1425 lines
48 KiB
Python
1425 lines
48 KiB
Python
#
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# For licensing see accompanying LICENSE file at https://github.com/apple/ml-fastvit/tree/main
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#
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# Original work is copyright (C) 2023 Apple Inc. All Rights Reserved.
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#
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import copy
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import os
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from functools import partial
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from typing import List, Tuple, Optional, Union
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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
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from timm.layers import DropPath, trunc_normal_, create_conv2d, ConvNormAct, SqueezeExcite, use_fused_attn
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from ._builder import build_model_with_cfg
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from ._registry import register_model, generate_default_cfgs
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def num_groups(group_size, channels):
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if not group_size: # 0 or None
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return 1 # normal conv with 1 group
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else:
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# NOTE group_size == 1 -> depthwise conv
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assert channels % group_size == 0
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return channels // group_size
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class MobileOneBlock(nn.Module):
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"""MobileOne building block.
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This block has a multi-branched architecture at train-time
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and plain-CNN style architecture at inference time
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For more details, please refer to our paper:
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`An Improved One millisecond Mobile Backbone` -
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https://arxiv.org/pdf/2206.04040.pdf
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"""
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def __init__(
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self,
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in_chs: int,
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out_chs: int,
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kernel_size: int,
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stride: int = 1,
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dilation: int = 1,
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group_size: int = 0,
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inference_mode: bool = False,
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use_se: bool = False,
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use_act: bool = True,
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use_scale_branch: bool = True,
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num_conv_branches: int = 1,
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act_layer: nn.Module = nn.GELU,
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) -> None:
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"""Construct a MobileOneBlock module.
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Args:
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in_chs: Number of channels in the input.
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out_chs: Number of channels produced by the block.
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kernel_size: Size of the convolution kernel.
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stride: Stride size.
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dilation: Kernel dilation factor.
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group_size: Convolution group size.
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inference_mode: If True, instantiates model in inference mode.
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use_se: Whether to use SE-ReLU activations.
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use_act: Whether to use activation. Default: ``True``
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use_scale_branch: Whether to use scale branch. Default: ``True``
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num_conv_branches: Number of linear conv branches.
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"""
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super(MobileOneBlock, self).__init__()
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self.inference_mode = inference_mode
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self.groups = num_groups(group_size, in_chs)
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self.stride = stride
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self.dilation = dilation
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self.kernel_size = kernel_size
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self.in_chs = in_chs
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self.out_chs = out_chs
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self.num_conv_branches = num_conv_branches
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# Check if SE-ReLU is requested
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self.se = SqueezeExcite(out_chs, rd_divisor=1) if use_se else nn.Identity()
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if inference_mode:
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self.reparam_conv = create_conv2d(
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in_chs,
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out_chs,
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kernel_size=kernel_size,
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stride=stride,
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dilation=dilation,
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groups=self.groups,
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bias=True,
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)
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else:
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# Re-parameterizable skip connection
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self.reparam_conv = None
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self.rbr_skip = (
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nn.BatchNorm2d(num_features=in_chs)
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if out_chs == in_chs and stride == 1
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else None
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)
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# Re-parameterizable conv branches
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if num_conv_branches > 0:
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self.rbr_conv = nn.ModuleList([
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ConvNormAct(
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self.in_chs,
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self.out_chs,
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kernel_size=kernel_size,
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stride=self.stride,
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groups=self.groups,
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apply_act=False,
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) for _ in range(self.num_conv_branches)
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])
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else:
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self.rbr_conv = None
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# Re-parameterizable scale branch
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self.rbr_scale = None
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if kernel_size > 1 and use_scale_branch:
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self.rbr_scale = ConvNormAct(
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self.in_chs,
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self.out_chs,
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kernel_size=1,
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stride=self.stride,
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groups=self.groups,
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apply_act=False
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)
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self.act = act_layer() if use_act else nn.Identity()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Apply forward pass."""
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# Inference mode forward pass.
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if self.reparam_conv is not None:
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return self.act(self.se(self.reparam_conv(x)))
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# Multi-branched train-time forward pass.
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# Skip branch output
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identity_out = 0
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if self.rbr_skip is not None:
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identity_out = self.rbr_skip(x)
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# Scale branch output
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scale_out = 0
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if self.rbr_scale is not None:
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scale_out = self.rbr_scale(x)
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# Other branches
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out = scale_out + identity_out
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if self.rbr_conv is not None:
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for rc in self.rbr_conv:
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out += rc(x)
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return self.act(self.se(out))
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def reparameterize(self):
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"""Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
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https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
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architecture used at training time to obtain a plain CNN-like structure
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for inference.
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"""
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if self.reparam_conv is not None:
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return
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kernel, bias = self._get_kernel_bias()
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self.reparam_conv = create_conv2d(
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in_channels=self.in_chs,
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out_channels=self.out_chs,
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kernel_size=self.kernel_size,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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bias=True,
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)
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self.reparam_conv.weight.data = kernel
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self.reparam_conv.bias.data = bias
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# Delete un-used branches
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for para in self.parameters():
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para.detach_()
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self.__delattr__("rbr_conv")
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self.__delattr__("rbr_scale")
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if hasattr(self, "rbr_skip"):
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self.__delattr__("rbr_skip")
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self.inference_mode = True
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def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Method to obtain re-parameterized kernel and bias.
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Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
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Returns:
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Tuple of (kernel, bias) after fusing branches.
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"""
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# get weights and bias of scale branch
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kernel_scale = 0
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bias_scale = 0
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if self.rbr_scale is not None:
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kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
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# Pad scale branch kernel to match conv branch kernel size.
