eradio_model.py

#!/usr/bin/env python3

# Copyright (c) 2024, 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.

# E-RADIO model from
# Mike Ranzinger, Greg Heinrich, Jan Kautz, and Pavlo Molchanov. "AM-RADIO: Agglomerative Model--Reduce All Domains Into One." arXiv preprint arXiv:2312.06709 (2023).

# based on FasterViT, Swin Transformer, YOLOv8

# FasterViT:
# Ali Hatamizadeh, Greg Heinrich, Hongxu Yin, Andrew Tao, Jose M. Alvarez, Jan Kautz, and Pavlo Molchanov. "FasterViT: Fast Vision Transformers with Hierarchical Attention." arXiv preprint arXiv:2306.06189 (2023).

import timm
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
import math
import warnings

#######################
## Codebase from YOLOv8
## BEGINNING
#######################

class C2f(nn.Module):
    """Faster Implementation of CSP Bottleneck with 2 convolutions."""
    """From YOLOv8 codebase"""
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, drop_path=None):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        if drop_path is None:
            drop_path = [0.0] * n

        self.c = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0, drop_path=drop_path[i]) for i in range(n))

    def forward(self, x):
        """Forward pass through C2f layer."""
        y = list(self.cv1(x).chunk(2, 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))

    def forward_split(self, x):
        """Forward pass using split() instead of chunk()."""
        y = list(self.cv1(x).split((self.c, self.c), 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))

class Bottleneck(nn.Module):
    """Standard bottleneck."""

    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5, drop_path=0.0):  # ch_in, ch_out, shortcut, groups, kernels, expand
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, k[0], 1)
        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
        self.add = shortcut and c1 == c2
        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        """'forward()' applies the YOLOv5 FPN to input data."""
        return x + self.drop_path1(self.cv2(self.cv1(x))) if self.add else self.cv2(self.cv1(x))


class Conv(nn.Module):
    """Modified to support layer fusion"""
    default_act = nn.SiLU()  # default activation

    def __init__(self, a, b, kernel_size=1, stride=1, padding=None, g=1, dilation=1, bn_weight_init=1, bias=False, act=True):
        super().__init__()

        self.conv = torch.nn.Conv2d(a, b, kernel_size, stride, autopad(kernel_size, padding, dilation), dilation, g, 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)
        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()


    def forward(self,x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.act(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 autopad(k, p=None, d=1):  # kernel, padding, dilation
    """Pad to 'same' shape outputs."""
    if d > 1:
        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p


#######################
## Codebase from YOLOv8
## END
#######################

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):
    """
    Function for partitioning image into windows and later do windowed attention
    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), mode="reflect")
        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
    Can be merged with Conv() function with additional arguments
    '''
    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):
        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):
    """
    Windows to the full feature map
    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):
    """
    2D positional embedding from Swin Transformer v2
    Added functionality to store the positional embedding in the model and not recompute it every time
    """
    def __init__(
        self, window_size, pretrained_window_size, num_heads, seq_length, no_log=False, cpb_mlp_hidden=512,
    ):
        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, cpb_mlp_hidden, bias=True),
            nn.ReLU(inplace=True),
            nn.Linear(cpb_mlp_hidden, num_heads, bias=False),
        )

        self.grid_exists = False
        self.seq_length = seq_length
        self.deploy = False
        self.num_heads = num_heads
        self.no_log = no_log
        self.pretrained_window_size = pretrained_window_size
        self.relative_bias_window_size = window_size

        relative_coords_table, relative_position_index, relative_bias = self.relative_bias_initialization(window_size, num_heads,
                                                                                                     pretrained_window_size, seq_length,
                                                                                                     no_log)

        self.register_buffer("relative_coords_table", relative_coords_table)
        self.register_buffer("relative_position_index", relative_position_index)
        self.register_buffer("relative_bias", relative_bias)  # for EMA

    def relative_bias_initialization(self, window_size, num_heads, pretrained_window_size, seq_length, no_log):
        # as in separate function to support window size chage after model weights loading
        relative_coords_h = torch.arange(
            -(window_size[0] - 1), window_size[0], dtype=torch.float32
        )
        relative_coords_w = torch.arange(
            -(window_size[1] - 1), 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)
            )

