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import torch
import torch.nn as nn
from .sam2_predictor import SAM2VideoPredictor
from .sam2_implementation.modeling.backbones.hieradet import Hiera
from .sam2_implementation.modeling.backbones.image_encoder import FpnNeck, ImageEncoder
from .sam2_implementation.modeling.position_encoding import PositionEmbeddingSine
from .sam2_implementation.modeling.memory_encoder import MemoryEncoder
from .sam2_implementation.modeling.memory_attention import MemoryAttentionLayer, MemoryAttention
from .sam2_implementation.modeling.sam.transformer import RoPEAttention
from .sam2_implementation.modeling.memory_encoder import MaskDownSampler
from .sam2_implementation.modeling.memory_encoder import Fuser
from .sam2_implementation.modeling.memory_encoder import CXBlock
def load_checkpoint_with_prefix(filename, prefix=None, map_location='cpu', logger='current'):
"""Load partial pretrained model with specific prefix.
Args:
prefix (str): The prefix of sub-module.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str | None): Same as :func:`torch.load`.
Defaults to None.
logger: logger
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint = torch.load(filename, map_location=map_location)
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
elif 'model' in checkpoint:
state_dict = checkpoint['model']
else:
state_dict = checkpoint
if not prefix:
return state_dict
if not prefix.endswith('.'):
prefix += '.'
prefix_len = len(prefix)
state_dict = {
k[prefix_len:]: v
for k, v in state_dict.items() if k.startswith(prefix)
}
assert state_dict, f'{prefix} is not in the pretrained model'
return state_dict
def load_state_dict_to_model(model, state_dict, logger='current'):
missing_keys, unexpected_keys = model.load_state_dict(state_dict)
if missing_keys:
print(missing_keys)
raise RuntimeError()
if unexpected_keys:
print(unexpected_keys)
raise RuntimeError()
print("Loaded checkpoint successfully")
class SAM2(nn.Module):
def __init__(
self,
ckpt_path: str = None,
):
super().__init__()
image_encoder = self.build_image_encoder()
memory_attention = self.build_memory_attention()
memory_encoder = self.build_memory_encoder()
sam2_model = SAM2VideoPredictor(
image_encoder=image_encoder,
memory_attention=memory_attention,
memory_encoder=memory_encoder,
num_maskmem = 7,
image_size = 1024,
# apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
sigmoid_scale_for_mem_enc = 20.0,
sigmoid_bias_for_mem_enc = -10.0,
use_mask_input_as_output_without_sam = True,
# Memory
directly_add_no_mem_embed = True,
# use high-resolution feature map in the SAM mask decoder
use_high_res_features_in_sam = True,
# output 3 masks on the first click on initial conditioning frames
multimask_output_in_sam = True,
# SAM heads
iou_prediction_use_sigmoid = True,
# cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
use_obj_ptrs_in_encoder = True,
add_tpos_enc_to_obj_ptrs = False,
only_obj_ptrs_in_the_past_for_eval = True,
# object occlusion prediction
pred_obj_scores = True,
pred_obj_scores_mlp = True,
fixed_no_obj_ptr = True,
# multimask tracking settings
multimask_output_for_tracking = True,
use_multimask_token_for_obj_ptr = True,
multimask_min_pt_num = 0,
multimask_max_pt_num = 1,
use_mlp_for_obj_ptr_proj = True,
# Compilation flag
compile_image_encoder = False,
sam_mask_decoder_extra_args={
'dynamic_multimask_via_stability':True,
'dynamic_multimask_stability_delta': 0.05,
'dynamic_multimask_stability_thresh': 0.98,
}
)
if ckpt_path is not None:
state_dict = load_checkpoint_with_prefix(ckpt_path)
load_state_dict_to_model(sam2_model, state_dict)
self.sam2_model = sam2_model
self.hidden_dim = self.sam2_model.hidden_dim
self.img_mean = (0.485, 0.456, 0.406)
self.img_std = (0.229, 0.224, 0.225)
def build_image_encoder(self):
def build_trunk():
embed_dim = 144
num_heads = 2
stages = [2, 6, 36, 4]
global_att_blocks = [23, 33, 43]
window_pos_embed_bkg_spatial_size = [7, 7]
window_spec = [8, 4, 16, 8]
ret = Hiera(
embed_dim=embed_dim,
num_heads=num_heads,
stages=stages,
global_att_blocks=global_att_blocks,
window_pos_embed_bkg_spatial_size=window_pos_embed_bkg_spatial_size,
window_spec=window_spec,
)
return ret
def build_neck():
def build_position_encoding():
num_pos_feats = 256
normalize = True
scale = None
temperature = 10000
ret = PositionEmbeddingSine(
num_pos_feats=num_pos_feats,
normalize=normalize,
scale=scale,
temperature=temperature,
)
return ret
d_model = 256
backbone_channel_list = [1152, 576, 288, 144]
fpn_top_down_levels = [2, 3] # output level 0 and 1 directly use the backbone features
fpn_interp_model = 'nearest'
position_encoding = build_position_encoding()
ret = FpnNeck(
d_model=d_model,
position_encoding=position_encoding,
backbone_channel_list=backbone_channel_list,
