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# ------------------------------------------------------------------------ | |
# Grounding DINO | |
# url: https://github.com/IDEA-Research/GroundingDINO | |
# Copyright (c) 2023 IDEA. All Rights Reserved. | |
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
# ------------------------------------------------------------------------ | |
# DINO | |
# Copyright (c) 2022 IDEA. All Rights Reserved. | |
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
# -------------------------------------------------------- | |
# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py | |
# -------------------------------------------------------- | |
import numpy as np | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.utils.checkpoint as checkpoint | |
from timm.models.layers import DropPath, to_2tuple, trunc_normal_ | |
from groundingdino.util.misc import NestedTensor | |
class Mlp(nn.Module): | |
"""Multilayer perceptron.""" | |
def __init__( | |
self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 | |
): | |
super().__init__() | |
out_features = out_features or in_features | |
hidden_features = hidden_features or in_features | |
self.fc1 = nn.Linear(in_features, hidden_features) | |
self.act = act_layer() | |
self.fc2 = nn.Linear(hidden_features, out_features) | |
self.drop = nn.Dropout(drop) | |
def forward(self, x): | |
x = self.fc1(x) | |
x = self.act(x) | |
x = self.drop(x) | |
x = self.fc2(x) | |
x = self.drop(x) | |
return x | |
def window_partition(x, window_size): | |
""" | |
Args: | |
x: (B, H, W, C) | |
window_size (int): window size | |
Returns: | |
windows: (num_windows*B, window_size, window_size, C) | |
""" | |
B, H, W, C = x.shape | |
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) | |
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) | |
return windows | |
def window_reverse(windows, window_size, H, W): | |
""" | |
Args: | |
windows: (num_windows*B, window_size, window_size, C) | |
window_size (int): Window size | |
H (int): Height of image | |
W (int): Width of image | |
Returns: | |
x: (B, H, W, C) | |
""" | |
B = int(windows.shape[0] / (H * W / window_size / window_size)) | |
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) | |
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) | |
return x | |
class WindowAttention(nn.Module): | |
"""Window based multi-head self attention (W-MSA) module with relative position bias. | |
It supports both of shifted and non-shifted window. | |
Args: | |
dim (int): Number of input channels. | |
window_size (tuple[int]): The height and width of the window. | |
num_heads (int): Number of attention heads. | |
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True | |
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set | |
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 | |
proj_drop (float, optional): Dropout ratio of output. Default: 0.0 | |
""" | |
def __init__( | |
self, | |
dim, | |
window_size, | |
num_heads, | |
qkv_bias=True, | |
qk_scale=None, | |
attn_drop=0.0, | |
proj_drop=0.0, | |
): | |
super().__init__() | |
self.dim = dim | |
self.window_size = window_size # Wh, Ww | |
self.num_heads = num_heads | |
head_dim = dim // num_heads | |
self.scale = qk_scale or head_dim**-0.5 | |
# define a parameter table of relative position bias | |
self.relative_position_bias_table = nn.Parameter( | |
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) | |
) # 2*Wh-1 * 2*Ww-1, nH | |
# get pair-wise relative position index for each token inside the window | |
coords_h = torch.arange(self.window_size[0]) | |
coords_w = torch.arange(self.window_size[1]) | |
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 | |
relative_coords[:, :, 1] += self.window_size[1] - 1 | |
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 | |
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
self.register_buffer("relative_position_index", relative_position_index) | |
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) | |
self.attn_drop = nn.Dropout(attn_drop) | |
self.proj = nn.Linear(dim, dim) | |
self.proj_drop = nn.Dropout(proj_drop) | |
trunc_normal_(self.relative_position_bias_table, std=0.02) | |
self.softmax = nn.Softmax(dim=-1) | |
def forward(self, x, mask=None): | |
"""Forward function. | |
Args: | |
x: input features with shape of (num_windows*B, N, C) | |
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None | |
""" | |
B_, N, C = x.shape | |
qkv = ( | |
self.qkv(x) | |
.reshape(B_, N, 3, self.num_heads, C // self.num_heads) | |
.permute(2, 0, 3, 1, 4) | |
) | |
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) | |
q = q * self.scale | |
attn = q @ k.transpose(-2, -1) | |
relative_position_bias = self.relative_position_bias_table[ | |
self.relative_position_index.view(-1) | |
].view( | |
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 | |
) # Wh*Ww,Wh*Ww,nH | |
relative_position_bias = relative_position_bias.permute( | |
2, 0, 1 | |
).contiguous() # nH, Wh*Ww, Wh*Ww | |
attn = attn + relative_position_bias.unsqueeze(0) | |
if mask is not None: | |
nW = mask.shape[0] | |
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) | |
attn = attn.view(-1, self.num_heads, N, N) | |
attn = self.softmax(attn) | |
else: | |
attn = self.softmax(attn) | |
attn = self.attn_drop(attn) | |
x = (attn @ v).transpose(1, 2).reshape(B_, N, C) | |
x = self.proj(x) | |
x = self.proj_drop(x) | |
return x | |
class SwinTransformerBlock(nn.Module): | |
"""Swin Transformer Block. | |
Args: | |
dim (int): Number of input channels. | |
num_heads (int): Number of attention heads. | |
window_size (int): Window size. | |
shift_size (int): Shift size for SW-MSA. | |
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. | |
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True | |
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. | |
drop (float, optional): Dropout rate. Default: 0.0 | |
attn_drop (float, optional): Attention dropout rate. Default: 0.0 | |
drop_path (float, optional): Stochastic depth rate. Default: 0.0 | |
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU | |
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm | |
""" | |
def __init__( | |
self, | |
dim, | |
num_heads, | |
window_size=7, | |
shift_size=0, | |
mlp_ratio=4.0, | |
qkv_bias=True, | |
qk_scale=None, | |
drop=0.0, | |
attn_drop=0.0, | |
drop_path=0.0, | |
act_layer=nn.GELU, | |
norm_layer=nn.LayerNorm, | |
): | |
super().__init__() | |
self.dim = dim | |
self.num_heads = num_heads | |
self.window_size = window_size | |
self.shift_size = shift_size | |
self.mlp_ratio = mlp_ratio | |
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" | |
self.norm1 = norm_layer(dim) | |
self.attn = WindowAttention( | |
dim, | |
window_size=to_2tuple(self.window_size), | |
num_heads=num_heads, | |
qkv_bias=qkv_bias, | |
qk_scale=qk_scale, | |
attn_drop=attn_drop, | |
proj_drop=drop, | |
) | |
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() | |
self.norm2 = norm_layer(dim) | |
mlp_hidden_dim = int(dim * mlp_ratio) | |
self.mlp = Mlp( | |
in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop | |
) | |
self.H = None | |
self.W = None | |
def forward(self, x, mask_matrix): | |
"""Forward function. | |
Args: | |
x: Input feature, tensor size (B, H*W, C). | |
H, W: Spatial resolution of the input feature. | |
mask_matrix: Attention mask for cyclic shift. | |
""" | |
B, L, C = x.shape | |
H, W = self.H, self.W | |
assert L == H * W, "input feature has wrong size" | |
shortcut = x | |
x = self.norm1(x) | |
x = x.view(B, H, W, C) | |
# pad feature maps to multiples of window size | |
pad_l = pad_t = 0 | |
pad_r = (self.window_size - W % self.window_size) % self.window_size | |
pad_b = (self.window_size - H % self.window_size) % self.window_size | |
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) | |
_, Hp, Wp, _ = x.shape | |
# cyclic shift | |
if self.shift_size > 0: | |
