Upload model

cls_token.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import torch
+from torch import nn
+
+
+class ClsToken(nn.Module):
+    def __init__(self, ndim: int,
+                 num_tokens: int = 1,
+                 enabled: bool = True,
+                 register_multiple: int = 0,
+    ):
+        super().__init__()
+
+        self.ndim = ndim
+        self.enabled = enabled
+        self.num_registers = 0
+        self.num_tokens = num_tokens
+        if enabled:
+            if register_multiple > 0:
+                self.num_registers = register_multiple - (num_tokens % register_multiple)
+
+            scale = ndim ** -0.5
+            self.token = nn.Parameter(torch.randn(num_tokens + self.num_registers, ndim) * scale)
+        else:
+            self.token = None
+
+        self.num_patches = self.num_tokens + self.num_registers
+
+    def disable(self):
+        self.token = None
+        self.enabled = False
+
+    def forward(self, x: torch.Tensor):
+        if self.token is None:
+            return x
+
+        token = self.token.unsqueeze(0).expand(x.shape[0], -1, -1)
+        x = torch.cat([
+            token,
+            x,
+        ], dim=1)
+
+        return x
+
+    def no_weight_decay(self):
+        return [
+            'token',
+        ]

enable_cpe_support.py

@@ -0,0 +1,67 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+from typing import Union, Tuple
+from types import MethodType
+
+import torch
+from torch import nn
+
+from timm.models import VisionTransformer, checkpoint_seq
+
+from .vit_patch_generator import ViTPatchGenerator
+
+
+def _forward_cpe(self: VisionTransformer, x: torch.Tensor) -> torch.Tensor:
+    x = self.patch_generator(x)
+    if self.grad_checkpointing and not torch.jit.is_scripting():
+        x = checkpoint_seq(self.blocks, x)
+    else:
+        x = self.blocks(x)
+    x = self.norm(x)
+    return x
+
+
+def enable_cpe(model: nn.Module,
+               max_img_size: Union[int, Tuple[int, int]] = 1024,
+               num_cls_tokens: int = 1,
+               pos_dropout: float = 0.1,
+               register_multiple: int = 0,
+):
+    if not isinstance(model, VisionTransformer):
+        raise ValueError("CPE only support for VisionTransformer models!")
+
+    patch_size = model.patch_embed.patch_size[0]
+    embed_dim = model.embed_dim
+    input_dims = model.patch_embed.img_size
+    normalize_patches = not isinstance(model.patch_embed.norm, nn.Identity)
+    cls_token = model.cls_token is not None
+
+    max_img_size = int(round(max_img_size / patch_size) * patch_size)
+
+    patch_generator = ViTPatchGenerator(
+        patch_size=patch_size,
+        embed_dim=embed_dim,
+        input_dims=input_dims,
+        normalize_patches=normalize_patches,
+        cls_token=cls_token,
+        max_input_dims=max_img_size,
+        pos_dropout=pos_dropout,
+        num_cls_tokens=num_cls_tokens,
+        register_multiple=register_multiple,
+    )
+
+    model.patch_generator = patch_generator
+    model.patch_embed = None
+    model.cls_token = None
+    model.pos_embed = None
+    model.pos_drop = None
+    model.num_cls_tokens = num_cls_tokens
+    model.num_registers = patch_generator.num_registers
+
+    model.forward_features = MethodType(_forward_cpe, model)

