diff --git a/ppcls/arch/backbone/model_zoo/strongbaseline_attr.py b/ppcls/arch/backbone/model_zoo/strongbaseline_attr.py
index 6bb445d1f..7e2725545 100644
--- a/ppcls/arch/backbone/model_zoo/strongbaseline_attr.py
+++ b/ppcls/arch/backbone/model_zoo/strongbaseline_attr.py
@@ -1,4 +1,4 @@
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/ppcls/configs/Attr/StrongBaselineAttr.yaml b/ppcls/configs/Attr/StrongBaselineAttr.yaml
index df6916bde..6718d8cbb 100644
--- a/ppcls/configs/Attr/StrongBaselineAttr.yaml
+++ b/ppcls/configs/Attr/StrongBaselineAttr.yaml
@@ -14,7 +14,6 @@ Global:
image_shape: [3, 256, 192]
save_inference_dir: "./inference"
use_multilabel: True
- metric_attr: True
# model architecture
Arch:
@@ -26,11 +25,15 @@ Arch:
# loss function config for traing/eval process
Loss:
Train:
- - BCELoss:
+ - MultiLabelLoss:
weight: 1.0
+ weight_ratio: True
+ size_sum: True
Eval:
- - BCELoss:
+ - MultiLabelLoss:
weight: 1.0
+ weight_ratio: True
+ size_sum: True
Optimizer:
name: Adam
@@ -47,10 +50,10 @@ Optimizer:
DataLoader:
Train:
dataset:
- name: AttrDataset
+ name: MultiLabelDataset
image_root: "dataset/xingrenfenxi/data/"
- cls_label_path: "dataset/xingrenfenxi/all_qiye.pkl"
- split: 'trainval'
+ cls_label_path: "dataset/xingrenfenxi/trainval.txt"
+ label_ratio: True
transform_ops:
- DecodeImage:
to_rgb: True
@@ -80,10 +83,10 @@ DataLoader:
use_shared_memory: True
Eval:
dataset:
- name: AttrDataset
+ name: MultiLabelDataset
image_root: "dataset/xingrenfenxi/data/"
- cls_label_path: "dataset/xingrenfenxi/all_qiye.pkl"
- split: 'test'
+ cls_label_path: "dataset/xingrenfenxi/test.txt"
+ label_ratio: True
transform_ops:
- DecodeImage:
to_rgb: True
diff --git a/ppcls/data/__init__.py b/ppcls/data/__init__.py
index 00bc50f0d..9fc4d760b 100644
--- a/ppcls/data/__init__.py
+++ b/ppcls/data/__init__.py
@@ -30,7 +30,6 @@ from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
from ppcls.data.dataloader.mix_dataset import MixDataset
from ppcls.data.dataloader.multi_scale_dataset import MultiScaleDataset
from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
-from ppcls.data.dataloader.attr_dataset import AttrDataset
# sampler
diff --git a/ppcls/data/dataloader/__init__.py b/ppcls/data/dataloader/__init__.py
index 7581daa0a..2b1d92b76 100644
--- a/ppcls/data/dataloader/__init__.py
+++ b/ppcls/data/dataloader/__init__.py
@@ -10,4 +10,3 @@ from ppcls.data.dataloader.mix_sampler import MixSampler
from ppcls.data.dataloader.multi_scale_sampler import MultiScaleSampler
from ppcls.data.dataloader.pk_sampler import PKSampler
from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
-from ppcls.data.dataloader.attr_dataset import AttrDataset
diff --git a/ppcls/data/dataloader/attr_dataset.py b/ppcls/data/dataloader/attr_dataset.py
deleted file mode 100644
index f4aaef2db..000000000
--- a/ppcls/data/dataloader/attr_dataset.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-
-import numpy as np
-import os
-import pickle
-
-from .common_dataset import CommonDataset
-from ppcls.data.preprocess import transform
-
-
-class AttrDataset(CommonDataset):
- def _load_anno(self, seed=None, split='trainval'):
- assert os.path.exists(self._cls_path)
- assert os.path.exists(self._img_root)
- anno_path = self._cls_path
- image_dir = self._img_root
- self.images = []
- self.labels = []
-
- dataset_info = pickle.load(open(anno_path, 'rb+'))
- img_id = dataset_info.image_name
-
- attr_label = dataset_info.label
- attr_label[attr_label == 2] = 0
- attr_id = dataset_info.attr_name
- if 'label_idx' in dataset_info.keys():
- eval_attr_idx = dataset_info.label_idx.eval
- attr_label = attr_label[:, eval_attr_idx]
- attr_id = [attr_id[i] for i in eval_attr_idx]
-
- attr_num = len(attr_id)
-
- # mapping category name to class id
- # first_class:0, second_class:1, ...