        # 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

        relative_bias = torch.zeros(1, num_heads, seq_length, seq_length)

        self.relative_bias_window_size = window_size

        return relative_coords_table, relative_position_index, relative_bias


    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

        if not self.deploy or self.training:
            self.grid_exists = False

        #compare if all elements in self.window_size list match those in self.relative_bias_window_size
        if not all([self.window_size[i] == self.relative_bias_window_size[i] for i in range(len(self.window_size))]):
            relative_coords_table, relative_position_index, relative_bias = self.relative_bias_initialization(self.window_size, self.num_heads,
                                                                                                        self.pretrained_window_size, self.seq_length,
                                                                                                        self.no_log)

            self.relative_coords_table = relative_coords_table.to(self.relative_coords_table.device)
            self.relative_position_index = relative_position_index.to(self.relative_position_index.device)
            self.relative_bias = relative_bias.to(self.relative_bias.device)

        if self.deploy and self.grid_exists:
            input_tensor = input_tensor + self.relative_bias
            return input_tensor

        if 1:
            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 = 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, use_shift=0,
                  cpb_mlp_hidden=512, conv_groups_ratio=0):
        '''
        Global Resolution Attention Block , see README for details
        Attention with subsampling to get a bigger receptive field for attention
        conv_base - use conv2d instead of avgpool2d for downsample / upsample


        '''
        super().__init__()

        self.shift_size=window_size//2 if use_shift else 0

        self.do_windowing = do_windowing
        self.subsample_ratio = subsample_ratio



        if do_windowing:
            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()
                    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.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()
                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()


        # in case there is no downsampling conv we want to have it separately
        # will help with information propagation between windows
        if subsample_ratio == 1:
            # conv_groups_ratio=0
            self.pre_conv = Conv2d_BN(dim_in, dim_in, kernel_size=3, stride=1, padding=1, groups=max(1,int(conv_groups_ratio*dim_in)), bias=False)
            # self.pre_conv = nn.Conv2d(dim_in, dim_in, kernel_size=3, stride=1, padding=1, groups=max(1,int(conv_groups_ratio*dim_in)), bias=False)
            # self.pre_conv_act = nn.ReLU6()
            #for simplicity:
            self.pre_conv_act = nn.Identity()
            if conv_groups_ratio == -1:
                self.pre_conv = nn.Identity()
                self.pre_conv_act = nn.Identity()

        self.window_size = window_size

        self.norm1 = norm_layer(dim_in)

        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,
            shift_size=self.shift_size, cpb_mlp_hidden=cpb_mlp_hidden)

        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()


    def forward(self, x):
        skip_connection = x
        attn_mask = None

        # in case there is no downsampling conv we want to have it separately
        # will help with information propagation
        if self.subsample_ratio == 1:
            x = self.pre_conv_act(self.pre_conv(x)) + skip_connection

        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]

            if self.shift_size > 0 and H>self.window_size and W>self.window_size:
                # @swin like cyclic shift, doesnt show better performance
                x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(2, 3))

            x, pad_hw = window_partition(x, self.window_size)

            if self.shift_size > 0 and H>self.window_size and W>self.window_size:
                # set atten matrix to have -100 and the top right square
                # attn[:, :, :-self.shift_size, -self.shift_size:] = -100.0
                # calculate attention mask for SW-MSA
                # not used in final version, can be useful for some cases especially for high res
                H, W = pad_hw
                img_mask = torch.zeros((1, H, W, 1), device=x.device)  # 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
                img_mask = img_mask.transpose(1,2).transpose(1,3)
                mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1

                mask_windows = mask_windows[0].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))

        # window attention
        x = x + self.drop_path1(self.gamma1*self.attn(self.norm1(x), attn_mask=attn_mask)) # or pass H,W
        # 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)

            # reverse cyclic shift
            if self.shift_size > 0 and H>self.window_size and W>self.window_size:
                # @swin like cyclic shift, not tested
                x = torch.roll(x, shifts=(self.shift_size, self.shift_size), dims=(2, 3))

            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]), mode="reflect")
        # 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 windowing
    """

    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,
                 use_shift=0, cpb_mlp_hidden=512, conv_groups_ratio=0) -> None:
        """
        Args:
            input_resolution: input image resolution
            window_size: window size
            compression_ratio: compression ratio
            max_depth: maximum depth of the GRA module
            use_shift: do window shifting
        """
        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,
                                            use_shift=use_shift, cpb_mlp_hidden=cpb_mlp_hidden, conv_groups_ratio=conv_groups_ratio),
                                        )

    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)