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) | |
attn_mask = mask_matrix | |
else: | |
shifted_x = x | |
attn_mask = None | |
# partition windows | |
x_windows = window_partition( | |
shifted_x, self.window_size | |
) # nW*B, window_size, window_size, C | |
x_windows = x_windows.view( | |
-1, self.window_size * self.window_size, C | |
) # nW*B, window_size*window_size, C | |
# W-MSA/SW-MSA | |
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C | |
# merge windows | |
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) | |
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C | |
# reverse cyclic shift | |
if self.shift_size > 0: | |
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) | |
else: | |
x = shifted_x | |
if pad_r > 0 or pad_b > 0: | |
x = x[:, :H, :W, :].contiguous() | |
x = x.view(B, H * W, C) | |
# FFN | |
x = shortcut + self.drop_path(x) | |
x = x + self.drop_path(self.mlp(self.norm2(x))) | |
return x | |
class PatchMerging(nn.Module): | |
"""Patch Merging Layer | |
Args: | |
dim (int): Number of input channels. | |
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm | |
""" | |
def __init__(self, dim, norm_layer=nn.LayerNorm): | |
super().__init__() | |
self.dim = dim | |
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) | |
self.norm = norm_layer(4 * dim) | |
def forward(self, x, H, W): | |
"""Forward function. | |
Args: | |
x: Input feature, tensor size (B, H*W, C). | |
H, W: Spatial resolution of the input feature. | |
""" | |
B, L, C = x.shape | |
assert L == H * W, "input feature has wrong size" | |
x = x.view(B, H, W, C) | |
# padding | |
pad_input = (H % 2 == 1) or (W % 2 == 1) | |
if pad_input: | |
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) | |
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C | |
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C | |
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C | |
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C | |
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C | |
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C | |
x = self.norm(x) | |
x = self.reduction(x) | |
return x | |
class BasicLayer(nn.Module): | |
"""A basic Swin Transformer layer for one stage. | |
Args: | |
dim (int): Number of feature channels | |
depth (int): Depths of this stage. | |
num_heads (int): Number of attention head. | |
window_size (int): Local window size. Default: 7. | |
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. | |
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True | |
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. | |
drop (float, optional): Dropout rate. Default: 0.0 | |
attn_drop (float, optional): Attention dropout rate. Default: 0.0 | |
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 | |
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm | |
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None | |
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. | |
""" | |
def __init__( | |
self, | |
dim, | |
depth, | |
num_heads, | |
window_size=7, | |
mlp_ratio=4.0, | |
qkv_bias=True, | |
qk_scale=None, | |
drop=0.0, | |
attn_drop=0.0, | |
drop_path=0.0, | |
norm_layer=nn.LayerNorm, | |
downsample=None, | |
use_checkpoint=False, | |
): | |
super().__init__() | |
self.window_size = window_size | |
self.shift_size = window_size // 2 | |
self.depth = depth | |
self.use_checkpoint = use_checkpoint | |
# build blocks | |
self.blocks = nn.ModuleList( | |
[ | |
SwinTransformerBlock( | |
dim=dim, | |
num_heads=num_heads, | |
window_size=window_size, | |
shift_size=0 if (i % 2 == 0) else window_size // 2, | |
mlp_ratio=mlp_ratio, | |
qkv_bias=qkv_bias, | |
qk_scale=qk_scale, | |
drop=drop, | |
attn_drop=attn_drop, | |
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, | |
norm_layer=norm_layer, | |
) | |
for i in range(depth) | |
] | |
) | |
# patch merging layer | |
if downsample is not None: | |
self.downsample = downsample(dim=dim, norm_layer=norm_layer) | |
