venv/lib/python3.11/site-packages/timm/models/sequencer.py

""" Sequencer

Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf

"""
#  Copyright (c) 2022. Yuki Tatsunami
#  Licensed under the Apache License, Version 2.0 (the "License");


import math
from functools import partial
from typing import Tuple

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
from .helpers import build_model_with_cfg, named_apply
from .layers import lecun_normal_, DropPath, Mlp, PatchEmbed as TimmPatchEmbed
from .registry import register_model


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': DEFAULT_CROP_PCT, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.proj', 'classifier': 'head',
        **kwargs
    }


default_cfgs = dict(
    sequencer2d_s=_cfg(url="https://github.com/okojoalg/sequencer/releases/download/weights/sequencer2d_s.pth"),
    sequencer2d_m=_cfg(url="https://github.com/okojoalg/sequencer/releases/download/weights/sequencer2d_m.pth"),
    sequencer2d_l=_cfg(url="https://github.com/okojoalg/sequencer/releases/download/weights/sequencer2d_l.pth"),
)


def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):
    if isinstance(module, nn.Linear):
        if name.startswith('head'):
            nn.init.zeros_(module.weight)
            nn.init.constant_(module.bias, head_bias)
        else:
            if flax:
                # Flax defaults
                lecun_normal_(module.weight)
                if module.bias is not None:
                    nn.init.zeros_(module.bias)
            else:
                nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    if 'mlp' in name:
                        nn.init.normal_(module.bias, std=1e-6)
                    else:
                        nn.init.zeros_(module.bias)
    elif isinstance(module, nn.Conv2d):
        lecun_normal_(module.weight)
        if module.bias is not None:
            nn.init.zeros_(module.bias)
    elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
        nn.init.ones_(module.weight)
        nn.init.zeros_(module.bias)
    elif isinstance(module, (nn.RNN, nn.GRU, nn.LSTM)):
        stdv = 1.0 / math.sqrt(module.hidden_size)
        for weight in module.parameters():
            nn.init.uniform_(weight, -stdv, stdv)
    elif hasattr(module, 'init_weights'):
        module.init_weights()


def get_stage(
        index, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer,
        norm_layer, act_layer, num_layers, bidirectional, union,
        with_fc, drop=0., drop_path_rate=0., **kwargs):
    assert len(layers) == len(patch_sizes) == len(embed_dims) == len(hidden_sizes) == len(mlp_ratios)
    blocks = []
    for block_idx in range(layers[index]):
        drop_path = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
        blocks.append(block_layer(
            embed_dims[index], hidden_sizes[index], mlp_ratio=mlp_ratios[index],
            rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer,
            num_layers=num_layers, bidirectional=bidirectional, union=union, with_fc=with_fc,
            drop=drop, drop_path=drop_path))

    if index < len(embed_dims) - 1:
        blocks.append(Downsample2D(embed_dims[index], embed_dims[index + 1], patch_sizes[index + 1]))

    blocks = nn.Sequential(*blocks)
    return blocks


class RNNIdentity(nn.Module):
    def __init__(self, *args, **kwargs):
        super(RNNIdentity, self).__init__()

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
        return x, None


class RNN2DBase(nn.Module):

    def __init__(
            self, input_size: int, hidden_size: int,
            num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
            union="cat", with_fc=True):
        super().__init__()

        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = 2 * hidden_size if bidirectional else hidden_size
        self.union = union

        self.with_vertical = True
        self.with_horizontal = True
        self.with_fc = with_fc

        self.fc = None
        if with_fc:
            if union == "cat":
                self.fc = nn.Linear(2 * self.output_size, input_size)
            elif union == "add":
                self.fc = nn.Linear(self.output_size, input_size)
            elif union == "vertical":
                self.fc = nn.Linear(self.output_size, input_size)
                self.with_horizontal = False
            elif union == "horizontal":
                self.fc = nn.Linear(self.output_size, input_size)
                self.with_vertical = False
            else:
                raise ValueError("Unrecognized union: " + union)
        elif union == "cat":
            pass
            if 2 * self.output_size != input_size:
                raise ValueError(f"The output channel {2 * self.output_size} is different from the input channel {input_size}.")
        elif union == "add":
            pass
            if self.output_size != input_size:
                raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
        elif union == "vertical":
            if self.output_size != input_size:
                raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
            self.with_horizontal = False
        elif union == "horizontal":
            if self.output_size != input_size:
                raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
            self.with_vertical = False
        else:
            raise ValueError("Unrecognized union: " + union)