    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.fc2(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)
            # pixel unshuffleging works well but doesnt provide any speedup
        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
            # therefore we remove it compared to the original implementation in FasterViT
            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
    Used to convert image into an initial set of feature maps with lower resolution
    """

    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, 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
    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,
                 ):
        super().__init__()

        self.conv1 = Conv2d_BN(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
        self.act1 = nn.GELU()

        self.conv2 = Conv2d_BN(dim, dim, kernel_size=kernel_size, stride=1, padding=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

        x = self.conv1(x)
        x = self.act1(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

    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, resolution=0,
                 seq_length=0, dim_out=None, multi_query=False, shift_size=0, cpb_mlp_hidden=512):
        # taken from EdgeViT and tweaked with attention bias.
        super().__init__()
        if not dim_out: dim_out = dim
        self.shift_size = shift_size
        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:
            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,
                                              cpb_mlp_hidden=cpb_mlp_hidden)

        self.resolution = resolution

    def forward(self, x, attn_mask = None):
        B, N, C = x.shape

        if not self.multi_query:
            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)

        #add window shift
        if attn_mask is not None:
            nW = attn_mask.shape[0]
            attn = attn.view(B // nW, nW, self.num_heads, N, N) + attn_mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)

        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(1, 2).reshape(B, -1, C)
        x = self.proj(x)
        return x



class ERADIOLayer(nn.Module):
    """
    E-RADIO Layer
    """

    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,
                 yolo_arch=False,
                 downsample_shuffle=False,
                 conv_base=False,
                 use_shift=False,
                 cpb_mlp_hidden=512,
                 conv_groups_ratio=0,
                 verbose: bool = True,

    ):
        """
        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.
            use_shift: SWIN like window shifting for half the window size for every alternating layer (considering multi-resolution)
            conv_groups_ratio: group ratio for conv when no subsampling in multi-res attention
        """

        super().__init__()
        self.conv = conv
        self.yolo_arch=False
        self.verbose = verbose
        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)
                    for i in range(depth)])
                self.blocks = nn.Sequential(*self.blocks)
            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]
            self.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

            #for v2_2
            if conv_groups_ratio != -1:
                self.do_single_windowing = False
                do_windowing = True

            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,
                                             cpb_mlp_hidden=cpb_mlp_hidden,
                                             use_shift =0 if ((not use_shift) or ((i) % 2 == 0)) else True    ,
                                             conv_groups_ratio=conv_groups_ratio,
                    ))
            self.blocks = nn.Sequential(*self.blocks)

        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

        # do padding for transforemr
        interpolate = True
        if self.transformer and interpolate:
            # Windowed Attention will split feature map into windows with the size of window_size x window_size
            # if the resolution is not divisible by window_size, we need to interpolate the feature map
            # can be done via padding, but doing so after training hurts the model performance.
            # interpolation affects the performance as well, but not as much as padding
            if isinstance(self.window_size, list) or isinstance(self.window_size, tuple):
                current_max_window_size = max(self.window_size)
            else:
                current_max_window_size = self.window_size

            max_window_size = max([res_upsample*current_max_window_size for res_upsample in self.sr_ratio])
            if H % max_window_size != 0 or W % max_window_size != 0:
                new_h = int(np.ceil(H/max_window_size)*max_window_size)
                new_w = int(np.ceil(W/max_window_size)*max_window_size)
                x = F.interpolate(x, size=(new_h, new_w), mode='nearest')
                if self.verbose:
                    warnings.warn(f"Choosen window size is not optimal for given resolution. Interpolation of features maps will be done and it can affect the performance. Max window size is {max_window_size}, feature map size is {H}x{W}, interpolated feature map size is {new_h}x{new_w}.")


        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)

        #run main blocks
        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.transformer and interpolate:
            #lets keep original resolution, might be not ideal, but for the upsampling tower we need to keep the expected resolution.
            x = F.interpolate(x, size=(H, W), mode='nearest')

        if self.downsample is None:
            return x, x

        return self.downsample(x), x  # changing to output pre downsampled features


class InterpolateLayer(nn.Module):
    def __init__(self, size=None, scale_factor=None, mode='nearest'):
        super(InterpolateLayer, self).__init__()
        self.size = size
        self.scale_factor = scale_factor
        self.mode = mode

    def forward(self, x):
        return F.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode)


class HiResNeck(nn.Module):
    """
    The block is used to output dense features from all stages
    Otherwise, by default, only the last stage features are returned with E-RADIO
    """
    def __init__(self, dim, depths, neck_start_stage, full_features_head_dim, downsample_enabled):

