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add multilabel feature

pull/1265/head
cuicheng01 2021-09-26 07:05:13 +00:00
parent e431fe3399
commit af9aae730e
20 changed files with 524 additions and 69 deletions

33
deploy/configs/inference_multilabel_cls.yaml 100644
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@ -0,0 +1,33 @@
Global:
infer_imgs: "./images/0517_2715693311.jpg"
inference_model_dir: "../inference/"
batch_size: 1
use_gpu: True
enable_mkldnn: False
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
PreProcess:
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: ''
channel_num: 3
- ToCHWImage:
PostProcess:
main_indicator: MultiLabelTopk
MultiLabelTopk:
topk: 5
class_id_map_file: None
SavePreLabel:
save_dir: ./pre_label/

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23
deploy/python/postprocess.py
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@ -81,12 +81,14 @@ class Topk(object):
class_id_map = None
return class_id_map
def __call__(self, x, file_names=None):
def __call__(self, x, file_names=None, multilabel=False):
if file_names is not None:
assert x.shape[0] == len(file_names)
y = []
for idx, probs in enumerate(x):
index = probs.argsort(axis=0)[-self.topk:][::-1].astype("int32")
index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
"int32") if not multilabel else np.where(
probs >= 0.5)[0].astype("int32")
clas_id_list = []
score_list = []
label_name_list = []
@ -108,6 +110,14 @@ class Topk(object):
return y
class MultiLabelTopk(Topk):
def __init__(self, topk=1, class_id_map_file=None):
super().__init__()
def __call__(self, x, file_names=None):
return super().__call__(x, file_names, multilabel=True)
class SavePreLabel(object):
def __init__(self, save_dir):
if save_dir is None:
@ -128,23 +138,24 @@ class SavePreLabel(object):
os.makedirs(output_dir, exist_ok=True)
shutil.copy(image_file, output_dir)
class Binarize(object):
def __init__(self, method = "round"):
def __init__(self, method="round"):
self.method = method
self.unit = np.array([[128, 64, 32, 16, 8, 4, 2, 1]]).T
def __call__(self, x, file_names=None):
if self.method == "round":
x = np.round(x + 1).astype("uint8") - 1
if self.method == "sign":
x = ((np.sign(x) + 1) / 2).astype("uint8")
embedding_size = x.shape[1]
assert embedding_size % 8 == 0, "The Binary index only support vectors with sizes multiple of 8"
byte = np.zeros([x.shape[0], embedding_size // 8], dtype=np.uint8)
for i in range(embedding_size // 8):
byte[:, i:i+1] = np.dot(x[:, i * 8: (i + 1)* 8], self.unit)
byte[:, i:i + 1] = np.dot(x[:, i * 8:(i + 1) * 8], self.unit)
return byte

2
deploy/python/predict_cls.py
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@ -71,7 +71,6 @@ class ClsPredictor(Predictor):
output_names = self.paddle_predictor.get_output_names()
output_tensor = self.paddle_predictor.get_output_handle(output_names[
0])
if self.benchmark:
self.auto_logger.times.start()
if not isinstance(images, (list, )):
@ -119,7 +118,6 @@ def main(config):
) == len(image_list):
if len(batch_imgs) == 0:
continue
batch_results = cls_predictor.predict(batch_imgs)
for number, result_dict in enumerate(batch_results):
filename = batch_names[number]

3
deploy/shell/predict.sh
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@ -1,6 +1,9 @@
# classification
python3.7 python/predict_cls.py -c configs/inference_cls.yaml
# multilabel_classification
#python3.7 python/predict_cls.py -c configs/inference_multilabel_cls.yaml
# feature extractor
# python3.7 python/predict_rec.py -c configs/inference_rec.yaml