else: | |
self.downsample = None | |
def forward(self, x, H, W): | |
"""Forward function. | |
Args: | |
x: Input feature, tensor size (B, H*W, C). | |
H, W: Spatial resolution of the input feature. | |
""" | |
# calculate attention mask for SW-MSA | |
Hp = int(np.ceil(H / self.window_size)) * self.window_size | |
Wp = int(np.ceil(W / self.window_size)) * self.window_size | |
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 | |
h_slices = ( | |
slice(0, -self.window_size), | |
slice(-self.window_size, -self.shift_size), | |
slice(-self.shift_size, None), | |
) | |
w_slices = ( | |
slice(0, -self.window_size), | |
slice(-self.window_size, -self.shift_size), | |
slice(-self.shift_size, None), | |
) | |
cnt = 0 | |
for h in h_slices: | |
for w in w_slices: | |
img_mask[:, h, w, :] = cnt | |
cnt += 1 | |
mask_windows = window_partition( | |
img_mask, self.window_size | |
) # nW, window_size, window_size, 1 | |
mask_windows = mask_windows.view(-1, self.window_size * self.window_size) | |
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) | |
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( | |
attn_mask == 0, float(0.0) | |
) | |
for blk in self.blocks: | |
blk.H, blk.W = H, W | |
if self.use_checkpoint: | |
x = checkpoint.checkpoint(blk, x, attn_mask) | |
else: | |
x = blk(x, attn_mask) | |
if self.downsample is not None: | |
x_down = self.downsample(x, H, W) | |
Wh, Ww = (H + 1) // 2, (W + 1) // 2 | |
return x, H, W, x_down, Wh, Ww | |
else: | |
return x, H, W, x, H, W | |
class PatchEmbed(nn.Module): | |
"""Image to Patch Embedding | |
Args: | |
patch_size (int): Patch token size. Default: 4. | |
in_chans (int): Number of input image channels. Default: 3. | |
embed_dim (int): Number of linear projection output channels. Default: 96. | |
norm_layer (nn.Module, optional): Normalization layer. Default: None | |
""" | |
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): | |
super().__init__() | |
patch_size = to_2tuple(patch_size) | |
self.patch_size = patch_size | |
self.in_chans = in_chans | |
self.embed_dim = embed_dim | |
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) | |
if norm_layer is not None: | |
self.norm = norm_layer(embed_dim) | |
else: | |
self.norm = None | |
def forward(self, x): | |
"""Forward function.""" | |
# padding | |
_, _, H, W = x.size() | |
if W % self.patch_size[1] != 0: | |
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) | |
if H % self.patch_size[0] != 0: | |
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) | |
x = self.proj(x) # B C Wh Ww | |
if self.norm is not None: | |
Wh, Ww = x.size(2), x.size(3) | |
x = x.flatten(2).transpose(1, 2) | |
x = self.norm(x) | |
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) | |
return x | |
class SwinTransformer(nn.Module): | |
"""Swin Transformer backbone. | |
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - | |
https://arxiv.org/pdf/2103.14030 | |
Args: | |
pretrain_img_size (int): Input image size for training the pretrained model, | |
used in absolute postion embedding. Default 224. | |
patch_size (int | tuple(int)): Patch size. Default: 4. | |
in_chans (int): Number of input image channels. Default: 3. | |
embed_dim (int): Number of linear projection output channels. Default: 96. | |
depths (tuple[int]): Depths of each Swin Transformer stage. | |
num_heads (tuple[int]): Number of attention head of each stage. | |
window_size (int): Window size. Default: 7. | |
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. | |
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True | |
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. | |
drop_rate (float): Dropout rate. | |
attn_drop_rate (float): Attention dropout rate. Default: 0. | |
drop_path_rate (float): Stochastic depth rate. Default: 0.2. | |
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. | |
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. | |
patch_norm (bool): If True, add normalization after patch embedding. Default: True. | |
