Source code for mmpretrain.models.backbones.vig
# Copyright (c) OpenMMLab. All rights reserved.
# modified from
# https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
from typing import Sequence
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_activation_layer
from mmcv.cnn.bricks import DropPath
from mmengine.model import ModuleList, Sequential
from torch.nn.modules.batchnorm import _BatchNorm
from mmpretrain.models.backbones.base_backbone import BaseBackbone
from mmpretrain.registry import MODELS
from ..utils import build_norm_layer
def get_2d_relative_pos_embed(embed_dim, grid_size):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, grid_size*grid_size]
"""
pos_embed = get_2d_sincos_pos_embed(embed_dim, grid_size)
relative_pos = 2 * np.matmul(pos_embed,
pos_embed.transpose()) / pos_embed.shape[1]
return relative_pos
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed],
axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def xy_pairwise_distance(x, y):
"""Compute pairwise distance of a point cloud.
Args:
x: tensor (batch_size, num_points, num_dims)
y: tensor (batch_size, num_points, num_dims)
Returns:
pairwise distance: (batch_size, num_points, num_points)
"""
with torch.no_grad():
xy_inner = -2 * torch.matmul(x, y.transpose(2, 1))
x_square = torch.sum(torch.mul(x, x), dim=-1, keepdim=True)
y_square = torch.sum(torch.mul(y, y), dim=-1, keepdim=True)
return x_square + xy_inner + y_square.transpose(2, 1)
def xy_dense_knn_matrix(x, y, k=16, relative_pos=None):
"""Get KNN based on the pairwise distance.
Args:
x: (batch_size, num_dims, num_points, 1)
y: (batch_size, num_dims, num_points, 1)
k: int
relative_pos:Whether to use relative_pos
Returns:
nearest neighbors:
(batch_size, num_points, k) (batch_size, num_points, k)
"""
with torch.no_grad():
x = x.transpose(2, 1).squeeze(-1)
y = y.transpose(2, 1).squeeze(-1)
batch_size, n_points, n_dims = x.shape
dist = xy_pairwise_distance(x.detach(), y.detach())
if relative_pos is not None:
dist += relative_pos
_, nn_idx = torch.topk(-dist, k=k)
center_idx = torch.arange(
0, n_points, device=x.device).repeat(batch_size, k,
1).transpose(2, 1)
return torch.stack((nn_idx, center_idx), dim=0)
class DenseDilated(nn.Module):
"""Find dilated neighbor from neighbor list.
edge_index: (2, batch_size, num_points, k)
"""
def __init__(self, k=9, dilation=1, use_stochastic=False, epsilon=0.0):
super(DenseDilated, self).__init__()
self.dilation = dilation
self.use_stochastic = use_stochastic
self.epsilon = epsilon
self.k = k
def forward(self, edge_index):
if self.use_stochastic:
if torch.rand(1) < self.epsilon and self.training:
num = self.k * self.dilation
randnum = torch.randperm(num)[:self.k]
edge_index = edge_index[:, :, :, randnum]
else:
edge_index = edge_index[:, :, :, ::self.dilation]
else:
edge_index = edge_index[:, :, :, ::self.dilation]
return edge_index
class DenseDilatedKnnGraph(nn.Module):
"""Find the neighbors' indices based on dilated knn."""
def __init__(self, k=9, dilation=1, use_stochastic=False, epsilon=0.0):
super(DenseDilatedKnnGraph, self).__init__()
self.dilation = dilation
self.use_stochastic = use_stochastic
self.epsilon = epsilon
self.k = k
self._dilated = DenseDilated(k, dilation, use_stochastic, epsilon)
def forward(self, x, y=None, relative_pos=None):
if y is not None:
x = F.normalize(x, p=2.0, dim=1)
y = F.normalize(y, p=2.0, dim=1)
edge_index = xy_dense_knn_matrix(x, y, self.k * self.dilation,
relative_pos)
else:
x = F.normalize(x, p=2.0, dim=1)
y = x.clone()
edge_index = xy_dense_knn_matrix(x, y, self.k * self.dilation,
relative_pos)
return self._dilated(edge_index)
class BasicConv(Sequential):
def __init__(self,
channels,
act_cfg,
norm_cfg=None,
graph_conv_bias=True,
drop=0.):
m = []
for i in range(1, len(channels)):
m.append(
nn.Conv2d(
channels[i - 1],
channels[i],
1,
bias=graph_conv_bias,
groups=4))
if norm_cfg is not None:
m.append(build_norm_layer(norm_cfg, channels[-1]))
if act_cfg is not None:
m.append(build_activation_layer(act_cfg))
if drop > 0:
m.append(nn.Dropout2d(drop))
super(BasicConv, self).__init__(*m)
def batched_index_select(x, idx):
r"""fetches neighbors features from a given neighbor idx
Args:
x (Tensor): input feature Tensor
:math:
`\mathbf{X} \in \mathbb{R}^{B \times C \times N \times 1}`.
