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from functools import partial
from itertools import repeat
#from torch._six import container_abcs
import collections.abc as container_abcs
import logging
import os
from collections import OrderedDict
import numpy as np
import scipy
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch import Rearrange
from timm.models.layers import DropPath, trunc_normal_
#from .registry import register_model
from config import config
from torchinfo import summary
import yaml
_model_entrypoints = {}
def register_model(fn):
module_name_split = fn.__module__.split('.')
model_name = module_name_split[-1]
_model_entrypoints[model_name] = fn
return fn
def model_entrypoints(model_name):
return _model_entrypoints[model_name]
def is_model(model_name):
return model_name in _model_entrypoints
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, container_abcs.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
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_in,
dim_out,
num_heads,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
method='dw_bn',
kernel_size=3,
stride_kv=1,
stride_q=1,
padding_kv=1,
padding_q=1,
with_cls_token=True,
**kwargs
):
super().__init__()
self.stride_kv = stride_kv
self.stride_q = stride_q
self.dim = dim_out
self.num_heads = num_heads
# head_dim = self.qkv_dim // num_heads
self.scale = dim_out ** -0.5
self.with_cls_token = with_cls_token
self.conv_proj_q = self._build_projection(
dim_in, dim_out, kernel_size, padding_q,
stride_q, 'linear' if method == 'avg' else method
)
self.conv_proj_k = self._build_projection(
dim_in, dim_out, kernel_size, padding_kv,
stride_kv, method
)
self.conv_proj_v = self._build_projection(
dim_in, dim_out, kernel_size, padding_kv,
stride_kv, method
)
self.proj_q = nn.Linear(dim_in, dim_out, bias=qkv_bias)
self.proj_k = nn.Linear(dim_in, dim_out, bias=qkv_bias)
self.proj_v = nn.Linear(dim_in, dim_out, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim_out, dim_out)
self.proj_drop = nn.Dropout(proj_drop)
def _build_projection(self,
dim_in,
dim_out,
kernel_size,
padding,
stride,
method):
if method == 'dw_bn':
proj = nn.Sequential(OrderedDict([
('conv', nn.Conv2d(
dim_in,
dim_in,
kernel_size=kernel_size,
padding=padding,
stride=stride,
bias=False,
groups=dim_in
)),
('bn', nn.BatchNorm2d(dim_in)),
('rearrage', Rearrange('b c h w -> b (h w) c')),
]))
elif method == 'avg':
proj = nn.Sequential(OrderedDict([
('avg', nn.AvgPool2d(
kernel_size=kernel_size,
padding=padding,
stride=stride,
ceil_mode=True
)),
('rearrage', Rearrange('b c h w -> b (h w) c')),
]))
elif method == 'linear':
proj = None
else:
raise ValueError('Unknown method ({})'.format(method))
return proj
def forward_conv(self, x, h, w):
if self.with_cls_token:
cls_token, x = torch.split(x, [1, h*w], 1)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
if self.conv_proj_q is not None:
q = self.conv_proj_q(x)
else:
q = rearrange(x, 'b c h w -> b (h w) c')
if self.conv_proj_k is not None:
k = self.conv_proj_k(x)
else:
k = rearrange(x, 'b c h w -> b (h w) c')
if self.conv_proj_v is not None:
v = self.conv_proj_v(x)
else:
v = rearrange(x, 'b c h w -> b (h w) c')
if self.with_cls_token:
q = torch.cat((cls_token, q), dim=1)
k = torch.cat((cls_token, k), dim=1)
v = torch.cat((cls_token, v), dim=1)
return q, k, v
def forward(self, x, h, w):
if (
self.conv_proj_q is not None
or self.conv_proj_k is not None