eradio_model.py

@@ -0,0 +1,1340 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+# Created by Pavlo Molchanov, LPR - DL Efficiency Research team
+# based on Fastervit1 from LPR
+
+import torch
+import torch.nn as nn
+from timm.models.registry import register_model
+
+from timm.models.layers import trunc_normal_, DropPath, LayerNorm2d
+import numpy as np
+import torch.nn.functional as F
+from .block import C2f
+TRT = False # should help for TRT
+
+import pickle
+global bias_indx
+bias_indx = -1
+DEBUG = False
+
+
+
+def pixel_unshuffle(data, factor=2):
+    # performs nn.PixelShuffle(factor) in reverse, torch has some bug for ONNX and TRT, so doing it manually
+    B, C, H, W = data.shape
+    return data.view(B, C, factor, H//factor, factor, W//factor).permute(0,1,2,4,3,5).reshape(B, -1, H//factor, W//factor)
+
+class SwiGLU(nn.Module):
+    # should be more advanced, but doesnt improve results so far
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, C, H, W)
+        window_size: window size
+    Returns:
+        windows - local window features (num_windows*B, window_size*window_size, C)
+        (Hp, Wp) -  the size of the padded image
+    """
+    B, C, H, W = x.shape
+
+    if window_size == 0 or (window_size==H and window_size==W):
+        windows = x.flatten(2).transpose(1, 2)
+        Hp, Wp = H, W
+    else:
+        pad_h = (window_size - H % window_size) % window_size
+        pad_w = (window_size - W % window_size) % window_size
+        if pad_h > 0 or pad_w > 0:
+            x = F.pad(x, (0, pad_w, 0, pad_h, 0, 0, 0, 0))
+        Hp, Wp = H + pad_h, W + pad_w
+
+        x = x.view(B, C, Hp // window_size, window_size, Wp // window_size, window_size)
+        windows = x.permute(0, 2, 4, 3, 5, 1).reshape(-1, window_size*window_size, C)
+
+    return windows, (Hp, Wp)
+
+class Conv2d_BN(nn.Module):
+    '''
+    Conv2d + BN layer with folding capability to speed up inference
+    '''
+    def __init__(self, a, b, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bn_weight_init=1, bias=False):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(a, b, kernel_size, stride, padding, dilation, groups, bias=False)
+        if 1:
+            self.bn = torch.nn.BatchNorm2d(b)
+            torch.nn.init.constant_(self.bn.weight, bn_weight_init)
+            torch.nn.init.constant_(self.bn.bias, 0)
+
+    def forward(self,x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+    @torch.no_grad()
+    def switch_to_deploy(self):
+
+        # return 1
+        if not isinstance(self.bn, nn.Identity):
+            c, bn = self.conv, self.bn
+            w = bn.weight / (bn.running_var + bn.eps) ** 0.5
+            w = c.weight * w[:, None, None, None]
+            b = bn.bias - bn.running_mean * bn.weight / \
+                (bn.running_var + bn.eps)**0.5
+            self.conv.weight.data.copy_(w)
+            self.conv.bias = nn.Parameter(b)
+            self.bn = nn.Identity()
+
+
+
+def window_reverse(windows, window_size, H, W, pad_hw):
+    """
+    Args:
+        windows: local window features (num_windows*B, window_size, window_size, C)
+        window_size: Window size
+        H: Height of image
+        W: Width of image
+        pad_w - a tuple of image passing used in windowing step
+    Returns:
+        x: (B, C, H, W)
+
+    """
+    # print(f"window_reverse, windows.shape {windows.shape}")
+    Hp, Wp = pad_hw
+    if window_size == 0 or (window_size==H and window_size==W):
+        B = int(windows.shape[0] / (Hp * Wp / window_size / window_size))
+        x = windows.transpose(1, 2).view(B, -1, H, W)
+    else:
+        B = int(windows.shape[0] / (Hp * Wp / window_size / window_size))
+        x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+        x = x.permute(0, 5, 1, 3, 2, 4).reshape(B,windows.shape[2], Hp, Wp)
+
+        if Hp > H or Wp > W:
+            x = x[:, :, :H, :W, ].contiguous()
+
+    return x
+
+
+
+class PosEmbMLPSwinv2D(nn.Module):
+    def __init__(self, window_size, pretrained_window_size, num_heads, seq_length, no_log=False):
+        super().__init__()
+        self.window_size = window_size
+        self.num_heads = num_heads
+        # mlp to generate continuous relative position bias
+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+                                     nn.ReLU(inplace=True),
+                                     nn.Linear(512, num_heads, bias=False))
+
+        # get relative_coords_table
+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+        relative_coords_table = torch.stack(
+            torch.meshgrid([relative_coords_h,
+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+        if pretrained_window_size[0] > 0:
+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+        else:
+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+
+        if not no_log:
+            relative_coords_table *= 8  # normalize to -8, 8
+            relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+                torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+        self.register_buffer("relative_coords_table", relative_coords_table)
+
+        # 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.grid_exists = False
+
+        self.deploy = False
+
+        relative_bias = torch.zeros(1, num_heads, seq_length, seq_length)
+        self.seq_length = seq_length
+        self.register_buffer("relative_bias", relative_bias) #for EMA
+
+    def switch_to_deploy(self):
+        self.deploy = True
+        self.grid_exists = True
+
+    def forward(self, input_tensor):
+        # for efficiency, we want this forward to be folded into a single operation (sum)
+        # if resolution stays the same, then we dont need to recompute MLP layers
+        #
+        # to dynamically adjust patch size over the step
+        # if not (input_tensor.shape[1:] == self.relative_bias.shape[1:]):
+        #     self.grid_exists = False
+
+        if self.training: self.grid_exists = False
+
+        if self.deploy and self.grid_exists:
+            input_tensor += self.relative_bias
+            return input_tensor
+
+        if not self.grid_exists:
+            self.grid_exists = True
+
+            relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+            relative_position_bias = 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
+            relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+
+            self.relative_bias = relative_position_bias.unsqueeze(0)
+
+        input_tensor += self.relative_bias
+        return input_tensor
+
+
+
+class GRAAttentionBlock(nn.Module):
+    def __init__(self, window_size, dim_in, dim_out,
+                 num_heads, drop_path=0., qk_scale=None, qkv_bias=False,
+                 norm_layer=nn.LayerNorm, layer_scale=None,
+                  use_swiglu=True,
+                  subsample_ratio=1, dim_ratio=1, conv_base=False,
+                  do_windowing=True, multi_query=False) -> None:
+        super().__init__()
+
+        dim = dim_in
+        # conv_base = True
+        SHUFFLE = True
+        SHUFFLE = False
+        self.do_windowing = do_windowing
+
+        if do_windowing:
+            if SHUFFLE:
+                self.downsample_op = torch.nn.PixelUnshuffle(subsample_ratio) if subsample_ratio>1 else torch.nn.Identity()
+                self.downsample_mixer = nn.Conv2d(dim_in * (subsample_ratio * subsample_ratio), dim_in * (dim_ratio), kernel_size=1, stride=1, padding=0, bias=False) if dim*dim_ratio != dim * subsample_ratio * subsample_ratio else torch.nn.Identity()
+            else:
+                if conv_base:
+                    self.downsample_op = nn.Conv2d(dim_in, dim_out, kernel_size=subsample_ratio, stride=subsample_ratio) if subsample_ratio > 1 else nn.Identity()
+                    self.downsample_mixer = nn.Identity()
+                else:
+                    self.downsample_op = nn.AvgPool2d(kernel_size=subsample_ratio, stride=subsample_ratio) if subsample_ratio > 1 else nn.Identity()
+                    self.downsample_mixer = Conv2d_BN(dim_in, dim_out, kernel_size=1, stride=1) if subsample_ratio > 1 else nn.Identity()
+
+
+        if do_windowing:
+            if SHUFFLE:
+                self.upsample_mixer =nn.Conv2d(dim_in * dim_ratio, dim_in * (subsample_ratio * subsample_ratio), kernel_size=1, stride=1, padding=0, bias=False) if dim*dim_ratio != dim * subsample_ratio * subsample_ratio else torch.nn.Identity()
+                self.upsample_op = torch.nn.PixelShuffle(subsample_ratio) if subsample_ratio>1 else torch.nn.Identity()
+            else:
+                if conv_base:
+                    self.upsample_mixer = nn.Identity()
+                    self.upsample_op = nn.ConvTranspose2d(dim_in, dim_out, kernel_size=subsample_ratio, stride=subsample_ratio) if subsample_ratio > 1 else nn.Identity()
+                else:
+                    self.upsample_mixer = nn.Upsample(scale_factor=subsample_ratio, mode='nearest') if subsample_ratio > 1 else nn.Identity()
+                    self.upsample_op = Conv2d_BN(dim_in, dim_out, kernel_size=1, stride=1, padding=0, bias=False) if subsample_ratio > 1 else nn.Identity()
+
+        self.window_size = window_size
+
+        self.norm1 = norm_layer(dim_in)
+        if DEBUG:
+            print(f"GRAAttentionBlock: input_resolution: , window_size: {window_size}, dim_in: {dim_in}, dim_out: {dim_out}, num_heads: {num_heads}, drop_path: {drop_path}, qk_scale: {qk_scale}, qkv_bias: {qkv_bias}, layer_scale: {layer_scale}")
+
+
+        self.attn = WindowAttention(
+            dim_in,
+            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
+            resolution=window_size,
+            seq_length=window_size**2, dim_out=dim_in, multi_query=multi_query)
+        if DEBUG:
+            print(f"Attention: dim_in: {dim_in}, num_heads: {num_heads}, qkv_bias: {qkv_bias}, qk_scale: {qk_scale}, resolution: {window_size}, seq_length: {window_size**2}, dim_out: {dim_in}")
+            print(f"drop_path: {drop_path}, layer_scale: {layer_scale}")
+
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        use_layer_scale = layer_scale is not None and type(layer_scale) in [int, float]
+        self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim_in))  if use_layer_scale else 1
+
+        ### mlp layer
+        mlp_ratio = 4
+        self.norm2 = norm_layer(dim_in)
