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PaddleClas
提交 f83ff59c 编写于 作者: Z zh-hike 提交者： Walter
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增加代码规范，删除一些无用的function

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docs/zh_CN/training/semi_supervised_learning/FixMatchCCSSL.md
浏览文件 @ f83ff59c
......@@ -79,14 +79,14 @@ python tools/train.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4
python -m paddle.distributed.launch --gpus='0,1,2,3' tools/train.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
```
2. **查看训练日志和保存的模型参数文件** 训练过程中屏幕会实时打印loss等指标信息，同时会保存日志文件 `train.log` ，模型参数文件 `*.pdparams`，优化器参数文件 `*.pdopt` 等内容到`Global.output_dir`指定的文件夹下，默认在 `PaddleClas/output/WideResNet/`文件夹下。
2. **查看训练日志和保存的模型参数文件** 训练过程中屏幕会实时打印loss等指标信息，同时会保存日志文件 `train.log` ，模型参数文件 `*.pdparams`，优化器参数文件 `*.pdopt` 等内容到`Global.output_dir`指定的文件夹下，默认在 `PaddleClas/output/RecModel/`文件夹下。
## 5. 模型评估与推理部署
### 5.1 模型评估
准备用于评估的 `*.pdparams` 模型参数文件，可以使用训练好的模型，也可以使用 *4. 模型训练* 中保存的模型。
* 以训练过程中保存的 `best_model_ema.ema.pdparams`为例，执行如下命令即可进行评估。
```
python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml -o Global.pretrained_model="./output/WideResNet/best_model_ema.ema"
python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml -o Global.pretrained_model="./output/RecModel/best_model_ema.ema"
```
* 以训练好的模型为例，下载提供的已经训练好的模型，到 `PaddleClas/pretrained_models` 文件夹中，执行如下命令即可进行评估。
......@@ -98,7 +98,7 @@ cd pretrained_models
wget
cd ..
# 评估
python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatch_CCSSL_cifar10_4000.yaml -o Global.pretrained_model=""
python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL_cifar10_4000.yaml -o Global.pretrained_model="./output/RecModel/best_model_ema.ema"
```
**注：** `pretrained_model` 后填入的地址不需要加 `.pdparams`后缀，在程序运行时会自动补上。
......@@ -114,15 +114,15 @@ python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatch_CCSSL_cifar10_4000.yaml -o
[2022/12/08 09:36:16] ppcls INFO: [Eval][Epoch 0][Iter: 140/157]CELoss: 0.03242, loss: 0.03242, top1: 0.95601, top5: 0.99945, batch_cost: 0.02084s, reader_cost: 0.00075, ips: 3071.00311 images/sec
[2022/12/08 09:36:16] ppcls INFO: [Eval][Epoch 0][Avg]CELoss: 0.16041, loss: 0.16041, top1: 0.95610, top5: 0.99950
```
默认评估日志保存在 `PaddleClas/output/WideResNetCCSSL/eval.log`中，可以看到我们提供的模型在cifar10数据集上的评估指标为top1: 95.61, top5: 99.95
默认评估日志保存在 `PaddleClas/output/RecModel/eval.log`中，可以看到我们提供的模型在cifar10数据集上的评估指标为top1: 95.57, top5: 99.95
### 5.2 模型推理
#### 5.2.1 推理模型准备
将训练过程中保存的模型文件转成inference模型，同样以 `best_model_ema.ema_pdparams`为例，执行以下命令进行转换
```
python3.7 tools/export_model.py \
-c ppcls/configs/ssl/FixMatch_CCSSL/FixMatch_CCSSL_cifar10_4000.yaml \
-o Global.pretrained_model="output/WideResNetCCSSL/best_model_ema.ema" \
-c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000.yaml \
-o Global.pretrained_model="output/RecModel/best_model_ema.ema" \
-o Global.save_inference_fir="./deploy/inference"
```
......
