437 lines
19 KiB
Python
Executable File
437 lines
19 KiB
Python
Executable File
# ------------------------------------------------------------------------
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# DINO
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# Copyright (c) 2022 IDEA. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Modified by Feng Li and Hao Zhang.
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import logging
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import numpy as np
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from typing import Callable, Dict, List, Optional, Tuple, Union
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import fvcore.nn.weight_init as weight_init
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
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from torch.cuda.amp import autocast
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from detectron2.layers import Conv2d, ShapeSpec, get_norm
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# from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
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from .registry import register_encoder
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from ...utils import configurable
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from ...modules import PositionEmbeddingSine
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from ..transformer_blocks import _get_clones, _get_activation_fn
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from .ops.modules import MSDeformAttn
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from torch.utils import checkpoint
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# MSDeformAttn Transformer encoder in deformable detr
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class MSDeformAttnTransformerEncoderOnly(nn.Module):
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def __init__(self, d_model=256, nhead=8,
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num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
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activation="relu",
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num_feature_levels=4, enc_n_points=4,):
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super().__init__()
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self.d_model = d_model
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self.nhead = nhead
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encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,
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dropout, activation,
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num_feature_levels, nhead, enc_n_points)
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self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)
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self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
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self._reset_parameters()
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def _reset_parameters(self):
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for p in self.parameters():
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if p.dim() > 1:
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nn.init.xavier_uniform_(p)
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for m in self.modules():
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if isinstance(m, MSDeformAttn):
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m._reset_parameters()
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normal_(self.level_embed)
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def get_valid_ratio(self, mask):
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_, H, W = mask.shape
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valid_H = torch.sum(~mask[:, :, 0], 1)
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valid_W = torch.sum(~mask[:, 0, :], 1)
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valid_ratio_h = valid_H.float() / H
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valid_ratio_w = valid_W.float() / W
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valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
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return valid_ratio
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def forward(self, srcs, masks, pos_embeds, use_ckpt=False):
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enable_mask=0
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if masks is not None:
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for src in srcs:
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if src.size(2)%32 or src.size(3)%32:
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enable_mask = 1
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if enable_mask==0:
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masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
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# prepare input for encoder
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src_flatten = []
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mask_flatten = []
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lvl_pos_embed_flatten = []
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spatial_shapes = []
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for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
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bs, c, h, w = src.shape
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spatial_shape = (h, w)
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spatial_shapes.append(spatial_shape)
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src = src.flatten(2).transpose(1, 2)
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mask = mask.flatten(1)
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pos_embed = pos_embed.flatten(2).transpose(1, 2)
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lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
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lvl_pos_embed_flatten.append(lvl_pos_embed)
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src_flatten.append(src)
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mask_flatten.append(mask)
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src_flatten = torch.cat(src_flatten, 1)
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mask_flatten = torch.cat(mask_flatten, 1)
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lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
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spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
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level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
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valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
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# encoder
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memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten, use_ckpt=use_ckpt)
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return memory, spatial_shapes, level_start_index
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class MSDeformAttnTransformerEncoderLayer(nn.Module):
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def __init__(self,
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d_model=256, d_ffn=1024,
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dropout=0.1, activation="relu",
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n_levels=4, n_heads=8, n_points=4):
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super().__init__()
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# self attention
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self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
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self.dropout1 = nn.Dropout(dropout)
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self.norm1 = nn.LayerNorm(d_model)
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# ffn
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self.linear1 = nn.Linear(d_model, d_ffn)
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self.activation = _get_activation_fn(activation)
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self.dropout2 = nn.Dropout(dropout)
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self.linear2 = nn.Linear(d_ffn, d_model)
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self.dropout3 = nn.Dropout(dropout)
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self.norm2 = nn.LayerNorm(d_model)
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@staticmethod
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def with_pos_embed(tensor, pos):
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return tensor if pos is None else tensor + pos
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def forward_ffn(self, src):
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src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
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src = src + self.dropout3(src2)
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src = self.norm2(src)
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return src
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def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
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# self attention
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src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
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src = src + self.dropout1(src2)
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src = self.norm1(src)
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# ffn
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src = self.forward_ffn(src)
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return src
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class MSDeformAttnTransformerEncoder(nn.Module):
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def __init__(self, encoder_layer, num_layers):
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super().__init__()
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self.layers = _get_clones(encoder_layer, num_layers)
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self.num_layers = num_layers
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@staticmethod
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def get_reference_points(spatial_shapes, valid_ratios, device):
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reference_points_list = []
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for lvl, (H_, W_) in enumerate(spatial_shapes):
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ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
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torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
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ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
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ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
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ref = torch.stack((ref_x, ref_y), -1)
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reference_points_list.append(ref)
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reference_points = torch.cat(reference_points_list, 1)
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reference_points = reference_points[:, :, None] * valid_ratios[:, None]
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return reference_points
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def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None, use_ckpt=False):
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output = src
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reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
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for _, layer in enumerate(self.layers):
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if use_ckpt:
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output = checkpoint.checkpoint(layer,output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
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else:
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output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
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return output
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class MaskDINOEncoder(nn.Module):
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"""
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This is the multi-scale encoder in detection models, also named as pixel decoder in segmentation models.