out_indices (Sequence[int]): Output from which stages. | |
frozen_stages (int): Stages to be frozen (stop grad and set eval mode). | |
-1 means not freezing any parameters. | |
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. | |
dilation (bool): if True, the output size if 16x downsample, ow 32x downsample. | |
""" | |
def __init__( | |
self, | |
pretrain_img_size=224, | |
patch_size=4, | |
in_chans=3, | |
embed_dim=96, | |
depths=[2, 2, 6, 2], | |
num_heads=[3, 6, 12, 24], | |
window_size=7, | |
mlp_ratio=4.0, | |
qkv_bias=True, | |
qk_scale=None, | |
drop_rate=0.0, | |
attn_drop_rate=0.0, | |
drop_path_rate=0.2, | |
norm_layer=nn.LayerNorm, | |
ape=False, | |
patch_norm=True, | |
out_indices=(0, 1, 2, 3), | |
frozen_stages=-1, | |
dilation=False, | |
use_checkpoint=False, | |
): | |
super().__init__() | |
self.pretrain_img_size = pretrain_img_size | |
self.num_layers = len(depths) | |
self.embed_dim = embed_dim | |
self.ape = ape | |
self.patch_norm = patch_norm | |
self.out_indices = out_indices | |
self.frozen_stages = frozen_stages | |
self.dilation = dilation | |
# if use_checkpoint: | |
# print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!") | |
# split image into non-overlapping patches | |
self.patch_embed = PatchEmbed( | |
patch_size=patch_size, | |
in_chans=in_chans, | |
embed_dim=embed_dim, | |
norm_layer=norm_layer if self.patch_norm else None, | |
) | |
# absolute position embedding | |
if self.ape: | |
pretrain_img_size = to_2tuple(pretrain_img_size) | |
patch_size = to_2tuple(patch_size) | |
patches_resolution = [ | |
pretrain_img_size[0] // patch_size[0], | |
pretrain_img_size[1] // patch_size[1], | |
] | |
self.absolute_pos_embed = nn.Parameter( | |
torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) | |
) | |
trunc_normal_(self.absolute_pos_embed, std=0.02) | |
self.pos_drop = nn.Dropout(p=drop_rate) | |
# stochastic depth | |
dpr = [ | |
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) | |
] # stochastic depth decay rule | |
# build layers | |
self.layers = nn.ModuleList() | |
# prepare downsample list | |
downsamplelist = [PatchMerging for i in range(self.num_layers)] | |
downsamplelist[-1] = None | |
num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] | |
if self.dilation: | |
downsamplelist[-2] = None | |
num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2 | |
for i_layer in range(self.num_layers): | |
layer = BasicLayer( | |
# dim=int(embed_dim * 2 ** i_layer), | |
dim=num_features[i_layer], | |
depth=depths[i_layer], | |
num_heads=num_heads[i_layer], | |
window_size=window_size, | |
mlp_ratio=mlp_ratio, | |
qkv_bias=qkv_bias, | |
qk_scale=qk_scale, | |
drop=drop_rate, | |
attn_drop=attn_drop_rate, | |
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], | |
norm_layer=norm_layer, | |
# downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, | |
downsample=downsamplelist[i_layer], | |
use_checkpoint=use_checkpoint, | |
) | |
self.layers.append(layer) | |
# num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] | |
self.num_features = num_features | |
# add a norm layer for each output | |
for i_layer in out_indices: | |
layer = norm_layer(num_features[i_layer]) | |
layer_name = f"norm{i_layer}" | |
self.add_module(layer_name, layer) | |
self._freeze_stages() | |
def _freeze_stages(self): | |
if self.frozen_stages >= 0: | |
self.patch_embed.eval() | |
for param in self.patch_embed.parameters(): | |
param.requires_grad = False | |
if self.frozen_stages >= 1 and self.ape: | |
self.absolute_pos_embed.requires_grad = False | |
if self.frozen_stages >= 2: | |
self.pos_drop.eval() | |
for i in range(0, self.frozen_stages - 1): | |
m = self.layers[i] | |
m.eval() | |
for param in m.parameters(): | |
param.requires_grad = False | |