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pad = self.kernel_size // 2
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kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
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# get weights and bias of skip branch
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kernel_identity = 0
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bias_identity = 0
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if self.rbr_skip is not None:
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kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
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# get weights and bias of conv branches
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kernel_conv = 0
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bias_conv = 0
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if self.rbr_conv is not None:
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for ix in range(self.num_conv_branches):
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_kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
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kernel_conv += _kernel
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bias_conv += _bias
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kernel_final = kernel_conv + kernel_scale + kernel_identity
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bias_final = bias_conv + bias_scale + bias_identity
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return kernel_final, bias_final
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def _fuse_bn_tensor(
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self, branch: Union[nn.Sequential, nn.BatchNorm2d]
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Method to fuse batchnorm layer with preceeding conv layer.
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Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
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Args:
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branch: Sequence of ops to be fused.
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Returns:
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Tuple of (kernel, bias) after fusing batchnorm.
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"""
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if isinstance(branch, nn.Sequential):
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kernel = branch.conv.weight
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running_mean = branch.bn.running_mean
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running_var = branch.bn.running_var
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gamma = branch.bn.weight
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beta = branch.bn.bias
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eps = branch.bn.eps
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else:
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assert isinstance(branch, nn.BatchNorm2d)
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if not hasattr(self, "id_tensor"):
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input_dim = self.in_chs // self.groups
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kernel_value = torch.zeros(
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(self.in_chs, input_dim, self.kernel_size, self.kernel_size),
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dtype=branch.weight.dtype,
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device=branch.weight.device,
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)
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for i in range(self.in_chs):
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kernel_value[
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i, i % input_dim, self.kernel_size // 2, self.kernel_size // 2
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] = 1
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self.id_tensor = kernel_value
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kernel = self.id_tensor
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running_mean = branch.running_mean
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running_var = branch.running_var
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gamma = branch.weight
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beta = branch.bias
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eps = branch.eps
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape(-1, 1, 1, 1)
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return kernel * t, beta - running_mean * gamma / std
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class ReparamLargeKernelConv(nn.Module):
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"""Building Block of RepLKNet
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This class defines overparameterized large kernel conv block
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introduced in `RepLKNet <https://arxiv.org/abs/2203.06717>`_
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Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
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"""
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def __init__(
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self,
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in_chs: int,
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out_chs: int,
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kernel_size: int,
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stride: int,
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group_size: int,
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small_kernel: Optional[int] = None,
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inference_mode: bool = False,
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act_layer: Optional[nn.Module] = None,
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) -> None:
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"""Construct a ReparamLargeKernelConv module.
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Args:
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in_chs: Number of input channels.
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out_chs: Number of output channels.
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kernel_size: Kernel size of the large kernel conv branch.
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stride: Stride size. Default: 1
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group_size: Group size. Default: 1
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small_kernel: Kernel size of small kernel conv branch.
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inference_mode: If True, instantiates model in inference mode. Default: ``False``
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act_layer: Activation module. Default: ``nn.GELU``
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"""
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super(ReparamLargeKernelConv, self).__init__()
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self.stride = stride
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self.groups = num_groups(group_size, in_chs)
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self.in_chs = in_chs
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self.out_chs = out_chs
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self.kernel_size = kernel_size
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self.small_kernel = small_kernel
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if inference_mode:
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self.lkb_reparam = create_conv2d(
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in_chs,
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out_chs,
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kernel_size=kernel_size,
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stride=stride,
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dilation=1,
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groups=self.groups,
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bias=True,
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)
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else:
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self.lkb_reparam = None
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self.lkb_origin = ConvNormAct(
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in_chs,
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out_chs,
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kernel_size=kernel_size,
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stride=self.stride,
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groups=self.groups,
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apply_act=False,
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)
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if small_kernel is not None:
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assert (
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small_kernel <= kernel_size
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), "The kernel size for re-param cannot be larger than the large kernel!"
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self.small_conv = ConvNormAct(
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in_chs,
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out_chs,
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kernel_size=small_kernel,
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stride=self.stride,
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groups=self.groups,
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apply_act=False,
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)
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# FIXME output of this act was not used in original impl, likely due to bug
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self.act = act_layer() if act_layer is not None else nn.Identity()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.lkb_reparam is not None:
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out = self.lkb_reparam(x)
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else:
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out = self.lkb_origin(x)
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if self.small_conv is not None:
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out = out + self.small_conv(x)
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out = self.act(out)
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return out
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def get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Method to obtain re-parameterized kernel and bias.
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Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
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Returns:
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Tuple of (kernel, bias) after fusing branches.
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"""
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eq_k, eq_b = self._fuse_bn(self.lkb_origin.conv, self.lkb_origin.bn)
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if hasattr(self, "small_conv"):
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small_k, small_b = self._fuse_bn(self.small_conv.conv, self.small_conv.bn)
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eq_b += small_b
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eq_k += nn.functional.pad(
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small_k, [(self.kernel_size - self.small_kernel) // 2] * 4
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)
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return eq_k, eq_b
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def reparameterize(self) -> None:
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"""
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Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
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https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
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architecture used at training time to obtain a plain CNN-like structure
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for inference.
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"""
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eq_k, eq_b = self.get_kernel_bias()
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self.lkb_reparam = create_conv2d(
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self.in_chs,
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self.out_chs,
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kernel_size=self.kernel_size,
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stride=self.stride,
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dilation=self.lkb_origin.conv.dilation,
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groups=self.groups,
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bias=True,
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)
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self.lkb_reparam.weight.data = eq_k
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self.lkb_reparam.bias.data = eq_b
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self.__delattr__("lkb_origin")
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if hasattr(self, "small_conv"):
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self.__delattr__("small_conv")
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@staticmethod
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def _fuse_bn(
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conv: torch.Tensor, bn: nn.BatchNorm2d
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Method to fuse batchnorm layer with conv layer.
