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test_main.py
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import logging
import os
from collections import OrderedDict
from pathlib import Path
import torch
import itertools
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
PascalVOCDetectionEvaluator,
SemSegEvaluator,
verify_results,
)
from detectron2.modeling import GeneralizedRCNNWithTTA
from tools.train_net import build_evaluator, Trainer, maskr_setup, retina_setup
def maskr_test_main_100(args):
cfg = maskr_setup(args)
cfg.defrost()
cfg.INPUT.WINDOW = (0, 1)
cfg.MODEL.WEIGHTS = 'maskr_final_100.pth'
cfg.OUTPUT_DIR = './maskr_outputs_100'
cfg.freeze()
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=args.output_dir).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.retrain)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
def maskr_test_main_995(args):
cfg = maskr_setup(args)
cfg.defrost()
cfg.INPUT.WINDOW = (0.005, 0.995)
cfg.MODEL.WEIGHTS = 'maskr_final_995.pth'
cfg.OUTPUT_DIR = './maskr_outputs_995'
cfg.freeze()
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=args.output_dir).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.retrain)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
def maskr_test_main_99(args):
cfg = maskr_setup(args)
cfg.defrost()
cfg.INPUT.WINDOW = (0.01, 0.99)
cfg.MODEL.WEIGHTS = 'maskr_final_99.pth'
cfg.OUTPUT_DIR = './maskr_outputs_99'
cfg.freeze()
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=args.output_dir).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.retrain)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
def retina_test_main_100(args):
cfg = retina_setup(args)
cfg.defrost()
cfg.INPUT.WINDOW = (0, 1)
cfg.MODEL.WEIGHTS = 'retina_final_100.pth'
cfg.OUTPUT_DIR = './retina_outputs_100'
cfg.freeze()
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=args.output_dir).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.retrain)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
def retina_test_main_995(args):
cfg = retina_setup(args)
cfg.defrost()
cfg.INPUT.WINDOW = (0.005, 0.995)
cfg.MODEL.WEIGHTS = 'retina_final_995.pth'
cfg.OUTPUT_DIR = './retina_outputs_995'
cfg.freeze()
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=args.output_dir).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.retrain)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
def retina_test_main_99(args):
cfg = retina_setup(args)
cfg.defrost()
cfg.INPUT.WINDOW = (0.01, 0.99)
cfg.MODEL.WEIGHTS = 'retina_final_99.pth'
cfg.OUTPUT_DIR = './retina_outputs_99'
cfg.freeze()
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=args.output_dir).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.retrain)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res