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add quick start demo

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littletomatodonkey 2020-04-20 03:54:09 +00:00
parent 5736d85b96
commit 42158c1867
9 changed files with 438 additions and 4 deletions
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70
configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml Normal file
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@ -0,0 +1,70 @@
mode: 'train'
ARCHITECTURE:
name: 'MobileNetV3_large_x1_0'
pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained"
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:

75
configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml Normal file
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@ -0,0 +1,75 @@
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
pretrained_model:
- "./pretrain/flowers102_R50_vd_final/ppcls"
- "./pretrained/MobileNetV3_large_x1_0_pretrained/"
model_save_dir: "./output/"
classes_num: 102
total_images: 7169
save_interval: 1
validate: True
valid_interval: 1
epochs: 20
topk: 5
image_shape: [3, 224, 224]
use_distillation: True
LEARNING_RATE:
function: 'Cosine'
params:
lr: 0.0125
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.00007
TRAIN:
batch_size: 32
num_workers: 4
file_list: "./dataset/flowers102/train_test_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:

70
configs/quick_start/ResNet50_vd.yaml Normal file
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@ -0,0 +1,70 @@
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd'
pretrained_model: ""
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: 0.0125
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:

70
configs/quick_start/ResNet50_vd_finetune.yaml Normal file
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@ -0,0 +1,70 @@
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd'
pretrained_model: "./pretrained/ResNet50_vd_pretrained"
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:

72
configs/quick_start/ResNet50_vd_ssld_finetune.yaml Normal file
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@ -0,0 +1,72 @@
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd'
params:
lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
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:

74
configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml Normal file
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@ -0,0 +1,74 @@
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd'
params:
lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
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: ''
- RandomErasing:
EPSILON: 0.5
- 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:

2
ppcls/modeling/architectures/__init__.py
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@ -44,4 +44,4 @@ from .darts_gs import DARTS_GS_6M, DARTS_GS_4M
from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet
# distillation model # distillation model
from .distillation_models import ResNet50_vd_distill_MobileNetV3_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd

4
ppcls/modeling/architectures/distillation_models.py
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@ -27,12 +27,12 @@ from .mobilenet_v3 import MobileNetV3_large_x1_0
from .resnext101_wsl import ResNeXt101_32x16d_wsl from .resnext101_wsl import ResNeXt101_32x16d_wsl
__all__ = [ __all__ = [
'ResNet50_vd_distill_MobileNetV3_x1_0', 'ResNet50_vd_distill_MobileNetV3_large_x1_0',
'ResNeXt101_32x16d_wsl_distill_ResNet50_vd' 'ResNeXt101_32x16d_wsl_distill_ResNet50_vd'
] ]
class ResNet50_vd_distill_MobileNetV3_x1_0(): class ResNet50_vd_distill_MobileNetV3_large_x1_0():
def net(self, input, class_dim=1000): def net(self, input, class_dim=1000):
# student # student
student = MobileNetV3_large_x1_0() student = MobileNetV3_large_x1_0()

5
ppcls/utils/save_load.py
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@ -118,7 +118,10 @@ def init_model(config, program, exe):
pretrained_model = config.get('pretrained_model') pretrained_model = config.get('pretrained_model')
if pretrained_model: if pretrained_model:
load_params(exe, program, pretrained_model) if not isinstance(pretrained_model, list):
pretrained_model = [pretrained_model]
for pretrain in pretrained_model:
load_params(exe, program, pretrain)
logger.info("Finish initing model from {}".format(pretrained_model)) logger.info("Finish initing model from {}".format(pretrained_model))