class TestDatasetJson: def setup(self): self.d = Dataset('test_data1', force=True) self.classes = ["A", "B", "C"] def test_load(self): assert (self.d.num_images() == 4) assert (self.d.classes == self.classes) def test_get_det_gt(self): gt = self.d.get_det_gt(with_diff=True, with_trun=False) df = Table( np.array([[0., 0., 0., 0., 0., 0, 0, 0.], [1., 1., 1., 1., 1., 0, 0, 0.], [1., 1., 1., 0., 0., 0, 0, 1.], [0., 0., 0., 0., 1., 0, 0, 2.], [0., 0., 0., 0., 2., 0, 0, 3.], [1., 1., 1., 1., 2., 0, 0, 3.]]), ['x', 'y', 'w', 'h', 'cls_ind', 'diff', 'trun', 'img_ind']) print(gt) print(df) assert (gt == df) def test_get_cls_counts_json(self): arr = np.array([[1, 1, 0], [1, 0, 0], [0, 1, 0], [0, 0, 2]]) print(self.d.get_cls_counts()) assert (np.all(self.d.get_cls_counts() == arr)) def test_get_cls_ground_truth_json(self): table = Table( np.array([[True, True, False], [True, False, False], [False, True, False], [False, False, True]]), ["A", "B", "C"]) assert (self.d.get_cls_ground_truth() == table) def test_det_ground_truth_for_class_json(self): gt = self.d.get_det_gt_for_class("A", with_diff=True, with_trun=True) arr = np.array([[0., 0., 0., 0., 0., 0., 0, 0.], [1., 1., 1., 0., 0., 0., 0., 1.]]) cols = ['x', 'y', 'w', 'h', 'cls_ind', 'diff', 'trun', 'img_ind'] print(gt.arr) assert (np.all(gt.arr == arr)) assert (gt.cols == cols) # no diff or trun gt = self.d.get_det_gt_for_class("A", with_diff=False, with_trun=False) arr = np.array([[0., 0., 0., 0., 0., 0., 0, 0.], [1., 1., 1., 0., 0., 0., 0., 1.]]) cols = ['x', 'y', 'w', 'h', 'cls_ind', 'diff', 'trun', 'img_ind'] print(gt.arr) assert (np.all(gt.arr == arr)) assert (gt.cols == cols) def test_set_values(self): assert (np.all(self.d.values == 1 / 3. * np.ones(len(self.classes)))) self.d.set_values('uniform') assert (np.all(self.d.values == 1 / 3. * np.ones(len(self.classes)))) self.d.set_values('inverse_prior') print(self.d.values) assert (np.all(self.d.values == np.array([0.25, 0.25, 0.5])))
class TestEvaluationSynthetic: def __init__(self): self.d = Dataset('test_data2',force=True) self.classes = ["A","B","C"] self.det_gt = self.d.get_det_gt() def test(self): scores = np.ones(self.det_gt.shape[0]) dets = self.det_gt.append_column('score',scores) scores = np.ones(self.d.get_det_gt_for_class('A').shape[0]) dets_just_A = self.d.get_det_gt_for_class('A') dets_just_A = dets_just_A.append_column('score',scores) self.d.set_values('uniform') assert(np.all(self.d.values == 1./3 * np.ones(len(self.classes)))) dp = DatasetPolicy(self.d,self.d,detector='perfect') ev = Evaluation(dp) ap = ev.compute_det_map(dets,self.det_gt) assert(ap==1) ap = ev.compute_det_map(dets_just_A,self.det_gt) print(ap) assert(ut.fequal(ap, 0.33333333333333)) self.d.set_values('inverse_prior') assert(np.all(self.d.values == np.array([0.25,0.25,0.5]))) dp = DatasetPolicy(self.d,self.d,detector='perfect') ev = Evaluation(dp) ap = ev.compute_det_map(dets,self.det_gt) assert(ap==1) ap = ev.compute_det_map(dets_just_A,self.det_gt) print(ap) assert(ut.fequal(ap, 0.25))
class TestEvaluationSynthetic: def __init__(self): self.d = Dataset('test_data2', force=True) self.classes = ["A", "B", "C"] self.det_gt = self.d.get_det_gt() def test(self): scores = np.ones(self.det_gt.shape[0]) dets = self.det_gt.append_column('score', scores) scores = np.ones(self.d.get_det_gt_for_class('A').shape[0]) dets_just_A = self.d.get_det_gt_for_class('A') dets_just_A = dets_just_A.append_column('score', scores) self.d.set_values('uniform') assert (np.all(self.d.values == 1. / 3 * np.ones(len(self.classes)))) dp = DatasetPolicy(self.d, self.d, detector='perfect') ev = Evaluation(dp) ap = ev.compute_det_map(dets, self.det_gt) assert (ap == 1) ap = ev.compute_det_map(dets_just_A, self.det_gt) print(ap) assert (ut.fequal(ap, 0.33333333333333)) self.d.set_values('inverse_prior') assert (np.all(self.d.values == np.array([0.25, 0.25, 0.5]))) dp = DatasetPolicy(self.d, self.d, detector='perfect') ev = Evaluation(dp) ap = ev.compute_det_map(dets, self.det_gt) assert (ap == 1) ap = ev.compute_det_map(dets_just_A, self.det_gt) print(ap) assert (ut.fequal(ap, 0.25))
