cs-mixer / timm /models /mymlp_old.py
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import os
import numpy as np
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_
from timm.models.registry import register_model
from timm.models.layers import to_2tuple
import math
from torch import Tensor
from torch.nn import init
from torch.nn.modules.utils import _pair
from torchvision.ops.deform_conv import deform_conv2d as deform_conv2d_tv
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .96, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
**kwargs
}
default_cfgs = {
'my_S': _cfg(crop_pct=0.9),
'my_M': _cfg(crop_pct=0.9),
'my_L': _cfg(crop_pct=0.875),
}
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 MyFC(nn.Module):
"""
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size, # re-defined kernel_size, represent the spatial area of staircase FC
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
conjugate: bool = False,
bias: bool = True,
):
super(MyFC, self).__init__()
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
if stride != 1:
raise ValueError('stride must be 1')
if padding != 0:
raise ValueError('padding must be 0')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.conjugate = conjugate
self.weight = nn.Parameter(torch.empty(out_channels, in_channels // groups, 1, 1)) # kernel size == 1
if bias:
self.bias = nn.Parameter(torch.empty(out_channels))
else:
self.register_parameter('bias', None)
self.register_buffer('offset', self.gen_offset())
self.reset_parameters()
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def gen_offset(self):
"""
offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width,
out_height, out_width]): offsets to be applied for each position in the
convolution kernel.
"""
# offset shape: (1, self.in_channels*2, 1, 1)
k = self.kernel_size
# Note: Unary - takes precedence over binary //
if self.conjugate:
return torch.Tensor([[i, j] for j in range(-(k//2), k//2+1) for i in range(-(k//2),k//2+1)][::-1] \
* math.ceil(self.in_channels / k**2)).view(1, -1, 1, 1)[:, :2 * self.in_channels]
return torch.Tensor([[i, j] for j in range(-(k//2), k//2+1) for i in range(-(k//2),k//2+1)] \
* math.ceil(self.in_channels / k**2)).view(1, -1, 1, 1)[:, :2 * self.in_channels]
def forward(self, input: Tensor) -> Tensor:
"""
Args:
input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
"""
B, C, H, W = input.size()
return deform_conv2d_tv(input, self.offset.expand(B, -1, H, W), self.weight, self.bias, stride=self.stride,
padding=self.padding, dilation=self.dilation)
def extra_repr(self) -> str:
s = self.__class__.__name__ + '('
s += '{in_channels}'
s += ', {out_channels}'
s += ', kernel_size={kernel_size}'
s += ', stride={stride}'
s += ', padding={padding}' if self.padding != (0, 0) else ''
s += ', dilation={dilation}' if self.dilation != (1, 1) else ''
s += ', groups={groups}' if self.groups != 1 else ''
s += ', bias=False' if self.bias is None else ''
s += ')'
return s.format(**self.__dict__)
class MyMLP(nn.Module):
def __init__(self, dim, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.mlp_c = nn.Linear(dim, dim, bias=qkv_bias)
self.sfc1 = MyFC(dim, dim, 3, conjugate=True)
self.sfc2 = MyFC(dim, dim, 3, conjugate=False)
self.reweight = Mlp(dim, dim // 4, dim * 3)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, H, W, C = x.shape
h = self.sfc1(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
w = self.sfc2(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
c = self.mlp_c(x)
a = (h + w + c).permute(0, 3, 1, 2).flatten(2).mean(2)
a = self.reweight(a).reshape(B, C, 3).permute(2, 0, 1).softmax(dim=0).unsqueeze(2).unsqueeze(2)
x = h * a[0] + w * a[1] + c * a[2]
x = self.proj(x)
x = self.proj_drop(x)
return x
class MyBlock(nn.Module):
def __init__(self, dim, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, skip_lam=1.0, mlp_fn=MyMLP):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = mlp_fn(dim, qkv_bias=qkv_bias, qk_scale=None, attn_drop=attn_drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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)
self.skip_lam = skip_lam
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x))) / self.skip_lam
x = x + self.drop_path(self.mlp(self.norm2(x))) / self.skip_lam
return x
class PatchEmbedOverlapping(nn.Module):
""" 2D Image to Patch Embedding with overlapping
"""
def __init__(self, patch_size=16, stride=16, padding=0, in_chans=3, embed_dim=768, norm_layer=None, groups=1):
super().__init__()
patch_size = to_2tuple(patch_size)
stride = to_2tuple(stride)
padding = to_2tuple(padding)
self.patch_size = patch_size
# remove image_size in model init to support dynamic image size
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding, groups=groups)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
return x
class Downsample(nn.Module):
""" Downsample transition stage
"""
def __init__(self, in_embed_dim, out_embed_dim, patch_size):
super().__init__()
assert patch_size == 2, patch_size