+        mlp_hidden_dim = int(dim_in * mlp_ratio)
+
+        activation = nn.GELU if not use_swiglu else SwiGLU
+        mlp_hidden_dim = int((4 * dim_in * 1 / 2) / 64) * 64 if use_swiglu else mlp_hidden_dim
+
+        self.mlp = Mlp(in_features=dim_in, hidden_features=mlp_hidden_dim, act_layer=activation, use_swiglu=use_swiglu)
+
+        self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim_in)) if layer_scale else 1
+        self.drop_path2=DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        if DEBUG:
+            print(f"MLP layer: dim_in: {dim_in}, dim_out: {dim_in}, mlp_hidden_dim: {mlp_hidden_dim}")
+            print(f"drop_path: {drop_path}, layer_scale: {layer_scale}")
+
+
+    def forward(self, x):
+        skip_connection = x
+
+        if self.do_windowing:
+            # performing windowing if required
+            x = self.downsample_op(x)
+            x = self.downsample_mixer(x)
+
+            if self.window_size>0:
+                H, W = x.shape[2], x.shape[3]
+
+            x, pad_hw = window_partition(x, self.window_size)
+
+        # window attention
+        x = x + self.drop_path1(self.gamma1*self.attn(self.norm1(x)))
+        # mlp layer
+        x = x + self.drop_path2(self.gamma2*self.mlp(self.norm2(x)))
+
+        if self.do_windowing:
+            if self.window_size > 0:
+                x = window_reverse(x, self.window_size, H, W, pad_hw)
+
+            x = self.upsample_mixer(x)
+            x = self.upsample_op(x)
+
+
+            if x.shape[2] != skip_connection.shape[2] or x.shape[3] != skip_connection.shape[3]:
+                x = torch.nn.functional.pad(x, ( 0, -x.shape[3] + skip_connection.shape[3], 0, -x.shape[2] + skip_connection.shape[2]))
+        # need to add skip connection because downsampling and upsampling will break residual connection
+        # 0.5 is needed to make sure that the skip connection is not too strong
+        # in case of no downsample / upsample we can show that 0.5 compensates for the residual connection
+        x = 0.5 * x + 0.5 * skip_connection
+
+        return x
+
+
+
+
+class MultiResolutionAttention(nn.Module):
+    """
+    MultiResolutionAttention (MRA) module
+    The idea is to use multiple attention blocks with different resolution
+    Feature maps are downsampled / upsampled for each attention block on different blocks
+    Every attention block supports
+
+    """
+
+    def __init__(self, window_size, sr_ratio,
+                 dim, dim_ratio, num_heads,
+                 do_windowing=True,
+                 layer_scale=1e-5, norm_layer=nn.LayerNorm,
+                 drop_path = 0, qkv_bias=False, qk_scale=1.0,
+                 use_swiglu=True, multi_query=False, conv_base=False) -> None:
+        """
+        Args:
+            input_resolution: input image resolution
+            window_size: window size
+            compression_ratio: compression ratio
+            max_depth: maximum depth of the GRA module
+        """
+        super().__init__()
+
+        depth = len(sr_ratio)
+
+
+        self.attention_blocks = nn.ModuleList()
+
+
+        for i in range(depth):
+            subsample_ratio = sr_ratio[i]
+            if len(window_size) > i:
+                window_size_local = window_size[i]
+            else:
+                window_size_local = window_size[0]
+
+            self.attention_blocks.append(GRAAttentionBlock(window_size=window_size_local,
+                                            dim_in=dim, dim_out=dim, num_heads=num_heads,
+                                            qkv_bias=qkv_bias, qk_scale=qk_scale, norm_layer=norm_layer,
+                                            layer_scale=layer_scale, drop_path=drop_path,
+                                            use_swiglu=use_swiglu, subsample_ratio=subsample_ratio, dim_ratio=dim_ratio,
+                                            do_windowing=do_windowing, multi_query=multi_query, conv_base=conv_base),
+                                        )
+
+
+
+    def forward(self, x):
+
+        for attention_block in self.attention_blocks:
+            x = attention_block(x)
+
+        return x
+
+
+
+class Mlp(nn.Module):
+    """
+    Multi-Layer Perceptron (MLP) block
+    """
+
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 use_swiglu=True,
+                 drop=0.):
+        """
+        Args:
+            in_features: input features dimension.
+            hidden_features: hidden features dimension.
+            out_features: output features dimension.
+            act_layer: activation function.
+            drop: dropout rate.
+        """
+
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features * (2 if use_swiglu else 1), bias=False)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=False)
+        # self.drop = GaussianDropout(drop)
+
+    def forward(self, x):
+        x_size = x.size()
+        x = x.view(-1, x_size[-1])
+        x = self.fc1(x)
+        x = self.act(x)
+        # x = self.drop(x)
+        x = self.fc2(x)
+        # x = self.drop(x)
+        x = x.view(x_size)
+        return x
+
+class Downsample(nn.Module):
+    """
+    Down-sampling block
+
+    Pixel Unshuffle is used for down-sampling, works great accuracy - wise but takes 10% more TRT time
+    """
+
+    def __init__(self,
+                 dim,
+                 shuffle = False,
+                 ):
+        """
+        Args:
+            dim: feature size dimension.
+            shuffle: idea with
+            keep_dim: bool argument for maintaining the resolution.
+        """
+
+        super().__init__()
+        dim_out = 2 * dim
+
+        if shuffle:
+            self.norm = lambda x: pixel_unshuffle(x, factor=2)
+            self.reduction = Conv2d_BN(dim*4, dim_out, 1, 1, 0, bias=False)
+        else:
+            #removed layer norm for better, in this formulation we are getting 10% better speed
+            # LayerNorm for high resolution inputs will be a pain as it pools over the entire spatial dimension
+            self.norm = nn.Identity()
+            self.reduction = Conv2d_BN(dim, dim_out, 3, 2, 1, bias=False)
+
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = self.reduction(x)
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """
+    Patch embedding block
+    """
+
+    def __init__(self, in_chans=3, in_dim=64, dim=96, shuffle_down=False):
+        """
+        Args:
+            in_chans: number of input channels.
+            in_dim: intermediate feature size dimension to speed up stem.
+            dim: final stem channel number
+            shuffle_down: use PixelUnshuffle for down-sampling, effectively increases the receptive field
+        """
+
+        super().__init__()
+        # shuffle_down = False
+        if not shuffle_down:
+            self.proj = nn.Identity()
+            self.conv_down = nn.Sequential(
+                Conv2d_BN(in_chans, in_dim, 3, 2, 1, bias=False),
+                nn.ReLU(),
+                Conv2d_BN(in_dim, dim, 3, 2, 1, bias=False),
+                nn.ReLU()
+                )
+        else:
+            self.proj = lambda x: pixel_unshuffle(x, factor=4)
+
+            # self.conv_down = nn.Sequential(Conv2d_BN(in_chans*16, in_dim, 3, 1, 1),
+            #                                nn.SiLU(),
+            #                                Conv2d_BN(in_dim, dim, 3, 1, 1),
+            #                                nn.SiLU(),
+            #                                )
+            self.conv_down = nn.Sequential(Conv2d_BN(in_chans*16, dim, 3, 1, 1),
+                                           nn.ReLU(),
+                                           )
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = self.conv_down(x)
+        return x
+
+
+
+class ConvBlock(nn.Module):
+    """
+    Convolutional block, used in first couple of stages
+    Experimented with plan resnet-18 like modules, they are the best in terms of throughput
+    Experimented with RepVGG, dont see significant improvement in accuracy
+    Finally, YOLOv8 idea seem to work fine (resnet-18 like block with squeezed feature dimension, and feature concatendation at the end)
+    """
+    def __init__(self, dim,
+                 drop_path=0.,
+                 layer_scale=None,
+                 kernel_size=3,
+                 rep_vgg=False):
+        super().__init__()
+        self.rep_vgg = rep_vgg
+        if not rep_vgg:
+            self.conv1 = Conv2d_BN(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
+            self.act1 = nn.GELU()
+        else:
+            self.conv1 = RepVGGBlock(dim, dim, kernel_size=kernel_size, stride=1, padding=1, groups=1)
+
+
+        if not rep_vgg:
+            self.conv2 = Conv2d_BN(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
+        else:
+            self.conv2 = RepVGGBlock(dim, dim, kernel_size=kernel_size, stride=1, padding=1, groups=1)
+
+        self.layer_scale = layer_scale
+        if layer_scale is not None and type(layer_scale) in [int, float]:
+            self.gamma = nn.Parameter(layer_scale * torch.ones(dim))
+            self.layer_scale = True
+        else:
+            self.layer_scale = False
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        if not self.rep_vgg:
+            x = self.conv1(x)
+            x = self.act1(x)
+            x = self.conv2(x)
+        else:
+            x = self.conv1(x)
+            x = self.conv2(x)
+        if self.layer_scale:
+            x = x * self.gamma.view(1, -1, 1, 1)
+        x = input + self.drop_path(x)
+        return x
+
+
+class WindowAttention(nn.Module):
+    # Windowed Attention from SwinV2
+    # use a MLP trick to deal with various input image resolutions, then fold it to improve speed
+    # tested multi-querry attention, but it is not as good as full attention:
+    # look into palm: https://github.com/lucidrains/PaLM-pytorch/blob/main/palm_pytorch/palm_pytorch.py
+    # single kv attention, mlp in parallel (didnt improve speed)
+
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, resolution=0,
+                 seq_length=0, dim_out=None, multi_query=False):
+        # taken from EdgeViT and tweaked with attention bias.
+        super().__init__()
+        if not dim_out: dim_out = dim
+        self.multi_query = multi_query
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.head_dim = dim // num_heads
+
