wehub-resource-sync
672 lines
30 KiB
Python
672 lines
30 KiB
Python
""" Vision Transformer (ViT) in PyTorch
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A PyTorch implement of Vision Transformers as described in
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'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
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The official jax code is released and available at https://github.com/google-research/vision_transformer
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Status/TODO:
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* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
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* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
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* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
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* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.
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Acknowledgments:
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* The paper authors for releasing code and weights, thanks!
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* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
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for some einops/einsum fun
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* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
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* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import warnings
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import math
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import torch
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from functools import partial
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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 drop_path, to_2tuple, trunc_normal_
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic',
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'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
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**kwargs
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}
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return 'p={}'.format(self.drop_prob)
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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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)
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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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# commit this for the orignal BERT implement
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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 Attention(nn.Module):
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def __init__(
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self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
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proj_drop=0., window_size=None, attn_head_dim=None):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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if attn_head_dim is not None:
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head_dim = attn_head_dim
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all_head_dim = head_dim * self.num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim ** -0.5
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self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
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if qkv_bias:
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self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
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self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
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else:
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self.q_bias = None
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self.v_bias = None
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if window_size:
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self.window_size = window_size
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self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(window_size[0])
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coords_w = torch.arange(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] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = self.num_relative_distance - 3
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relative_position_index[0:, 0] = self.num_relative_distance - 2
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relative_position_index[0, 0] = self.num_relative_distance - 1
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self.register_buffer("relative_position_index", relative_position_index)
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# trunc_normal_(self.relative_position_bias_table, std=.0)
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else:
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self.window_size = None
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self.relative_position_bias_table = None
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self.relative_position_index = None
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(all_head_dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x, rel_pos_bias=None, training_window_size=None):
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B, N, C = x.shape
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qkv_bias = None
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if self.q_bias is not None:
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qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
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# qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[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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if self.relative_position_bias_table is not None:
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if training_window_size == self.window_size:
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relative_position_bias = \
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self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1] + 1,
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self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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else:
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training_window_size = tuple(training_window_size.tolist())
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new_num_relative_distance = (2 * training_window_size[0] - 1) * (2 * training_window_size[1] - 1) + 3
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# new_num_relative_dis 为 所有可能的相对位置选项，包含cls-cls，tok-cls，与cls-tok
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new_relative_position_bias_table = F.interpolate(
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self.relative_position_bias_table[:-3, :].permute(1, 0).view(1, self.num_heads,
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2 * self.window_size[0] - 1,
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2 * self.window_size[1] - 1),
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size=(2 * training_window_size[0] - 1, 2 * training_window_size[1] - 1), mode='bicubic',
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align_corners=False)
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new_relative_position_bias_table = new_relative_position_bias_table.view(self.num_heads,
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new_num_relative_distance - 3).permute(
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1, 0)
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new_relative_position_bias_table = torch.cat(
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[new_relative_position_bias_table, self.relative_position_bias_table[-3::]], dim=0)
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(training_window_size[0])
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coords_w = torch.arange(training_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] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += training_window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += training_window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * training_window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(training_window_size[0] * training_window_size[1] + 1,) * 2,
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dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = new_num_relative_distance - 3
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relative_position_index[0:, 0] = new_num_relative_distance - 2
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relative_position_index[0, 0] = new_num_relative_distance - 1
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relative_position_bias = \
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new_relative_position_bias_table[relative_position_index.view(-1)].view(
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training_window_size[0] * training_window_size[1] + 1,
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training_window_size[0] * training_window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(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 rel_pos_bias is not None:
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attn = attn + rel_pos_bias
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
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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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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
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window_size=None, attn_head_dim=None):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(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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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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if init_values is not None:
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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else:
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self.gamma_1, self.gamma_2 = None, None
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def forward(self, x, rel_pos_bias=None, training_window_size=None):
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if self.gamma_1 is None:
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x = x + self.drop_path(
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self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, training_window_size=training_window_size))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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else:
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x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias,
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training_window_size=training_window_size))
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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return x
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class PatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
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self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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self.num_patches_w = self.patch_shape[0]
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self.num_patches_h = self.patch_shape[1]
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# the so-called patch_shape is the patch shape during pre-training
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x, position_embedding=None, **kwargs):
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# FIXME look at relaxing size constraints
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# assert H == self.img_size[0] and W == self.img_size[1], \
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# f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x)
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Hp, Wp = x.shape[2], x.shape[3]
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if position_embedding is not None:
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# interpolate the position embedding to the corresponding size
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position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1).permute(0, 3,
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1, 2)
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position_embedding = F.interpolate(position_embedding, size=(Hp, Wp), mode='bicubic')
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x = x + position_embedding
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x = x.flatten(2).transpose(1, 2)
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return x, (Hp, Wp)
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class HybridEmbed(nn.Module):
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""" CNN Feature Map Embedding
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Extract feature map from CNN, flatten, project to embedding dim.
