mirrors
/
PaddleClas
mirror of https://github.com/PaddlePaddle/PaddleClas.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

fix pact bug for circlemargin arcmargin cosmargin

pull/1243/head
dongshuilong 2021-09-16 12:17:02 +00:00
parent 1abbc82635
commit 18e1cf040b
10 changed files with 461 additions and 40 deletions

3
.gitignore vendored
View File

@ -3,10 +3,11 @@ __pycache__/
*.sw*
*/workerlog*
checkpoints/
output/
output*/
pretrained/
.ipynb_checkpoints/
*.ipynb*
_build/
build/
log/
nohup.out

19
ppcls/arch/gears/arcmargin.py
View File

@ -24,30 +24,25 @@ class ArcMargin(nn.Layer):
margin=0.5,
scale=80.0,
easy_margin=False):
super(ArcMargin, self).__init__()
super().__init__()
self.embedding_size = embedding_size
self.class_num = class_num
self.margin = margin
self.scale = scale
self.easy_margin = easy_margin
weight_attr = paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierNormal())
self.fc = nn.Linear(
self.embedding_size,
self.class_num,
weight_attr=weight_attr,
bias_attr=False)
self.weight = self.create_parameter(
shape=[self.embedding_size, self.class_num],
is_bias=False,
default_initializer=paddle.nn.initializer.XavierNormal())
def forward(self, input, label=None):
input_norm = paddle.sqrt(
paddle.sum(paddle.square(input), axis=1, keepdim=True))
input = paddle.divide(input, input_norm)
weight = self.fc.weight
weight_norm = paddle.sqrt(
paddle.sum(paddle.square(weight), axis=0, keepdim=True))
weight = paddle.divide(weight, weight_norm)
paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
weight = paddle.divide(self.weight, weight_norm)
cos = paddle.matmul(input, weight)
if not self.training or label is None:

17
ppcls/arch/gears/circlemargin.py
View File

@ -26,20 +26,19 @@ class CircleMargin(nn.Layer):
self.embedding_size = embedding_size
self.class_num = class_num
weight_attr = paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierNormal())
self.fc = paddle.nn.Linear(
self.embedding_size, self.class_num, weight_attr=weight_attr)
self.weight = self.create_parameter(
shape=[self.embedding_size, self.class_num],
is_bias=False,
default_initializer=paddle.nn.initializer.XavierNormal())
def forward(self, input, label):
feat_norm = paddle.sqrt(
paddle.sum(paddle.square(input), axis=1, keepdim=True))
input = paddle.divide(input, feat_norm)
weight = self.fc.weight
weight_norm = paddle.sqrt(
paddle.sum(paddle.square(weight), axis=0, keepdim=True))
weight = paddle.divide(weight, weight_norm)
paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
weight = paddle.divide(self.weight, weight_norm)
logits = paddle.matmul(input, weight)
if not self.training or label is None:
@ -49,9 +48,9 @@ class CircleMargin(nn.Layer):
alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
delta_p = 1 - self.margin
delta_n = self.margin
m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
logits_p = alpha_p * (logits - delta_p)
logits_n = alpha_n * (logits - delta_n)
pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)

18
ppcls/arch/gears/cosmargin.py
View File

@ -25,13 +25,10 @@ class CosMargin(paddle.nn.Layer):
self.embedding_size = embedding_size
self.class_num = class_num
weight_attr = paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierNormal())
self.fc = nn.Linear(
self.embedding_size,
self.class_num,
weight_attr=weight_attr,
bias_attr=False)
self.weight = self.create_parameter(
shape=[self.embedding_size, self.class_num],
is_bias=False,
default_initializer=paddle.nn.initializer.XavierNormal())
def forward(self, input, label):
label.stop_gradient = True
@ -40,15 +37,14 @@ class CosMargin(paddle.nn.Layer):
paddle.sum(paddle.square(input), axis=1, keepdim=True))
input = paddle.divide(input, input_norm)
weight = self.fc.weight
weight_norm = paddle.sqrt(
paddle.sum(paddle.square(weight), axis=0, keepdim=True))
weight = paddle.divide(weight, weight_norm)
paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
weight = paddle.divide(self.weight, weight_norm)
cos = paddle.matmul(input, weight)
if not self.training or label is None:
return cos
cos_m = cos - self.margin
one_hot = paddle.nn.functional.one_hot(label, self.class_num)

7
ppcls/configs/Vehicle/ResNet50.yaml
View File

@ -2,7 +2,7 @@
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
output_dir: "./output_vehicle_cls/"
device: "gpu"
save_interval: 1
eval_during_train: True
@ -51,11 +51,8 @@ Optimizer:
name: Momentum
momentum: 0.9
lr:
name: MultiStepDecay
name: Cosine
learning_rate: 0.01
milestones: [30, 60, 70, 80, 90, 100, 120, 140]
gamma: 0.5
verbose: False
last_epoch: -1
regularizer:
name: 'L2'

2
ppcls/configs/Vehicle/ResNet50_ReID.yaml
View File

@ -2,7 +2,7 @@
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
output_dir: "./output_vehicle_reid/"
device: "gpu"
save_interval: 1
eval_during_train: True

136
ppcls/configs/slim/ResNet50_vehicle_cls_prune.yaml 100644
View File

@ -0,0 +1,136 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output_vehicle_cls_prune/"
device: "gpu"
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 160
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: "./inference"
Slim:
prune:
name: fpgm
pruned_ratio: 0.3
# model architecture
Arch:
name: "RecModel"
infer_output_key: "features"
infer_add_softmax: False
Backbone:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
Neck:
name: "VehicleNeck"
in_channels: 2048
out_channels: 512
Head:
name: "ArcMargin"
embedding_size: 512
class_num: 431
margin: 0.15
scale: 32
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
- SupConLoss:
weight: 1.0
views: 2
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.01
last_epoch: -1
regularizer:
name: 'L2'
coeff: 0.0005
# data loader for train and eval
DataLoader:
Train:
dataset:
name: "CompCars"
image_root: "./dataset/CompCars/image/"
label_root: "./dataset/CompCars/label/"
bbox_crop: True
cls_label_path: "./dataset/CompCars/train_test_split/classification/train_label.txt"
transform_ops:
- ResizeImage:
size: 224
- RandFlipImage:
flip_code: 1
- AugMix:
prob: 0.5
- 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: 128
num_instances: 2
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: "CompCars"
image_root: "./dataset/CompCars/image/"
label_root: "./dataset/CompCars/label/"
cls_label_path: "./dataset/CompCars/train_test_split/classification/test_label.txt"
bbox_crop: True
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: 128
drop_last: False
shuffle: False
loader:
num_workers: 8
use_shared_memory: True
Metric:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

135
ppcls/configs/slim/ResNet50_vehicle_cls_quantization.yaml 100644
View File

@ -0,0 +1,135 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output_vehicle_cls_pact/"
device: "gpu"
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 80
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: "./inference"
Slim:
quant:
name: pact
# model architecture
Arch:
name: "RecModel"
infer_output_key: "features"
infer_add_softmax: False
Backbone:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
Neck:
name: "VehicleNeck"
in_channels: 2048
out_channels: 512
Head:
name: "ArcMargin"
embedding_size: 512
class_num: 431
margin: 0.15
scale: 32
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
- SupConLoss:
weight: 1.0
views: 2
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.001
last_epoch: -1
regularizer:
name: 'L2'
coeff: 0.0005
# data loader for train and eval
DataLoader:
Train:
dataset:
name: "CompCars"
image_root: "./dataset/CompCars/image/"
label_root: "./dataset/CompCars/label/"
bbox_crop: True
cls_label_path: "./dataset/CompCars/train_test_split/classification/train_label.txt"
transform_ops:
- ResizeImage:
size: 224
- RandFlipImage:
flip_code: 1
- AugMix:
prob: 0.5
- 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: 128
num_instances: 2
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: "CompCars"
image_root: "./dataset/CompCars/image/"
label_root: "./dataset/CompCars/label/"
cls_label_path: "./dataset/CompCars/train_test_split/classification/test_label.txt"
bbox_crop: True
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: 128
drop_last: False
shuffle: False
loader:
num_workers: 8
use_shared_memory: True
Metric:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

2
ppcls/configs/slim/ResNet50_vehicle_reid_prune.yaml
View File

@ -2,7 +2,7 @@
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
output_dir: "./output_fpgm/"
device: "gpu"
save_interval: 1
eval_during_train: True

162
ppcls/configs/slim/ResNet50_vehicle_reid_quantization.yaml 100644
View File

@ -0,0 +1,162 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: "./output_vehicle_reid_pact/"
device: "gpu"
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 40
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"
# for quantizaiton or prune model
Slim:
## for prune
quant:
name: pact
# model architecture
Arch:
name: "RecModel"
infer_output_key: "features"
infer_add_softmax: False
Backbone:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
Neck:
name: "VehicleNeck"
in_channels: 2048
out_channels: 512
Head:
name: "ArcMargin"
embedding_size: 512
class_num: 30671
margin: 0.15
scale: 32
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
- SupConLoss:
weight: 1.0
views: 2
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.001
last_epoch: -1
regularizer:
name: 'L2'
coeff: 0.0005
# data loader for train and eval
DataLoader:
Train:
dataset:
name: "VeriWild"
image_root: "./dataset/VeRI-Wild/images/"
cls_label_path: "./dataset/VeRI-Wild/train_test_split/train_list_start0.txt"
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: 224
- RandFlipImage:
flip_code: 1
- AugMix:
prob: 0.5
- 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: True
Eval:
Query:
dataset:
name: "VeriWild"
image_root: "./dataset/VeRI-Wild/images"
cls_label_path: "./dataset/VeRI-Wild/train_test_split/test_3000_id_query.txt"
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- 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: True
Gallery:
dataset:
name: "VeriWild"
image_root: "./dataset/VeRI-Wild/images"
cls_label_path: "./dataset/VeRI-Wild/train_test_split/test_3000_id.txt"
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- 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: True
Metric:
Eval:
- Recallk:
topk: [1, 5]
- mAP: {}