        '''
        Hi Resolution neck to support output of high res features that are useful for dense tasks.
        depths - total number of layers in the base model
        neck_start_stage - when to start the neck, 0 - start from the first stage, 1 - start from the second stage etc.
                            earlier layers result in higher resolution features at the cost of compute
        full_features_head_dim - number of channels in the dense features head
        '''
        super().__init__()
        # 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()
                if False:
                    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:
                    # B, in_channels, H, W -> B, in_channels, H*upsample_ratio, W*upsample_ratio
                    # print("upsample ratio", upsample_ratio, level_n_features_output, level_n_features_output)
                    feature_projection.add_module("upsample", InterpolateLayer(scale_factor=upsample_ratio, mode='nearest'))
                    feature_projection.add_module("conv1", nn.Conv2d(level_n_features_output, level_n_features_output, kernel_size=3, stride=1, padding=1, groups=level_n_features_output))
                    feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output))
                    # B, in_channels, H*upsample_ratio, W*upsample_ratio -> B, full_features_head_dim, H*upsample_ratio, W*upsample_ratio
                    feature_projection.add_module("conv2", nn.Conv2d(level_n_features_output, full_features_head_dim, kernel_size=1, stride=1, padding=0))
            else:
                feature_projection = nn.Sequential()

            self.neck_features_proj.append(feature_projection)

            if i>0 and downsample_enabled[i]:
                upsample_ratio *= 2

    def forward(self, x, il_level=-1, full_features=None):
        if self.neck_start_stage > il_level:
            return full_features

        if full_features is None:
            full_features = self.neck_features_proj[il_level - self.neck_start_stage](x)
        else:
            #upsample torch tensor x to match full_features size, and add to full_features
            feature_projection = self.neck_features_proj[il_level - self.neck_start_stage](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 = full_features + feature_projection
        return full_features

class ERADIO(nn.Module):
    """
    Efficient RADIO
    """

    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,
                 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,
                 use_shift=False,
                 cpb_mlp_hidden=512,
                 conv_groups_ratio=0,
                 verbose: bool = 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
            use_shift: SWIN like window shifting but without masking attention
            conv_groups_ratio: will be used for conv blocks where there is no multires attention,
                                if 0 then normal conv,
                                if 1 then channels are independent,
                                if -1 then no conv at all

        """
        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 = ERADIOLayer(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 < len(depths) - 1),
                                   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,
                                   yolo_arch=yolo_arch,
                                   downsample_shuffle=downsample_shuffle,
                                   conv_base=conv_base,
                                   cpb_mlp_hidden=cpb_mlp_hidden,
                                   use_shift=use_shift,
                                   conv_groups_ratio=conv_groups_ratio,
                                   verbose=verbose)

            self.levels.append(level)

        if self.return_full_features or self.use_neck:
            #num_heads
            downsample_enabled = [self.levels[i-1].downsample is not None for i in range(len(self.levels))]
            self.high_res_neck = HiResNeck(dim, depths, neck_start_stage, full_features_head_dim, downsample_enabled)

        self.switched_to_deploy = False

        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)

    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):
        _, _, H, W = x.shape
        if H % 32 != 0 or W % 32 != 0:
            raise ValueError(f"E-RADIO requires input dimensions to be divisible by 32 but got H x W: {H} x {W}")
        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:
                full_features = self.high_res_neck(pre_downsample_x, il, full_features)

        # x = self.norm(full_features if (self.return_full_features or self.use_neck) else x)
        x = self.norm(x) # new version for

        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.avgpool(x)
        x = torch.flatten(x, 1)

        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
        '''
        if not self.switched_to_deploy:
            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()
        self.switched_to_deploy = True


    def change_window_size(self, new_window_size):
        """
        E-RADIO employs windowed attention, which may be sensitive to the choice of this parameter,
        especially in cases of uneven partitioning of the feature maps.
        E-RADIO allows for the adjustment of the window size after training,
        making it adaptable to different input image resolutions.
        The recommended values for window size based on input resolution are as follows:

