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418 lines
14 KiB
418 lines
14 KiB
""" Sequencer
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Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
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"""
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# Copyright (c) 2022. Yuki Tatsunami
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# Licensed under the Apache License, Version 2.0 (the "License");
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import math
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from functools import partial
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from typing import Tuple
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
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from .helpers import build_model_with_cfg, named_apply
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from .layers import lecun_normal_, DropPath, Mlp, PatchEmbed as TimmPatchEmbed
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from .registry import register_model
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': DEFAULT_CROP_PCT, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = dict(
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sequencer2d_s=_cfg(url="https://github.com/okojoalg/sequencer/releases/download/weights/sequencer2d_s.pth"),
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sequencer2d_m=_cfg(url="https://github.com/okojoalg/sequencer/releases/download/weights/sequencer2d_m.pth"),
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sequencer2d_l=_cfg(url="https://github.com/okojoalg/sequencer/releases/download/weights/sequencer2d_l.pth"),
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)
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def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):
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if isinstance(module, nn.Linear):
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if name.startswith('head'):
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nn.init.zeros_(module.weight)
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nn.init.constant_(module.bias, head_bias)
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else:
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if flax:
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# Flax defaults
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lecun_normal_(module.weight)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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else:
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nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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if 'mlp' in name:
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nn.init.normal_(module.bias, std=1e-6)
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else:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Conv2d):
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lecun_normal_(module.weight)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.ones_(module.weight)
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nn.init.zeros_(module.bias)
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elif isinstance(module, (nn.RNN, nn.GRU, nn.LSTM)):
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stdv = 1.0 / math.sqrt(module.hidden_size)
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for weight in module.parameters():
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nn.init.uniform_(weight, -stdv, stdv)
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elif hasattr(module, 'init_weights'):
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module.init_weights()
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def get_stage(
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index, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer,
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norm_layer, act_layer, num_layers, bidirectional, union,
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with_fc, drop=0., drop_path_rate=0., **kwargs):
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assert len(layers) == len(patch_sizes) == len(embed_dims) == len(hidden_sizes) == len(mlp_ratios)
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blocks = []
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for block_idx in range(layers[index]):
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drop_path = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
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blocks.append(block_layer(
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embed_dims[index], hidden_sizes[index], mlp_ratio=mlp_ratios[index],
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rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer,
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num_layers=num_layers, bidirectional=bidirectional, union=union, with_fc=with_fc,
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drop=drop, drop_path=drop_path))
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if index < len(embed_dims) - 1:
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blocks.append(Downsample2D(embed_dims[index], embed_dims[index + 1], patch_sizes[index + 1]))
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blocks = nn.Sequential(*blocks)
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return blocks
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class RNNIdentity(nn.Module):
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def __init__(self, *args, **kwargs):
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super(RNNIdentity, self).__init__()
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
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return x, None
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class RNN2DBase(nn.Module):
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def __init__(
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self, input_size: int, hidden_size: int,
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num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
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union="cat", with_fc=True):
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super().__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.output_size = 2 * hidden_size if bidirectional else hidden_size
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self.union = union
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self.with_vertical = True
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self.with_horizontal = True
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self.with_fc = with_fc
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self.fc = None
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if with_fc:
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if union == "cat":
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self.fc = nn.Linear(2 * self.output_size, input_size)
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elif union == "add":
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self.fc = nn.Linear(self.output_size, input_size)
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elif union == "vertical":
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self.fc = nn.Linear(self.output_size, input_size)
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self.with_horizontal = False
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elif union == "horizontal":
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self.fc = nn.Linear(self.output_size, input_size)
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self.with_vertical = False
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else:
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raise ValueError("Unrecognized union: " + union)
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elif union == "cat":
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pass
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if 2 * self.output_size != input_size:
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raise ValueError(f"The output channel {2 * self.output_size} is different from the input channel {input_size}.")
