Swin-Free.patch

diff --git a/config.py b/config.py
index 88acdb6..bdb69f1 100644
--- a/config.py
+++ b/config.py
@@ -83,6 +83,20 @@ _C.MODEL.SWIN.QK_SCALE = None
 _C.MODEL.SWIN.APE = False
 _C.MODEL.SWIN.PATCH_NORM = True
 
+# Swin-Free Transformer parameters
+_C.MODEL.SWINFREE = CN()
+_C.MODEL.SWINFREE.PATCH_SIZE = 4
+_C.MODEL.SWINFREE.IN_CHANS = 3
+_C.MODEL.SWINFREE.EMBED_DIM = 96
+_C.MODEL.SWINFREE.DEPTHS = [2, 2, 6, 2]
+_C.MODEL.SWINFREE.NUM_HEADS = [3, 6, 12, 24]
+_C.MODEL.SWINFREE.WINDOW_SIZES = [7, 14, 14, 7]
+_C.MODEL.SWINFREE.MLP_RATIO = 4.
+_C.MODEL.SWINFREE.QKV_BIAS = True
+_C.MODEL.SWINFREE.QK_SCALE = None
+_C.MODEL.SWINFREE.APE = False
+_C.MODEL.SWINFREE.PATCH_NORM = True
+
 # Swin Transformer V2 parameters
 _C.MODEL.SWINV2 = CN()
 _C.MODEL.SWINV2.PATCH_SIZE = 4
diff --git a/configs/Swin-Free-BR/Swin-Free-BR-DR12_base_patch4_window7_224.yaml b/configs/Swin-Free-BR/Swin-Free-BR-DR12_base_patch4_window7_224.yaml
new file mode 100755
index 0000000..ec6881c
--- /dev/null
+++ b/configs/Swin-Free-BR/Swin-Free-BR-DR12_base_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free-BR
+  NAME: Swin-Free-BR-DR12_base_patch4_window7_224
+  DROP_PATH_RATE: 0.5
+  SWINFREE:
+    EMBED_DIM: 128
+    DEPTHS: [ 2, 2, 12, 2 ]
+    NUM_HEADS: [ 4, 8, 16, 32 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
diff --git a/configs/Swin-Free-BR/Swin-Free-BR_base_patch4_window7_224.yaml b/configs/Swin-Free-BR/Swin-Free-BR_base_patch4_window7_224.yaml
new file mode 100755
index 0000000..6ea5076
--- /dev/null
+++ b/configs/Swin-Free-BR/Swin-Free-BR_base_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free-BR
+  NAME: Swin-Free-BR_base_patch4_window7_224
+  DROP_PATH_RATE: 0.5
+  SWINFREE:
+    EMBED_DIM: 128
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 4, 8, 16, 32 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
\ No newline at end of file
diff --git a/configs/Swin-Free-BR/Swin-Free-BR_small_patch4_window7_224.yaml b/configs/Swin-Free-BR/Swin-Free-BR_small_patch4_window7_224.yaml
new file mode 100755
index 0000000..8a1c962
--- /dev/null
+++ b/configs/Swin-Free-BR/Swin-Free-BR_small_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free-BR
+  NAME: Swin-Free-BR_small_patch4_window7_224
+  DROP_PATH_RATE: 0.3
+  SWINFREE:
+    EMBED_DIM: 96
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 3, 6, 12, 24 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
\ No newline at end of file
diff --git a/configs/Swin-Free-BR/Swin-Free-BR_tiny_patch4_window7_224.yaml b/configs/Swin-Free-BR/Swin-Free-BR_tiny_patch4_window7_224.yaml
new file mode 100755
index 0000000..27b6b18
--- /dev/null
+++ b/configs/Swin-Free-BR/Swin-Free-BR_tiny_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free-BR
+  NAME: Swin-Free-BR_tiny_patch4_window7_224
+  DROP_PATH_RATE: 0.2
+  SWINFREE:
+    EMBED_DIM: 96
+    DEPTHS: [ 2, 2, 6, 2 ]
+    NUM_HEADS: [ 3, 6, 12, 24 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
\ No newline at end of file
diff --git a/configs/Swin-Free/Swin-Free-DR12_base_patch4_window7_224.yaml b/configs/Swin-Free/Swin-Free-DR12_base_patch4_window7_224.yaml
new file mode 100755
index 0000000..d36188f
--- /dev/null
+++ b/configs/Swin-Free/Swin-Free-DR12_base_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free
+  NAME: Swin-Free-DR12_base_patch4_window7_224
