mirror of https://github.com/open-mmlab/mmpose
1067 lines
40 KiB
Python
1067 lines
40 KiB
Python
# --------------------------------------------------------
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# Swin Transformer MoE
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# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------
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import numpy as np
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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try:
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from tutel import moe as tutel_moe
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except ImportError:
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tutel_moe = None
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print(
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'Tutel has not been installed. To use Swin-MoE, please install Tutel;'
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'otherwise, just ignore this.')
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class Mlp(nn.Module):
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.,
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mlp_fc2_bias=True):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features, bias=mlp_fc2_bias)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class MoEMlp(nn.Module):
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def __init__(self,
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in_features,
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hidden_features,
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num_local_experts,
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top_value,
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capacity_factor=1.25,
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cosine_router=False,
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normalize_gate=False,
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use_bpr=True,
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is_gshard_loss=True,
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gate_noise=1.0,
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cosine_router_dim=256,
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cosine_router_init_t=0.5,
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moe_drop=0.0,
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init_std=0.02,
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mlp_fc2_bias=True):
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super().__init__()
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self.in_features = in_features
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self.hidden_features = hidden_features
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self.num_local_experts = num_local_experts
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self.top_value = top_value
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self.capacity_factor = capacity_factor
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self.cosine_router = cosine_router
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self.normalize_gate = normalize_gate
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self.use_bpr = use_bpr
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self.init_std = init_std
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self.mlp_fc2_bias = mlp_fc2_bias
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self.dist_rank = dist.get_rank()
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self._dropout = nn.Dropout(p=moe_drop)
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_gate_type = {
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'type': 'cosine_top' if cosine_router else 'top',
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'k': top_value,
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'capacity_factor': capacity_factor,
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'gate_noise': gate_noise,
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'fp32_gate': True
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}
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if cosine_router:
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_gate_type['proj_dim'] = cosine_router_dim
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_gate_type['init_t'] = cosine_router_init_t
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self._moe_layer = tutel_moe.moe_layer(
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gate_type=_gate_type,
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model_dim=in_features,
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experts={
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'type': 'ffn',
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'count_per_node': num_local_experts,
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'hidden_size_per_expert': hidden_features,
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'activation_fn': lambda x: self._dropout(F.gelu(x))
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},
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scan_expert_func=lambda name, param: setattr(
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param, 'skip_allreduce', True),
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seeds=(1, self.dist_rank + 1, self.dist_rank + 1),
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batch_prioritized_routing=use_bpr,
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normalize_gate=normalize_gate,
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is_gshard_loss=is_gshard_loss,
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)
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if not self.mlp_fc2_bias:
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self._moe_layer.experts.batched_fc2_bias.requires_grad = False
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def forward(self, x):
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x = self._moe_layer(x)
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return x, x.l_aux
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def extra_repr(self) -> str:
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return (f'[Statistics-{self.dist_rank}] param count for MoE, '
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f'in_features = {self.in_features}, '
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f'hidden_features = {self.hidden_features}, '
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f'num_local_experts = {self.num_local_experts}, '
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f'top_value = {self.top_value}, '
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f'cosine_router={self.cosine_router} '
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f'normalize_gate={self.normalize_gate}, '
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f'use_bpr = {self.use_bpr}')
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def _init_weights(self):
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if hasattr(self._moe_layer, 'experts'):
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trunc_normal_(
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self._moe_layer.experts.batched_fc1_w, std=self.init_std)
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trunc_normal_(
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self._moe_layer.experts.batched_fc2_w, std=self.init_std)
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nn.init.constant_(self._moe_layer.experts.batched_fc1_bias, 0)
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nn.init.constant_(self._moe_layer.experts.batched_fc2_bias, 0)
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def window_partition(x, window_size):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size, window_size, W // window_size, window_size,
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C)
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windows = x.permute(0, 1, 3, 2, 4,
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5).contiguous().view(-1, window_size, window_size, C)
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return windows
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def window_reverse(windows, window_size, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size,
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window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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class WindowAttention(nn.Module):
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r""" Window based multi-head self attention (W-MSA) module with relative
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position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query,
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key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of
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head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight.