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elif union == "add":
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pass
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if self.output_size != input_size:
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raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
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elif union == "vertical":
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if self.output_size != input_size:
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raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
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self.with_horizontal = False
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elif union == "horizontal":
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if self.output_size != input_size:
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raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
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self.with_vertical = False
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else:
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raise ValueError("Unrecognized union: " + union)
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self.rnn_v = RNNIdentity()
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self.rnn_h = RNNIdentity()
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def forward(self, x):
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B, H, W, C = x.shape
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if self.with_vertical:
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v = x.permute(0, 2, 1, 3)
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v = v.reshape(-1, H, C)
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v, _ = self.rnn_v(v)
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v = v.reshape(B, W, H, -1)
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v = v.permute(0, 2, 1, 3)
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else:
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v = None
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if self.with_horizontal:
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h = x.reshape(-1, W, C)
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h, _ = self.rnn_h(h)
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h = h.reshape(B, H, W, -1)
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else:
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h = None
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if v is not None and h is not None:
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if self.union == "cat":
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x = torch.cat([v, h], dim=-1)
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else:
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x = v + h
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elif v is not None:
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x = v
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elif h is not None:
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x = h
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if self.fc is not None:
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x = self.fc(x)
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return x
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class LSTM2D(RNN2DBase):
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def __init__(
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self, input_size: int, hidden_size: int,
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num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
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union="cat", with_fc=True):
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super().__init__(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)
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if self.with_vertical:
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self.rnn_v = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bias=bias, bidirectional=bidirectional)
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if self.with_horizontal:
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self.rnn_h = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bias=bias, bidirectional=bidirectional)
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class Sequencer2DBlock(nn.Module):
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def __init__(
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self, dim, hidden_size, mlp_ratio=3.0, rnn_layer=LSTM2D, mlp_layer=Mlp,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU,
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num_layers=1, bidirectional=True, union="cat", with_fc=True, drop=0., drop_path=0.):
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super().__init__()
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channels_dim = int(mlp_ratio * dim)
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self.norm1 = norm_layer(dim)
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self.rnn_tokens = rnn_layer(dim, hidden_size, num_layers=num_layers, bidirectional=bidirectional,
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union=union, with_fc=with_fc)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.mlp_channels = mlp_layer(dim, channels_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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x = x + self.drop_path(self.rnn_tokens(self.norm1(x)))
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x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
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return x
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class PatchEmbed(TimmPatchEmbed):
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def forward(self, x):
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x = self.proj(x)
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if self.flatten:
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x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
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else:
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x = x.permute(0, 2, 3, 1) # BCHW -> BHWC
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x = self.norm(x)
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return x
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class Shuffle(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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if self.training:
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B, H, W, C = x.shape
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r = torch.randperm(H * W)
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x = x.reshape(B, -1, C)
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x = x[:, r, :].reshape(B, H, W, -1)
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return x
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class Downsample2D(nn.Module):
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def __init__(self, input_dim, output_dim, patch_size):
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super().__init__()
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self.down = nn.Conv2d(input_dim, output_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x):
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x = x.permute(0, 3, 1, 2)
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x = self.down(x)
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x = x.permute(0, 2, 3, 1)
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return x
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class Sequencer2D(nn.Module):
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def __init__(
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self,
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num_classes=1000,
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img_size=224,
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in_chans=3,
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global_pool='avg',
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layers=[4, 3, 8, 3],
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patch_sizes=[7, 2, 1, 1],
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embed_dims=[192, 384, 384, 384],
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hidden_sizes=[48, 96, 96, 96],
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mlp_ratios=[3.0, 3.0, 3.0, 3.0],
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block_layer=Sequencer2DBlock,
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rnn_layer=LSTM2D,
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mlp_layer=Mlp,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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act_layer=nn.GELU,
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num_rnn_layers=1,
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bidirectional=True,
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union="cat",
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with_fc=True,
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drop_rate=0.,
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drop_path_rate=0.,
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nlhb=False,
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stem_norm=False,
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):
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super().__init__()
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assert global_pool in ('', 'avg')
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self.num_classes = num_classes
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self.global_pool = global_pool
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self.num_features = embed_dims[-1] # num_features for consistency with other models
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self.feature_dim = -1 # channel dim index for feature outputs (rank 4, NHWC)
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self.embed_dims = embed_dims
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self.stem = PatchEmbed(
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img_size=img_size, patch_size=patch_sizes[0], in_chans=in_chans,
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embed_dim=embed_dims[0], norm_layer=norm_layer if stem_norm else None,
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flatten=False)
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self.blocks = nn.Sequential(*[
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get_stage(
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i, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer=block_layer,
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rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer,
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num_layers=num_rnn_layers, bidirectional=bidirectional,
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union=union, with_fc=with_fc, drop=drop_rate, drop_path_rate=drop_path_rate,
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)
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for i, _ in enumerate(embed_dims)])
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self.norm = norm_layer(embed_dims[-1])
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self.head = nn.Linear(embed_dims[-1], self.num_classes) if num_classes > 0 else nn.Identity()
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self.init_weights(nlhb=nlhb)
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def init_weights(self, nlhb=False):
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head_bias = -math.log(self.num_classes) if nlhb else 0.
