mirror of
https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM.git
synced 2026-09-27 03:51:46 +02:00
348 lines
14 KiB
Python
348 lines
14 KiB
Python
# coding=utf-8
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import logging
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import math
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from os.path import join as pjoin
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import torch
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import torch.nn as nn
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import numpy as np
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from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm
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from torch.nn.modules.utils import _pair
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from scipy import ndimage
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import models.configs as configs
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from .modeling_resnet import ResNetV2
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logger = logging.getLogger(__name__)
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ATTENTION_Q = "MultiHeadDotProductAttention_1/query"
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ATTENTION_K = "MultiHeadDotProductAttention_1/key"
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ATTENTION_V = "MultiHeadDotProductAttention_1/value"
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ATTENTION_OUT = "MultiHeadDotProductAttention_1/out"
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FC_0 = "MlpBlock_3/Dense_0"
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FC_1 = "MlpBlock_3/Dense_1"
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ATTENTION_NORM = "LayerNorm_0"
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MLP_NORM = "LayerNorm_2"
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def np2th(weights, conv=False):
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"""Possibly convert HWIO to OIHW."""
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if conv:
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weights = weights.transpose([3, 2, 0, 1])
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return torch.from_numpy(weights)
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def swish(x):
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return x * torch.sigmoid(x)
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ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish}
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class Attention(nn.Module):
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def __init__(self, config, vis):
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super(Attention, self).__init__()
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self.vis = vis
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self.num_attention_heads = config.transformer["num_heads"]
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self.attention_head_size = int(config.hidden_size / self.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = Linear(config.hidden_size, self.all_head_size)
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self.key = Linear(config.hidden_size, self.all_head_size)
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self.value = Linear(config.hidden_size, self.all_head_size)
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self.out = Linear(config.hidden_size, config.hidden_size)
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self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"])
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self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"])
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self.softmax = Softmax(dim=-1)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(self, hidden_states):
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(hidden_states)
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mixed_value_layer = self.value(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_value_layer)
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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attention_probs = self.softmax(attention_scores)
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weights = attention_probs if self.vis else None
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attention_probs = self.attn_dropout(attention_probs)
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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attention_output = self.out(context_layer)
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attention_output = self.proj_dropout(attention_output)
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return attention_output, weights
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class Mlp(nn.Module):
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def __init__(self, config):
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super(Mlp, self).__init__()
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self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"])
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self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size)
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self.act_fn = ACT2FN["gelu"]
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self.dropout = Dropout(config.transformer["dropout_rate"])
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self._init_weights()
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def _init_weights(self):
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nn.init.xavier_uniform_(self.fc1.weight)
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nn.init.xavier_uniform_(self.fc2.weight)
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nn.init.normal_(self.fc1.bias, std=1e-6)
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nn.init.normal_(self.fc2.bias, std=1e-6)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act_fn(x)
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x = self.dropout(x)
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x = self.fc2(x)
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x = self.dropout(x)
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return x
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class Embeddings(nn.Module):
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"""Construct the embeddings from patch, position embeddings.
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"""
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def __init__(self, config, img_size, in_channels=3):
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super(Embeddings, self).__init__()
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self.hybrid = None
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img_size = _pair(img_size)
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if config.patches.get("grid") is not None:
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grid_size = config.patches["grid"]
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patch_size = (img_size[0] // 16 // grid_size[0], img_size[1] // 16 // grid_size[1])
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n_patches = (img_size[0] // 16) * (img_size[1] // 16)
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self.hybrid = True
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else:
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patch_size = _pair(config.patches["size"])
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n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
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self.hybrid = False
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if self.hybrid:
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self.hybrid_model = ResNetV2(block_units=config.resnet.num_layers,
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width_factor=config.resnet.width_factor)
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in_channels = self.hybrid_model.width * 16
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self.patch_embeddings = Conv2d(in_channels=in_channels,
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out_channels=config.hidden_size,
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kernel_size=patch_size,
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stride=patch_size)
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self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches+1, config.hidden_size))
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.dropout = Dropout(config.transformer["dropout_rate"])
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def forward(self, x):
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B = x.shape[0]
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cls_tokens = self.cls_token.expand(B, -1, -1)
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if self.hybrid:
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x = self.hybrid_model(x)
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x = self.patch_embeddings(x)
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x = x.flatten(2)
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x = x.transpose(-1, -2)
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x = torch.cat((cls_tokens, x), dim=1)
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embeddings = x + self.position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class Block(nn.Module):
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def __init__(self, config, vis):
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super(Block, self).__init__()
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self.hidden_size = config.hidden_size
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self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)
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self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)
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self.ffn = Mlp(config)
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self.attn = Attention(config, vis)
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def forward(self, x):
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h = x
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x = self.attention_norm(x)
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x, weights = self.attn(x)
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x = x + h
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h = x
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x = self.ffn_norm(x)
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x = self.ffn(x)
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x = x + h
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return x, weights
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def load_from(self, weights, n_block):
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ROOT = f"Transformer/encoderblock_{n_block}"
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with torch.no_grad():
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query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1)
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key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1)
