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from typing import List, Optional, Iterable, Dict
import logging
from omegaconf import DictConfig
import numpy as np
import torch
import torch.nn.functional as F
from tracker.inference.memory_manager import MemoryManager
from tracker.inference.object_manager import ObjectManager
from tracker.inference.image_feature_store import ImageFeatureStore
from tracker.model.cutie import CUTIE
from tracker.utils.tensor_utils import pad_divide_by, unpad, aggregate
log = logging.getLogger()
class InferenceCore:
def __init__(self,
network: CUTIE,
cfg: DictConfig,
*,
image_feature_store: ImageFeatureStore = None):
self.network = network
self.cfg = cfg
self.mem_every = cfg.mem_every
stagger_updates = cfg.stagger_updates
self.chunk_size = cfg.chunk_size
self.save_aux = cfg.save_aux
self.max_internal_size = cfg.max_internal_size
self.flip_aug = cfg.flip_aug
self.curr_ti = -1
self.last_mem_ti = 0
# at which time indices should we update the sensory memory
if stagger_updates >= self.mem_every:
self.stagger_ti = set(range(1, self.mem_every + 1))
else:
self.stagger_ti = set(
np.round(np.linspace(1, self.mem_every, stagger_updates)).astype(int))
self.object_manager = ObjectManager()
self.memory = MemoryManager(cfg=cfg, object_manager=self.object_manager)
if image_feature_store is None:
self.image_feature_store = ImageFeatureStore(self.network)
else:
self.image_feature_store = image_feature_store
self.last_mask = None
def clear_memory(self):
self.curr_ti = -1
self.last_mem_ti = 0
self.memory = MemoryManager(cfg=self.cfg, object_manager=self.object_manager)
def clear_non_permanent_memory(self):
self.curr_ti = -1
self.last_mem_ti = 0
self.memory.clear_non_permanent_memory()
def clear_sensory_memory(self):
self.curr_ti = -1
self.last_mem_ti = 0
self.memory.clear_sensory_memory()
def update_config(self, cfg):
self.mem_every = cfg['mem_every']
self.memory.update_config(cfg)
def _add_memory(self,
image: torch.Tensor,
pix_feat: torch.Tensor,
prob: torch.Tensor,
key: torch.Tensor,
shrinkage: torch.Tensor,
selection: torch.Tensor,
*,
is_deep_update: bool = True,
force_permanent: bool = False) -> None:
"""
Memorize the given segmentation in all memory stores.
The batch dimension is 1 if flip augmentation is not used.
image: RGB image, (1/2)*3*H*W
pix_feat: from the key encoder, (1/2)*_*H*W
prob: (1/2)*num_objects*H*W, in [0, 1]
key/shrinkage/selection: for anisotropic l2, (1/2)*_*H*W
selection can be None if not using long-term memory
is_deep_update: whether to use deep update (e.g. with the mask encoder)
force_permanent: whether to force the memory to be permanent
"""
if prob.shape[1] == 0:
# nothing to add
log.warn('Trying to add an empty object mask to memory!')
return
if force_permanent:
as_permanent = 'all'
else:
as_permanent = 'first'
self.memory.initialize_sensory_if_needed(key, self.object_manager.all_obj_ids)
msk_value, sensory, obj_value, self.obj_logits = self.network.encode_mask(
image,
pix_feat,
self.memory.get_sensory(self.object_manager.all_obj_ids),
prob,
deep_update=is_deep_update,
chunk_size=self.chunk_size,
need_weights=self.save_aux)
self.memory.add_memory(key,
shrinkage,
msk_value,
obj_value,
self.object_manager.all_obj_ids,
selection=selection,
as_permanent=as_permanent)
self.last_mem_ti = self.curr_ti
if is_deep_update:
self.memory.update_sensory(sensory, self.object_manager.all_obj_ids)
def _segment(self,
key: torch.Tensor,
selection: torch.Tensor,
pix_feat: torch.Tensor,
ms_features: Iterable[torch.Tensor],
update_sensory: bool = True) -> torch.Tensor:
"""
Produce a segmentation using the given features and the memory
The batch dimension is 1 if flip augmentation is not used.
key/selection: for anisotropic l2: (1/2) * _ * H * W
pix_feat: from the key encoder, (1/2) * _ * H * W
ms_features: an iterable of multiscale features from the encoder, each is (1/2)*_*H*W
with strides 16, 8, and 4 respectively
update_sensory: whether to update the sensory memory
Returns: (num_objects+1)*H*W normalized probability; the first channel is the background
"""
bs = key.shape[0]
if self.flip_aug:
assert bs == 2
else:
assert bs == 1
if not self.memory.engaged:
log.warn('Trying to segment without any memory!')
