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ppwwyyxx/maskrcnn_LazyCall.py

Created January 22, 2021 01:13
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maskrcnn_LazyCall.py
from detectron2.layers import ShapeSpec
from detectron2.modeling import FPN, GeneralizedRCNN, ResNet, StandardROIHeads
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
from detectron2.modeling.backbone.fpn import LastLevelMaxPool
from detectron2.modeling.backbone.resnet import BasicStem, BottleneckBlock
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.matcher import Matcher
from detectron2.modeling.poolers import ROIPooler
from detectron2.modeling.proposal_generator.rpn import RPN, StandardRPNHead
from detectron2.modeling.roi_heads import FastRCNNOutputLayers, MaskRCNNConvUpsampleHead
from detectron2.modeling.roi_heads.box_head import FastRCNNConvFCHead
from newconfig import LazyCall as L
model = L(GeneralizedRCNN)(
backbone=L(FPN)(
bottom_up=L(ResNet)(
stem=L(BasicStem)(in_channels=3, out_channels=64),
# can create some specializations such as "ResNet50" to avoid writing this
stages=[
L(ResNet.make_stage)(
block_class=BottleneckBlock,
num_blocks=n,
stride_per_block=[s] + [1] * (n - 1),
in_channels=i,
bottleneck_channels=o // 4,
out_channels=o,
stride_in_1x1=True,
)
for (n, s, i, o) in zip(
[3, 4, 6, 3], [1, 2, 2, 2], [64, 256, 512, 1024], [256, 512, 1024, 2048]
)
],
out_features=["res2", "res3", "res4", "res5"],
# freeze_at=2,
),
in_features=["res2", "res3", "res4", "res5"],
out_channels=256,
top_block=L(LastLevelMaxPool)(),
),
proposal_generator=L(RPN)(
in_features=["p2", "p3", "p4", "p5", "p6"],
head=L(StandardRPNHead)(in_channels=256, num_anchors=3),
anchor_generator=L(DefaultAnchorGenerator)(
sizes=[[32], [64], [128], [256], [512]],
aspect_ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64],
offset=0.0,
),
anchor_matcher=L(Matcher)(
thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True
),
box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
batch_size_per_image=256,
positive_fraction=0.5,
pre_nms_topk=(2000, 1000),
post_nms_topk=(1000, 1000),
nms_thresh=0.7,
),
roi_heads=L(StandardROIHeads)(
num_classes=80,
batch_size_per_image=512,
positive_fraction=0.25,
proposal_matcher=L(Matcher)(
thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
),
box_in_features=["p2", "p3", "p4", "p5"],
box_pooler=L(ROIPooler)(
output_size=7,
scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
sampling_ratio=0,
pooler_type="ROIAlignV2",
),
box_head=L(FastRCNNConvFCHead)(
input_shape=ShapeSpec(channels=256, height=7, width=7),
conv_dims=[],
fc_dims=[1024, 1024],
),
box_predictor=L(FastRCNNOutputLayers)(
input_shape=ShapeSpec(channels=1024),
test_score_thresh=0.05,
box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
# NOTE: interpolation supported by OmegaConf
num_classes="${..num_classes}",
),
mask_in_features=["p2", "p3", "p4", "p5"],
mask_pooler=L(ROIPooler)(
output_size=14,
scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
sampling_ratio=0,
pooler_type="ROIAlignV2",
),
mask_head=L(MaskRCNNConvUpsampleHead)(
input_shape=ShapeSpec(channels=256, width=14, height=14),
num_classes="${..num_classes}",
conv_dims=[256, 256, 256, 256, 256],
),
),
pixel_mean=[103.530, 116.280, 123.675],
pixel_std=[1.0, 1.0, 1.0],
input_format="BGR",
)
Raw
maskrcnn_mmdet.py
from detectron2.layers import ShapeSpec