88
docs/zh_CN/advanced_tutorials/multilabel/multilabel.md
View File

@ -25,58 +25,66 @@ tar -xf NUS-SCENE-dataset.tar
cd ../../
```
## 二、环境准备
### 2.1 下载预训练模型
本例展示基于ResNet50_vd模型的多标签分类流程，因此首先下载ResNet50_vd的预训练模型
```bash
mkdir pretrained
cd pretrained
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
cd ../
```
## 三、模型训练
## 二、模型训练
```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
--gpus="0" \
export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m paddle.distributed.launch \
--gpus="0,1,2,3" \
tools/train.py \
-c ./configs/quick_start/ResNet50_vd_multilabel.yaml
-c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml
```
训练10epoch之后，验证集最好的正确率应该在0.72左右。
训练10epoch之后，验证集最好的正确率应该在0.95左右。
## 四、模型评估
## 三、模型评估
```bash
python tools/eval.py \
-c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
-o pretrained_model="./output/ResNet50_vd/best_model/ppcls" \
-o load_static_weights=False
python3 tools/eval.py \
-c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml \
-o Arch.pretrained="./output/MobileNetV1/best_model"
```
评估指标采用mAP，验证集的mAP应该在0.57左右。
## 五、模型预测
## 四、模型预测
```bash
python tools/infer/infer.py \
-i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
--model ResNet50_vd \
--pretrained_model "./output/ResNet50_vd/best_model/ppcls" \
--use_gpu True \
--load_static_weights False \
--multilabel True \
--class_num 33
python3 tools/infer.py \
-c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml \
-o Arch.pretrained="./output/MobileNetV1/best_model"
```
得到类似下面的输出：
```
class id: 3, probability: 0.6025
class id: 23, probability: 0.5491
class id: 32, probability: 0.7006
```
```
[{'class_ids': [6, 13, 17, 23, 26, 30], 'scores': [0.95683, 0.5567, 0.55211, 0.99088, 0.5943, 0.78767], 'file_name': './deploy/images/0517_2715693311.jpg', 'label_names': []}]
```
## 五、基于预测引擎预测
### 5.1 导出inference model
```bash
python3 tools/export_model.py \
-c ./ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml \
-o Arch.pretrained="./output/MobileNetV1/best_model"
```
inference model的路径默认在当前路径下`./inference`
### 5.2 基于预测引擎预测
首先进入deploy目录下：
```bash
cd ./deploy
```
通过预测引擎推理预测：
```
python3 python/predict_cls.py \
-c configs/inference_multilabel_cls.yaml
```
得到类似下面的输出：
```
0517_2715693311.jpg: class id(s): [6, 13, 17, 23, 26, 30], score(s): [0.96, 0.56, 0.55, 0.99, 0.59, 0.79], label_name(s): []
```

129
ppcls/configs/quick_start/MobileNetV1_multilabel.yaml 100644
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@ -0,0 +1,129 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 10
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
use_multilabel: True
# model architecture
Arch:
name: MobileNetV1
class_num: 33
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- MultiLabelLoss:
weight: 1.0
Eval:
- MultiLabelLoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.1
regularizer:
name: 'L2'
coeff: 0.00004
# data loader for train and eval
DataLoader:
Train:
dataset:
name: MultiLabelDataset
image_root: ./dataset/NUS-SCENE-dataset/images/
cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_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: 64
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: MultiLabelDataset
image_root: ./dataset/NUS-SCENE-dataset/images/
cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_test_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: 256
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: dataset/NUS-SCENE-dataset/images/0001_109549716.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: MutiLabelTopk
topk: 5
class_id_map_file: None
Metric:
Train:
- HammingDistance:
- AccuracyScore:
Eval:
- HammingDistance:
- AccuracyScore:

129
ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml 100644
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@ -0,0 +1,129 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 10
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
use_multilabel: True
# model architecture
Arch:
name: MobileNetV1
class_num: 33
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- MultiLabelLoss:
weight: 1.0
Eval:
- MultiLabelLoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.1
regularizer:
name: 'L2'
coeff: 0.00004
# data loader for train and eval
DataLoader:
Train:
dataset:
name: MultiLabelDataset
image_root: ./dataset/NUS-SCENE-dataset/images/
cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_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: 64
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: MultiLabelDataset
image_root: ./dataset/NUS-SCENE-dataset/images/
cls_label_path: ./dataset/NUS-SCENE-dataset/multilabel_test_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: 256
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: ./deploy/images/0517_2715693311.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: MultiLabelTopk
topk: 5
class_id_map_file: None
Metric:
Train:
- HammingDistance:
- AccuracyScore:
Eval:
- HammingDistance:
- AccuracyScore:

7
ppcls/data/dataloader/multilabel_dataset.py