ppcls/arch/gears/decoup.py 已删除 100644 → 0
浏览文件 @ 692b8d8c
import paddle
import paddle.nn as nn
class Decoup(nn.Layer):
def __init__(self, logits_index, features_index, **kwargs):
super(Decoup, self).__init__()
self.logits_index = logits_index
self.features_index = features_index
def forward(self, out, **kwargs):
assert isinstance(out, (list, tuple)), 'out must be list or tuple'
out = {'logits': out[self.logits_index], 'features':out[self.features_index]}
return out
ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
浏览文件 @ f83ff59c
......@@ -64,10 +64,9 @@ Optimizer:
use_nesterov: true
weight_decay: 0.001
lr:
name: 'cosine_schedule_with_warmup'
name: 'CosineFixmatch'
learning_rate: 0.03
num_warmup_steps: 0
num_training_steps: 524800
DataLoader:
mean: [0.4914, 0.4822, 0.4465]
......
ppcls/data/dataloader/__init__.py
浏览文件 @ f83ff59c
......@@ -13,4 +13,3 @@ from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
from ppcls.data.dataloader.face_dataset import AdaFaceDataset, FiveValidationDataset
from ppcls.data.dataloader.custom_label_dataset import CustomLabelDataset
from ppcls.data.dataloader.cifar import Cifar10, Cifar100
# from ppcls.data.dataloader.cifar import CIFAR10SSL, CIFAR100SSL
ppcls/data/dataloader/cifar.py
浏览文件 @ f83ff59c
......@@ -15,16 +15,11 @@
from __future__ import print_function
import numpy as np
import cv2
import shutil
from ppcls.data import preprocess
from ppcls.data.preprocess import transform
# from ppcls.data.preprocess import BaseTransform, ListTransform
from ppcls.data.dataloader.common_dataset import create_operators
from paddle.vision.datasets import Cifar10 as Cifar10_paddle
from paddle.vision.datasets import Cifar100 as Cifar100_paddle
# from paddle.vision.datasets import cifar
import os
# from PIL import Image
class Cifar10(Cifar10_paddle):
......@@ -128,176 +123,4 @@ class Cifar100(Cifar100_paddle):
image3 = transform(image, self._transform_ops_strong)
image3 = image3.transpose((2, 0, 1))
return (image2, image3, np.int64(label))
# def np_convert_pil(array):
# """
# array conver image
# Args:
# array: array and dim is 1
# """
# assert len(array.shape), "dim of array should 1"
# img = Image.fromarray(array.reshape(3, 32, 32).transpose(1, 2, 0))
# return img
# class CIFAR10(cifar.Cifar10):
# """
# cifar10 dataset
# """
# def __init__(self, data_file, download=True, mode='train'):
# super().__init__(download=download, mode=mode)
# if data_file is not None:
# os.makedirs(data_file, exist_ok=True)
# if not os.path.exists(os.path.join(data_file, 'cifar-10-python.tar.gz')):
# shutil.move('~/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz', data_file)
# self.num_classes = 10
# self.x = []
# self.y = []
# for d in self.data:
# self.x.append(d[0])
# self.y.append(d[1])
# self.x = np.array(self.x)
# self.y = np.array(self.y)
# def __getitem__(self, idx):
# return self.x[idx], self.y[idx]
# def __len__(self):
# return self.x.shape[0]
# class CIFAR100(cifar.Cifar100):
# """
# cifar10 dataset
# """
# def __init__(self, data_file, download=True, mode='train'):
# super().__init__(download=download, mode=mode)
# if data_file is not None:
# os.makedirs(data_file, exist_ok=True)
# if not os.path.exists(os.path.join(data_file, 'cifar-100-python.tar.gz')):
# shutil.move('~/.cache/paddle/dataset/cifar/cifar-100-python.tar.gz', data_file)
# self.num_classes = 100
# self.x = []
# self.y = []
# for d in self.data:
# self.x.append(d[0])
# self.y.append(d[1])
# self.x = np.array(self.x)
# self.y = np.array(self.y)
# def __getitem__(self, idx):
# return self.x[idx], self.y[idx]
# def __len__(self):
# return self.x.shape[0]
# class CIFAR10SSL(CIFAR10):
# """
# from Cifar10
# """
# def __init__(self,
# data_file=None,
# sample_per_label=None,
# download=True,
# expand_labels=1,
# mode='train',
# transform_ops=None,
# transform_w=None,
# transform_s1=None,
# transform_s2=None):
# super().