from detectron2.modeling import FPN, GeneralizedRCNN, ResNet, StandardROIHeads
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
from detectron2.modeling.backbone.fpn import LastLevelMaxPool
from detectron2.modeling.backbone.resnet import BasicStem, BottleneckBlock
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.matcher import Matcher
from detectron2.modeling.poolers import ROIPooler
from detectron2.modeling.proposal_generator.rpn import RPN, StandardRPNHead
from detectron2.modeling.roi_heads import FastRCNNOutputLayers, MaskRCNNConvUpsampleHead
from detectron2.modeling.roi_heads.box_head import FastRCNNConvFCHead
model = dict(
_target_=GeneralizedRCNN,
backbone=dict(
_target_=FPN,
bottom_up=dict(
_target_=ResNet,
stem=dict(_target_=BasicStem, in_channels=3, out_channels=64),
# can create some specializations such as "ResNet50" to avoid writing this
stages=[
dict(
_target_=ResNet.make_stage,
block_class=BottleneckBlock,
num_blocks=n,
stride_per_block=[s] + [1] * (n - 1),
in_channels=i,
bottleneck_channels=o // 4,
out_channels=o,
stride_in_1x1=True,
)
for (n, s, i, o) in zip(
[3, 4, 6, 3], [1, 2, 2, 2], [64, 256, 512, 1024], [256, 512, 1024, 2048]
)
],
out_features=["res2", "res3", "res4", "res5"],
# freeze_at=2,
),
in_features=["res2", "res3", "res4", "res5"],
out_channels=256,
top_block=dict(_target_=LastLevelMaxPool),
),
proposal_generator=dict(
_target_=RPN,
in_features=["p2", "p3", "p4", "p5", "p6"],
head=dict(_target_=StandardRPNHead, in_channels=256, num_anchors=3),
anchor_generator=dict(
_target_=DefaultAnchorGenerator,
sizes=[[32], [64], [128], [256], [512]],
aspect_ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64],
offset=0.0,
),
anchor_matcher=dict(
_target_=Matcher,
thresholds=[0.3, 0.7],
labels=[0, -1, 1],
allow_low_quality_matches=True,
),
box2box_transform=dict(_target_=Box2BoxTransform, weights=[1.0, 1.0, 1.0, 1.0]),
batch_size_per_image=256,
positive_fraction=0.5,
pre_nms_topk=(2000, 1000),
post_nms_topk=(1000, 1000),
nms_thresh=0.7,
),
roi_heads=dict(
_target_=StandardROIHeads,
num_classes=80,
batch_size_per_image=512,
positive_fraction=0.25,
proposal_matcher=dict(
_target_=Matcher, thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
),
box_in_features=["p2", "p3", "p4", "p5"],
box_pooler=dict(
_target_=ROIPooler,
output_size=7,
scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
sampling_ratio=0,
pooler_type="ROIAlignV2",
),
box_head=dict(
_target_=FastRCNNConvFCHead,
input_shape=ShapeSpec(channels=256, height=7, width=7),
conv_dims=[],
fc_dims=[1024, 1024],
),
box_predictor=dict(
_target_=FastRCNNOutputLayers,
input_shape=ShapeSpec(channels=1024),
test_score_thresh=0.05,
box2box_transform=dict(_target_=Box2BoxTransform, weights=(10, 10, 5, 5)),
# NOTE: interpolation supported by OmegaConf
num_classes="${..num_classes}",
),
mask_in_features=["p2", "p3", "p4", "p5"],
mask_pooler=dict(
_target_=ROIPooler,
output_size=14,
scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
sampling_ratio=0,
pooler_type="ROIAlignV2",
),
mask_head=dict(
_target_=MaskRCNNConvUpsampleHead,
input_shape=ShapeSpec(channels=256, width=14, height=14),
num_classes="${..num_classes}",
conv_dims=[256, 256, 256, 256, 256],
),
),
pixel_mean=[103.530, 116.280, 123.675],
pixel_std=[1.0, 1.0, 1.0],
input_format="BGR",
)
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