# def init_weights(self, pretrained=None): | |
# """Initialize the weights in backbone. | |
# Args: | |
# pretrained (str, optional): Path to pre-trained weights. | |
# Defaults to None. | |
# """ | |
# def _init_weights(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) | |
# if isinstance(pretrained, str): | |
# self.apply(_init_weights) | |
# logger = get_root_logger() | |
# load_checkpoint(self, pretrained, strict=False, logger=logger) | |
# elif pretrained is None: | |
# self.apply(_init_weights) | |
# else: | |
# raise TypeError('pretrained must be a str or None') | |
def forward_raw(self, x): | |
"""Forward function.""" | |
x = self.patch_embed(x) | |
Wh, Ww = x.size(2), x.size(3) | |
if self.ape: | |
# interpolate the position embedding to the corresponding size | |
absolute_pos_embed = F.interpolate( | |
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" | |
) | |
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C | |
else: | |
x = x.flatten(2).transpose(1, 2) | |
x = self.pos_drop(x) | |
outs = [] | |
for i in range(self.num_layers): | |
layer = self.layers[i] | |
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) | |
# import ipdb; ipdb.set_trace() | |
if i in self.out_indices: | |
norm_layer = getattr(self, f"norm{i}") | |
x_out = norm_layer(x_out) | |
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() | |
outs.append(out) | |
# in: | |
# torch.Size([2, 3, 1024, 1024]) | |
# outs: | |
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ | |
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] | |
return tuple(outs) | |
def forward(self, tensor_list: NestedTensor): | |
x = tensor_list.tensors | |
"""Forward function.""" | |
x = self.patch_embed(x) | |
Wh, Ww = x.size(2), x.size(3) | |
if self.ape: | |
# interpolate the position embedding to the corresponding size | |
absolute_pos_embed = F.interpolate( | |
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" | |
) | |
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C | |
else: | |
x = x.flatten(2).transpose(1, 2) | |
x = self.pos_drop(x) | |
outs = [] | |
for i in range(self.num_layers): | |
layer = self.layers[i] | |
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) | |
if i in self.out_indices: | |
norm_layer = getattr(self, f"norm{i}") | |
x_out = norm_layer(x_out) | |
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() | |
outs.append(out) | |
# in: | |
# torch.Size([2, 3, 1024, 1024]) | |
# out: | |
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ | |
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] | |
# collect for nesttensors | |
outs_dict = {} | |
for idx, out_i in enumerate(outs): | |
m = tensor_list.mask | |
assert m is not None | |
mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0] | |
outs_dict[idx] = NestedTensor(out_i, mask) | |
return outs_dict | |
def train(self, mode=True): | |
"""Convert the model into training mode while keep layers freezed.""" | |
super(SwinTransformer, self).train(mode) | |
self._freeze_stages() | |
def build_swin_transformer(modelname, pretrain_img_size, **kw): | |
assert modelname in [ | |
"swin_T_224_1k", | |
"swin_B_224_22k", | |
"swin_B_384_22k", | |
"swin_L_224_22k", | |
"swin_L_384_22k", | |
] | |
model_para_dict = { | |
"swin_T_224_1k": dict( | |
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7 | |
), | |
"swin_B_224_22k": dict( | |
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7 | |
), | |
"swin_B_384_22k": dict( | |
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12 | |
), | |
"swin_L_224_22k": dict( | |
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7 | |
), | |
"swin_L_384_22k": dict( | |
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12 | |
), | |
} | |
kw_cgf = model_para_dict[modelname] | |
kw_cgf.update(kw) | |
model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf) | |
return model | |
if __name__ == "__main__": | |
model = build_swin_transformer("swin_L_384_22k", 384, dilation=True) | |
x = torch.rand(2, 3, 1024, 1024) | |
y = model.forward_raw(x) | |
import ipdb | |
ipdb.set_trace() | |
x = torch.rand(2, 3, 384, 384) | |
y = model.forward_raw(x) | |