release infer and demo
This commit is contained in:
pq-yang
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from typing import List, Dict, Iterable, Tuple
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import logging
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from omegaconf import DictConfig
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from omegaconf import OmegaConf
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from huggingface_hub import PyTorchModelHubMixin
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from matanyone2.model.big_modules import PixelEncoder, UncertPred, KeyProjection, MaskEncoder, PixelFeatureFuser, MaskDecoder
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from matanyone2.model.aux_modules import AuxComputer
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from matanyone2.model.utils.memory_utils import get_affinity, readout
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from matanyone2.model.transformer.object_transformer import QueryTransformer
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from matanyone2.model.transformer.object_summarizer import ObjectSummarizer
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from matanyone2.utils.tensor_utils import aggregate
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from matanyone2.utils.device import get_default_device, safe_autocast
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device = get_default_device()
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log = logging.getLogger()
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class MatAnyone2(nn.Module,
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PyTorchModelHubMixin,
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library_name="matanyone2",
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repo_url="https://github.com/pq-yang/MatAnyone2",
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coders={
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DictConfig: (
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lambda x: OmegaConf.to_container(x),
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lambda data: OmegaConf.create(data),
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)
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},
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):
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def __init__(self, cfg: DictConfig, *, single_object=False):
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super().__init__()
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self.cfg = cfg
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model_cfg = cfg.model
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self.ms_dims = model_cfg.pixel_encoder.ms_dims
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self.key_dim = model_cfg.key_dim
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self.value_dim = model_cfg.value_dim
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self.sensory_dim = model_cfg.sensory_dim
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self.pixel_dim = model_cfg.pixel_dim
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self.embed_dim = model_cfg.embed_dim
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self.single_object = single_object
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log.info(f'Single object: {self.single_object}')
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self.pixel_encoder = PixelEncoder(model_cfg)
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self.pix_feat_proj = nn.Conv2d(self.ms_dims[0], self.pixel_dim, kernel_size=1)
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self.key_proj = KeyProjection(model_cfg)
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self.mask_encoder = MaskEncoder(model_cfg, single_object=single_object)
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self.mask_decoder = MaskDecoder(model_cfg)
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self.pixel_fuser = PixelFeatureFuser(model_cfg, single_object=single_object)
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self.object_transformer = QueryTransformer(model_cfg)
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self.object_summarizer = ObjectSummarizer(model_cfg)
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self.aux_computer = AuxComputer(cfg)
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self.temp_sparity = UncertPred(model_cfg)
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self.register_buffer("pixel_mean", torch.Tensor(model_cfg.pixel_mean).view(-1, 1, 1), False)
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self.register_buffer("pixel_std", torch.Tensor(model_cfg.pixel_std).view(-1, 1, 1), False)
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def _get_others(self, masks: torch.Tensor) -> torch.Tensor:
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# for each object, return the sum of masks of all other objects
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if self.single_object:
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return None
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num_objects = masks.shape[1]
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if num_objects >= 1:
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others = (masks.sum(dim=1, keepdim=True) - masks).clamp(0, 1)
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else:
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others = torch.zeros_like(masks)
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return others
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def pred_uncertainty(self, last_pix_feat: torch.Tensor, cur_pix_feat: torch.Tensor, last_mask: torch.Tensor, mem_val_diff:torch.Tensor):
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logits = self.temp_sparity(last_frame_feat=last_pix_feat,
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cur_frame_feat=cur_pix_feat,
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last_mask=last_mask,
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mem_val_diff=mem_val_diff)
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prob = torch.sigmoid(logits)
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mask = (prob > 0) + 0
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uncert_output = {"logits": logits,
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"prob": prob,
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"mask": mask}
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return uncert_output
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def encode_image(self, image: torch.Tensor, seq_length=None, last_feats=None) -> (Iterable[torch.Tensor], torch.Tensor): # type: ignore
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self.pixel_mean = self.pixel_mean.to(device)
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self.pixel_std = self.pixel_std.to(device)
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image = (image - self.pixel_mean) / self.pixel_std
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ms_image_feat = self.pixel_encoder(image, seq_length) # f16, f8, f4, f2, f1
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return ms_image_feat, self.pix_feat_proj(ms_image_feat[0])
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def encode_mask(
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self,
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image: torch.Tensor,
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ms_features: List[torch.Tensor],
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sensory: torch.Tensor,
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masks: torch.Tensor,
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*,
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deep_update: bool = True,
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chunk_size: int = -1,
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need_weights: bool = False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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image = (image - self.pixel_mean) / self.pixel_std
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others = self._get_others(masks)
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mask_value, new_sensory = self.mask_encoder(image,
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ms_features,
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sensory,
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masks,
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others,
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deep_update=deep_update,
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chunk_size=chunk_size)
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object_summaries, object_logits = self.object_summarizer(masks, mask_value, need_weights)
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return mask_value, new_sensory, object_summaries, object_logits
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def transform_key(self,
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final_pix_feat: torch.Tensor,
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*,
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need_sk: bool = True,
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need_ek: bool = True) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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key, shrinkage, selection = self.key_proj(final_pix_feat, need_s=need_sk, need_e=need_ek)
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return key, shrinkage, selection
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# Used in training only.
