增加代码规范，删除一些无用的function
zh-hike
Walter
parent
692b8d8c1f
commit
f83ff59c97
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@ -79,14 +79,14 @@ python tools/train.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4
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python -m paddle.distributed.launch --gpus='0,1,2,3' tools/train.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
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```
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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。
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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。
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## 5. 模型评估与推理部署
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### 5.1 模型评估
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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。
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* 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。
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```
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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"
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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"
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```
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* 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。
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@ -98,7 +98,7 @@ cd pretrained_models
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wget
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cd ..
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# 评估
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python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatch_CCSSL_cifar10_4000.yaml -o Global.pretrained_model=""
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python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL_cifar10_4000.yaml -o Global.pretrained_model="./output/RecModel/best_model_ema.ema"
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```
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**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。
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@ -114,15 +114,15 @@ python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatch_CCSSL_cifar10_4000.yaml -o
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[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
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[2022/12/08 09:36:16] ppcls INFO: [Eval][Epoch 0][Avg]CELoss: 0.16041, loss: 0.16041, top1: 0.95610, top5: 0.99950
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```
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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
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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
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### 5.2 模型推理
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#### 5.2.1 推理模型准备
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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
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```
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python3.7 tools/export_model.py \
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-c ppcls/configs/ssl/FixMatch_CCSSL/FixMatch_CCSSL_cifar10_4000.yaml \
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-o Global.pretrained_model="output/WideResNetCCSSL/best_model_ema.ema" \
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-c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000.yaml \
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-o Global.pretrained_model="output/RecModel/best_model_ema.ema" \
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-o Global.save_inference_fir="./deploy/inference"
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```
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@ -1,16 +0,0 @@
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import paddle
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import paddle.nn as nn
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class Decoup(nn.Layer):
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def __init__(self, logits_index, features_index, **kwargs):
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super(Decoup, self).__init__()
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self.logits_index = logits_index
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self.features_index = features_index
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def forward(self, out, **kwargs):
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assert isinstance(out, (list, tuple)), 'out must be list or tuple'
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out = {'logits': out[self.logits_index], 'features':out[self.features_index]}
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return out
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@ -64,10 +64,9 @@ Optimizer:
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use_nesterov: true
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weight_decay: 0.001
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lr:
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name: 'cosine_schedule_with_warmup'
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name: 'CosineFixmatch'
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learning_rate: 0.03
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num_warmup_steps: 0
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num_training_steps: 524800
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DataLoader:
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mean: [0.4914, 0.4822, 0.4465]
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@ -13,4 +13,3 @@ from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
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from ppcls.data.dataloader.face_dataset import AdaFaceDataset, FiveValidationDataset
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from ppcls.data.dataloader.custom_label_dataset import CustomLabelDataset
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from ppcls.data.dataloader.cifar import Cifar10, Cifar100
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# from ppcls.data.dataloader.cifar import CIFAR10SSL, CIFAR100SSL
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@ -15,16 +15,11 @@
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from __future__ import print_function
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import numpy as np
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import cv2
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import shutil
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from ppcls.data import preprocess
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from ppcls.data.preprocess import transform
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# from ppcls.data.preprocess import BaseTransform, ListTransform
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from ppcls.data.dataloader.common_dataset import create_operators
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from paddle.vision.datasets import Cifar10 as Cifar10_paddle
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from paddle.vision.datasets import Cifar100 as Cifar100_paddle
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# from paddle.vision.datasets import cifar
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import os
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# from PIL import Image
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class Cifar10(Cifar10_paddle):
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@ -128,176 +123,4 @@ class Cifar100(Cifar100_paddle):
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image3 = transform(image, self._transform_ops_strong)
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image3 = image3.transpose((2, 0, 1))
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return (image2, image3, np.int64(label))
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# def np_convert_pil(array):
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# """
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# array conver image
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# Args:
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# array: array and dim is 1
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# """
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# assert len(array.shape), "dim of array should 1"
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# img = Image.fromarray(array.reshape(3, 32, 32).transpose(1, 2, 0))
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# return img
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# class CIFAR10(cifar.Cifar10):
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# """
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# cifar10 dataset
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# """
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# def __init__(self, data_file, download=True, mode='train'):
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# super().__init__(download=download, mode=mode)
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# if data_file is not None:
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# os.makedirs(data_file, exist_ok=True)
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# if not os.path.exists(os.path.join(data_file, 'cifar-10-python.tar.gz')):
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# shutil.move('~/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz', data_file)
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# self.num_classes = 10
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# self.x = []
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# self.y = []
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# for d in self.data:
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# self.x.append(d[0])
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# self.y.append(d[1])
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# self.x = np.array(self.x)
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# self.y = np.array(self.y)
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# def __getitem__(self, idx):
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# return self.x[idx], self.y[idx]
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# def __len__(self):
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# return self.x.shape[0]
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# class CIFAR100(cifar.Cifar100):
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# """
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# cifar10 dataset
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# """
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# def __init__(self, data_file, download=True, mode='train'):
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# super().__init__(download=download, mode=mode)
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# if data_file is not None:
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# os.makedirs(data_file, exist_ok=True)
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# if not os.path.exists(os.path.join(data_file, 'cifar-100-python.tar.gz')):
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# shutil.move('~/.cache/paddle/dataset/cifar/cifar-100-python.tar.gz', data_file)
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# self.num_classes = 100
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# self.x = []
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# self.y = []
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# for d in self.data:
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# self.x.append(d[0])
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# self.y.append(d[1])
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# self.x = np.array(self.x)
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# self.y = np.array(self.y)
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# def __getitem__(self, idx):
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# return self.x[idx], self.y[idx]
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# def __len__(self):
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# return self.x.shape[0]
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# class CIFAR10SSL(CIFAR10):
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# """
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# from Cifar10
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# """
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# def __init__(self,
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# data_file=None,
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# sample_per_label=None,
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# download=True,
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# expand_labels=1,
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# mode='train',
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# transform_ops=None,
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# transform_w=None,
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# transform_s1=None,
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# transform_s2=None):
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# super().__init__(data_file, download=download, mode=mode)
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# self.data_type = 'unlabeled_train' if mode == 'train' else 'val'
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# if transform_ops is not None and sample_per_label is not None:
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# index = []
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# self.data_type = 'labeled_train'
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# for c in range(self.num_classes):
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# idx = np.where(self.y == c)[0]
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# idx = np.random.choice(idx, sample_per_label, False)
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# index.extend(idx)
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# index = index * expand_labels
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# # print(index)
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# self.x = self.x[index]
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# self.y = self.y[index]
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# self.transforms = [transform_ops] if transform_ops is not None else [transform_w, transform_s1, transform_s2]
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# self.mode = mode
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# def __getitem__(self, idx):
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# img, label = np_convert_pil(self.x[idx]), self.y[idx]
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# results = ListTransform(self.transforms)(img)
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# if self.data_type == 'unlabeled_train':
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# return results
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# return results[0], label
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# def __len__(self):
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# return self.x.shape[0]
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# class CIFAR100SSL(CIFAR100):
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# """
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# from Cifar100
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# """
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# def __init__(self,
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# data_file=None,
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# sample_per_label=None,
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# download=True,
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# expand_labels=1,
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# mode='train',
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# transform_ops=None,
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# transform_w=None,
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# transform_s1=None,
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# transform_s2=None):
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# super().__init__(data_file, download=download, mode=mode)
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# self.data_type = 'unlabeled_train' if mode == 'train' else 'val'
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# if transform_ops is not None and sample_per_label is not None:
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# index = []
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# self.data_type = 'labeled_train'
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# for c in range(self.num_classes):
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# idx = np.where(self.y == c)[0]
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# idx = np.random.choice(idx, sample_per_label, False)
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# index.extend(idx)
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# index = index * expand_labels
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# # print(index)
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# self.x = self.x[index]
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# self.y = self.y[index]
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# self.transforms = [transform_ops] if transform_ops is not None else [transform_w, transform_s1, transform_s2]
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# self.mode = mode
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# def __getitem__(self, idx):
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# img, label = np_convert_pil(self.x[idx]), self.y[idx]
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# results = ListTransform(self.transforms)(img)
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# if self.data_type == 'unlabeled_train':
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# return results
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# return results[0], label
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# def __len__(self):
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# return self.x.shape[0]
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# def x_u_split(num_labeled, num_classes, label):
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# """
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# split index of dataset to labeled x and unlabeled u
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# Args:
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# num_labeled: num of labeled dataset
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# label: list or array, label
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# """
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# assert num_labeled <= len(label), "arg num_labeled should <= num of label"
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# label = np.array(label) if isinstance(label, list) else label
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# label_per_class = num_labeled // num_classes
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# labeled_idx = []
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# unlabeled_idx = np.array(list(range(label.shape[0])))
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# for c in range(num_classes):
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# idx = np.where(label == c)[0]
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# idx = np.random.choice(idx, label_per_class, False)
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# labeled_idx.extend(idx)
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# np.random.shuffle(labeled_idx)
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# return labeled_idx, unlabeled_idx
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return (image2, image3, np.int64(label))
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@ -56,8 +56,6 @@ from ppcls.data.preprocess.batch_ops.batch_operators import MixupCutmixHybrid
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import numpy as np
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from PIL import Image
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import random
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from paddle.vision.transforms import transforms as T
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from paddle.vision.transforms.transforms import RandomCrop, ToTensor, Normalize
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def transform(data, ops=[]):
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@ -123,41 +121,4 @@ class TimmAutoAugment(RawTimmAutoAugment):
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img = np.asarray(img)
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return img
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# class BaseTransform:
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# def __init__(self, cfg) -> None:
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# """
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# Args:
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# cfg: list [dict, dict, dict]
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# """
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# ts = []
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# for op in cfg:
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# name = list(op.keys())[0]
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# if op[name] is None:
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# ts.append(eval(name)())
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# else:
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# ts.append(eval(name)(**(op[name])))
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# self.t = T.Compose(ts)
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# def __call__(self, img):
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# return self.t(img)
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# class ListTransform:
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# def __init__(self, ops) -> None:
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# """
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# Args:
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# ops: list[list[dict, dict], ...]
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# """
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# self.ts = []
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# for op in ops:
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# self.ts.append(BaseTransform(op))
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# def __call__(self, img):
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# results = []
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# for op in self.ts:
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# results.append(op(img))
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# return results
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@ -22,7 +22,6 @@ from .pairwisecosface import PairwiseCosface
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from .dmlloss import DMLLoss
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from .distanceloss import DistanceLoss
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from .softtargetceloss import SoftTargetCrossEntropy
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from .ccssl_loss import CCSSLLoss
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from .distillationloss import DistillationCELoss
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from .distillationloss import DistillationGTCELoss
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from .distillationloss import DistillationDMLLoss
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@ -1,53 +1,14 @@
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from ppcls.engine.train.train import forward
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from .softsuploss import SoftSupConLoss
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import copy
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import paddle.nn as nn
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class CCSSLLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(CCSSLLoss, self).__init__()
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ce_cfg = copy.deepcopy(kwargs['CELoss'])
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self.ce_weight = ce_cfg.pop('weight')
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softsupconloss_cfg = copy.deepcopy(kwargs['SoftSupConLoss'])
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self.softsupconloss_weight = softsupconloss_cfg.pop('weight')
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self.softsuploss = SoftSupConLoss(**softsupconloss_cfg)
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self.celoss = nn.CrossEntropyLoss(reduction='none')
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def forward(self, feats, batch, **kwargs):
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"""
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Args:
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feats: feature of s1 and s2, (n, 2, d)
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batch: dict
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"""
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logits_w = batch['logits_w']
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logits_s1 = batch['logits_s1']
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p_targets_u_w = batch['p_targets_u_w']
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mask = batch['mask']
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max_probs = batch['max_probs']
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# reduction = batch['reduction']
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loss_u = self.celoss(logits_s1, p_targets_u_w) * mask
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loss_u = loss_u.mean()
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loss_c = self.softsuploss(feats, max_probs, p_targets_u_w)
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return {'CCSSLLoss': self.ce_weight*loss_u + self.softsupconloss_weight * loss_c}
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class CCSSLCeLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(CCSSLCeLoss, self).__init__()
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self.celoss = nn.CrossEntropyLoss(reduction='none')
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def forward(self, inputs, batch, **kwargs):
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p_targets_u_w = batch['p_targets_u_w']
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logits_s1 = batch['logits_s1']
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@ -56,6 +17,3 @@ class CCSSLCeLoss(nn.Layer):
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loss_u = loss_u.mean()
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return {'CCSSLCeLoss': loss_u}
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@ -41,7 +41,6 @@ class SoftSupConLoss(nn.Layer):
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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}
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue