modify the cifar10 dataset format

ppcls/configs/DeepHash/DCH.yaml

@@ -62,8 +62,20 @@ Optimizer:
 DataLoader:
   Train:
     dataset:
-      name: CustomizedCifar10
-      mode: 'train'
+      name: ImageNetDataset
+      image_root: ./dataset/
+      cls_label_path: ./dataset/cifar10-2/train.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
     sampler:
       batch_size: 128
       drop_last: False
@@ -74,9 +86,21 @@ DataLoader:
 
   Eval:
     Query:
-      dataset: 
-        name: CustomizedCifar10
-        mode: 'test'
+      dataset:
+        name: ImageNetDataset
+        image_root: ./dataset/
+        cls_label_path: ./dataset/cifar10-2/test.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
       sampler:
         batch_size: 128
         drop_last: False
@@ -86,9 +110,21 @@ DataLoader:
         use_shared_memory: True
 
     Gallery:
-      dataset: 
-        name: CustomizedCifar10
-        mode: 'train'
+      dataset:
+        name: ImageNetDataset
+        image_root: ./dataset/
+        cls_label_path: ./dataset/cifar10-2/database.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
       sampler:
         batch_size: 128
         drop_last: False

ppcls/configs/DeepHash/DSHSD.yaml

@@ -63,8 +63,20 @@ Optimizer:
 DataLoader:
   Train:
     dataset:
-      name: CustomizedCifar10
-      mode: 'train'
+      name: ImageNetDataset
+      image_root: ./dataset/
+      cls_label_path: ./dataset/cifar10-2/train.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
     sampler:
       batch_size: 128
       drop_last: False
@@ -75,9 +87,21 @@ DataLoader:
 
   Eval:
     Query:
-      dataset: 
-        name: CustomizedCifar10
-        mode: 'test'
+      dataset:
+        name: ImageNetDataset
+        image_root: ./dataset/
+        cls_label_path: ./dataset/cifar10-2/test.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
       sampler:
         batch_size: 128
         drop_last: False
@@ -87,9 +111,21 @@ DataLoader:
         use_shared_memory: True
 
     Gallery:
-      dataset: 
-        name: CustomizedCifar10
-        mode: 'train'
+      dataset:
+        name: ImageNetDataset
+        image_root: ./dataset/
+        cls_label_path: ./dataset/cifar10-2/database.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
       sampler:
         batch_size: 128
         drop_last: False

ppcls/configs/DeepHash/LCDSH.yaml

@@ -59,8 +59,20 @@ Optimizer:
 DataLoader:
   Train:
     dataset:
-      name: CustomizedCifar10
-      mode: 'train'
+      name: ImageNetDataset
+      image_root: ./dataset/
+      cls_label_path: ./dataset/cifar10-2/train.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
     sampler:
       batch_size: 128
       drop_last: False
@@ -71,9 +83,21 @@ DataLoader:
 
   Eval:
     Query:
-      dataset: 
-        name: CustomizedCifar10
-        mode: 'test'
+      dataset:
+        name: ImageNetDataset
+        image_root: ./dataset/
+        cls_label_path: ./dataset/cifar10-2/test.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
       sampler:
         batch_size: 128
         drop_last: False
@@ -83,9 +107,21 @@ DataLoader:
         use_shared_memory: True
 
     Gallery:
-      dataset: 
-        name: CustomizedCifar10
-        mode: 'train'
+      dataset:
+        name: ImageNetDataset
+        image_root: ./dataset/
+        cls_label_path: ./dataset/cifar10-2/database.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
       sampler:
         batch_size: 128
         drop_last: False

ppcls/data/__init__.py

@@ -28,7 +28,6 @@ from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
 from ppcls.data.dataloader.logo_dataset import LogoDataset
 from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
 from ppcls.data.dataloader.mix_dataset import MixDataset
-from ppcls.data.dataloader.customized_cifar10 import CustomizedCifar10
 
 # sampler
 from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler

ppcls/data/dataloader/__init__.py

@@ -4,7 +4,6 @@ from ppcls.data.dataloader.common_dataset import create_operators
 from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
 from ppcls.data.dataloader.logo_dataset import LogoDataset
 from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
-from ppcls.data.dataloader.customized_cifar10 import CustomizedCifar10
 from ppcls.data.dataloader.mix_dataset import MixDataset
 from ppcls.data.dataloader.mix_sampler import MixSampler
 from ppcls.data.dataloader.pk_sampler import PKSampler

ppcls/data/dataloader/customized_cifar10.py

-#   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.
-
-import paddle
-from paddle.vision.datasets import Cifar10
-from paddle.vision import transforms
-from paddle.dataset.common import _check_exists_and_download
-
-import numpy as np
-import os
-from PIL import Image
-
-
-class CustomizedCifar10(Cifar10):
-    def __init__(self,
-                 data_file=None,
-                 mode='train',
-                 download=True,
-                 backend=None):
-        assert mode.lower() in ['train', 'test'], \
-            "mode should be 'train', 'test', but got {}".format(mode)
-        self.mode = mode.lower()
-
-        if backend is None:
-            backend = paddle.vision.get_image_backend()
-        if backend not in ['pil', 'cv2']:
-            raise ValueError(
-                "Expected backend are one of ['pil', 'cv2'], but got {}"
-                .format(backend))
-        self.backend = backend
-
-        self._init_url_md5_flag()
-
-        self.data_file = data_file
-        if self.data_file is None:
-            assert download, "data_file is not set and downloading automatically is disabled"
-            self.data_file = _check_exists_and_download(
-                data_file, self.data_url, self.data_md5, 'cifar', download)
-
-        self.transform = transforms.Compose([
-            transforms.Resize(224), transforms.ToTensor(),
-            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        ])
-
-        self._load_data()
-        self.dtype = paddle.get_default_dtype()
-
-    def __getitem__(self, index):
-        img, target = self.data[index]
-        img = np.reshape(img, [3, 32, 32])
-        img = img.transpose([1, 2, 0]).astype("uint8")
-        img = Image.fromarray(img)
-        img = self.transform(img)
-        return (img, target)

ppcls/loss/deephashloss.py

@@ -132,7 +132,7 @@ class DCHLoss(paddle.nn.Layer):
                            paddle.log(1 + self.gamma / d_hi_hj))
 
         all_one = paddle.ones_like(u, dtype="float32")
-        quantization_loss = paddle.log(1 + self.d(u.abs(), all_one) /
+        quantization_loss = paddle.log(1 + self.distance(u.abs(), all_one) /
                                        self.gamma)
 
         loss = cauchy_loss.mean() + self._lambda * quantization_loss.mean()

ppcls/optimizer/optimizer.py

@@ -49,13 +49,11 @@ class SGD(object):
                  learning_rate=0.001,
                  weight_decay=None,
                  grad_clip=None,
-                 name=None,
-                 multi_precision=False):
+                 name=None):
         self.learning_rate = learning_rate
         self.weight_decay = weight_decay
         self.grad_clip = grad_clip
         self.name = name
-        self.multi_precision = multi_precision
 
     def __call__(self, model_list):
         # model_list is None in static graph
@@ -65,8 +63,7 @@ class SGD(object):
                         parameters=parameters,
                         weight_decay=self.weight_decay,
                         grad_clip=self.grad_clip,
-                        name=self.name,
-                        multi_precision=self.multi_precision)
+                        name=self.name)
         return opt