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Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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pretrained_window_size (tuple[int]): The height and width of the
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window in pretraining.
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"""
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def __init__(self,
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dim,
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window_size,
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num_heads,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.,
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pretrained_window_size=[0, 0]):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.pretrained_window_size = pretrained_window_size
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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# mlp to generate continuous relative position bias
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self.cpb_mlp = nn.Sequential(
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nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True),
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nn.Linear(512, num_heads, bias=False))
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# get relative_coords_table
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relative_coords_h = torch.arange(
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-(self.window_size[0] - 1),
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self.window_size[0],
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dtype=torch.float32)
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relative_coords_w = torch.arange(
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-(self.window_size[1] - 1),
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self.window_size[1],
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dtype=torch.float32)
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relative_coords_table = torch.stack(
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torch.meshgrid([relative_coords_h, relative_coords_w])).permute(
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1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
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if pretrained_window_size[0] > 0:
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relative_coords_table[:, :, :, 0] /= (
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pretrained_window_size[0] - 1)
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relative_coords_table[:, :, :, 1] /= (
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pretrained_window_size[1] - 1)
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else:
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relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
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relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
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relative_coords_table *= 8 # normalize to -8, 8
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relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
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torch.abs(relative_coords_table) + 1.0) / np.log2(8)
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self.register_buffer('relative_coords_table', relative_coords_table)
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# get pair-wise relative position index for each token inside the
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# window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = (coords_flatten[:, :, None] -
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coords_flatten[:, None, :]) # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(
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1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.window_size[0] - 1
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.register_buffer('relative_position_index',
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relative_position_index)
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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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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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"""
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or
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None
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"""
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[
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2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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relative_position_bias_table = self.cpb_mlp(
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self.relative_coords_table).view(-1, self.num_heads)
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relative_position_bias = relative_position_bias_table[
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self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1],
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self.window_size[0] * self.window_size[1],
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-1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(
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2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, self.num_heads, N,
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N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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def extra_repr(self) -> str:
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return f'dim={self.dim}, window_size={self.window_size}, ' \
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(f'pretrained_window_size={self.pretrained_window_size}, '
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f'num_heads={self.num_heads}')
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def flops(self, N):
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# calculate flops for 1 window with token length of N
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flops = 0
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# qkv = self.qkv(x)
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flops += N * self.dim * 3 * self.dim
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# attn = (q @ k.transpose(-2, -1))
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flops += self.num_heads * N * (self.dim // self.num_heads) * N
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# x = (attn @ v)
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flops += self.num_heads * N * N * (self.dim // self.num_heads)
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# x = self.proj(x)
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flops += N * self.dim * self.dim
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return flops
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class SwinTransformerBlock(nn.Module):
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r""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resolution.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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shift_size (int): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query,
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key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of
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head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default:
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nn.LayerNorm
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mlp_fc2_bias (bool): Whether to add bias in fc2 of Mlp. Default: True
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init_std: Initialization std. Default: 0.02
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pretrained_window_size (int): Window size in pretraining.
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is_moe (bool): If True, this block is a MoE block.
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num_local_experts (int): number of local experts in each device (
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GPU). Default: 1
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top_value (int): the value of k in top-k gating. Default: 1
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capacity_factor (float): the capacity factor in MoE. Default: 1.25
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cosine_router (bool): Whether to use cosine router. Default: False
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normalize_gate (bool): Whether to normalize the gating score in top-k
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gating. Default: False
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use_bpr (bool): Whether to use batch-prioritized-routing. Default: True
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is_gshard_loss (bool): If True, use Gshard balance loss.
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If False, use the load loss and importance
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loss in "arXiv:1701.06538". Default: False
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gate_noise (float): the noise ratio in top-k gating. Default: 1.0
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cosine_router_dim (int): Projection dimension in cosine router.
