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Merge pull request #800 from cuicheng01/develop_reg 

Add products configs
pull/802/head
Wei Shengyu 2021-06-08 10:40:03 +08:00 committed by GitHub
parent 56d82ea052 05aa04f7d3
commit a175ce5efe
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4 changed files with 454 additions and 1 deletions
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2
ppcls/arch/gears/fc.py
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@ -30,6 +30,6 @@ class FC(nn.Layer):
self.fc = paddle.nn.Linear(
self.embedding_size, self.class_num, weight_attr=weight_attr)
def forward(self, input):
def forward(self, input, label=None):
out = self.fc(input)
return out

127
ppcls/configs/Products/ResNet50_vd_Aliproduct.yaml 100644
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@ -0,0 +1,127 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
device: "gpu"
class_num: 50030
save_interval: 10
eval_during_train: False
eval_interval: 1
epochs: 120
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: "./inference"
eval_mode: "classification"
# model architecture
Arch:
name: "RecModel"
Backbone:
name: "ResNet50_vd"
pretrained: False
BackboneStopLayer:
name: "flatten_0"
Neck:
name: "FC"
embedding_size: 2048
class_num: 512
Head:
name: "FC"
embedding_size: 512
class_num: 50030
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.05
regularizer:
name: 'L2'
coeff: 0.00007
# data loader for train and eval
DataLoader:
Train:
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Aliproduct/"
cls_label_path: "./dataset/Aliproduct/train_list.txt"
transform_ops:
- ResizeImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 6
use_shared_memory: False
Eval:
# TOTO: modify to the latest trainer
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Aliproduct/"
cls_label_path: "./dataset/Aliproduct/val_list.txt"
transform_ops:
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 6
use_shared_memory: False
Metric:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]
Infer:
infer_imgs: "docs/images/whl/demo.jpg"
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:

163
ppcls/configs/Products/ResNet50_vd_Inshop.yaml 100644
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@ -0,0 +1,163 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
device: "gpu"
class_num: 3997
save_interval: 10
eval_during_train: False
eval_interval: 1
epochs: 120
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: "./inference"
eval_mode: "retrieval"
# model architecture
Arch:
name: "RecModel"
Backbone:
name: "ResNet50_vd"
pretrained: False
BackboneStopLayer:
name: "flatten_0"
Neck:
name: "FC"
embedding_size: 2048
class_num: 512
Head:
name: "ArcMargin"
embedding_size: 512
class_num: 3997
margin: 0.15
scale: 30
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
- TripletLossV2:
weight: 1.0
margin: 0.5
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: MultiStepDecay
learning_rate: 0.01
milestones: [30, 60, 70, 80, 90, 100]
gamma: 0.5
verbose: False
last_epoch: -1
regularizer:
name: 'L2'
coeff: 0.0005
# data loader for train and eval
DataLoader:
Train:
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Inshop/"
cls_label_path: "./dataset/Inshop/train_list.txt"
transform_ops:
- ResizeImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.5
sl: 0.02
sh: 0.4
r1: 0.3
mean: [0., 0., 0.]
sampler:
name: DistributedRandomIdentitySampler
batch_size: 64
num_instances: 2
drop_last: False
shuffle: True
loader:
num_workers: 6
use_shared_memory: False
Eval:
Query:
# TOTO: modify to the latest trainer
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Inshop/"
cls_label_path: "./dataset/Inshop/query_list.txt"
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 6
use_shared_memory: False
Gallery:
# TOTO: modify to the latest trainer
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Inshop/"
cls_label_path: "./dataset/Inshop/gallery_list.txt"
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 6
use_shared_memory: False
Metric:
Eval:
- Recallk:
topk: [1, 5]
Infer:
infer_imgs: "docs/images/whl/demo.jpg"
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:

163
ppcls/configs/Products/ResNet50_vd_SOP.yaml 100644
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@ -0,0 +1,163 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
device: "gpu"
class_num: 11319
save_interval: 10
eval_during_train: False
eval_interval: 1
epochs: 120
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: "./inference"
eval_mode: "retrieval"
# model architecture
Arch:
name: "RecModel"
Backbone:
name: "ResNet50_vd"
pretrained: False
BackboneStopLayer:
name: "flatten_0"
Neck:
name: "FC"
embedding_size: 2048
class_num: 512
Head:
name: "ArcMargin"
embedding_size: 512
class_num: 11319
margin: 0.15
scale: 30
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
- TripletLossV2:
weight: 1.0
margin: 0.5
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: MultiStepDecay
learning_rate: 0.01
milestones: [30, 60, 70, 80, 90, 100]
gamma: 0.5
verbose: False
last_epoch: -1
regularizer:
name: 'L2'
coeff: 0.0005
# data loader for train and eval
DataLoader:
Train:
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Stanford_Online_Products/"
cls_label_path: "./dataset/Stanford_Online_Products/train_list.txt"
transform_ops:
- ResizeImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.5
sl: 0.02
sh: 0.4
r1: 0.3
mean: [0., 0., 0.]
sampler:
name: DistributedRandomIdentitySampler
batch_size: 64
num_instances: 2
drop_last: False
shuffle: True
loader:
num_workers: 6
use_shared_memory: False
Eval:
Query:
# TOTO: modify to the latest trainer
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Stanford_Online_Products/"
cls_label_path: "./dataset/Stanford_Online_Products/test_list.txt"
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 32
drop_last: False
shuffle: False
loader:
num_workers: 6
use_shared_memory: False
Gallery:
# TOTO: modify to the latest trainer
dataset:
name: "ImageNetDataset"
image_root: "./dataset/Stanford_Online_Products/"
cls_label_path: "./dataset/Stanford_Online_Products/test_list.txt"
transform_ops:
- ResizeImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 32
drop_last: False
shuffle: False
loader:
num_workers: 6
use_shared_memory: False
Metric:
Eval:
- Recallk:
topk: [1, 5]
Infer:
infer_imgs: "docs/images/whl/demo.jpg"
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage: