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https://github.com/nod-ai/SHARK-Studio.git
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47f8a79c75
* [MiniGPT4] Add MiniGPT4 to SHARK -- This is the first installment of MiniGPT4 in SHARK. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> * Add int8 support for MiniGPT4 -- This commit adds int8 support for MiniGPT4. Signed-off-by: Abhishek Varma <abhishek@nod-lab.com> * Update .spec for MiniGPT4's config files * black format MiniGPT4 --------- Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> Signed-off-by: Abhishek Varma <abhishek@nod-lab.com>
630 lines
21 KiB
Python
630 lines
21 KiB
Python
# Based on EVA, BEIT, timm and DeiT code bases
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# https://github.com/baaivision/EVA
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm
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# https://github.com/microsoft/unilm/tree/master/beit
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# https://github.com/facebookresearch/deit/
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# https://github.com/facebookresearch/dino
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# --------------------------------------------------------'
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import math
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import requests
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from functools import partial
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import torch
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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,
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"input_size": (3, 224, 224),
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"pool_size": None,
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"crop_pct": 0.9,
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"interpolation": "bicubic",
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"mean": (0.5, 0.5, 0.5),
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"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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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__(
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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=0.0,
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):
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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,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.0,
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proj_drop=0.0,
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window_size=None,
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attn_head_dim=None,
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):
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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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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) * (
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2 * window_size[1] - 1
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) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)
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) # 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(
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torch.meshgrid([coords_h, coords_w])
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) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = (
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coords_flatten[:, :, None] - coords_flatten[:, None, :]
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) # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(
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1, 2, 0
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).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += (
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window_size[0] - 1
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) # 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 = torch.zeros(
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size=(window_size[0] * window_size[1] + 1,) * 2,
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dtype=relative_coords.dtype,
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)
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relative_position_index[1:, 1:] = relative_coords.sum(
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-1
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) # 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(
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"relative_position_index", relative_position_index
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)
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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):
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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(
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(
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self.q_bias,
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torch.zeros_like(self.v_bias, requires_grad=False),
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self.v_bias,
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)
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)
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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 = (
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qkv[0],
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qkv[1],
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qkv[2],
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) # 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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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index.view(-1)
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].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,
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-1,
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) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(
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2, 0, 1
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).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__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.0,
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qkv_bias=False,
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qk_scale=None,
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drop=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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init_values=None,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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window_size=None,
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attn_head_dim=None,
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):
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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,
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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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window_size=window_size,
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attn_head_dim=attn_head_dim,
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)
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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 = (
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DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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)
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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(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop,