or self.conv_proj_v is not None
):
q, k, v = self.forward_conv(x, h, w)
q = rearrange(self.proj_q(q), 'b t (h d) -> b h t d', h=self.num_heads)
k = rearrange(self.proj_k(k), 'b t (h d) -> b h t d', h=self.num_heads)
v = rearrange(self.proj_v(v), 'b t (h d) -> b h t d', h=self.num_heads)
attn_score = torch.einsum('bhlk,bhtk->bhlt', [q, k]) * self.scale
attn = F.softmax(attn_score, dim=-1)
attn = self.attn_drop(attn)
x = torch.einsum('bhlt,bhtv->bhlv', [attn, v])
x = rearrange(x, 'b h t d -> b t (h d)')
x = self.proj(x)
x = self.proj_drop(x)
return x
@staticmethod
def compute_macs(module, input, output):
# T: num_token
# S: num_token
input = input[0]
flops = 0
_, T, C = input.shape
H = W = int(np.sqrt(T-1)) if module.with_cls_token else int(np.sqrt(T))
H_Q = H / module.stride_q
W_Q = H / module.stride_q
T_Q = H_Q * W_Q + 1 if module.with_cls_token else H_Q * W_Q
H_KV = H / module.stride_kv
W_KV = W / module.stride_kv
T_KV = H_KV * W_KV + 1 if module.with_cls_token else H_KV * W_KV
# C = module.dim
# S = T
# Scaled-dot-product macs
# [B x T x C] x [B x C x T] --> [B x T x S]
# multiplication-addition is counted as 1 because operations can be fused
flops += T_Q * T_KV * module.dim
# [B x T x S] x [B x S x C] --> [B x T x C]
flops += T_Q * module.dim * T_KV
if (
hasattr(module, 'conv_proj_q')
and hasattr(module.conv_proj_q, 'conv')
):
params = sum(
[
p.numel()
for p in module.conv_proj_q.conv.parameters()
]
)
flops += params * H_Q * W_Q
if (
hasattr(module, 'conv_proj_k')
and hasattr(module.conv_proj_k, 'conv')
):
params = sum(
[
p.numel()
for p in module.conv_proj_k.conv.parameters()
]
)
flops += params * H_KV * W_KV
if (
hasattr(module, 'conv_proj_v')
and hasattr(module.conv_proj_v, 'conv')
):
params = sum(
[
p.numel()
for p in module.conv_proj_v.conv.parameters()
]
)
flops += params * H_KV * W_KV
params = sum([p.numel() for p in module.proj_q.parameters()])
flops += params * T_Q
params = sum([p.numel() for p in module.proj_k.parameters()])
flops += params * T_KV
params = sum([p.numel() for p in module.proj_v.parameters()])
flops += params * T_KV
params = sum([p.numel() for p in module.proj.parameters()])
flops += params * T
module.__flops__ += flops
class Block(nn.Module):
def __init__(self,
dim_in,
dim_out,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
**kwargs):
super().__init__()
self.with_cls_token = kwargs['with_cls_token']
self.norm1 = norm_layer(dim_in)
self.attn = Attention(
dim_in, dim_out, num_heads, qkv_bias, attn_drop, drop,
**kwargs
)
self.drop_path = DropPath(drop_path) \
if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim_out)
dim_mlp_hidden = int(dim_out * mlp_ratio)
self.mlp = Mlp(
in_features=dim_out,
hidden_features=dim_mlp_hidden,
act_layer=act_layer,
drop=drop
)
def forward(self, x, h, w):
res = x
x = self.norm1(x)
attn = self.attn(x, h, w)
x = res + self.drop_path(attn)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class ConvEmbed(nn.Module):
""" Image to Conv Embedding
"""
def __init__(self,
patch_size=7,
in_chans=1, #1 for spectrogram, 3 for rgb image
embed_dim=64,
stride=4,
padding=2,
norm_layer=None):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_chans, embed_dim,
kernel_size=patch_size,
stride=stride,
padding=padding
)
self.norm = norm_layer(embed_dim) if norm_layer else None
def forward(self, x):
x = self.proj(x)
B, C, H, W = x.shape
x = rearrange(x, 'b c h w -> b (h w) c')
if self.norm:
