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import torch
import torch.nn as nn
import math
# from transformers import CLIPConfig,AutoConfig
from typing import Any, Optional, Tuple, Union
import torch.distributed.nn as nn_dist
import torch.nn.functional as F
import numpy as np
from collections import OrderedDict
from typing import Tuple, Union
from .modeling_clip import CLIPModel, CLIPTextTransformer, CLIPVisionTransformer, CLIPOutput, CLIPAttention, CLIPMLP
import torch.distributed as dist
from torch.nn import AvgPool2d
from transformers import (
AutoImageProcessor,
AutoModel,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from .modeling_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
from torch import nn, einsum
from einops import rearrange, repeat, reduce
from einops.layers.torch import Rearrange, Reduce
import math
from torchvision.ops import roi_align
class FGCLIPConfig(CLIPConfig):
model_type = "clip"
class FGCLIPModel(CLIPModel):
config_class = FGCLIPConfig
main_input_name = "text_long"
def __init__(self, config):
super(CLIPModel, self).__init__(config)
if not isinstance(config.text_config, CLIPTextConfig):
raise ValueError(
"config.text_config is expected to be of type CLIPTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, CLIPVisionConfig):
raise ValueError(
"config.vision_config is expected to be of type CLIPVisionConfig but is of type"
f" {type(config.vision_config)}."
)
text_config = config.text_config
vision_config = config.vision_config
text_config.eos_token_id = 49407
text_config.pad_token_id = 49407
text_config.bos_token_id = 49406
self.projection_dim = config.projection_dim
self.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = CLIPTextTransformer(text_config)
self.vision_model = CLIPVisionTransformer(vision_config)
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
self.text_filip_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
self.logit_scale_finegraind = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
self.logit_scale_hardneg = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
self.embed_dim = text_config.hidden_size
self.world_size = 0
# Initialize weights and apply final processing
self.post_init()
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = vision_outputs[1] # pooled_output
image_features = self.visual_projection(pooled_output)
return image_features
def get_image_box_roi_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
box_info=None,
) -> torch.FloatTensor:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict
)
bs = pixel_values.shape[0]
length = vision_outputs[0].shape[1]-1
h = int(math.sqrt(length))
w = h
feature_map = vision_outputs.hidden_states[-2]#[:, 1:, :]
feature_map = self.forward_without_attn(feature_map)[:, 1:]
feature_map = self.vision_model.post_layernorm(feature_map)
feature_map = self.visual_projection(feature_map)
feature_map = feature_map.view(bs, h, w, -1).permute(0, 3, 1, 2)
x_rois = roi_align(feature_map.type(torch.float32),box_info, (1, 1), 1.0, -1, True)[..., 0, 0]
x_rois = x_rois / x_rois.norm(p=2, dim=-1, keepdim=True)
return x_rois
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
walk_short_pos: Optional[bool] = True,
use_bbox: Optional[bool] = False
) -> torch.FloatTensor:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
pos_flag = walk_short_pos or use_bbox
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
walk_short_pos=pos_flag,
)
pooled_output = text_outputs[1]
if walk_short_pos:
text_features = self.text_projection(pooled_output)
else:
text_features = self.text_filip_projection(pooled_output)
return text_features
@staticmethod
def _denormalize_boxes(normed_boxes, x):
h, w = x.shape[-2:]
denormed_boxes = []
for boxes in normed_boxes:
new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!
new_boxes[:, [0, 2]] *= w
new_boxes[:, [1, 3]] *= h
denormed_boxes.append(new_boxes.type(torch.float32))
return denormed_boxes
def forward_without_attn(self, x):
# get last layer
residual = x
x = self.vision_model.encoder.layers[-1].layer_norm1(x)
x = F.linear(input=x, weight=self.vision_model.encoder.layers[-1].self_attn.v_proj.weight, bias=self.vision_model.encoder.layers[-1].self_attn.v_proj.bias)
x = self.vision_model.encoder.layers[-1].self_attn.out_proj(x)
x = residual+x
residual = x
x = self.vision_model.encoder.layers[-1].layer_norm2(x)
x = self.vision_model.encoder.layers[-1].mlp(x)
x = residual + x
return x
def get_image_dense_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding=False,
box_info=None,
) -> torch.FloatTensor:
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
bs = pixel_values.shape[0]
length = vision_outputs[0].shape[1]-1
h = int(math.sqrt(length))
w = h
feature_map = vision_outputs.hidden_states[-2]#[:, 1:, :]
feature_map = self.forward_without_attn(feature_map)[:, 1:]
feature_map = self.vision_model.post_layernorm(feature_map)
feature_map = self.visual_projection(feature_map)
return feature_map
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