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import torch
from einops import rearrange
from typing import Optional, Tuple, Union
from torch import nn
from transformers import CLIPModel as HFCLIPModel, CLIPVisionConfig
from transformers.modeling_outputs import BaseModelOutputWithPooling
from transformers.models.clip.modeling_clip import CLIP_VISION_INPUTS_DOCSTRING
from transformers.utils import replace_return_docstrings, add_start_docstrings_to_model_forward
# class VT_CLIP(nn.Module):
# output_dict: torch.jit.Final[bool]
#
# def __init__(
# self,
# embed_dim: int,
# vision_cfg: CLIPVisionCfg,
# text_cfg: CLIPTextCfg,
# quick_gelu: bool = False,
# cast_dtype: Optional[torch.dtype] = None,
# output_dict: bool = False,
# ):
# super().__init__()
# self.output_dict = output_dict
# self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
#
# text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
# self.transformer = text.transformer
# self.context_length = text.context_length
# self.vocab_size = text.vocab_size
# self.token_embedding = text.token_embedding
# self.positional_embedding = text.positional_embedding
# self.ln_final = text.ln_final
# self.text_projection = text.text_projection
# self.register_buffer('attn_mask', text.attn_mask, persistent=False)
#
# self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
#
#
#
# def unlock_time_attn(self):
# for name, param in self.named_parameters():
# if 'time' in name:
# param.requires_grad = True
#
# def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
# # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
# self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
#
# def lock_text_tower(self, unlocked_layers=0, freeze_layer_norm=False):
# for param in self.transformer.parameters():
# param.requires_grad = False
# for param in self.token_embedding.parameters():
# param.requires_grad = False
# for param in self.ln_final.parameters():
# param.requires_grad = False
# self.positional_embedding.requires_grad = False
# self.text_projection.requires_grad = False
#
# if unlocked_layers != 0:
# groups = [
# [
# self.token_embedding,
# self.positional_embedding,
# ],
# *self.transformer.resblocks[:-1],
# [
# self.transformer.resblocks[-1],
# self.ln_final,
# ],
# self.text_projection,
# ]
#
# def _unlock(x):
# if isinstance(x, Sequence):
# for g in x:
# _unlock(g)
# else:
# if isinstance(x, torch.nn.Parameter):
# x.requires_grad = True
# else:
# for p in x.parameters():
# p.requires_grad = True
#
# _unlock(groups[-unlocked_layers:])
#
# @torch.jit.ignore
# def set_grad_checkpointing(self, enable=True):
# self.visual.set_grad_checkpointing(enable)
# self.transformer.grad_checkpointing = enable
#
# def encode_image(self, image, normalize: bool = False):
# features = self.visual(image)
# return F.normalize(features, dim=-1) if normalize else features
#
# def encode_text(self, text, normalize: bool = False):
# cast_dtype = self.transformer.get_cast_dtype()
#
# x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]
#
# x = x + self.positional_embedding.to(cast_dtype)
# x = x.permute(1, 0, 2) # NLD -> LND
# x = self.transformer(x, attn_mask=self.attn_mask)
# x = x.permute(1, 0, 2) # LND -> NLD
# x = self.ln_final(x) # [batch_size, n_ctx, transformer.width]
# # take features from the eot embedding (eot_token is the highest number in each sequence)
# x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
# return F.normalize(x, dim=-1) if normalize else x
#
# def forward(
# self,
# image: Optional[torch.Tensor] = None,
# text: Optional[torch.Tensor] = None,
# ):
# image_features = self.encode_image(image, normalize=True) if image is not None else None
# text_features = self.encode_text(text, normalize=True) if text is not None else None
# if self.output_dict:
# return {
# "image_features": image_features,
# "text_features": text_features,
# "logit_scale": self.logit_scale.exp()
# }
# return image_features, text_features, self.logit_scale.exp()
from model.process_clip import get_global_value, set_global_value
def SET_GLOBAL_VALUE(k, v):
set_global_value(k, v)
class CLIPVisionEmbeddings(nn.Module):
def __init__(self, config: CLIPVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
# (b t) c h w
batch_size = pixel_values.shape[0]
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
embeddings = embeddings + self.position_embedding(self.position_ids) # b hw c
return embeddings
class CLIPVisionEmbeddings3D(nn.Module):
def __init__(self, config: CLIPVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.num_frames = config.num_frames
self.tube_size = config.tube_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
def expand3d(self):
state_dict = self.patch_embedding.state_dict()
state_dict_expand = state_dict['weight'].unsqueeze(2)
device, dtype = state_dict_expand.device, state_dict_expand.dtype
# print(device, dtype)
zero = torch.zeros_like(state_dict_expand).to(device=device, dtype=dtype)
state_dict_expand3d = torch.cat([state_dict_expand] + (self.tube_size-1)*[zero], dim=2)