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Args:
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conv: Convolutional kernel weights.
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bn: Batchnorm 2d layer.
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Returns:
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Tuple of (kernel, bias) after fusing batchnorm.
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"""
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kernel = conv.weight
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running_mean = bn.running_mean
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running_var = bn.running_var
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gamma = bn.weight
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beta = bn.bias
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eps = bn.eps
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape(-1, 1, 1, 1)
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return kernel * t, beta - running_mean * gamma / std
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def convolutional_stem(
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in_chs: int,
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out_chs: int,
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inference_mode: bool = False
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) -> nn.Sequential:
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"""Build convolutional stem with MobileOne blocks.
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Args:
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in_chs: Number of input channels.
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out_chs: Number of output channels.
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inference_mode: Flag to instantiate model in inference mode. Default: ``False``
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Returns:
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nn.Sequential object with stem elements.
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"""
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return nn.Sequential(
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MobileOneBlock(
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in_chs=in_chs,
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out_chs=out_chs,
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kernel_size=3,
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stride=2,
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inference_mode=inference_mode,
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),
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MobileOneBlock(
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in_chs=out_chs,
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out_chs=out_chs,
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kernel_size=3,
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stride=2,
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group_size=1,
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inference_mode=inference_mode,
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),
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MobileOneBlock(
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in_chs=out_chs,
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out_chs=out_chs,
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kernel_size=1,
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stride=1,
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inference_mode=inference_mode,
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),
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)
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|
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class Attention(nn.Module):
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"""Multi-headed Self Attention module.
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|
|
|
Source modified from:
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https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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"""
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fused_attn: torch.jit.Final[bool]
|
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|
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def __init__(
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self,
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dim: int,
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head_dim: int = 32,
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qkv_bias: bool = False,
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attn_drop: float = 0.0,
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|
proj_drop: float = 0.0,
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) -> None:
|
|
"""Build MHSA module that can handle 3D or 4D input tensors.
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|
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|
Args:
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dim: Number of embedding dimensions.
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head_dim: Number of hidden dimensions per head. Default: ``32``
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qkv_bias: Use bias or not. Default: ``False``
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attn_drop: Dropout rate for attention tensor.
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proj_drop: Dropout rate for projection tensor.
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"""
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super().__init__()
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assert dim % head_dim == 0, "dim should be divisible by head_dim"
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self.head_dim = head_dim
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self.num_heads = dim // head_dim
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self.scale = head_dim ** -0.5
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self.fused_attn = use_fused_attn()
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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B, C, H, W = x.shape
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N = H * W
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x = x.flatten(2).transpose(-2, -1) # (B, N, C)
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qkv = (
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self.qkv(x)
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.reshape(B, N, 3, self.num_heads, self.head_dim)
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.permute(2, 0, 3, 1, 4)
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)
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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if self.fused_attn:
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x = torch.nn.functional.scaled_dot_product_attention(
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q, k, v,
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dropout_p=self.attn_drop.p,
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)
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else:
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q = q * self.scale
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attn = q @ k.transpose(-2, -1)
|
|
attn = attn.softmax(dim=-1)
|
|
attn = self.attn_drop(attn)
|
|
x = attn @ v
|
|
|
|
x = x.transpose(1, 2).reshape(B, N, C)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
x = x.transpose(-2, -1).reshape(B, C, H, W)
|
|
|
|
return x
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
"""Convolutional patch embedding layer."""
|
|
|
|
def __init__(
|
|
self,
|
|
patch_size: int,
|
|
stride: int,
|
|
in_chs: int,
|
|
embed_dim: int,
|
|
inference_mode: bool = False,
|
|
) -> None:
|
|
"""Build patch embedding layer.
|
|
|
|
Args:
|
|
patch_size: Patch size for embedding computation.
|
|
stride: Stride for convolutional embedding layer.
|
|
in_chs: Number of channels of input tensor.
|
|
embed_dim: Number of embedding dimensions.
|
|
inference_mode: Flag to instantiate model in inference mode. Default: ``False``
|
|
"""
|
|
super().__init__()
|
|
self.proj = nn.Sequential(
|
|
ReparamLargeKernelConv(
|
|
in_chs=in_chs,
|
|
out_chs=embed_dim,
|
|
kernel_size=patch_size,
|
|
stride=stride,
|
|
group_size=1,
|
|
small_kernel=3,
|
|
inference_mode=inference_mode,
|
|
act_layer=None, # activation was not used in original impl
|
|
),
|
|
MobileOneBlock(
|
|
in_chs=embed_dim,
|
|
out_chs=embed_dim,
|
|
kernel_size=1,
|
|
stride=1,
|
|
inference_mode=inference_mode,
|
|
)
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.proj(x)
|
|
return x
|
|
|
|
|
|
class RepMixer(nn.Module):
|
|
"""Reparameterizable token mixer.
|
|
|
|
For more details, please refer to our paper:
|
|
`FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
kernel_size=3,
|
|
use_layer_scale=True,
|
|
layer_scale_init_value=1e-5,
|
|
inference_mode: bool = False,
|
|
):
|
|
"""Build RepMixer Module.
