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PaddleClas
提交 45b1296c 编写于 作者: C cuicheng01
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Add cls_demo_person code

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ppcls/arch/backbone/legendary_models/pp_lcnet.py
浏览文件 @ 45b1296c
......@@ -17,7 +17,7 @@ from __future__ import absolute_import, division, print_function
import paddle
import paddle.nn as nn
from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
......@@ -83,7 +83,8 @@ class ConvBNLayer(TheseusLayer):
filter_size,
num_filters,
stride,
num_groups=1):
num_groups=1,
lr_mult=1.0):
super().__init__()
self.conv = Conv2D(
......@@ -93,13 +94,13 @@ class ConvBNLayer(TheseusLayer):
stride=stride,
padding=(filter_size - 1) // 2,
groups=num_groups,
weight_attr=ParamAttr(initializer=KaimingNormal()),
weight_attr=ParamAttr(initializer=KaimingNormal(), learning_rate=lr_mult),
bias_attr=False)
self.bn = BatchNorm(
self.bn = BatchNorm2D(
num_filters,
param_attr=ParamAttr(regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
weight_attr=ParamAttr(regularizer=L2Decay(0.0), learning_rate=lr_mult),
bias_attr=ParamAttr(regularizer=L2Decay(0.0), learning_rate=lr_mult))
self.hardswish = nn.Hardswish()
def forward(self, x):
......@@ -115,7 +116,8 @@ class DepthwiseSeparable(TheseusLayer):
num_filters,
stride,
dw_size=3,
use_se=False):
use_se=False,
lr_mult=1.0):
super().__init__()
self.use_se = use_se
self.dw_conv = ConvBNLayer(
......@@ -123,14 +125,17 @@ class DepthwiseSeparable(TheseusLayer):
num_filters=num_channels,
filter_size=dw_size,
stride=stride,
num_groups=num_channels)
num_groups=num_channels,
lr_mult=lr_mult)
if use_se:
self.se = SEModule(num_channels)
self.se = SEModule(num_channels,
lr_mult=lr_mult)
self.pw_conv = ConvBNLayer(
num_channels=num_channels,
filter_size=1,
num_filters=num_filters,
stride=1)
stride=1,
lr_mult=lr_mult)
def forward(self, x):
x = self.dw_conv(x)
......@@ -141,7 +146,7 @@ class DepthwiseSeparable(TheseusLayer):
class SEModule(TheseusLayer):
def __init__(self, channel, reduction=4):
def __init__(self, channel, reduction=4, lr_mult=1.0):
super().__init__()
self.avg_pool = AdaptiveAvgPool2D(1)
self.conv1 = Conv2D(
......@@ -149,14 +154,18 @@ class SEModule(TheseusLayer):
out_channels=channel // reduction,
kernel_size=1,
stride=1,
padding=0)
padding=0,
weight_attr=ParamAttr(learning_rate=lr_mult),
bias_attr=ParamAttr(learning_rate=lr_mult))
self.relu = nn.ReLU()
self.conv2 = Conv2D(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0)
padding=0,
weight_attr=ParamAttr(learning_rate=lr_mult),
bias_attr=ParamAttr(learning_rate=lr_mult))
self.hardsigmoid = nn.Hardsigmoid()
def forward(self, x):
......@@ -175,19 +184,34 @@ class PPLCNet(TheseusLayer):
stages_pattern,
scale=1.0,
class_num=1000,
dropout_prob=0.2,
dropout_prob=0.0,
class_expand=1280,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
use_last_conv=True,
return_patterns=None,
return_stages=None):
super().__init__()
self.scale = scale
self.class_expand = class_expand
self.lr_mult_list = lr_mult_list
self.use_last_conv = use_last_conv
if isinstance(self.lr_mult_list, str):
self.lr_mult_list = eval(self.lr_mult_list)
assert isinstance(self.lr_mult_list, (
list, tuple
)), "lr_mult_list should be in (list, tuple) but got {}".format(
type(self.lr_mult_list))
assert len(self.lr_mult_list
) == 6, "lr_mult_list length should be 5 but got {}".format(
len(self.lr_mult_list))
self.conv1 = ConvBNLayer(
num_channels=3,
filter_size=3,
num_filters=make_divisible(16 * scale),
stride=2)
stride=2,
lr_mult=self.lr_mult_list[0])
self.blocks2 = nn.Sequential(* [
DepthwiseSeparable(
......@@ -195,7 +219,8 @@ class PPLCNet(TheseusLayer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
lr_mult=self.lr_mult_list[1])
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"])
])
......@@ -205,7 +230,8 @@ class PPLCNet(TheseusLayer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
lr_mult=self.lr_mult_list[2])
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"])
])
......@@ -215,7 +241,8 @@ class PPLCNet(TheseusLayer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
lr_mult=self.lr_mult_list[3])
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"])
])
......@@ -225,7 +252,8 @@ class PPLCNet(TheseusLayer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
lr_mult=self.lr_mult_list[4])
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"])
])
......@@ -235,25 +263,26 @@ class PPLCNet(TheseusLayer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
lr_mult=self.lr_mult_list[5])
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"])
])
self.avg_pool = AdaptiveAvgPool2D(1)
self.last_conv = Conv2D(
in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * scale),
out_channels=self.class_expand,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False)
self.hardswish = nn.Hardswish()
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
