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from torch import nn, Tensor
from einops import rearrange
from typing import Dict
import torch
from config import AutoConfig
from timm.layers.norm import LayerNorm2d
from timm.models.convnext import ConvNeXtBlock
from timm.layers.mlp import Mlp
class SimpleConvBlocks(nn.Module):
def __init__(
self,
in_chs,
out_chs,
depth=3,
kernel_size=5,
max_dim=1024,
stride=1,
padding="same",
norm_layer=LayerNorm2d,
act=nn.SiLU,
groups=1,
bias=False,
conv1x1=False,
reduce_dim=False,
skip_connection=True,
):
super(SimpleConvBlocks, self).__init__()
if isinstance(kernel_size, int):
kernel_size = [kernel_size] * depth
if reduce_dim:
ch1 = min(in_chs, out_chs)
else:
ch1 = max(in_chs, out_chs)
# ch1 = min(ch1, max_dim)
layers = []
self.reduce_dim = False
if in_chs > max_dim:
self.reduce_block = nn.Conv2d(in_chs, max_dim, 1, bias=False)
in_chs = max_dim
ch1 = max_dim
self.reduce_dim = True
# norm_shape = None
# if norm_layer == nn.BatchNorm2d:
# norm_shape = ch1
# if norm_layer == nn.LayerNorm:
# norm_shape = [ch1, 16, 16] # not elegant
for i in range(depth - 1):
block = nn.Sequential(
nn.Conv2d(
in_chs if i == 0 else ch1,
ch1,
kernel_size[i],
stride,
padding,
groups,
bias=bias,
),
norm_layer(ch1),
act(inplace=True),
)
layers.append(block)
if not conv1x1:
block = nn.Sequential(
nn.Conv2d(
ch1, out_chs, kernel_size[-1], stride, padding, groups, bias=bias
),
act(inplace=True),
)
layers.append(block)
if conv1x1:
block = nn.Sequential(
nn.Conv2d(
ch1, ch1, kernel_size[-1], stride, padding, groups, bias=bias
),
norm_layer(ch1),
act(inplace=True),
nn.Conv2d(ch1, out_chs, 1, bias=bias),
act(inplace=True),
)
layers.append(block)
self.block = nn.Sequential(*layers)
self.skip_connection = skip_connection
self.depth = depth
def forward(self, x):
if self.reduce_dim:
x = self.reduce_block(x)
for i, b in enumerate(self.block):
x_prev = x
x_next = b(x)
if i < self.depth - 1:
x = x_next + x_prev if self.skip_connection else x_next
else:
x = x_next
return x
class ConvBlocks(nn.Module):
def __init__(
self,
in_chs,
out_chs,
max_dim=1024,
depth=3,
kernel_size=5,
):
super().__init__()
dim = min(in_chs, max_dim)
self.blocks = []
for i in range(depth):
_in_chs = in_chs if i == 0 else dim
norm_layer = None # defaults to LayerNorm
# if i == depth - 1 and skip_last_norm:
# norm_layer = nn.Identity
self.blocks.append(
ConvNeXtBlock(_in_chs, dim, kernel_size,
norm_layer=norm_layer),
)
self.blocks.append(nn.Conv2d(dim, out_chs, 3, padding="same"))
self.blocks.append(nn.GELU())
self.blocks = nn.Sequential(*self.blocks)
def forward(self, x: Tensor):
return self.blocks(x)
class DictConvBlocks(nn.Module):
def __init__(
self,
layers=[5, 11, 17, 23],
in_dims=[1024, 1024, 1024, 1024],
out_dim=256,
max_dim=1024,
kernel_sizes=[5, 5, 5, 5],
depths=[3, 3, 3, 3],
block=ConvBlocks,
):
super().__init__()
self.blocks_dict = nn.ModuleDict()
for i, layer in enumerate(layers):
self.blocks_dict[str(layer)] = block(
in_dims[i],
out_dim,
max_dim=max_dim,
depth=depths[i],
kernel_size=kernel_sizes[i],
)
def forward(self, x: Dict[str, Tensor]):
for layer, block in self.blocks_dict.items():
x[layer] = block(x[layer])
return x