+        self.dim_internal = dim
+
+        self.scale = qk_scale or head_dim ** -0.5
+        if not multi_query:
+            if TRT:
+                self.q = nn.Linear(dim, dim, bias=qkv_bias)
+                self.k = nn.Linear(dim, dim, bias=qkv_bias)
+                self.v = nn.Linear(dim, dim, bias=qkv_bias)
+            else:
+                self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        else:
+            self.qkv = nn.Linear(dim, dim + 2*self.head_dim, bias=qkv_bias)
+
+        self.proj = nn.Linear(dim, dim_out, bias=False)
+        # attention positional bias
+        self.pos_emb_funct = PosEmbMLPSwinv2D(window_size=[resolution, resolution],
+                                              pretrained_window_size=[resolution, resolution],
+                                              num_heads=num_heads,
+                                              seq_length=seq_length)
+
+        self.resolution = resolution
+
+    def forward(self, x):
+        B, N, C = x.shape
+
+        if not self.multi_query:
+            if TRT:
+                q = self.q(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+                k = self.k(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+                v = self.v(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+            else:
+                qkv = self.qkv(x).reshape(B, -1, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+                q, k, v = qkv[0], qkv[1], qkv[2]
+        else:
+            qkv = self.qkv(x)
+            (q, k, v) = qkv.split([self.dim_internal, self.head_dim, self.head_dim], dim=2)
+
+            q = q.reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+            k = k.reshape(B, -1, 1, C // self.num_heads).permute(0, 2, 1, 3)
+            v = v.reshape(B, -1, 1, C // self.num_heads).permute(0, 2, 1, 3)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+
+        attn = self.pos_emb_funct(attn)
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).transpose(1, 2).reshape(B, -1, C)
+        x = self.proj(x)
+        return x
+
+
+
+class FasterViTLayer(nn.Module):
+    """
+    fastervitlayer
+    """
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size,
+                 conv=False,
+                 downsample=True,
+                 mlp_ratio=4.,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 norm_layer=nn.LayerNorm,
+                 drop_path=0.,
+                 layer_scale=None,
+                 layer_scale_conv=None,
+                 sr_dim_ratio=1,
+                 sr_ratio=1,
+                 multi_query=False,
+                 use_swiglu=True,
+                 rep_vgg=False,
+                 yolo_arch=False,
+                 downsample_shuffle=False,
+                 conv_base=False,
+
+    ):
+        """
+        Args:
+            dim: feature size dimension.
+            depth: number of layers in each stage.
+            input_resolution: input image resolution.
+            window_size: window size in each stage.
+            downsample: bool argument for down-sampling.
+            mlp_ratio: MLP ratio.
+            num_heads: number of heads in each stage.
+            qkv_bias: bool argument for query, key, value learnable bias.
+            qk_scale: bool argument to scaling query, key.
+            drop: dropout rate.
+            attn_drop: attention dropout rate.
+            drop_path: drop path rate.
+            norm_layer: normalization layer.
+            layer_scale: layer scaling coefficient.
+        """
+
+        super().__init__()
+        self.conv = conv
+        self.yolo_arch=False
+        if conv:
+            if not yolo_arch:
+                self.blocks = nn.ModuleList([
+                    ConvBlock(dim=dim,
+                            drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                            layer_scale=layer_scale_conv, rep_vgg=rep_vgg)
+                    for i in range(depth)])
+            else:
+                self.blocks = C2f(dim,dim,n=depth,shortcut=True,e=0.5)
+                self.yolo_arch=True
+        else:
+            if not isinstance(window_size, list): window_size = [window_size]
+            self.window_size = window_size[0]
+            self.do_single_windowing = True
+            if not isinstance(sr_ratio, list): sr_ratio = [sr_ratio]
+            if any([sr!=1 for sr in sr_ratio]) or len(set(window_size))>1:
+                self.do_single_windowing = False
+                do_windowing = True
+            else:
+                self.do_single_windowing = True
+                do_windowing = False
+
+            self.blocks = nn.ModuleList()
+            for i in range(depth):
+
+                self.blocks.append(
+                    MultiResolutionAttention(window_size=window_size,
+                                             sr_ratio=sr_ratio,
+                                             dim=dim,
+                                             dim_ratio = sr_dim_ratio,
+                                             num_heads=num_heads,
+                                             norm_layer=norm_layer,
+                                             drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                             layer_scale=layer_scale,
+                                             qkv_bias=qkv_bias,
+                                             qk_scale=qk_scale,
+                                             use_swiglu=use_swiglu,
+                                             do_windowing=do_windowing,
+                                             multi_query=multi_query,
+                                             conv_base=conv_base,
+                    ))
+
+        self.transformer = not conv
+
+
+        self.downsample = None if not downsample else Downsample(dim=dim, shuffle=downsample_shuffle)
+
+
+
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+
+        if self.transformer and self.do_single_windowing:
+            H, W = x.shape[2], x.shape[3]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        if not self.yolo_arch:
+            for bn, blk in enumerate(self.blocks):
+                x = blk(x)
+        else:
+            x = self.blocks(x)
+
+        if self.transformer and self.do_single_windowing:
+            x = window_reverse(x, self.window_size, H, W, pad_hw)
+
+
+        if self.downsample is None:
+            return x, x
+
+        return self.downsample(x), x #changing to output pre downsampled features
+
+
+class FasterViT(nn.Module):
+    """
+    FasterViT
+    """
+
+    def __init__(self,
+                 dim,
+                 in_dim,
+                 depths,
+                 window_size,
+                 mlp_ratio,
+                 num_heads,
+                 drop_path_rate=0.2,
+                 in_chans=3,
+                 num_classes=1000,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 layer_scale=None,
+                 layer_scale_conv=None,
+                 layer_norm_last=False,
+                 sr_ratio = [1, 1, 1, 1],
+                 max_depth = -1,
+                 conv_base=False,
+                 use_swiglu=False,
+                 multi_query=False,
+                 norm_layer=nn.LayerNorm,
+                 rep_vgg=False,
+                 drop_uniform=False,
+                 yolo_arch=False,
+                 shuffle_down=False,
+                 downsample_shuffle=False,
+                 return_full_features=False,
+                 full_features_head_dim=128,
+                 neck_start_stage=1,
+                 use_neck=False,
+                 **kwargs):
+        """
+        Args:
+            dim: feature size dimension.
+            depths: number of layers in each stage.
+            window_size: window size in each stage.
+            mlp_ratio: MLP ratio.
+            num_heads: number of heads in each stage.
+            drop_path_rate: drop path rate.
+            in_chans: number of input channels.
+            num_classes: number of classes.
+            qkv_bias: bool argument for query, key, value learnable bias.
+            qk_scale: bool argument to scaling query, key.
+            drop_rate: dropout rate.
+            attn_drop_rate: attention dropout rate.
+            norm_layer: normalization layer.
+            layer_scale: layer scaling coefficient.
+            return_full_features: output dense features as well as logits
+            full_features_head_dim: number of channels in the dense features head
+            neck_start_stage: a stage id to start full feature neck. Model has 4 stages, indix starts with 0
+                                for 224 resolution, the output of the stage before downsample:
+                                stage 0: 56x56, stage 1: 28x28, stage 2: 14x14, stage 3: 7x7
+            use_neck: even for summarization embedding use neck
+        """
+        super().__init__()
+
+        num_features = int(dim * 2 ** (len(depths) - 1))
+        self.num_classes = num_classes
+        self.patch_embed = PatchEmbed(in_chans=in_chans, in_dim=in_dim, dim=dim, shuffle_down=shuffle_down)
+        # set return_full_features true if we want to return full features from all stages
+        self.return_full_features = return_full_features
+        self.use_neck = use_neck
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        if drop_uniform:
+            dpr = [drop_path_rate for x in range(sum(depths))]
+
+        if not isinstance(max_depth, list): max_depth = [max_depth] * len(depths)
+
+        self.levels = nn.ModuleList()
+        for i in range(len(depths)):
+            conv = True if (i == 0 or i == 1) else False
+
+            level = FasterViTLayer(dim=int(dim * 2 ** i),
+                                   depth=depths[i],
+                                   num_heads=num_heads[i],
+                                   window_size=window_size[i],
+                                   mlp_ratio=mlp_ratio,
+                                   qkv_bias=qkv_bias,
+                                   qk_scale=qk_scale,
+                                   conv=conv,
+                                   drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],
+                                   downsample=(i < 3),
+                                   layer_scale=layer_scale,