if self.use_last_conv:
self.last_conv = Conv2D(
in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * scale),
out_channels=self.class_expand,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False)
self.hardswish = nn.Hardswish()
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
else:
self.last_conv = None
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
self.fc = Linear(self.class_expand, class_num)
self.fc = Linear(self.class_expand if self.use_last_conv else NET_CONFIG["blocks6"][-1][2], class_num)
super().init_res(
stages_pattern,
......@@ -270,9 +299,10 @@ class PPLCNet(TheseusLayer):
x = self.blocks6(x)
x = self.avg_pool(x)
x = self.last_conv(x)
x = self.hardswish(x)
x = self.dropout(x)
if self.last_conv is not None:
x = self.last_conv(x)
x = self.hardswish(x)
x = self.dropout(x)
x = self.flatten(x)
x = self.fc(x)
return x
......@@ -291,7 +321,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld):
)
def PPLCNet_x0_25(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x0_25(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x0_25
Args:
......@@ -307,7 +337,7 @@ def PPLCNet_x0_25(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x0_35(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x0_35(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x0_35
Args:
......@@ -323,7 +353,7 @@ def PPLCNet_x0_35(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x0_5(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x0_5
Args:
......@@ -339,7 +369,7 @@ def PPLCNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x0_75(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x0_75(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x0_75
Args:
......@@ -355,7 +385,7 @@ def PPLCNet_x0_75(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x1_0(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x1_0
Args:
......@@ -371,7 +401,7 @@ def PPLCNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x1_5(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x1_5(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x1_5
Args:
......@@ -387,7 +417,7 @@ def PPLCNet_x1_5(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x2_0(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x2_0(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x2_0
Args:
......@@ -403,7 +433,7 @@ def PPLCNet_x2_0(pretrained=False, use_ssld=False, **kwargs):
return model
def PPLCNet_x2_5(pretrained=False, use_ssld=False, **kwargs):
def PPLCNet_x2_5(pretrained=False, use_ssld=False, use_sync_bn=False, **kwargs):
"""
PPLCNet_x2_5
Args:
......
ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml 0 → 100644
浏览文件 @ 45b1296c
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output
device: gpu
save_interval: 1
eval_during_train: True
start_eval_epoch: 1
eval_interval: 1
epochs: 20
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
use_dali: False
# model architecture
Arch:
name: "DistillationModel"
class_num: &class_num 2
# if not null, its lengths should be same as models
pretrained_list:
# if not null, its lengths should be same as models
freeze_params_list:
- True
- False
use_sync_bn: True
models:
- Teacher:
name: ResNet101_vd
class_num: *class_num
pretrained: "./output/TEACHER_ResNet101_vd/ResNet101_vd/best_model"
- Student:
name: PPLCNet_x1_0
class_num: *class_num
pretrained: True
use_ssld: True
infer_model_name: "Student"
# loss function config for traing/eval process
Loss:
Train:
- DistillationDMLLoss:
weight: 1.0
model_name_pairs:
- ["Student", "Teacher"]
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.01
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00004
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/coco/
cls_label_path: ./dataset/coco/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
prob: 0.0
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.0
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 16
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/coco/
cls_label_path: ./dataset/coco/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
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: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
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:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Train:
- DistillationTopkAcc:
model_key: "Student"
topk: [1, 2]
Eval:
- TprAtFpr:
- TopkAcc:
topk: [1, 2]
ppcls/configs/cls_demo/person/OtherModels/MobileNetV3_large_x1_0.yaml 0 → 100644
浏览文件 @ 45b1296c
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
start_eval_epoch: 15
epochs: 20
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
use_dali: False
# mixed precision training
AMP:
scale_loss: 128.0
use_dynamic_loss_scaling: True
# O1: mixed fp16
level: O1
# model architecture
Arch:
name: MobileNetV3_large_x1_0
class_num: 2
pretrained: True
use_sync_bn: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.13