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cosine_router_init_t (float): Initialization temperature in cosine
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router.
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moe_drop (float): Dropout rate in MoE. Default: 0.0
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"""
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def __init__(self,
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dim,
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input_resolution,
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num_heads,
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window_size=7,
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shift_size=0,
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mlp_ratio=4.,
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qkv_bias=True,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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mlp_fc2_bias=True,
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init_std=0.02,
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pretrained_window_size=0,
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is_moe=False,
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num_local_experts=1,
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top_value=1,
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capacity_factor=1.25,
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cosine_router=False,
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normalize_gate=False,
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use_bpr=True,
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is_gshard_loss=True,
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gate_noise=1.0,
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cosine_router_dim=256,
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cosine_router_init_t=0.5,
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moe_drop=0.0):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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self.is_moe = is_moe
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self.capacity_factor = capacity_factor
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self.top_value = top_value
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if min(self.input_resolution) <= self.window_size:
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# if window size is larger than input resolution, we don't
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# partition windows
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self.shift_size = 0
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self.window_size = min(self.input_resolution)
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assert 0 <= self.shift_size < self.window_size, ('shift_size must in '
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'0-window_size')
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim,
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window_size=to_2tuple(self.window_size),
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop,
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pretrained_window_size=to_2tuple(pretrained_window_size))
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self.drop_path = DropPath(
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drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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if self.is_moe:
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self.mlp = MoEMlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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num_local_experts=num_local_experts,
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top_value=top_value,
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capacity_factor=capacity_factor,
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cosine_router=cosine_router,
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normalize_gate=normalize_gate,
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use_bpr=use_bpr,
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is_gshard_loss=is_gshard_loss,
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gate_noise=gate_noise,
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cosine_router_dim=cosine_router_dim,
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cosine_router_init_t=cosine_router_init_t,
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moe_drop=moe_drop,
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mlp_fc2_bias=mlp_fc2_bias,
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init_std=init_std)
|
|
else:
|
|
self.mlp = Mlp(
|
|
in_features=dim,
|
|
hidden_features=mlp_hidden_dim,
|
|
act_layer=act_layer,
|
|
drop=drop,
|
|
mlp_fc2_bias=mlp_fc2_bias)
|
|
|
|
if self.shift_size > 0:
|
|
# calculate attention mask for SW-MSA
|
|
H, W = self.input_resolution
|
|
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
|
h_slices = (slice(0, -self.window_size),
|
|
slice(-self.window_size,
|
|
-self.shift_size), slice(-self.shift_size, None))
|
|
w_slices = (slice(0, -self.window_size),
|
|
slice(-self.window_size,
|
|
-self.shift_size), slice(-self.shift_size, None))
|
|
cnt = 0
|
|
for h in h_slices:
|
|
for w in w_slices:
|
|
img_mask[:, h, w, :] = cnt
|
|
cnt += 1
|
|
|
|
mask_windows = window_partition(
|
|
img_mask, self.window_size) # nW, window_size, window_size, 1
|
|
mask_windows = mask_windows.view(
|
|
-1, self.window_size * self.window_size)
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0,
|
|
float(-100.0)).masked_fill(
|
|
attn_mask == 0, float(0.0))
|
|
else:
|
|
attn_mask = None
|
|
|
|
self.register_buffer('attn_mask', attn_mask)
|
|
|
|
def forward(self, x):
|
|
H, W = self.input_resolution
|
|
B, L, C = x.shape
|
|
assert L == H * W, 'input feature has wrong size'
|
|
|
|
shortcut = x
|
|
x = self.norm1(x)
|
|
x = x.view(B, H, W, C)
|
|
|
|
# cyclic shift
|
|
if self.shift_size > 0:
|
|
shifted_x = torch.roll(
|
|
x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
|
else:
|
|
shifted_x = x
|
|
|
|
# partition windows
|
|
x_windows = window_partition(
|
|
shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
|
x_windows = x_windows.view(-1, self.window_size * self.window_size,
|
|
C) # nW*B, window_size*window_size, C
|
|
|
|
# W-MSA/SW-MSA
|
|
attn_windows = self.attn(
|
|
x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
|
|
|
|
# merge windows
|
|
attn_windows = attn_windows.view(-1, self.window_size,
|
|
self.window_size, C)
|
|
shifted_x = window_reverse(attn_windows, self.window_size, H,
|
|
W) # B H' W' C
|
|
|
|
# reverse cyclic shift
|
|
if self.shift_size > 0:
|
|
x = torch.roll(
|
|
shifted_x,
|
|
shifts=(self.shift_size, self.shift_size),
|
|
dims=(1, 2))
|
|
else:
|
|
x = shifted_x
|
|
x = x.view(B, H * W, C)
|
|
x = shortcut + self.drop_path(x)
|
|
|
|
# FFN
|
|
shortcut = x
|
|
x = self.norm2(x)
|
|
if self.is_moe:
|
|
x, l_aux = self.mlp(x)
|
|
x = shortcut + self.drop_path(x)
|
|
return x, l_aux
|
|
else:
|
|
x = shortcut + self.drop_path(self.mlp(x))
|
|
return x
|
|
|
|
def extra_repr(self) -> str:
|
|
return (f'dim={self.dim}, '
|
|
f'input_resolution={self.input_resolution}, '
|
|
f'num_heads={self.num_heads}, '
|
|
f'window_size={self.window_size}, '
|
|
f'shift_size={self.shift_size}, '
|
|
f'mlp_ratio={self.mlp_ratio}')
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
H, W = self.input_resolution
|
|
# norm1
|
|
flops += self.dim * H * W
|
|
# W-MSA/SW-MSA
|
|
nW = H * W / self.window_size / self.window_size
|
|
flops += nW * self.attn.flops(self.window_size * self.window_size)
|
|
# mlp
|
|
if self.is_moe:
|
|
flops += (2 * H * W * self.dim * self.dim * self.mlp_ratio *
|
|
self.capacity_factor * self.top_value)
|
|
else:
|
|
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
|
|
# norm2
|
|
flops += self.dim * H * W
|
|
return flops
|
|
|
|
|
|
class PatchMerging(nn.Module):
|
|
r""" Patch Merging Layer.
|
|
|
|
Args:
|
|
input_resolution (tuple[int]): Resolution of input feature.
|
|
dim (int): Number of input channels.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default:
|
|
nn.LayerNorm
|
|
"""
|
|
|
|
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
|
|
super().__init__()
|
|
self.input_resolution = input_resolution
|
|
self.dim = dim
|
|
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
|
self.norm = norm_layer(4 * dim)
|
|
|
|
def forward(self, x):
|
|
"""
|
|
x: B, H*W, C
|
|
"""
|
|
H, W = self.input_resolution
|
|
B, L, C = x.shape
|
|
assert L == H * W, 'input feature has wrong size'
|
|
assert H % 2 == 0 and W % 2 == 0, f'x size ({H}*{W}) are not even.'
|
|
|
|
x = x.view(B, H, W, C)
|
|
|
|
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
|
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
|
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
|
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
|
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
|
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
|
|
|
x = self.norm(x)
|
|
x = self.reduction(x)
|
|
|
|
return x
|
|
|
|
def extra_repr(self) -> str:
|
|
return f'input_resolution={self.input_resolution}, dim={self.dim}'
|
|
|
|
def flops(self):
|
|
H, W = self.input_resolution
|
|
flops = H * W * self.dim
|
|
flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
|
|
return flops
|
|
|
|
|
|
class BasicLayer(nn.Module):
|
|
"""A basic Swin Transformer layer for one stage.
