AutoAndroidController/py/venv/Lib/site-packages/timm/models/pit.py

""" Pooling-based Vision Transformer (PiT) in PyTorch

A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302

This code was adapted from the original version at https://github.com/naver-ai/pit, original copyright below.

Modifications for timm by / Copyright 2020 Ross Wightman
"""
# PiT
# Copyright 2021-present NAVER Corp.
# Apache License v2.0

import math
import re
from functools import partial
from typing import List, Optional, Sequence, Tuple, Union, Type, Any

import torch
from torch import nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, to_2tuple, calculate_drop_path_rates
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._registry import register_model, generate_default_cfgs
from .vision_transformer import Block


__all__ = ['PoolingVisionTransformer']  # model_registry will add each entrypoint fn to this


class SequentialTuple(nn.Sequential):
    """ This module exists to work around torchscript typing issues list -> list"""
    def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
        for module in self:
            x = module(x)
        return x


class Transformer(nn.Module):
    def __init__(
            self,
            base_dim: int,
            depth: int,
            heads: int,
            mlp_ratio: float,
            pool: Optional[Any] = None,
            proj_drop: float = .0,
            attn_drop: float = .0,
            drop_path_prob: Optional[List[float]] = None,
            norm_layer: Optional[Type[nn.Module]] = None,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        embed_dim = base_dim * heads

        self.pool = pool
        self.norm = norm_layer(embed_dim, **dd) if norm_layer else nn.Identity()
        self.blocks = nn.Sequential(*[
            Block(
                dim=embed_dim,
                num_heads=heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=True,
                proj_drop=proj_drop,
                attn_drop=attn_drop,
                drop_path=drop_path_prob[i],
                norm_layer=partial(nn.LayerNorm, eps=1e-6),
                **dd,
            )
            for i in range(depth)])

    def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
        x, cls_tokens = x
        token_length = cls_tokens.shape[1]
        if self.pool is not None:
            x, cls_tokens = self.pool(x, cls_tokens)

        B, C, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = torch.cat((cls_tokens, x), dim=1)

        x = self.norm(x)
        x = self.blocks(x)

        cls_tokens = x[:, :token_length]
        x = x[:, token_length:]
        x = x.transpose(1, 2).reshape(B, C, H, W)

        return x, cls_tokens


class Pooling(nn.Module):
    def __init__(
            self,
            in_feature: int,
            out_feature: int,
            stride: int,
            padding_mode: str = 'zeros',
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()

        self.conv = nn.Conv2d(
            in_feature,
            out_feature,
            kernel_size=stride + 1,
            padding=stride // 2,
            stride=stride,
            padding_mode=padding_mode,
            groups=in_feature,
            **dd,
        )
        self.fc = nn.Linear(in_feature, out_feature, **dd)

    def forward(self, x, cls_token) -> Tuple[torch.Tensor, torch.Tensor]:
        x = self.conv(x)
        cls_token = self.fc(cls_token)
        return x, cls_token


class ConvEmbedding(nn.Module):
    def __init__(
            self,
            in_channels: int,
            out_channels: int,
            img_size: int = 224,
            patch_size: int = 16,
            stride: int = 8,
            padding: int = 0,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        padding = padding
        self.img_size = to_2tuple(img_size)
        self.patch_size = to_2tuple(patch_size)
        self.height = math.floor((self.img_size[0] + 2 * padding - self.patch_size[0]) / stride + 1)
        self.width = math.floor((self.img_size[1] + 2 * padding - self.patch_size[1]) / stride + 1)
        self.grid_size = (self.height, self.width)

        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=patch_size,
            stride=stride,
            padding=padding,
            bias=True,
            **dd,
        )

    def forward(self, x):
        x = self.conv(x)
        return x


class PoolingVisionTransformer(nn.Module):
    """ Pooling-based Vision Transformer

    A PyTorch implement of 'Rethinking Spatial Dimensions of Vision Transformers'
        - https://arxiv.org/abs/2103.16302
    """
    def __init__(
            self,
            img_size: int = 224,
            patch_size: int = 16,
            stride: int = 8,
            stem_type: str = 'overlap',
            base_dims: Sequence[int] = (48, 48, 48),
            depth: Sequence[int] = (2, 6, 4),
            heads: Sequence[int] = (2, 4, 8),
            mlp_ratio: float = 4,
            num_classes: int = 1000,
            in_chans: int = 3,
            global_pool: str = 'token',
            distilled: bool = False,
            drop_rate: float = 0.,
            pos_drop_drate: float = 0.,
            proj_drop_rate: float = 0.,
            attn_drop_rate: float = 0.,
            drop_path_rate: float = 0.,
            device=None,
            dtype=None,
    ):
        super().__init__()
        dd = {'device': device, 'dtype': dtype}
        assert global_pool in ('token',)

        self.base_dims = base_dims
        self.heads = heads
        embed_dim = base_dims[0] * heads[0]
        self.num_classes = num_classes
        self.global_pool = global_pool
        self.num_tokens = 2 if distilled else 1
        self.feature_info = []