|
|
|
|
Args:
|
|
dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
|
|
kernel_size: Kernel size for spatial mixing. Default: 3
|
|
use_layer_scale: If True, learnable layer scale is used. Default: ``True``
|
|
layer_scale_init_value: Initial value for layer scale. Default: 1e-5
|
|
inference_mode: If True, instantiates model in inference mode. Default: ``False``
|
|
"""
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.kernel_size = kernel_size
|
|
self.inference_mode = inference_mode
|
|
|
|
if inference_mode:
|
|
self.reparam_conv = nn.Conv2d(
|
|
self.dim,
|
|
self.dim,
|
|
kernel_size=self.kernel_size,
|
|
stride=1,
|
|
padding=self.kernel_size // 2,
|
|
groups=self.dim,
|
|
bias=True,
|
|
)
|
|
else:
|
|
self.reparam_conv = None
|
|
self.norm = MobileOneBlock(
|
|
dim,
|
|
dim,
|
|
kernel_size,
|
|
group_size=1,
|
|
use_act=False,
|
|
use_scale_branch=False,
|
|
num_conv_branches=0,
|
|
)
|
|
self.mixer = MobileOneBlock(
|
|
dim,
|
|
dim,
|
|
kernel_size,
|
|
group_size=1,
|
|
use_act=False,
|
|
)
|
|
self.use_layer_scale = use_layer_scale
|
|
if use_layer_scale:
|
|
self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
if self.reparam_conv is not None:
|
|
x = self.reparam_conv(x)
|
|
return x
|
|
else:
|
|
if self.use_layer_scale:
|
|
x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
|
|
else:
|
|
x = x + self.mixer(x) - self.norm(x)
|
|
return x
|
|
|
|
def reparameterize(self) -> None:
|
|
"""Reparameterize mixer and norm into a single
|
|
convolutional layer for efficient inference.
|
|
"""
|
|
if self.inference_mode:
|
|
return
|
|
|
|
self.mixer.reparameterize()
|
|
self.norm.reparameterize()
|
|
|
|
if self.use_layer_scale:
|
|
w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
|
|
self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
|
|
)
|
|
b = torch.squeeze(self.layer_scale) * (
|
|
self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
|
|
)
|
|
else:
|
|
w = (
|
|
self.mixer.id_tensor
|
|
+ self.mixer.reparam_conv.weight
|
|
- self.norm.reparam_conv.weight
|
|
)
|
|
b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
|
|
|
|
self.reparam_conv = create_conv2d(
|
|
self.dim,
|
|
self.dim,
|
|
kernel_size=self.kernel_size,
|
|
stride=1,
|
|
groups=self.dim,
|
|
bias=True,
|
|
)
|
|
self.reparam_conv.weight.data = w
|
|
self.reparam_conv.bias.data = b
|
|
|
|
for para in self.parameters():
|
|
para.detach_()
|
|
self.__delattr__("mixer")
|
|
self.__delattr__("norm")
|
|
if self.use_layer_scale:
|
|
self.__delattr__("layer_scale")
|
|
|
|
|
|
class ConvMlp(nn.Module):
|
|
"""Convolutional FFN Module."""
|
|
|
|
def __init__(
|
|
self,
|
|
in_chs: int,
|
|
hidden_channels: Optional[int] = None,
|
|
out_chs: Optional[int] = None,
|
|
act_layer: nn.Module = nn.GELU,
|
|
drop: float = 0.0,
|
|
) -> None:
|
|
"""Build convolutional FFN module.
|
|
|
|
Args:
|
|
in_chs: Number of input channels.
|
|
hidden_channels: Number of channels after expansion. Default: None
|
|
out_chs: Number of output channels. Default: None
|
|
act_layer: Activation layer. Default: ``GELU``
|
|
drop: Dropout rate. Default: ``0.0``.
|
|
"""
|
|
super().__init__()
|
|
out_chs = out_chs or in_chs
|
|
hidden_channels = hidden_channels or in_chs
|
|
# self.conv = nn.Sequential()
|
|
# self.conv.add_module(
|
|
# "conv",
|
|
# nn.Conv2d(
|
|
# in_chs,
|
|
# out_chs,
|
|
# kernel_size=7,
|
|
# padding=3,
|
|
# groups=in_chs,
|
|
# bias=False,
|
|
# ),
|
|
# )
|
|
# self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_chs))
|
|
self.conv = ConvNormAct(
|
|
in_chs,
|
|
out_chs,
|
|
kernel_size=7,
|
|
groups=in_chs,
|
|
apply_act=False,
|
|
)
|
|
self.fc1 = nn.Conv2d(in_chs, hidden_channels, kernel_size=1)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Conv2d(hidden_channels, out_chs, kernel_size=1)
|
|
self.drop = nn.Dropout(drop)
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m: nn.Module) -> None:
|
|
if isinstance(m, nn.Conv2d):
|
|
trunc_normal_(m.weight, std=0.02)
|
|
if m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.conv(x)
|
|
x = self.fc1(x)
|
|
x = self.act(x)
|
|
x = self.drop(x)
|
|
x = self.fc2(x)
|
|
x = self.drop(x)
|
|
return x
|
|
|
|
|
|
class RepCPE(nn.Module):
|
|
"""Implementation of conditional positional encoding.
|
|
|
|
For more details refer to paper:
|
|
`Conditional Positional Encodings for Vision Transformers <https://arxiv.org/pdf/2102.10882.pdf>`_
|
|
|
|
In our implementation, we can reparameterize this module to eliminate a skip connection.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_chs: int,
|
|
embed_dim: int = 768,
|
|
spatial_shape: Union[int, Tuple[int, int]] = (7, 7),
|
|
inference_mode=False,
|
|
) -> None:
|
|
"""Build reparameterizable conditional positional encoding
|
|
|
|
Args:
|
|
in_chs: Number of input channels.
|
|
embed_dim: Number of embedding dimensions. Default: 768
|
|
spatial_shape: Spatial shape of kernel for positional encoding. Default: (7, 7)
|
|
inference_mode: Flag to instantiate block in inference mode. Default: ``False``
|
|
"""
|
|
super(RepCPE, self).__init__()
|
|
if isinstance(spatial_shape, int):
|
|
spatial_shape = tuple([spatial_shape] * 2)
|
|
assert isinstance(spatial_shape, Tuple), (
|
|
f'"spatial_shape" must by a sequence or int, '
|
|
f"get {type(spatial_shape)} instead."
