Spaces:
Runtime error
Runtime error
File size: 7,215 Bytes
a1ebdce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
from collections import OrderedDict
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers import BridgeTowerPreTrainedModel, BridgeTowerModel
from transformers.models.bridgetower.modeling_bridgetower import BridgeTowerTextModel
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class BridgeTowerImageFeatureExtractor(nn.Module):
def __init__(
self,
patch_size=14,
width=1024,
resolution_after=294,
ckpt_path=None,
):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
scale = width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(width))
self.positional_embedding = nn.Parameter(scale * torch.randn((resolution_after // patch_size) ** 2 + 1, width))
self.ln_pre = LayerNorm(width)
if ckpt_path is not None:
sd = torch.load(ckpt_path)
if 'state_dict' in sd:
sd = sd["state_dict"]
print(f'Loading feature extractor checkpoint from {ckpt_path}')
self.load_state_dict(sd)
def forward(self, x: torch.Tensor):
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
t=self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device)
x = torch.cat([t, x], dim=1) # shape = [*, grid ** 2 + 1, width]
x = x + self.positional_embedding.to(x.dtype)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
return x
class BridgeTowerITCHead(nn.Module):
def __init__(self, hidden_size, embed_size):
super().__init__()
self.fc = nn.Linear(hidden_size, embed_size)
def forward(self, x):
x = self.fc(x)
return x
class _BridgeTowerTextModelWrapper(nn.Module):
def __init__(self, config):
super().__init__()
self.text_model = BridgeTowerTextModel(config)
def forward(self, **kwargs):
return self.text_model(**kwargs)
class BridgeTowerTextFeatureExtractor(BridgeTowerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bridgetower = _BridgeTowerTextModelWrapper(config.text_config)
self.itc_text_head = BridgeTowerITCHead(config.hidden_size, config.contrastive_hidden_size)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
):
outputs = self.bridgetower(input_ids=input_ids, attention_mask=attention_mask)
final_hidden_cls = outputs.last_hidden_state[:,0,:]
final_hidden_cls = F.normalize(self.itc_text_head(final_hidden_cls), dim=-1, p=2)
return final_hidden_cls
class BridgeTowerForITC(BridgeTowerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bridgetower = BridgeTowerModel(config)
self.itc_text_head = BridgeTowerITCHead(config.hidden_size, config.contrastive_hidden_size)
self.itc_image_head = BridgeTowerITCHead(config.hidden_size, config.contrastive_hidden_size)
self.itc_cross_modal_head = BridgeTowerITCHead(config.hidden_size * 2, config.contrastive_hidden_size)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
assert output_hidden_states, 'output_hidden_states should be set to True for BridgeTowerForITC'
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bridgetower(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
pixel_values=pixel_values,
pixel_mask=pixel_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
image_embeds=image_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooler_output = outputs.pooler_output if return_dict else outputs[2]
hidden_states_txt, hidden_states_img, hidden_states_cross_modal = outputs.hidden_states
final_hidden_txt = hidden_states_txt[-1]
final_hidden_img = hidden_states_img[-1]
image_embeds_with_ln = self.bridgetower.vision_model.visual.forward_post(final_hidden_img)
image_token_type_embeddings = self.bridgetower.token_type_embeddings(
torch.full((1,), 1, dtype=torch.long, device=self.bridgetower.token_type_embeddings.weight.device)
).expand_as(image_embeds_with_ln)
final_hidden_img = (
self.bridgetower.cross_modal_image_transform(image_embeds_with_ln)
+ image_token_type_embeddings
)
final_hidden_txt = F.normalize(self.itc_text_head(final_hidden_txt[:,0,:]), dim=-1, p=2)
final_hidden_img = F.normalize(self.itc_image_head(final_hidden_img[:,0,:]), dim=-1, p=2)
final_hidden_cross = F.normalize(self.itc_cross_modal_head(pooler_output), dim=-1, p=2)
logits = torch.stack([final_hidden_txt, final_hidden_img, final_hidden_cross], dim=-2)
if not return_dict:
return tuple(logits)
return SequenceClassifierOutput(
loss=None,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
|