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#!/usr/bin/env python3
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# Created by Pavlo Molchanov, LPR - DL Efficiency Research team
# based on Fastervit1 from LPR
import 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
from .block import C2f
TRT = False # should help for TRT
import pickle
global bias_indx
bias_indx = -1
DEBUG = False
def pixel_unshuffle(data, factor=2):
# performs nn.PixelShuffle(factor) in reverse, torch has some bug for ONNX and TRT, so doing it manually
B, C, H, W = data.shape
return data.view(B, C, factor, H//factor, factor, W//factor).permute(0,1,2,4,3,5).reshape(B, -1, H//factor, W//factor)
class SwiGLU(nn.Module):
# should be more advanced, but doesnt improve results so far
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
return F.silu(gate) * x
def window_partition(x, window_size):
"""
Args:
x: (B, C, H, W)
window_size: window size
Returns:
windows - local window features (num_windows*B, window_size*window_size, C)
(Hp, Wp) - the size of the padded image
"""
B, C, H, W = x.shape
if window_size == 0 or (window_size==H and window_size==W):
windows = x.flatten(2).transpose(1, 2)
Hp, Wp = H, W
else:
pad_h = (window_size - H % window_size) % window_size
pad_w = (window_size - W % window_size) % window_size
if pad_h > 0 or pad_w > 0:
x = F.pad(x, (0, pad_w, 0, pad_h, 0, 0, 0, 0))
Hp, Wp = H + pad_h, W + pad_w
x = x.view(B, C, Hp // window_size, window_size, Wp // window_size, window_size)
windows = x.permute(0, 2, 4, 3, 5, 1).reshape(-1, window_size*window_size, C)
return windows, (Hp, Wp)
class Conv2d_BN(nn.Module):
'''
Conv2d + BN layer with folding capability to speed up inference
'''
def __init__(self, a, b, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bn_weight_init=1, bias=False):
super().__init__()
self.conv = torch.nn.Conv2d(a, b, kernel_size, stride, padding, dilation, groups, bias=False)
if 1:
self.bn = torch.nn.BatchNorm2d(b)
torch.nn.init.constant_(self.bn.weight, bn_weight_init)
torch.nn.init.constant_(self.bn.bias, 0)
def forward(self,x):
x = self.conv(x)
x = self.bn(x)
return x
@torch.no_grad()
def switch_to_deploy(self):
# return 1
if not isinstance(self.bn, nn.Identity):
c, bn = self.conv, self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps)**0.5
self.conv.weight.data.copy_(w)
self.conv.bias = nn.Parameter(b)
self.bn = nn.Identity()
def window_reverse(windows, window_size, H, W, pad_hw):
"""
Args:
windows: local window features (num_windows*B, window_size, window_size, C)
window_size: Window size
H: Height of image
W: Width of image
pad_w - a tuple of image passing used in windowing step
Returns:
x: (B, C, H, W)
"""
# print(f"window_reverse, windows.shape {windows.shape}")
Hp, Wp = pad_hw
if window_size == 0 or (window_size==H and window_size==W):
B = int(windows.shape[0] / (Hp * Wp / window_size / window_size))
x = windows.transpose(1, 2).view(B, -1, H, W)
else:
B = int(windows.shape[0] / (Hp * Wp / window_size / window_size))
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
x = x.permute(0, 5, 1, 3, 2, 4).reshape(B,windows.shape[2], Hp, Wp)
if Hp > H or Wp > W:
x = x[:, :, :H, :W, ].contiguous()
return x
class PosEmbMLPSwinv2D(nn.Module):
def __init__(self, window_size, pretrained_window_size, num_heads, seq_length, no_log=False):
super().__init__()
self.window_size = window_size
self.num_heads = num_heads
# mlp to generate continuous relative position bias
self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
nn.ReLU(inplace=True),
nn.Linear(512, num_heads, bias=False))
# get relative_coords_table
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
relative_coords_table = torch.stack(
torch.meshgrid([relative_coords_h,
relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
if pretrained_window_size[0] > 0:
relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