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"""
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def __init__(self, backbone, img_size=[224, 224], feature_size=None, in_chans=3, embed_dim=768):
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super().__init__()
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assert isinstance(backbone, nn.Module)
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img_size = to_2tuple(img_size)
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self.img_size = img_size
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self.backbone = backbone
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if feature_size is None:
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with torch.no_grad():
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# FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
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# map for all networks, the feature metadata has reliable channel and stride info, but using
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# stride to calc feature dim requires info about padding of each stage that isn't captured.
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training = backbone.training
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if training:
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backbone.eval()
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o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
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feature_size = o.shape[-2:]
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feature_dim = o.shape[1]
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backbone.train(training)
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else:
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feature_size = to_2tuple(feature_size)
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feature_dim = self.backbone.feature_info.channels()[-1]
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self.num_patches = feature_size[0] * feature_size[1]
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self.proj = nn.Linear(feature_dim, embed_dim)
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def forward(self, x):
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x = self.backbone(x)[-1]
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x = x.flatten(2).transpose(1, 2)
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x = self.proj(x)
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return x
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class RelativePositionBias(nn.Module):
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def __init__(self, window_size, num_heads):
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super().__init__()
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self.window_size = window_size
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self.num_heads = num_heads
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self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(window_size[0])
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coords_w = torch.arange(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] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = self.num_relative_distance - 3
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relative_position_index[0:, 0] = self.num_relative_distance - 2
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relative_position_index[0, 0] = self.num_relative_distance - 1
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self.register_buffer("relative_position_index", relative_position_index)
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# trunc_normal_(self.relative_position_bias_table, std=.02)
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def forward(self, training_window_size):
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if training_window_size == self.window_size:
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relative_position_bias = \
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self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1] + 1,
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self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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else:
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training_window_size = tuple(training_window_size.tolist())
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new_num_relative_distance = (2 * training_window_size[0] - 1) * (2 * training_window_size[1] - 1) + 3
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# new_num_relative_dis 为 所有可能的相对位置选项，包含cls-cls，tok-cls，与cls-tok
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new_relative_position_bias_table = F.interpolate(
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self.relative_position_bias_table[:-3, :].permute(1, 0).view(1, self.num_heads,
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2 * self.window_size[0] - 1,
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2 * self.window_size[1] - 1),
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size=(2 * training_window_size[0] - 1, 2 * training_window_size[1] - 1), mode='bicubic',
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align_corners=False)
