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from models.med import BertConfig, BertModel
from transformers import BertTokenizer
import torch
from torch import nn
import torch.nn.functional as F
from models.blip import create_vit, init_tokenizer, load_checkpoint
class BLIP_Retrieval(nn.Module):
def __init__(self,
med_config = 'configs/med_config.json',
image_size = 384,
vit = 'base',
vit_grad_ckpt = False,
vit_ckpt_layer = 0,
embed_dim = 256,
queue_size = 57600,
momentum = 0.995,
negative_all_rank = False,
):
"""
Args:
med_config (str): path for the mixture of encoder-decoder model's configuration file
image_size (int): input image size
vit (str): model size of vision transformer
"""
super().__init__()
self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
self.tokenizer = init_tokenizer()
med_config = BertConfig.from_json_file(med_config)
med_config.encoder_width = vision_width
self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
text_width = self.text_encoder.config.hidden_size
self.vision_proj = nn.Linear(vision_width, embed_dim)
self.text_proj = nn.Linear(text_width, embed_dim)
self.itm_head = nn.Linear(text_width, 2)
# create momentum encoders
self.visual_encoder_m, vision_width = create_vit(vit,image_size)
self.vision_proj_m = nn.Linear(vision_width, embed_dim)
self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False)
self.text_proj_m = nn.Linear(text_width, embed_dim)
self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
[self.vision_proj,self.vision_proj_m],
[self.text_encoder,self.text_encoder_m],
[self.text_proj,self.text_proj_m],
]
self.copy_params()
# create the queue
self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
self.register_buffer("idx_queue", torch.full((1,queue_size),-100))
self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long))
self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
self.queue_size = queue_size
self.momentum = momentum
self.temp = nn.Parameter(0.07*torch.ones([]))
self.negative_all_rank = negative_all_rank
def forward(self, image, caption, alpha, idx):
with torch.no_grad():
self.temp.clamp_(0.001,0.5)
image_embeds = self.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)
text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35,
return_tensors="pt").to(image.device)
text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
return_dict = True, mode = 'text')
text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)
###============== Image-text Contrastive Learning ===================###
idx = idx.view(-1,1)
idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1)
pos_idx = torch.eq(idx, idx_all).float()
sim_targets = pos_idx / pos_idx.sum(1,keepdim=True)
# get momentum features
with torch.no_grad():
self._momentum_update()
image_embeds_m = self.visual_encoder_m(image)
image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)
image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)
text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,
return_dict = True, mode = 'text')
text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1)
text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp
sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp
sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets
sim_i2t = image_feat @ text_feat_m_all / self.temp
sim_t2i = text_feat @ image_feat_m_all / self.temp
loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean()
loss_ita = (loss_i2t+loss_t2i)/2
idxs = concat_all_gather(idx)
self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs)
###============== Image-text Matching ===================###
encoder_input_ids = text.input_ids.clone()
encoder_input_ids[:,0] = self.tokenizer.enc_token_id
# forward the positve image-text pair
bs = image.size(0)
output_pos = self.text_encoder(encoder_input_ids,
attention_mask = text.attention_mask,
encoder_hidden_states = image_embeds,
encoder_attention_mask = image_atts,
return_dict = True,
)
if self.negative_all_rank:
# compute sample similarity
with torch.no_grad():
mask = torch.eq(idx, idxs.t())
image_feat_world = concat_all_gather(image_feat)
text_feat_world = concat_all_gather(text_feat)
sim_i2t = image_feat @ text_feat_world.t() / self.temp
sim_t2i = text_feat @ image_feat_world.t() / self.temp
weights_i2t = F.softmax(sim_i2t,dim=1)
weights_i2t.masked_fill_(mask, 0)
weights_t2i = F.softmax(sim_t2i,dim=1)
weights_t2i.masked_fill_(mask, 0)
image_embeds_world = all_gather_with_grad(image_embeds)
# select a negative image (from all ranks) for each text
image_embeds_neg = []
for b in range(bs):