|
|
)
|
|
assert len(spatial_shape) == 2, (
|
|
f'Length of "spatial_shape" should be 2, '
|
|
f"got {len(spatial_shape)} instead."
|
|
)
|
|
|
|
self.spatial_shape = spatial_shape
|
|
self.embed_dim = embed_dim
|
|
self.in_chs = in_chs
|
|
self.groups = embed_dim
|
|
|
|
if inference_mode:
|
|
self.reparam_conv = nn.Conv2d(
|
|
self.in_chs,
|
|
self.embed_dim,
|
|
kernel_size=self.spatial_shape,
|
|
stride=1,
|
|
padding=spatial_shape[0] // 2,
|
|
groups=self.embed_dim,
|
|
bias=True,
|
|
)
|
|
else:
|
|
self.reparam_conv = None
|
|
self.pe = nn.Conv2d(
|
|
in_chs,
|
|
embed_dim,
|
|
spatial_shape,
|
|
1,
|
|
int(spatial_shape[0] // 2),
|
|
groups=embed_dim,
|
|
bias=True,
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
if self.reparam_conv is not None:
|
|
x = self.reparam_conv(x)
|
|
return x
|
|
else:
|
|
x = self.pe(x) + x
|
|
return x
|
|
|
|
def reparameterize(self) -> None:
|
|
# Build equivalent Id tensor
|
|
input_dim = self.in_chs // self.groups
|
|
kernel_value = torch.zeros(
|
|
(
|
|
self.in_chs,
|
|
input_dim,
|
|
self.spatial_shape[0],
|
|
self.spatial_shape[1],
|
|
),
|
|
dtype=self.pe.weight.dtype,
|
|
device=self.pe.weight.device,
|
|
)
|
|
for i in range(self.in_chs):
|
|
kernel_value[
|
|
i,
|
|
i % input_dim,
|
|
self.spatial_shape[0] // 2,
|
|
self.spatial_shape[1] // 2,
|
|
] = 1
|
|
id_tensor = kernel_value
|
|
|
|
# Reparameterize Id tensor and conv
|
|
w_final = id_tensor + self.pe.weight
|
|
b_final = self.pe.bias
|
|
|
|
# Introduce reparam conv
|
|
self.reparam_conv = nn.Conv2d(
|
|
self.in_chs,
|
|
self.embed_dim,
|
|
kernel_size=self.spatial_shape,
|
|
stride=1,
|
|
padding=int(self.spatial_shape[0] // 2),
|
|
groups=self.embed_dim,
|
|
bias=True,
|
|
)
|
|
self.reparam_conv.weight.data = w_final
|
|
self.reparam_conv.bias.data = b_final
|
|
|
|
for para in self.parameters():
|
|
para.detach_()
|
|
self.__delattr__("pe")
|
|
|
|
|
|
class RepMixerBlock(nn.Module):
|
|
"""Implementation of Metaformer block with RepMixer as token mixer.
|
|
|
|
For more details on Metaformer structure, please refer to:
|
|
`MetaFormer Is Actually What You Need for Vision <https://arxiv.org/pdf/2111.11418.pdf>`_
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
kernel_size: int = 3,
|
|
mlp_ratio: float = 4.0,
|
|
act_layer: nn.Module = nn.GELU,
|
|
drop: float = 0.0,
|
|
drop_path: float = 0.0,
|
|
use_layer_scale: bool = True,
|
|
layer_scale_init_value: float = 1e-5,
|
|
inference_mode: bool = False,
|
|
):
|
|
"""Build RepMixer Block.
|
|
|
|
Args:
|
|
dim: Number of embedding dimensions.
|
|
kernel_size: Kernel size for repmixer. Default: 3
|
|
mlp_ratio: MLP expansion ratio. Default: 4.0
|
|
act_layer: Activation layer. Default: ``nn.GELU``
|
|
drop: Dropout rate. Default: 0.0
|
|
drop_path: Drop path rate. Default: 0.0
|
|
use_layer_scale: Flag to turn on layer scale. Default: ``True``
|
|
layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
|
|
inference_mode: Flag to instantiate block in inference mode. Default: ``False``
|
|
"""
|
|
|
|
super().__init__()
|
|
|
|
self.token_mixer = RepMixer(
|
|
dim,
|
|
kernel_size=kernel_size,
|
|
use_layer_scale=use_layer_scale,
|
|
layer_scale_init_value=layer_scale_init_value,
|
|
inference_mode=inference_mode,
|
|
)
|
|
|
|
assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
|
|
mlp_ratio
|
|
)
|
|
self.convffn = ConvMlp(
|
|
in_chs=dim,
|
|
hidden_channels=int(dim * mlp_ratio),
|
|
act_layer=act_layer,
|
|
drop=drop,
|
|
)
|
|
|
|
# Drop Path
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
|
|
|
# Layer Scale
|
|
self.use_layer_scale = use_layer_scale
|
|
if use_layer_scale:
|
|
self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
|
|
|
|
def forward(self, x):
|
|
if self.use_layer_scale:
|
|
x = self.token_mixer(x)
|
|
x = x + self.drop_path(self.layer_scale * self.convffn(x))
|
|
else:
|
|
x = self.token_mixer(x)
|
|
x = x + self.drop_path(self.convffn(x))
|
|
return x
|
|
|
|
|
|
class AttentionBlock(nn.Module):
|
|
"""Implementation of metaformer block with MHSA as token mixer.
