diff --git a/ppcls/arch/backbone/model_zoo/swin_transformer_v2.py b/ppcls/arch/backbone/model_zoo/swin_transformer_v2.py
index d2f89446a..bfb4a17ba 100644
--- a/ppcls/arch/backbone/model_zoo/swin_transformer_v2.py
+++ b/ppcls/arch/backbone/model_zoo/swin_transformer_v2.py
@@ -422,8 +422,36 @@ class SwinTransformerBlock(nn.Layer):
 
         self.register_buffer("attn_mask", attn_mask)
 
-    def forward(self, x):
-        H, W = self.input_resolution
+    def get_attn_mask(self, height, width, dtype):
+        if self.shift_size > 0:
+            # calculate attention mask for shifted window multihead self attention
+            img_mask = paddle.zeros((1, height, width, 1), dtype=dtype)
+            height_slices = (
+                slice(0, -self.window_size),
+                slice(-self.window_size, -self.shift_size),
+                slice(-self.shift_size, None), )
+            width_slices = (
+                slice(0, -self.window_size),
+                slice(-self.window_size, -self.shift_size),
+                slice(-self.shift_size, None), )
+            count = 0
+            for height_slice in height_slices:
+                for width_slice in width_slices:
+                    img_mask[:, height_slice, width_slice, :] = count
+                    count += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)
+            mask_windows = mask_windows.reshape(
+                (-1, self.window_size * self.window_size))
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = masked_fill(attn_mask, attn_mask != 0, float(-100.0))
+            attn_mask = masked_fill(attn_mask, attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+        return attn_mask
+
+    def forward(self, x, input_dimensions):
+        H, W = input_dimensions
         B, L, C = x.shape
         assert L == H * W, "input feature has wrong size"
 
@@ -431,6 +459,12 @@ class SwinTransformerBlock(nn.Layer):
 
         x = x.reshape([B, H, W, C])
 
+        x, pad_values = pading_for_not_divisible(x, H, W, self.window_size,
+                                                 "BHWC")
+        _, height_pad, width_pad, _ = x.shape
+
+        padding_state = pad_values[3] > 0 or pad_values[
+            5] > 0  # change variable name
         # cyclic shift
         if self.shift_size > 0:
             shifted_x = paddle.roll(
@@ -511,16 +545,23 @@ class PatchMerging(nn.Layer):
         self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
         self.norm = norm_layer(2 * dim)
 
-    def forward(self, x):
+    def forward(self, x, input_dimensions):
         """
         x: B, H*W, C
         """
         H, W = input_dimensions
         B, L, C = x.shape
+        x = x.reshape([B, H, W, C])
+        x, _ = pading_for_not_divisible(x, H, W, 2, "BHWC", function="merge")
 
-        x = x.reshape([B, H // 2, 2, W // 2, 2, C])
-        x = x.transpose((0, 1, 3, 4, 2, 5))
-        x = x.reshape([B, H * W // 4, 4 * C])  # B H/2*W/2 4*C
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = paddle.reshape(x, x.shape)
+        x = paddle.concat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+
+        x = x.reshape([B,  -1, 4 * C])  # B H/2*W/2 4*C
         x = self.reduction(x)
         x = self.norm(x)
         return x
@@ -683,7 +724,11 @@ class PatchEmbed(nn.Layer):
         # FIXME look at relaxing size constraints
         assert H == self.img_size[0] and W == self.img_size[1], \
             f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose([0, 2, 1])  # B Ph*Pw C
+        x = self.proj(x)
+        _, _, height, width = x.shape
+        output_dimensions = (height, width)
+        x = x.flatten(2).transpose([0, 2, 1])  # B Ph*Pw C
+        # x = self.proj(x).flatten(2).transpose([0, 2, 1])  # B Ph*Pw C
         if self.norm is not None:
             x = self.norm(x)
         return x, output_dimensions