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from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from .llava.model.language_model.llava_llama import (LlavaLlamaForCausalLM, LlavaLlamaModel)
from .segment_anything import build_sam_vit_h
embedding_dict = {}
def dice_loss(
inputs: torch.Tensor,
targets: torch.Tensor,
num_masks: float,
scale=1000, # 100000.0,
eps=1e-6,
) -> torch.Tensor:
"""
Compute the DICE loss, similar to generalized IOU for masks.
Arguments 'num_masks', 'scale', 'eps' and return value 'loss' are undocumented in original project
https://github.com/dvlab-research/LISA
About 'num_masks': it's similar to 'avg_factor' in weight_reduce_loss() from
https://github.com/open-mmlab/mmdetection/blob/e9cae2d0787cd5c2fc6165a6061f92fa09e48fb1/mmdet/models/losses/utils.py#L30
Args:
inputs: A float tensor of arbitrary shape.
The predictions for each example.
targets: A float tensor with the same shape as inputs. Stores the binary
classification label for each element in inputs
(0 for the negative class and 1 for the positive class).
num_masks: Average factor when computing the mean of losses (?)
scale: weight factor applied before computing mean of losses (?)
eps: Avoid dividing by zero (?)
return:
Processed loss values.
"""
inputs = inputs.sigmoid()
inputs = inputs.flatten(1, 2)
targets = targets.flatten(1, 2)
numerator = 2 * (inputs / scale * targets).sum(-1)
denominator = (inputs / scale).sum(-1) + (targets / scale).sum(-1)
loss = 1 - (numerator + eps) / (denominator + eps)
loss = loss.sum() / (num_masks + 1e-8)
return loss
def sigmoid_ce_loss(
inputs: torch.Tensor,
targets: torch.Tensor,
num_masks: float,
):
"""
Args:
inputs: A float tensor of arbitrary shape.
The predictions for each example.
targets: A float tensor with the same shape as inputs. Stores the binary
classification label for each element in inputs
(0 for the negative class and 1 for the positive class).
num_masks: Average factor when computing the mean of losses (?)
Returns:
Loss tensor
"""
loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
loss = loss.flatten(1, 2).mean(1).sum() / (num_masks + 1e-8)
return loss
class LisaMetaModel:
def __init__(
self,
config,
**kwargs,
):
super(LisaMetaModel, self).__init__(config)
self.config = config
if not hasattr(self.config, "train_mask_decoder"):
self.config.train_mask_decoder = kwargs["train_mask_decoder"]
self.config.out_dim = kwargs["out_dim"]
self.vision_pretrained = kwargs.get("vision_pretrained", None)
else:
self.vision_pretrained = kwargs.get("vision_pretrained", None)
self.initialize_lisa_modules(self.config)
def initialize_lisa_modules(self, config):
# SAM
self.visual_model = build_sam_vit_h(self.vision_pretrained)
for param in self.visual_model.parameters():
param.requires_grad = False
if config.train_mask_decoder:
self.visual_model.mask_decoder.train()
for param in self.visual_model.mask_decoder.parameters():
param.requires_grad = True
# Projection layer
in_dim = config.hidden_size
out_dim = config.out_dim
text_fc = [
nn.Linear(in_dim, in_dim),
nn.ReLU(inplace=True),
nn.Linear(in_dim, out_dim),
nn.Dropout(0.0),
]
self.text_hidden_fcs = nn.ModuleList([nn.Sequential(*text_fc)])
self.text_hidden_fcs.train()
for param in self.text_hidden_fcs.parameters():
param.requires_grad = True
class LisaModel(LisaMetaModel, LlavaLlamaModel):
def __init__(
self,
config,
**kwargs,
):
super(LisaModel, self).__init__(config, **kwargs)
self.config.use_cache = False
self.config.vision_tower = self.config.mm_vision_tower
self.config.mm_vision_select_feature = "patch"
self.config.image_aspect_ratio = "square"
self.config.image_grid_pinpoints = None
self.config.tune_mm_mlp_adapter = False
self.config.freeze_mm_mlp_adapter = True
self.config.pretrain_mm_mlp_adapter = None
self.config.mm_use_im_patch_token = False
class LISAForCausalLM(LlavaLlamaForCausalLM):
def __init__(
self,
config,
**kwargs,
):
if not hasattr(config, "train_mask_decoder"):
config.mm_use_im_start_end = kwargs.pop("use_mm_start_end", True)
config.mm_vision_tower = kwargs.get(
"vision_tower", "openai/clip-vit-large-patch14"
)
self.ce_loss_weight = kwargs.pop("ce_loss_weight", None)
self.dice_loss_weight = kwargs.pop("dice_loss_weight", None)
self.bce_loss_weight = kwargs.pop("bce_loss_weight", None)
else:
config.mm_vision_tower = config.vision_tower
self.seg_token_idx = kwargs.pop("seg_token_idx")
super().__init__(config)
self.model = LisaModel(config, **kwargs)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_visual_embs(self, pixel_values: torch.FloatTensor):
with torch.no_grad():
image_embeddings_list = []
for i in range(pixel_values.shape[0]):
torch.cuda.empty_cache()
image_embeddings = self.model.visual_model.image_encoder(
pixel_values[i].unsqueeze(0)
)
image_embeddings_list.append(image_embeddings)
torch.cuda.empty_cache()
image_embeddings = torch.cat(image_embeddings_list, 0)
return image_embeddings
def forward(self, **kwargs):
if "past_key_values" in kwargs:
return super().forward(**kwargs)
return self.model_forward(**kwargs)
def model_forward(
self,
images: torch.FloatTensor,
images_clip: torch.FloatTensor,
input_ids: torch.LongTensor,
labels: torch.LongTensor,
attention_masks: torch.LongTensor,
offset: torch.LongTensor,
masks_list: List[torch.FloatTensor],
label_list: List[torch.Tensor],
resize_list: List[tuple],
inference: bool = False,
**kwargs,
):
image_embeddings = self.get_visual_embs(images)
batch_size = image_embeddings.shape[0]
assert batch_size == len(offset) - 1
seg_token_mask = input_ids[:, 1:] == self.seg_token_idx
seg_token_mask = torch.cat(
[
seg_token_mask,
torch.zeros((seg_token_mask.shape[0], 1)).bool().cuda(),
],
dim=1,
)
# hack for IMAGE_TOKEN_INDEX (we suppose that there is only one image, and it is in the front)
seg_token_mask = torch.cat(
[torch.zeros((seg_token_mask.shape[0], 255)).bool().cuda(), seg_token_mask],
dim=1,
)
if inference:
n_batch = 1
length = input_ids.shape[0]
assert images_clip.shape[0] == 1
images_clip_extend = images_clip.expand(length, -1, -1, -1).contiguous()
output_hidden_states = []
for i in range(n_batch):
start_i, end_i = i * length, min((i + 1) * length, input_ids.shape[0])
output_i = super().forward(
images=images_clip_extend[: end_i - start_i],
attention_mask=attention_masks[start_i:end_i],
input_ids=input_ids[start_i:end_i],
output_hidden_states=True,
)
output_hidden_states.append(output_i.hidden_states)
torch.cuda.empty_cache()
output_hidden_states_list = []
output_hidden_states_level = torch.cat(output_hidden_states, dim=0)
output_hidden_states_list.append(output_hidden_states_level)
output_hidden_states = output_hidden_states_list
output = None
else:
images_clip_list = []
for i in range(len(offset) - 1):
start_i, end_i = offset[i], offset[i + 1]
images_clip_i = (
images_clip[i]
.unsqueeze(0)
.expand(end_i - start_i, -1, -1, -1)
.contiguous()
)
images_clip_list.append(images_clip_i)
images_clip = torch.cat(images_clip_list, dim=0)
output = super().forward(
images=images_clip,
attention_mask=attention_masks,
input_ids=input_ids,
labels=labels,
output_hidden_states=True,
)
output_hidden_states = output.hidden_states
hidden_states = []
assert len(self.model.text_hidden_fcs) == 1
hidden_states.append(self.model.text_hidden_fcs[0](output_hidden_states[-1]))
last_hidden_state = torch.stack(hidden_states, dim=-1).sum(dim=-1)
pred_embeddings = last_hidden_state[seg_token_mask]
seg_token_counts = seg_token_mask.int().sum(-1) # [bs, ]
seg_token_offset = seg_token_counts.cumsum(-1)
seg_token_offset = torch.cat(
[torch.zeros(1).long().cuda(), seg_token_offset], dim=0
)
seg_token_offset = seg_token_offset[offset]
pred_embeddings_ = []
for i in range(len(seg_token_offset) - 1):
start_i, end_i = seg_token_offset[i], seg_token_offset[i + 1]
pred_embeddings_.append(pred_embeddings[start_i:end_i])
pred_embeddings = pred_embeddings_
multimask_output = False
pred_masks = []
for i in range(len(pred_embeddings)):
(
sparse_embeddings,
dense_embeddings,
) = self.model.visual_model.prompt_encoder(
points=None,
boxes=None,
masks=None,
text_embeds=pred_embeddings[i].unsqueeze(1),
)
sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype)
low_res_masks, iou_predictions = self.model.visual_model.mask_decoder(
image_embeddings=image_embeddings[i].unsqueeze(0),
image_pe=self.model.visual_model.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output,
)
pred_mask = self.model.visual_model.postprocess_masks(
low_res_masks,
input_size=resize_list[i],
original_size=label_list[i].shape,
)
pred_masks.append(pred_mask[:, 0])
model_output = output
gt_masks = masks_list
if inference:
return {
"pred_masks": pred_masks,
"gt_masks": gt_masks,
}
output = model_output.logits
ce_loss = model_output.loss
ce_loss = ce_loss * self.ce_loss_weight
mask_bce_loss = 0
mask_dice_loss = 0
num_masks = 0
for batch_idx in range(len(pred_masks)):
gt_mask = gt_masks[batch_idx]
pred_mask = pred_masks[batch_idx]
assert (
gt_mask.shape[0] == pred_mask.shape[0]
), "gt_mask.shape: {}, pred_mask.shape: {}".format(
gt_mask.shape, pred_mask.shape
)
mask_bce_loss += (
sigmoid_ce_loss(pred_mask, gt_mask, num_masks=gt_mask.shape[0])
* gt_mask.shape[0]
)
mask_dice_loss += (
dice_loss(pred_mask, gt_mask, num_masks=gt_mask.shape[0])
* gt_mask.shape[0]
)
num_masks += gt_mask.shape[0]
mask_bce_loss = self.bce_loss_weight * mask_bce_loss / (num_masks + 1e-8)
mask_dice_loss = self.dice_loss_weight * mask_dice_loss / (num_masks + 1e-8)
mask_loss = mask_bce_loss + mask_dice_loss
loss = ce_loss + mask_loss
return {
"loss": loss,
"ce_loss": ce_loss,
"mask_bce_loss": mask_bce_loss,
"mask_dice_loss": mask_dice_loss,
"mask_loss": mask_loss,
}
def evaluate(
self,
images_clip,
images,
input_ids,
resize_list,
original_size_list,
max_new_tokens=32,
tokenizer=None,
model_logger=None,
embedding_key=None
):
with torch.no_grad():
if model_logger is None:
import logging
model_logger = logging
model_logger.debug("start output generation...")