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
input_resolution (tuple[int]): Input resolution.
|
|
depth (int): Number of blocks.
|
|
num_heads (int): Number of attention heads.
|
|
window_size (int): Local window size.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query,
|
|
key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of
|
|
head_dim ** -0.5 if set.
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate.
|
|
Default: 0.0
|
|
norm_layer (nn.Module, optional): Normalization layer. Default:
|
|
nn.LayerNorm
|
|
downsample (nn.Module | None, optional): Downsample layer at the end
|
|
of the layer. Default: None
|
|
mlp_fc2_bias (bool): Whether to add bias in fc2 of Mlp. Default: True
|
|
init_std: Initialization std. Default: 0.02
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory.
|
|
Default: False.
|
|
pretrained_window_size (int): Local window size in pretraining.
|
|
moe_blocks (tuple(int)): The index of each MoE block.
|
|
num_local_experts (int): number of local experts in each device (
|
|
GPU). Default: 1
|
|
top_value (int): the value of k in top-k gating. Default: 1
|
|
capacity_factor (float): the capacity factor in MoE. Default: 1.25
|
|
cosine_router (bool): Whether to use cosine router Default: False
|
|
normalize_gate (bool): Whether to normalize the gating score in top-k
|
|
gating. Default: False
|
|
use_bpr (bool): Whether to use batch-prioritized-routing. Default: True
|
|
is_gshard_loss (bool): If True, use Gshard balance loss.
|
|
If False, use the load loss and importance
|
|
loss in "arXiv:1701.06538". Default: False
|
|
gate_noise (float): the noise ratio in top-k gating. Default: 1.0
|
|
cosine_router_dim (int): Projection dimension in cosine router.
|
|
cosine_router_init_t (float): Initialization temperature in cosine
|
|
router.
|
|
moe_drop (float): Dropout rate in MoE. Default: 0.0
|
|
"""
|
|
|
|
def __init__(self,
|
|
dim,
|
|
input_resolution,
|
|
depth,
|
|
num_heads,
|
|
window_size,
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop=0.,
|
|
attn_drop=0.,
|
|
drop_path=0.,
|
|
norm_layer=nn.LayerNorm,
|
|
downsample=None,
|
|
mlp_fc2_bias=True,
|
|
init_std=0.02,
|
|
use_checkpoint=False,
|
|
pretrained_window_size=0,
|
|
moe_block=[-1],
|
|
num_local_experts=1,
|
|
top_value=1,
|
|
capacity_factor=1.25,
|
|
cosine_router=False,
|
|
normalize_gate=False,
|
|
use_bpr=True,
|
|
is_gshard_loss=True,
|
|
cosine_router_dim=256,
|
|
cosine_router_init_t=0.5,
|
|
gate_noise=1.0,
|
|
moe_drop=0.0):
|
|
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.input_resolution = input_resolution
|
|
self.depth = depth
|
|
self.use_checkpoint = use_checkpoint
|
|
|
|
# build blocks
|
|
self.blocks = nn.ModuleList([
|
|
SwinTransformerBlock(
|
|
dim=dim,
|
|
input_resolution=input_resolution,
|
|
num_heads=num_heads,
|
|
window_size=window_size,
|
|
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop,
|
|
attn_drop=attn_drop,
|
|
drop_path=drop_path[i]
|
|
if isinstance(drop_path, list) else drop_path,
|
|
norm_layer=norm_layer,
|
|
mlp_fc2_bias=mlp_fc2_bias,
|
|
init_std=init_std,
|
|
pretrained_window_size=pretrained_window_size,
|
|
is_moe=True if i in moe_block else False,
|
|
num_local_experts=num_local_experts,
|
|
top_value=top_value,
|
|
capacity_factor=capacity_factor,
|
|
cosine_router=cosine_router,
|
|
normalize_gate=normalize_gate,
|
|
use_bpr=use_bpr,
|
|
is_gshard_loss=is_gshard_loss,
|
|
