RADIO / eradio_model.py

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	#!/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 (FasterViTv2) 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_windowsB, window_sizewindow_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, 2Wh-1, 2Ww-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, WhWw, WhWw
	relative_coords = relative_coords.permute(
	1, 2, 0
	).contiguous() # WhWw, WhWw, 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) # WhWw, WhWw

	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,
	) # WhWw,WhWw,nH

	relative_position_bias = relative_position_bias.permute(
	2, 0, 1
	).contiguous() # nH, WhWw, WhWw
	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 FasterViTv1
	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 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,
	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 FasterViTv2
	"""
	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, Hupsample_ratio, Wupsample_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, Hupsample_ratio, Wupsample_ratio -> B, full_features_head_dim, Hupsample_ratio, Wupsample_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 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,
	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 = 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 < 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):
	"""
	FasterViT employs windowed attention, which may be sensitive to the choice of this parameter,
	especially in cases of uneven partitioning of the feature maps.
	FasterViT 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.

	FasterViT employs windowed attention, which may be sensitive to the choice of this parameter,
	especially in cases of uneven partitioning of the feature maps.
	FasterViT 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)

	# 83.44200001953125
	@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)["state_dict"])
	return model

	# 82.61
	@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)["state_dict"])
	return model

	#'top1', 84.31800001220704
	@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)["state_dict"])
	return model

	#84.39999999267579
	@register_model
	def fastervit2_base_v1(pretrained=False, **kwargs):
	model = FasterViT(depths=[4, 4, 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,
	downsample_shuffle=False,
	**kwargs)
	if pretrained:
	model.load_state_dict(torch.load(pretrained)["state_dict"])
	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)["state_dict"])
	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)["state_dict"])
	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)["state_dict"])
	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)["state_dict"])
	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)["state_dict"])
	return model

	#84.87
	@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=0.3,
	sr_ratio=[1, 1, [2, 1], 1],
	use_swiglu=False,
	yolo_arch=False,
	shuffle_down=False,
	cpb_mlp_hidden=64,
	conv_base=True,
	**kwargs)
	if pretrained:
	model.load_state_dict(torch.load(pretrained)["state_dict"])
	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=0.0,
	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 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=0.0,
	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 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=0.0,
	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 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=0.0,
	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

	#85.23% top1
	@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=0.4,
	sr_ratio=[1, 1, [2, 1], 1],
	use_swiglu=False,
	yolo_arch=False,
	shuffle_down=False,
	cpb_mlp_hidden=64,
	**kwargs)
	if pretrained:
	model.load_state_dict(torch.load(pretrained)["state_dict"])
	return model

	@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)["state_dict"])
	return model


	# 81.61
	@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,
	cpb_mlp_hidden=64,
	**kwargs)
	if pretrained:
	model.load_state_dict(torch.load(pretrained)["state_dict"])
	return model


	# 80.19
	@register_model
	def fastervit2_xxtiny(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,
	cpb_mlp_hidden=64,
	**kwargs)
	if pretrained:
	model.load_state_dict(torch.load(pretrained)["state_dict"])
	return model

	@register_model
	# 77.0
	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,
	cpb_mlp_hidden=64,
	**kwargs)
	if pretrained:
	model.load_state_dict(torch.load(pretrained)["state_dict"])
	return model


	@register_model
	def fastervit2_xxxtiny_fullres(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,
	cpb_mlp_hidden=64,
	use_neck=True,
	full_features_head_dim=128,
	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_xxxtiny(pretrained=False, **kwargs): # ,
	model = FasterViT(
	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 = FasterViT(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 = FasterViT(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 fastervit2_large_fullres_ws16(pretrained=pretrained, **kwargs)