return torch.zeros((1, key.shape[-2] * 16, key.shape[-1] * 16),
device=key.device,
dtype=key.dtype)
memory_readout = self.memory.read(pix_feat, key, selection, self.last_mask, self.network)
memory_readout = self.object_manager.realize_dict(memory_readout)
sensory, _, pred_prob_with_bg = self.network.segment(ms_features,
memory_readout,
self.memory.get_sensory(
self.object_manager.all_obj_ids),
chunk_size=self.chunk_size,
update_sensory=update_sensory)
# remove batch dim
if self.flip_aug:
# average predictions of the non-flipped and flipped version
pred_prob_with_bg = (pred_prob_with_bg[0] +
torch.flip(pred_prob_with_bg[1], dims=[-1])) / 2
else:
pred_prob_with_bg = pred_prob_with_bg[0]
if update_sensory:
self.memory.update_sensory(sensory, self.object_manager.all_obj_ids)
return pred_prob_with_bg
def step(self,
image: torch.Tensor,
mask: Optional[torch.Tensor] = None,
objects: Optional[List[int]] = None,
*,
idx_mask: bool = True,
end: bool = False,
delete_buffer: bool = True,
force_permanent: bool = False) -> torch.Tensor:
"""
Take a step with a new incoming image.
If there is an incoming mask with new objects, we will memorize them.
If there is no incoming mask, we will segment the image using the memory.
In both cases, we will update the memory and return a segmentation.
image: 3*H*W
mask: H*W (if idx mask) or len(objects)*H*W or None
objects: list of object ids that are valid in the mask Tensor.
The ids themselves do not need to be consecutive/in order, but they need to be
in the same position in the list as the corresponding mask
in the tensor in non-idx-mask mode.
objects is ignored if the mask is None.
If idx_mask is False and objects is None, we sequentially infer the object ids.
idx_mask: if True, mask is expected to contain an object id at every pixel.
If False, mask should have multiple channels with each channel representing one object.
end: if we are at the end of the sequence, we do not need to update memory
if unsure just set it to False
delete_buffer: whether to delete the image feature buffer after this step
force_permanent: the memory recorded this frame will be added to the permanent memory
"""
if objects is None and mask is not None:
assert not idx_mask
objects = list(range(1, mask.shape[0] + 1))
# resize input if needed -- currently only used for the GUI
resize_needed = False
if self.max_internal_size > 0:
h, w = image.shape[-2:]
min_side = min(h, w)
if min_side > self.max_internal_size:
resize_needed = True
new_h = int(h / min_side * self.max_internal_size)
new_w = int(w / min_side * self.max_internal_size)
image = F.interpolate(image.unsqueeze(0),
size=(new_h, new_w),
mode='bilinear',
align_corners=False)[0]
if mask is not None:
if idx_mask:
mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0).float(),
size=(new_h, new_w),
mode='nearest',
align_corners=False)[0, 0].round().long()
else:
mask = F.interpolate(mask.unsqueeze(0),
size=(new_h, new_w),
mode='bilinear',
align_corners=False)[0]
self.curr_ti += 1
image, self.pad = pad_divide_by(image, 16)
image = image.unsqueeze(0) # add the batch dimension
if self.flip_aug:
image = torch.cat([image, torch.flip(image, dims=[-1])], dim=0)
# whether to update the working memory
is_mem_frame = ((self.curr_ti - self.last_mem_ti >= self.mem_every) or
(mask is not None)) and (not end)
# segment when there is no input mask or when the input mask is incomplete
need_segment = (mask is None) or (self.object_manager.num_obj > 0
and not self.object_manager.has_all(objects))
update_sensory = ((self.curr_ti - self.last_mem_ti) in self.stagger_ti) and (not end)
# encoding the image
ms_feat, pix_feat = self.image_feature_store.get_features(self.curr_ti, image)
key, shrinkage, selection = self.image_feature_store.get_key(self.curr_ti, image)
# segmentation from memory if needed
if need_segment:
pred_prob_with_bg = self._segment(key,
selection,
pix_feat,
ms_feat,
update_sensory=update_sensory)
# use the input mask if provided
if mask is not None:
# inform the manager of the new objects, and get a list of temporary id
# temporary ids -- indicates the position of objects in the tensor
# (starts with 1 due to the background channel)
corresponding_tmp_ids, _ = self.object_manager.add_new_objects(objects)
mask, _ = pad_divide_by(mask, 16)
if need_segment:
# merge predicted mask with the incomplete input mask
pred_prob_no_bg = pred_prob_with_bg[1:]
# use the mutual exclusivity of segmentation
if idx_mask:
pred_prob_no_bg[:, mask > 0] = 0
else:
pred_prob_no_bg[:, mask.max(0) > 0.5] = 0
new_masks = []
for mask_id, tmp_id in enumerate(corresponding_tmp_ids):
if idx_mask:
this_mask = (mask == objects[mask_id]).type_as(pred_prob_no_bg)
else:
this_mask = mask[tmp_id]
if tmp_id >= pred_prob_no_bg.shape[0]:
new_masks.append(this_mask.unsqueeze(0))
else:
# +1 for padding the background channel
pred_prob_no_bg[tmp_id + 1] = this_mask
# new_masks are always in the order of tmp_id
mask = torch.cat([pred_prob_no_bg, *new_masks], dim=0)
elif idx_mask:
# simply convert cls to one-hot representation
if len(objects) == 0:
if delete_buffer:
self.image_feature_store.delete(self.curr_ti)
log.warn('Trying to insert an empty mask as memory!')