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# This step is replaced by MemoryManager in test time
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def read_memory(self, query_key: torch.Tensor, query_selection: torch.Tensor,
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memory_key: torch.Tensor, memory_shrinkage: torch.Tensor,
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msk_value: torch.Tensor, obj_memory: torch.Tensor, pix_feat: torch.Tensor,
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sensory: torch.Tensor, last_mask: torch.Tensor,
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selector: torch.Tensor, uncert_output=None, seg_pass=False,
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last_pix_feat=None, last_pred_mask=None) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
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"""
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query_key : B * CK * H * W
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query_selection : B * CK * H * W
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memory_key : B * CK * T * H * W
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memory_shrinkage: B * 1 * T * H * W
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msk_value : B * num_objects * CV * T * H * W
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obj_memory : B * num_objects * T * num_summaries * C
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pixel_feature : B * C * H * W
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"""
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batch_size, num_objects = msk_value.shape[:2]
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uncert_mask = uncert_output["mask"] if uncert_output is not None else None
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# read using visual attention
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with safe_autocast(enabled=False):
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affinity = get_affinity(memory_key.float(), memory_shrinkage.float(), query_key.float(),
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query_selection.float(), uncert_mask=uncert_mask)
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msk_value = msk_value.flatten(start_dim=1, end_dim=2).float()
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# B * (num_objects*CV) * H * W
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pixel_readout = readout(affinity, msk_value, uncert_mask)
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pixel_readout = pixel_readout.view(batch_size, num_objects, self.value_dim,
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*pixel_readout.shape[-2:])
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uncert_output = self.pred_uncertainty(last_pix_feat, pix_feat, last_pred_mask, pixel_readout[:,0]-msk_value[:,:,-1])
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uncert_prob = uncert_output["prob"].unsqueeze(1) # b n 1 h w
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pixel_readout = pixel_readout*uncert_prob + msk_value[:,:,-1].unsqueeze(1)*(1-uncert_prob)
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pixel_readout = self.pixel_fusion(pix_feat, pixel_readout, sensory, last_mask)
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# read from query transformer
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mem_readout, aux_features = self.readout_query(pixel_readout, obj_memory, selector=selector, seg_pass=seg_pass)
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aux_output = {
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'sensory': sensory,
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'q_logits': aux_features['logits'] if aux_features else None,
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'attn_mask': aux_features['attn_mask'] if aux_features else None,
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}
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return mem_readout, aux_output, uncert_output
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def read_first_frame_memory(self, pixel_readout,
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obj_memory: torch.Tensor, pix_feat: torch.Tensor,
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sensory: torch.Tensor, last_mask: torch.Tensor,
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selector: torch.Tensor, seg_pass=False) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
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"""
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query_key : B * CK * H * W
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query_selection : B * CK * H * W
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memory_key : B * CK * T * H * W
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memory_shrinkage: B * 1 * T * H * W
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msk_value : B * num_objects * CV * T * H * W
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obj_memory : B * num_objects * T * num_summaries * C
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pixel_feature : B * C * H * W
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"""
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pixel_readout = self.pixel_fusion(pix_feat, pixel_readout, sensory, last_mask)
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# read from query transformer
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mem_readout, aux_features = self.readout_query(pixel_readout, obj_memory, selector=selector, seg_pass=seg_pass)
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aux_output = {
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'sensory': sensory,
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'q_logits': aux_features['logits'] if aux_features else None,
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'attn_mask': aux_features['attn_mask'] if aux_features else None,
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}
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return mem_readout, aux_output
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def pixel_fusion(self,
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pix_feat: torch.Tensor,
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pixel: torch.Tensor,
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sensory: torch.Tensor,
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last_mask: torch.Tensor,
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*,
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chunk_size: int = -1) -> torch.Tensor:
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last_mask = F.interpolate(last_mask, size=sensory.shape[-2:], mode='area')
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last_others = self._get_others(last_mask)
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fused = self.pixel_fuser(pix_feat,
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pixel,
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sensory,
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last_mask,
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last_others,
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chunk_size=chunk_size)
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return fused
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def readout_query(self,
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pixel_readout,
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obj_memory,
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*,
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selector=None,
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need_weights=False,
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seg_pass=False) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
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return self.object_transformer(pixel_readout,
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obj_memory,
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selector=selector,
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need_weights=need_weights,
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seg_pass=seg_pass)
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def segment(self,
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ms_image_feat: List[torch.Tensor],
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memory_readout: torch.Tensor,
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sensory: torch.Tensor,
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*,
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selector: bool = None,
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chunk_size: int = -1,
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update_sensory: bool = True,
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seg_pass: bool = False,
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clamp_mat: bool = True,
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last_mask=None,
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sigmoid_residual=False,
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seg_mat=False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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multi_scale_features is from the key encoder for skip-connection
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memory_readout is from working/long-term memory
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sensory is the sensory memory
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last_mask is the mask from the last frame, supplementing sensory memory
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selector is 1 if an object exists, and 0 otherwise. We use it to filter padded objects
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during training.
