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vit.py
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vit.py
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import torch.nn.functional as F
import torch
import torch.nn as nn
from functools import partial
from networks.helpers import DropPath, trunc_normal_
# mp stuff
from utils import comm
from distributed.layers import DistributedMatmul, DistributedMLP, DistributedAttention
class MLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
qk_norm=False,
attn_drop=0.,
proj_drop=0.,
norm_layer=nn.LayerNorm,
):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = True
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
comm_inp_name="col_matmul", comm_hidden_name="row_matmul"):
super().__init__()
if (comm.get_size(comm_inp_name) * comm.get_size(comm_hidden_name)) > 1:
self.attn = DistributedAttention(
dim, comm_inp_name=comm_inp_name, comm_hidden_name=comm_hidden_name,
num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
norm_layer=norm_layer)
else:
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
norm_layer=norm_layer)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm1 = norm_layer(dim)
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
# distribute MLP for model parallelism
if (comm.get_size(comm_inp_name) * comm.get_size(comm_hidden_name)) > 1:
self.mlp = DistributedMLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop,
comm_inp_name=comm_inp_name,
comm_hidden_name=comm_hidden_name
)
else:
self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
y = self.attn(self.norm1(x))
x = x + self.drop_path(y)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=[224,224], patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
# grid of patches
self.h = img_size[0] // patch_size
self.w = img_size[1] // patch_size
num_patches = self.h * self.w
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class VisionTransformer(nn.Module):
def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, out_chans=3, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm,
comm_inp_name="col_matmul", comm_hidden_name="row_matmul", **kwargs):
super().__init__()
self.num_features = self.embed_dim = embed_dim
self.patch_size = patch_size
self.img_size = img_size
self.out_ch = out_chans
self.drop_rate = drop_rate
self.comm_inp_name = comm_inp_name
self.comm_hidden_name = comm_hidden_name
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=self.embed_dim)
num_patches = self.patch_embed.num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
comm_inp_name=comm_inp_name, comm_hidden_name=comm_hidden_name)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
self.out_size = self.out_ch * self.patch_size * self.patch_size
self.head = nn.Linear(embed_dim, self.out_size, bias=False)
trunc_normal_(self.pos_embed, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def prepare_tokens(self, x):
B, nc, w, h = x.shape
x = self.patch_embed(x) # patch linear embedding
# add positional encoding to each token
x = x + self.pos_embed
return self.pos_drop(x)
def forward_head(self, x):
B, _, _ = x.shape # B x N x embed_dim
x = x.reshape(B, self.patch_embed.h, self.patch_embed.w, self.embed_dim)
B, h, w, _ = x.shape
# apply head
x = self.head(x)
x = x.reshape(shape=(B, h, w, self.patch_size, self.patch_size, self.out_ch))
x = torch.einsum("nhwpqc->nchpwq", x)
x = x.reshape(shape=(B, self.out_ch, self.img_size[0], self.img_size[1]))
return x
def forward(self, x):
x = self.prepare_tokens(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
x = self.forward_head(x)
return x
def ViT(params, **kwargs):
model = VisionTransformer(
img_size=params.img_size,
in_chans=params.n_in_channels, out_chans=params.n_out_channels,
patch_size=params.patch_size,
embed_dim=params.embed_dim, depth=params.depth, num_heads=params.num_heads, mlp_ratio=4,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6),
drop_path_rate=params.dropout,
drop_rate=params.dropout,
attn_drop_rate=params.dropout,
**kwargs)
return model