Model tests

configs/solo/solo_mvitv2_DC2_gold.py

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+""" This is a demo "solo config" file for use in solo_test_run_transformers.py.
+
+This uses template configs cascade_mask_rcnn_swin_b_in21k_50ep and yaml_style_defaults."""
+
+from omegaconf import OmegaConf
+import numpy as np
+# ---------------------------------------------------------------------------- #
+# Local variables and metadata
+# ---------------------------------------------------------------------------- #
+bs = 1
+
+metadata = OmegaConf.create() 
+metadata.classes = ["object"]
+
+numclasses = len(metadata.classes)
+
+# ---------------------------------------------------------------------------- #
+# Standard config (this has always been the LazyConfig/.py-style config)
+# ---------------------------------------------------------------------------- #
+# Get values from templates
+from ..COCO.cascade_mask_rcnn_mvitv2_b_in21k_100ep import dataloader, model, train, lr_multiplier, optimizer
+from deepdisc.model.models import RedshiftPDFCasROIHeads, OldRedshiftPDFCasROIHeads, WeightedRedshiftPDFCasROIHeadsGold, RedshiftPDFCasROIHeadsGold
+from deepdisc.model.loaders import (RedshiftDictMapper, RedshiftDictMapperEval, 
+GoldRedshiftDictMapperEval, GoldRedshiftDictMapper, WCSDictmapper, MagRedshiftDictMapper, MagRedshiftDictMapperNlim)
+from deepdisc.data_format.augment_image import dc2_train_augs, dc2_train_augs_full
+from deepdisc.data_format.image_readers import DC2ImageReader
+
+# Overrides
+dataloader.augs = dc2_train_augs
+dataloader.train.total_batch_size = bs
+
+model.proposal_generator.anchor_generator.sizes = [[8], [16], [32], [64], [128]]
+#model.proposal_generator.batch_size_per_image=128
+
+
+model.roi_heads.num_classes = numclasses
+model.roi_heads.batch_size_per_image = 512
+model.roi_heads.positive_fraction = 0.5
+model.backbone.bottom_up.in_chans = 6
+
+model.pixel_mean = [0.05485338, 0.05625433, 0.08389594, 0.11460936, 0.15186928, 0.22200608]
+model.pixel_std =[2.7777975, 2.009476, 2.780979, 3.7311685, 4.749582, 7.093875 ]
+
+model.backbone.square_pad = 1100
+
+model.roi_heads.num_components = 5
+model.roi_heads.zloss_factor = 1
+#model.roi_heads.zbins = np.linspace(0,3,300)
+#model.roi_heads.weights = np.load('/home/g4merz/rail_deepdisc/configs/solo/zweights.npy')
+model.roi_heads.zloss_factor = 1
+
+model.roi_heads._target_ = RedshiftPDFCasROIHeadsGold
+
+#model.proposal_generator.pre_nms_topk=[10000,10000]
+#model.proposal_generator.post_nms_topk=[10000,10000]
+#model.proposal_generator.pre_nms_topk=[200,1000]
+model.proposal_generator.post_nms_topk=[6000,1000]
+#model.proposal_generator.nms_thresh = 0.3
+for box_predictor in model.roi_heads.box_predictors:
+    box_predictor.test_topk_per_image = 3000
+    box_predictor.test_score_thresh = 0.3
+    box_predictor.test_nms_thresh = 0.5
+
+
+train.init_checkpoint = "/home/shared/hsc/detectron2/projects/ViTDet/model_final_8c3da3.pkl"
+#train.init_checkpoint = "/home/g4merz/DC2/model_tests/MViTv2_baseline.pth"
+
+optimizer.lr = 0.001
+dataloader.test.mapper = WCSDictmapper
+dataloader.train.mapper = MagRedshiftDictMapperNlim
+
+reader = DC2ImageReader()
+dataloader.imagereader = reader
+
+# ---------------------------------------------------------------------------- #
+# Yaml-style config (was formerly saved as a .yaml file, loaded to cfg_loader)
+# ---------------------------------------------------------------------------- #
+# Get values from template
+from .yacs_style_defaults import MISC, DATALOADER, DATASETS, GLOBAL, INPUT, MODEL, SOLVER, TEST
+
+# Overrides
+SOLVER.IMS_PER_BATCH = bs
+
+DATASETS.TRAIN = "astro_train"
+DATASETS.TEST = "astro_val"
+
+SOLVER.BASE_LR = 0.001
+SOLVER.CLIP_GRADIENTS.ENABLED = True
+# Type of gradient clipping, currently 2 values are supported:
+# - "value": the absolute values of elements of each gradients are clipped
+# - "norm": the norm of the gradient for each parameter is clipped thus
+#   affecting all elements in the parameter
+SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "norm"
+# Maximum absolute value used for clipping gradients
+# Floating point number p for L-p norm to be used with the "norm"
+# gradient clipping type; for L-inf, please specify .inf
+SOLVER.CLIP_GRADIENTS.NORM_TYPE = 5.0
+
+
+SOLVER.STEPS = []  # do not decay learning rate for retraining
+SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
+SOLVER.WARMUP_ITERS = 0
+TEST.DETECTIONS_PER_IMAGE = 3000