__init__(data_file, download=download, mode=mode)
# self.data_type = 'unlabeled_train' if mode == 'train' else 'val'
# if transform_ops is not None and sample_per_label is not None:
# index = []
# self.data_type = 'labeled_train'
# for c in range(self.num_classes):
# idx = np.where(self.y == c)[0]
# idx = np.random.choice(idx, sample_per_label, False)
# index.extend(idx)
# index = index * expand_labels
# # print(index)
# self.x = self.x[index]
# self.y = self.y[index]
# self.transforms = [transform_ops] if transform_ops is not None else [transform_w, transform_s1, transform_s2]
# self.mode = mode
# def __getitem__(self, idx):
# img, label = np_convert_pil(self.x[idx]), self.y[idx]
# results = ListTransform(self.transforms)(img)
# if self.data_type == 'unlabeled_train':
# return results
# return results[0], label
# def __len__(self):
# return self.x.shape[0]
# class CIFAR100SSL(CIFAR100):
# """
# from Cifar100
# """
# def __init__(self,
# data_file=None,
# sample_per_label=None,
# download=True,
# expand_labels=1,
# mode='train',
# transform_ops=None,
# transform_w=None,
# transform_s1=None,
# transform_s2=None):
# super().__init__(data_file, download=download, mode=mode)
# self.data_type = 'unlabeled_train' if mode == 'train' else 'val'
# if transform_ops is not None and sample_per_label is not None:
# index = []
# self.data_type = 'labeled_train'
# for c in range(self.num_classes):
# idx = np.where(self.y == c)[0]
# idx = np.random.choice(idx, sample_per_label, False)
# index.extend(idx)
# index = index * expand_labels
# # print(index)
# self.x = self.x[index]
# self.y = self.y[index]
# self.transforms = [transform_ops] if transform_ops is not None else [transform_w, transform_s1, transform_s2]
# self.mode = mode
# def __getitem__(self, idx):
# img, label = np_convert_pil(self.x[idx]), self.y[idx]
# results = ListTransform(self.transforms)(img)
# if self.data_type == 'unlabeled_train':
# return results
# return results[0], label
# def __len__(self):
# return self.x.shape[0]
# def x_u_split(num_labeled, num_classes, label):
# """
# split index of dataset to labeled x and unlabeled u
# Args:
# num_labeled: num of labeled dataset
# label: list or array, label
# """
# assert num_labeled <= len(label), "arg num_labeled should <= num of label"
# label = np.array(label) if isinstance(label, list) else label
# label_per_class = num_labeled // num_classes
# labeled_idx = []
# unlabeled_idx = np.array(list(range(label.shape[0])))
# for c in range(num_classes):
# idx = np.where(label == c)[0]
# idx = np.random.choice(idx, label_per_class, False)
# labeled_idx.extend(idx)
# np.random.shuffle(labeled_idx)
# return labeled_idx, unlabeled_idx
\ No newline at end of file
return (image2, image3, np.int64(label))
\ No newline at end of file
ppcls/data/preprocess/__init__.py
浏览文件 @ f83ff59c
......@@ -56,8 +56,6 @@ from ppcls.data.preprocess.batch_ops.batch_operators import MixupCutmixHybrid
import numpy as np
from PIL import Image
import random
from paddle.vision.transforms import transforms as T
from paddle.vision.transforms.transforms import RandomCrop, ToTensor, Normalize
def transform(data, ops=[]):
......@@ -123,41 +121,4 @@ class TimmAutoAugment(RawTimmAutoAugment):
img = np.asarray(img)
return img
# class BaseTransform:
# def __init__(self, cfg) -> None:
# """
# Args:
# cfg: list [dict, dict, dict]
# """
# ts = []
# for op in cfg:
# name = list(op.keys())[0]
# if op[name] is None:
# ts.append(eval(name)())
# else:
# ts.append(eval(name)(**(op[name])))
# self.t = T.Compose(ts)
# def __call__(self, img):
# return self.t(img)
# class ListTransform:
# def __init__(self, ops) -> None:
# """
# Args:
# ops: list[list[dict, dict], ...]
# """
# self.ts = []
# for op in ops:
# self.ts.append(BaseTransform(op))
# def __call__(self, img):
# results = []
# for op in self.ts:
# results.append(op(img))
# return results
\ No newline at end of file
ppcls/loss/__init__.py
浏览文件 @ f83ff59c
......@@ -22,7 +22,6 @@ from .pairwisecosface import PairwiseCosface
from .dmlloss import DMLLoss
from .distanceloss import DistanceLoss
from .softtargetceloss import SoftTargetCrossEntropy
from .ccssl_loss import CCSSLLoss
from .distillationloss import DistillationCELoss
from .distillationloss import DistillationGTCELoss
from .distillationloss import DistillationDMLLoss
......