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)
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if init_values is not None and init_values > 0:
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self.gamma_1 = nn.Parameter(
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init_values * torch.ones((dim)), requires_grad=True
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)
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self.gamma_2 = nn.Parameter(
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init_values * torch.ones((dim)), requires_grad=True
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)
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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):
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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)
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)
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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(
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self.gamma_1
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* self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)
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)
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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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def __init__(self, img_size=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]) * (
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img_size[0] // patch_size[0]
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)
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self.patch_shape = (
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img_size[0] // patch_size[0],
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img_size[1] // patch_size[1],
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)
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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(
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in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
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)
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def forward(self, x, **kwargs):
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B, C, H, W = x.shape
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# FIXME look at relaxing size constraints
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assert (
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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).flatten(2).transpose(1, 2)
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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_relative_distance = (2 * window_size[0] - 1) * (
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2 * window_size[1] - 1
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) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)
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) # 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 = (
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coords_flatten[:, :, None] - coords_flatten[:, None, :]
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) # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(
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1, 2, 0
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).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 = torch.zeros(
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size=(window_size[0] * window_size[1] + 1,) * 2,
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dtype=relative_coords.dtype,
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)
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relative_position_index[1:, 1:] = relative_coords.sum(
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-1
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) # 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(
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"relative_position_index", relative_position_index
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)
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# trunc_normal_(self.relative_position_bias_table, std=.02)
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def forward(self):
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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index.view(-1)
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].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,
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-1,
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) # Wh*Ww,Wh*Ww,nH
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return relative_position_bias.permute(
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2, 0, 1
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).contiguous() # nH, Wh*Ww, Wh*Ww
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class VisionTransformer(nn.Module):
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"""Vision Transformer with support for patch or hybrid CNN input stage"""
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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num_classes=1000,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.0,
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qkv_bias=False,
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qk_scale=None,
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drop_rate=0.0,
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attn_drop_rate=0.0,
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drop_path_rate=0.0,
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norm_layer=nn.LayerNorm,
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init_values=None,
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use_abs_pos_emb=True,
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use_rel_pos_bias=False,
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use_shared_rel_pos_bias=False,
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use_mean_pooling=True,
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init_scale=0.001,
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use_checkpoint=False,
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):
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super().__init__()
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self.image_size = img_size
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self.num_classes = num_classes
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self.num_features = (
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self.embed_dim
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) = embed_dim # num_features for consistency with other models
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self.patch_embed = PatchEmbed(
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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if use_abs_pos_emb:
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self.pos_embed = nn.Parameter(
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torch.zeros(1, num_patches + 1, embed_dim)
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)
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else:
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self.pos_embed = None
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self.pos_drop = nn.Dropout(p=drop_rate)
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if use_shared_rel_pos_bias:
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self.rel_pos_bias = RelativePositionBias(
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window_size=self.patch_embed.patch_shape, num_heads=num_heads