+                                   layer_scale_conv=layer_scale_conv,
+                                   sr_ratio=sr_ratio[i],
+                                   use_swiglu=use_swiglu,
+                                   multi_query=multi_query,
+                                   norm_layer=norm_layer,
+                                   rep_vgg=rep_vgg,
+                                   yolo_arch=yolo_arch,
+                                   downsample_shuffle=downsample_shuffle,
+                                   conv_base=conv_base)
+
+            self.levels.append(level)
+
+        if self.return_full_features or self.use_neck:
+            # create feature projection layers for segmentation output
+            self.neck_features_proj = nn.ModuleList()
+            self.neck_start_stage = neck_start_stage
+            upsample_ratio = 1
+            for i in range(len(depths)):
+                level_n_features_output = int(dim * 2 ** i)
+
+                if self.neck_start_stage > i: continue
+
+                if (upsample_ratio > 1) or full_features_head_dim!=level_n_features_output:
+                    feature_projection = nn.Sequential()
+                    # feature_projection.add_module("norm",LayerNorm2d(level_n_features_output)) #slow, but better
+
+
+                    if 0    :
+                        # Train: 0 [1900/10009 ( 19%)]  Loss: 6.113 (6.57)  Time: 0.548s,  233.40/s  (0.549s,  233.04/s)  LR: 1.000e-05  Data: 0.015 (0.013)
+                        feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output)) #fast, but worse
+                        feature_projection.add_module("dconv", nn.ConvTranspose2d(level_n_features_output,
+                                                                                full_features_head_dim, kernel_size=upsample_ratio, stride=upsample_ratio))
+                    else:
+                        # pixel shuffle based upsampling
+                        # Train: 0 [1950/10009 ( 19%)]  Loss: 6.190 (6.55)  Time: 0.540s,  236.85/s  (0.548s,  233.38/s)  LR: 1.000e-05  Data: 0.015 (0.013)
+                        feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output)) #fast, but worse
+                        feature_projection.add_module("conv", nn.Conv2d(level_n_features_output,
+                                                                                full_features_head_dim*upsample_ratio*upsample_ratio, kernel_size=1, stride=1))
+                        feature_projection.add_module("upsample_pixelshuffle", nn.PixelShuffle(upsample_ratio))
+
+                else:
+                    feature_projection = nn.Sequential()
+                    feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output))
+
+
+                self.neck_features_proj.append(feature_projection)
+
+                if i>0 and self.levels[i-1].downsample is not None:
+                    upsample_ratio *= 2
+
+
+        num_features = full_features_head_dim if (self.return_full_features or self.use_neck) else num_features
+
+        self.num_features = num_features
+
+        self.norm = LayerNorm2d(num_features) if layer_norm_last else nn.BatchNorm2d(num_features)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.head = nn.Linear(num_features, num_classes) if num_classes > 0 else nn.Identity()
+        self.apply(self._init_weights)
+        # pass
+
+    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)
+        elif isinstance(m, LayerNorm2d):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+        elif isinstance(m, nn.BatchNorm2d):
+            nn.init.ones_(m.weight)
+            nn.init.zeros_(m.bias)
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'rpb'}
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        full_features = None
+        for il, level in enumerate(self.levels):
+            x, pre_downsample_x = level(x)
+
+            if self.return_full_features or self.use_neck:
+                if self.neck_start_stage > il: continue
+                if full_features is None:
+                    full_features = self.neck_features_proj[il - self.neck_start_stage](pre_downsample_x)
+                else:
+                    #upsample torch tensor x to match full_features size, and add to full_features
+                    feature_projection = self.neck_features_proj[il - self.neck_start_stage](pre_downsample_x)
+                    if feature_projection.shape[2] != full_features.shape[2] or feature_projection.shape[3] != full_features.shape[3]:
+                        feature_projection = torch.nn.functional.pad(feature_projection, ( 0, -feature_projection.shape[3] + full_features.shape[3], 0, -feature_projection.shape[2] + full_features.shape[2]))
+                    full_features += feature_projection
+
+        # x = self.norm(full_features if (self.return_full_features or self.use_neck) else x)
+        x = self.norm(x) # new version for
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+
+        if not self.return_full_features:
+            return x, None
+
+        return x, full_features
+
+    def forward(self, x):
+        x, full_features = self.forward_features(x)
+        x = self.head(x)
+        if full_features is not None:
+            return x, full_features
+        return x
+
+    def switch_to_deploy(self):
+        '''
+        A method to perform model self-compression
+        merges BN into conv layers
+        converts MLP relative positional bias into precomputed buffers
+        '''
+        for level in [self.patch_embed, self.levels, self.head]:
+            for module in level.modules():
+                if hasattr(module, 'switch_to_deploy'):
+                    module.switch_to_deploy()
+
+@register_model
+def fastervit2_small(pretrained=False, **kwargs): #,
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=96,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [1, 2], 1],
+                     use_swiglu=False,
+                     downsample_shuffle=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_tiny(pretrained=False, **kwargs): #,
+    model = FasterViT(depths=[1, 3, 4, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=80,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     downsample_shuffle=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_base(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_base_fullres1(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=1024,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_base_fullres2(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=512,
+                     neck_start_stage=1,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_base_fullres3(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=256,
+                     neck_start_stage=1,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_base_fullres4(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=256,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_base_fullres5(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=512,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+#pyt: 1934, 4202 TRT
+@register_model
+def fastervit2_large(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128+64,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_large_fullres(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[None, None, [7, 7], 7],
+                     dim=192,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=1536,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_large_fullres_ws8(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[None, None, [8, 8], 8],
+                     dim=192,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=1536,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_large_fullres_ws16(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[None, None, [16, 16], 16],
+                     dim=192,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=1536,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_large_fullres_ws32(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[None, None, [32, 32], 32],
+                     dim=192,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     conv_base=True,
+                     use_neck=True,
+                     full_features_head_dim=1536,
+                     neck_start_stage=2,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+#pyt: 897
+@register_model
+def fastervit2_xlarge(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128+128+64,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+
+#pyt:
+@register_model
+def fastervit2_huge(pretrained=False, **kwargs):
+    model = FasterViT(depths=[3, 3, 5, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=128+128+128+64,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.2,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+
+@register_model
+def fastervit2_xtiny(pretrained=False, **kwargs): #,
+    model = FasterViT(depths=[1, 3, 4, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=64,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.1,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     downsample_shuffle=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+
+@register_model
+def fastervit2_xxtiny_5(pretrained=False, **kwargs): #,
+    model = FasterViT(depths=[1, 3, 4, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=48,
+                     in_dim=64,
+                     mlp_ratio=4,
+                     drop_path_rate=0.05,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     downsample_shuffle=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+@register_model
+def fastervit2_xxxtiny(pretrained=False, **kwargs): #,
+    model = FasterViT(depths=[1, 3, 4, 5],
+                     num_heads=[2, 4, 8, 16],
+                     window_size=[8, 8, [7, 7], 7],
+                     dim=32,
+                     in_dim=32,
+                     mlp_ratio=4,
+                     drop_path_rate=0.0,
+                     sr_ratio=[1, 1, [2, 1], 1],
+                     use_swiglu=False,
+                     downsample_shuffle=False,
+                     yolo_arch=True,
+                     shuffle_down=False,
+                     **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pretrained))
+    return model
+
+
+@register_model
+def eradio(pretrained=False, **kwargs):
+    return fastervit2_large_fullres(pretrained=pretrained, **kwargs)