ppcls/loss/ccssl_loss.py
浏览文件 @ f83ff59c
from ppcls.engine.train.train import forward
from .softsuploss import SoftSupConLoss
import copy
import paddle.nn as nn
class CCSSLLoss(nn.Layer):
def __init__(self, **kwargs):
super(CCSSLLoss, self).__init__()
ce_cfg = copy.deepcopy(kwargs['CELoss'])
self.ce_weight = ce_cfg.pop('weight')
softsupconloss_cfg = copy.deepcopy(kwargs['SoftSupConLoss'])
self.softsupconloss_weight = softsupconloss_cfg.pop('weight')
self.softsuploss = SoftSupConLoss(**softsupconloss_cfg)
self.celoss = nn.CrossEntropyLoss(reduction='none')
def forward(self, feats, batch, **kwargs):
"""
Args:
feats: feature of s1 and s2, (n, 2, d)
batch: dict
"""
logits_w = batch['logits_w']
logits_s1 = batch['logits_s1']
p_targets_u_w = batch['p_targets_u_w']
mask = batch['mask']
max_probs = batch['max_probs']
# reduction = batch['reduction']
loss_u = self.celoss(logits_s1, p_targets_u_w) * mask
loss_u = loss_u.mean()
loss_c = self.softsuploss(feats, max_probs, p_targets_u_w)
return {'CCSSLLoss': self.ce_weight*loss_u + self.softsupconloss_weight * loss_c}
class CCSSLCeLoss(nn.Layer):
def __init__(self, **kwargs):
super(CCSSLCeLoss, self).__init__()
self.celoss = nn.CrossEntropyLoss(reduction='none')
def forward(self, inputs, batch, **kwargs):
p_targets_u_w = batch['p_targets_u_w']
logits_s1 = batch['logits_s1']
......@@ -56,6 +17,3 @@ class CCSSLCeLoss(nn.Layer):
loss_u = loss_u.mean()
return {'CCSSLCeLoss': loss_u}
ppcls/loss/softsuploss.py
浏览文件 @ f83ff59c
......@@ -41,7 +41,6 @@ class SoftSupConLoss(nn.Layer):
score_mask = paddle.matmul(max_probs, max_probs.T)
mask = paddle.multiply(mask, score_mask)
contrast_count = feat.shape[1]
......@@ -55,7 +54,6 @@ class SoftSupConLoss(nn.Layer):
mask = paddle.concat([mask, mask], axis=0)
mask = paddle.concat([mask, mask], axis=1)
# mask = paddle.repeat_interleave(paddle.repeat_interleave(mask, 2, 0), 2, 1)
logits_mask = 1 - paddle.eye(batch_size * contrast_count, dtype=paddle.float64)
mask = mask * logits_mask
exp_logits = paddle.exp(logits) * logits_mask
......@@ -68,6 +66,4 @@ class SoftSupConLoss(nn.Layer):
loss = loss.mean()
return {"SoftSupConLoss": loss}
\ No newline at end of file
ppcls/optimizer/learning_rate.py
浏览文件 @ f83ff59c
......@@ -519,21 +519,3 @@ class CosineFixmatch(LRBase):
last_epoch=self.last_epoch)
setattr(learning_rate, "by_epoch", self.by_epoch)
return learning_rate
def cosine_schedule_with_warmup(learning_rate,
num_warmup_steps,
num_training_steps,
num_cycles=7./16,
last_epoch=-1,
**kwargs):
def _lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
no_progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0., math.cos(math.pi * num_cycles * no_progress))
return lr.LambdaDecay(learning_rate=learning_rate, lr_lambda=_lr_lambda)
\ No newline at end of file
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