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"""
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@configurable
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def __init__(
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self,
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input_shape: Dict[str, ShapeSpec],
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*,
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transformer_dropout: float,
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transformer_nheads: int,
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transformer_dim_feedforward: int,
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transformer_enc_layers: int,
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conv_dim: int,
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mask_dim: int,
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norm: Optional[Union[str, Callable]] = None,
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# deformable transformer encoder args
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transformer_in_features: List[str],
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common_stride: int,
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num_feature_levels: int,
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total_num_feature_levels: int,
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feature_order: str,
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use_ckpt=False,
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):
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"""
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NOTE: this interface is experimental.
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Args:
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input_shape: shapes (channels and stride) of the input features
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transformer_dropout: dropout probability in transformer
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transformer_nheads: number of heads in transformer
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transformer_dim_feedforward: dimension of feedforward network
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transformer_enc_layers: number of transformer encoder layers
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conv_dims: number of output channels for the intermediate conv layers.
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mask_dim: number of output channels for the final conv layer.
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norm (str or callable): normalization for all conv layers
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num_feature_levels: feature scales used
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total_num_feature_levels: total feautre scales used (include the downsampled features)
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feature_order: 'low2high' or 'high2low', i.e., 'low2high' means low-resolution features are put in the first.
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"""
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super().__init__()
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self.use_ckpt = use_ckpt
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transformer_input_shape = {
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k: v for k, v in input_shape.items() if k in transformer_in_features
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}
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# this is the input shape of pixel decoder
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input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
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self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
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self.feature_strides = [v.stride for k, v in input_shape]
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self.feature_channels = [v.channels for k, v in input_shape]
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self.feature_order = feature_order
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if feature_order == "low2high":
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transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: -x[1].stride)
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else:
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transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)
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self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5"
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transformer_in_channels = [v.channels for k, v in transformer_input_shape]
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self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers
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self.maskdino_num_feature_levels = num_feature_levels # always use 3 scales
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self.total_num_feature_levels = total_num_feature_levels
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self.common_stride = common_stride
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self.transformer_num_feature_levels = len(self.transformer_in_features)
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self.low_resolution_index = transformer_in_channels.index(max(transformer_in_channels))
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self.high_resolution_index = 0 if self.feature_order == 'low2high' else -1
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if self.transformer_num_feature_levels > 1:
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input_proj_list = []
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for in_channels in transformer_in_channels[::-1]:
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input_proj_list.append(nn.Sequential(
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nn.Conv2d(in_channels, conv_dim, kernel_size=1),
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nn.GroupNorm(32, conv_dim),
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))
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# input projectino for downsample
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in_channels = max(transformer_in_channels)
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for _ in range(self.total_num_feature_levels - self.transformer_num_feature_levels): # exclude the res2
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input_proj_list.append(nn.Sequential(
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nn.Conv2d(in_channels, conv_dim, kernel_size=3, stride=2, padding=1),
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nn.GroupNorm(32, conv_dim),
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))
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in_channels = conv_dim
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self.input_proj = nn.ModuleList(input_proj_list)
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else:
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self.input_proj = nn.ModuleList([
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nn.Sequential(
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nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
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nn.GroupNorm(32, conv_dim),
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)])
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for proj in self.input_proj:
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nn.init.xavier_uniform_(proj[0].weight, gain=1)
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nn.init.constant_(proj[0].bias, 0)
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self.transformer = MSDeformAttnTransformerEncoderOnly(
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d_model=conv_dim,
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dropout=transformer_dropout,
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nhead=transformer_nheads,
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dim_feedforward=transformer_dim_feedforward,
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num_encoder_layers=transformer_enc_layers,
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num_feature_levels=self.total_num_feature_levels,
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)
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N_steps = conv_dim // 2
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self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
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self.mask_dim = mask_dim
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# use 1x1 conv instead
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self.mask_features = Conv2d(
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conv_dim,
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mask_dim,
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kernel_size=1,
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stride=1,
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padding=0,
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)
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weight_init.c2_xavier_fill(self.mask_features)
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# extra fpn levels
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stride = min(self.transformer_feature_strides)
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self.num_fpn_levels = max(int(np.log2(stride) - np.log2(self.common_stride)), 1)
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lateral_convs = []
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output_convs = []
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use_bias = norm == ""
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for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):
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lateral_norm = get_norm(norm, conv_dim)
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output_norm = get_norm(norm, conv_dim)
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lateral_conv = Conv2d(
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in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
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)
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output_conv = Conv2d(
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conv_dim,
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conv_dim,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=use_bias,
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norm=output_norm,
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activation=F.relu,
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)
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weight_init.c2_xavier_fill(lateral_conv)
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weight_init.c2_xavier_fill(output_conv)
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self.add_module("adapter_{}".format(idx + 1), lateral_conv)
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self.add_module("layer_{}".format(idx + 1), output_conv)
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lateral_convs.append(lateral_conv)
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output_convs.append(output_conv)
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# Place convs into top-down order (from low to high resolution)
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# to make the top-down computation in forward clearer.