x = self.norm(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)
return x
class VisionTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
patch_size=16,
patch_stride=16,
patch_padding=0,
in_chans=1, #1for spectrogram, 3 for RGB
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.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
init='trunc_norm',
**kwargs):
super().__init__()
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.rearrage = None
self.patch_embed = ConvEmbed(
# img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
stride=patch_stride,
padding=patch_padding,
embed_dim=embed_dim,
norm_layer=norm_layer
)
with_cls_token = kwargs['with_cls_token']
if with_cls_token:
self.cls_token = nn.Parameter(
torch.zeros(1, 1, embed_dim)
)
else:
self.cls_token = None
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
blocks = []
for j in range(depth):
blocks.append(
Block(
dim_in=embed_dim,
dim_out=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[j],
act_layer=act_layer,
norm_layer=norm_layer,
**kwargs
)
)
self.blocks = nn.ModuleList(blocks)
if self.cls_token is not None:
trunc_normal_(self.cls_token, std=.02)
if init == 'xavier':
self.apply(self._init_weights_xavier)
else:
self.apply(self._init_weights_trunc_normal)
def _init_weights_trunc_normal(self, m):
if isinstance(m, nn.Linear):
logging.info('=> init weight of Linear from trunc norm')
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
logging.info('=> init bias of Linear to zeros')
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def _init_weights_xavier(self, m):
if isinstance(m, nn.Linear):
logging.info('=> init weight of Linear from xavier uniform')
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
logging.info('=> init bias of Linear to zeros')
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x):
x = self.patch_embed(x)
B, C, H, W = x.size()
x = rearrange(x, 'b c h w -> b (h w) c')
cls_tokens = None
if self.cls_token is not None:
# stole cls_tokens impl from Phil Wang, thanks
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x = self.pos_drop(x)
for i, blk in enumerate(self.blocks):
x = blk(x, H, W)
if self.cls_token is not None:
cls_tokens, x = torch.split(x, [1, H*W], 1)
x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)
return x, cls_tokens
class ConvolutionalVisionTransformer(nn.Module):
def __init__(self,
in_chans=1, #3 for RGB, 1 for Spectrogram
num_classes=1000,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
init='trunc_norm',
spec=None):
super().__init__()
self.num_classes = num_classes
self.num_stages = spec['NUM_STAGES']
for i in range(self.num_stages):
kwargs = {
'patch_size': spec['PATCH_SIZE'][i],
'patch_stride': spec['PATCH_STRIDE'][i],
'patch_padding': spec['PATCH_PADDING'][i],
'embed_dim': spec['DIM_EMBED'][i],
'depth': spec['DEPTH'][i],
'num_heads': spec['NUM_HEADS'][i],
'mlp_ratio': spec['MLP_RATIO'][i],
'qkv_bias': spec['QKV_BIAS'][i],
'drop_rate': spec['DROP_RATE'][i],
'attn_drop_rate': spec['ATTN_DROP_RATE'][i],
'drop_path_rate': spec['DROP_PATH_RATE'][i],
'with_cls_token': spec['CLS_TOKEN'][i],
'method': spec['QKV_PROJ_METHOD'][i],
'kernel_size': spec['KERNEL_QKV'][i],
'padding_q': spec['PADDING_Q'][i],
'padding_kv': spec['PADDING_KV'][i],
'stride_kv': spec['STRIDE_KV'][i],
'stride_q': spec['STRIDE_Q'][i],
}
stage = VisionTransformer(
in_chans=in_chans,
init=init,
act_layer=act_layer,
norm_layer=norm_layer,
**kwargs
)
