image-match/python/py/Lib/site-packages/timm/models/inception_next.py

"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""

from functools import partial
from typing import List, Optional, Tuple, Union, Type

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, DropPath, calculate_drop_path_rates, to_2tuple, get_padding, SelectAdaptivePool2d
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs

__all__ = ['MetaNeXt']


class InceptionDWConv2d(nn.Module):
    """ Inception depthwise convolution
    """

    def __init__(
            self,
            in_chs: int,
            square_kernel_size: int = 3,
            band_kernel_size: int = 11,
            branch_ratio: float = 0.125,
            dilation: int = 1,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        gc = int(in_chs * branch_ratio)  # channel numbers of a convolution branch
        square_padding = get_padding(square_kernel_size, dilation=dilation)
        band_padding = get_padding(band_kernel_size, dilation=dilation)
        self.dwconv_hw = nn.Conv2d(
            gc, gc, square_kernel_size,
            padding=square_padding, dilation=dilation, groups=gc, **dd)
        self.dwconv_w = nn.Conv2d(
            gc, gc, (1, band_kernel_size),
            padding=(0, band_padding), dilation=(1, dilation), groups=gc, **dd)
        self.dwconv_h = nn.Conv2d(
            gc, gc, (band_kernel_size, 1),
            padding=(band_padding, 0), dilation=(dilation, 1), groups=gc, **dd)
        self.split_indexes = (in_chs - 3 * gc, gc, gc, gc)

    def forward(self, x):
        x_id, x_hw, x_w, x_h = torch.split(x, self.split_indexes, dim=1)
        return torch.cat((
            x_id,
            self.dwconv_hw(x_hw),
            self.dwconv_w(x_w),
            self.dwconv_h(x_h)
            ), dim=1,
        )


class ConvMlp(nn.Module):
    """ MLP using 1x1 convs that keeps spatial dims
    copied from timm: https://github.com/huggingface/pytorch-image-models/blob/v0.6.11/timm/models/layers/mlp.py
    """

    def __init__(
            self,
            in_features: int,
            hidden_features: Optional[int] = None,
            out_features: Optional[int] = None,
            act_layer: Type[nn.Module] = nn.ReLU,
            norm_layer: Optional[Type[nn.Module]] = None,
            bias: bool = True,
            drop: float = 0.,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0], **dd)
        self.norm = norm_layer(hidden_features, **dd) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1], **dd)

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x


class MlpClassifierHead(nn.Module):
    """ MLP classification head
    """

    def __init__(
            self,
            in_features: int,
            num_classes: int = 1000,
            pool_type: str = 'avg',
            mlp_ratio: float = 3,
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Type[nn.Module] = partial(nn.LayerNorm, eps=1e-6),
            drop: float = 0.,
            bias: bool = True,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        self.use_conv = False
        self.in_features = in_features
        self.num_features = hidden_features = int(mlp_ratio * in_features)

        assert pool_type, 'Cannot disable pooling'
        self.global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=True)

        self.fc1 = nn.Linear(in_features * self.global_pool.feat_mult(), hidden_features, bias=bias, **dd)
        self.act = act_layer()
        self.norm = norm_layer(hidden_features, **dd)
        self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias, **dd)
        self.drop = nn.Dropout(drop)

    def reset(self, num_classes: int, pool_type: Optional[str] = None):
        if pool_type is not None:
            assert pool_type, 'Cannot disable pooling'
            self.global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=True)

        self.fc2 = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward(self, x, pre_logits: bool = False):
        x = self.global_pool(x)
        x = self.fc1(x)
        x = self.act(x)
        x = self.norm(x)
        x = self.drop(x)
        return x if pre_logits else self.fc2(x)


class MetaNeXtBlock(nn.Module):
    """ MetaNeXtBlock Block
    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
        ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(
            self,
            dim: int,
            dilation: int = 1,
            token_mixer: Type[nn.Module] = InceptionDWConv2d,
            norm_layer: Type[nn.Module] = nn.BatchNorm2d,
            mlp_layer: Type[nn.Module] = ConvMlp,
            mlp_ratio: float = 4,
            act_layer: Type[nn.Module] = nn.GELU,
            ls_init_value: float = 1e-6,
            drop_path: float = 0.,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        self.token_mixer = token_mixer(dim, dilation=dilation, **dd)
        self.norm = norm_layer(dim, **dd)
        self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer, **dd)
        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim, **dd)) if ls_init_value else None
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        shortcut = x
        x = self.token_mixer(x)
        x = self.norm(x)
        x = self.mlp(x)
        if self.gamma is not None:
            x = x.mul(self.gamma.reshape(1, -1, 1, 1))
        x = self.drop_path(x) + shortcut
        return x


