chore: support onnx export

yolo_world/models/layers/yolo_bricks.py

@@ -11,6 +11,42 @@
 from mmyolo.registry import MODELS
 from mmyolo.models.layers import CSPLayerWithTwoConv
 
+#AdaptiveAvgPool2dCustom and AdaptiveMaxPool2dCustom are compatible when exporting onnx format models
+# reference: https://github.com/pytorch/pytorch/issues/42653#issuecomment-1168816422
+class AdaptiveAvgPool2dCustom(nn.Module):
+    def __init__(self, output_size):
+        super(AdaptiveAvgPool2dCustom, self).__init__()
+        self.output_size = torch.tensor(output_size)
+
+    def forward(self, x: torch.Tensor):
+        # Calculate the stride size required to achieve the desired output size
+        stride_size = torch.floor(torch.tensor(x.shape[-2:]) / self.output_size).to(torch.int32)
+
+        # Calculate the kernel size based on the stride size and desired output size
+        kernel_size = torch.tensor(x.shape[-2:]) - (self.output_size - 1) * stride_size
+
+        # Create a AvgPool2d layer with the calculated kernel and stride sizes
+        avg = nn.AvgPool2d(kernel_size.tolist(), stride=stride_size.tolist())
+
+        x = avg(x)
+        return x
+class AdaptiveMaxPool2dCustom(nn.Module):
+    def __init__(self, output_size):
+        super(AdaptiveMaxPool2dCustom, self).__init__()
+        self.output_size = torch.tensor(output_size)
+
+    def forward(self, x: torch.Tensor):
+        # Calculate the stride size required to achieve the desired output size
+        stride_size = torch.floor(torch.tensor(x.shape[-2:]) / self.output_size).to(torch.int32)
+
+        # Calculate the kernel size based on the stride size and desired output size
+        kernel_size = torch.tensor(x.shape[-2:]) - (self.output_size - 1) * stride_size
+
+        # Create a MaxPool2d layer with the calculated kernel and stride sizes
+        max_pool = nn.MaxPool2d(kernel_size.tolist(), stride=stride_size.tolist())
+
+        x = max_pool(x)
+        return x
 
 @MODELS.register_module()
 class MaxSigmoidAttnBlock(BaseModule):
@@ -31,7 +67,7 @@ def __init__(self,
                                              momentum=0.03,
                                              eps=0.001),
                  init_cfg: OptMultiConfig = None,
-                 use_einsum: bool = True) -> None:
+                 export_onnx: bool = True) -> None:
         super().__init__(init_cfg=init_cfg)
         conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
 
@@ -40,7 +76,7 @@ def __init__(self,
             'out_channels and embed_channels should be divisible by num_heads.'
         self.num_heads = num_heads
         self.head_channels = out_channels // num_heads
-        self.use_einsum = use_einsum
+        self.export_onnx = export_onnx
 
         self.embed_conv = ConvModule(
             in_channels,
@@ -73,8 +109,7 @@ def forward(self, x: Tensor, guide: Tensor) -> Tensor:
         guide = guide.reshape(B, -1, self.num_heads, self.head_channels)
         embed = self.embed_conv(x) if self.embed_conv is not None else x
         embed = embed.reshape(B, self.num_heads, self.head_channels, H, W)
-
-        if self.use_einsum:
+        if self.export_onnx == False:
             attn_weight = torch.einsum('bmchw,bnmc->bmhwn', embed, guide)
         else:
             batch, m, channel, height, width = embed.shape
@@ -116,7 +151,7 @@ def __init__(self,
                                              momentum=0.03,
                                              eps=0.001),
                  init_cfg: OptMultiConfig = None,
-                 use_einsum: bool = True) -> None:
+                 export_onnx: bool = True) -> None:
         super().__init__(init_cfg=init_cfg)
         conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
 
