"))
def save_model(net, optimizer, model_path, epoch_id, prefix='ppcls'):

30
tools/infer/infer.py
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@ -18,6 +18,8 @@ import numpy as np
import paddle.fluid as fluid
from ppcls.modeling import architectures
from ppcls.utils.save_load import load_dygraph_pretrain
def parse_args():
def str2bool(v):
@ -28,9 +30,11 @@ def parse_args():
parser.add_argument("-m", "--model", type=str)
parser.add_argument("-p", "--pretrained_model", type=str)
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--load_static_weights", type=str2bool, default=True)
return parser.parse_args()
def create_operators():
size = 224
img_mean = [0.485, 0.456, 0.406]
@ -66,32 +70,32 @@ def main():
args = parse_args()
operators = create_operators()
# assign the place
gpu_id = fluid.dygraph.parallel.Env().dev_id
place = fluid.CUDAPlace(gpu_id)
pre_weights_dict = fluid.load_program_state(args.pretrained_model)
if args.use_gpu:
gpu_id = fluid.dygraph.parallel.Env().dev_id
place = fluid.CUDAPlace(gpu_id)
else:
place = fluid.CPUPlace()
with fluid.dygraph.guard(place):
net = architectures.__dict__[args.model]()
data = preprocess(args.image_file, operators)
data = np.expand_dims(data, axis=0)
data = fluid.dygraph.to_variable(data)
dy_weights_dict = net.state_dict()
pre_weights_dict_new = {}
for key in dy_weights_dict:
weights_name = dy_weights_dict[key].name
pre_weights_dict_new[key] = pre_weights_dict[weights_name]
net.set_dict(pre_weights_dict_new)
load_dygraph_pretrain(net, args.pretrained_model,
args.load_static_weights)
net.eval()
outputs = net(data)
outputs = fluid.layers.softmax(outputs)
outputs = outputs.numpy()
probs = postprocess(outputs)
rank = 1
for idx, prob in probs:
print("top{:d}, class id: {:d}, probability: {:.4f}".format(
rank, idx, prob))
print("top{:d}, class id: {:d}, probability: {:.4f}".format(rank, idx,
prob))
rank += 1
return
if __name__ == "__main__":
main()

2
tools/program.py
View File

@ -71,6 +71,8 @@ def create_model(architecture, classes_num):
"""
name = architecture["name"]
params = architecture.get("params", {})
print(name)
print(params)
return architectures.__dict__[name](class_dim=classes_num, **params)

2
tools/train.py
View File

@ -102,7 +102,7 @@ def main(args):
config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(net, optimizer, model_path,
"best_model_in_epoch_" + str(epoch_id))
"best_model")
# 3. save the persistable model
if epoch_id % config.save_interval == 0: