Merge pull request #2286 from zengshao0622/merge_CAE

fix cae_large

ppcls/arch/backbone/model_zoo/cae.py

@@ -842,7 +842,7 @@ def cae_large_patch16_224(pretrained=True, use_ssld=False, **kwargs):
         qkv_bias=True,
         norm_layer=partial(
             nn.LayerNorm, epsilon=1e-6),
-        **kwargs)
+        **config)
 
     if enable_linear_eval:
         _enable_linear_eval(model)

ppcls/configs/CAE/cae_large_patch16_224_finetune.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 20
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 100
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+# model architecture
+Arch:
+  name: cae_large_patch16_224
+  class_num: 102
+  drop_rate: 0.0
+  drop_path_rate: 0.2
+  attn_drop_rate: 0.0
+
+  use_mean_pooling: True
+  init_scale: 0.001
+  use_rel_pos_bias: True
+  use_abs_pos_emb: False
+  init_values: 0.1
+  lin_probe: False
+
+  sin_pos_emb: True
+
+  abs_pos_emb: False
+  enable_linear_eval: False
+  model_key: model|module|state_dict
+  rel_pos_bias: True
+  model_ema:
+    enable_model_ema: False 
+    model_ema_decay: 0.9999
+    model_ema_force_cpu: False
+  pretrained: True
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - SoftTargetCrossEntropy:
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: AdamWDL
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.05
+  layerwise_decay: 0.75
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    eta_min: 1e-6
+    warmup_epoch: 10
+    warmup_start_lr: 1e-6
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/flowers102/
+      cls_label_path: ./dataset/flowers102/train_list.txt
+      batch_transform_ops:
+        - MixupCutmixHybrid:
+            mixup_alpha: 0.8
+            cutmix_alpha: 1.0
+            switch_prob: 0.5
+            num_classes: 102
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+            interpolation: bilinear
+        - RandFlipImage:
+            flip_code: 1
+        - RandAugment:
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - RandomErasing:
+            EPSILON: 0.5
+            sl: 0.02
+            sh: 0.3
+            r1: 0.3
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 16
+      drop_last: True
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/flowers102/
+      cls_label_path: ./dataset/flowers102/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 16
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.5, 0.5, 0.5]
+        std: [0.5, 0.5, 0.5]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]