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import torch.nn as nn
import torch.nn.functional as F
from xtuner.registry import BUILDER
from mmengine.model import BaseModel
from xtuner.model.utils import guess_load_checkpoint
from .utils import compute_mask_IoU
class FrozenLlava(BaseModel):
def __init__(self,
model,
mask_head,
merge='mean',
loss_mask=None,
loss_dice=None,
pretrained=None,
**kwargs):
super().__init__()
self.llava = BUILDER.build(model)
self.llava.requires_grad_(False)
in_channels = (self.llava.config.text_config.num_attention_heads *
self.llava.config.text_config.num_hidden_layers)
mask_head.update(in_channels=in_channels)
self.mask_head = BUILDER.build(mask_head)
self.patch_size = self.llava.config.vision_config.patch_size
self.merge = merge
assert merge in ['mean', 'max']
self.loss_mask = BUILDER.build(loss_mask)
self.loss_dice = BUILDER.build(loss_dice)
self.text_layer_weights = nn.Parameter(
torch.ones(self.llava.config.text_config.num_hidden_layers))
if pretrained is not None:
_ = self.load_state_dict(
guess_load_checkpoint(pretrained), strict=False)
def get_text_layer_weights(self):
return torch.softmax(self.text_layer_weights, dim=0)
def apply_merge(self, x, dim=1):
if self.merge == 'mean':
return x.mean(dim=dim)
elif self.merge == 'max':
return x.max(dim=dim).values
else:
raise NotImplementedError
def init_weights(self):
pass
def train(self, mode=True):
super().train(mode=mode)
self.llava.train(mode=False)
self.training = mode
return self
def forward(self, data, data_samples=None, mode='loss'):
if mode == 'loss':
return self.compute_loss(data)
elif mode == 'predict':
return self.predict(data)
elif mode == 'tensor':
return self._forward(data)
else:
raise NotImplementedError
def _compute(self, pred_masks, gt_masks):
mask_cnt = pred_masks.shape[0]
loss_dice = self.loss_dice(
pred_masks.view(mask_cnt, -1), gt_masks.view(mask_cnt, -1),
avg_factor=mask_cnt)
loss_mask = self.loss_mask(
pred_masks.view(-1),
gt_masks.view(-1),
avg_factor=pred_masks.numel())
accuracy = torch.eq((pred_masks.detach().sigmoid() > 0.5).to(gt_masks),
gt_masks).to(gt_masks).mean()
aiou = compute_mask_IoU((pred_masks.detach().sigmoid() > 0.5).to(gt_masks).view(mask_cnt, -1),
gt_masks.view(mask_cnt, -1)).mean()
return loss_dice, loss_mask, accuracy, aiou
class FrozenLlavaSAM(FrozenLlava):
def __init__(self, sam, *args, **kwargs):
pretrained = kwargs.pop('pretrained', None)
super().__init__(*args, **kwargs)
self.sam = BUILDER.build(sam)
self.text_proj = nn.Linear(self.llava.config.text_config.hidden_size,
self.sam.model.prompt_encoder.embed_dim)
if pretrained is not None:
_ = self.load_state_dict(
guess_load_checkpoint(pretrained), strict=False)
def _forward(self, data_sample):
text_layer_weights = self.get_text_layer_weights()
inputs = dict(input_ids=data_sample['input_ids'][None].to(self.llava.device),
mask_ids=data_sample['mask_ids'][None].to(
self.llava.device),
pixel_values=data_sample['pixel_values'][None].to(device=self.llava.device,
dtype=self.llava.dtype),
labels=data_sample['labels'][None].to(self.llava.device)
)
attention_mask = torch.ones(inputs['input_ids'].shape, device=self.llava.device,
dtype=torch.bool)
meta_data = data_sample['meta_data']
with torch.no_grad():
outputs = self.llava(**inputs,
attention_mask=attention_mask,
output_hidden_states=True,
output_attentions=True)
mask_ids = outputs['mask_ids'][0]
attentions = [attn[0, ..., outputs['image_to_overwrite'][0]]
for attn in outputs.attentions]