idx (Tensor): edge_idx
:math:`\mathbf{X} \in \mathbb{R}^{B \times N \times l}`.
Returns:
Tensor: output neighbors features
:math:`\mathbf{X} \in \mathbb{R}^{B \times C \times N \times k}`.
"""
batch_size, num_dims, num_vertices_reduced = x.shape[:3]
_, num_vertices, k = idx.shape
idx_base = torch.arange(
0, batch_size, device=idx.device).view(-1, 1, 1) * num_vertices_reduced
idx = idx + idx_base
idx = idx.contiguous().view(-1)
x = x.transpose(2, 1)
feature = x.contiguous().view(batch_size * num_vertices_reduced,
-1)[idx, :]
feature = feature.view(batch_size, num_vertices, k,
num_dims).permute(0, 3, 1, 2).contiguous()
return feature
class MRConv2d(nn.Module):
"""Max-Relative Graph Convolution (Paper: https://arxiv.org/abs/1904.03751)
for dense data type."""
def __init__(self,
in_channels,
out_channels,
act_cfg,
norm_cfg=None,
graph_conv_bias=True):
super(MRConv2d, self).__init__()
self.nn = BasicConv([in_channels * 2, out_channels], act_cfg, norm_cfg,
graph_conv_bias)
def forward(self, x, edge_index, y=None):
x_i = batched_index_select(x, edge_index[1])
if y is not None:
x_j = batched_index_select(y, edge_index[0])
else:
x_j = batched_index_select(x, edge_index[0])
x_j, _ = torch.max(x_j - x_i, -1, keepdim=True)
b, c, n, _ = x.shape
x = torch.cat([x.unsqueeze(2), x_j.unsqueeze(2)],
dim=2).reshape(b, 2 * c, n, _)
return self.nn(x)
class EdgeConv2d(nn.Module):
"""Edge convolution layer (with activation, batch normalization) for dense
data type."""
def __init__(self,
in_channels,
out_channels,
act_cfg,
norm_cfg=None,
graph_conv_bias=True):
super(EdgeConv2d, self).__init__()
self.nn = BasicConv([in_channels * 2, out_channels], act_cfg, norm_cfg,
graph_conv_bias)
def forward(self, x, edge_index, y=None):
x_i = batched_index_select(x, edge_index[1])
if y is not None:
x_j = batched_index_select(y, edge_index[0])
else:
x_j = batched_index_select(x, edge_index[0])
max_value, _ = torch.max(
self.nn(torch.cat([x_i, x_j - x_i], dim=1)), -1, keepdim=True)
return max_value
class GraphSAGE(nn.Module):
"""GraphSAGE Graph Convolution (Paper: https://arxiv.org/abs/1706.02216)
for dense data type."""
def __init__(self,
in_channels,
out_channels,
act_cfg,
norm_cfg=None,
graph_conv_bias=True):
super(GraphSAGE, self).__init__()
self.nn1 = BasicConv([in_channels, in_channels], act_cfg, norm_cfg,
graph_conv_bias)
self.nn2 = BasicConv([in_channels * 2, out_channels], act_cfg,
norm_cfg, graph_conv_bias)
def forward(self, x, edge_index, y=None):
if y is not None:
x_j = batched_index_select(y, edge_index[0])
else:
x_j = batched_index_select(x, edge_index[0])
x_j, _ = torch.max(self.nn1(x_j), -1, keepdim=True)
return self.nn2(torch.cat([x, x_j], dim=1))
class GINConv2d(nn.Module):
"""GIN Graph Convolution (Paper: https://arxiv.org/abs/1810.00826) for
dense data type."""
