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fix: limit box detections per images to 10

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Former-commit-id: 3b49b7b2c9d5093ab989e8757c6b0429eb9746bd [formerly abf1897a28a7591c6fdf9cf51b9c832ca48cc10b]
Former-commit-id: c1db1fe1e9475adbdf10f067db9206d69fe5ed2f
This commit is contained in:
Laurent Fainsin 2022-09-12 09:26:42 +02:00
parent f50b758102
commit c6c08ac98a
1 changed files with 17 additions and 12 deletions
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29
src/mrcnn/module.py
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@ -15,7 +15,10 @@ from torchvision.models.detection.mask_rcnn import (
def get_model_instance_segmentation(num_classes):
# load an instance segmentation model pre-trained on COCO
model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT)
model = torchvision.models.detection.maskrcnn_resnet50_fpn(
weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT,
box_detections_per_img=10, # cap numbers of detections, else memory explosion
)
# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features
@ -97,20 +100,22 @@ class MRCNNModule(pl.LightningModule):
optimizer = torch.optim.Adam(
self.parameters(),
lr=wandb.config.LEARNING_RATE,
momentum=wandb.config.MOMENTUM,
weight_decay=wandb.config.WEIGHT_DECAY,
# momentum=wandb.config.MOMENTUM,
# weight_decay=wandb.config.WEIGHT_DECAY,
)
scheduler = LinearWarmupCosineAnnealingLR(
optimizer,
warmup_epochs=10,
max_epochs=40,
)
# scheduler = LinearWarmupCosineAnnealingLR(
# optimizer,
# warmup_epochs=1,
# max_epochs=30,
# )
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "map",
},
# "lr_scheduler": {
# "scheduler": scheduler,
# "interval": "step",
# "frequency": 10,
# "monitor": "bbox/map",
# },
}