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00002
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/person/
cls_label_path: ./dataset/person/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 512
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/person/
cls_label_path: ./dataset/person/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
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: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
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:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Train:
- TopkAcc:
topk: [1, 2]
Eval:
- TprAtFpr:
- TopkAcc:
topk: [1, 2]
ppcls/configs/cls_demo/person/OtherModels/SwinTransformer_tiny_patch4_window7_224.yaml 0 → 100644
浏览文件 @ 45b1296c
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
start_eval_epoch: 1
epochs: 20
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
use_dali: False
# mixed precision training
AMP:
scale_loss: 128.0
use_dynamic_loss_scaling: True
# O1: mixed fp16
level: O1
# model architecture
Arch:
name: SwinTransformer_tiny_patch4_window7_224
class_num: 2
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
name: Cosine
learning_rate: 1e-4
eta_min: 2e-6
warmup_epoch: 5
warmup_start_lr: 2e-7
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/person/
cls_label_path: ./dataset/person/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/person/
cls_label_path: ./dataset/person/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
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: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 8
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
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:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Train:
- TopkAcc:
topk: [1, 2]
Eval:
- TprAtFpr:
- TopkAcc:
topk: [1, 2]
ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml 0 → 100644
浏览文件 @ 45b1296c
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
start_eval_epoch: 1
epochs: 20
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
use_dali: False
# model architecture
Arch:
name: PPLCNet_x1_0
class_num: 2
pretrained: True
use_ssld: True
use_sync_bn: True
# 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: 0.01
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.00004
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/person/
cls_label_path: ./dataset/person/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
prob: 0.0
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.0
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: True
loader:
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/person/
cls_label_path: ./dataset/person/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
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: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
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:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Train:
- TopkAcc:
topk: [1, 2]
Eval:
- TprAtFpr:
- TopkAcc:
topk: [1, 2]
ppcls/data/preprocess/__init__.py
浏览文件 @ 45b1296c
......@@ -38,7 +38,7 @@ from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, Cutmi
import numpy as np
from PIL import Image
import random
def transform(data, ops=[]):
""" transform """
......@@ -88,16 +88,16 @@ class RandAugment(RawRandAugment):
class TimmAutoAugment(RawTimmAutoAugment):
""" TimmAutoAugment wrapper to auto fit different img tyeps. """
def __init__(self, *args, **kwargs):
def __init__(self, prob=1.0, *args, **kwargs):
super().__init__(*args, **kwargs)
self.prob = prob
def __call__(self, img):
if not isinstance(img, Image.Image):
img = np.ascontiguousarray(img)
img = Image.fromarray(img)
img = super().__call__(img)
if random.random() < self.prob:
img = super().__call__(img)
if isinstance(img, Image.Image):
img = np.asarray(img)
......
ppcls/engine/engine.py
浏览文件 @ 45b1296c
......@@ -312,7 +312,7 @@ class Engine(object):
print_batch_step = self.config['Global']['print_batch_step']
save_interval = self.config["Global"]["save_interval"]
best_metric = {
"metric": 0.0,
"metric": -1.0,
"epoch": 0,
}
# key:
......@@ -345,17 +345,17 @@ class Engine(object):
if self.use_dali:
self.train_dataloader.reset()
metric_msg = ", ".join([
"{}: {:.5f}".format(key, self.output_info[key].avg)
for key in self.output_info
self.output_info[key].avg_info for key in self.output_info
])
logger.info("[Train][Epoch {}/{}][Avg]{}".format(
epoch_id, self.config["Global"]["epochs"], metric_msg))
self.output_info.clear()
# eval model and save model if possible
start_eval_epoch = self.config["Global"].get("start_eval_epoch", 0) - 1
if self.config["Global"][
"eval_during_train"] and epoch_id % self.config["Global"][
"eval_interval"] == 0:
"eval_interval"] == 0 and epoch_id > start_eval_epoch:
acc = self.eval(epoch_id)
if acc > best_metric["metric"]:
best_metric["metric"] = acc
......