- cname2cid = {attr_id[i]: i for i in range(attr_num)}
-
- assert split in dataset_info.partition.keys(
- ), f'split {split} is not exist'
-
- img_idx = dataset_info.partition[split]
-
- if isinstance(img_idx, list):
- img_idx = img_idx[0] # default partition 0
-
- img_num = img_idx.shape[0]
- img_id = [img_id[i] for i in img_idx]
- label = attr_label[img_idx] # [:, [0, 12]]
- self.label_ratio = label.mean(0)
- print("label_ratio:", self.label_ratio)
- for i, (img_i, label_i) in enumerate(zip(img_id, label)):
- imgname = os.path.join(image_dir, img_i)
- self.images.append(imgname)
- self.labels.append(np.int64(label_i))
-
- def __getitem__(self, idx):
- try:
- 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))
- return (img, [self.labels[idx], self.label_ratio])
-
- except Exception as ex:
- logger.error("Exception occured when parse line: {} with msg: {}".
- format(self.images[idx], ex))
- rnd_idx = np.random.randint(self.__len__())
- return self.__getitem__(rnd_idx)
diff --git a/ppcls/data/dataloader/common_dataset.py b/ppcls/data/dataloader/common_dataset.py
index fb251a7fc..88bab0f1d 100644
--- a/ppcls/data/dataloader/common_dataset.py
+++ b/ppcls/data/dataloader/common_dataset.py
@@ -48,7 +48,7 @@ class CommonDataset(Dataset):
image_root,
cls_label_path,
transform_ops=None,
- split='trainval'):
+ label_ratio=False):
self._img_root = image_root
self._cls_path = cls_label_path
if transform_ops:
@@ -56,7 +56,10 @@ class CommonDataset(Dataset):
self.images = []
self.labels = []
- self._load_anno(split=split)
+ if label_ratio:
+ self.label_ratio = self._load_anno(label_ratio=label_ratio)
+ else:
+ self._load_anno()
def _load_anno(self):
pass
diff --git a/ppcls/data/dataloader/multilabel_dataset.py b/ppcls/data/dataloader/multilabel_dataset.py
index 2c1ed7703..16bd5481f 100644
--- a/ppcls/data/dataloader/multilabel_dataset.py
+++ b/ppcls/data/dataloader/multilabel_dataset.py
@@ -25,7 +25,7 @@ from .common_dataset import CommonDataset
class MultiLabelDataset(CommonDataset):
- def _load_anno(self):
+ def _load_anno(self, label_ratio=False):
assert os.path.exists(self._cls_path)
assert os.path.exists(self._img_root)
self.images = []
@@ -41,6 +41,8 @@ class MultiLabelDataset(CommonDataset):
self.labels.append(labels)
assert os.path.exists(self.images[-1])
+ if label_ratio:
+ return np.array(self.labels).mean(0)
def __getitem__(self, idx):
try:
@@ -50,7 +52,10 @@ class MultiLabelDataset(CommonDataset):
img = transform(img, self._transform_ops)
img = img.transpose((2, 0, 1))
label = np.array(self.labels[idx]).astype("float32")
- return (img, label)
+ if self.label_ratio is not None:
+ return (img, [label, self.label_ratio])
+ else:
+ return (img, label)
except Exception as ex:
logger.error("Exception occured when parse line: {} with msg: {}".
diff --git a/ppcls/engine/evaluation/classification.py b/ppcls/engine/evaluation/classification.py
index d543b0b3f..ea78fe5b5 100644
--- a/ppcls/engine/evaluation/classification.py
+++ b/ppcls/engine/evaluation/classification.py
@@ -32,8 +32,8 @@ def classification_eval(engine, epoch_id=0):
}
print_batch_step = engine.config["Global"]["print_batch_step"]
- if engine.eval_metric_func is not None and engine.config["Global"][
- "metric_attr"]:
+ if engine.eval_metric_func is not None and engine.config["Arch"][
+ "name"] == "StrongBaselineAttr":
output_info["attr"] = AttrMeter(threshold=0.5)
metric_key = None
@@ -128,7 +128,7 @@ def classification_eval(engine, epoch_id=0):
# calc metric
if engine.eval_metric_func is not None:
- if engine.config["Global"]["metric_attr"]:
+ if engine.config["Arch"]["name"] == "StrongBaselineAttr":
metric_dict = engine.eval_metric_func(preds, labels)
metric_key = "attr"
output_info["attr"].update(metric_dict)
@@ -153,7 +153,7 @@ def classification_eval(engine, epoch_id=0):
ips_msg = "ips: {:.5f} images/sec".format(
batch_size / time_info["batch_cost"].avg)
- if engine.config["Global"]["metric_attr"]:
+ if engine.config["Arch"]["name"] == "StrongBaselineAttr":
metric_msg = ""
else:
metric_msg = ", ".join([
@@ -168,7 +168,7 @@ def classification_eval(engine, epoch_id=0):
if engine.use_dali:
engine.eval_dataloader.reset()
- if engine.config["Global"]["metric_attr"]:
+ if engine.config["Arch"]["name"] == "StrongBaselineAttr":
metric_msg = ", ".join([
"evalres: ma: {:.5f} label_f1: {:.5f} label_pos_recall: {:.5f} label_neg_recall: {:.5f} instance_f1: {:.5f} instance_acc: {:.5f} instance_prec: {:.5f} instance_recall: {:.5f}".