        self.patch_embed = ConvEmbedding(in_chans, embed_dim, img_size, patch_size, stride, **dd)
        self.pos_embed = nn.Parameter(torch.randn(1, embed_dim, self.patch_embed.height, self.patch_embed.width, **dd))
        self.cls_token = nn.Parameter(torch.randn(1, self.num_tokens, embed_dim, **dd))
        self.pos_drop = nn.Dropout(p=pos_drop_drate)

        transformers = []
        # stochastic depth decay rule
        dpr = calculate_drop_path_rates(drop_path_rate, depth, stagewise=True)
        prev_dim = embed_dim
        for i in range(len(depth)):
            pool = None
            embed_dim = base_dims[i] * heads[i]
            if i > 0:
                pool = Pooling(
                    prev_dim,
                    embed_dim,
                    stride=2,
                    **dd,
                )
            transformers += [Transformer(
                base_dims[i],
                depth[i],
                heads[i],
                mlp_ratio,
                pool=pool,
                proj_drop=proj_drop_rate,
                attn_drop=attn_drop_rate,
                drop_path_prob=dpr[i],
                **dd,
            )]
            prev_dim = embed_dim
            self.feature_info += [dict(num_chs=prev_dim, reduction=(stride - 1) * 2**i, module=f'transformers.{i}')]

        self.transformers = SequentialTuple(*transformers)
        self.norm = nn.LayerNorm(base_dims[-1] * heads[-1], eps=1e-6, **dd)
        self.num_features = self.head_hidden_size = self.embed_dim = embed_dim

        # Classifier head
        self.head_drop = nn.Dropout(drop_rate)
        self.head = nn.Linear(self.embed_dim, num_classes, **dd) if num_classes > 0 else nn.Identity()
        self.head_dist = None
        if distilled:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes, **dd) if num_classes > 0 else nn.Identity()
        self.distilled_training = False  # must set this True to train w/ distillation token

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    @torch.jit.ignore
    def set_distilled_training(self, enable=True):
        self.distilled_training = enable

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

    def get_classifier(self) -> nn.Module:
        if self.head_dist is not None:
            return self.head, self.head_dist
        else:
            return self.head

    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
        self.num_classes = num_classes
        if global_pool is not None:
            self.global_pool = global_pool
        device = self.head.weight.device if hasattr(self.head, 'weight') else None
        dtype = self.head.weight.dtype if hasattr(self.head, 'weight') else None
        self.head = nn.Linear(self.embed_dim, num_classes, device=device, dtype=dtype) if num_classes > 0 else nn.Identity()
        if self.head_dist is not None:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes, device=device, dtype=dtype) if num_classes > 0 else nn.Identity()

    def forward_intermediates(
            self,
            x: torch.Tensor,
            indices: Optional[Union[int, List[int]]] = None,
            norm: bool = False,
            stop_early: bool = False,
            output_fmt: str = 'NCHW',
            intermediates_only: bool = False,
    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
        """ Forward features that returns intermediates.

        Args:
            x: Input image tensor
            indices: Take last n blocks if int, all if None, select matching indices if sequence
            norm: Apply norm layer to compatible intermediates
            stop_early: Stop iterating over blocks when last desired intermediate hit
            output_fmt: Shape of intermediate feature outputs
            intermediates_only: Only return intermediate features
        Returns:

        """
        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
        intermediates = []
        take_indices, max_index = feature_take_indices(len(self.transformers), indices)

        # forward pass
        x = self.patch_embed(x)
        x = self.pos_drop(x + self.pos_embed)
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)

        last_idx = len(self.transformers) - 1
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.transformers
        else:
            stages = self.transformers[:max_index + 1]

        for feat_idx, stage in enumerate(stages):
            x, cls_tokens = stage((x, cls_tokens))
            if feat_idx in take_indices:
                intermediates.append(x)

        if intermediates_only:
            return intermediates

        if feat_idx == last_idx:
            cls_tokens = self.norm(cls_tokens)

        return cls_tokens, intermediates

    def prune_intermediate_layers(
            self,
            indices: Union[int, List[int]] = 1,
            prune_norm: bool = False,
            prune_head: bool = True,
    ):
        """ Prune layers not required for specified intermediates.
        """
        take_indices, max_index = feature_take_indices(len(self.transformers), indices)
        self.transformers = self.transformers[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_norm:
            self.norm = nn.Identity()
        if prune_head:
            self.reset_classifier(0, '')
        return take_indices

    def forward_features(self, x):
        x = self.patch_embed(x)
        x = self.pos_drop(x + self.pos_embed)
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
        x, cls_tokens = self.transformers((x, cls_tokens))
        cls_tokens = self.norm(cls_tokens)
        return cls_tokens

    def forward_head(self, x, pre_logits: bool = False) -> torch.Tensor:
        if self.head_dist is not None:
            assert self.global_pool == 'token'
            x, x_dist = x[:, 0], x[:, 1]
            x = self.head_drop(x)
            x_dist = self.head_drop(x)
            if not pre_logits:
                x = self.head(x)
                x_dist = self.head_dist(x_dist)
            if self.distilled_training and self.training and not torch.jit.is_scripting():
                # only return separate classification predictions when training in distilled mode
                return x, x_dist
            else:
                # during standard train / finetune, inference average the classifier predictions
                return (x + x_dist) / 2
        else:
            if self.global_pool == 'token':
                x = x[:, 0]
            x = self.head_drop(x)
            if not pre_logits:
                x = self.head(x)
            return x