@@ -125,7 +160,7 @@ def __init__(self,
             'out_channels and embed_channels should be divisible by num_heads.'
         self.num_heads = num_heads
         self.head_channels = out_channels // num_heads
-        self.use_einsum = use_einsum
+        self.export_onnx = export_onnx
 
         self.embed_conv = ConvModule(
             in_channels,
@@ -272,7 +307,7 @@ def __init__(
             norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001),
             act_cfg: ConfigType = dict(type='SiLU', inplace=True),
             init_cfg: OptMultiConfig = None,
-            use_einsum: bool = True) -> None:
+            export_onnx: bool = True) -> None:
         super().__init__(in_channels=in_channels,
                          out_channels=out_channels,
                          expand_ratio=expand_ratio,
@@ -298,7 +333,7 @@ def __init__(
                                               with_scale=with_scale,
                                               conv_cfg=conv_cfg,
                                               norm_cfg=norm_cfg,
-                                              use_einsum=use_einsum)
+                                              export_onnx=export_onnx)
 
     def forward(self, x: Tensor, guide: Tensor) -> Tensor:
         """Forward process."""
@@ -328,7 +363,7 @@ def __init__(
             norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001),
             act_cfg: ConfigType = dict(type='SiLU', inplace=True),
             init_cfg: OptMultiConfig = None,
-            use_einsum: bool = True) -> None:
+            export_onnx: bool = True) -> None:
         super().__init__(in_channels=in_channels,
                          out_channels=out_channels,
                          expand_ratio=expand_ratio,
@@ -412,7 +447,7 @@ def __init__(
             with_scale=with_scale,
             conv_cfg=conv_cfg,
             norm_cfg=norm_cfg,
-            use_einsum=use_einsum)
+            export_onnx=export_onnx)
 
     def forward(self, x: Tensor, guide: Tensor) -> Tensor:
         """Forward process."""
@@ -434,7 +469,7 @@ def __init__(self,
                  num_feats: int = 3,
                  num_heads: int = 8,
                  pool_size: int = 3,
-                 use_einsum: bool = True):
+                 export_onnx: bool = True):
         super().__init__()
 
         self.text_channels = text_channels
@@ -443,7 +478,7 @@ def __init__(self,
         self.num_feats = num_feats
         self.head_channels = embed_channels // num_heads
         self.pool_size = pool_size
-        self.use_einsum = use_einsum
+        self.export_onnx = export_onnx
         if with_scale:
             self.scale = nn.Parameter(torch.tensor([0.]), requires_grad=True)
         else:
@@ -459,11 +494,16 @@ def __init__(self,
         self.value = nn.Sequential(nn.LayerNorm(embed_channels),
                                    Linear(embed_channels, embed_channels))
         self.proj = Linear(embed_channels, text_channels)
-
-        self.image_pools = nn.ModuleList([
-            nn.AdaptiveMaxPool2d((pool_size, pool_size))
-            for _ in range(num_feats)
-        ])
+        if self.export_onnx == False:
+            self.image_pools = nn.ModuleList([
+                nn.AdaptiveMaxPool2d((pool_size, pool_size))
+                for _ in range(num_feats)
+            ])
+        else:
+            self.image_pools = nn.ModuleList([
+                AdaptiveMaxPool2dCustom((pool_size, pool_size))
+                for _ in range(num_feats)
+            ])
 
     def forward(self, text_features, image_features):
         B = image_features[0].shape[0]
@@ -483,7 +523,7 @@ def forward(self, text_features, image_features):
         q = q.reshape(B, -1, self.num_heads, self.head_channels)
         k = k.reshape(B, -1, self.num_heads, self.head_channels)
         v = v.reshape(B, -1, self.num_heads, self.head_channels)
-        if self.use_einsum:
+        if self.export_onnx == False:
             attn_weight = torch.einsum('bnmc,bkmc->bmnk', q, k)
         else:
             q = q.permute(0, 2, 1, 3)
@@ -492,7 +532,7 @@ def forward(self, text_features, image_features):
 
         attn_weight = attn_weight / (self.head_channels**0.5)
         attn_weight = F.softmax(attn_weight, dim=-1)
-        if self.use_einsum:
+        if self.export_onnx == False:
             x = torch.einsum('bmnk,bkmc->bnmc', attn_weight, v)
         else:
             v = v.permute(0, 2, 1, 3)