gate_noise=gate_noise,
|
|
cosine_router_dim=cosine_router_dim,
|
|
cosine_router_init_t=cosine_router_init_t,
|
|
moe_drop=moe_drop) for i in range(depth)
|
|
])
|
|
|
|
# patch merging layer
|
|
if downsample is not None:
|
|
self.downsample = downsample(
|
|
input_resolution, dim=dim, norm_layer=norm_layer)
|
|
else:
|
|
self.downsample = None
|
|
|
|
def forward(self, x):
|
|
l_aux = 0.0
|
|
for blk in self.blocks:
|
|
if self.use_checkpoint:
|
|
out = checkpoint.checkpoint(blk, x)
|
|
else:
|
|
out = blk(x)
|
|
if isinstance(out, tuple):
|
|
x = out[0]
|
|
cur_l_aux = out[1]
|
|
l_aux = cur_l_aux + l_aux
|
|
else:
|
|
x = out
|
|
|
|
if self.downsample is not None:
|
|
x = self.downsample(x)
|
|
return x, l_aux
|
|
|
|
def extra_repr(self) -> str:
|
|
return (f'dim={self.dim}, input_resolution={self.input_resolution}, '
|
|
f'depth={self.depth}')
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
for blk in self.blocks:
|
|
flops += blk.flops()
|
|
if self.downsample is not None:
|
|
flops += self.downsample.flops()
|
|
return flops
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
r""" Image to Patch Embedding
|
|
|
|
Args:
|
|
img_size (int): Image size. Default: 224.
|
|
patch_size (int): Patch token size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels.
|
|
Default: 96.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
|
"""
|
|
|
|
def __init__(self,
|
|
img_size=224,
|
|
patch_size=4,
|
|
in_chans=3,
|
|
embed_dim=96,
|
|
norm_layer=None):
|
|
super().__init__()
|
|
img_size = to_2tuple(img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
patches_resolution = [
|
|
img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
|
]
|
|
self.img_size = img_size
|
|
self.patch_size = patch_size
|
|
self.patches_resolution = patches_resolution
|
|
self.num_patches = patches_resolution[0] * patches_resolution[1]
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
self.proj = nn.Conv2d(
|
|
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
if norm_layer is not None:
|
|
self.norm = norm_layer(embed_dim)
|
|
else:
|
|
self.norm = None
|
|
|
|
def forward(self, x):
|
|
B, C, H, W = x.shape
|
|
# FIXME look at relaxing size constraints
|
|
assert H == self.img_size[0] and W == self.img_size[1], \
|
|
(f"Input image size ({H}*{W}) doesn't match model ("
|
|
f'{self.img_size[0]}*{self.img_size[1]}).')
|
|
x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
|
|
if self.norm is not None:
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
def flops(self):
|
|
Ho, Wo = self.patches_resolution
|
|
flops = Ho * Wo * self.embed_dim * self.in_chans * (
|
|
self.patch_size[0] * self.patch_size[1])
|
|
if self.norm is not None:
|
|
flops += Ho * Wo * self.embed_dim
|
|
return flops
|
|
|
|
|
|
class SwinTransformerMoE(nn.Module):
|
|
r""" Swin Transformer
|
|
A PyTorch impl of : `Swin Transformer: Hierarchical Vision
|
|
Transformer using Shifted Windows` -
|
|
https://arxiv.org/pdf/2103.14030
|
|
|
|
Args:
|
|
img_size (int | tuple(int)): Input image size. Default 224
|
|
patch_size (int | tuple(int)): Patch size. Default: 4
|
|
in_chans (int): Number of input image channels. Default: 3
|
|
num_classes (int): Number of classes for classification head.
|
|
Default: 1000
|
|
embed_dim (int): Patch embedding dimension. Default: 96
|
|
depths (tuple(int)): Depth of each Swin Transformer layer.
|
|
num_heads (tuple(int)): Number of attention heads in different layers.