outputs = self.generate(
images=images_clip,
input_ids=input_ids,
max_new_tokens=max_new_tokens,
num_beams=1,
output_hidden_states=True,
return_dict_in_generate=True,
)
model_logger.debug("done output generation...")
output_hidden_states = outputs.hidden_states[-1]
output_ids = outputs.sequences
seg_token_mask = output_ids[:, 1:] == self.seg_token_idx
# hack for IMAGE_TOKEN_INDEX (we suppose that there is only one image, and it is in the front)
model_logger.debug(f"start torch.cat to seg_token_mask...")
seg_token_mask = torch.cat(
[
torch.zeros((seg_token_mask.shape[0], 255)).bool().cuda(),
seg_token_mask,
],
dim=1,
)
model_logger.debug("done torch.cat to seg_token_mask...")
hidden_states = []
assert len(self.model.text_hidden_fcs) == 1
hidden_states.append(self.model.text_hidden_fcs[0](output_hidden_states))
model_logger.debug("start torch.stack to last_hidden_state...")
last_hidden_state = torch.stack(hidden_states, dim=-1).sum(dim=-1)
model_logger.debug("done torch.stack to last_hidden_state...")
pred_embeddings = last_hidden_state[seg_token_mask]
seg_token_counts = seg_token_mask.int().sum(-1) # [bs, ]
seg_token_offset = seg_token_counts.cumsum(-1)
model_logger.debug(f"start torch.cat to seg_token_offset...")
seg_token_offset = torch.cat(
[torch.zeros(1).long().cuda(), seg_token_offset], dim=0
)
model_logger.debug("done torch.cat to seg_token_offset...")
pred_embeddings_ = []
for i in range(len(seg_token_offset) - 1):
start_i, end_i = seg_token_offset[i], seg_token_offset[i + 1]
pred_embeddings_.append(pred_embeddings[start_i:end_i])
pred_embeddings = pred_embeddings_
model_logger.debug(f"start get_visual_embs to image_embeddings with embedding_key {embedding_key}.")
if embedding_key is None:
image_embeddings = self.get_visual_embs(images)
else:
try:
image_embeddings = embedding_dict[embedding_key]
except KeyError:
model_logger.debug(f"embedding_key {embedding_key} not in embedding_dict, creating embedding now!")
image_embeddings = self.get_visual_embs(images)
embedding_dict[embedding_key] = image_embeddings
model_logger.debug(f"image embedding added in embedding_dict with embedding_key {embedding_key}!")
model_logger.debug("done get_visual_embs to image_embeddings...")
multimask_output = False
pred_masks = []
for i in range(len(pred_embeddings)):
model_logger.debug(f"start ({i}nth time) visual_model.prompt_encoder to sparse/dense")
(
sparse_embeddings,
dense_embeddings,
) = self.model.visual_model.prompt_encoder(
points=None,
boxes=None,
masks=None,
text_embeds=pred_embeddings[i].unsqueeze(1),
)
model_logger.debug(f"done ({i}nth) visual_model.prompt_encoder to sparse/dense, start sparse2sparse")
sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype)
model_logger.debug(f"done ({i}nth) sparse2sparse, start visual_model.mask_decoder")
low_res_masks, iou_predictions = self.model.visual_model.mask_decoder(
image_embeddings=image_embeddings[i].unsqueeze(0),
image_pe=self.model.visual_model.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output,
)
model_logger.debug(f"done ({i}nth) visual_model.mask_decoder, start postprocess_masks")
pred_mask = self.model.visual_model.postprocess_masks(
low_res_masks,
input_size=resize_list[i],
original_size=original_size_list[i],
)
model_logger.debug(f"done ({i}nth) postprocess_masks")
pred_masks.append(pred_mask[:, 0])
model_logger.debug(f"env evaluate! ")
return output_ids, pred_masks
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