else:
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
if not no_log:
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
torch.abs(relative_coords_table) + 1.0) / np.log2(8)
self.register_buffer("relative_coords_table", relative_coords_table)
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.grid_exists = False
self.deploy = False
relative_bias = torch.zeros(1, num_heads, seq_length, seq_length)
self.seq_length = seq_length
self.register_buffer("relative_bias", relative_bias) #for EMA
def switch_to_deploy(self):
self.deploy = True
self.grid_exists = True
def forward(self, input_tensor):
# for efficiency, we want this forward to be folded into a single operation (sum)
# if resolution stays the same, then we dont need to recompute MLP layers
#
# to dynamically adjust patch size over the step
# if not (input_tensor.shape[1:] == self.relative_bias.shape[1:]):
# self.grid_exists = False
if self.training: self.grid_exists = False
if self.deploy and self.grid_exists:
input_tensor += self.relative_bias
return input_tensor
if not self.grid_exists:
self.grid_exists = True
relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1],
-1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
self.relative_bias = relative_position_bias.unsqueeze(0)
input_tensor += self.relative_bias
return input_tensor
class GRAAttentionBlock(nn.Module):
def __init__(self, window_size, dim_in, dim_out,
num_heads, drop_path=0., qk_scale=None, qkv_bias=False,
norm_layer=nn.LayerNorm, layer_scale=None,
use_swiglu=True,
subsample_ratio=1, dim_ratio=1, conv_base=False,
do_windowing=True, multi_query=False) -> None:
super().__init__()
dim = dim_in
# conv_base = True
SHUFFLE = True
SHUFFLE = False
self.do_windowing = do_windowing
if do_windowing:
if SHUFFLE:
self.downsample_op = torch.nn.PixelUnshuffle(subsample_ratio) if subsample_ratio>1 else torch.nn.Identity()
self.downsample_mixer = nn.Conv2d(dim_in * (subsample_ratio * subsample_ratio), dim_in * (dim_ratio), kernel_size=1, stride=1, padding=0, bias=False) if dim*dim_ratio != dim * subsample_ratio * subsample_ratio else torch.nn.Identity()
else:
if conv_base:
self.downsample_op = nn.Conv2d(dim_in, dim_out, kernel_size=subsample_ratio, stride=subsample_ratio) if subsample_ratio > 1 else nn.Identity()
self.downsample_mixer = nn.Identity()
else:
self.downsample_op = nn.AvgPool2d(kernel_size=subsample_ratio, stride=subsample_ratio) if subsample_ratio > 1 else nn.Identity()
self.downsample_mixer = Conv2d_BN(dim_in, dim_out, kernel_size=1, stride=1) if subsample_ratio > 1 else nn.Identity()
if do_windowing:
if SHUFFLE:
self.upsample_mixer =nn.Conv2d(dim_in * dim_ratio, dim_in * (subsample_ratio * subsample_ratio), kernel_size=1, stride=1, padding=0, bias=False) if dim*dim_ratio != dim * subsample_ratio * subsample_ratio else torch.nn.Identity()
self.upsample_op = torch.nn.PixelShuffle(subsample_ratio) if subsample_ratio>1 else torch.nn.Identity()
else:
if conv_base:
self.upsample_mixer = nn.Identity()
self.upsample_op = nn.ConvTranspose2d(dim_in, dim_out, kernel_size=subsample_ratio, stride=subsample_ratio) if subsample_ratio > 1 else nn.Identity()
else:
self.upsample_mixer = nn.Upsample(scale_factor=subsample_ratio, mode='nearest') if subsample_ratio > 1 else nn.Identity()
self.upsample_op = Conv2d_BN(dim_in, dim_out, kernel_size=1, stride=1, padding=0, bias=False) if subsample_ratio > 1 else nn.Identity()
self.window_size = window_size
self.norm1 = norm_layer(dim_in)
if DEBUG:
print(f"GRAAttentionBlock: input_resolution: , window_size: {window_size}, dim_in: {dim_in}, dim_out: {dim_out}, num_heads: {num_heads}, drop_path: {drop_path}, qk_scale: {qk_scale}, qkv_bias: {qkv_bias}, layer_scale: {layer_scale}")
self.attn = WindowAttention(
dim_in,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
resolution=window_size,