ppcls/engine/evaluation/classification.py
浏览文件 @ 45b1296c
......@@ -23,6 +23,8 @@ from ppcls.utils import logger
def classification_eval(engine, epoch_id=0):
if hasattr(engine.eval_metric_func, "reset"):
engine.eval_metric_func.reset()
output_info = dict()
time_info = {
"batch_cost": AverageMeter(
......@@ -123,16 +125,7 @@ def classification_eval(engine, epoch_id=0):
current_samples)
# calc metric
if engine.eval_metric_func is not None:
metric_dict = engine.eval_metric_func(preds, labels)
for key in metric_dict:
if metric_key is None:
metric_key = key
if key not in output_info:
output_info[key] = AverageMeter(key, '7.5f')
output_info[key].update(metric_dict[key].numpy()[0],
current_samples)
engine.eval_metric_func(preds, labels)
time_info["batch_cost"].update(time.time() - tic)
if iter_id % print_batch_step == 0:
......@@ -148,6 +141,7 @@ def classification_eval(engine, epoch_id=0):
"{}: {:.5f}".format(key, output_info[key].val)
for key in output_info
])
metric_msg += ", {}".format(engine.eval_metric_func.avg_info)
logger.info("[Eval][Epoch {}][Iter: {}/{}]{}, {}, {}".format(
epoch_id, iter_id,
len(engine.eval_dataloader), metric_msg, time_msg, ips_msg))
......@@ -158,10 +152,11 @@ def classification_eval(engine, epoch_id=0):
metric_msg = ", ".join([
"{}: {:.5f}".format(key, output_info[key].avg) for key in output_info
])
metric_msg += ", {}".format(engine.eval_metric_func.avg_info)
logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
# do not try to save best eval.model
if engine.eval_metric_func is None:
return -1
# return 1st metric in the dict
return output_info[metric_key].avg
return engine.eval_metric_func.avg
ppcls/metric/__init__.py
浏览文件 @ 45b1296c
......@@ -12,17 +12,18 @@
#See the License for the specific language governing permissions and
#limitations under the License.
from paddle import nn
import copy
from collections import OrderedDict
from .avg_metrics import AvgMetrics
from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
from .metrics import DistillationTopkAcc
from .metrics import GoogLeNetTopkAcc
from .metrics import HammingDistance, AccuracyScore
from .metrics import TprAtFpr
class CombinedMetrics(nn.Layer):
class CombinedMetrics(AvgMetrics):
def __init__(self, config_list):
super().__init__()
self.metric_func_list = []
......@@ -39,13 +40,22 @@ class CombinedMetrics(nn.Layer):
else:
self.metric_func_list.append(eval(metric_name)())
def __call__(self, *args, **kwargs):
def forward(self, *args, **kwargs):
metric_dict = OrderedDict()
for idx, metric_func in enumerate(self.metric_func_list):
metric_dict.update(metric_func(*args, **kwargs))
return metric_dict
@property
def avg_info(self):
return ", ".join([metric.avg_info for metric in self.metric_func_list])
@property
def avg(self):
return self.metric_func_list[0].avg
def build_metrics(config):
metrics_list = CombinedMetrics(copy.deepcopy(config))
return metrics_list
ppcls/metric/avg_metrics.py 0 → 100644
浏览文件 @ 45b1296c
from paddle import nn
class AvgMetrics(nn.Layer):
def __init__(self):
super().__init__()
self.avg_meters = {}
def reset(self):
self.avg_meters = {}
@property
def avg(self):
if self.avg_meters:
for metric_key in self.avg_meters:
return self.avg_meters[metric_key].avg
@property
def avg_info(self):
return ", ".join([self.avg_meters[key].avg_info for key in self.avg_meters])
ppcls/metric/metrics.py
浏览文件 @ 45b1296c
......@@ -22,14 +22,18 @@ from sklearn.metrics import accuracy_score as accuracy_metric
from sklearn.metrics import multilabel_confusion_matrix
from sklearn.preprocessing import binarize
from ppcls.metric.avg_metrics import AvgMetrics
from ppcls.utils.misc import AverageMeter
class TopkAcc(nn.Layer):
class TopkAcc(AvgMetrics):
def __init__(self, topk=(1, 5)):
super().__init__()
assert isinstance(topk, (int, list, tuple))
if isinstance(topk, int):
topk = [topk]
self.topk = topk
self.avg_meters = {"top{}".format(k): AverageMeter("top{}".format(k)) for k in self.topk}
def forward(self, x, label):
if isinstance(x, dict):
......@@ -39,6 +43,7 @@ class TopkAcc(nn.Layer):
for k in self.topk:
metric_dict["top{}".format(k)] = paddle.metric.accuracy(
x, label, k=k)
self.avg_meters["top{}".format(k)].update(metric_dict["top{}".format(k)].numpy()[0], x.shape[0])
return metric_dict
......@@ -129,6 +134,57 @@ class mINP(nn.Layer):
return metric_dict
class TprAtFpr(nn.Layer):
def __init__(self, max_fpr=1/1000.):
super().__init__()
self.gt_pos_score_list = []
self.gt_neg_score_list = []
self.softmax = nn.Softmax(axis=-1)
self.max_fpr = max_fpr
self.max_tpr = 0.
