Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,952 Bytes
9b33fca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
"""Mask RCNN model implementation and runtime."""
from __future__ import annotations
from typing import NamedTuple
import torch
from torch import nn
from vis4d.common.ckpt import load_model_checkpoint
from vis4d.op.base import BaseModel, ResNet
from vis4d.op.box.box2d import apply_mask, scale_and_clip_boxes
from vis4d.op.box.encoder import DeltaXYWHBBoxDecoder
from vis4d.op.detect.common import DetOut
from vis4d.op.detect.faster_rcnn import FasterRCNNHead, FRCNNOut
from vis4d.op.detect.mask_rcnn import (
Det2Mask,
MaskOut,
MaskRCNNHead,
MaskRCNNHeadOut,
)
from vis4d.op.detect.rcnn import RoI2Det
from vis4d.op.fpp.fpn import FPN
class MaskDetectionOut(NamedTuple):
"""Mask detection output."""
boxes: DetOut
masks: MaskOut
class MaskRCNNOut(NamedTuple):
"""Mask RCNN output."""
boxes: FRCNNOut
masks: MaskRCNNHeadOut
REV_KEYS = [
(r"^backbone\.", "basemodel."),
(r"^rpn_head.rpn_reg\.", "rpn_head.rpn_box."),
(r"^roi_head.bbox_head\.", "roi_head."),
(r"^roi_head.mask_head\.", "mask_head."),
(r"^convs\.", "mask_head.convs."),
(r"^upsample\.", "mask_head.upsample."),
(r"^conv_logits\.", "mask_head.conv_logits."),
(r"^roi_head\.", "faster_rcnn_head.roi_head."),
(r"^rpn_head\.", "faster_rcnn_head.rpn_head."),
(r"^neck.lateral_convs\.", "fpn.inner_blocks."),
(r"^neck.fpn_convs\.", "fpn.layer_blocks."),
(r"\.conv.weight", ".weight"),
(r"\.conv.bias", ".bias"),
]
class MaskRCNN(nn.Module):
"""Mask RCNN model.
Args:
num_classes (int): Number of classes.
basemodel (BaseModel, optional): Base model network. Defaults to
None. If None, will use ResNet50.
faster_rcnn_head (FasterRCNNHead, optional): Faster RCNN head.
Defaults to None. if None, will use default FasterRCNNHead.
mask_head (MaskRCNNHead, optional): Mask RCNN head. Defaults to
None. if None, will use default MaskRCNNHead.
rcnn_box_decoder (DeltaXYWHBBoxDecoder, optional): Decoder for RCNN
bounding boxes. Defaults to None.
no_overlap (bool, optional): Whether to remove overlapping pixels
between masks. Defaults to False.
weights (None | str, optional): Weights to load for model. If set
to "mmdet", will load MMDetection pre-trained weights.
Defaults to None.
"""
def __init__(
self,
num_classes: int,
basemodel: BaseModel | None = None,
faster_rcnn_head: FasterRCNNHead | None = None,
mask_head: MaskRCNNHead | None = None,
rcnn_box_decoder: DeltaXYWHBBoxDecoder | None = None,
no_overlap: bool = False,
weights: None | str = None,
) -> None:
"""Creates an instance of the class."""