seq_length=window_size**2, dim_out=dim_in, multi_query=multi_query)
if DEBUG:
print(f"Attention: dim_in: {dim_in}, num_heads: {num_heads}, qkv_bias: {qkv_bias}, qk_scale: {qk_scale}, resolution: {window_size}, seq_length: {window_size**2}, dim_out: {dim_in}")
print(f"drop_path: {drop_path}, layer_scale: {layer_scale}")
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
use_layer_scale = layer_scale is not None and type(layer_scale) in [int, float]
self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim_in)) if use_layer_scale else 1
### mlp layer
mlp_ratio = 4
self.norm2 = norm_layer(dim_in)
mlp_hidden_dim = int(dim_in * mlp_ratio)
activation = nn.GELU if not use_swiglu else SwiGLU
mlp_hidden_dim = int((4 * dim_in * 1 / 2) / 64) * 64 if use_swiglu else mlp_hidden_dim
self.mlp = Mlp(in_features=dim_in, hidden_features=mlp_hidden_dim, act_layer=activation, use_swiglu=use_swiglu)
self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim_in)) if layer_scale else 1
self.drop_path2=DropPath(drop_path) if drop_path > 0. else nn.Identity()
if DEBUG:
print(f"MLP layer: dim_in: {dim_in}, dim_out: {dim_in}, mlp_hidden_dim: {mlp_hidden_dim}")
print(f"drop_path: {drop_path}, layer_scale: {layer_scale}")
def forward(self, x):
skip_connection = x
if self.do_windowing:
# performing windowing if required
x = self.downsample_op(x)
x = self.downsample_mixer(x)
if self.window_size>0:
H, W = x.shape[2], x.shape[3]
x, pad_hw = window_partition(x, self.window_size)
# window attention
x = x + self.drop_path1(self.gamma1*self.attn(self.norm1(x)))
# mlp layer
x = x + self.drop_path2(self.gamma2*self.mlp(self.norm2(x)))
if self.do_windowing:
if self.window_size > 0:
x = window_reverse(x, self.window_size, H, W, pad_hw)
x = self.upsample_mixer(x)
x = self.upsample_op(x)
if x.shape[2] != skip_connection.shape[2] or x.shape[3] != skip_connection.shape[3]:
x = torch.nn.functional.pad(x, ( 0, -x.shape[3] + skip_connection.shape[3], 0, -x.shape[2] + skip_connection.shape[2]))
# need to add skip connection because downsampling and upsampling will break residual connection
# 0.5 is needed to make sure that the skip connection is not too strong
# in case of no downsample / upsample we can show that 0.5 compensates for the residual connection
x = 0.5 * x + 0.5 * skip_connection
return x
class MultiResolutionAttention(nn.Module):
"""
MultiResolutionAttention (MRA) module
The idea is to use multiple attention blocks with different resolution
Feature maps are downsampled / upsampled for each attention block on different blocks
Every attention block supports
"""
def __init__(self, window_size, sr_ratio,
dim, dim_ratio, num_heads,
do_windowing=True,
layer_scale=1e-5, norm_layer=nn.LayerNorm,
drop_path = 0, qkv_bias=False, qk_scale=1.0,
use_swiglu=True, multi_query=False, conv_base=False) -> None:
"""
Args:
input_resolution: input image resolution
window_size: window size
compression_ratio: compression ratio
max_depth: maximum depth of the GRA module
"""
super().__init__()
depth = len(sr_ratio)
self.attention_blocks = nn.ModuleList()
for i in range(depth):
subsample_ratio = sr_ratio[i]
if len(window_size) > i:
window_size_local = window_size[i]
else:
window_size_local = window_size[0]
self.attention_blocks.append(GRAAttentionBlock(window_size=window_size_local,
dim_in=dim, dim_out=dim, num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, norm_layer=norm_layer,
layer_scale=layer_scale, drop_path=drop_path,
use_swiglu=use_swiglu, subsample_ratio=subsample_ratio, dim_ratio=dim_ratio,
do_windowing=do_windowing, multi_query=multi_query, conv_base=conv_base),
)
def forward(self, x):
for attention_block in self.attention_blocks:
x = attention_block(x)
return x
class Mlp(nn.Module):
"""
Multi-Layer Perceptron (MLP) block
"""
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
use_swiglu=True,
drop=0.):
"""
Args:
in_features: input features dimension.