# state_dict_expand3d = torch.cat([state_dict_expand / self.tube_size] * self.tube_size, dim=2)
patch_embedding = nn.Conv3d(
in_channels=self.patch_embedding.in_channels,
out_channels=self.embed_dim,
kernel_size=(self.tube_size, self.patch_size, self.patch_size),
stride=(self.tube_size, self.patch_size, self.patch_size),
bias=False,
).to(device=device, dtype=dtype)
patch_embedding.load_state_dict({'weight': state_dict_expand3d})
self.patch_embedding = patch_embedding
class_embedding = nn.Parameter(self.class_embedding.data.repeat(self.num_frames // self.tube_size, 1)).to(device=device, dtype=dtype)
self.class_embedding = class_embedding
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
# (b t) c h w
batch_size = pixel_values.shape[0] // self.num_frames
pixel_values = rearrange(pixel_values, '(b t) c h w -> b c t h w', b=batch_size, t=self.num_frames)
# print('pixel_values', pixel_values.shape)
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, t, grid, grid]
# print('patch_embeds', patch_embeds.shape)
# SET_GLOBAL_VALUE('NUM_FRAMES', patch_embeds.shape[2])
patch_embeds = rearrange(patch_embeds, 'b c t h w -> b t (h w) c')
class_embeds = self.class_embedding.unsqueeze(1).unsqueeze(0).repeat(batch_size, 1, 1, 1) # b t 1 c
# print('class_embeds', class_embeds.device, class_embeds.dtype)
# print('patch_embeds', patch_embeds.device, patch_embeds.dtype)
embeddings = torch.cat([class_embeds, patch_embeds], dim=2) # b t hw+1 c
embeddings = embeddings + self.position_embedding(self.position_ids)
embeddings = rearrange(embeddings, 'b t hw_1 c -> (b t) hw_1 c')
return embeddings
class CLIPModel(HFCLIPModel):
def __init__(self, config, num_frames, add_time_attn, vl_new, tube_size):
super(CLIPModel, self).__init__(config)
config.vision_config.num_frames = num_frames
config.vision_config.tube_size = tube_size
if add_time_attn:
if vl_new:
self.vision_model.embeddings = CLIPVisionEmbeddings3D(config.vision_config)
else:
self.vision_model.embeddings = CLIPVisionEmbeddings(config.vision_config)
self.T = config.vision_config.num_frames // config.vision_config.tube_size
self.vision_model.forward = self.vision_model_forward
@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig)
def vision_model_forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.vision_model.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.vision_model.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.vision_model.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
if len(pixel_values.shape) == 7:
b_new, pair_new, T, bs_new, channel_new, h_new, w_new = pixel_values.shape
# print(pixel_values.shape)
B = b_new * pair_new * bs_new
pixel_values = pixel_values.reshape(B*T, channel_new, h_new, w_new)
elif len(pixel_values.shape) == 5:
B, _, T, _, _ = pixel_values.shape
# print(pixel_values.shape)
pixel_values = rearrange(pixel_values, 'b c t h w -> (b t) c h w')
else:
# print(pixel_values.shape)
B, _, _, _ = pixel_values.shape
T = 1
hidden_states = self.vision_model.embeddings(pixel_values)
# print('hidden_states', hidden_states.shape)
#
# if self.temporal_embedding is not None and get_global_value()['NUM_FRAMES'] != 1:
# n = hidden_states.shape[1]
# hidden_states = rearrange(hidden_states, '(b t) n d -> (b n) t d', t=T)
# hidden_states = hidden_states + self.temporal_embedding[:, :T, :]
# hidden_states = rearrange(hidden_states, '(b n) t d -> (b t) n d', n=n)
T = self.T
# print('B.shape, T.shape', B.shape, T.shape)
hidden_states = self.vision_model.patch_dropout(hidden_states, B, T)
# print('patch_dropout', hidden_states.shape)
hidden_states = self.vision_model.pre_layrnorm(hidden_states)
encoder_outputs = self.vision_model.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.vision_model.post_layernorm(pooled_output)
pooled_output = pooled_output.reshape(B, T, -1).mean(1)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def encode_image(self, image, normalize: bool = False):
vision_outputs = self.vision_model(
pixel_values=image,
return_dict=True,
)
image_embeds = vision_outputs[1]
image_embeds = self.visual_projection(image_embeds)
return image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) if normalize else image_embeds
def encode_text(self, input_ids, attention_mask, normalize: bool = False):
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True,
)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(text_embeds)
return text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) if normalize else text_embeds
def forward(
self,
image=None,
input_ids=None, attention_mask=None
):
image_features = self.encode_image(image, normalize=True) if image is not None else None
text_features = self.encode_text(input_ids, attention_mask, normalize=True) if input_ids is not None else None
# if self.output_dict:
return {
"image_features": image_features,
"text_features": text_features,
"logit_scale": self.logit_scale.exp()
}
# return image_features, text_features, self.logit_scale.exp()
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