fpn_top_down_levels=fpn_top_down_levels,
fpn_interp_model=fpn_interp_model,
)
return ret
scalp = 1
trunk = build_trunk()
neck = build_neck()
ret = ImageEncoder(scalp=scalp, trunk=trunk, neck=neck)
return ret
def build_memory_attention(self):
def build_layer():
def build_self_attention():
rope_theta = 10000.0
feat_sizes = [32, 32]
embedding_dim = 256
num_heads = 1
downsample_rate = 1
dropout = 0.1
ret = RoPEAttention(
rope_theta=rope_theta,
feat_sizes=feat_sizes,
embedding_dim=embedding_dim,
num_heads=num_heads,
downsample_rate=downsample_rate,
dropout=dropout
)
return ret
def build_cross_attention():
rope_theta = 10000.0
feat_sizes = [32, 32]
rope_k_repeat = True
embedding_dim = 256
num_heads = 1
downsample_rate = 1
dropout = 0.1
kv_in_dim = 64
ret = RoPEAttention(
rope_theta=rope_theta,
feat_sizes=feat_sizes,
rope_k_repeat=rope_k_repeat,
embedding_dim=embedding_dim,
num_heads=num_heads,
downsample_rate=downsample_rate,
dropout=dropout,
kv_in_dim=kv_in_dim
)
return ret
activation = 'relu'
dim_feedforward = 2048
dropout = 0.1
pos_enc_at_attn = False
d_model = 256
pos_enc_at_cross_attn_keys = True
pos_enc_at_cross_attn_queries = False
self_attention = build_self_attention()
cross_attention = build_cross_attention()
ret = MemoryAttentionLayer(
activation=activation,
dim_feedforward=dim_feedforward,
dropout=dropout,
pos_enc_at_attn=pos_enc_at_attn,
d_model=d_model,
pos_enc_at_cross_attn_queries=pos_enc_at_cross_attn_queries,
pos_enc_at_cross_attn_keys=pos_enc_at_cross_attn_keys,
self_attention=self_attention,
cross_attention=cross_attention,
)
return ret
d_model = 256
pos_enc_at_input = True
num_layers = 4
layer = build_layer()
ret = MemoryAttention(
d_model=d_model,
pos_enc_at_input=pos_enc_at_input,
num_layers=num_layers,
layer=layer,
)
return ret
def build_memory_encoder(self):
def build_position_encoding():
num_pos_feats = 64
normalize = True
scale = None
temperature = 10000
ret = PositionEmbeddingSine(
num_pos_feats=num_pos_feats,
normalize=normalize,
scale=scale,
temperature=temperature,
)
return ret
def build_mask_downsampler():
kernel_size = 3
stride = 2
padding = 1
ret = MaskDownSampler(
kernel_size=kernel_size,
stride=stride,
padding=padding,
)
return ret
def build_fuser():
def build_layer():
dim = 256
kernel_size = 7
padding = 3
layer_scale_init_value = 1e-6
use_dwconv = True # depth-wise convs
ret = CXBlock(
dim=dim, kernel_size=kernel_size,
padding=padding, layer_scale_init_value=layer_scale_init_value,
use_dwconv=use_dwconv,
)
return ret
num_layers = 2
layer = build_layer()
ret = Fuser(
layer=layer,
num_layers=num_layers
)
return ret
out_dim = 64
position_encoding = build_position_encoding()
mask_downsampler = build_mask_downsampler()
fuser = build_fuser()
ret = MemoryEncoder(
out_dim=out_dim,
position_encoding=position_encoding,
mask_downsampler=mask_downsampler,
fuser=fuser,
)
return ret
def inject_language_embd(self, inference_state, language_embd):
num_frame = len(language_embd)
num_obj = len(language_embd[0])
mask_out = []
for frame_idx in range(num_frame):
frame_mask_out = []
for obj_idx in range(num_obj):
_language_embd = language_embd[frame_idx][obj_idx][None][None]
_, _, out_mask_logits = self.sam2_model.add_language_embd(inference_state, frame_idx, obj_idx + 100, _language_embd)
frame_mask_out.append(out_mask_logits)
frame_mask_out = torch.cat(frame_mask_out, dim=1)
mask_out.append(frame_mask_out)
mask_out = torch.cat(mask_out, dim=0)
return mask_out
def language_embd_inference(self, inference_state, language_embd):
num_frame = len(language_embd)
num_obj = len(language_embd[0])
mask_out = []
with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
for frame_idx in range(num_frame):
frame_mask_out = []
for obj_idx in range(num_obj):
_language_embd = language_embd[frame_idx][obj_idx][None][None]
_, _, out_mask_logits = self.sam2_model.add_language_embd(
inference_state,
frame_idx,
obj_idx + 100,
_language_embd,
inference=True,
)
frame_mask_out.append(out_mask_logits)
frame_mask_out = torch.cat(frame_mask_out, dim=1)
mask_out.append(frame_mask_out)
mask_out = []
for out_frame_idx, out_obj_ids, out_mask_logits in self.sam2_model.propagate_in_video(inference_state):
mask_out.append(out_mask_logits)
mask_out = torch.cat(mask_out, dim=0)
return mask_out
def get_sam2_embeddings(self, images):
return self.sam2_model.init_state(images)
def forward(self, batch):
raise NotImplementedError
def preprocess_image(self, image: torch.Tensor, dtype=torch.float32) -> torch.Tensor:
image = image / 255.
img_mean = torch.tensor(self.img_mean, dtype=dtype, device=image.device)[:, None, None]
img_std = torch.tensor(self.img_std, dtype=dtype, device=image.device)[:, None, None]
image -= img_mean
image /= img_std
return image
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