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@ -33,7 +33,7 @@ class MultiLabelDataset(CommonDataset):
with open(self._cls_path) as fd:
lines = fd.readlines()
for l in lines:
l = l.strip().split(" ")
l = l.strip().split("\t")
self.images.append(os.path.join(self._img_root, l[0]))
labels = l[1].split(',')
@ -44,13 +44,14 @@ class MultiLabelDataset(CommonDataset):
def __getitem__(self, idx):
try:
img = cv2.imread(self.images[idx])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
with open(self.images[idx], 'rb') as f:
img = f.read()
if self._transform_ops:
img = transform(img, self._transform_ops)
img = img.transpose((2, 0, 1))
label = np.array(self.labels[idx]).astype("float32")
return (img, label)
except Exception as ex:
logger.error("Exception occured when parse line: {} with msg: {}".
format(self.images[idx], ex))

2
ppcls/data/postprocess/__init__.py
View File

@ -16,7 +16,7 @@ import importlib
from . import topk
from .topk import Topk
from .topk import Topk, MultiLabelTopk
def build_postprocess(config):

16
ppcls/data/postprocess/topk.py
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@ -45,15 +45,17 @@ class Topk(object):
class_id_map = None
return class_id_map
def __call__(self, x, file_names=None):
def __call__(self, x, file_names=None, multilabel=False):
assert isinstance(x, paddle.Tensor)
if file_names is not None:
assert x.shape[0] == len(file_names)
x = F.softmax(x, axis=-1)
x = F.softmax(x, axis=-1) if not multilabel else F.sigmoid(x)
x = x.numpy()
y = []
for idx, probs in enumerate(x):
index = probs.argsort(axis=0)[-self.topk:][::-1].astype("int32")
index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
"int32") if not multilabel else np.where(
probs >= 0.5)[0].astype("int32")
clas_id_list = []
score_list = []
label_name_list = []
@ -73,3 +75,11 @@ class Topk(object):
result["label_names"] = label_name_list
y.append(result)
return y
class MultiLabelTopk(Topk):
def __init__(self, topk=1, class_id_map_file=None):
super().__init__()
def __call__(self, x, file_names=None):
return super().__call__(x, file_names, multilabel=True)

19
ppcls/engine/engine.py
View File

@ -355,7 +355,8 @@ class Engine(object):
def export(self):
assert self.mode == "export"
model = ExportModel(self.config["Arch"], self.model)
use_multilabel = self.config["Global"].get("use_multilabel", False)
model = ExportModel(self.config["Arch"], self.model, use_multilabel)
if self.config["Global"]["pretrained_model"] is not None:
load_dygraph_pretrain(model.base_model,
self.config["Global"]["pretrained_model"])
@ -388,10 +389,9 @@ class ExportModel(nn.Layer):
ExportModel: add softmax onto the model
"""
def __init__(self, config, model):
def __init__(self, config, model, use_multilabel):
super().__init__()
self.base_model = model
# we should choose a final model to export
if isinstance(self.base_model, DistillationModel):
self.infer_model_name = config["infer_model_name"]
@ -402,10 +402,13 @@ class ExportModel(nn.Layer):
if self.infer_output_key == "features" and isinstance(self.base_model,
RecModel):
self.base_model.head = IdentityHead()
if config.get("infer_add_softmax", True):
self.softmax = nn.Softmax(axis=-1)
if use_multilabel:
self.out_act = nn.Sigmoid()
else:
self.softmax = None
if config.get("infer_add_softmax", True):
self.out_act = nn.Softmax(axis=-1)
else:
self.out_act = None
def eval(self):
self.training = False
@ -421,6 +424,6 @@ class ExportModel(nn.Layer):
x = x[self.infer_model_name]
if self.infer_output_key is not None:
x = x[self.infer_output_key]
if self.softmax is not None:
x = self.softmax(x)
if self.out_act is not None:
x = self.out_act(x)
return x

3
ppcls/engine/evaluation/classification.py
View File

@ -52,7 +52,8 @@ def classification_eval(evaler, epoch_id=0):
time_info["reader_cost"].update(time.time() - tic)
batch_size = batch[0].shape[0]
batch[0] = paddle.to_tensor(batch[0]).astype("float32")
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
if not evaler.config["Global"].get("use_multilabel", False):
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
# image input
out = evaler.model(batch[0])
# calc loss

4
ppcls/engine/train/train.py
View File

@ -36,8 +36,8 @@ def train_epoch(trainer, epoch_id, print_batch_step):
paddle.to_tensor(batch[0]['label'])
]
batch_size = batch[0].shape[0]
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
if not trainer.config["Global"].get("use_multilabel", False):
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
trainer.global_step += 1
# image input
if trainer.amp:

1
ppcls/loss/__init__.py
View File

@ -20,6 +20,7 @@ from .distanceloss import DistanceLoss
from .distillationloss import DistillationCELoss
from .distillationloss import DistillationGTCELoss
from .distillationloss import DistillationDMLLoss
from .multilabelloss import MultiLabelLoss
class CombinedLoss(nn.Layer):

43
ppcls/loss/multilabelloss.py 100644
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@ -0,0 +1,43 @@