class MetaNeXtStage(nn.Module):
    def __init__(
            self,
            in_chs: int,
            out_chs: int,
            stride: int = 2,
            depth: int = 2,
            dilation: Tuple[int, int] = (1, 1),
            drop_path_rates: Optional[List[float]] = None,
            ls_init_value: float = 1.0,
            token_mixer: Type[nn.Module] = InceptionDWConv2d,
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Optional[Type[nn.Module]] = None,
            mlp_ratio: float = 4,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        self.grad_checkpointing = False
        if stride > 1 or dilation[0] != dilation[1]:
            self.downsample = nn.Sequential(
                norm_layer(in_chs, **dd),
                nn.Conv2d(
                    in_chs,
                    out_chs,
                    kernel_size=2,
                    stride=stride,
                    dilation=dilation[0],
                    **dd,
                ),
            )
        else:
            self.downsample = nn.Identity()

        drop_path_rates = drop_path_rates or [0.] * depth
        stage_blocks = []
        for i in range(depth):
            stage_blocks.append(MetaNeXtBlock(
                dim=out_chs,
                dilation=dilation[1],
                drop_path=drop_path_rates[i],
                ls_init_value=ls_init_value,
                token_mixer=token_mixer,
                act_layer=act_layer,
                norm_layer=norm_layer,
                mlp_ratio=mlp_ratio,
                **dd,
            ))
        self.blocks = nn.Sequential(*stage_blocks)

    def forward(self, x):
        x = self.downsample(x)
        if self.grad_checkpointing and not torch.jit.is_scripting():
            x = checkpoint_seq(self.blocks, x)
        else:
            x = self.blocks(x)
        return x


class MetaNeXt(nn.Module):
    r""" MetaNeXt
        A PyTorch impl of : `InceptionNeXt: When Inception Meets ConvNeXt` - https://arxiv.org/abs/2303.16900

    Args:
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        depths (tuple(int)): Number of blocks at each stage. Default: (3, 3, 9, 3)
        dims (tuple(int)): Feature dimension at each stage. Default: (96, 192, 384, 768)
        token_mixers: Token mixer function. Default: nn.Identity
        norm_layer: Normalization layer. Default: nn.BatchNorm2d
        act_layer: Activation function for MLP. Default: nn.GELU
        mlp_ratios (int or tuple(int)): MLP ratios. Default: (4, 4, 4, 3)
        drop_rate (float): Head dropout rate
        drop_path_rate (float): Stochastic depth rate. Default: 0.
        ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(
            self,
            in_chans: int = 3,
            num_classes: int = 1000,
            global_pool: str = 'avg',
            output_stride: int = 32,
            depths: Tuple[int, ...] = (3, 3, 9, 3),
            dims: Tuple[int, ...] = (96, 192, 384, 768),
            token_mixers: Union[Type[nn.Module], List[Type[nn.Module]]] = InceptionDWConv2d,
            norm_layer: Type[nn.Module] = nn.BatchNorm2d,
            act_layer: Type[nn.Module] = nn.GELU,
            mlp_ratios: Union[int, Tuple[int, ...]] = (4, 4, 4, 3),
            drop_rate: float = 0.,
            drop_path_rate: float = 0.,
            ls_init_value: float = 1e-6,
            device=None,
            dtype=None,
    ):
        super().__init__()
        dd = {'device': device, 'dtype': dtype}
        num_stage = len(depths)
        if not isinstance(token_mixers, (list, tuple)):
            token_mixers = [token_mixers] * num_stage
        if not isinstance(mlp_ratios, (list, tuple)):
            mlp_ratios = [mlp_ratios] * num_stage
        self.num_classes = num_classes
        self.in_chans = in_chans
        self.global_pool = global_pool
        self.drop_rate = drop_rate
        self.feature_info = []

        self.stem = nn.Sequential(
            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4, **dd),
            norm_layer(dims[0], **dd)
        )