def forward(self, x, label):
if isinstance(x, dict):
x = x["logits"]
x = self.softmax(x)
for i, label_i in enumerate(label):
if label_i[0] == 0:
self.gt_neg_score_list.append(x[i][1].numpy())
else:
self.gt_pos_score_list.append(x[i][1].numpy())
return {}
def reset(self):
self.gt_pos_score_list = []
self.gt_neg_score_list = []
self.max_tpr = 0.
@property
def avg(self):
return self.max_tpr
@property
def avg_info(self):
max_tpr = 0.
result = ""
gt_pos_score_list = np.array(self.gt_pos_score_list)
gt_neg_score_list = np.array(self.gt_neg_score_list)
for i in range(0, 10000):
threshold = i / 10000.
if len(gt_pos_score_list) == 0:
continue
tpr = np.sum(gt_pos_score_list > threshold) / len(gt_pos_score_list)
if len(gt_neg_score_list) == 0 and tpr > max_tpr:
max_tpr = tpr
result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(threshold, fpr, tpr)
fpr = np.sum(gt_neg_score_list > threshold) / len(gt_neg_score_list)
if fpr <= self.max_fpr and tpr > max_tpr:
max_tpr = tpr
result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(threshold, fpr, tpr)
self.max_tpr = max_tpr
return result
class Recallk(nn.Layer):
def __init__(self, topk=(1, 5)):
super().__init__()
......@@ -241,20 +297,17 @@ class GoogLeNetTopkAcc(TopkAcc):
return super().forward(x[0], label)
class MutiLabelMetric(object):
def __init__(self):
pass
def _multi_hot_encode(self, logits, threshold=0.5):
return binarize(logits, threshold=threshold)
class MultiLabelMetric(AvgMetrics):
def __init__(self, bi_threshold=0.5):
super().__init__()
self.bi_threshold = bi_threshold
def __call__(self, output):
output = F.sigmoid(output)
preds = self._multi_hot_encode(logits=output.numpy(), threshold=0.5)
return preds
def _multi_hot_encode(self, output):
logits = F.sigmoid(output).numpy()
return binarize(logits, threshold=self.bi_threshold)
class HammingDistance(MutiLabelMetric):
class HammingDistance(MultiLabelMetric):
"""
Soft metric based label for multilabel classification
Returns:
......@@ -263,16 +316,18 @@ class HammingDistance(MutiLabelMetric):
def __init__(self):
super().__init__()
self.avg_meters = {"HammingDistance": AverageMeter("HammingDistance")}
def __call__(self, output, target):
preds = super().__call__(output)
def forward(self, output, target):
preds = super()._multi_hot_encode(output)
metric_dict = dict()
metric_dict["HammingDistance"] = paddle.to_tensor(
hamming_loss(target, preds))
self.avg_meters["HammingDistance"].update(metric_dict["HammingDistance"].numpy()[0], output.shape[0])
return metric_dict
class AccuracyScore(MutiLabelMetric):
class AccuracyScore(MultiLabelMetric):
"""
Hard metric for multilabel classification
Args:
......@@ -289,8 +344,8 @@ class AccuracyScore(MutiLabelMetric):
], 'must be one of ["sample", "label"]'
self.base = base
def __call__(self, output, target):
preds = super().__call__(output)
def forward(self, output, target):
preds = super()._multi_hot_encode(output)
metric_dict = dict()
if self.base == "sample":
accuracy = accuracy_metric(target, preds)
......@@ -303,4 +358,5 @@ class AccuracyScore(MutiLabelMetric):
accuracy = (sum(tps) + sum(tns)) / (
sum(tps) + sum(tns) + sum(fns) + sum(fps))
metric_dict["AccuracyScore"] = paddle.to_tensor(accuracy)
self.avg_meters["AccuracyScore"].update(metric_dict["AccuracyScore"].numpy()[0], output.shape[0])
return metric_dict
ppcls/utils/misc.py
浏览文件 @ 45b1296c
......@@ -42,6 +42,10 @@ class AverageMeter(object):
self.count += n
self.avg = self.sum / self.count
@property
def avg_info(self):
return "{}: {:.5f}".format(self.name, self.avg)
@property
def total(self):
return '{self.name}_sum: {self.sum:{self.fmt}}{self.postfix}'.format(
......
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