|
|
window_size (int): Window size. Default: 7
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
|
|
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
|
Default: True
|
|
qk_scale (float): Override default qk scale of head_dim ** -0.5 if
|
|
set. Default: None
|
|
drop_rate (float): Dropout rate. Default: 0
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
|
ape (bool): If True, add absolute position embedding to the patch
|
|
embedding. Default: False
|
|
patch_norm (bool): If True, add normalization after patch embedding.
|
|
Default: True
|
|
mlp_fc2_bias (bool): Whether to add bias in fc2 of Mlp. Default: True
|
|
init_std: Initialization std. Default: 0.02
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory.
|
|
Default: False
|
|
pretrained_window_sizes (tuple(int)): Pretrained window sizes of each
|
|
layer.
|
|
moe_blocks (tuple(tuple(int))): The index of each MoE block in each
|
|
layer.
|
|
num_local_experts (int): number of local experts in each device (
|
|
GPU). Default: 1
|
|
top_value (int): the value of k in top-k gating. Default: 1
|
|
capacity_factor (float): the capacity factor in MoE. Default: 1.25
|
|
cosine_router (bool): Whether to use cosine router Default: False
|
|
normalize_gate (bool): Whether to normalize the gating score in top-k
|
|
gating. Default: False
|
|
use_bpr (bool): Whether to use batch-prioritized-routing. Default: True
|
|
is_gshard_loss (bool): If True, use Gshard balance loss.
|
|
If False, use the load loss and importance
|
|
loss in "arXiv:1701.06538". Default: False
|
|
gate_noise (float): the noise ratio in top-k gating. Default: 1.0
|
|
cosine_router_dim (int): Projection dimension in cosine router.
|
|
cosine_router_init_t (float): Initialization temperature in cosine
|
|
router.
|
|
moe_drop (float): Dropout rate in MoE. Default: 0.0
|
|
aux_loss_weight (float): auxiliary loss weight. Default: 0.1
|
|
"""
|
|
|
|
def __init__(self,
|
|
img_size=224,
|
|
patch_size=4,
|
|
in_chans=3,
|
|
num_classes=1000,
|
|
embed_dim=96,
|
|
depths=[2, 2, 6, 2],
|
|
num_heads=[3, 6, 12, 24],
|
|
window_size=7,
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.1,
|
|
norm_layer=nn.LayerNorm,
|
|
ape=False,
|
|
patch_norm=True,
|
|
mlp_fc2_bias=True,
|
|
init_std=0.02,
|
|
use_checkpoint=False,
|
|
pretrained_window_sizes=[0, 0, 0, 0],
|
|
moe_blocks=[[-1], [-1], [-1], [-1]],
|
|
num_local_experts=1,
|
|
top_value=1,
|
|
capacity_factor=1.25,
|
|
cosine_router=False,
|
|
normalize_gate=False,
|
|
use_bpr=True,
|
|
is_gshard_loss=True,
|
|
gate_noise=1.0,
|
|
cosine_router_dim=256,
|
|
cosine_router_init_t=0.5,
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|
moe_drop=0.0,
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|
aux_loss_weight=0.01,
|
|
**kwargs):
|
|
super().__init__()
|
|
self._ddp_params_and_buffers_to_ignore = list()
|
|
|
|
self.num_classes = num_classes
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.ape = ape
|
|
self.patch_norm = patch_norm
|
|
self.num_features = int(embed_dim * 2**(self.num_layers - 1))
|
|
self.mlp_ratio = mlp_ratio
|
|
self.init_std = init_std
|
|
self.aux_loss_weight = aux_loss_weight
|
|
self.num_local_experts = num_local_experts
|
|
self.global_experts = num_local_experts * dist.get_world_size() if (
|
|
num_local_experts > 0) \
|
|
else dist.get_world_size() // (-num_local_experts)
|
|
self.sharded_count = (
|
|
1.0 / num_local_experts) if num_local_experts > 0 else (
|
|