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class MultiLabelLoss(nn.Layer):
"""
Multi-label loss
"""
def __init__(self, epsilon=None):
super().__init__()
if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
epsilon = None
self.epsilon = epsilon
def _labelsmoothing(self, target, class_num):
if target.ndim == 1 or target.shape[-1] != class_num:
one_hot_target = F.one_hot(target, class_num)
else:
one_hot_target = target
soft_target = F.label_smooth(one_hot_target, epsilon=self.epsilon)
soft_target = paddle.reshape(soft_target, shape=[-1, class_num])
return soft_target
def _binary_crossentropy(self, input, target, class_num):
if self.epsilon is not None:
target = self._labelsmoothing(target, class_num)
cost = F.binary_cross_entropy_with_logits(
logit=input, label=target)
else:
cost = F.binary_cross_entropy_with_logits(
logit=input, label=target)
return cost
def forward(self, x, target):
if isinstance(x, dict):
x = x["logits"]
class_num = x.shape[-1]
loss = self._binary_crossentropy(x, target, class_num)
loss = loss.mean()
return {"MultiLabelLoss": loss}

6
ppcls/metric/__init__.py
View File

@ -19,6 +19,8 @@ from collections import OrderedDict
from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
from .metrics import DistillationTopkAcc
from .metrics import GoogLeNetTopkAcc
from .metrics import HammingDistance, AccuracyScore
class CombinedMetrics(nn.Layer):
def __init__(self, config_list):
@ -32,7 +34,8 @@ class CombinedMetrics(nn.Layer):
metric_name = list(config)[0]
metric_params = config[metric_name]
if metric_params is not None:
self.metric_func_list.append(eval(metric_name)(**metric_params))
self.metric_func_list.append(
eval(metric_name)(**metric_params))
else:
self.metric_func_list.append(eval(metric_name)())
@ -42,6 +45,7 @@ class CombinedMetrics(nn.Layer):
metric_dict.update(metric_func(*args, **kwargs))
return metric_dict
def build_metrics(config):
metrics_list = CombinedMetrics(copy.deepcopy(config))
return metrics_list

76
ppcls/metric/metrics.py
View File

@ -15,6 +15,12 @@
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from sklearn.metrics import hamming_loss
from sklearn.metrics import accuracy_score as accuracy_metric
from sklearn.metrics import multilabel_confusion_matrix
from sklearn.preprocessing import binarize
class TopkAcc(nn.Layer):
@ -198,7 +204,7 @@ class Precisionk(nn.Layer):
equal_flag = paddle.logical_and(equal_flag,
keep_mask.astype('bool'))
equal_flag = paddle.cast(equal_flag, 'float32')
Ns = paddle.arange(gallery_img_id.shape[0]) + 1
equal_flag_cumsum = paddle.cumsum(equal_flag, axis=1)
Precision_at_k = (paddle.mean(equal_flag_cumsum, axis=0) / Ns).numpy()
@ -232,3 +238,71 @@ class GoogLeNetTopkAcc(TopkAcc):
def forward(self, x, label):
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)
def __call__(self, output):
output = F.sigmoid(output)
preds = self._multi_hot_encode(logits=output.numpy(), threshold=0.5)
return preds
class HammingDistance(MutiLabelMetric):
"""
Soft metric based label for multilabel classification
Returns:
The smaller the return value is, the better model is.
"""
def __init__(self):
super().__init__()
def __call__(self, output, target):
preds = super().__call__(output)
metric_dict = dict()
metric_dict["HammingDistance"] = paddle.to_tensor(
hamming_loss(target, preds))
return metric_dict
class AccuracyScore(MutiLabelMetric):
"""
Hard metric for multilabel classification
Args:
base: ["sample", "label"], default="sample"
if "sample", return metric score based sample,
if "label", return metric score based label.
Returns:
accuracy:
"""
def __init__(self, base="label"):
super().__init__()
assert base in ["sample", "label"
], 'must be one of ["sample", "label"]'
self.base = base
def __call__(self, output, target):
preds = super().__call__(output)
metric_dict = dict()
if self.base == "sample":
accuracy = accuracy_metric(target, preds)
elif self.base == "label":
mcm = multilabel_confusion_matrix(target, preds)
tns = mcm[:, 0, 0]
fns = mcm[:, 1, 0]
tps = mcm[:, 1, 1]
fps = mcm[:, 0, 1]
accuracy = (sum(tps) + sum(tns)) / (
sum(tps) + sum(tns) + sum(fns) + sum(fps))
precision = sum(tps) / (sum(tps) + sum(fps))
recall = sum(tps) / (sum(tps) + sum(fns))
F1 = 2 * (accuracy * recall) / (accuracy + recall)
metric_dict["AccuracyScore"] = paddle.to_tensor(accuracy)
return metric_dict

2
tools/train.sh
View File

@ -4,4 +4,4 @@
# python3.7 tools/train.py -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml
# for multi-cards train
python3.7 -m paddle.distributed.launch --gpus="0,1,2,3" tools/train.py -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml
python3.7 -m paddle.distributed.launch --gpus="0,1,2,3" tools/train.py -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml

7
train.sh 100755
View File

@ -0,0 +1,7 @@
#!/usr/bin/env bash
# for single card train
# python3.7 tools/train.py -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml
# for multi-cards train
python3.7 -m paddle.distributed.launch --gpus="0" tools/train.py -c ./MobileNetV2.yaml