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"""
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#### use mat head for seg data
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if seg_mat:
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assert seg_pass
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seg_pass = False
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####
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sensory, logits = self.mask_decoder(ms_image_feat,
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memory_readout,
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sensory,
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chunk_size=chunk_size,
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update_sensory=update_sensory,
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seg_pass = seg_pass,
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last_mask=last_mask,
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sigmoid_residual=sigmoid_residual)
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if seg_pass:
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prob = torch.sigmoid(logits)
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if selector is not None:
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prob = prob * selector
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# Softmax over all objects[]
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logits = aggregate(prob, dim=1)
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prob = F.softmax(logits, dim=1)
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else:
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if clamp_mat:
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logits = logits.clamp(0.0, 1.0)
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logits = torch.cat([torch.prod(1 - logits, dim=1, keepdim=True), logits], 1)
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prob = logits
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return sensory, logits, prob
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def compute_aux(self, pix_feat: torch.Tensor, aux_inputs: Dict[str, torch.Tensor],
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selector: torch.Tensor, seg_pass=False) -> Dict[str, torch.Tensor]:
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return self.aux_computer(pix_feat, aux_inputs, selector, seg_pass=seg_pass)
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def forward(self, *args, **kwargs):
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raise NotImplementedError
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def load_weights(self, src_dict, init_as_zero_if_needed=False) -> None:
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if not self.single_object:
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# Map single-object weight to multi-object weight (4->5 out channels in conv1)
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for k in list(src_dict.keys()):
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if k == 'mask_encoder.conv1.weight':
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if src_dict[k].shape[1] == 4:
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log.info(f'Converting {k} from single object to multiple objects.')
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pads = torch.zeros((64, 1, 7, 7), device=src_dict[k].device)
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if not init_as_zero_if_needed:
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nn.init.orthogonal_(pads)
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log.info(f'Randomly initialized padding for {k}.')
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else:
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log.info(f'Zero-initialized padding for {k}.')
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src_dict[k] = torch.cat([src_dict[k], pads], 1)
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elif k == 'pixel_fuser.sensory_compress.weight':
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if src_dict[k].shape[1] == self.sensory_dim + 1:
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log.info(f'Converting {k} from single object to multiple objects.')
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pads = torch.zeros((self.value_dim, 1, 1, 1), device=src_dict[k].device)
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if not init_as_zero_if_needed:
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nn.init.orthogonal_(pads)
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log.info(f'Randomly initialized padding for {k}.')
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else:
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log.info(f'Zero-initialized padding for {k}.')
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src_dict[k] = torch.cat([src_dict[k], pads], 1)
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elif self.single_object:
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"""
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If the model is multiple-object and we are training in single-object,
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we strip the last channel of conv1.
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This is not supposed to happen in standard training except when users are trying to
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finetune a trained model with single object datasets.
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"""
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if src_dict['mask_encoder.conv1.weight'].shape[1] == 5:
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log.warning('Converting mask_encoder.conv1.weight from multiple objects to single object.'
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'This is not supposed to happen in standard training.')
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src_dict['mask_encoder.conv1.weight'] = src_dict['mask_encoder.conv1.weight'][:, :-1]
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src_dict['pixel_fuser.sensory_compress.weight'] = src_dict['pixel_fuser.sensory_compress.weight'][:, :-1]
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for k in src_dict:
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if k not in self.state_dict():
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log.info(f'Key {k} found in src_dict but not in self.state_dict()!!!')
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for k in self.state_dict():
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if k not in src_dict:
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log.info(f'Key {k} found in self.state_dict() but not in src_dict!!!')
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self.load_state_dict(src_dict, strict=False)
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@property
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def device(self) -> torch.device:
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return self.pixel_mean.device
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