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value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1)
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out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1)
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self.attn.query.weight.copy_(query_weight)
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self.attn.key.weight.copy_(key_weight)
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self.attn.value.weight.copy_(value_weight)
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self.attn.out.weight.copy_(out_weight)
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self.attn.query.bias.copy_(query_bias)
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self.attn.key.bias.copy_(key_bias)
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self.attn.value.bias.copy_(value_bias)
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self.attn.out.bias.copy_(out_bias)
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mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, "kernel")]).t()
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mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, "kernel")]).t()
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mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, "bias")]).t()
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mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, "bias")]).t()
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self.ffn.fc1.weight.copy_(mlp_weight_0)
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self.ffn.fc2.weight.copy_(mlp_weight_1)
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self.ffn.fc1.bias.copy_(mlp_bias_0)
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self.ffn.fc2.bias.copy_(mlp_bias_1)
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self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "scale")]))
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self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "bias")]))
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self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "scale")]))
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self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "bias")]))
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class Encoder(nn.Module):
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def __init__(self, config, vis):
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super(Encoder, self).__init__()
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self.vis = vis
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self.layer = nn.ModuleList()
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self.encoder_norm = LayerNorm(config.hidden_size, eps=1e-6)
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for _ in range(config.transformer["num_layers"]):
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layer = Block(config, vis)
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self.layer.append(copy.deepcopy(layer))
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def forward(self, hidden_states):
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attn_weights = []
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for layer_block in self.layer:
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hidden_states, weights = layer_block(hidden_states)
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if self.vis:
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attn_weights.append(weights)
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encoded = self.encoder_norm(hidden_states)
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return encoded, attn_weights
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class Transformer(nn.Module):
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def __init__(self, config, img_size, vis):
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super(Transformer, self).__init__()
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self.embeddings = Embeddings(config, img_size=img_size)
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self.encoder = Encoder(config, vis)
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def forward(self, input_ids):
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embedding_output = self.embeddings(input_ids)
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encoded, attn_weights = self.encoder(embedding_output)
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return encoded, attn_weights
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class VisionTransformer(nn.Module):
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def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False):
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super(VisionTransformer, self).__init__()
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self.num_classes = num_classes
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self.zero_head = zero_head
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self.classifier = config.classifier
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self.transformer = Transformer(config, img_size, vis)
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self.head = Linear(config.hidden_size, num_classes)
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def forward(self, x, labels=None):
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x, attn_weights = self.transformer(x)
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logits = self.head(x[:, 0])
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_classes), labels.view(-1))
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return loss
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else:
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return logits, attn_weights
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def load_from(self, weights):
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with torch.no_grad():
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if self.zero_head:
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nn.init.zeros_(self.head.weight)
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nn.init.zeros_(self.head.bias)
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else:
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self.head.weight.copy_(np2th(weights["head/kernel"]).t())
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self.head.bias.copy_(np2th(weights["head/bias"]).t())
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self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights["embedding/kernel"], conv=True))
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self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights["embedding/bias"]))
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self.transformer.embeddings.cls_token.copy_(np2th(weights["cls"]))
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self.transformer.encoder.encoder_norm.weight.copy_(np2th(weights["Transformer/encoder_norm/scale"]))
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self.transformer.encoder.encoder_norm.bias.copy_(np2th(weights["Transformer/encoder_norm/bias"]))
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posemb = np2th(weights["Transformer/posembed_input/pos_embedding"])
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posemb_new = self.transformer.embeddings.position_embeddings
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if posemb.size() == posemb_new.size():
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self.transformer.embeddings.position_embeddings.copy_(posemb)
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else:
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logger.info("load_pretrained: resized variant: %s to %s" % (posemb.size(), posemb_new.size()))
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ntok_new = posemb_new.size(1)
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if self.classifier == "token":
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posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
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ntok_new -= 1
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else:
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posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
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gs_old = int(np.sqrt(len(posemb_grid)))
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gs_new = int(np.sqrt(ntok_new))
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print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new))
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posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)
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zoom = (gs_new / gs_old, gs_new / gs_old, 1)
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posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1)
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posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1)
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posemb = np.concatenate([posemb_tok, posemb_grid], axis=1)
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self.transformer.embeddings.position_embeddings.copy_(np2th(posemb))
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for bname, block in self.transformer.encoder.named_children():
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for uname, unit in block.named_children():
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unit.load_from(weights, n_block=uname)
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if self.transformer.embeddings.hybrid:
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self.transformer.embeddings.hybrid_model.root.conv.weight.copy_(np2th(weights["conv_root/kernel"], conv=True))
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gn_weight = np2th(weights["gn_root/scale"]).view(-1)
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gn_bias = np2th(weights["gn_root/bias"]).view(-1)
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self.transformer.embeddings.hybrid_model.root.gn.weight.copy_(gn_weight)
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self.transformer.embeddings.hybrid_model.root.gn.bias.copy_(gn_bias)
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for bname, block in self.transformer.embeddings.hybrid_model.body.named_children():
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for uname, unit in block.named_children():
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unit.load_from(weights, n_block=bname, n_unit=uname)
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CONFIGS = {
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'ViT-B_16': configs.get_b16_config(),
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'ViT-B_32': configs.get_b32_config(),
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'ViT-L_16': configs.get_l16_config(),
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'ViT-L_32': configs.get_l32_config(),
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'ViT-H_14': configs.get_h14_config(),
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'R50-ViT-B_16': configs.get_r50_b16_config(),
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'testing': configs.get_testing(),
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}
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