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


def checkpoint_filter_fn(state_dict, model):
    """ preprocess checkpoints """
    out_dict = {}
    p_blocks = re.compile(r'pools\.(\d)\.')
    for k, v in state_dict.items():
        # FIXME need to update resize for PiT impl
        # if k == 'pos_embed' and v.shape != model.pos_embed.shape:
        #     # To resize pos embedding when using model at different size from pretrained weights
        #     v = resize_pos_embed(v, model.pos_embed)
        k = p_blocks.sub(lambda exp: f'transformers.{int(exp.group(1)) + 1}.pool.', k)
        out_dict[k] = v
    return out_dict


def _create_pit(variant, pretrained=False, **kwargs):
    default_out_indices = tuple(range(3))
    out_indices = kwargs.pop('out_indices', default_out_indices)

    model = build_model_with_cfg(
        PoolingVisionTransformer,
        variant,
        pretrained,
        pretrained_filter_fn=checkpoint_filter_fn,
        feature_cfg=dict(feature_cls='hook', out_indices=out_indices),
        **kwargs,
    )
    return model


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.conv', 'classifier': 'head',
        'license': 'apache-2.0',
        **kwargs
    }


default_cfgs = generate_default_cfgs({
    # deit models (FB weights)
    'pit_ti_224.in1k': _cfg(hf_hub_id='timm/'),
    'pit_xs_224.in1k': _cfg(hf_hub_id='timm/'),
    'pit_s_224.in1k': _cfg(hf_hub_id='timm/'),
    'pit_b_224.in1k': _cfg(hf_hub_id='timm/'),
    'pit_ti_distilled_224.in1k': _cfg(
        hf_hub_id='timm/',
        classifier=('head', 'head_dist')),
    'pit_xs_distilled_224.in1k': _cfg(
        hf_hub_id='timm/',
        classifier=('head', 'head_dist')),
    'pit_s_distilled_224.in1k': _cfg(
        hf_hub_id='timm/',
        classifier=('head', 'head_dist')),
    'pit_b_distilled_224.in1k': _cfg(
        hf_hub_id='timm/',
        classifier=('head', 'head_dist')),
})


@register_model
def pit_b_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=14,
        stride=7,
        base_dims=[64, 64, 64],
        depth=[3, 6, 4],
        heads=[4, 8, 16],
        mlp_ratio=4,
    )
    return _create_pit('pit_b_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_s_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=16,
        stride=8,
        base_dims=[48, 48, 48],
        depth=[2, 6, 4],
        heads=[3, 6, 12],
        mlp_ratio=4,
    )
    return _create_pit('pit_s_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_xs_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=16,
        stride=8,
        base_dims=[48, 48, 48],
        depth=[2, 6, 4],
        heads=[2, 4, 8],
        mlp_ratio=4,
    )
    return _create_pit('pit_xs_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_ti_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=16,
        stride=8,
        base_dims=[32, 32, 32],
        depth=[2, 6, 4],
        heads=[2, 4, 8],
        mlp_ratio=4,
    )
    return _create_pit('pit_ti_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_b_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=14,
        stride=7,
        base_dims=[64, 64, 64],
        depth=[3, 6, 4],
        heads=[4, 8, 16],
        mlp_ratio=4,
        distilled=True,
    )
    return _create_pit('pit_b_distilled_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_s_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=16,
        stride=8,
        base_dims=[48, 48, 48],
        depth=[2, 6, 4],
        heads=[3, 6, 12],
        mlp_ratio=4,
        distilled=True,
    )
    return _create_pit('pit_s_distilled_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_xs_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=16,
        stride=8,
        base_dims=[48, 48, 48],
        depth=[2, 6, 4],
        heads=[2, 4, 8],
        mlp_ratio=4,
        distilled=True,
    )
    return _create_pit('pit_xs_distilled_224', pretrained, **dict(model_args, **kwargs))


@register_model
def pit_ti_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
    model_args = dict(
        patch_size=16,
        stride=8,
        base_dims=[32, 32, 32],
        depth=[2, 6, 4],
        heads=[2, 4, 8],
        mlp_ratio=4,
        distilled=True,
    )
    return _create_pit('pit_ti_distilled_224', pretrained, **dict(model_args, **kwargs))