Fix #2212 map florence2 image tower to davit with a few changes

timm/models/davit.py

@@ -34,7 +34,14 @@ class ConvPosEnc(nn.Module):
     def __init__(self, dim: int, k: int = 3, act: bool = False):
         super(ConvPosEnc, self).__init__()
 
-        self.proj = nn.Conv2d(dim, dim, k, 1, k // 2, groups=dim)
+        self.proj = nn.Conv2d(
+            dim,
+            dim,
+            kernel_size=k,
+            stride=1,
+            padding=k // 2,
+            groups=dim,
+        )
         self.act = nn.GELU() if act else nn.Identity()
 
     def forward(self, x: Tensor):
@@ -84,30 +91,65 @@ def __init__(
             self,
             in_chs,
             out_chs,
+            kernel_size=3,
             norm_layer=LayerNorm2d,
     ):
         super().__init__()
         self.in_chs = in_chs
         self.out_chs = out_chs
 
         self.norm = norm_layer(in_chs)
+        self.even_k = kernel_size % 2 == 0
         self.conv = nn.Conv2d(
             in_chs,
             out_chs,
-            kernel_size=2,
+            kernel_size=kernel_size,
             stride=2,
-            padding=0,
+            padding=0 if self.even_k else kernel_size // 2,
         )
 
     def forward(self, x: Tensor):
         B, C, H, W = x.shape
         x = self.norm(x)
-        x = F.pad(x, (0, (2 - W % 2) % 2))
-        x = F.pad(x, (0, 0, 0, (2 - H % 2) % 2))
+        if self.even_k:
+            k_h, k_w = self.conv.kernel_size
+            x = F.pad(x, (0, (k_w - W % k_w) % k_w))
+            x = F.pad(x, (0, 0, 0, (k_h - H % k_h) % k_h))
         x = self.conv(x)
         return x
 
 
+class ChannelAttentionV2(nn.Module):
+
+    def __init__(self, dim, num_heads=8, qkv_bias=True, dynamic_scale=True):
+        super().__init__()
+        self.groups = num_heads
+        self.head_dim = dim // num_heads
+        self.dynamic_scale = dynamic_scale
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x):
+        B, N, C = x.shape
+
+        qkv = self.qkv(x).reshape(B, N, 3, self.groups, C // self.groups).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+
+        if self.dynamic_scale:
+            q = q * float(N) ** -0.5
+        else:
+            q = q * self.head_dim ** -0.5
+        attn = q.transpose(-1, -2) @ k
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v.transpose(-1, -2)).transpose(-1, -2)
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        return x
+
+
+
 class ChannelAttention(nn.Module):
 
     def __init__(self, dim, num_heads=8, qkv_bias=False):
@@ -147,13 +189,19 @@ def __init__(
             norm_layer=nn.LayerNorm,
             ffn=True,
             cpe_act=False,
+            v2=False,
     ):
         super().__init__()
 
         self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
         self.ffn = ffn
         self.norm1 = norm_layer(dim)
-        self.attn = ChannelAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
+        attn_layer = ChannelAttentionV2 if v2 else ChannelAttention
+        self.attn = attn_layer(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+        )
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
         self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
 
@@ -372,21 +420,24 @@ def __init__(
             attn_types=('spatial', 'channel'),
             num_heads=3,
             window_size=7,
-            mlp_ratio=4,
+            mlp_ratio=4.,
             qkv_bias=True,
             drop_path_rates=(0, 0),
             norm_layer=LayerNorm2d,
             norm_layer_cl=nn.LayerNorm,
             ffn=True,
-            cpe_act=False
+            cpe_act=False,
+            down_kernel_size=2,
+            named_blocks=False,
+            channel_attn_v2=False,
     ):
         super().__init__()
 
         self.grad_checkpointing = False
 
         # downsample embedding layer at the beginning of each stage
         if downsample:
-            self.downsample = Downsample(in_chs, out_chs, norm_layer=norm_layer)
+            self.downsample = Downsample(in_chs, out_chs, kernel_size=down_kernel_size, norm_layer=norm_layer)
         else:
             self.downsample = nn.Identity()
 
@@ -399,10 +450,11 @@ def __init__(
         '''
         stage_blocks = []
         for block_idx in range(depth):
+            from collections import OrderedDict
             dual_attention_block = []
             for attn_idx, attn_type in enumerate(attn_types):
                 if attn_type == 'spatial':
-                    dual_attention_block.append(SpatialBlock(
+                    dual_attention_block.append(('spatial_block', SpatialBlock(
                         dim=out_chs,
                         num_heads=num_heads,
                         mlp_ratio=mlp_ratio,
@@ -412,19 +464,23 @@ def __init__(
                         ffn=ffn,
                         cpe_act=cpe_act,
                         window_size=window_size,
-                    ))
+                    )))
                 elif attn_type == 'channel':
-                    dual_attention_block.append(ChannelBlock(
+                    dual_attention_block.append(('channel_block', ChannelBlock(
                         dim=out_chs,
                         num_heads=num_heads,
                         mlp_ratio=mlp_ratio,
                         qkv_bias=qkv_bias,
                         drop_path=drop_path_rates[block_idx],
                         norm_layer=norm_layer_cl,
                         ffn=ffn,
-                        cpe_act=cpe_act
-                    ))
-            stage_blocks.append(nn.Sequential(*dual_attention_block))
+                        cpe_act=cpe_act,
+                        v2=channel_attn_v2,
+                    )))
+            if named_blocks:
+                stage_blocks.append(nn.Sequential(OrderedDict(dual_attention_block)))
+            else:
+                stage_blocks.append(nn.Sequential(*[b[1] for b in dual_attention_block]))
         self.blocks = nn.Sequential(*stage_blocks)
 