def __init__(self,
in_channels,
out_channels,
act_cfg,
norm_cfg=None,
graph_conv_bias=True):
super(GINConv2d, self).__init__()
self.nn = BasicConv([in_channels, out_channels], act_cfg, norm_cfg,
graph_conv_bias)
eps_init = 0.0
self.eps = nn.Parameter(torch.Tensor([eps_init]))
def forward(self, x, edge_index, y=None):
if y is not None:
x_j = batched_index_select(y, edge_index[0])
else:
x_j = batched_index_select(x, edge_index[0])
x_j = torch.sum(x_j, -1, keepdim=True)
return self.nn((1 + self.eps) * x + x_j)
class GraphConv2d(nn.Module):
"""Static graph convolution layer."""
def __init__(self,
in_channels,
out_channels,
graph_conv_type,
act_cfg,
norm_cfg=None,
graph_conv_bias=True):
super(GraphConv2d, self).__init__()
if graph_conv_type == 'edge':
self.gconv = EdgeConv2d(in_channels, out_channels, act_cfg,
norm_cfg, graph_conv_bias)
elif graph_conv_type == 'mr':
self.gconv = MRConv2d(in_channels, out_channels, act_cfg, norm_cfg,
graph_conv_bias)
elif graph_conv_type == 'sage':
self.gconv = GraphSAGE(in_channels, out_channels, act_cfg,
norm_cfg, graph_conv_bias)
elif graph_conv_type == 'gin':
self.gconv = GINConv2d(in_channels, out_channels, act_cfg,
norm_cfg, graph_conv_bias)
else:
raise NotImplementedError(
'graph_conv_type:{} is not supported'.format(graph_conv_type))
def forward(self, x, edge_index, y=None):
return self.gconv(x, edge_index, y)
class DyGraphConv2d(GraphConv2d):
"""Dynamic graph convolution layer."""
def __init__(self,
in_channels,
out_channels,
k=9,
dilation=1,
graph_conv_type='mr',
act_cfg=dict(type='GELU'),
norm_cfg=None,
graph_conv_bias=True,
use_stochastic=False,
epsilon=0.2,
r=1):
super(DyGraphConv2d,
self).__init__(in_channels, out_channels, graph_conv_type,
act_cfg, norm_cfg, graph_conv_bias)
self.k = k
self.d = dilation
self.r = r
self.dilated_knn_graph = DenseDilatedKnnGraph(k, dilation,
use_stochastic, epsilon)
def forward(self, x, relative_pos=None):
B, C, H, W = x.shape
y = None
if self.r > 1:
y = F.avg_pool2d(x, self.r, self.r)
y = y.reshape(B, C, -1, 1).contiguous()
x = x.reshape(B, C, -1, 1).contiguous()
edge_index = self.dilated_knn_graph(x, y, relative_pos)
x = super(DyGraphConv2d, self).forward(x, edge_index, y)
return x.reshape(B, -1, H, W).contiguous()
class Grapher(nn.Module):
"""Grapher module with graph convolution and fc layers."""
