PP-HGNet_base.yaml 3.6 KB

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  1. # global configs
  2. Global:
  3. checkpoints: null
  4. pretrained_model: null
  5. output_dir: ./output/
  6. device: gpu
  7. save_interval: 1
  8. eval_during_train: True
  9. eval_interval: 1
  10. epochs: 600
  11. print_batch_step: 10
  12. use_visualdl: False
  13. # used for static mode and model export
  14. image_shape: [3, 224, 224]
  15. save_inference_dir: ./inference
  16. # training model under @to_static
  17. to_static: False
  18. use_dali: False
  19. # mixed precision training
  20. AMP:
  21. use_amp: True
  22. use_fp16_test: False
  23. scale_loss: 128.0
  24. use_dynamic_loss_scaling: True
  25. use_promote: False
  26. # O1: mixed fp16, O2: pure fp16
  27. level: O1
  28. # model architecture
  29. Arch:
  30. name: PPHGNet_base
  31. class_num: 102
  32. # loss function config for traing/eval process
  33. Loss:
  34. Train:
  35. - CELoss:
  36. weight: 1.0
  37. epsilon: 0.1
  38. Eval:
  39. - CELoss:
  40. weight: 1.0
  41. Optimizer:
  42. name: Momentum
  43. momentum: 0.9
  44. lr:
  45. name: Cosine
  46. learning_rate: 0.5
  47. warmup_epoch: 5
  48. regularizer:
  49. name: 'L2'
  50. coeff: 0.00004
  51. # data loader for train and eval
  52. DataLoader:
  53. Train:
  54. dataset:
  55. name: ImageNetDataset
  56. image_root: ./dataset/ILSVRC2012/
  57. cls_label_path: ./dataset/ILSVRC2012/train_list.txt
  58. transform_ops:
  59. - DecodeImage:
  60. to_rgb: True
  61. channel_first: False
  62. - RandCropImage:
  63. size: 224
  64. interpolation: bicubic
  65. backend: pil
  66. - RandFlipImage:
  67. flip_code: 1
  68. - TimmAutoAugment:
  69. config_str: rand-m15-mstd0.5-inc1
  70. interpolation: bicubic
  71. img_size: 224
  72. - NormalizeImage:
  73. scale: 1.0/255.0
  74. mean: [0.485, 0.456, 0.406]
  75. std: [0.229, 0.224, 0.225]
  76. order: ''
  77. - RandomErasing:
  78. EPSILON: 0.4
  79. sl: 0.02
  80. sh: 1.0/3.0
  81. r1: 0.3
  82. attempt: 10
  83. use_log_aspect: True
  84. mode: pixel
  85. batch_transform_ops:
  86. - OpSampler:
  87. MixupOperator:
  88. alpha: 0.4
  89. prob: 0.5
  90. CutmixOperator:
  91. alpha: 1.0
  92. prob: 0.5
  93. sampler:
  94. name: DistributedBatchSampler
  95. batch_size: 128
  96. drop_last: False
  97. shuffle: True
  98. loader:
  99. num_workers: 16
  100. use_shared_memory: True
  101. Eval:
  102. dataset:
  103. name: ImageNetDataset
  104. image_root: ./dataset/ILSVRC2012/
  105. cls_label_path: ./dataset/ILSVRC2012/val_list.txt
  106. transform_ops:
  107. - DecodeImage:
  108. to_rgb: True
  109. channel_first: False
  110. - ResizeImage:
  111. resize_short: 236
  112. interpolation: bicubic
  113. backend: pil
  114. - CropImage:
  115. size: 224
  116. - NormalizeImage:
  117. scale: 1.0/255.0
  118. mean: [0.485, 0.456, 0.406]
  119. std: [0.229, 0.224, 0.225]
  120. order: ''
  121. sampler:
  122. name: DistributedBatchSampler
  123. batch_size: 128
  124. drop_last: False
  125. shuffle: False
  126. loader:
  127. num_workers: 16
  128. use_shared_memory: True
  129. Infer:
  130. infer_imgs: docs/images/inference_deployment/whl_demo.jpg
  131. batch_size: 10
  132. transforms:
  133. - DecodeImage:
  134. to_rgb: True
  135. channel_first: False
  136. - ResizeImage:
  137. resize_short: 236
  138. interpolation: bicubic
  139. backend: pil
  140. - CropImage:
  141. size: 224
  142. - NormalizeImage:
  143. scale: 1.0/255.0
  144. mean: [0.485, 0.456, 0.406]
  145. std: [0.229, 0.224, 0.225]
  146. order: ''
  147. - ToCHWImage:
  148. PostProcess:
  149. name: Topk
  150. topk: 5
  151. class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
  152. Metric:
  153. Train:
  154. - TopkAcc:
  155. topk: [1, 5]
  156. Eval:
  157. - TopkAcc:
  158. topk: [1, 5]