        dp_rates = calculate_drop_path_rates(drop_path_rate, depths, stagewise=True)
        prev_chs = dims[0]
        curr_stride = 4
        dilation = 1
        # feature resolution stages, each consisting of multiple residual blocks
        self.stages = nn.Sequential()
        for i in range(num_stage):
            stride = 2 if curr_stride == 2 or i > 0 else 1
            if curr_stride >= output_stride and stride > 1:
                dilation *= stride
                stride = 1
            curr_stride *= stride
            first_dilation = 1 if dilation in (1, 2) else 2
            out_chs = dims[i]
            self.stages.append(MetaNeXtStage(
                prev_chs,
                out_chs,
                stride=stride if i > 0 else 1,
                dilation=(first_dilation, dilation),
                depth=depths[i],
                drop_path_rates=dp_rates[i],
                ls_init_value=ls_init_value,
                act_layer=act_layer,
                token_mixer=token_mixers[i],
                norm_layer=norm_layer,
                mlp_ratio=mlp_ratios[i],
                **dd,
            ))
            prev_chs = out_chs
            self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
        self.num_features = prev_chs
        self.head = MlpClassifierHead(self.num_features, num_classes, pool_type=self.global_pool, drop=drop_rate, **dd)
        self.head_hidden_size = self.head.num_features
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        return dict(
            stem=r'^stem',
            blocks=r'^stages\.(\d+)' if coarse else [
                (r'^stages\.(\d+)\.downsample', (0,)),  # blocks
                (r'^stages\.(\d+)\.blocks\.(\d+)', None),
            ]
        )

    @torch.jit.ignore
    def get_classifier(self) -> nn.Module:
        return self.head.fc2

    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
        self.num_classes = num_classes
        self.head.reset(num_classes, global_pool)

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        for s in self.stages:
            s.grad_checkpointing = enable

    @torch.jit.ignore
    def no_weight_decay(self):
        return set()

    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.stages), indices)

        # forward pass
        x = self.stem(x)
        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
            stages = self.stages
        else:
            stages = self.stages[:max_index + 1]

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

        if intermediates_only:
            return intermediates

        return x, 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.stages), indices)
        self.stages = self.stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
        if prune_head:
            self.reset_classifier(0, 'avg')
        return take_indices

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

    def forward_head(self, x, pre_logits: bool = False):
        return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)

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


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.0', 'classifier': 'head.fc2',
        'license': 'apache-2.0',
        **kwargs
    }


default_cfgs = generate_default_cfgs({
    'inception_next_atto.sail_in1k': _cfg(
        hf_hub_id='timm/',
        # url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_atto.pth',
    ),
    'inception_next_tiny.sail_in1k': _cfg(
        hf_hub_id='timm/',
        # url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_tiny.pth',
    ),
    'inception_next_small.sail_in1k': _cfg(
        hf_hub_id='timm/',
        # url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_small.pth',
    ),
    'inception_next_base.sail_in1k': _cfg(
        hf_hub_id='timm/',
        # url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
        crop_pct=0.95,
    ),
    'inception_next_base.sail_in1k_384': _cfg(
        hf_hub_id='timm/',
        # url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base_384.pth',
        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
    ),
})


def _create_inception_next(variant, pretrained=False, **kwargs):
    model = build_model_with_cfg(
        MetaNeXt, variant, pretrained,
        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
        **kwargs,
    )
    return model


@register_model
def inception_next_atto(pretrained=False, **kwargs):
    model_args = dict(
        depths=(2, 2, 6, 2), dims=(40, 80, 160, 320),
        token_mixers=partial(InceptionDWConv2d, band_kernel_size=9, branch_ratio=0.25)
    )
    return _create_inception_next('inception_next_atto', pretrained=pretrained, **dict(model_args, **kwargs))


@register_model
def inception_next_tiny(pretrained=False, **kwargs):
    model_args = dict(
        depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
        token_mixers=InceptionDWConv2d,
    )
    return _create_inception_next('inception_next_tiny', pretrained=pretrained, **dict(model_args, **kwargs))


@register_model
def inception_next_small(pretrained=False, **kwargs):
    model_args = dict(
        depths=(3, 3, 27, 3), dims=(96, 192, 384, 768),
        token_mixers=InceptionDWConv2d,
    )
    return _create_inception_next('inception_next_small', pretrained=pretrained, **dict(model_args, **kwargs))


@register_model
def inception_next_base(pretrained=False, **kwargs):
    model_args = dict(
        depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024),
        token_mixers=InceptionDWConv2d,
    )
    return _create_inception_next('inception_next_base', pretrained=pretrained, **dict(model_args, **kwargs))