hidden_states = outputs.hidden_states[-self.llava.config.text_config.num_hidden_layers:]
labels = outputs.labels[0]
# num_layers, seq_len, dim
hidden_states = torch.stack([hs[0] for hs in hidden_states])
# seq_len, dim
hidden_states = (
hidden_states * text_layer_weights.view(-1, 1, 1)).sum(0)
del outputs
padded_h, padded_w = meta_data['padded_shape']['height'], meta_data['padded_shape']['width']
llava_h, llava_w = padded_h // self.patch_size, padded_w // self.patch_size
attentions = [attn.view(*attn.shape[:-1], llava_h, llava_w)
for attn in attentions]
masks = data_sample['masks']
mask_attentions = []
text_embeds = []
for mask_id in range(len(masks)):
matched = mask_ids == mask_id
assert matched.sum() > 0
mask_attentions.append(torch.cat(
[self.apply_merge(attn[:, matched], dim=1) for attn in attentions]))
text_embeds.append(self.text_proj(hidden_states[matched]))
del attentions
mask_attentions = torch.stack(mask_attentions).to(self.mask_head.dtype)
# if self.training:
# mask_attentions.requires_grad = True
pred_masks = self.mask_head(mask_attentions)[:, 0]
# todo: unpad pred_masks
padded_mask_h, padded_mask_w = pred_masks.shape[-2:]
before_height = int(
meta_data['padding']['before_height'] * padded_mask_h / padded_h)
before_width = int(
meta_data['padding']['before_width'] * padded_mask_w / padded_w)
mask_h = int(meta_data['image_shape']['height']
* padded_mask_h / padded_h + 0.5)
mask_w = int(meta_data['image_shape']['width']
* padded_mask_w / padded_w + 0.5)
pred_masks \
= pred_masks[:, before_height:before_height + mask_h, before_width:before_width + mask_w].contiguous()
sam_pred_masks = self.sam(
data_sample['image'], pred_masks, text_embeds)
output = dict(pred_masks=pred_masks, sam_pred_masks=sam_pred_masks,
labels=labels, mask_ids=mask_ids, hidden_states=hidden_states)
return output
@torch.no_grad()
def predict(self, data_sample):
return self._forward(data_sample)['sam_pred_masks']
def compute_loss(self, data):
mask_cnts = 0
loss_dice = 0
loss_mask = 0
accuracy = 0
aiou = 0
sam_loss_dice = 0
sam_loss_mask = 0
sam_accuracy = 0
sam_aiou = 0
for data_sample in data:
forward_output = self._forward(data_sample)
pred_masks, sam_pred_masks = forward_output['pred_masks'], forward_output['sam_pred_masks']
masks = data_sample['masks'].to(self.llava.device)
gt_masks = F.interpolate(masks[None].float(),
size=pred_masks.shape[-2:])[0].to(pred_masks)
sam_gt_masks = F.interpolate(masks[None].float(),
size=sam_pred_masks.shape[-2:])[0].to(sam_pred_masks)
mask_cnt = pred_masks.shape[0]
assert pred_masks.shape == gt_masks.shape
mask_cnts += mask_cnt
loss_dice_, loss_mask_, accuracy_, aiou_ = self._compute(
pred_masks, gt_masks)
loss_dice += loss_dice_ * mask_cnt
loss_mask += loss_mask_ * mask_cnt
accuracy += accuracy_ * mask_cnt
aiou += aiou_ * mask_cnt
sam_loss_dice_, sam_loss_mask_, sam_accuracy_, sam_aiou_ = self._compute(
sam_pred_masks, sam_gt_masks)
sam_loss_dice += sam_loss_dice_ * mask_cnt
sam_loss_mask += sam_loss_mask_ * mask_cnt
sam_accuracy += sam_accuracy_ * mask_cnt
sam_aiou += sam_aiou_ * mask_cnt
assert mask_cnts > 0
loss_dict = {'loss_mask': loss_mask / mask_cnts,
'loss_dice': loss_dice / mask_cnts,
'accuracy': accuracy / mask_cnts,
'aiou': aiou / mask_cnts,
'sam_loss_mask': sam_loss_mask / mask_cnts,
'sam_loss_dice': sam_loss_dice / mask_cnts,
'sam_accuracy': sam_accuracy / mask_cnts,
'sam_aiou': sam_aiou / mask_cnts,
}
return loss_dict
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