hidden_features: hidden features dimension.
out_features: output features dimension.
act_layer: activation function.
drop: dropout rate.
"""
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features * (2 if use_swiglu else 1), bias=False)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features, bias=False)
# self.drop = GaussianDropout(drop)
def forward(self, x):
x_size = x.size()
x = x.view(-1, x_size[-1])
x = self.fc1(x)
x = self.act(x)
# x = self.drop(x)
x = self.fc2(x)
# x = self.drop(x)
x = x.view(x_size)
return x
class Downsample(nn.Module):
"""
Down-sampling block
Pixel Unshuffle is used for down-sampling, works great accuracy - wise but takes 10% more TRT time
"""
def __init__(self,
dim,
shuffle = False,
):
"""
Args:
dim: feature size dimension.
shuffle: idea with
keep_dim: bool argument for maintaining the resolution.
"""
super().__init__()
dim_out = 2 * dim
if shuffle:
self.norm = lambda x: pixel_unshuffle(x, factor=2)
self.reduction = Conv2d_BN(dim*4, dim_out, 1, 1, 0, bias=False)
else:
#removed layer norm for better, in this formulation we are getting 10% better speed
# LayerNorm for high resolution inputs will be a pain as it pools over the entire spatial dimension
self.norm = nn.Identity()
self.reduction = Conv2d_BN(dim, dim_out, 3, 2, 1, bias=False)
def forward(self, x):
x = self.norm(x)
x = self.reduction(x)
return x
class PatchEmbed(nn.Module):
"""
Patch embedding block
"""
def __init__(self, in_chans=3, in_dim=64, dim=96, shuffle_down=False):
"""
Args:
in_chans: number of input channels.
in_dim: intermediate feature size dimension to speed up stem.