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)
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else:
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self.rel_pos_bias = None
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self.use_checkpoint = use_checkpoint
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, depth)
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] # stochastic depth decay rule
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self.use_rel_pos_bias = use_rel_pos_bias
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self.blocks = nn.ModuleList(
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[
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Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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init_values=init_values,
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window_size=self.patch_embed.patch_shape
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if use_rel_pos_bias
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else None,
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)
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for i in range(depth)
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]
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)
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# self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
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# self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
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# self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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if self.pos_embed is not None:
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trunc_normal_(self.pos_embed, std=0.02)
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trunc_normal_(self.cls_token, std=0.02)
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# trunc_normal_(self.mask_token, std=.02)
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# if isinstance(self.head, nn.Linear):
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# trunc_normal_(self.head.weight, std=.02)
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self.apply(self._init_weights)
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self.fix_init_weight()
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# if isinstance(self.head, nn.Linear):
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# self.head.weight.data.mul_(init_scale)
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# self.head.bias.data.mul_(init_scale)
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def fix_init_weight(self):
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def rescale(param, layer_id):
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param.div_(math.sqrt(2.0 * layer_id))
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for layer_id, layer in enumerate(self.blocks):
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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=0.02)
|
|
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 get_classifier(self):
|
|
return self.head
|
|
|
|
def reset_classifier(self, num_classes, global_pool=""):
|
|
self.num_classes = num_classes
|
|
self.head = (
|
|
nn.Linear(self.embed_dim, num_classes)
|
|
if num_classes > 0
|
|
else nn.Identity()
|
|
)
|
|
|
|
def forward_features(self, x):
|
|
x = self.patch_embed(x)
|
|
batch_size, seq_len, _ = x.size()
|
|
|
|
cls_tokens = self.cls_token.expand(
|
|
batch_size, -1, -1
|
|
) # stole cls_tokens impl from Phil Wang, thanks
|
|
x = torch.cat((cls_tokens, x), dim=1)
|
|
if self.pos_embed is not None:
|
|
x = x + self.pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
rel_pos_bias = (
|
|
self.rel_pos_bias() if self.rel_pos_bias is not None else None
|
|
)
|
|
for blk in self.blocks:
|
|
if self.use_checkpoint:
|
|
x = checkpoint.checkpoint(blk, x, rel_pos_bias)
|
|
else:
|
|
x = blk(x, rel_pos_bias)
|
|
return x
|
|
|
|
# x = self.norm(x)
|
|
|
|
# if self.fc_norm is not None:
|
|
# t = x[:, 1:, :]
|
|
# return self.fc_norm(t.mean(1))
|
|
# else:
|
|
# return x[:, 0]
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
# x = self.head(x)
|
|
return x
|
|
|
|
def get_intermediate_layers(self, x):
|
|
x = self.patch_embed(x)
|
|
batch_size, seq_len, _ = x.size()
|
|
|
|
cls_tokens = self.cls_token.expand(
|
|
batch_size, -1, -1
|
|
) # stole cls_tokens impl from Phil Wang, thanks
|
|
x = torch.cat((cls_tokens, x), dim=1)
|
|
if self.pos_embed is not None:
|
|
x = x + self.pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
features = []
|
|
rel_pos_bias = (
|
|
self.rel_pos_bias() if self.rel_pos_bias is not None else None
|
|
)
|
|
for blk in self.blocks:
|
|
x = blk(x, rel_pos_bias)
|
|
features.append(x)
|
|
|
|
return features
|
|
|
|
|
|
def interpolate_pos_embed(model, checkpoint_model):
|
|
if "pos_embed" in checkpoint_model:
|
|
pos_embed_checkpoint = checkpoint_model["pos_embed"].float()
|
|
embedding_size = pos_embed_checkpoint.shape[-1]
|
|
num_patches = model.patch_embed.num_patches
|
|
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
|
|
# height (== width) for the checkpoint position embedding
|
|
orig_size = int(
|
|
(pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5
|
|
)
|
|
# height (== width) for the new position embedding
|
|
new_size = int(num_patches**0.5)
|
|
# class_token and dist_token are kept unchanged
|
|
if orig_size != new_size:
|
|
print(
|
|
"Position interpolate from %dx%d to %dx%d"
|
|
% (orig_size, orig_size, new_size, new_size)
|
|
)
|
|
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
|
# only the position tokens are interpolated
|
|
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
|
pos_tokens = pos_tokens.reshape(
|
|
-1, orig_size, orig_size, embedding_size
|
|
).permute(0, 3, 1, 2)
|
|
pos_tokens = torch.nn.functional.interpolate(
|
|
pos_tokens,
|
|
size=(new_size, new_size),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
)
|
|
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
|
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
|
checkpoint_model["pos_embed"] = new_pos_embed
|
|
|
|
|
|
def convert_weights_to_fp16(model: nn.Module):
|
|
"""Convert applicable model parameters to fp16"""
|
|
|
|
def _convert_weights_to_fp16(l):
|
|
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
|
|
# l.weight.data = l.weight.data.half()
|
|
l.weight.data = l.weight.data
|
|
if l.bias is not None:
|
|
# l.bias.data = l.bias.data.half()
|
|
l.bias.data = l.bias.data
|
|
|
|
# if isinstance(l, (nn.MultiheadAttention, Attention)):
|
|
# for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
|
|
# tensor = getattr(l, attr)
|
|
# if tensor is not None:
|
|
# tensor.data = tensor.data.half()
|
|
|
|
model.apply(_convert_weights_to_fp16)
|
|
|
|
|
|
def create_eva_vit_g(
|
|
img_size=224, drop_path_rate=0.4, use_checkpoint=False, precision="fp16"
|
|
):
|
|
model = VisionTransformer(
|
|
img_size=img_size,
|
|
patch_size=14,
|
|
use_mean_pooling=False,
|
|
embed_dim=1408,
|
|
depth=39,
|
|
num_heads=1408 // 88,
|
|
mlp_ratio=4.3637,
|
|
qkv_bias=True,
|
|
drop_path_rate=drop_path_rate,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
|
use_checkpoint=use_checkpoint,
|
|
)
|
|
url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth"
|
|
|
|
local_filename = "eva_vit_g.pth"
|
|
response = requests.get(url)
|
|
if response.status_code == 200:
|
|
with open(local_filename, "wb") as f:
|
|
f.write(response.content)
|
|
print("File downloaded successfully.")
|
|
state_dict = torch.load(local_filename, map_location="cpu")
|
|
interpolate_pos_embed(model, state_dict)
|
|
|
|
incompatible_keys = model.load_state_dict(state_dict, strict=False)
|
|
|
|
if precision == "fp16":
|
|
# model.to("cuda")
|
|
convert_weights_to_fp16(model)
|
|
return model
|