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cvt.py
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cvt.py
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import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
# helper methods
def group_dict_by_key(cond, d):
return_val = [dict(), dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def group_by_key_prefix_and_remove_prefix(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(lambda x: x.startswith(prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs
# classes
class LayerNorm(nn.Module): # layernorm, but done in the channel dimension #1
def __init__(self, dim, eps = 1e-5):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
mean = torch.mean(x, dim = 1, keepdim = True)
return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
class FeedForward(nn.Module):
def __init__(self, dim, mult = 4, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
LayerNorm(dim),
nn.Conv2d(dim, dim * mult, 1),
nn.GELU(),
nn.Dropout(dropout),
nn.Conv2d(dim * mult, dim, 1),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class DepthWiseConv2d(nn.Module):
def __init__(self, dim_in, dim_out, kernel_size, padding, stride, bias = True):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
nn.BatchNorm2d(dim_in),
nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, proj_kernel, kv_proj_stride, heads = 8, dim_head = 64, dropout = 0.):
super().__init__()
inner_dim = dim_head * heads
padding = proj_kernel // 2
self.heads = heads
self.scale = dim_head ** -0.5
self.norm = LayerNorm(dim)
self.attend = nn.Softmax(dim = -1)
self.dropout = nn.Dropout(dropout)
self.to_q = DepthWiseConv2d(dim, inner_dim, proj_kernel, padding = padding, stride = 1, bias = False)
self.to_kv = DepthWiseConv2d(dim, inner_dim * 2, proj_kernel, padding = padding, stride = kv_proj_stride, bias = False)
self.to_out = nn.Sequential(
nn.Conv2d(inner_dim, dim, 1),
nn.Dropout(dropout)
)
def forward(self, x):
shape = x.shape
b, n, _, y, h = *shape, self.heads
x = self.norm(x)
q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim = 1))
q, k, v = map(lambda t: rearrange(t, 'b (h d) x y -> (b h) (x y) d', h = h), (q, k, v))
dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
attn = self.attend(dots)
attn = self.dropout(attn)
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) (x y) d -> b (h d) x y', h = h, y = y)
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self, dim, proj_kernel, kv_proj_stride, depth, heads, dim_head = 64, mlp_mult = 4, dropout = 0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
Attention(dim, proj_kernel = proj_kernel, kv_proj_stride = kv_proj_stride, heads = heads, dim_head = dim_head, dropout = dropout),
FeedForward(dim, mlp_mult, dropout = dropout)
]))
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return x
class CvT(nn.Module):
def __init__(
self,
*,
num_classes,
s1_emb_dim = 64,
s1_emb_kernel = 7,
s1_emb_stride = 4,
s1_proj_kernel = 3,
s1_kv_proj_stride = 2,
s1_heads = 1,
s1_depth = 1,
s1_mlp_mult = 4,
s2_emb_dim = 192,
s2_emb_kernel = 3,
s2_emb_stride = 2,
s2_proj_kernel = 3,
s2_kv_proj_stride = 2,
s2_heads = 3,
s2_depth = 2,
s2_mlp_mult = 4,
s3_emb_dim = 384,
s3_emb_kernel = 3,
s3_emb_stride = 2,
s3_proj_kernel = 3,
s3_kv_proj_stride = 2,
s3_heads = 6,
s3_depth = 10,
s3_mlp_mult = 4,
dropout = 0.,
channels = 3
):
super().__init__()
kwargs = dict(locals())
dim = channels
layers = []
for prefix in ('s1', 's2', 's3'):
config, kwargs = group_by_key_prefix_and_remove_prefix(f'{prefix}_', kwargs)
layers.append(nn.Sequential(
nn.Conv2d(dim, config['emb_dim'], kernel_size = config['emb_kernel'], padding = (config['emb_kernel'] // 2), stride = config['emb_stride']),
LayerNorm(config['emb_dim']),
Transformer(dim = config['emb_dim'], proj_kernel = config['proj_kernel'], kv_proj_stride = config['kv_proj_stride'], depth = config['depth'], heads = config['heads'], mlp_mult = config['mlp_mult'], dropout = dropout)
))
dim = config['emb_dim']
self.layers = nn.Sequential(*layers)
self.to_logits = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
Rearrange('... () () -> ...'),
nn.Linear(dim, num_classes)
)
def forward(self, x):
latents = self.layers(x)
return self.to_logits(latents)