hf_model.py

@@ -15,12 +15,70 @@ from collections import namedtuple
 from typing import Optional
 
 from einops import rearrange
+from timm.models import VisionTransformer
 import torch
 from transformers import PretrainedConfig, PreTrainedModel
 
-#from radio.model import create_model_from_args
-from radio.input_conditioner import get_default_conditioner, InputConditioner
-from .model import eradio
+
+from .eradio_model import eradio
+from .radio_model import create_model_from_args
+from .radio_model import RADIOModel as RADIOModelBase
+from .input_conditioner import get_default_conditioner, InputConditioner
+
+
+class RADIOConfig(PretrainedConfig):
+    """Pretrained Hugging Face configuration for RADIO models."""
+
+    def __init__(
+        self,
+        args: Optional[dict] = None,
+        version: Optional[str] = "v1",
+        return_summary: Optional[bool] = True,
+        return_spatial_features: Optional[bool] = True,
+        **kwargs,
+    ):
+        self.args = args
+        self.version = version
+        self.return_summary = return_summary
+        self.return_spatial_features = return_spatial_features
+        super().__init__(**kwargs)
+
+
+class RADIOModel(PreTrainedModel):
+    """Pretrained Hugging Face model for RADIO.
+
+    This class inherits from PreTrainedModel, which provides
+    HuggingFace's functionality for loading and saving models.
+    """
+
+    config_class = RADIOConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        RADIOArgs = namedtuple("RADIOArgs", config.args.keys())
+        args = RADIOArgs(**config.args)
+        self.config = config
+        model = create_model_from_args(args)
+        input_conditioner: InputConditioner = get_default_conditioner()
+
+        self.radio_model = RADIOModelBase(
+            model,
+            input_conditioner,
+            config.return_summary,
+            config.return_spatial_features,
+        )
+
+    @property
+    def model(self) -> VisionTransformer:
+        return self.radio_model.model
+
+    @property
+    def input_conditioner(self) -> InputConditioner:
+        return self.radio_model.input_conditioner
+
+    def forward(self, x: torch.Tensor):
+        return self.radio_model.forward(x)
 
 
 class ERADIOConfig(PretrainedConfig):

input_conditioner.py

@@ -0,0 +1,49 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+from typing import Union, Tuple
+
+import torch
+from torch import nn
+
+
+norm_t = Union[Tuple[float, float, float], torch.Tensor]
+
+class InputConditioner(nn.Module):
+    def __init__(self,
+                 input_scale: float,
+                 norm_mean: norm_t,
+                 norm_std: norm_t,
+                 dtype: torch.dtype = torch.float32,
+    ):
+        super().__init__()
+
+        self.dtype = dtype
+
+        # self.input_scale = input_scale
+        self.register_buffer("norm_mean", _to_tensor(norm_mean) / input_scale)
+        self.register_buffer("norm_std", _to_tensor(norm_std) / input_scale)
+
+    def forward(self, x: torch.Tensor):
+        # x = x * self.input_scale
+        y = (x - self.norm_mean) / self.norm_std
+        return y.to(self.dtype)
+
+
+def get_default_conditioner():
+    from timm.data.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+
+    return InputConditioner(
+        input_scale=1.0,
+        norm_mean=OPENAI_CLIP_MEAN,
+        norm_std=OPENAI_CLIP_STD,
+    )
+
+
+def _to_tensor(v: norm_t):
+    return torch.as_tensor(v, dtype=torch.float32).view(-1, 1, 1)