return torch.zeros((1, key.shape[-2] * 16, key.shape[-1] * 16),
device=key.device,
dtype=key.dtype)
mask = torch.stack(
[mask == objects[mask_id] for mask_id, _ in enumerate(corresponding_tmp_ids)],
dim=0)
pred_prob_with_bg = aggregate(mask, dim=0)
pred_prob_with_bg = torch.softmax(pred_prob_with_bg, dim=0)
self.last_mask = pred_prob_with_bg[1:].unsqueeze(0)
if self.flip_aug:
self.last_mask = torch.cat(
[self.last_mask, torch.flip(self.last_mask, dims=[-1])], dim=0)
# save as memory if needed
if is_mem_frame or force_permanent:
self._add_memory(image,
pix_feat,
self.last_mask,
key,
shrinkage,
selection,
force_permanent=force_permanent)
if delete_buffer:
self.image_feature_store.delete(self.curr_ti)
output_prob = unpad(pred_prob_with_bg, self.pad)
if resize_needed:
# restore output to the original size
output_prob = F.interpolate(output_prob.unsqueeze(0),
size=(h, w),
mode='bilinear',
align_corners=False)[0]
return output_prob
def get_aux_outputs(self, image: torch.Tensor) -> Dict[str, torch.Tensor]:
image, pads = pad_divide_by(image, 16)
image = image.unsqueeze(0) # add the batch dimension
_, pix_feat = self.image_feature_store.get_features(self.curr_ti, image)
aux_inputs = self.memory.aux
aux_outputs = self.network.compute_aux(pix_feat, aux_inputs, selector=None)
aux_outputs['q_weights'] = aux_inputs['q_weights']
aux_outputs['p_weights'] = aux_inputs['p_weights']
for k, v in aux_outputs.items():
if len(v.shape) == 5:
aux_outputs[k] = F.interpolate(v[0],
size=image.shape[-2:],
mode='bilinear',
align_corners=False)
elif 'weights' in k:
b, num_objects, num_heads, num_queries, h, w = v.shape
v = v.view(num_objects * num_heads, num_queries, h, w)
v = F.interpolate(v, size=image.shape[-2:], mode='bilinear', align_corners=False)
aux_outputs[k] = v.view(num_objects, num_heads, num_queries, *image.shape[-2:])
else:
aux_outputs[k] = F.interpolate(v,
size=image.shape[-2:],
mode='bilinear',
align_corners=False)[0]
aux_outputs[k] = unpad(aux_outputs[k], pads)
if 'weights' in k:
weights = aux_outputs[k]
weights = weights / (weights.max(-1, keepdim=True)[0].max(-2, keepdim=True)[0] +
1e-8)
aux_outputs[k] = (weights * 255).cpu().numpy()
else:
aux_outputs[k] = (aux_outputs[k].softmax(dim=0) * 255).cpu().numpy()
self.image_feature_store.delete(self.curr_ti)
return aux_outputs
def get_aux_object_weights(self, image: torch.Tensor) -> np.ndarray:
image, pads = pad_divide_by(image, 16)
# B*num_objects*H*W*num_queries -> num_objects*num_queries*H*W
# weights = F.softmax(self.obj_logits, dim=-1)[0]
weights = F.sigmoid(self.obj_logits)[0]
weights = weights.permute(0, 3, 1, 2).contiguous()
weights = F.interpolate(weights,
size=image.shape[-2:],
mode='bilinear',
align_corners=False)
# weights = weights / (weights.max(-1, keepdim=True)[0].max(-2, keepdim=True)[0])
weights = unpad(weights, pads)
weights = (weights * 255).cpu().numpy()
return weights
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