|
|
|
|
For more details on Metaformer structure, please refer to:
|
|
`MetaFormer Is Actually What You Need for Vision <https://arxiv.org/pdf/2111.11418.pdf>`_
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
mlp_ratio: float = 4.0,
|
|
act_layer: nn.Module = nn.GELU,
|
|
norm_layer: nn.Module = nn.BatchNorm2d,
|
|
drop: float = 0.0,
|
|
drop_path: float = 0.0,
|
|
use_layer_scale: bool = True,
|
|
layer_scale_init_value: float = 1e-5,
|
|
):
|
|
"""Build Attention Block.
|
|
|
|
Args:
|
|
dim: Number of embedding dimensions.
|
|
mlp_ratio: MLP expansion ratio. Default: 4.0
|
|
act_layer: Activation layer. Default: ``nn.GELU``
|
|
norm_layer: Normalization layer. Default: ``nn.BatchNorm2d``
|
|
drop: Dropout rate. Default: 0.0
|
|
drop_path: Drop path rate. Default: 0.0
|
|
use_layer_scale: Flag to turn on layer scale. Default: ``True``
|
|
layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
|
|
"""
|
|
|
|
super().__init__()
|
|
|
|
self.norm = norm_layer(dim)
|
|
self.token_mixer = Attention(dim=dim)
|
|
|
|
assert mlp_ratio > 0, "MLP ratio should be greater than 0, found: {}".format(
|
|
mlp_ratio
|
|
)
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.convffn = ConvMlp(
|
|
in_chs=dim,
|
|
hidden_channels=mlp_hidden_dim,
|
|
act_layer=act_layer,
|
|
drop=drop,
|
|
)
|
|
|
|
# Drop path
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
|
|
|
# Layer Scale
|
|
self.use_layer_scale = use_layer_scale
|
|
if use_layer_scale:
|
|
self.layer_scale_1 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
|
|
self.layer_scale_2 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)))
|
|
|
|
def forward(self, x):
|
|
if self.use_layer_scale:
|
|
x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(self.norm(x)))
|
|
x = x + self.drop_path(self.layer_scale_2 * self.convffn(x))
|
|
else:
|
|
x = x + self.drop_path(self.token_mixer(self.norm(x)))
|
|
x = x + self.drop_path(self.convffn(x))
|
|
return x
|
|
|
|
|
|
def basic_blocks(
|
|
dim: int,
|
|
block_index: int,
|
|
num_blocks: List[int],
|
|
token_mixer_type: str,
|
|
kernel_size: int = 3,
|
|
mlp_ratio: float = 4.0,
|
|
act_layer: nn.Module = nn.GELU,
|
|
norm_layer: nn.Module = nn.BatchNorm2d,
|
|
drop_rate: float = 0.0,
|
|
drop_path_rate: float = 0.0,
|
|
use_layer_scale: bool = True,
|
|
layer_scale_init_value: float = 1e-5,
|
|
inference_mode=False,
|
|
) -> nn.Sequential:
|
|
"""Build FastViT blocks within a stage.
|
|
|
|
Args:
|
|
dim: Number of embedding dimensions.
|
|
block_index: block index.
|
|
num_blocks: List containing number of blocks per stage.
|
|
token_mixer_type: Token mixer type.
|
|
kernel_size: Kernel size for repmixer.
|
|
mlp_ratio: MLP expansion ratio.
|
|
act_layer: Activation layer.
|
|
norm_layer: Normalization layer.
|
|
drop_rate: Dropout rate.
|
|
drop_path_rate: Drop path rate.
|
|
use_layer_scale: Flag to turn on layer scale regularization.
|
|
layer_scale_init_value: Layer scale value at initialization.
|
|
inference_mode: Flag to instantiate block in inference mode.
|
|
|
|
Returns:
|
|
nn.Sequential object of all the blocks within the stage.
|
|
"""
|
|
blocks = []
|
|
for block_idx in range(num_blocks[block_index]):
|
|
block_dpr = (
|
|
drop_path_rate
|
|
* (block_idx + sum(num_blocks[:block_index]))
|
|
/ (sum(num_blocks) - 1)
|
|
)
|
|
if token_mixer_type == "repmixer":
|
|
blocks.append(RepMixerBlock(
|
|
dim,
|
|
kernel_size=kernel_size,
|
|
mlp_ratio=mlp_ratio,
|
|
act_layer=act_layer,
|
|
drop=drop_rate,
|
|
drop_path=block_dpr,
|
|
use_layer_scale=use_layer_scale,
|
|
layer_scale_init_value=layer_scale_init_value,
|
|
inference_mode=inference_mode,
|
|
))
|
|
elif token_mixer_type == "attention":
|
|
blocks.append(AttentionBlock(
|
|
dim,
|
|
mlp_ratio=mlp_ratio,
|
|
act_layer=act_layer,
|
|
norm_layer=norm_layer,
|
|
drop=drop_rate,
|
|
drop_path=block_dpr,
|
|
use_layer_scale=use_layer_scale,
|
|
layer_scale_init_value=layer_scale_init_value,
|
|
))
|
|
else:
|
|
raise ValueError(
|
|
"Token mixer type: {} not supported".format(token_mixer_type)
|
|
)
|
|
blocks = nn.Sequential(*blocks)
|
|
|
|
return blocks
|
|
|
|
|
|
class FastVit(nn.Module):
|
|
fork_feat: torch.jit.Final[bool]
|
|
|
|
"""
|
|
This class implements `FastViT architecture <https://arxiv.org/pdf/2303.14189.pdf>`_
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_chans=3,
|
|
layers=(2, 2, 6, 2),
|
|
token_mixers: Tuple[str, ...] = ("repmixer", "repmixer", "repmixer", "repmixer"),
|
|
embed_dims=None,
|
|
mlp_ratios=None,
|
|
downsamples=None,
|
|
repmixer_kernel_size=3,
|
|
norm_layer: nn.Module = nn.BatchNorm2d,
|
|
act_layer: nn.Module = nn.GELU,
|
|
num_classes=1000,
|
|
pos_embs=None,
|
|
down_patch_size=7,
|
|
down_stride=2,
|
|
drop_rate=0.0,
|
|