dim: final stem channel number
shuffle_down: use PixelUnshuffle for down-sampling, effectively increases the receptive field
"""
super().__init__()
# shuffle_down = False
if not shuffle_down:
self.proj = nn.Identity()
self.conv_down = nn.Sequential(
Conv2d_BN(in_chans, in_dim, 3, 2, 1, bias=False),
nn.ReLU(),
Conv2d_BN(in_dim, dim, 3, 2, 1, bias=False),
nn.ReLU()
)
else:
self.proj = lambda x: pixel_unshuffle(x, factor=4)
# self.conv_down = nn.Sequential(Conv2d_BN(in_chans*16, in_dim, 3, 1, 1),
# nn.SiLU(),
# Conv2d_BN(in_dim, dim, 3, 1, 1),
# nn.SiLU(),
# )
self.conv_down = nn.Sequential(Conv2d_BN(in_chans*16, dim, 3, 1, 1),
nn.ReLU(),
)
def forward(self, x):
x = self.proj(x)
x = self.conv_down(x)
return x
class ConvBlock(nn.Module):
"""
Convolutional block, used in first couple of stages
Experimented with plan resnet-18 like modules, they are the best in terms of throughput
Experimented with RepVGG, dont see significant improvement in accuracy
Finally, YOLOv8 idea seem to work fine (resnet-18 like block with squeezed feature dimension, and feature concatendation at the end)
"""
def __init__(self, dim,
drop_path=0.,
layer_scale=None,
kernel_size=3,
rep_vgg=False):
super().__init__()
self.rep_vgg = rep_vgg
if not rep_vgg:
self.conv1 = Conv2d_BN(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
self.act1 = nn.GELU()
else:
self.conv1 = RepVGGBlock(dim, dim, kernel_size=kernel_size, stride=1, padding=1, groups=1)
if not rep_vgg:
self.conv2 = Conv2d_BN(dim, dim, kernel_size=kernel_size, stride=1, padding=1)
else:
self.conv2 = RepVGGBlock(dim, dim, kernel_size=kernel_size, stride=1, padding=1, groups=1)
self.layer_scale = layer_scale
if layer_scale is not None and type(layer_scale) in [int, float]:
self.gamma = nn.Parameter(layer_scale * torch.ones(dim))
self.layer_scale = True
else:
self.layer_scale = False
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
input = x
if not self.rep_vgg:
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
else:
x = self.conv1(x)
x = self.conv2(x)
if self.layer_scale:
x = x * self.gamma.view(1, -1, 1, 1)
x = input + self.drop_path(x)
return x
class WindowAttention(nn.Module):
# Windowed Attention from SwinV2
# use a MLP trick to deal with various input image resolutions, then fold it to improve speed
# tested multi-querry attention, but it is not as good as full attention:
# look into palm: https://github.com/lucidrains/PaLM-pytorch/blob/main/palm_pytorch/palm_pytorch.py
# single kv attention, mlp in parallel (didnt improve speed)
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, resolution=0,
seq_length=0, dim_out=None, multi_query=False):
# taken from EdgeViT and tweaked with attention bias.
super().__init__()
if not dim_out: dim_out = dim
self.multi_query = multi_query
self.num_heads = num_heads
head_dim = dim // num_heads
self.head_dim = dim // num_heads
self.dim_internal = dim
self.scale = qk_scale or head_dim ** -0.5
if not multi_query:
if TRT:
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.k = nn.Linear(dim, dim, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
else:
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
else:
self.qkv = nn.Linear(dim, dim + 2*self.head_dim, bias=qkv_bias)
self.proj = nn.Linear(dim, dim_out, bias=False)
# attention positional bias
self.pos_emb_funct = PosEmbMLPSwinv2D(window_size=[resolution, resolution],
pretrained_window_size=[resolution, resolution],
num_heads=num_heads,
seq_length=seq_length)
self.resolution = resolution
def forward(self, x):
B, N, C = x.shape
if not self.multi_query:
if TRT:
q = self.q(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
k = self.k(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
v = self.v(x).reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
else:
qkv = self.qkv(x).reshape(B, -1, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
else:
qkv = self.qkv(x)
(q, k, v) = qkv.split([self.dim_internal, self.head_dim, self.head_dim], dim=2)
q = q.reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
k = k.reshape(B, -1, 1, C // self.num_heads).permute(0, 2, 1, 3)
v = v.reshape(B, -1, 1, C // self.num_heads).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = self.pos_emb_funct(attn)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, -1, C)
x = self.proj(x)
return x
class FasterViTLayer(nn.Module):
"""
fastervitlayer
"""
def __init__(self,
dim,
depth,
num_heads,
window_size,
conv=False,
downsample=True,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
norm_layer=nn.LayerNorm,
drop_path=0.,
layer_scale=None,
layer_scale_conv=None,
sr_dim_ratio=1,
sr_ratio=1,
multi_query=False,
use_swiglu=True,
rep_vgg=False,
yolo_arch=False,
downsample_shuffle=False,
conv_base=False,
):
"""
Args:
dim: feature size dimension.
depth: number of layers in each stage.
input_resolution: input image resolution.
window_size: window size in each stage.
downsample: bool argument for down-sampling.
mlp_ratio: MLP ratio.
num_heads: number of heads in each stage.
qkv_bias: bool argument for query, key, value learnable bias.
qk_scale: bool argument to scaling query, key.
drop: dropout rate.
attn_drop: attention dropout rate.
drop_path: drop path rate.
norm_layer: normalization layer.
layer_scale: layer scaling coefficient.