def __init__(self,
in_channels,
k=9,
dilation=1,
graph_conv_type='mr',
act_cfg=dict(type='GELU'),
norm_cfg=None,
graph_conv_bias=True,
use_stochastic=False,
epsilon=0.2,
r=1,
n=196,
drop_path=0.0,
relative_pos=False):
super(Grapher, self).__init__()
self.channels = in_channels
self.n = n
self.r = r
self.fc1 = Sequential(
nn.Conv2d(in_channels, in_channels, 1, stride=1, padding=0),
build_norm_layer(dict(type='BN'), in_channels),
)
self.graph_conv = DyGraphConv2d(in_channels, in_channels * 2, k,
dilation, graph_conv_type, act_cfg,
norm_cfg, graph_conv_bias,
use_stochastic, epsilon, r)
self.fc2 = Sequential(
nn.Conv2d(in_channels * 2, in_channels, 1, stride=1, padding=0),
build_norm_layer(dict(type='BN'), in_channels),
)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
self.relative_pos = None
if relative_pos:
relative_pos_tensor = torch.from_numpy(
np.float32(
get_2d_relative_pos_embed(in_channels, int(
n**0.5)))).unsqueeze(0).unsqueeze(1)
relative_pos_tensor = F.interpolate(
relative_pos_tensor,
size=(n, n // (r * r)),
mode='bicubic',
align_corners=False)
self.relative_pos = nn.Parameter(
-relative_pos_tensor.squeeze(1), requires_grad=False)
def _get_relative_pos(self, relative_pos, H, W):
if relative_pos is None or H * W == self.n:
return relative_pos
else:
N = H * W
N_reduced = N // (self.r * self.r)
return F.interpolate(
relative_pos.unsqueeze(0), size=(N, N_reduced),
mode='bicubic').squeeze(0)
def forward(self, x):
B, C, H, W = x.shape
relative_pos = self._get_relative_pos(self.relative_pos, H, W)
shortcut = x
x = self.fc1(x)
x = self.graph_conv(x, relative_pos)
x = self.fc2(x)
x = self.drop_path(x) + shortcut
return x
class FFN(nn.Module):
""""out_features = out_features or in_features\n
hidden_features = hidden_features or in_features"""
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_cfg=dict(type='GELU'),
drop_path=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = Sequential(
nn.Conv2d(in_features, hidden_features, 1, stride=1, padding=0),
build_norm_layer(dict(type='BN'), hidden_features),
)
self.act = build_activation_layer(act_cfg)
self.fc2 = Sequential(
nn.Conv2d(hidden_features, out_features, 1, stride=1, padding=0),
build_norm_layer(dict(type='BN'), out_features),
)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
x = self.drop_path(x) + shortcut
return x
[docs]@MODELS.register_module()
class Vig(BaseBackbone):
"""Vision GNN backbone.
A PyTorch implementation of `Vision GNN: An Image is Worth Graph of Nodes
<https://arxiv.org/abs/2206.00272>`_.
Modified from the official implementation
https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
Args:
arch(str): Vision GNN architecture,
choose from 'tiny', 'small' and 'base'.
in_channels (int): The number of channels of input images.
Defaults to 3.
k (int): The number of KNN's k. Defaults to 9.
out_indices (Sequence | int): Output from which blocks.
Defaults to -1, means the last block.
act_cfg (dict): The config of activative functions.
Defaults to ``dict(type='GELU'))``.
norm_cfg (dict): The config of normalization layers.
Defaults to ``dict(type='BN', eps=1e-6)``.
graph_conv_bias (bool): Whether to use bias in the convolution
layers in Grapher. Defaults to True.
graph_conv_type (str): The type of graph convolution，choose
from 'edge', 'mr', 'sage' and 'gin'. Defaults to 'mr'.
epsilon (float): Probability of random arrangement in KNN. It only
works when ``use_dilation=True`` and ``use_stochastic=True``.
Defaults to 0.2.
use_dilation(bool): Whether to use dilation in KNN. Defaults to True.
use_stochastic(bool): Whether to use stochastic in KNN.
Defaults to False.
drop_path (float): stochastic depth rate. Default 0.0
relative_pos(bool): Whether to use relative position embedding.
Defaults to False.
norm_eval (bool): Whether to set the normalization layer to eval mode.
Defaults to False.
frozen_stages (int): Blocks to be frozen (all param fixed).
Defaults to 0, which means not freezing any parameters.
init_cfg (dict, optional): The initialization configs.
Defaults to None.
""" # noqa: E501
arch_settings = {
'tiny': dict(num_blocks=12, channels=192),
'small': dict(num_blocks=16, channels=320),
'base': dict(num_blocks=16, channels=640),
}
def __init__(self,
arch,
in_channels=3,
k=9,
out_indices=-1,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='BN'),
graph_conv_bias=True,
graph_conv_type='mr',
epsilon=0.2,
use_dilation=True,
use_stochastic=False,
drop_path=0.,
relative_pos=False,
norm_eval=False,
frozen_stages=0,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
arch = self.arch_settings[arch]
self.num_blocks = arch['num_blocks']
channels = arch['channels']
if isinstance(out_indices, int):
out_indices = [out_indices]
elif isinstance(out_indices, tuple):
out_indices = list(out_indices)
elif not isinstance(out_indices, list):
raise TypeError('"out_indices" must by a tuple, list or int, '
f'get {type(out_indices)} instead.')
