Adjust arg order for recent vit model args, add a few comments

timm/models/vision_transformer.py

@@ -325,8 +325,8 @@ class VisionTransformer(nn.Module):
     def __init__(
             self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
             embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
-            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='', class_token=True,
-            fc_norm=None, embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
+            class_token=True, fc_norm=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='',
+            embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
         """
         Args:
             img_size (int, tuple): input image size
@@ -340,12 +340,12 @@ class VisionTransformer(nn.Module):
             mlp_ratio (int): ratio of mlp hidden dim to embedding dim
             qkv_bias (bool): enable bias for qkv if True
             init_values: (float): layer-scale init values
+            class_token (bool): use class token
+            fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
             drop_rate (float): dropout rate
             attn_drop_rate (float): attention dropout rate
             drop_path_rate (float): stochastic depth rate
             weight_init (str): weight init scheme
-            class_token (bool): use class token
-            fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
             embed_layer (nn.Module): patch embedding layer
             norm_layer: (nn.Module): normalization layer
             act_layer: (nn.Module): MLP activation layer

timm/models/vision_transformer_relpos.py

@@ -240,13 +240,19 @@ class ResPostRelPosBlock(nn.Module):
 
 class VisionTransformerRelPos(nn.Module):
     """ Vision Transformer w/ Relative Position Bias
+
+    Differing from classic vit, this impl
+      * uses relative position index (swin v1 / beit) or relative log coord + mlp (swin v2) pos embed
+      * defaults to no class token (can be enabled)
+      * defaults to global avg pool for head (can be changed)
+      * layer-scale (residual branch gain) enabled
     """
 
     def __init__(
             self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
-            embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
-            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', class_token=False,
-            rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
+            embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-5,
+            class_token=False, rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
+            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip',
             embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
         """
         Args:
@@ -254,21 +260,21 @@ class VisionTransformerRelPos(nn.Module):
             patch_size (int, tuple): patch size
             in_chans (int): number of input channels
             num_classes (int): number of classes for classification head
-            global_pool (str): type of global pooling for final sequence (default: 'token')
+            global_pool (str): type of global pooling for final sequence (default: 'avg')
             embed_dim (int): embedding dimension
             depth (int): depth of transformer
             num_heads (int): number of attention heads
             mlp_ratio (int): ratio of mlp hidden dim to embedding dim
             qkv_bias (bool): enable bias for qkv if True
             init_values: (float): layer-scale init values
+            class_token (bool): use class token (default: False)
+            rel_pos_ty pe (str): type of relative position
+            shared_rel_pos (bool): share relative pos across all blocks
+            fc_norm (bool): use pre classifier norm instead of pre-pool
             drop_rate (float): dropout rate
             attn_drop_rate (float): attention dropout rate
             drop_path_rate (float): stochastic depth rate
             weight_init (str): weight init scheme
-            class_token (bool): use class token (default: False)
-            rel_pos_ty pe (str): type of relative position
-            shared_rel_pos (bool): share relative pos across all blocks
-            fc_norm (bool): use pre classifier norm
             embed_layer (nn.Module): patch embedding layer
             norm_layer: (nn.Module): normalization layer
             act_layer: (nn.Module): MLP activation layer
@@ -384,11 +390,10 @@ def _create_vision_transformer_relpos(variant, pretrained=False, **kwargs):
 
 @register_model
 def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/32+) w/ relative log-coord position  and residual post-norm, no class token
+    """ ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
     """
     model_kwargs = dict(
-        patch_size=32, embed_dim=896, depth=12, num_heads=14,  init_values=1e-5,
-        block_fn=ResPostRelPosBlock, **kwargs)
+        patch_size=32, embed_dim=896, depth=12, num_heads=14, block_fn=ResPostRelPosBlock, **kwargs)
     model = _create_vision_transformer_relpos(
         'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **model_kwargs)
     return model
@@ -398,7 +403,7 @@ def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs):
 def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
     """
-    model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, **kwargs)
     model = _create_vision_transformer_relpos('vit_relpos_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -408,8 +413,7 @@ def vit_relpos_base_patch16_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
-        fc_norm=True, **kwargs)
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, **kwargs)
     model = _create_vision_transformer_relpos('vit_relpos_base_patch16_224', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -419,7 +423,6 @@ def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
-        block_fn=ResPostRelPosBlock, **kwargs)
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs)
     model = _create_vision_transformer_relpos('vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
     return model