setattr(self, f'stage{i}', stage)
in_chans = spec['DIM_EMBED'][i]
dim_embed = spec['DIM_EMBED'][-1]
self.norm = norm_layer(dim_embed)
self.cls_token = spec['CLS_TOKEN'][-1]
# Classifier head
#self.head = nn.Linear(dim_embed, num_classes) if num_classes > 0 else nn.Identity()
#trunc_normal_(self.head.weight, std=0.02)
self.head = nn.Identity()
def init_weights(self, pretrained='', pretrained_layers=[], verbose=True):
if os.path.isfile(pretrained):
pretrained_dict = torch.load(pretrained, map_location='cpu')
logging.info(f'=> loading pretrained model {pretrained}')
model_dict = self.state_dict()
pretrained_dict = {
k: v for k, v in pretrained_dict.items()
if k in model_dict.keys()
}
need_init_state_dict = {}
for k, v in pretrained_dict.items():
need_init = (
k.split('.')[0] in pretrained_layers
#or pretrained_layers[0] is '*'
or pretrained_layers[0] == '*'
)
if need_init:
if verbose:
logging.info(f'=> init {k} from {pretrained}')
if 'pos_embed' in k and v.size() != model_dict[k].size():
size_pretrained = v.size()
size_new = model_dict[k].size()
logging.info(
'=> load_pretrained: resized variant: {} to {}'
.format(size_pretrained, size_new)
)
ntok_new = size_new[1]
ntok_new -= 1
posemb_tok, posemb_grid = v[:, :1], v[0, 1:]
gs_old = int(np.sqrt(len(posemb_grid)))
gs_new = int(np.sqrt(ntok_new))
logging.info(
'=> load_pretrained: grid-size from {} to {}'
.format(gs_old, gs_new)
)
posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)
zoom = (gs_new / gs_old, gs_new / gs_old, 1)
posemb_grid = scipy.ndimage.zoom(
posemb_grid, zoom, order=1
)
posemb_grid = posemb_grid.reshape(1, gs_new ** 2, -1)
v = torch.tensor(
np.concatenate([posemb_tok, posemb_grid], axis=1)
)
need_init_state_dict[k] = v
self.load_state_dict(need_init_state_dict, strict=False)
@torch.jit.ignore
def no_weight_decay(self):
layers = set()
for i in range(self.num_stages):
layers.add(f'stage{i}.pos_embed')
layers.add(f'stage{i}.cls_token')
return layers
def forward_features(self, x):
for i in range(self.num_stages):
x, cls_tokens = getattr(self, f'stage{i}')(x)
if self.cls_token:
x = self.norm(cls_tokens)
#x = cls_tokens
x = torch.squeeze(x)
else:
x = rearrange(x, 'b c h w -> b (h w) c')
x = self.norm(x)
x = torch.mean(x, dim=1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
@register_model
def get_cls_model(**kwargs):
msvit_spec = config.MODEL.SPEC
msvit = ConvolutionalVisionTransformer(
in_chans=1, #1 for spectrogram 3 for RGB
num_classes=config.MODEL.NUM_CLASSES,
act_layer=QuickGELU,
norm_layer=partial(LayerNorm, eps=1e-5),
init=getattr(msvit_spec, 'INIT', 'trunc_norm'),
spec=msvit_spec
)
# if config.MODEL.INIT_WEIGHTS:
# msvit.init_weights(
# config.MODEL.PRETRAINED,
# config.MODEL.PRETRAINED_LAYERS,
# config.VERBOSE
# )
return msvit
def build_model(config, **kwargs):
model_name = config.MODEL.NAME
if not is_model(model_name):
raise ValueError(f'Unkown model: {model_name}')
return model_entrypoints(model_name)(config, **kwargs)
def cvt13(**kwargs):
f = open('config/cvt-13-224x224.yaml', 'r')
config = yaml.safe_load(f)
return ConvolutionalVisionTransformer(spec=config['MODEL']['SPEC']) # only loades the config, no pretraining
if __name__ == '__main__':
f = open('config/cvt-13-224x224.yaml', 'r')
config = yaml.safe_load(f)
model = ConvolutionalVisionTransformer(spec=config['MODEL']['SPEC'])
print(summary(model))
quit()
print(summary(model, input_size=(4, 1, 128, 301))) |