radio_model.py

@@ -0,0 +1,100 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import torch
+from torch import nn
+
+from timm.models import create_model, VisionTransformer
+
+from .enable_cpe_support import enable_cpe
+from .input_conditioner import InputConditioner
+
+
+class RADIOModel(nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        input_conditioner: InputConditioner,
+        return_summary: bool,
+        return_spatial_features: bool,
+    ):
+        super().__init__()
+
+        self.model = model
+        self.input_conditioner = input_conditioner
+        self.return_summary = return_summary
+        self.return_spatial_features = return_spatial_features
+
+    def forward(self, x: torch.Tensor):
+        x = self.input_conditioner(x)
+
+        y = self.model.forward_features(x)
+
+        if isinstance(y, (list, tuple)):
+            summary, all_feat = y
+        elif isinstance(self.model, VisionTransformer):
+            patch_gen = getattr(self.model, "patch_generator", None)
+            if patch_gen is not None:
+                summary = y[:, : patch_gen.num_cls_tokens].flatten(1)
+                all_feat = y[:, patch_gen.num_skip :]
+            elif self.model.global_pool == "avg":
+                summary = y[:, self.model.num_prefix_tokens :].mean(dim=1)
+                all_feat = y
+            else:
+                summary = y[:, 0]
+                all_feat = y[:, 1:]
+        else:
+            raise ValueError("Unsupported model type")
+
+        if self.return_summary and self.return_spatial_features:
+            return summary, all_feat
+        elif self.return_summary:
+            return summary
+        return all_feat
+
+
+def create_model_from_args(args) -> nn.Module:
+    in_chans = 3
+    if args.in_chans is not None:
+        in_chans = args.in_chans
+    elif args.input_size is not None:
+        in_chans = args.input_size[0]
+
+    # Skip weight initialization unless it's explicitly requested.
+    weight_init = args.model_kwargs.pop("weight_init", "skip")
+
+    model = create_model(
+        args.model,
+        pretrained=args.pretrained,
+        in_chans=in_chans,
+        num_classes=args.num_classes,
+        drop_rate=args.drop,
+        drop_path_rate=args.drop_path,
+        drop_block_rate=args.drop_block,
+        global_pool=args.gp,
+        bn_momentum=args.bn_momentum,
+        bn_eps=args.bn_eps,
+        scriptable=args.torchscript,
+        checkpoint_path=args.initial_checkpoint,
+        weight_init=weight_init,
+        **args.model_kwargs,
+    )
+
+    assert (
+        not args.cls_token_per_teacher or args.cpe_max_size is not None
+    ), "CPE must be enabled for multiple CLS tokens!"
+
+    if args.cpe_max_size is not None:
+        enable_cpe(
+            model,
+            args.cpe_max_size,
+            num_cls_tokens=len(args.teachers) if args.cls_token_per_teacher else 1,
+            register_multiple=args.register_multiple,
+        )
+
+    return model