        self.rnn_v = RNNIdentity()
        self.rnn_h = RNNIdentity()

    def forward(self, x):
        B, H, W, C = x.shape

        if self.with_vertical:
            v = x.permute(0, 2, 1, 3)
            v = v.reshape(-1, H, C)
            v, _ = self.rnn_v(v)
            v = v.reshape(B, W, H, -1)
            v = v.permute(0, 2, 1, 3)
        else:
            v = None

        if self.with_horizontal:
            h = x.reshape(-1, W, C)
            h, _ = self.rnn_h(h)
            h = h.reshape(B, H, W, -1)
        else:
            h = None

        if v is not None and h is not None:
            if self.union == "cat":
                x = torch.cat([v, h], dim=-1)
            else:
                x = v + h
        elif v is not None:
            x = v
        elif h is not None:
            x = h

        if self.fc is not None:
            x = self.fc(x)

        return x


class LSTM2D(RNN2DBase):

    def __init__(
            self, input_size: int, hidden_size: int,
            num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
            union="cat", with_fc=True):
        super().__init__(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)
        if self.with_vertical:
            self.rnn_v = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bias=bias, bidirectional=bidirectional)
        if self.with_horizontal:
            self.rnn_h = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bias=bias, bidirectional=bidirectional)


class Sequencer2DBlock(nn.Module):
    def __init__(
            self, dim, hidden_size, mlp_ratio=3.0, rnn_layer=LSTM2D, mlp_layer=Mlp,
            norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU,
            num_layers=1, bidirectional=True, union="cat", with_fc=True, drop=0., drop_path=0.):
        super().__init__()
        channels_dim = int(mlp_ratio * dim)
        self.norm1 = norm_layer(dim)
        self.rnn_tokens = rnn_layer(dim, hidden_size, num_layers=num_layers, bidirectional=bidirectional,
                                    union=union, with_fc=with_fc)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.mlp_channels = mlp_layer(dim, channels_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        x = x + self.drop_path(self.rnn_tokens(self.norm1(x)))
        x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
        return x


class PatchEmbed(TimmPatchEmbed):
    def forward(self, x):
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        else:
            x = x.permute(0, 2, 3, 1)  # BCHW -> BHWC
        x = self.norm(x)
        return x


class Shuffle(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        if self.training:
            B, H, W, C = x.shape
            r = torch.randperm(H * W)
            x = x.reshape(B, -1, C)
            x = x[:, r, :].reshape(B, H, W, -1)
        return x


class Downsample2D(nn.Module):
    def __init__(self, input_dim, output_dim, patch_size):
        super().__init__()
        self.down = nn.Conv2d(input_dim, output_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        x = x.permute(0, 3, 1, 2)
        x = self.down(x)
        x = x.permute(0, 2, 3, 1)
        return x


class Sequencer2D(nn.Module):
    def __init__(
            self,
            num_classes=1000,
            img_size=224,
            in_chans=3,
            global_pool='avg',
            layers=[4, 3, 8, 3],
            patch_sizes=[7, 2, 1, 1],
            embed_dims=[192, 384, 384, 384],
            hidden_sizes=[48, 96, 96, 96],
            mlp_ratios=[3.0, 3.0, 3.0, 3.0],
            block_layer=Sequencer2DBlock,
            rnn_layer=LSTM2D,
            mlp_layer=Mlp,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            act_layer=nn.GELU,
            num_rnn_layers=1,
            bidirectional=True,
            union="cat",
            with_fc=True,
            drop_rate=0.,
            drop_path_rate=0.,
            nlhb=False,
            stem_norm=False,
    ):
        super().__init__()
        assert global_pool in ('', 'avg')
        self.num_classes = num_classes
        self.global_pool = global_pool
        self.num_features = embed_dims[-1]  # num_features for consistency with other models
        self.feature_dim = -1  # channel dim index for feature outputs (rank 4, NHWC)
        self.embed_dims = embed_dims
        self.stem = PatchEmbed(
            img_size=img_size, patch_size=patch_sizes[0], in_chans=in_chans,
            embed_dim=embed_dims[0], norm_layer=norm_layer if stem_norm else None,
            flatten=False)

