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fix: limit box detections per images to 10
Former-commit-id: 3b49b7b2c9d5093ab989e8757c6b0429eb9746bd [formerly abf1897a28a7591c6fdf9cf51b9c832ca48cc10b] Former-commit-id: c1db1fe1e9475adbdf10f067db9206d69fe5ed2f
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@ -15,7 +15,10 @@ from torchvision.models.detection.mask_rcnn import (
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def get_model_instance_segmentation(num_classes):
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# load an instance segmentation model pre-trained on COCO
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model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT)
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model = torchvision.models.detection.maskrcnn_resnet50_fpn(
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weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT,
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box_detections_per_img=10, # cap numbers of detections, else memory explosion
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)
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# get number of input features for the classifier
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in_features = model.roi_heads.box_predictor.cls_score.in_features
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@ -97,20 +100,22 @@ class MRCNNModule(pl.LightningModule):
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optimizer = torch.optim.Adam(
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self.parameters(),
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lr=wandb.config.LEARNING_RATE,
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momentum=wandb.config.MOMENTUM,
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weight_decay=wandb.config.WEIGHT_DECAY,
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# momentum=wandb.config.MOMENTUM,
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# weight_decay=wandb.config.WEIGHT_DECAY,
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)
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scheduler = LinearWarmupCosineAnnealingLR(
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optimizer,
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warmup_epochs=10,
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max_epochs=40,
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)
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# scheduler = LinearWarmupCosineAnnealingLR(
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# optimizer,
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# warmup_epochs=1,
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# max_epochs=30,
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# )
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return {
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"optimizer": optimizer,
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"lr_scheduler": {
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"scheduler": scheduler,
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"monitor": "map",
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},
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# "lr_scheduler": {
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# "scheduler": scheduler,
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# "interval": "step",
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# "frequency": 10,
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# "monitor": "bbox/map",
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# },
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}
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