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Update davit.py

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pull/1630/head
Fredo Guan 3 years ago
parent 4ecf422cd3
commit 447dd0d23f
1 changed files with 248 additions and 7 deletions
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255
timm/models/davit.py
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@ -45,7 +45,7 @@ class SequentialWithSize(nn.Sequential):
return x, size
class ConvPosEnc(nn.Module):
class ConvPosEncOld(nn.Module):
def __init__(self, dim : int, k : int=3, act : bool=False, normtype : str='none'):
super(ConvPosEnc, self).__init__()
@ -79,10 +79,44 @@ class ConvPosEnc(nn.Module):
return x
class ConvPosEnc(nn.Module):
def __init__(self, dim : int, k : int=3, act : bool=False, normtype : str='none'):
super(ConvPosEnc, self).__init__()
self.proj = nn.Conv2d(dim,
dim,
to_2tuple(k),
to_2tuple(1),
to_2tuple(k // 2),
groups=dim)
self.normtype = normtype
self.norm = nn.Identity()
if self.normtype == 'batch':
self.norm = nn.BatchNorm2d(dim)
elif self.normtype == 'layer':
self.norm = nn.LayerNorm(dim)
self.activation = nn.GELU() if act else nn.Identity()
def forward(self, x : Tensor):
B, C, H, W = x.shape
#feat = x.transpose(1, 2).view(B, C, H, W)
feat = self.proj(feat)
if self.normtype == 'batch':
feat = self.norm(feat).flatten(2).transpose(1, 2)
elif self.normtype == 'layer':
feat = self.norm(feat.flatten(2).transpose(1, 2))
else:
feat = feat.flatten(2).transpose(1, 2)
x = x + self.activation(feat).transpose(1, 2).view(B, C, H, W)
return x
# reason: dim in control sequence
# FIXME reimplement to allow tracing
@register_notrace_module
class PatchEmbed(nn.Module):
class PatchEmbedOld(nn.Module):
""" Size-agnostic implementation of 2D image to patch embedding,
allowing input size to be adjusted during model forward operation
"""
@ -141,6 +175,60 @@ class PatchEmbed(nn.Module):
x = self.norm(x)
return x, newsize
@register_notrace_module
class PatchEmbed(nn.Module):
""" Size-agnostic implementation of 2D image to patch embedding,
allowing input size to be adjusted during model forward operation
"""
def __init__(
self,
patch_size=4,
in_chans=3,
embed_dim=96,
overlapped=False):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.in_chans = in_chans
self.embed_dim = embed_dim
if patch_size[0] == 4:
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=(7, 7),
stride=patch_size,
padding=(3, 3))
self.norm = nn.LayerNorm(embed_dim)
if patch_size[0] == 2:
kernel = 3 if overlapped else 2
pad = 1 if overlapped else 0
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=to_2tuple(kernel),
stride=patch_size,
padding=to_2tuple(pad))
self.norm = nn.LayerNorm(in_chans)
def forward(self, x : Tensor):
B, C, H, W = x.shape
if self.norm.normalized_shape[0] == self.in_chans:
x = self.norm(x.flatten(2).transpose(1, 2)).transpose(1, 2).view(B, C, H, W)
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)
#x = x.flatten(2).transpose(1, 2)
if self.norm.normalized_shape[0] == self.embed_dim:
x = self.norm(x.flatten(2).transpose(1, 2)).transpose(1, 2).view(B, C, H, W)
return x
class ChannelAttention(nn.Module):
@ -166,9 +254,37 @@ class ChannelAttention(nn.Module):
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x
class ChannelAttentionNew(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
class ChannelBlock(nn.Module):
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
def forward(self, x : Tensor):
B, C, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
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]
k = k * self.scale
attention = k.transpose(-1, -2) @ v
attention = attention.softmax(dim=-1)
x = (attention @ q.transpose(-1, -2)).transpose(-1, -2)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = x.transpose(1, 2).view(B, C, H, W)
return x
class ChannelBlockOld(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
@ -204,6 +320,46 @@ class ChannelBlock(nn.Module):
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x, size
class ChannelBlock(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
ffn=True, cpe_act=False):
super().__init__()
self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
self.ffn = ffn
self.norm1 = norm_layer(dim)
self.attn = ChannelAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
if self.ffn:
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)
def forward(self, x : Tensor):
B, C, H, W = x.shape
x = self.cpe1(x).flatten(2).transpose(1, 2)
cur = self.norm1(x)
cur = self.attn(cur)
x = x + self.drop_path(cur)
x = self.cpe2(x.transpose(1, 2).view(B, C, H, W)).flatten(2).transpose(1, 2)
if self.ffn:
x = x + self.drop_path(self.mlp(self.norm2(x)))
x = x.transpose(1, 2).view(B, C, H, W)
return x
def window_partition(x : Tensor, window_size: int):
"""
@ -319,6 +475,91 @@ class SpatialBlock(nn.Module):
act_layer=act_layer)
def forward(self, x : Tensor, size: Tuple[int, int]):
B, C, H, W = x.shape
shortcut = self.cpe1(x, size).flatten(2).transpose(1, 2)
x = self.norm1(shortcut)
x = x.view(B, H, W, C)
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
x_windows = window_partition(x, self.window_size)
x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows)
# merge windows
attn_windows = attn_windows.view(-1,
self.window_size,
self.window_size,
C)
x = window_reverse(attn_windows, self.window_size, Hp, Wp)
#if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :].contiguous()
x = x.view(B, H * W, C)
x = shortcut + self.drop_path(x)
x = self.cpe2(x, size)
if self.ffn:
x = x + self.drop_path(self.mlp(self.norm2(x)))
x = x.transpose(1, 2).view(B, C, H, W)
return x, size
class SpatialBlockOld(nn.Module):
r""" Windows Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (int): Window size.
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
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,
mlp_ratio=4., qkv_bias=True, drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm,
ffn=True, cpe_act=False):
super().__init__()
self.dim = dim
self.ffn = ffn
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
if self.ffn:
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)
def forward(self, x : Tensor, size: Tuple[int, int]):
H, W = size
@ -357,7 +598,7 @@ class SpatialBlock(nn.Module):
if self.ffn:
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x, size
class DaViTStage(nn.Module):
def __init__(
self,
@ -424,9 +665,9 @@ class DaViTStage(nn.Module):
cpe_act=cpe_act
))
stage_blocks.append(SequentialWithSize(*dual_attention_block))
stage_blocks.append(nn.Sequential(*dual_attention_block))
self.blocks = SequentialWithSize(*stage_blocks)
self.blocks = nn.Sequential(*stage_blocks)
def forward(self, x : Tensor, size: Tuple[int, int]):
x, size = self.patch_embed(x, size)
@ -519,7 +760,7 @@ class DaViT(nn.Module):
self.feature_info += [dict(num_chs=self.embed_dims[stage_id], reduction=2, module=f'stages.{stage_id}')]
self.stages = SequentialWithSize(*stages)
self.stages = nn.Sequential(*stages)
self.norms = norm_layer(self.num_features)
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)

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