        Input Resolution | Window Size
        224 | 7
        256 | 8
        386 | 12
        512 | 16
        Ideally, the window size should be a factor of the input resolution. In the third stage, we divide the resolution by 16, so the window size should be
        img_res/16/2
        for the third stage and img_res/32 for the last stage. While this can be applied in a brute-force manner, a better way is to do model.change_window_size.
        Manual way to change resolution -> model.change_window_size(resolution)
        """
        window_size = new_window_size
        print(f"Setting window size to {window_size}")
        for module in self.modules():
            if hasattr(module, "window_size"):
                # check if tuple or a number
                if isinstance(module.window_size, tuple):
                    if module.window_size[0] != window_size:
                        module.window_size = (window_size, window_size)
                elif isinstance(module.window_size, list):
                    if module.window_size[0] != window_size:
                        module.window_size = [window_size, window_size]
                else:
                    module.window_size = window_size


    def set_optimal_window_size(self, image_dim, max_window_size = 16):
        """
        Using hand picked window size for various resolutions.

        E-RADIO employs windowed attention, which may be sensitive to the choice of this parameter,
        especially in cases of uneven partitioning of the feature maps.
        E-RADIO allows for the adjustment of the window size after training,
        making it adaptable to different input image resolutions.
        The recommended values for window size based on input resolution are as follows:

        Input Resolution | Window Size
        224 | 7
        256 | 8
        386 | 12
        512 | 16
        Ideally, the window size should be a factor of the input resolution. In the third stage, we divide the resolution by 16, so the window size should be
        img_res/16/2
        for the third stage and img_res/32 for the last stage. While this can be applied in a brute-force manner, a better way is to do model.change_window_size.
        Manual way to change resolution -> model.change_window_size(resolution)

        """
        # import math

        def divisorGenerator(n):
            large_divisors = []
            for i in range(1, int(math.sqrt(n) + 1)):
                if n % i == 0:
                    yield i
                    if i*i != n:
                        large_divisors.append(n / i)
            for divisor in reversed(large_divisors):
                yield divisor

        if isinstance(image_dim, list) or isinstance(image_dim, tuple):
            image_dim = min(image_dim)

        # we do windowed attention in the 3rd stage for the first time, therefore //16,
        # we do subsampled attention with downsample by 2 so need to get //32 actually
        # ideally we should rewrite this to be dependent on the structure of the model like what if subsampled is removed etc
        all_divisors = np.array(list(divisorGenerator(image_dim//32)))
        new_window_size = int(min(all_divisors[all_divisors <= max_window_size][-1], max_window_size))

        # for image_dim in [128, 224, 256, 384, 512, 768, 1024]:
        #     all_divisors = np.array(list(divisorGenerator(image_dim//32)))
        #     new_window_size = int(min(all_divisors[all_divisors <= max_window_size][-1], max_window_size))
        #     print(f"Setting window size to {new_window_size} for image resolution {image_dim}")

        self.change_window_size(new_window_size = new_window_size)


@register_model
def eradio_large_fullres_ws16(pretrained=False, **kwargs):
    model = ERADIO(
        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=0.0,
        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)["state_dict"])
    return model


@register_model
def eradio_xxxtiny(pretrained=False, **kwargs):  # ,
    model = ERADIO(
        depths=[1, 3, 4, 5],
        num_heads=[2, 4, 8, 16],
        window_size=[None, None, [16, 16], 16],
        dim=32,
        in_dim=32,
        mlp_ratio=4,
        drop_path_rate=0.0,
        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=256,
        neck_start_stage=2,
        **kwargs,
    )
    if pretrained:
        model.load_state_dict(torch.load(pretrained))
    return model

@register_model
def eradio_xxxtiny_8x_ws12(pretrained=False, **kwargs):
    model = ERADIO(depths=[1, 3, 4, 5],
        num_heads=[2, 4, 8, 16],
        window_size=[None, None, [12, 12], 12],
        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,
        cpb_mlp_hidden=64,
        use_neck=True,
        full_features_head_dim=256,
        neck_start_stage=2,
        conv_groups_ratio = 1,
        **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(pretrained)["state_dict"])
    return model


@register_model
def eradio_xxxtiny_8x_ws16(pretrained=False, **kwargs):
    model = ERADIO(depths=[1, 3, 4, 5],
        num_heads=[2, 4, 8, 16],
        window_size=[None, None, [16, 16], 16],
        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,
        cpb_mlp_hidden=64,
        use_neck=True,
        full_features_head_dim=256,
        neck_start_stage=1,
        conv_groups_ratio = 1,
        **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(pretrained)["state_dict"])
    return model

@register_model
def eradio(pretrained=False, **kwargs):
    return eradio_large_fullres_ws16(pretrained=pretrained, **kwargs)