neg_idx = torch.multinomial(weights_t2i[b], 1).item()
image_embeds_neg.append(image_embeds_world[neg_idx])
image_embeds_neg = torch.stack(image_embeds_neg,dim=0)
# select a negative text (from all ranks) for each image
input_ids_world = concat_all_gather(encoder_input_ids)
att_mask_world = concat_all_gather(text.attention_mask)
text_ids_neg = []
text_atts_neg = []
for b in range(bs):
neg_idx = torch.multinomial(weights_i2t[b], 1).item()
text_ids_neg.append(input_ids_world[neg_idx])
text_atts_neg.append(att_mask_world[neg_idx])
else:
with torch.no_grad():
mask = torch.eq(idx, idx.t())
sim_i2t = image_feat @ text_feat.t() / self.temp
sim_t2i = text_feat @ image_feat.t() / self.temp
weights_i2t = F.softmax(sim_i2t,dim=1)
weights_i2t.masked_fill_(mask, 0)
weights_t2i = F.softmax(sim_t2i,dim=1)
weights_t2i.masked_fill_(mask, 0)
# select a negative image (from same rank) for each text
image_embeds_neg = []
for b in range(bs):
neg_idx = torch.multinomial(weights_t2i[b], 1).item()
image_embeds_neg.append(image_embeds[neg_idx])
image_embeds_neg = torch.stack(image_embeds_neg,dim=0)
# select a negative text (from same rank) for each image
text_ids_neg = []
text_atts_neg = []
for b in range(bs):
neg_idx = torch.multinomial(weights_i2t[b], 1).item()
text_ids_neg.append(encoder_input_ids[neg_idx])
text_atts_neg.append(text.attention_mask[neg_idx])
text_ids_neg = torch.stack(text_ids_neg,dim=0)
text_atts_neg = torch.stack(text_atts_neg,dim=0)
text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)
text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)
image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
image_atts_all = torch.cat([image_atts,image_atts],dim=0)
output_neg = self.text_encoder(text_ids_all,
attention_mask = text_atts_all,
encoder_hidden_states = image_embeds_all,
encoder_attention_mask = image_atts_all,
return_dict = True,
)
vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
vl_output = self.itm_head(vl_embeddings)
itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
dim=0).to(image.device)
loss_itm = F.cross_entropy(vl_output, itm_labels)
return loss_ita, loss_itm
@torch.no_grad()
def copy_params(self):
for model_pair in self.model_pairs:
for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
param_m.data.copy_(param.data) # initialize
param_m.requires_grad = False # not update by gradient
@torch.no_grad()
def _momentum_update(self):
for model_pair in self.model_pairs:
for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
@torch.no_grad()
def _dequeue_and_enqueue(self, image_feat, text_feat, idxs):
# gather keys before updating queue
image_feats = concat_all_gather(image_feat)
text_feats = concat_all_gather(text_feat)
batch_size = image_feats.shape[0]
ptr = int(self.ptr_queue)
assert self.queue_size % batch_size == 0 # for simplicity
# replace the keys at ptr (dequeue and enqueue)
self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
self.idx_queue[:, ptr:ptr + batch_size] = idxs.T
ptr = (ptr + batch_size) % self.queue_size # move pointer
self.ptr_queue[0] = ptr
def blip_retrieval(pretrained='',**kwargs):
model = BLIP_Retrieval(**kwargs)
if pretrained:
model,msg = load_checkpoint(model,pretrained)
print("missing keys:")
print(msg.missing_keys)
return model
@torch.no_grad()
def concat_all_gather(tensor):
"""
Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
tensors_gather = [torch.ones_like(tensor)
for _ in range(torch.distributed.get_world_size())]
torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
output = torch.cat(tensors_gather, dim=0)
return output
class GatherLayer(torch.autograd.Function):
"""
Gather tensors from all workers with support for backward propagation:
This implementation does not cut the gradients as torch.distributed.all_gather does.
"""
@staticmethod
def forward(ctx, x):
output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())]
torch.distributed.all_gather(output, x)
return tuple(output)
@staticmethod
def backward(ctx, *grads):
all_gradients = torch.stack(grads)
torch.distributed.all_reduce(all_gradients)
return all_gradients[torch.distributed.get_rank()]
def all_gather_with_grad(tensors):
"""
Performs all_gather operation on the provided tensors.
Graph remains connected for backward grad computation.
"""
# Queue the gathered tensors
world_size = torch.distributed.get_world_size()
# There is no need for reduction in the single-proc case
if world_size == 1:
return tensors
tensor_all = GatherLayer.apply(tensors)
return torch.cat(tensor_all, dim=0)
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