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named_apply(partial(_init_weights, head_bias=head_bias), module=self) # depth-first
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@torch.jit.ignore
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def group_matcher(self, coarse=False):
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return dict(
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stem=r'^stem',
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blocks=[
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(r'^blocks\.(\d+)\..*\.down', (99999,)),
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(r'^blocks\.(\d+)', None) if coarse else (r'^blocks\.(\d+)\.(\d+)', None),
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(r'^norm', (99999,))
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]
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)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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assert not enable, 'gradient checkpointing not supported'
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@torch.jit.ignore
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def get_classifier(self):
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return self.head
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def reset_classifier(self, num_classes, global_pool=None):
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self.num_classes = num_classes
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if global_pool is not None:
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assert global_pool in ('', 'avg')
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self.global_pool = global_pool
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x):
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x = self.stem(x)
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x = self.blocks(x)
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x = self.norm(x)
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return x
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def forward_head(self, x, pre_logits: bool = False):
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if self.global_pool == 'avg':
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x = x.mean(dim=(1, 2))
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return x if pre_logits else self.head(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _create_sequencer2d(variant, pretrained=False, **kwargs):
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Sequencer2D models.')
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model = build_model_with_cfg(Sequencer2D, variant, pretrained, **kwargs)
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return model
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# main
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@register_model
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def sequencer2d_s(pretrained=False, **kwargs):
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model_args = dict(
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layers=[4, 3, 8, 3],
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patch_sizes=[7, 2, 1, 1],
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embed_dims=[192, 384, 384, 384],
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hidden_sizes=[48, 96, 96, 96],
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mlp_ratios=[3.0, 3.0, 3.0, 3.0],
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rnn_layer=LSTM2D,
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bidirectional=True,
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union="cat",
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with_fc=True,
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**kwargs)
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model = _create_sequencer2d('sequencer2d_s', pretrained=pretrained, **model_args)
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return model
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@register_model
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def sequencer2d_m(pretrained=False, **kwargs):
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model_args = dict(
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layers=[4, 3, 14, 3],
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patch_sizes=[7, 2, 1, 1],
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embed_dims=[192, 384, 384, 384],
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hidden_sizes=[48, 96, 96, 96],
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mlp_ratios=[3.0, 3.0, 3.0, 3.0],
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rnn_layer=LSTM2D,
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bidirectional=True,
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union="cat",
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with_fc=True,
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**kwargs)
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model = _create_sequencer2d('sequencer2d_m', pretrained=pretrained, **model_args)
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return model
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@register_model
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def sequencer2d_l(pretrained=False, **kwargs):
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model_args = dict(
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layers=[8, 8, 16, 4],
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patch_sizes=[7, 2, 1, 1],
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embed_dims=[192, 384, 384, 384],
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hidden_sizes=[48, 96, 96, 96],
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mlp_ratios=[3.0, 3.0, 3.0, 3.0],
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rnn_layer=LSTM2D,
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bidirectional=True,
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union="cat",
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with_fc=True,
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**kwargs)
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model = _create_sequencer2d('sequencer2d_l', pretrained=pretrained, **model_args)
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return model
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