        self.blocks = nn.Sequential(*[
            get_stage(
                i, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer=block_layer,
                rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer,
                num_layers=num_rnn_layers, bidirectional=bidirectional,
                union=union, with_fc=with_fc, drop=drop_rate, drop_path_rate=drop_path_rate,
            )
            for i, _ in enumerate(embed_dims)])

        self.norm = norm_layer(embed_dims[-1])
        self.head = nn.Linear(embed_dims[-1], self.num_classes) if num_classes > 0 else nn.Identity()

        self.init_weights(nlhb=nlhb)

    def init_weights(self, nlhb=False):
        head_bias = -math.log(self.num_classes) if nlhb else 0.
        named_apply(partial(_init_weights, head_bias=head_bias), module=self)  # depth-first

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        return dict(
            stem=r'^stem',
            blocks=[
                (r'^blocks\.(\d+)\..*\.down', (99999,)),
                (r'^blocks\.(\d+)', None) if coarse else (r'^blocks\.(\d+)\.(\d+)', None),
                (r'^norm', (99999,))
            ]
        )

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        assert not enable, 'gradient checkpointing not supported'

    @torch.jit.ignore
    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=None):
        self.num_classes = num_classes
        if global_pool is not None:
            assert global_pool in ('', 'avg')
            self.global_pool = global_pool
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        x = self.stem(x)
        x = self.blocks(x)
        x = self.norm(x)
        return x

    def forward_head(self, x, pre_logits: bool = False):
        if self.global_pool == 'avg':
            x = x.mean(dim=(1, 2))
        return x if pre_logits else self.head(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def _create_sequencer2d(variant, pretrained=False, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Sequencer2D models.')

    model = build_model_with_cfg(Sequencer2D, variant, pretrained, **kwargs)
    return model


# main

@register_model
def sequencer2d_s(pretrained=False, **kwargs):
    model_args = dict(
        layers=[4, 3, 8, 3],
        patch_sizes=[7, 2, 1, 1],
        embed_dims=[192, 384, 384, 384],
        hidden_sizes=[48, 96, 96, 96],
        mlp_ratios=[3.0, 3.0, 3.0, 3.0],
        rnn_layer=LSTM2D,
        bidirectional=True,
        union="cat",
        with_fc=True,
        **kwargs)
    model = _create_sequencer2d('sequencer2d_s', pretrained=pretrained, **model_args)
    return model


@register_model
def sequencer2d_m(pretrained=False, **kwargs):
    model_args = dict(
        layers=[4, 3, 14, 3],
        patch_sizes=[7, 2, 1, 1],
        embed_dims=[192, 384, 384, 384],
        hidden_sizes=[48, 96, 96, 96],
        mlp_ratios=[3.0, 3.0, 3.0, 3.0],
        rnn_layer=LSTM2D,
        bidirectional=True,
        union="cat",
        with_fc=True,
        **kwargs)
    model = _create_sequencer2d('sequencer2d_m', pretrained=pretrained, **model_args)
    return model


@register_model
def sequencer2d_l(pretrained=False, **kwargs):
    model_args = dict(
        layers=[8, 8, 16, 4],
        patch_sizes=[7, 2, 1, 1],
        embed_dims=[192, 384, 384, 384],
        hidden_sizes=[48, 96, 96, 96],
        mlp_ratios=[3.0, 3.0, 3.0, 3.0],
        rnn_layer=LSTM2D,
        bidirectional=True,
        union="cat",
        with_fc=True,
        **kwargs)
    model = _create_sequencer2d('sequencer2d_l', pretrained=pretrained, **model_args)
    return model