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import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionEmbs(nn.Module):
def __init__(self, num_patches, emb_dim, dropout_rate=0.1):
super(PositionEmbs, self).__init__()
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, emb_dim))
if dropout_rate > 0:
self.dropout = nn.Dropout(dropout_rate)
else:
self.dropout = None
def forward(self, x):
out = x + self.pos_embedding
if self.dropout:
out = self.dropout(out)
return out
class MlpBlock(nn.Module):
""" Transformer Feed-Forward Block """
def __init__(self, in_dim, mlp_dim, out_dim, dropout_rate=0.1):
super(MlpBlock, self).__init__()
# init layers
self.fc1 = nn.Linear(in_dim, mlp_dim)
self.fc2 = nn.Linear(mlp_dim, out_dim)
self.act = nn.GELU()
if dropout_rate > 0.0:
self.dropout1 = nn.Dropout(dropout_rate)
self.dropout2 = nn.Dropout(dropout_rate)
else:
self.dropout1 = None
self.dropout2 = None
def forward(self, x):
out = self.fc1(x)
out = self.act(out)
if self.dropout1:
out = self.dropout1(out)
out = self.fc2(out)
if self.dropout2:
out = self.dropout2(out)
return out
class LinearGeneral(nn.Module):
def __init__(self, in_dim=(768,), feat_dim=(12, 64)):
super(LinearGeneral, self).__init__()
#self.weight = nn.Parameter(torch.randn(*in_dim, *feat_dim))
self.weight = torch.randn(*in_dim, *feat_dim)
self.weight.normal_(0, 0.02)
self.weight = nn.Parameter(self.weight)
self.bias = nn.Parameter(torch.zeros(*feat_dim))
def forward(self, x, dims):
a = torch.tensordot(x, self.weight, dims=dims) + self.bias
return a
class SelfAttention(nn.Module):
def __init__(self, in_dim, heads=8, dropout_rate=0.1):
super(SelfAttention, self).__init__()
self.heads = heads
self.head_dim = in_dim // heads
self.scale = self.head_dim ** 0.5
self.query = LinearGeneral((in_dim,), (self.heads, self.head_dim))
self.key = LinearGeneral((in_dim,), (self.heads, self.head_dim))
self.value = LinearGeneral((in_dim,), (self.heads, self.head_dim))
self.out = LinearGeneral((self.heads, self.head_dim), (in_dim,))
if dropout_rate > 0:
self.dropout = nn.Dropout(dropout_rate)
else:
self.dropout = None
def forward(self, x, vis_attn = False):
b, n, _ = x.shape
q = self.query(x, dims=([2], [0]))
k = self.key(x, dims=([2], [0]))
v = self.value(x, dims=([2], [0]))
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
attn_weights = torch.matmul(q, k.transpose(-2, -1)) / self.scale
attn_weights = F.softmax(attn_weights, dim=-1)
out = torch.matmul(attn_weights, v)
out = out.permute(0, 2, 1, 3)
out = self.out(out, dims=([2, 3], [0, 1]))
if not vis_attn:
return out
else:
return out, attn_weights
class EncoderBlock(nn.Module):
def __init__(self, in_dim, mlp_dim, num_heads, dropout_rate=0.1, attn_dropout_rate=0.1, normalize = 'layer_norm'):
super(EncoderBlock, self).__init__()
if normalize == 'layer_norm':
self.norm1 = nn.LayerNorm(in_dim)
self.norm2 = nn.LayerNorm(in_dim)
elif normalize == 'group_norm':
self.norm1 = Normalize(in_dim)
self.norm2 = Normalize(in_dim)
self.attn = SelfAttention(in_dim, heads=num_heads, dropout_rate=attn_dropout_rate)
if dropout_rate > 0:
self.dropout = nn.Dropout(dropout_rate)
else:
self.dropout = None
self.mlp = MlpBlock(in_dim, mlp_dim, in_dim, dropout_rate)
def forward(self, x, vis_attn = False):
residual = x
out = self.norm1(x)
if vis_attn:
out, attn_weights = self.attn(out, vis_attn)
else:
out = self.attn(out, vis_attn)
if self.dropout:
out = self.dropout(out)
out += residual
residual = out
out = self.norm2(out)
out = self.mlp(out)
out += residual
if vis_attn:
return out, attn_weights
else:
return out
class Encoder(nn.Module):
def __init__(self, num_patches, emb_dim, mlp_dim, num_layers=12, num_heads=12, dropout_rate=0.1, attn_dropout_rate=0.0):
super(Encoder, self).__init__()
# positional embedding
self.pos_embedding = PositionEmbs(num_patches, emb_dim, dropout_rate)
# encoder blocks
in_dim = emb_dim
self.encoder_layers = nn.ModuleList()
for i in range(num_layers):
layer = EncoderBlock(in_dim, mlp_dim, num_heads, dropout_rate, attn_dropout_rate)
self.encoder_layers.append(layer)
self.norm = nn.LayerNorm(in_dim)
def forward(self, x):
out = self.pos_embedding(x)
for layer in self.encoder_layers:
out = layer(out)
out = self.norm(out)
return out
class VisionTransformer(nn.Module):
""" Vision Transformer """
def __init__(self,
image_size=(256, 256),
patch_size=(16, 16),
emb_dim=768,
mlp_dim=3072,
num_heads=12,
num_layers=12,
num_classes=1000,
attn_dropout_rate=0.0,
dropout_rate=0.1,
feat_dim=None):
super(VisionTransformer, self).__init__()
h, w = image_size
# embedding layer
fh, fw = patch_size
gh, gw = h // fh, w // fw
num_patches = gh * gw
self.embedding = nn.Conv2d(3, emb_dim, kernel_size=(fh, fw), stride=(fh, fw))
# class token
self.cls_token = nn.Parameter(torch.zeros(1, 1, emb_dim))
# transformer
self.transformer = Encoder(
num_patches=num_patches,
emb_dim=emb_dim,
mlp_dim=mlp_dim,
num_layers=num_layers,
num_heads=num_heads,
dropout_rate=dropout_rate,
attn_dropout_rate=attn_dropout_rate)
# classfier
self.classifier = nn.Linear(emb_dim, num_classes)
def forward(self, x):
emb = self.embedding(x) # (n, c, gh, gw)
emb = emb.permute(0, 2, 3, 1) # (n, gh, hw, c)
b, h, w, c = emb.shape
emb = emb.reshape(b, h * w, c)
# prepend class token
cls_token = self.cls_token.repeat(b, 1, 1)
emb = torch.cat([cls_token, emb], dim=1)
# transformer
feat = self.transformer(emb)
# classifier
logits = self.classifier(feat[:, 0])
return logits
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
if __name__ == '__main__':
model = VisionTransformer(num_layers=2)
import pdb; pdb.set_trace()
x = torch.randn((2, 3, 256, 256))
out = model(x) |