+  DROP_PATH_RATE: 0.5
+  SWINFREE:
+    EMBED_DIM: 128
+    DEPTHS: [ 2, 2, 12, 2 ]
+    NUM_HEADS: [ 4, 8, 16, 32 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
diff --git a/configs/Swin-Free/Swin-Free_base_patch4_window7_224.yaml b/configs/Swin-Free/Swin-Free_base_patch4_window7_224.yaml
new file mode 100755
index 0000000..da927fe
--- /dev/null
+++ b/configs/Swin-Free/Swin-Free_base_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free
+  NAME: Swin-Free_base_patch4_window7_224
+  DROP_PATH_RATE: 0.5
+  SWINFREE:
+    EMBED_DIM: 128
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 4, 8, 16, 32 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
\ No newline at end of file
diff --git a/configs/Swin-Free/Swin-Free_small_patch4_window7_224.yaml b/configs/Swin-Free/Swin-Free_small_patch4_window7_224.yaml
new file mode 100755
index 0000000..c1649df
--- /dev/null
+++ b/configs/Swin-Free/Swin-Free_small_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free
+  NAME: Swin-Free_small_patch4_window7_224
+  DROP_PATH_RATE: 0.3
+  SWINFREE:
+    EMBED_DIM: 96
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 3, 6, 12, 24 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
\ No newline at end of file
diff --git a/configs/Swin-Free/Swin-Free_tiny_patch4_window7_224.yaml b/configs/Swin-Free/Swin-Free_tiny_patch4_window7_224.yaml
new file mode 100755
index 0000000..3bc3307
--- /dev/null
+++ b/configs/Swin-Free/Swin-Free_tiny_patch4_window7_224.yaml
@@ -0,0 +1,9 @@
+MODEL:
+  TYPE: Swin-Free
+  NAME: Swin-Free_tiny_patch4_window7_224
+  DROP_PATH_RATE: 0.2
+  SWINFREE:
+    EMBED_DIM: 96
+    DEPTHS: [ 2, 2, 6, 2 ]
+    NUM_HEADS: [ 3, 6, 12, 24 ]
+    WINDOW_SIZES: [7, 14, 14, 7]
\ No newline at end of file
diff --git a/main.py b/main.py
index 7fa03ca..f91b1f2 100644
--- a/main.py
+++ b/main.py
@@ -104,7 +104,7 @@ def main(config):
     model_without_ddp = model
 
     optimizer = build_optimizer(config, model)
-    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
+    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False, find_unused_parameters=True)
     loss_scaler = NativeScalerWithGradNormCount()
 
     if config.TRAIN.ACCUMULATION_STEPS > 1:
diff --git a/models/build.py b/models/build.py
index c37384d..d1f6dcb 100644
--- a/models/build.py
+++ b/models/build.py
@@ -6,6 +6,8 @@
 # --------------------------------------------------------
 
 from .swin_transformer import SwinTransformer
+from .swin_free import SwinFree
+from .swin_free_br import SwinFreeBR
 from .swin_transformer_v2 import SwinTransformerV2
 from .swin_transformer_moe import SwinTransformerMoE
 from .swin_mlp import SwinMLP
@@ -50,6 +52,45 @@ def build_model(config, is_pretrain=False):
                                 patch_norm=config.MODEL.SWIN.PATCH_NORM,
                                 use_checkpoint=config.TRAIN.USE_CHECKPOINT,
                                 fused_window_process=config.FUSED_WINDOW_PROCESS)
+    elif model_type == 'Swin-Free':
+        model = SwinFree(img_size=config.DATA.IMG_SIZE,
+                                patch_size=config.MODEL.SWINFREE.PATCH_SIZE,
+                                in_chans=config.MODEL.SWINFREE.IN_CHANS,
+                                num_classes=config.MODEL.NUM_CLASSES,
+                                embed_dim=config.MODEL.SWINFREE.EMBED_DIM,
+                                depths=config.MODEL.SWINFREE.DEPTHS,
+                                num_heads=config.MODEL.SWINFREE.NUM_HEADS,
+                                window_sizes=config.MODEL.SWINFREE.WINDOW_SIZES,
+                                mlp_ratio=config.MODEL.SWINFREE.MLP_RATIO,
+                                qkv_bias=config.MODEL.SWINFREE.QKV_BIAS,
+                                qk_scale=config.MODEL.SWINFREE.QK_SCALE,
+                                drop_rate=config.MODEL.DROP_RATE,
+                                drop_path_rate=config.MODEL.DROP_PATH_RATE,
+                                ape=config.MODEL.SWINFREE.APE,
+                                norm_layer=layernorm,
+                                patch_norm=config.MODEL.SWINFREE.PATCH_NORM,
+                                use_checkpoint=config.TRAIN.USE_CHECKPOINT,
+                                fused_window_process=config.FUSED_WINDOW_PROCESS)
+    elif model_type == 'Swin-Free-BR':
+        model = SwinFreeBR(img_size=config.DATA.IMG_SIZE,
+                                patch_size=config.MODEL.SWINFREE.PATCH_SIZE,
+                                in_chans=config.MODEL.SWINFREE.IN_CHANS,
+                                num_classes=config.MODEL.NUM_CLASSES,
+                                embed_dim=config.MODEL.SWINFREE.EMBED_DIM,
+                                depths=config.MODEL.SWINFREE.DEPTHS,
+                                num_heads=config.MODEL.SWINFREE.NUM_HEADS,
+                                window_sizes=config.MODEL.SWINFREE.WINDOW_SIZES,
+                                mlp_ratio=config.MODEL.SWINFREE.MLP_RATIO,
+                                qkv_bias=config.MODEL.SWINFREE.QKV_BIAS,
+                                qk_scale=config.MODEL.SWINFREE.QK_SCALE,
+                                drop_rate=config.MODEL.DROP_RATE,
+                                drop_path_rate=config.MODEL.DROP_PATH_RATE,
+                                ape=config.MODEL.SWINFREE.APE,
+                                norm_layer=nn.BatchNorm1d,
+                                patch_norm=config.MODEL.SWINFREE.PATCH_NORM,
+                                use_checkpoint=config.TRAIN.USE_CHECKPOINT,
+                                fused_window_process=config.FUSED_WINDOW_PROCESS)
+
     elif model_type == 'swinv2':
         model = SwinTransformerV2(img_size=config.DATA.IMG_SIZE,
                                   patch_size=config.MODEL.SWINV2.PATCH_SIZE,
diff --git a/models/swin_free.py b/models/swin_free.py
new file mode 100755
index 0000000..bcd6c53
--- /dev/null
+++ b/models/swin_free.py
@@ -0,0 +1,611 @@
+###########################################################
+# Swin-Free Transformer
+# Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Licensed under NSCLv1 [see LICENSE-NSCLv1 for details]
+###########################################################
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+try:
+    import os, sys
+
+    kernel_path = os.path.abspath(os.path.join('..'))
+    sys.path.append(kernel_path)
+    from kernels.window_process.window_process import WindowProcess, WindowProcessReverse
+
+except:
+    WindowProcess = None
+    WindowProcessReverse = None
+    print("[Warning] Fused window process have not been installed. Please refer to get_started.md for installation.")
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+                 fused_window_process=False):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+        self.fused_window_process = fused_window_process
+
+    def forward(self, x):
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            if not self.fused_window_process:
+                shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+                # partition windows
+                x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+            else:
+                x_windows = WindowProcess.apply(x, B, H, W, C, -self.shift_size, self.window_size)
+        else:
+            shifted_x = x
+            # partition windows
+            x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            if not self.fused_window_process:
+                shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+                x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+            else:
+                x = WindowProcessReverse.apply(attn_windows, B, H, W, C, self.shift_size, self.window_size)
+        else:
+            shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+            x = shifted_x
+        x = x.view(B, H * W, C)
+        x = shortcut + self.drop_path(x)
+
+        # FFN
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, 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 = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 fused_window_process=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0, # do not use the shifted window
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer,
+                                 fused_window_process=fused_window_process)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # 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(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
+
+
+class SwinFree(nn.Module):
+    r""" Swin-Free Transformer
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 224
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_sizes (tuple(int)): Window size at each stage.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
+                 window_sizes=[7, 14, 14, 7], mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, fused_window_process=False, **kwargs):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
+                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                                 patches_resolution[1] // (2 ** i_layer)),
+                               depth=depths[i_layer],
+                               num_heads=num_heads[i_layer],
+                               window_size=window_sizes[i_layer],
+                               mlp_ratio=self.mlp_ratio,
+                               qkv_bias=qkv_bias, qk_scale=qk_scale,
+                               drop=drop_rate, attn_drop=attn_drop_rate,
+                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                               norm_layer=norm_layer,
+                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                               use_checkpoint=use_checkpoint,
+                               fused_window_process=fused_window_process)
+            self.layers.append(layer)
+
+        self.norm = norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+
+        x = self.norm(x)  # B L C
+        x = self.avgpool(x.transpose(1, 2))  # B C 1
+        x = torch.flatten(x, 1)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
+        flops += self.num_features * self.num_classes
+        return flops
diff --git a/models/swin_free_br.py b/models/swin_free_br.py
new file mode 100755
index 0000000..f333294
--- /dev/null
+++ b/models/swin_free_br.py
@@ -0,0 +1,614 @@
+###########################################################
+# Swin-Free Transformer
+# Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Licensed under NSCLv1 [see LICENSE-NSCLv1 for details]
+###########################################################
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+try:
+    import os, sys
+
+    kernel_path = os.path.abspath(os.path.join('..'))
+    sys.path.append(kernel_path)
+    from kernels.window_process.window_process import WindowProcess, WindowProcessReverse
+
+except:
+    WindowProcess = None
+    WindowProcessReverse = None
+    print("[Warning] Fused window process have not been installed. Please refer to get_started.md for installation.")
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.ReLU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.BatchNorm1d
+        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.ReLU, norm_layer=nn.BatchNorm1d,
+                 fused_window_process=False):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        H, W = self.input_resolution
+        self.norm1 = norm_layer(H*W)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(H*W)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+        self.fused_window_process = fused_window_process
+
+    def forward(self, x):
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            if not self.fused_window_process:
+                shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+                # partition windows
+                x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+            else:
+                x_windows = WindowProcess.apply(x, B, H, W, C, -self.shift_size, self.window_size)
+        else:
+            shifted_x = x
+            # partition windows
+            x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            if not self.fused_window_process:
+                shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+                x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+            else:
+                x = WindowProcessReverse.apply(attn_windows, B, H, W, C, self.shift_size, self.window_size)
+        else:
+            shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+            x = shifted_x
+        x = x.view(B, H * W, C)
+        x = shortcut + self.drop_path(x)
+
+        # FFN
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.BatchNorm1d
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.BatchNorm1d):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+        
+        self.expansion = nn.Linear(dim, 2 * dim, bias=False)
+        
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, 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 = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.reduction(x)
+        return x
+        
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.BatchNorm1d
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.BatchNorm1d, downsample=None, use_checkpoint=False,
+                 fused_window_process=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0, # do not use the shifted window
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer,
+                                 fused_window_process=fused_window_process)
+            for i in range(depth)])
+
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(56*56)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # 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(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
+
+
+class SwinFreeBR(nn.Module):
+    r""" Swin-Free-BR Transformer
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 224
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_sizes (tuple(int)): Window size at each stage.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.BatchNorm1d.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
+                 window_sizes=[7, 14, 14, 7], mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.BatchNorm1d, ape=False, patch_norm=True,
+                 use_checkpoint=False, fused_window_process=False, **kwargs):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
+                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                                 patches_resolution[1] // (2 ** i_layer)),
+                               depth=depths[i_layer],
+                               num_heads=num_heads[i_layer],
+                               window_size=window_sizes[i_layer],
+                               mlp_ratio=self.mlp_ratio,
+                               qkv_bias=qkv_bias, qk_scale=qk_scale,
+                               drop=drop_rate, attn_drop=attn_drop_rate,
+                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                               norm_layer=norm_layer,
+                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                               use_checkpoint=use_checkpoint,
+                               fused_window_process=fused_window_process)
+            self.layers.append(layer)
+
+        self.norm = norm_layer(window_sizes[-1]**2)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+
+        x = self.norm(x)  # B L C
+        x = self.avgpool(x.transpose(1, 2))  # B C 1
+        x = torch.flatten(x, 1)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
+        flops += self.num_features * self.num_classes
+        return flops