cae config fix

ppcls/arch/backbone/model_zoo/cae.py

@@ -630,17 +630,17 @@ def _load_pretrained(pretrained,
                      model,
                      model_keys,
                      model_ema_configs,
-                     abs_pos_emb,
-                     rel_pos_bias,
+                     use_abs_pos_emb,
+                     use_rel_pos_bias,
                      use_ssld=False):
     if pretrained is False:
-        pass
+        return
     elif pretrained is True:
         local_weight_path = get_weights_path_from_url(pretrained_url).replace(
             ".pdparams", "")
         checkpoint = paddle.load(local_weight_path + ".pdparams")
     elif isinstance(pretrained, str):
-        checkpoint = paddle.load(local_weight_path + ".pdparams")
+        checkpoint = paddle.load(pretrained + ".pdparams")
 
     checkpoint_model = None
     for model_key in model_keys.split('|'):
@@ -693,10 +693,10 @@ def _load_pretrained(pretrained,
         if "relative_position_index" in key:
             checkpoint_model.pop(key)
 
-        if "relative_position_bias_table" in key and rel_pos_bias:
+        if "relative_position_bias_table" in key and use_rel_pos_bias:
             rel_pos_bias = checkpoint_model[key]
-            src_num_pos, num_attn_heads = rel_pos_bias.size()
-            dst_num_pos, _ = model.state_dict()[key].size()
+            src_num_pos, num_attn_heads = rel_pos_bias.shape
+            dst_num_pos, _ = model.state_dict()[key].shape
             dst_patch_shape = model.patch_embed.patch_shape
             if dst_patch_shape[0] != dst_patch_shape[1]:
                 raise NotImplementedError()
@@ -742,8 +742,8 @@ def _load_pretrained(pretrained,
                                                 src_size).float().numpy()
                     f = interpolate.interp2d(x, y, z, kind='cubic')
                     all_rel_pos_bias.append(
-                        paddle.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(
-                            rel_pos_bias.device))
+                        paddle.Tensor(f(dx, dy)).astype('float32').reshape(
+                            [-1, 1]))
 
                 rel_pos_bias = paddle.concat(all_rel_pos_bias, axis=-1)
 
@@ -752,7 +752,7 @@ def _load_pretrained(pretrained,
                 checkpoint_model[key] = new_rel_pos_bias
 
     # interpolate position embedding
-    if 'pos_embed' in checkpoint_model and abs_pos_emb:
+    if 'pos_embed' in checkpoint_model and use_abs_pos_emb:
         pos_embed_checkpoint = checkpoint_model['pos_embed']
         embedding_size = pos_embed_checkpoint.shape[-1]
         num_patches = model.patch_embed.num_patches
@@ -791,8 +791,8 @@ def cae_base_patch16_224(pretrained=True, use_ssld=False, **kwargs):
     enable_linear_eval = config.pop('enable_linear_eval')
     model_keys = config.pop('model_key')
     model_ema_configs = config.pop('model_ema')
-    abs_pos_emb = config.pop('abs_pos_emb')
-    rel_pos_bias = config.pop('rel_pos_bias')
+    use_abs_pos_emb = config.get('use_abs_pos_emb', False)
+    use_rel_pos_bias = config.get('use_rel_pos_bias', True)
     if pretrained in config:
         pretrained = config.pop('pretrained')
 
@@ -816,8 +816,8 @@ def cae_base_patch16_224(pretrained=True, use_ssld=False, **kwargs):
         model,
         model_keys,
         model_ema_configs,
-        abs_pos_emb,
-        rel_pos_bias,
+        use_abs_pos_emb,
+        use_rel_pos_bias,
         use_ssld=False)
 
     return model
@@ -828,8 +828,8 @@ def cae_large_patch16_224(pretrained=True, use_ssld=False, **kwargs):
     enable_linear_eval = config.pop('enable_linear_eval')
     model_keys = config.pop('model_key')
     model_ema_configs = config.pop('model_ema')
-    abs_pos_emb = config.pop('abs_pos_emb')
-    rel_pos_bias = config.pop('rel_pos_bias')
+    use_abs_pos_emb = config.get('use_abs_pos_emb', False)
+    use_rel_pos_bias = config.get('use_rel_pos_bias', True)
     if pretrained in config:
         pretrained = config.pop('pretrained')
 
@@ -853,8 +853,8 @@ def cae_large_patch16_224(pretrained=True, use_ssld=False, **kwargs):
         model,
         model_keys,
         model_ema_configs,
-        abs_pos_emb,
-        rel_pos_bias,
+        use_abs_pos_emb,
+        use_rel_pos_bias,
         use_ssld=False)
 
     return model

ppcls/configs/CAE/cae_base_patch16_224_finetune.yaml

@@ -31,10 +31,8 @@ Arch:
 
   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
@@ -83,23 +81,27 @@ DataLoader:
         - DecodeImage:
             to_rgb: True
             channel_first: False
-        - RandCropImage:
+        - RandomResizedCrop:
             size: 224
-            interpolation: bilinear
-        - RandFlipImage:
-            flip_code: 1
-        - RandAugment:
+        - RandomHorizontalFlip:
+            prob: 0.5
+        - TimmAutoAugment:
+            config_str: rand-m9-mstd0.5-inc1
+            interpolation: bicubic
+            img_size: 224
         - NormalizeImage:
             scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
             order: ''
         - RandomErasing:
-            EPSILON: 0.5
+            EPSILON: 0.25
             sl: 0.02
-            sh: 0.3
+            sh: 1.0/3.0
             r1: 0.3
-
+            attempt: 10
+            use_log_aspect: True
+            mode: pixel
     sampler:
       name: DistributedBatchSampler
       batch_size: 16
@@ -110,7 +112,7 @@ DataLoader:
       use_shared_memory: True
 
   Eval:
-    dataset: 
+    dataset:
       name: ImageNetDataset
       image_root: ./dataset/flowers102/
       cls_label_path: ./dataset/flowers102/val_list.txt
@@ -124,8 +126,8 @@ DataLoader:
             size: 224
         - NormalizeImage:
             scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
             order: ''
     sampler:
       name: DistributedBatchSampler

ppcls/configs/CAE/cae_large_patch16_224_finetune.yaml

@@ -31,10 +31,8 @@ Arch:
 
   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
@@ -83,23 +81,27 @@ DataLoader:
         - DecodeImage:
             to_rgb: True
             channel_first: False
-        - RandCropImage:
+        - RandomResizedCrop:
             size: 224
-            interpolation: bilinear
-        - RandFlipImage:
-            flip_code: 1
-        - RandAugment:
+        - RandomHorizontalFlip:
+            prob: 0.5
+        - TimmAutoAugment:
+            config_str: rand-m9-mstd0.5-inc1
+            interpolation: bicubic
+            img_size: 224
         - NormalizeImage:
             scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
             order: ''
         - RandomErasing:
-            EPSILON: 0.5
+            EPSILON: 0.25
             sl: 0.02
-            sh: 0.3
+            sh: 1.0/3.0
             r1: 0.3
-
+            attempt: 10
+            use_log_aspect: True
+            mode: pixel
     sampler:
       name: DistributedBatchSampler
       batch_size: 16
@@ -110,7 +112,7 @@ DataLoader:
       use_shared_memory: True
 
   Eval:
-    dataset: 
+    dataset:
       name: ImageNetDataset
       image_root: ./dataset/flowers102/
       cls_label_path: ./dataset/flowers102/val_list.txt
@@ -124,8 +126,8 @@ DataLoader:
             size: 224
         - NormalizeImage:
             scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
             order: ''
     sampler:
       name: DistributedBatchSampler