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new_relative_position_bias_table = new_relative_position_bias_table.view(self.num_heads,
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new_num_relative_distance - 3).permute(
|
||
1, 0)
|
||
new_relative_position_bias_table = torch.cat(
|
||
[new_relative_position_bias_table, self.relative_position_bias_table[-3::]], dim=0)
|
||
|
||
# get pair-wise relative position index for each token inside the window
|
||
coords_h = torch.arange(training_window_size[0])
|
||
coords_w = torch.arange(training_window_size[1])
|
||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||
relative_coords[:, :, 0] += training_window_size[0] - 1 # shift to start from 0
|
||
relative_coords[:, :, 1] += training_window_size[1] - 1
|
||
relative_coords[:, :, 0] *= 2 * training_window_size[1] - 1
|
||
relative_position_index = \
|
||
torch.zeros(size=(training_window_size[0] * training_window_size[1] + 1,) * 2,
|
||
dtype=relative_coords.dtype)
|
||
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||
relative_position_index[0, 0:] = new_num_relative_distance - 3
|
||
relative_position_index[0:, 0] = new_num_relative_distance - 2
|
||
relative_position_index[0, 0] = new_num_relative_distance - 1
|
||
|
||
relative_position_bias = \
|
||
new_relative_position_bias_table[relative_position_index.view(-1)].view(
|
||
training_window_size[0] * training_window_size[1] + 1,
|
||
training_window_size[0] * training_window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
|
||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||
|
||
return relative_position_bias
|
||
|
||
|
||
class BEiT(nn.Module):
|
||
""" Vision Transformer with support for patch or hybrid CNN input stage
|
||
"""
|
||
|
||
def __init__(self,
|
||
img_size=[224, 224],
|
||
patch_size=16,
|
||
in_chans=3,
|
||
num_classes=80,
|
||
embed_dim=768,
|
||
depth=12,
|
||
num_heads=12,
|
||
mlp_ratio=4.,
|
||
qkv_bias=False,
|
||
qk_scale=None,
|
||
drop_rate=0.,
|
||
attn_drop_rate=0.,
|
||
drop_path_rate=0.,
|
||
hybrid_backbone=None,
|
||
norm_layer=None,
|
||
init_values=None,
|
||
use_abs_pos_emb=False,
|
||
use_rel_pos_bias=False,
|
||
use_shared_rel_pos_bias=False,
|
||
use_checkpoint=True,
|
||
pretrained=None,
|
||
out_features=None,
|
||
):
|
||
|
||
super(BEiT, self).__init__()
|
||
|
||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||
self.num_classes = num_classes
|
||
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
||
self.use_checkpoint = use_checkpoint
|
||
|
||
if hybrid_backbone is not None:
|
||
self.patch_embed = HybridEmbed(
|
||
hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
|
||
else:
|
||
self.patch_embed = PatchEmbed(
|
||
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
||
num_patches = self.patch_embed.num_patches
|
||
self.out_features = out_features
|
||
self.out_indices = [int(name[5:]) for name in out_features]
|
||
|
||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||
# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||
if use_abs_pos_emb:
|
||
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
|
||
else:
|
||
self.pos_embed = None
|
||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||
|
||
self.use_shared_rel_pos_bias = use_shared_rel_pos_bias
|
||
if use_shared_rel_pos_bias:
|
||
self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
|
||
else:
|
||
self.rel_pos_bias = None
|
||
|
||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||
self.use_rel_pos_bias = use_rel_pos_bias
|
||
self.blocks = nn.ModuleList([
|
||
Block(
|
||
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
|
||
init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None)
|
||
for i in range(depth)])
|
||
|
||
# trunc_normal_(self.mask_token, std=.02)
|
||
|
||
if patch_size == 16:
|
||
self.fpn1 = nn.Sequential(
|
||
nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
|
||
# nn.SyncBatchNorm(embed_dim),
|
||
nn.BatchNorm2d(embed_dim),
|
||
nn.GELU(),
|
||
nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
|
||
)
|
||
|
||
self.fpn2 = nn.Sequential(
|
||
nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
|
||
)
|
||
|
||
self.fpn3 = nn.Identity()
|
||
|
||
self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
|
||
elif patch_size == 8:
|
||
self.fpn1 = nn.Sequential(
|
||
nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
|
||
)
|
||
|
||
self.fpn2 = nn.Identity()
|
||
|
||
self.fpn3 = nn.Sequential(
|
||
nn.MaxPool2d(kernel_size=2, stride=2),
|
||
)
|
||
|
||
self.fpn4 = nn.Sequential(
|
||
nn.MaxPool2d(kernel_size=4, stride=4),
|
||
)
|
||
|
||
if self.pos_embed is not None:
|
||
trunc_normal_(self.pos_embed, std=.02)
|
||
trunc_normal_(self.cls_token, std=.02)
|
||
self.apply(self._init_weights)
|
||
self.fix_init_weight()
|
||
|
||
def fix_init_weight(self):
|
||
def rescale(param, layer_id):
|
||
param.div_(math.sqrt(2.0 * layer_id))
|
||
|
||
for layer_id, layer in enumerate(self.blocks):
|
||
rescale(layer.attn.proj.weight.data, layer_id + 1)
|
||
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
|
||
|
||
def _init_weights(self, m):
|
||
if isinstance(m, nn.Linear):
|
||
trunc_normal_(m.weight, std=.02)
|
||
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)
|
||
|
||
'''
|
||
def init_weights(self):
|
||
"""Initialize the weights in backbone.
|
||
|
||
Args:
|
||
pretrained (str, optional): Path to pre-trained weights.
|
||
Defaults to None.