for i, index in enumerate(out_indices):
if index < 0:
out_indices[i] = self.num_blocks + index
assert 0 <= out_indices[i] <= self.num_blocks, \
f'Invalid out_indices {index}'
self.out_indices = out_indices
self.stem = Sequential(
nn.Conv2d(in_channels, channels // 8, 3, stride=2, padding=1),
build_norm_layer(norm_cfg, channels // 8),
build_activation_layer(act_cfg),
nn.Conv2d(channels // 8, channels // 4, 3, stride=2, padding=1),
build_norm_layer(norm_cfg, channels // 4),
build_activation_layer(act_cfg),
nn.Conv2d(channels // 4, channels // 2, 3, stride=2, padding=1),
build_norm_layer(norm_cfg, channels // 2),
build_activation_layer(act_cfg),
nn.Conv2d(channels // 2, channels, 3, stride=2, padding=1),
build_norm_layer(norm_cfg, channels),
build_activation_layer(act_cfg),
nn.Conv2d(channels, channels, 3, stride=1, padding=1),
build_norm_layer(norm_cfg, channels),
)
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path, self.num_blocks)]
# number of knn's k
num_knn = [
int(x.item()) for x in torch.linspace(k, 2 * k, self.num_blocks)
]
max_dilation = 196 // max(num_knn)
self.pos_embed = nn.Parameter(torch.zeros(1, channels, 14, 14))
self.blocks = ModuleList([
Sequential(
Grapher(
in_channels=channels,
k=num_knn[i],
dilation=min(i // 4 +
1, max_dilation) if use_dilation else 1,
graph_conv_type=graph_conv_type,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
graph_conv_bias=graph_conv_bias,
use_stochastic=use_stochastic,
epsilon=epsilon,
drop_path=dpr[i],
relative_pos=relative_pos),
FFN(in_features=channels,
hidden_features=channels * 4,
act_cfg=act_cfg,
drop_path=dpr[i])) for i in range(self.num_blocks)
])
self.norm_eval = norm_eval
self.frozen_stages = frozen_stages
def forward(self, inputs):
outs = []
x = self.stem(inputs) + self.pos_embed
for i, block in enumerate(self.blocks):
x = block(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
def _freeze_stages(self):
self.stem.eval()
for i in range(self.frozen_stages):
m = self.blocks[i]
m.eval()
for param in m.parameters():
param.requires_grad = False
def train(self, mode=True):
super(Vig, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
if isinstance(m, _BatchNorm):
m.eval()
[docs]@MODELS.register_module()
class PyramidVig(BaseBackbone):
"""Pyramid Vision GNN backbone.
A PyTorch implementation of `Vision GNN: An Image is Worth Graph of Nodes
<https://arxiv.org/abs/2206.00272>`_.
Modified from the official implementation
https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
Args:
arch (str): Vision GNN architecture, choose from 'tiny',
'small' and 'base'.
in_channels (int): The number of channels of input images.
Defaults to 3.
k (int): The number of KNN's k. Defaults to 9.
out_indices (Sequence | int): Output from which stages.
Defaults to -1, means the last stage.
act_cfg (dict): The config of activative functions.
Defaults to ``dict(type='GELU'))``.
norm_cfg (dict): The config of normalization layers.
Defaults to ``dict(type='BN')``.
graph_conv_bias (bool): Whether to use bias in the convolution
layers in Grapher. Defaults to True.
graph_conv_type (str): The type of graph convolution，choose
from 'edge', 'mr', 'sage' and 'gin'. Defaults to 'mr'.
epsilon (float): Probability of random arrangement in KNN. It only
works when ``use_stochastic=True``. Defaults to 0.2.
use_stochastic (bool): Whether to use stochastic in KNN.
Defaults to False.
drop_path (float): stochastic depth rate. Default 0.0
norm_eval (bool): Whether to set the normalization layer to eval mode.
Defaults to False.
frozen_stages (int): Stages to be frozen (all param fixed).