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self.lateral_convs = lateral_convs[::-1]
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self.output_convs = output_convs[::-1]
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@classmethod
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def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec], *args, **kwargs):
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enc_cfg = cfg['MODEL']['ENCODER']
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dec_cfg = cfg['MODEL']['DECODER']
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ret = {}
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ret["input_shape"] = {
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k: v for k, v in input_shape.items() if k in enc_cfg['IN_FEATURES']
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}
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ret["conv_dim"] = enc_cfg['CONVS_DIM']
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ret["mask_dim"] = enc_cfg['MASK_DIM']
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ret["norm"] = enc_cfg['NORM']
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ret["transformer_dropout"] = dec_cfg['DROPOUT']
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ret["transformer_nheads"] = dec_cfg['NHEADS']
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ret["transformer_dim_feedforward"] = dec_cfg['DIM_FEEDFORWARD'] # deformable transformer encoder
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ret[
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"transformer_enc_layers"
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] = enc_cfg['TRANSFORMER_ENC_LAYERS'] # a separate config
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ret["transformer_in_features"] = enc_cfg['DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES'] # ['res3', 'res4', 'res5']
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ret["common_stride"] = enc_cfg['COMMON_STRIDE']
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ret["total_num_feature_levels"] = enc_cfg['TOTAL_NUM_FEATURE_LEVELS']
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ret["num_feature_levels"] = enc_cfg['NUM_FEATURE_LEVELS']
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ret["feature_order"] = enc_cfg['FEATURE_ORDER']
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ret["use_ckpt"] = enc_cfg.get('USE_CKPT', False)
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return ret
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@autocast(enabled=False)
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def forward_features(self, features, masks):
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"""
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:param features: multi-scale features from the backbone
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:param masks: image mask
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:return: enhanced multi-scale features and mask feature (1/4 resolution) for the decoder to produce binary mask
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"""
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# backbone features
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srcs = []
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pos = []
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# additional downsampled features
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srcsl = []
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posl = []
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if self.total_num_feature_levels > self.transformer_num_feature_levels:
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smallest_feat = features[self.transformer_in_features[self.low_resolution_index]].float()
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_len_srcs = self.transformer_num_feature_levels
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for l in range(_len_srcs, self.total_num_feature_levels):
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if l == _len_srcs:
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src = self.input_proj[l](smallest_feat)
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else:
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src = self.input_proj[l](srcsl[-1])
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srcsl.append(src)
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posl.append(self.pe_layer(src))
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srcsl = srcsl[::-1]
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# Reverse feature maps
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for idx, f in enumerate(self.transformer_in_features[::-1]):
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x = features[f].float() # deformable detr does not support half precision
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srcs.append(self.input_proj[idx](x))
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pos.append(self.pe_layer(x))
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srcs.extend(srcsl) if self.feature_order == 'low2high' else srcsl.extend(srcs)
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pos.extend(posl) if self.feature_order == 'low2high' else posl.extend(pos)
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if self.feature_order != 'low2high':
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srcs = srcsl
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pos = posl
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y, spatial_shapes, level_start_index = self.transformer(srcs, masks, pos, use_ckpt=self.use_ckpt)
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bs = y.shape[0]
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split_size_or_sections = [None] * self.total_num_feature_levels
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for i in range(self.total_num_feature_levels):
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if i < self.total_num_feature_levels - 1:
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split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
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else:
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split_size_or_sections[i] = y.shape[1] - level_start_index[i]
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y = torch.split(y, split_size_or_sections, dim=1)
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out = []
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multi_scale_features = []
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num_cur_levels = 0
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for i, z in enumerate(y):
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out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
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# append `out` with extra FPN levels
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# Reverse feature maps into top-down order (from low to high resolution)
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for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):
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x = features[f].float()
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lateral_conv = self.lateral_convs[idx]
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output_conv = self.output_convs[idx]
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cur_fpn = lateral_conv(x)
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# Following FPN implementation, we use nearest upsampling here
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y = cur_fpn + F.interpolate(out[self.high_resolution_index], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
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y = output_conv(y)
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out.append(y)
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for o in out:
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if num_cur_levels < self.total_num_feature_levels:
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multi_scale_features.append(o)
|
|
num_cur_levels += 1
|
|
return self.mask_features(out[-1]), out[0], multi_scale_features
|
|
|
|
|
|
|
|
@register_encoder
|
|
def get_maskdino_encoder_deform(cfg, input_shape):
|
|
"""
|
|
Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`.
|
|
"""
|
|
model = MaskDINOEncoder(cfg, input_shape)
|
|
forward_features = getattr(model, "forward_features", None)
|
|
if not callable(forward_features):
|
|
raise ValueError(
|
|
"Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
|
|
f"Please implement forward_features for {name} to only return mask features."
|
|
)
|
|
return model |