format(*output_info["attr"].res())
diff --git a/ppcls/loss/__init__.py b/ppcls/loss/__init__.py
index 65e1b0b76..c1f2f95df 100644
--- a/ppcls/loss/__init__.py
+++ b/ppcls/loss/__init__.py
@@ -26,7 +26,6 @@ from .distillationloss import DistillationKLDivLoss
from .distillationloss import DistillationDKDLoss
from .multilabelloss import MultiLabelLoss
from .afdloss import AFDLoss
-from .bceloss import BCELoss
from .deephashloss import DSHSDLoss
from .deephashloss import LCDSHLoss
diff --git a/ppcls/loss/bceloss.py b/ppcls/loss/bceloss.py
deleted file mode 100644
index 58418058d..000000000
--- a/ppcls/loss/bceloss.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-
-def ratio2weight(targets, ratio):
- # print(targets)
- pos_weights = targets * (1. - ratio)
- neg_weights = (1. - targets) * ratio
- weights = paddle.exp(neg_weights + pos_weights)
-
- # for RAP dataloader, targets element may be 2, with or without smooth, some element must great than 1
- weights = weights - weights * (targets > 1)
-
- return weights
-
-
-class BCELoss(nn.Layer):
- """BCE Loss.
- Args:
-
- """
-
- def __init__(self,
- sample_weight=True,
- size_sum=True,
- smoothing=None,
- weight=1.0):
- super(BCELoss, self).__init__()
-
- self.sample_weight = sample_weight
- self.size_sum = size_sum
- self.hyper = 0.8
- self.smoothing = smoothing
-
- def forward(self, logits, labels):
- targets, ratio = labels
-
- if self.smoothing is not None:
- targets = (1 - self.smoothing) * targets + self.smoothing * (
- 1 - targets)
-
- targets = paddle.cast(targets, 'float32')
-
- loss_m = F.binary_cross_entropy_with_logits(
- logits, targets, reduction='none')
-
- targets_mask = paddle.cast(targets > 0.5, 'float32')
- if self.sample_weight:
- weight = ratio2weight(targets_mask, ratio[0])
- weight = weight * (targets > -1)
- loss_m = loss_m * weight
-
- loss = loss_m.sum(1).mean() if self.size_sum else loss_m.sum()
-
- return {"BCELoss": loss}
diff --git a/ppcls/loss/multilabelloss.py b/ppcls/loss/multilabelloss.py
index 550db40f2..52c31c7da 100644
--- a/ppcls/loss/multilabelloss.py
+++ b/ppcls/loss/multilabelloss.py
@@ -19,12 +19,13 @@ class MultiLabelLoss(nn.Layer):
Multi-label loss
"""
- def __init__(self, epsilon=None, weight_ratio=None):
+ def __init__(self, epsilon=None, size_sum=False, weight_ratio=False):
super().__init__()
if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
epsilon = None
self.epsilon = epsilon
self.weight_ratio = weight_ratio
+ self.size_sum = size_sum
def _labelsmoothing(self, target, class_num):
if target.ndim == 1 or target.shape[-1] != class_num:
@@ -36,18 +37,21 @@ class MultiLabelLoss(nn.Layer):
return soft_target
def _binary_crossentropy(self, input, target, class_num):
+ if self.weight_ratio:
+ target, label_ratio = target
if self.epsilon is not None:
target = self._labelsmoothing(target, class_num)
- cost = F.binary_cross_entropy_with_logits(logit=input, label=target)
+ cost = F.binary_cross_entropy_with_logits(
+ logit=input, label=target, reduction='none')
- if self.weight_ratio is not None:
+ if self.weight_ratio:
targets_mask = paddle.cast(target > 0.5, 'float32')
- weight = ratio2weight(targets_mask,
- paddle.to_tensor(self.weight_ratio))
+ weight = ratio2weight(targets_mask, paddle.to_tensor(label_ratio))
weight = weight * (target > -1)
cost = cost * weight
- import pdb
- pdb.set_trace()
+
+ if self.size_sum:
+ cost = cost.sum(1).mean() if self.size_sum else cost.mean()
return cost
diff --git a/requirements.txt b/requirements.txt
index 79f548c22..5e927756a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,3 +9,4 @@ scipy
scikit-learn==0.23.2
gast==0.3.3
faiss-cpu==1.7.1.post2
+easydict