"""
super().__init__()
self.conv = conv
self.yolo_arch=False
if conv:
if not yolo_arch:
self.blocks = nn.ModuleList([
ConvBlock(dim=dim,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
layer_scale=layer_scale_conv, rep_vgg=rep_vgg)
for i in range(depth)])
else:
self.blocks = C2f(dim,dim,n=depth,shortcut=True,e=0.5)
self.yolo_arch=True
else:
if not isinstance(window_size, list): window_size = [window_size]
self.window_size = window_size[0]
self.do_single_windowing = True
if not isinstance(sr_ratio, list): sr_ratio = [sr_ratio]
if any([sr!=1 for sr in sr_ratio]) or len(set(window_size))>1:
self.do_single_windowing = False
do_windowing = True
else:
self.do_single_windowing = True
do_windowing = False
self.blocks = nn.ModuleList()
for i in range(depth):
self.blocks.append(
MultiResolutionAttention(window_size=window_size,
sr_ratio=sr_ratio,
dim=dim,
dim_ratio = sr_dim_ratio,
num_heads=num_heads,
norm_layer=norm_layer,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
layer_scale=layer_scale,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
use_swiglu=use_swiglu,
do_windowing=do_windowing,
multi_query=multi_query,
conv_base=conv_base,
))
self.transformer = not conv
self.downsample = None if not downsample else Downsample(dim=dim, shuffle=downsample_shuffle)
def forward(self, x):
B, C, H, W = x.shape
if self.transformer and self.do_single_windowing:
H, W = x.shape[2], x.shape[3]
x, pad_hw = window_partition(x, self.window_size)
if not self.yolo_arch:
for bn, blk in enumerate(self.blocks):
x = blk(x)
else:
x = self.blocks(x)
if self.transformer and self.do_single_windowing:
x = window_reverse(x, self.window_size, H, W, pad_hw)
if self.downsample is None:
return x, x
return self.downsample(x), x #changing to output pre downsampled features
class FasterViT(nn.Module):
"""
FasterViT
"""
def __init__(self,
dim,
in_dim,
depths,
window_size,
mlp_ratio,
num_heads,
drop_path_rate=0.2,
in_chans=3,
num_classes=1000,
qkv_bias=False,
qk_scale=None,
layer_scale=None,
layer_scale_conv=None,
layer_norm_last=False,
sr_ratio = [1, 1, 1, 1],
max_depth = -1,
conv_base=False,
use_swiglu=False,
multi_query=False,
norm_layer=nn.LayerNorm,
rep_vgg=False,
drop_uniform=False,
yolo_arch=False,
shuffle_down=False,
downsample_shuffle=False,
return_full_features=False,
full_features_head_dim=128,
neck_start_stage=1,
use_neck=False,
**kwargs):
"""
Args:
dim: feature size dimension.
depths: number of layers in each stage.
window_size: window size in each stage.
mlp_ratio: MLP ratio.
num_heads: number of heads in each stage.
drop_path_rate: drop path rate.
in_chans: number of input channels.
num_classes: number of classes.
qkv_bias: bool argument for query, key, value learnable bias.
qk_scale: bool argument to scaling query, key.
drop_rate: dropout rate.
attn_drop_rate: attention dropout rate.
norm_layer: normalization layer.
layer_scale: layer scaling coefficient.
