Merge pull request #797 from FredHuang16/patch-6

add logo config 

ppcls/arch/gears/circlemargin.py

@@ -20,7 +20,7 @@ import paddle.nn.functional as F
 
 class CircleMargin(nn.Layer):
     def __init__(self, embedding_size, class_num, margin, scale):
-        super(CircleSoftmax, self).__init__()
+        super(CircleMargin, self).__init__()
         self.scale = scale
         self.margin = margin
         self.embedding_size = embedding_size

ppcls/configs/Logo/ResNet50_ReID.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output/"
+  device: "gpu"
+  class_num: 3000
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 120
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: "./inference"
+  eval_mode: "retrieval"
+
+# model architecture
+Arch:
+  name: "RecModel"
+  Backbone:
+    name: "ResNet50_last_stage_stride1"
+    pretrained: True
+  BackboneStopLayer:
+    name: "adaptive_avg_pool2d_0"
+  Neck:
+    name: "VehicleNeck"
+    in_channels: 2048
+    out_channels: 512
+  Head:
+    name: "CircleMargin"
+    margin: 0.35
+    scale:  64
+    embedding_size: 512
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+    - PairwiseCosface:
+        margin: 0.35
+        gamma: 64
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.01
+  regularizer:
+    name: 'L2'
+    coeff: 0.0001
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+        name: LogoDataset
+        image_root: "dataset/LogoDet-3K-crop/train/"
+        cls_label_path: "dataset/LogoDet-3K-crop/LogoDet-3K+train.txt"
+        transform_ops:
+          - ResizeImage:
+              size: 224
+          - RandFlipImage:
+              flip_code: 1
+          - AugMix:
+              prob: 0.5
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+          - RandomErasing:
+              EPSILON: 0.5
+    sampler:
+        name: DistributedRandomIdentitySampler
+        batch_size: 128
+        num_instances: 2
+        drop_last: False
+
+    loader:
+        num_workers: 6
+        use_shared_memory: False
+  Eval:
+    Query:
+      # TOTO: modify to the latest trainer
+      dataset:
+          name: LogoDataset
+          image_root: "dataset/LogoDet-3K-crop/val/"
+          cls_label_path: "LogoDet-3K-crop/LogoDet-3K+query.txt"
+          transform_ops:
+            - ResizeImage:
+                size: 224
+            - NormalizeImage:
+                scale: 0.00392157
+                mean: [0.485, 0.456, 0.406]
+                std: [0.229, 0.224, 0.225]
+                order: ''
+      sampler:
+          name: DistributedBatchSampler
+          batch_size: 128
+          drop_last: False
+          shuffle: False
+      loader:
+          num_workers: 10
+          use_shared_memory: False
+
+    Gallery:
+      # TOTO: modify to the latest trainer
+      dataset:
+          name: LogoDataset
+          image_root: "dataset/LogoDet-3K-crop/train/"
+          cls_label_path: "dataset/LogoDet-3K-crop/LogoDet-3K+gallery.txt"
+          transform_ops:
+            - ResizeImage:
+                size: 224
+            - NormalizeImage:
+                scale: 0.00392157
+                mean: [0.485, 0.456, 0.406]
+                std: [0.229, 0.224, 0.225]
+                order: ''
+      sampler:
+          name: DistributedBatchSampler
+          batch_size: 128
+          drop_last: False
+          shuffle: False
+      loader:
+          num_workers: 10
+          use_shared_memory: False
+
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]
+    - mAP: {}
+
+
+Infer:
+  infer_imgs: "docs/images/whl/demo.jpg"
+  batch_size: 10
+  transforms:
+      - DecodeImage:
+          to_rgb: True
+          channel_first: False
+      - ResizeImage:
+          resize_short: 224
+      - NormalizeImage:
+          scale: 1.0/255.0
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: ''
+      - ToCHWImage:

ppcls/data/__init__.py

@@ -25,8 +25,10 @@ from ppcls.data.dataloader.imagenet_dataset import ImageNetDataset
 from ppcls.data.dataloader.multilabel_dataset import MultiLabelDataset
 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
 
+
 # sampler
 from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler
 from ppcls.data.preprocess import transform

ppcls/data/dataloader/logo_dataset.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.
+
+from __future__ import print_function
+
+import io
+import tarfile
+import numpy as np
+from PIL import Image  #all use default backend
+
+import paddle
+from paddle.io import Dataset
+import pickle
+import os
+import cv2
+import random
+
+from .common_dataset import CommonDataset
+
+class LogoDataset(CommonDataset):
+    def _load_anno(self):
+        assert os.path.exists(self._cls_path)
+        assert os.path.exists(self._img_root)
+        self.images = []
+        self.labels = []
+        with open(self._cls_path) as fd:
+            lines = fd.readlines()
+            for l in lines:
+                l = l.strip().split("\t")
+                if l[0] == 'image_id':
+                    continue
+                self.images.append(os.path.join(self._img_root, l[3]))
+                self.labels.append(int(l[1])-1)
+                assert os.path.exists(self.images[-1])
+
+