vit_patch_generator.py

@@ -0,0 +1,299 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import math
+from typing import Union, Tuple, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from einops import rearrange
+
+from .cls_token import ClsToken
+
+input_dim_t = Union[int, Tuple[int, int]]
+
+try:
+    # raise ImportError()
+    from indirect_grid_sample import indirect_grid_sample
+except ImportError:
+    indirect_grid_sample = None
+
+class ViTPatchGenerator(nn.Module):
+    def __init__(self,
+                 patch_size: int,
+                 embed_dim: int,
+                 input_dims: input_dim_t,
+                 abs_pos: bool = True,
+                 normalize_patches: bool = False,
+                 cls_token: bool = False,
+                 max_input_dims: Optional[input_dim_t] = None,
+                 pos_dropout: float = 0.0,
+                 return_pos_enc: bool = False,
+                 num_cls_tokens: int = 1,
+                 register_multiple: int = 0,
+                 device=None, dtype=None,
+    ):
+        super().__init__()
+
+        if isinstance(input_dims, int):
+            input_dims = (input_dims, input_dims)
+
+        if max_input_dims is None:
+            max_input_dims = input_dims
+        if isinstance(max_input_dims, int):
+            max_input_dims = (max_input_dims, max_input_dims)
+
+        max_input_dims = tuple(
+            int(math.ceil(d / patch_size) * patch_size)
+            for d in max_input_dims
+        )
+
+        self.cpe_mode = max_input_dims != input_dims
+        self.pos_dropout = pos_dropout
+        self.return_pos_enc = return_pos_enc
+
+        factory = dict(device=device, dtype=dtype)
+
+        self.patch_size = patch_size
+        self.abs_pos = abs_pos
+        self.embed_dim = embed_dim
+
+        self.num_rows = max_input_dims[0] // patch_size
+        self.num_cols = max_input_dims[1] // patch_size
+        self.input_dims = tuple(d // patch_size for d in input_dims)
+        self.num_patches = self.num_rows * self.num_cols
+        self.max_input_dims = max_input_dims
+
+        self.im_to_patches = Im2Patches(patch_size)
+        self.embedder = ViTPatchLinear(patch_size, embed_dim, **factory)
+
+        if abs_pos:
+            scale = embed_dim ** -0.5
+            self.pos_embed = nn.Parameter(torch.randn(1, self.num_patches, embed_dim, **factory) * scale)
+
+        self.cls_token = ClsToken(
+            embed_dim,
+            num_tokens=num_cls_tokens,
+            enabled=cls_token,
+            register_multiple=register_multiple,
+        )
+
+        self.patch_normalizer = nn.LayerNorm(embed_dim) if normalize_patches else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        patches = self.embed_patches(x)
+        patches, pos_enc = self.apply_pos_enc(patches, input_size=x.shape[2:])
+        patches = self.cls_token(patches)
+        patches = self.patch_normalizer(patches)
+        if self.return_pos_enc:
+            return patches, pos_enc
+        return patches
+
+    @property
+    def apply_cls_token(self):
+        return self.cls_token.enabled
+
+    @property
+    def num_cls_tokens(self):
+        return self.cls_token.num_tokens
+
+    @property
+    def num_registers(self):
+        return self.cls_token.num_registers
+
+    @property
+    def num_skip(self):
+        return self.num_cls_tokens + self.num_registers
+
+    def no_weight_decay(self):
+        return [
+            'pos_embed',
+        ]
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        if self.abs_pos:
+            self._load_embed(state_dict[f'{prefix}pos_embed'], self.pos_embed)
+
+    def _load_embed(self, src_embed: torch.Tensor, targ_embed: nn.Parameter):
+        if src_embed.shape != targ_embed.shape:
+            src_size = int(math.sqrt(src_embed.shape[1]))
+
+            assert src_size ** 2 == src_embed.shape[1], 'Unable to interpolate non-square embedding'
+
+            src_embed = rearrange(src_embed, 'b (h w) c -> b c h w', h=src_size, w=src_size)
+            src_embed = F.interpolate(src_embed, size=(self.num_rows, self.num_cols), mode='bicubic', align_corners=True, antialias=False)
+            src_embed = rearrange(src_embed, 'b c h w -> b (h w) c')
+        targ_embed.data.copy_(src_embed)
+
+    def _load_projection(self, src_proj_weight: torch.Tensor, targ_proj_weight: torch.Tensor):
+        if src_proj_weight.shape != targ_proj_weight.shape:
+            src_patch_size = int(math.sqrt(src_proj_weight.shape[1] // 3))
+
+            assert (src_patch_size ** 2) * 3 == src_proj_weight.shape[1], 'Unable to interpolate non-square patch size'
+
+            src_proj_weight = rearrange(src_proj_weight, 'b (c h w) -> b c h w', c=3, h=src_patch_size, w=src_patch_size)
+            src_proj_weight = F.interpolate(src_proj_weight, size=(self.patch_size, self.patch_size), mode='bicubic', align_corners=True, antialias=False)
+            src_proj_weight = rearrange(src_proj_weight, 'b c h w -> b (c h w)')
+        targ_proj_weight.data.copy_(src_proj_weight)
+
+    def embed_patches(self, x: torch.Tensor) -> torch.Tensor:
+        patches = self.im_to_patches(x)
+        patches = self.embedder(patches)
+        return patches
+
+    def apply_pos_enc(self,
+                      patches: torch.Tensor,
+                      patch_idxs: Optional[torch.Tensor] = None,
+                      input_size: Optional[Tuple[int, int]] = None,
+    ) -> torch.Tensor:
+        if not self.abs_pos:
+            return patches
+
+        pos_enc = self.get_pos_enc(patches.shape[0], patch_idxs, input_size)
+
+        if self.training and self.pos_dropout > 0:
+            keeps = torch.rand(patches.shape[0], 1, 1, dtype=pos_enc.dtype, device=pos_enc.device) > self.pos_dropout
+            pos_enc_drop = torch.where(keeps, pos_enc, 0)
+        else:
+            pos_enc_drop = pos_enc
+
+        return patches + pos_enc_drop, pos_enc
+
+    def get_pos_enc(self,
+                    batch_size: int,
+                    patch_idxs: Optional[torch.Tensor] = None,
+                    input_size: Optional[Tuple[int, int]] = None,
+    ) -> torch.Tensor:
+        if input_size is None:
+            input_dims = self.input_dims
+        else:
+            input_dims = tuple(d // self.patch_size for d in input_size)
+
+        pos_embed = self._get_pos_embeddings(batch_size, input_dims)
+
+        if patch_idxs is None:
+            return pos_embed
+
+        exp_patch_idxs = patch_idxs.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1])
+
+        pos_embed = torch.gather(pos_embed.expand(patch_idxs.shape[0], -1, -1), dim=1, index=exp_patch_idxs)
+        return pos_embed
+
+
+    def _get_pos_embeddings(self, batch_size: int, input_dims: Tuple[int, int]):
+        if (self.num_rows, self.num_cols) == input_dims:
+            return self.pos_embed
+
+        pos_embed = self.pos_embed.reshape(1, self.num_rows, self.num_cols, -1).permute(0, 3, 1, 2)
+
+        def window_select(pos_embed):
+            if input_dims[0] < pos_embed.shape[-2]:
+                pos_embed = pos_embed[..., :input_dims[0], :]
+            if input_dims[1] < pos_embed.shape[-1]:
+                pos_embed = pos_embed[..., :, :input_dims[1]]
+            return pos_embed
+
+        if self.cpe_mode:
+            if self.training:
+                min_scale = math.sqrt(0.1)
+                scale = torch.rand(batch_size, 1, 1, device=pos_embed.device) * (1 - min_scale) + min_scale
+                aspect_min = math.log(3 / 4)
+                aspect_max = -aspect_min
+                aspect = torch.exp(torch.rand(batch_size, 1, 1, device=pos_embed.device) * (aspect_max - aspect_min) + aspect_min)
+
+                scale_x = scale * aspect
+                scale_y = scale * (1 / aspect)
+                scale_xy = torch.stack([scale_x, scale_y], dim=-1).clamp_(0, 1)
+
+                pos_xy = torch.rand(batch_size, 1, 1, 2, device=pos_embed.device) * (1 - scale_xy)
+
+                lin_x = torch.linspace(0, 1, steps=input_dims[1], device=pos_embed.device)[None, None].expand(batch_size, input_dims[0], -1)
+                lin_y = torch.linspace(0, 1, steps=input_dims[0], device=pos_embed.device)[None, :, None].expand(batch_size, -1, input_dims[1])
+
+                lin_xy = torch.stack([lin_x, lin_y], dim=-1)
+
+                grid_xy = lin_xy * scale_xy + pos_xy
+
+                # Convert to [-1, 1] range
+                grid_xy.mul_(2).sub_(1)
+
+                pos_embed = F.grid_sample(
+                    pos_embed.expand(batch_size, -1, -1, -1),
+                    grid=grid_xy,
+                    mode='bilinear',
+                    padding_mode='zeros',
+                    align_corners=True,
+                )
+            else:
+                # i_rows, i_cols = input_dims
+                # p_rows, p_cols = pos_embed.shape[2:]
+                # if i_rows <= p_rows and i_cols <= p_cols:
+                #     left = (p_cols - i_cols) // 2
+                #     top = (p_rows - i_rows) // 2
+                #     pos_embed = pos_embed[..., top:top+i_rows, left:left+i_cols]
+                # else:
+                max_dim = max(input_dims)
+                pos_embed = F.interpolate(pos_embed.float(), size=(max_dim, max_dim), align_corners=True, mode='bilinear').to(pos_embed.dtype)
+
+                pos_embed = window_select(pos_embed)
+        else:
+            pos_embed = window_select(pos_embed)
+
+        if pos_embed.shape[-2:] != input_dims:
+            pos_embed = F.interpolate(pos_embed.float(), size=input_dims, align_corners=True, mode='bilinear').to(pos_embed.dtype)
+
+        pos_embed = pos_embed.flatten(2).permute(0, 2, 1)
+
+        return pos_embed
+
+
+class Im2Patches(nn.Module):
+    def __init__(self, patch_size: int):
+        super().__init__()
+        self.patch_size = patch_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.patch_size == 1:
+            patches = x.flatten(2)
+            patches = patches.permute(0, 2, 1)
+            return patches
+
+        py = x.shape[-2] // self.patch_size
+        px = x.shape[-1] // self.patch_size
+        patches = rearrange(x, 'b c (py yy) (px xx) -> b (py px) (c yy xx)',
+                            py=py, yy=self.patch_size,
+                            px=px, xx=self.patch_size,
+        )
+        return patches
+
+
+class ViTPatchLinear(nn.Linear):
+    def __init__(self, patch_size: int, embed_dim: int, **factory):
+        super().__init__(
+            3 * (patch_size ** 2),
+            embed_dim,
+            bias=False,
+            **factory
+        )
+        self.patch_size = patch_size
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        if self.bias is not None:
+            self.bias.data.copy_(state_dict[f'{prefix}bias'])
+
+        chk_weight = state_dict[f'{prefix}weight']
+        if chk_weight.shape != self.weight.shape:
+            src_patch_size = int(math.sqrt(chk_weight.shape[1] // 3))
+
+            assert (src_patch_size ** 2) * 3 == chk_weight.shape[1], 'Unable to interpolate non-square patch size'
+
+            chk_weight = rearrange(chk_weight, 'b (c h w) -> b c h w', c=3, h=src_patch_size, w=src_patch_size)
+            chk_weight = F.interpolate(chk_weight, size=(self.patch_size, self.patch_size), mode='bicubic', align_corners=True, antialias=False)
+            chk_weight = rearrange(chk_weight, 'b c h w -> b (c h w)')
+        self.weight.data.copy_(chk_weight)