Defaults to 0, which means not freezing any parameters.
init_cfg (dict, optional): The initialization configs.
Defaults to None.
""" # noqa: E501
arch_settings = {
'tiny': dict(blocks=[2, 2, 6, 2], channels=[48, 96, 240, 384]),
'small': dict(blocks=[2, 2, 6, 2], channels=[80, 160, 400, 640]),
'medium': dict(blocks=[2, 2, 16, 2], channels=[96, 192, 384, 768]),
'base': dict(blocks=[2, 2, 18, 2], channels=[128, 256, 512, 1024]),
}
def __init__(self,
arch,
in_channels=3,
k=9,
out_indices=-1,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='BN'),
graph_conv_bias=True,
graph_conv_type='mr',
epsilon=0.2,
use_stochastic=False,
drop_path=0.,
norm_eval=False,
frozen_stages=0,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
arch = self.arch_settings[arch]
self.blocks = arch['blocks']
self.num_blocks = sum(self.blocks)
self.num_stages = len(self.blocks)
channels = arch['channels']
self.channels = channels
if isinstance(out_indices, int):
out_indices = [out_indices]
assert isinstance(out_indices, Sequence), \
f'"out_indices" must by a sequence or int, ' \
f'get {type(out_indices)} instead.'
for i, index in enumerate(out_indices):
if index < 0:
out_indices[i] = self.num_stages + index
assert 0 <= out_indices[i] <= self.num_stages, \
f'Invalid out_indices {index}'
self.out_indices = out_indices
self.stem = Sequential(
nn.Conv2d(in_channels, channels[0] // 2, 3, stride=2, padding=1),
build_norm_layer(norm_cfg, channels[0] // 2),
build_activation_layer(act_cfg),
nn.Conv2d(channels[0] // 2, channels[0], 3, stride=2, padding=1),
build_norm_layer(norm_cfg, channels[0]),
build_activation_layer(act_cfg),
nn.Conv2d(channels[0], channels[0], 3, stride=1, padding=1),
build_norm_layer(norm_cfg, channels[0]),
)
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path, self.num_blocks)]
# number of knn's k
num_knn = [
int(x.item()) for x in torch.linspace(k, k, self.num_blocks)
]
max_dilation = 49 // max(num_knn)
self.pos_embed = nn.Parameter(
torch.zeros(1, channels[0], 224 // 4, 224 // 4))
HW = 224 // 4 * 224 // 4
reduce_ratios = [4, 2, 1, 1]
self.stages = ModuleList()
block_idx = 0
for stage_idx, num_blocks in enumerate(self.blocks):
mid_channels = channels[stage_idx]
reduce_ratio = reduce_ratios[stage_idx]
blocks = []
if stage_idx > 0:
blocks.append(
Sequential(
nn.Conv2d(
self.channels[stage_idx - 1],
mid_channels,
kernel_size=3,
stride=2,
padding=1),
build_norm_layer(norm_cfg, mid_channels),
))
HW = HW // 4
for _ in range(num_blocks):
blocks.append(
Sequential(
Grapher(
in_channels=mid_channels,
k=num_knn[block_idx],
dilation=min(block_idx // 4 + 1, max_dilation),
graph_conv_type=graph_conv_type,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
graph_conv_bias=graph_conv_bias,
use_stochastic=use_stochastic,
epsilon=epsilon,
r=reduce_ratio,
n=HW,
drop_path=dpr[block_idx],
relative_pos=True),
FFN(in_features=mid_channels,
hidden_features=mid_channels * 4,
act_cfg=act_cfg,
drop_path=dpr[block_idx])))
block_idx += 1
self.stages.append(Sequential(*blocks))
self.norm_eval = norm_eval
self.frozen_stages = frozen_stages
def forward(self, inputs):
outs = []
x = self.stem(inputs) + self.pos_embed
for i, blocks in enumerate(self.stages):
x = blocks(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
def _freeze_stages(self):
self.stem.eval()
for i in range(self.frozen_stages):
m = self.stages[i]
m.eval()
for param in m.parameters():
param.requires_grad = False
def train(self, mode=True):
super(PyramidVig, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
if isinstance(m, _BatchNorm):
m.eval()