self.proj = nn.Conv2d(in_embed_dim, out_embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=1)
def forward(self, x):
x = x.permute(0, 3, 1, 2)
x = self.proj(x) # B, C, H, W
x = x.permute(0, 2, 3, 1)
return x
def basic_blocks(dim, index, layers, mlp_ratio=3., qkv_bias=False, qk_scale=None, attn_drop=0.,
drop_path_rate=0., skip_lam=1.0, mlp_fn=MyMLP, **kwargs):
blocks = []
for block_idx in range(layers[index]):
block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
blocks.append(MyBlock(dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, drop_path=block_dpr, skip_lam=skip_lam, mlp_fn=mlp_fn))
blocks = nn.Sequential(*blocks)
return blocks
class MyNet(nn.Module):
""" MyMLP Network """
def __init__(self, layers, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
embed_dims=None, transitions=None, segment_dim=None, mlp_ratios=None, skip_lam=1.0,
qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
norm_layer=nn.LayerNorm, mlp_fn=MyMLP, fork_feat=False, **kwargs):
super().__init__()
if not fork_feat:
self.num_classes = num_classes
self.fork_feat = fork_feat
self.patch_embed = PatchEmbedOverlapping(patch_size=7, stride=4, padding=2, in_chans=3, embed_dim=embed_dims[0])
network = []
for i in range(len(layers)):
stage = basic_blocks(embed_dims[i], i, layers, mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias,
qk_scale=qk_scale, attn_drop=attn_drop_rate, drop_path_rate=drop_path_rate,
norm_layer=norm_layer, skip_lam=skip_lam, mlp_fn=mlp_fn)
network.append(stage)
if i >= len(layers) - 1:
break
if transitions[i] or embed_dims[i] != embed_dims[i+1]:
patch_size = 2 if transitions[i] else 1
network.append(Downsample(embed_dims[i], embed_dims[i+1], patch_size))
self.network = nn.ModuleList(network)
if self.fork_feat:
# add a norm layer for each output
self.out_indices = [0, 2, 4, 6]
for i_emb, i_layer in enumerate(self.out_indices):
if i_emb == 0 and os.environ.get('FORK_LAST3', None):
# TODO: more elegant way
"""For RetinaNet, `start_level=1`. The first norm layer will not used.
cmd: `FORK_LAST3=1 python -m torch.distributed.launch ...`
"""
layer = nn.Identity()
else:
layer = norm_layer(embed_dims[i_emb])
layer_name = f'norm{i_layer}'
self.add_module(layer_name, layer)
else:
# Classifier head
self.norm = norm_layer(embed_dims[-1])
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self.cls_init_weights)
def cls_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)
elif isinstance(m, MyFC):
trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_embeddings(self, x):
x = self.patch_embed(x)
# B,C,H,W-> B,H,W,C
x = x.permute(0, 2, 3, 1)
return x
def forward_tokens(self, x):
outs = []
for idx, block in enumerate(self.network):
x = block(x)
if self.fork_feat and idx in self.out_indices:
norm_layer = getattr(self, f'norm{idx}')
x_out = norm_layer(x)
outs.append(x_out.permute(0, 3, 1, 2).contiguous())
if self.fork_feat:
return outs
B, H, W, C = x.shape
x = x.reshape(B, -1, C)
return x
def forward(self, x):
x = self.forward_embeddings(x)
# B, H, W, C -> B, N, C
x = self.forward_tokens(x)
if self.fork_feat:
return x
x = self.norm(x)
cls_out = self.head(x.mean(1))
return cls_out
@register_model
def MyMLP_B1(pretrained=False, **kwargs):
transitions = [True, True, True, True]
layers = [2, 2, 4, 2]
mlp_ratios = [4, 4, 4, 4]
embed_dims = [64, 128, 320, 512]
model = MyNet(layers, embed_dims=embed_dims, patch_size=7, transitions=transitions,
mlp_ratios=mlp_ratios, mlp_fn=MyMLP, **kwargs)
model.default_cfg = default_cfgs['my_S']
return model
@register_model
def MyMLP_B2(pretrained=False, **kwargs):
transitions = [True, True, True, True]
layers = [2, 3, 10, 3]
mlp_ratios = [4, 4, 4, 4]
embed_dims = [64, 128, 320, 512]
model = MyNet(layers, embed_dims=embed_dims, patch_size=7, transitions=transitions,
mlp_ratios=mlp_ratios, mlp_fn=MyMLP, **kwargs)
model.default_cfg = default_cfgs['my_S']
return model
@register_model
def MyMLP_B3(pretrained=False, **kwargs):
transitions = [True, True, True, True]
layers = [3, 4, 18, 3]
mlp_ratios = [8, 8, 4, 4]
embed_dims = [64, 128, 320, 512]
model = MyNet(layers, embed_dims=embed_dims, patch_size=7, transitions=transitions,
mlp_ratios=mlp_ratios, mlp_fn=MyMLP, **kwargs)
model.default_cfg = default_cfgs['my_M']
return model
@register_model
def MyMLP_B4(pretrained=False, **kwargs):
transitions = [True, True, True, True]
layers = [3, 8, 27, 3]
mlp_ratios = [8, 8, 4, 4]
embed_dims = [64, 128, 320, 512]
model = MyNet(layers, embed_dims=embed_dims, patch_size=7, transitions=transitions,
mlp_ratios=mlp_ratios, mlp_fn=MyMLP, **kwargs)
model.default_cfg = default_cfgs['my_L']
return model
@register_model
def MyMLP_B5(pretrained=False, **kwargs):
transitions = [True, True, True, True]
layers = [3, 4, 24, 3]
mlp_ratios = [4, 4, 4, 4]
embed_dims = [96, 192, 384, 768]
model = MyNet(layers, embed_dims=embed_dims, patch_size=7, transitions=transitions,
mlp_ratios=mlp_ratios, mlp_fn=MyMLP, **kwargs)
model.default_cfg = default_cfgs['my_L']
return model