return_full_features: output dense features as well as logits
full_features_head_dim: number of channels in the dense features head
neck_start_stage: a stage id to start full feature neck. Model has 4 stages, indix starts with 0
for 224 resolution, the output of the stage before downsample:
stage 0: 56x56, stage 1: 28x28, stage 2: 14x14, stage 3: 7x7
use_neck: even for summarization embedding use neck
"""
super().__init__()
num_features = int(dim * 2 ** (len(depths) - 1))
self.num_classes = num_classes
self.patch_embed = PatchEmbed(in_chans=in_chans, in_dim=in_dim, dim=dim, shuffle_down=shuffle_down)
# set return_full_features true if we want to return full features from all stages
self.return_full_features = return_full_features
self.use_neck = use_neck
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
if drop_uniform:
dpr = [drop_path_rate for x in range(sum(depths))]
if not isinstance(max_depth, list): max_depth = [max_depth] * len(depths)
self.levels = nn.ModuleList()
for i in range(len(depths)):
conv = True if (i == 0 or i == 1) else False
level = FasterViTLayer(dim=int(dim * 2 ** i),
depth=depths[i],
num_heads=num_heads[i],
window_size=window_size[i],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
conv=conv,
drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],
downsample=(i < 3),
layer_scale=layer_scale,
layer_scale_conv=layer_scale_conv,
sr_ratio=sr_ratio[i],
use_swiglu=use_swiglu,
multi_query=multi_query,
norm_layer=norm_layer,
rep_vgg=rep_vgg,
yolo_arch=yolo_arch,
downsample_shuffle=downsample_shuffle,
conv_base=conv_base)
self.levels.append(level)
if self.return_full_features or self.use_neck:
# create feature projection layers for segmentation output
self.neck_features_proj = nn.ModuleList()
self.neck_start_stage = neck_start_stage
upsample_ratio = 1
for i in range(len(depths)):
level_n_features_output = int(dim * 2 ** i)
if self.neck_start_stage > i: continue
if (upsample_ratio > 1) or full_features_head_dim!=level_n_features_output:
feature_projection = nn.Sequential()
# feature_projection.add_module("norm",LayerNorm2d(level_n_features_output)) #slow, but better
if 0 :
# Train: 0 [1900/10009 ( 19%)] Loss: 6.113 (6.57) Time: 0.548s, 233.40/s (0.549s, 233.04/s) LR: 1.000e-05 Data: 0.015 (0.013)
feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output)) #fast, but worse
feature_projection.add_module("dconv", nn.ConvTranspose2d(level_n_features_output,
full_features_head_dim, kernel_size=upsample_ratio, stride=upsample_ratio))
else:
# pixel shuffle based upsampling
# Train: 0 [1950/10009 ( 19%)] Loss: 6.190 (6.55) Time: 0.540s, 236.85/s (0.548s, 233.38/s) LR: 1.000e-05 Data: 0.015 (0.013)
feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output)) #fast, but worse
feature_projection.add_module("conv", nn.Conv2d(level_n_features_output,
full_features_head_dim*upsample_ratio*upsample_ratio, kernel_size=1, stride=1))
feature_projection.add_module("upsample_pixelshuffle", nn.PixelShuffle(upsample_ratio))
else:
feature_projection = nn.Sequential()
feature_projection.add_module("norm",nn.BatchNorm2d(level_n_features_output))
self.neck_features_proj.append(feature_projection)
if i>0 and self.levels[i-1].downsample is not None:
upsample_ratio *= 2
num_features = full_features_head_dim if (self.return_full_features or self.use_neck) else num_features
self.num_features = num_features
self.norm = LayerNorm2d(num_features) if layer_norm_last else nn.BatchNorm2d(num_features)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.head = nn.Linear(num_features, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
# pass
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, LayerNorm2d):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'rpb'}
def forward_features(self, x):
x = self.patch_embed(x)
full_features = None
for il, level in enumerate(self.levels):
x, pre_downsample_x = level(x)
if self.return_full_features or self.use_neck:
if self.neck_start_stage > il: continue
if full_features is None:
full_features = self.neck_features_proj[il - self.neck_start_stage](pre_downsample_x)
else:
#upsample torch tensor x to match full_features size, and add to full_features
feature_projection = self.neck_features_proj[il - self.neck_start_stage](pre_downsample_x)
if feature_projection.shape[2] != full_features.shape[2] or feature_projection.shape[3] != full_features.shape[3]:
feature_projection = torch.nn.functional.pad(feature_projection, ( 0, -feature_projection.shape[3] + full_features.shape[3], 0, -feature_projection.shape[2] + full_features.shape[2]))
full_features += feature_projection