drop_path_rate=0.0,
|
|
use_layer_scale=True,
|
|
layer_scale_init_value=1e-5,
|
|
fork_feat=False,
|
|
cls_ratio=2.0,
|
|
inference_mode=False,
|
|
) -> None:
|
|
super().__init__()
|
|
self.num_classes = 0 if fork_feat else num_classes
|
|
self.fork_feat = fork_feat
|
|
|
|
if pos_embs is None:
|
|
pos_embs = [None] * len(layers)
|
|
|
|
# Convolutional stem
|
|
self.patch_embed = convolutional_stem(
|
|
in_chans, embed_dims[0], inference_mode)
|
|
|
|
# Build the main stages of the network architecture
|
|
network = []
|
|
for i in range(len(layers)):
|
|
# Add position embeddings if requested
|
|
if pos_embs[i] is not None:
|
|
network.append(pos_embs[i](
|
|
embed_dims[i],
|
|
embed_dims[i],
|
|
inference_mode=inference_mode,
|
|
))
|
|
stage = basic_blocks(
|
|
embed_dims[i],
|
|
i,
|
|
layers,
|
|
token_mixer_type=token_mixers[i],
|
|
kernel_size=repmixer_kernel_size,
|
|
mlp_ratio=mlp_ratios[i],
|
|
act_layer=act_layer,
|
|
norm_layer=norm_layer,
|
|
drop_rate=drop_rate,
|
|
drop_path_rate=drop_path_rate,
|
|
use_layer_scale=use_layer_scale,
|
|
layer_scale_init_value=layer_scale_init_value,
|
|
inference_mode=inference_mode,
|
|
)
|
|
network.append(stage)
|
|
if i >= len(layers) - 1:
|
|
break
|
|
|
|
# Patch merging/downsampling between stages.
|
|
if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
|
|
network += [PatchEmbed(
|
|
patch_size=down_patch_size,
|
|
stride=down_stride,
|
|
in_chs=embed_dims[i],
|
|
embed_dim=embed_dims[i + 1],
|
|
inference_mode=inference_mode,
|
|
)]
|
|
|
|
self.network = nn.ModuleList(network)
|
|
|
|
# For segmentation and detection, extract intermediate output
|
|
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):
|
|
"""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.gap = nn.AdaptiveAvgPool2d(output_size=1)
|
|
self.conv_exp = MobileOneBlock(
|
|
in_chs=embed_dims[-1],
|
|
out_chs=int(embed_dims[-1] * cls_ratio),
|
|
kernel_size=3,
|
|
stride=1,
|
|
group_size=1,
|
|
inference_mode=inference_mode,
|
|
use_se=True,
|
|
num_conv_branches=1,
|
|
)
|
|
self.head = (
|
|
nn.Linear(int(embed_dims[-1] * cls_ratio), num_classes)
|
|
if num_classes > 0
|
|
else nn.Identity()
|
|
)
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m: nn.Module) -> None:
|
|
"""Init. for classification"""
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=0.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
@staticmethod
|
|
def _scrub_checkpoint(checkpoint, model):
|
|
sterile_dict = {}
|
|
for k1, v1 in checkpoint.items():
|
|
if k1 not in model.state_dict():
|
|
continue
|
|
if v1.shape == model.state_dict()[k1].shape:
|
|
sterile_dict[k1] = v1
|
|
return sterile_dict
|
|
|
|
def forward_embeddings(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.patch_embed(x)
|
|
return x
|
|
|
|
def forward_tokens(self, x: torch.Tensor) -> torch.Tensor:
|
|
outs = []
|
|
for idx, block in enumerate(self.network):
|
|
x = block(x)
|
|
if self.fork_feat:
|
|
if idx in self.out_indices:
|
|
norm_layer = getattr(self, f"norm{idx}")
|
|
x_out = norm_layer(x)
|
|
outs.append(x_out)
|
|
if self.fork_feat:
|
|
# output the features of four stages for dense prediction
|
|
return outs
|
|
# output only the features of last layer for image classification
|
|
return x
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
# input embedding
|
|
x = self.forward_embeddings(x)
|
|
# through backbone
|
|
x = self.forward_tokens(x)
|
|
if self.fork_feat:
|
|
# output features of four stages for dense prediction
|
|
return x
|
|
# for image classification
|
|
x = self.conv_exp(x)
|
|
x = self.gap(x)
|
|
x = x.view(x.size(0), -1)
|
|
cls_out = self.head(x)
|
|
return cls_out
|
|
|
|
|
|
def _cfg(url="", **kwargs):
|
|
return {
|
|
"url": url,
|
|
"num_classes": 1000,
|
|
"input_size": (3, 256, 256),
|
|
"pool_size": None,
|
|
"crop_pct": 0.9,
|
|
"interpolation": "bicubic",
|
|
"mean": IMAGENET_DEFAULT_MEAN,
|
|
"std": IMAGENET_DEFAULT_STD,
|
|
"classifier": "head",
|
|
**kwargs,
|
|
}
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
"fastvit_t8.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_t8.pth.tar'
|
|
),
|
|
"fastvit_t12.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_t12.pth.tar'
|
|
),
|
|
|
|
"fastvit_s12.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_s12.pth.tar'),
|
|
"fastvit_sa12.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_sa12.pth.tar'),
|
|
"fastvit_sa24.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_sa24.pth.tar'),
|
|
"fastvit_sa36.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_sa36.pth.tar'),
|
|
|
|
"fastvit_ma36.apple_in1k": _cfg(
|
|
url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_models/fastvit_ma36.pth.tar',
|
|
crop_pct=0.95
|
|
),
|
|
|
|