def build_conv_blocks(cfg: AutoConfig):
return DictConvBlocks(
layers=cfg.MODEL.BACKBONE.LAYERS,
in_dims=cfg.MODEL.BACKBONE.FEATURE_DIMS,
out_dim=cfg.MODEL.CONV_HEAD.WIDTH,
max_dim=cfg.MODEL.CONV_HEAD.MAX_DIM,
kernel_sizes=cfg.MODEL.CONV_HEAD.KERNEL_SIZES,
depths=cfg.MODEL.CONV_HEAD.DEPTHS,
block=SimpleConvBlocks if cfg.MODEL.CONV_HEAD.SIMPLE else ConvBlocks,
)
class ClassTokenMLPs(nn.Module):
def __init__(
self,
layers=[5, 11, 17, 23],
in_dims=[1024, 1024, 1024, 1024],
out_dim=256,
):
super().__init__()
self.mlp_dict = nn.ModuleDict()
for i, layer in enumerate(layers):
self.mlp_dict[str(layer)] = Mlp(
in_features=in_dims[i], out_features=out_dim
)
def forward(self, x: Dict[str, Tensor]):
for layer, mlp in self.mlp_dict.items():
x[layer] = mlp(x[layer])
return x
def build_class_token_mlp(cfg: AutoConfig):
return ClassTokenMLPs(
layers=cfg.MODEL.BACKBONE.LAYERS,
in_dims=cfg.MODEL.BACKBONE.CLS_DIMS,
out_dim=cfg.MODEL.CONV_HEAD.WIDTH,
)
def build_class_token_mlp_prev(cfg: AutoConfig):
return ClassTokenMLPs(
layers=cfg.MODEL.BACKBONE_SMALL.LAYERS,
in_dims=cfg.MODEL.BACKBONE_SMALL.CLS_DIMS,
out_dim=cfg.MODEL.BACKBONE_SMALL.WIDTH,
)
class PreviousFeatureMLPs(nn.Module):
def __init__(
self,
feat_dim=1024,
c_dim=256,
t_dim=128,
out_dim=256,
):
super().__init__()
self.mlp = Mlp(
in_features=feat_dim + c_dim + t_dim,
out_features=out_dim,
)
self.feature_dim = feat_dim
self.c_dim = c_dim
self.t_dim = t_dim
def forward(self, x: Tensor, t: Tensor, c: Tensor=None):
bsz, tsz, _ = x.shape
t = rearrange(t, "(b t) c -> b t c", b=bsz)
tsz = t.shape[1]
if c is None:
c = torch.zeros(bsz, tsz, self.c_dim, device=x.device)
else:
c = rearrange(c, "(b t) c -> b t c", b=bsz)
x = torch.cat([x, c, t], dim=-1)
return self.mlp(x)
def build_prev_feat_mlp(cfg: AutoConfig):
return PreviousFeatureMLPs(
feat_dim=cfg.MODEL.PREV_FEAT.DIM,
c_dim=cfg.MODEL.COND.DIM,
t_dim=cfg.MODEL.BACKBONE_SMALL.T_DIM,
out_dim=cfg.MODEL.BACKBONE_SMALL.WIDTH,
)
class SubjectPreviousFrameCompress(nn.Module):
def __init__(
self,
num_time_steps,
in_width,
merge_width,
subject_list,
hidden_ratio=4,
):
super().__init__()
self.subject_list = subject_list
self.hidden_dim = int(merge_width * hidden_ratio)
self.t = num_time_steps
self.subject_layer = nn.ModuleDict()
for subject in self.subject_list:
self.subject_layer[subject] = nn.Sequential(
nn.Linear(int(in_width * num_time_steps), self.hidden_dim),
nn.GELU(),
)
self.merge_layer = Mlp(self.hidden_dim, out_features=merge_width)
def forward(
self,
x: Tensor, # [(B T), C]
subject: str,
):
x = rearrange(x, "b t c -> b (t c)", t=self.t)
x = self.subject_layer[subject](x)
x = self.merge_layer(x)
return x
def build_prev_compress(cfg: AutoConfig):
return SubjectPreviousFrameCompress(
num_time_steps=cfg.DATASET.N_PREV_FRAMES-1,
in_width=cfg.MODEL.BACKBONE_SMALL.WIDTH,
merge_width=cfg.MODEL.BACKBONE_SMALL.MERGE_WIDTH,
subject_list=cfg.DATASET.SUBJECT_LIST,
hidden_ratio=4,
)
def build_ftr_compress(cfg: AutoConfig):
return SubjectPreviousFrameCompress(
num_time_steps=cfg.DATASET.N_FTR_FRAMES-1,
in_width=cfg.MODEL.BACKBONE_SMALL.WIDTH,
merge_width=cfg.MODEL.BACKBONE_SMALL.MERGE_WIDTH,
subject_list=cfg.DATASET.SUBJECT_LIST,
hidden_ratio=4,
) |