# x = self.norm(full_features if (self.return_full_features or self.use_neck) else x)
x = self.norm(x) # new version for
x = self.avgpool(x)
x = torch.flatten(x, 1)
if not self.return_full_features:
return x, None
return x, full_features
def forward(self, x):
x, full_features = self.forward_features(x)
x = self.head(x)
if full_features is not None:
return x, full_features
return x
def switch_to_deploy(self):
'''
A method to perform model self-compression
merges BN into conv layers
converts MLP relative positional bias into precomputed buffers
'''
for level in [self.patch_embed, self.levels, self.head]:
for module in level.modules():
if hasattr(module, 'switch_to_deploy'):
module.switch_to_deploy()
@register_model
def fastervit2_small(pretrained=False, **kwargs): #,
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=96,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [1, 2], 1],
use_swiglu=False,
downsample_shuffle=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_tiny(pretrained=False, **kwargs): #,
model = FasterViT(depths=[1, 3, 4, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=80,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
downsample_shuffle=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_base(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_base_fullres1(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=1024,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_base_fullres2(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=512,
neck_start_stage=1,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_base_fullres3(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=256,
neck_start_stage=1,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_base_fullres4(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=256,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_base_fullres5(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=512,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
#pyt: 1934, 4202 TRT
@register_model
def fastervit2_large(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128+64,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_large_fullres(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[None, None, [7, 7], 7],
dim=192,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=1536,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_large_fullres_ws8(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[None, None, [8, 8], 8],
dim=192,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=1536,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_large_fullres_ws16(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[None, None, [16, 16], 16],
dim=192,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=1536,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_large_fullres_ws32(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[None, None, [32, 32], 32],
dim=192,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
conv_base=True,
use_neck=True,
full_features_head_dim=1536,
neck_start_stage=2,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
#pyt: 897
@register_model
def fastervit2_xlarge(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128+128+64,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
#pyt:
@register_model
def fastervit2_huge(pretrained=False, **kwargs):
model = FasterViT(depths=[3, 3, 5, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=128+128+128+64,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.2,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_xtiny(pretrained=False, **kwargs): #,
model = FasterViT(depths=[1, 3, 4, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=64,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.1,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
downsample_shuffle=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_xxtiny_5(pretrained=False, **kwargs): #,
model = FasterViT(depths=[1, 3, 4, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=48,
in_dim=64,
mlp_ratio=4,
drop_path_rate=0.05,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
downsample_shuffle=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def fastervit2_xxxtiny(pretrained=False, **kwargs): #,
model = FasterViT(depths=[1, 3, 4, 5],
num_heads=[2, 4, 8, 16],
window_size=[8, 8, [7, 7], 7],
dim=32,
in_dim=32,
mlp_ratio=4,
drop_path_rate=0.0,
sr_ratio=[1, 1, [2, 1], 1],
use_swiglu=False,
downsample_shuffle=False,
yolo_arch=True,
shuffle_down=False,
**kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
return model
@register_model
def eradio(pretrained=False, **kwargs):
return fastervit2_large_fullres(pretrained=pretrained, **kwargs)