-num_local_experts)
|
|
|
|
# split image into non-overlapping patches
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size,
|
|
patch_size=patch_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None)
|
|
num_patches = self.patch_embed.num_patches
|
|
patches_resolution = self.patch_embed.patches_resolution
|
|
self.patches_resolution = patches_resolution
|
|
|
|
# absolute position embedding
|
|
if self.ape:
|
|
self.absolute_pos_embed = nn.Parameter(
|
|
torch.zeros(1, num_patches, embed_dim))
|
|
trunc_normal_(self.absolute_pos_embed, std=self.init_std)
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
# stochastic depth
|
|
dpr = [
|
|
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
|
|
] # stochastic depth decay rule
|
|
|
|
# build layers
|
|
self.layers = nn.ModuleList()
|
|
for i_layer in range(self.num_layers):
|
|
layer = BasicLayer(
|
|
dim=int(embed_dim * 2**i_layer),
|
|
input_resolution=(patches_resolution[0] // (2**i_layer),
|
|
patches_resolution[1] // (2**i_layer)),
|
|
depth=depths[i_layer],
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_size,
|
|
mlp_ratio=self.mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
|
norm_layer=norm_layer,
|
|
downsample=PatchMerging if
|
|
(i_layer < self.num_layers - 1) else None,
|
|
mlp_fc2_bias=mlp_fc2_bias,
|
|
init_std=init_std,
|
|
use_checkpoint=use_checkpoint,
|
|
pretrained_window_size=pretrained_window_sizes[i_layer],
|
|
moe_block=moe_blocks[i_layer],
|
|
num_local_experts=num_local_experts,
|
|
top_value=top_value,
|
|
capacity_factor=capacity_factor,
|
|
cosine_router=cosine_router,
|
|
normalize_gate=normalize_gate,
|
|
use_bpr=use_bpr,
|
|
is_gshard_loss=is_gshard_loss,
|
|
gate_noise=gate_noise,
|
|
cosine_router_dim=cosine_router_dim,
|
|
cosine_router_init_t=cosine_router_init_t,
|
|
moe_drop=moe_drop)
|
|
self.layers.append(layer)
|
|
|
|
self.norm = norm_layer(self.num_features)
|
|
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
|
self.head = nn.Linear(
|
|
self.num_features,
|
|
num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=self.init_std)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
elif isinstance(m, MoEMlp):
|
|
m._init_weights()
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'absolute_pos_embed'}
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay_keywords(self):
|
|
return {
|
|
'cpb_mlp', 'relative_position_bias_table', 'fc1_bias', 'fc2_bias',
|
|
'temperature', 'cosine_projector', 'sim_matrix'
|
|
}
|
|
|
|
def forward_features(self, x):
|
|
x = self.patch_embed(x)
|
|
if self.ape:
|
|
x = x + self.absolute_pos_embed
|
|
x = self.pos_drop(x)
|
|
l_aux = 0.0
|
|
for layer in self.layers:
|
|
x, cur_l_aux = layer(x)
|
|
l_aux = cur_l_aux + l_aux
|
|
|
|
x = self.norm(x) # B L C
|
|
x = self.avgpool(x.transpose(1, 2)) # B C 1
|
|
x = torch.flatten(x, 1)
|
|
return x, l_aux
|
|
|
|
def forward(self, x):
|
|
x, l_aux = self.forward_features(x)
|
|
x = self.head(x)
|
|
return x, l_aux * self.aux_loss_weight
|
|
|
|
def add_param_to_skip_allreduce(self, param_name):
|
|
self._ddp_params_and_buffers_to_ignore.append(param_name)
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
flops += self.patch_embed.flops()
|
|
for i, layer in enumerate(self.layers):
|
|
flops += layer.flops()
|
|
flops += self.num_features * self.patches_resolution[
|
|
0] * self.patches_resolution[1] // (2**self.num_layers)
|
|
flops += self.num_features * self.num_classes
|
|
return flops
|