|
||
"""
|
||
logger = get_root_logger()
|
||
|
||
if self.pos_embed is not None:
|
||
trunc_normal_(self.pos_embed, std=.02)
|
||
trunc_normal_(self.cls_token, std=.02)
|
||
self.apply(self._init_weights)
|
||
self.fix_init_weight()
|
||
|
||
if self.init_cfg is None:
|
||
logger.warn(f'No pre-trained weights for '
|
||
f'{self.__class__.__name__}, '
|
||
f'training start from scratch')
|
||
else:
|
||
assert 'checkpoint' in self.init_cfg, f'Only support ' \
|
||
f'specify `Pretrained` in ' \
|
||
f'`init_cfg` in ' \
|
||
f'{self.__class__.__name__} '
|
||
logger.info(f"Will load ckpt from {self.init_cfg['checkpoint']}")
|
||
load_checkpoint(self,
|
||
filename=self.init_cfg['checkpoint'],
|
||
strict=False,
|
||
logger=logger,
|
||
beit_spec_expand_rel_pos = self.use_rel_pos_bias,
|
||
)
|
||
'''
|
||
|
||
def get_num_layers(self):
|
||
return len(self.blocks)
|
||
|
||
@torch.jit.ignore
|
||
def no_weight_decay(self):
|
||
return {'pos_embed', 'cls_token'}
|
||
|
||
def forward_features(self, x):
|
||
B, C, H, W = x.shape
|
||
x, (Hp, Wp) = self.patch_embed(x, self.pos_embed[:, 1:, :] if self.pos_embed is not None else None)
|
||
# Hp, Wp are HW for patches
|
||
batch_size, seq_len, _ = x.size()
|
||
|
||
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||
if self.pos_embed is not None:
|
||
cls_tokens = cls_tokens + self.pos_embed[:, :1, :]
|
||
x = torch.cat((cls_tokens, x), dim=1)
|
||
x = self.pos_drop(x)
|
||
|
||
features = []
|
||
training_window_size = torch.tensor([Hp, Wp])
|
||
|
||
rel_pos_bias = self.rel_pos_bias(training_window_size) if self.rel_pos_bias is not None else None
|
||
|
||
for i, blk in enumerate(self.blocks):
|
||
if self.use_checkpoint:
|
||
x = checkpoint.checkpoint(blk, x, rel_pos_bias, training_window_size)
|
||
else:
|
||
x = blk(x, rel_pos_bias=rel_pos_bias, training_window_size=training_window_size)
|
||
if i in self.out_indices:
|
||
xp = x[:, 1:, :].permute(0, 2, 1).reshape(B, -1, Hp, Wp)
|
||
features.append(xp.contiguous())
|
||
|
||
ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
|
||
for i in range(len(features)):
|
||
features[i] = ops[i](features[i])
|
||
|
||
feat_out = {}
|
||
|
||
for name, value in zip(self.out_features, features):
|
||
feat_out[name] = value
|
||
|
||
return feat_out
|
||
|
||
def forward(self, x):
|
||
x = self.forward_features(x)
|
||
return x
|
||
|
||
|
||
def beit_base_patch16(pretrained=False, **kwargs):
|
||
model = BEiT(
|
||
patch_size=16,
|
||
embed_dim=768,
|
||
depth=12,
|
||
num_heads=12,
|
||
mlp_ratio=4,
|
||
qkv_bias=True,
|
||
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||
init_values=None,
|
||
**kwargs)
|
||
model.default_cfg = _cfg()
|
||
return model
|
||
|
||
def beit_large_patch16(pretrained=False, **kwargs):
|
||
model = BEiT(
|
||
patch_size=16,
|
||
embed_dim=1024,
|
||
depth=24,
|
||
num_heads=16,
|
||
mlp_ratio=4,
|
||
qkv_bias=True,
|
||
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||
init_values=None,
|
||
**kwargs)
|
||
model.default_cfg = _cfg()
|
||
return model
|
||
|
||
def dit_base_patch16(pretrained=False, **kwargs):
|
||
model = BEiT(
|
||
patch_size=16,
|
||
embed_dim=768,
|
||
depth=12,
|
||
num_heads=12,
|
||
mlp_ratio=4,
|
||
qkv_bias=True,
|
||
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||
init_values=0.1,
|
||
**kwargs)
|
||
model.default_cfg = _cfg()
|
||
return model
|
||
|
||
def dit_large_patch16(pretrained=False, **kwargs):
|
||
model = BEiT(
|
||
patch_size=16,
|
||
embed_dim=1024,
|
||
depth=24,
|
||
num_heads=16,
|
||
mlp_ratio=4,
|
||
qkv_bias=True,
|
||
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||
init_values=1e-5,
|
||
**kwargs)
|
||
model.default_cfg = _cfg()
|
||
return model
|
||
|
||
if __name__ == '__main__':
|
||
model = BEiT(use_checkpoint=True, use_shared_rel_pos_bias=True)
|
||
model = model.to("cuda:0")
|
||
input1 = torch.rand(2, 3, 512, 762).to("cuda:0")
|
||
input2 = torch.rand(2, 3, 800, 1200).to("cuda:0")
|
||
input3 = torch.rand(2, 3, 720, 1000).to("cuda:0")
|
||
output1 = model(input1)
|
||
output2 = model(input2)
|
||
output3 = model(input3)
|
||
print("all done")
|