     @torch.jit.ignore
@@ -473,6 +529,9 @@ def __init__(
             attn_types=('spatial', 'channel'),
             ffn=True,
             cpe_act=False,
+            down_kernel_size=2,
+            channel_attn_v2=False,
+            named_blocks=False,
             drop_rate=0.,
             drop_path_rate=0.,
             num_classes=1000,
@@ -512,6 +571,9 @@ def __init__(
                 norm_layer_cl=norm_layer_cl,
                 ffn=ffn,
                 cpe_act=cpe_act,
+                down_kernel_size=down_kernel_size,
+                channel_attn_v2=channel_attn_v2,
+                named_blocks=named_blocks,
             )
             in_chs = out_chs
             stages.append(stage)
@@ -589,6 +651,34 @@ def forward(self, x):
         return x
 
 
+def _convert_florence2(state_dict, model, prefix='vision_tower.'):
+    import re
+    out_dict = {}
+
+    for k, v in state_dict.items():
+        if k.startswith(prefix):
+            k = k.replace(prefix, '')
+        else:
+            continue
+        k = re.sub(r'convs.([0-9]+)', r'stages.\1.downsample', k)
+        k = re.sub(r'blocks.([0-9]+)', r'stages.\1.blocks', k)
+        k = k.replace('downsample.proj', 'downsample.conv')
+        k = k.replace('stages.0.downsample', 'stem')
+        #k = k.replace('head.', 'head.fc.')
+        #k = k.replace('norms.', 'head.norm.')
+        k = k.replace('window_attn.norm.', 'norm1.')
+        k = k.replace('window_attn.fn.', 'attn.')
+        k = k.replace('channel_attn.norm.', 'norm1.')
+        k = k.replace('channel_attn.fn.', 'attn.')
+        k = k.replace('ffn.norm.', 'norm2.')
+        k = k.replace('ffn.fn.net.', 'mlp.')
+        k = k.replace('conv1.fn.dw', 'cpe1.proj')
+        k = k.replace('conv2.fn.dw', 'cpe2.proj')
+        out_dict[k] = v
+
+    return out_dict
+
+
 def checkpoint_filter_fn(state_dict, model):
     """ Remap MSFT checkpoints -> timm """
     if 'head.fc.weight' in state_dict:
@@ -597,6 +687,9 @@ def checkpoint_filter_fn(state_dict, model):
     if 'state_dict' in state_dict:
         state_dict = state_dict['state_dict']
 
+    if 'vision_tower.convs.0.proj.weight' in state_dict:
+        return _convert_florence2(state_dict, model)
+
     import re
     out_dict = {}
     for k, v in state_dict.items():
@@ -615,13 +708,17 @@ def checkpoint_filter_fn(state_dict, model):
 def _create_davit(variant, pretrained=False, **kwargs):
     default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
     out_indices = kwargs.pop('out_indices', default_out_indices)
-
+    strict = True
+    if variant.endswith('_fl'):
+        # FIXME cleaner approach to missing head norm?
+        strict = False
     model = build_model_with_cfg(
         DaVit,
         variant,
         pretrained,
         pretrained_filter_fn=checkpoint_filter_fn,
         feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
+        pretrained_strict=strict,
         **kwargs)
 
     return model
@@ -650,6 +747,12 @@ def _cfg(url='', **kwargs):
     'davit_large': _cfg(),
     'davit_huge': _cfg(),
     'davit_giant': _cfg(),
+    'davit_base_fl.msft_florence2': _cfg(
+        hf_hub_id='microsoft/Florence-2-base',
+        num_classes=0, input_size=(3, 768, 768)),
+    'davit_huge_fl.msft_florence2': _cfg(
+        hf_hub_id='microsoft/Florence-2-large',
+        num_classes=0, input_size=(3, 768, 768)),
 })
 
 
@@ -687,3 +790,23 @@ def davit_huge(pretrained=False, **kwargs) -> DaVit:
 def davit_giant(pretrained=False, **kwargs) -> DaVit:
     model_args = dict(depths=(1, 1, 12, 3), embed_dims=(384, 768, 1536, 3072), num_heads=(12, 24, 48, 96))
     return _create_davit('davit_giant', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+
+@register_model
+def davit_base_fl(pretrained=False, **kwargs) -> DaVit:
+    model_args = dict(
+        depths=(1, 1, 9, 1), embed_dims=(128, 256, 512, 1024), num_heads=(4, 8, 16, 32),
+        window_size=12, down_kernel_size=3, channel_attn_v2=True, named_blocks=True,
+    )
+    return _create_davit('davit_base_fl', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def davit_huge_fl(pretrained=False, **kwargs) -> DaVit:
+    # NOTE: huge image tower used in 'large' Florence2 model
+    model_args = dict(
+        depths=(1, 1, 9, 1), embed_dims=(256, 512, 1024, 2048), num_heads=(8, 16, 32, 64),
+        window_size=12, down_kernel_size=3, channel_attn_v2=True, named_blocks=True,
+    )
+    return _create_davit('davit_huge_fl', pretrained=pretrained, **dict(model_args, **kwargs))