super().__init__()
self.basemodel = (
ResNet(resnet_name="resnet50", pretrained=True, trainable_layers=3)
if basemodel is None
else basemodel
)
self.fpn = FPN(self.basemodel.out_channels[2:], 256)
if faster_rcnn_head is None:
self.faster_rcnn_head = FasterRCNNHead(num_classes=num_classes)
else:
self.faster_rcnn_head = faster_rcnn_head
if mask_head is None:
self.mask_head = MaskRCNNHead(num_classes=num_classes)
else:
self.mask_head = mask_head
self.transform_outs = RoI2Det(rcnn_box_decoder)
self.det2mask = Det2Mask(no_overlap=no_overlap)
if weights is not None:
if weights == "mmdet":
weights = (
"mmdet://mask_rcnn/mask_rcnn_r50_fpn_2x_coco/"
"mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_"
"20200505_003907-3e542a40.pth"
)
if weights.startswith("mmdet://") or weights.startswith(
"bdd100k://"
):
load_model_checkpoint(self, weights, rev_keys=REV_KEYS)
else:
load_model_checkpoint(self, weights)
def forward(
self,
images: torch.Tensor,
input_hw: list[tuple[int, int]],
boxes2d: None | list[torch.Tensor] = None,
boxes2d_classes: None | list[torch.Tensor] = None,
original_hw: None | list[tuple[int, int]] = None,
) -> MaskRCNNOut | MaskDetectionOut:
"""Forward pass.
Args:
images (torch.Tensor): Input images.
input_hw (list[tuple[int, int]]): Input image resolutions.
boxes2d (None | list[torch.Tensor], optional): Bounding box
labels. Required for training. Defaults to None.
boxes2d_classes (None | list[torch.Tensor], optional): Class
labels. Required for training. Defaults to None.
original_hw (None | list[tuple[int, int]], optional): Original
image resolutions (before padding and resizing). Required for
testing. Defaults to None.
Returns:
MaskRCNNOut | MaskDetectionOut: Either raw model
outputs (for training) or predicted outputs (for testing).
"""
if self.training:
assert boxes2d is not None and boxes2d_classes is not None
return self.forward_train(
images, input_hw, boxes2d, boxes2d_classes
)
assert original_hw is not None
return self.forward_test(images, input_hw, original_hw)
def forward_train(
self,
images: torch.Tensor,
images_hw: list[tuple[int, int]],
target_boxes: list[torch.Tensor],
target_classes: list[torch.Tensor],
) -> MaskRCNNOut:
"""Forward training stage.
Args:
images (torch.Tensor): Input images.
images_hw (list[tuple[int, int]]): Input image resolutions.
target_boxes (list[torch.Tensor]): Bounding box labels. Required
for training. Defaults to None.
target_classes (list[torch.Tensor]): Class labels. Required for
training. Defaults to None.
Returns:
MaskRCNNOut: Raw model outputs.
"""
features = self.fpn(self.basemodel(images))
outputs = self.faster_rcnn_head(
features, images_hw, target_boxes, target_classes
)
assert outputs.sampled_proposals is not None
assert outputs.sampled_targets is not None
pos_proposals = apply_mask(
[torch.eq(label, 1) for label in outputs.sampled_targets.labels],
outputs.sampled_proposals.boxes,
)[0]
mask_outs = self.mask_head(features, pos_proposals)
return MaskRCNNOut(outputs, mask_outs)
def forward_test(
self,
images: torch.Tensor,
images_hw: list[tuple[int, int]],
original_hw: list[tuple[int, int]],
) -> MaskDetectionOut:
"""Forward testing stage.
Args:
images (torch.Tensor): Input images.
images_hw (list[tuple[int, int]]): Input image resolutions.
original_hw (list[tuple[int, int]]): Original image resolutions
(before padding and resizing).
Returns:
MaskDetectionOut: Predicted outputs.
"""
features = self.fpn(self.basemodel(images))
outs = self.faster_rcnn_head(features, images_hw)
boxes, scores, class_ids = self.transform_outs(
*outs.roi, outs.proposals.boxes, images_hw
)
mask_outs = self.mask_head(features, boxes)
for i, boxs in enumerate(boxes):
boxes[i] = scale_and_clip_boxes(boxs, original_hw[i], images_hw[i])
mask_preds = [m.sigmoid() for m in mask_outs.mask_pred]
masks = self.det2mask(
mask_preds, boxes, scores, class_ids, original_hw
)
return MaskDetectionOut(DetOut(boxes, scores, class_ids), masks)
|