# "fastvit_t8.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_t8.pth.tar'
|
|
# ),
|
|
# "fastvit_t12.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_t12.pth.tar'
|
|
# ),
|
|
#
|
|
# "fastvit_s12.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_s12.pth.tar'),
|
|
# "fastvit_sa12.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_sa12.pth.tar'),
|
|
# "fastvit_sa24.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_sa24.pth.tar'),
|
|
# "fastvit_sa36.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_sa36.pth.tar'),
|
|
#
|
|
# "fastvit_ma36.apple_dist_in1k": _cfg(
|
|
# url='https://docs-assets.developer.apple.com/ml-research/models/fastvit/image_classification_distilled_models/fastvit_ma36.pth.tar',
|
|
# crop_pct=0.95
|
|
# ),
|
|
})
|
|
|
|
|
|
def checkpoint_filter_fn(state_dict, model):
|
|
# FIXME temporary for remapping
|
|
state_dict = state_dict.get('state_dict', state_dict)
|
|
msd = model.state_dict()
|
|
out_dict = {}
|
|
for ka, kb, va, vb in zip(msd.keys(), state_dict.keys(), msd.values(), state_dict.values()):
|
|
if va.ndim == 4 and vb.ndim == 2:
|
|
vb = vb[:, :, None, None]
|
|
out_dict[ka] = vb
|
|
|
|
return out_dict
|
|
|
|
|
|
def _create_fastvit(variant, pretrained=False, **kwargs):
|
|
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
|
|
model = build_model_with_cfg(
|
|
FastVit,
|
|
variant,
|
|
pretrained,
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
|
|
**kwargs
|
|
)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def fastvit_t8(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-T8 model variant."""
|
|
model_args = dict(
|
|
layers=(2, 2, 4, 2),
|
|
embed_dims=(48, 96, 192, 384),
|
|
mlp_ratios=(3, 3, 3, 3),
|
|
downsamples=(True, True, True, True),
|
|
token_mixers=("repmixer", "repmixer", "repmixer", "repmixer")
|
|
)
|
|
return _create_fastvit('fastvit_t8', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def fastvit_t12(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-T12 model variant."""
|
|
model_args = dict(
|
|
layers=(2, 2, 6, 2),
|
|
embed_dims=(64, 128, 256, 512),
|
|
mlp_ratios=(3, 3, 3, 3),
|
|
downsamples=(True, True, True, True),
|
|
token_mixers = ("repmixer", "repmixer", "repmixer", "repmixer"),
|
|
)
|
|
return _create_fastvit('fastvit_t12', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def fastvit_s12(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-S12 model variant."""
|
|
model_args = dict(
|
|
layers=(2, 2, 6, 2),
|
|
embed_dims=(64, 128, 256, 512),
|
|
mlp_ratios=(4, 4, 4, 4),
|
|
downsamples=(True, True, True, True),
|
|
token_mixers=("repmixer", "repmixer", "repmixer", "repmixer"),
|
|
)
|
|
return _create_fastvit('fastvit_s12', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def fastvit_sa12(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-SA12 model variant."""
|
|
model_args = dict(
|
|
layers=(2, 2, 6, 2),
|
|
embed_dims=(64, 128, 256, 512),
|
|
mlp_ratios=(4, 4, 4, 4),
|
|
downsamples=(True, True, True, True),
|
|
pos_embs=(None, None, None, partial(RepCPE, spatial_shape=(7, 7))),
|
|
token_mixers=("repmixer", "repmixer", "repmixer", "attention"),
|
|
)
|
|
return _create_fastvit('fastvit_sa12', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def fastvit_sa24(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-SA24 model variant."""
|
|
model_args = dict(
|
|
layers=(4, 4, 12, 4),
|
|
embed_dims=(64, 128, 256, 512),
|
|
mlp_ratios=(4, 4, 4, 4),
|
|
downsamples=(True, True, True, True),
|
|
pos_embs=(None, None, None, partial(RepCPE, spatial_shape=(7, 7))),
|
|
token_mixers=("repmixer", "repmixer", "repmixer", "attention"),
|
|
)
|
|
return _create_fastvit('fastvit_sa24', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def fastvit_sa36(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-SA36 model variant."""
|
|
model_args = dict(
|
|
layers=(6, 6, 18, 6),
|
|
embed_dims=(64, 128, 256, 512),
|
|
mlp_ratios=(4, 4, 4, 4),
|
|
downsamples=(True, True, True, True),
|
|
pos_embs=(None, None, None, partial(RepCPE, spatial_shape=(7, 7))),
|
|
token_mixers=("repmixer", "repmixer", "repmixer", "attention"),
|
|
)
|
|
return _create_fastvit('fastvit_sa36', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
@register_model
|
|
def fastvit_ma36(pretrained=False, **kwargs):
|
|
"""Instantiate FastViT-MA36 model variant."""
|
|
model_args = dict(
|
|
layers=(6, 6, 18, 6),
|
|
embed_dims=(76, 152, 304, 608),
|
|
mlp_ratios=(4, 4, 4, 4),
|
|
downsamples=(True, True, True, True),
|
|
pos_embs=(None, None, None, partial(RepCPE, spatial_shape=(7, 7))),
|
|
token_mixers=("repmixer", "repmixer", "repmixer", "attention")
|
|
)
|
|
return _create_fastvit('fastvit_ma36', pretrained=pretrained, **dict(model_args, **kwargs)) |