Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,388 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 |
"""RetinaNet model implementation and runtime."""
from __future__ import annotations
from torch import Tensor, nn
from vis4d.common.ckpt import load_model_checkpoint
from vis4d.common.typing import LossesType
from vis4d.op.base.resnet import ResNet
from vis4d.op.box.anchor import AnchorGenerator
from vis4d.op.box.box2d import scale_and_clip_boxes
from vis4d.op.box.encoder import DeltaXYWHBBoxEncoder
from vis4d.op.box.matchers import Matcher
from vis4d.op.box.samplers import Sampler
from vis4d.op.detect.common import DetOut
from vis4d.op.detect.retinanet import (
Dense2Det,
RetinaNetHead,
RetinaNetHeadLoss,
RetinaNetOut,
)
from vis4d.op.fpp.fpn import FPN, ExtraFPNBlock
REV_KEYS = [
(r"^backbone\.", "basemodel."),
(r"^bbox_head\.", "retinanet_head."),
(r"^neck.lateral_convs\.", "fpn.inner_blocks."),
(r"^neck.fpn_convs\.", "fpn.layer_blocks."),
(r"^fpn.layer_blocks.3\.", "fpn.extra_blocks.convs.0."),
(r"^fpn.layer_blocks.4\.", "fpn.extra_blocks.convs.1."),
(r"\.conv.weight", ".weight"),
(r"\.conv.bias", ".bias"),
]
class RetinaNet(nn.Module):
"""RetinaNet wrapper class for checkpointing etc."""
def __init__(self, num_classes: int, weights: None | str = None) -> None:
"""Creates an instance of the class.
Args:
num_classes (int): Number of classes.
weights (None | str, optional): Weights to load for model. If
set to "mmdet", will load MMDetection pre-trained weights.
Defaults to None.
"""
super().__init__()
self.basemodel = ResNet(
"resnet50", pretrained=True, trainable_layers=3
)
self.fpn = FPN(
self.basemodel.out_channels[3:],
256,
ExtraFPNBlock(2, 2048, 256, add_extra_convs="on_input"),
start_index=3,
)
self.retinanet_head = RetinaNetHead(
num_classes=num_classes, in_channels=256
)
self.transform_outs = Dense2Det(
self.retinanet_head.anchor_generator,
self.retinanet_head.box_decoder,
num_pre_nms=1000,
max_per_img=100,
nms_threshold=0.5,
score_thr=0.05,
)
if weights == "mmdet":
weights = (
"mmdet://retinanet/retinanet_r50_fpn_2x_coco/"
"retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth"
)
load_model_checkpoint(self, weights, rev_keys=REV_KEYS)
elif weights is not None:
load_model_checkpoint(self, weights)
def forward(
self,
images: Tensor,
input_hw: None | list[tuple[int, int]] = None,
original_hw: None | list[tuple[int, int]] = None,
) -> RetinaNetOut | DetOut:
"""Forward pass.
Args:
images (Tensor): Input images.
input_hw (None | list[tuple[int, int]], optional): Input image
resolutions. 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:
RetinaNetOut | DetOut: Either raw model outputs (for training) or
predicted outputs (for testing).
"""
if self.training:
return self.forward_train(images)
assert input_hw is not None and original_hw is not None
return self.forward_test(images, input_hw, original_hw)
def forward_train(self, images: Tensor) -> RetinaNetOut:
"""Forward training stage.
Args:
images (Tensor): Input images.
Returns:
RetinaNetOut: Raw model outputs.
"""
features = self.fpn(self.basemodel(images))
return self.retinanet_head(features[-5:])
def forward_test(
self,
images: Tensor,
images_hw: list[tuple[int, int]],
original_hw: list[tuple[int, int]],
) -> DetOut:
"""Forward testing stage.
Args:
images (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:
DetOut: Predicted outputs.
"""
features = self.fpn(self.basemodel(images))
outs = self.retinanet_head(features[-5:])
boxes, scores, class_ids = self.transform_outs(
cls_outs=outs.cls_score,
reg_outs=outs.bbox_pred,
images_hw=images_hw,
)
for i, boxs in enumerate(boxes):
boxes[i] = scale_and_clip_boxes(boxs, original_hw[i], images_hw[i])
return DetOut(boxes, scores, class_ids)
class RetinaNetLoss(nn.Module):
"""RetinaNet Loss."""
def __init__(
self,
anchor_generator: AnchorGenerator,
box_encoder: DeltaXYWHBBoxEncoder,
box_matcher: Matcher,
box_sampler: Sampler,
) -> None:
"""Creates an instance of the class.
Args:
anchor_generator (AnchorGenerator): Anchor generator for RPN.
box_encoder (BoxEncoder2D): Bounding box encoder.
box_matcher (BaseMatcher): Bounding box matcher.
box_sampler (BaseSampler): Bounding box sampler.
"""
super().__init__()
self.retinanet_loss = RetinaNetHeadLoss(
anchor_generator, box_encoder, box_matcher, box_sampler
)
def forward(
self,
outputs: RetinaNetOut,
images_hw: list[tuple[int, int]],
target_boxes: list[Tensor],
target_classes: list[Tensor],
) -> LossesType:
"""Forward of loss function.
Args:
outputs (RetinaNetOut): Raw model outputs.
images_hw (list[tuple[int, int]]): Input image resolutions.
target_boxes (list[Tensor]): Bounding box labels.
target_classes (list[Tensor]): Class labels.
Returns:
LossesType: Dictionary of model losses.
"""
losses = self.retinanet_loss(
outputs.cls_score,
outputs.bbox_pred,
target_boxes,
images_hw,
target_classes,
)
return losses._asdict()
|