def main(): parser = argparse.ArgumentParser(description="Run experiments with the timely detection system.") parser.add_argument( "--test_dataset", choices=["val", "test", "trainval"], default="val", help="""Dataset to use for testing. Run on val until final runs. The training dataset is inferred (val->train; test->trainval; trainval->trainval).""", ) parser.add_argument("--first_n", type=int, help="only take the first N images in the test dataset") parser.add_argument("--first_n_train", type=int, help="only take the first N images in the train dataset") parser.add_argument( "--config", help="""Config file name that specifies the experiments to run. Give name such that the file is configs/#{name}.json or configs/#{name}/ In the latter case, all files within the directory will be loaded.""", ) parser.add_argument("--suffix", help="Overwrites the suffix in the config(s).") parser.add_argument("--bounds10", action="store_true", default=False, help="set bounds to [0,10]") parser.add_argument("--bounds515", action="store_true", default=False, help="set bounds to [5,15]") parser.add_argument("--force", action="store_true", default=False, help="force overwrite") parser.add_argument("--wholeset_prs", action="store_true", default=False, help="evaluate in the final p-r regime") parser.add_argument( "--no_apvst", action="store_true", default=False, help="do NOT evaluate in the ap vs. time regime" ) parser.add_argument( "--det_configs", action="store_true", default=False, help="output detector statistics to det_configs" ) parser.add_argument("--inverse_prior", action="store_true", default=False, help="use inverse prior class values") args = parser.parse_args() print(args) # If config file is not given, just run one experiment using default config if not args.config: configs = [DatasetPolicy.default_config] else: configs = load_configs(args.config) # Load the dataset dataset = Dataset("full_pascal_" + args.test_dataset) if args.first_n: dataset.images = dataset.images[: args.first_n] # Infer train_dataset if args.test_dataset == "test": train_dataset = Dataset("full_pascal_trainval") elif args.test_dataset == "val": train_dataset = Dataset("full_pascal_train") elif args.test_dataset == "trainval": train_dataset = Dataset("full_pascal_trainval") else: None # impossible by argparse settings # Only need to set training dataset values; evaluation gets it from there if args.inverse_prior: train_dataset.set_values("inverse_prior") # TODO: hack if args.first_n_train: train_dataset.images = train_dataset.images[: args.first_n_train] # In both the above cases, we use the val dataset for weights weights_dataset_name = "full_pascal_val" dets_tables = [] dets_tables_whole = [] clses_tables_whole = [] all_bounds = [] plot_infos = [] for config_f in configs: if args.suffix: config_f["suffix"] = args.suffix if args.bounds10: config_f["bounds"] = [0, 10] if args.bounds515: config_f["bounds"] = [5, 15] assert not (args.bounds10 and args.bounds515) if args.inverse_prior: config_f["suffix"] += "_inverse_prior" config_f["values"] = "inverse_prior" dp = DatasetPolicy(dataset, train_dataset, weights_dataset_name, **config_f) ev = Evaluation(dp) all_bounds.append(dp.bounds) plot_infos.append(dict((k, config_f[k]) for k in ("label", "line", "color") if k in config_f)) # output the det configs first if args.det_configs: dp.output_det_statistics() # evaluate in the AP vs. Time regime, unless told not to if not args.no_apvst: dets_table = ev.evaluate_vs_t(None, None, force=args.force) # dets_table_whole,clses_table_whole = ev.evaluate_vs_t_whole(None,None,force=args.force) if comm_rank == 0: dets_tables.append(dets_table) # dets_tables_whole.append(dets_table_whole) # clses_tables_whole.append(clses_table_whole) # optionally, evaluate in the standard PR regime if args.wholeset_prs: ev.evaluate_detections_whole(None, force=args.force) # and plot the comparison if multiple config files were given if not args.no_apvst and len(configs) > 1 and comm_rank == 0: # filename of the final plot is the config file name dirname = config.get_evals_dir(dataset.get_name()) filename = args.config if args.inverse_prior: filename += "_inverse_prior" # det avg ff = opjoin(dirname, "%s_det_avg.png" % filename) ff_nl = opjoin(dirname, "%s_det_avg_nl.png" % filename) # make sure directory exists ut.makedirs(os.path.dirname(ff)) Evaluation.plot_ap_vs_t(dets_tables, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos) Evaluation.plot_ap_vs_t(dets_tables, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos) if False: # det whole ff = opjoin(dirname, "%s_det_whole.png" % filename) ff_nl = opjoin(dirname, "%s_det_whole_nl.png" % filename) Evaluation.plot_ap_vs_t( dets_tables_whole, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos ) Evaluation.plot_ap_vs_t( dets_tables_whole, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos ) # cls whole ff = opjoin(dirname, "%s_cls_whole.png" % filename) ff_nl = opjoin(dirname, "%s_cls_whole_nl.png" % filename) Evaluation.plot_ap_vs_t( clses_tables_whole, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos ) Evaluation.plot_ap_vs_t( clses_tables_whole, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos )
def main(): parser = argparse.ArgumentParser( description="Run experiments with the timely detection system.") parser.add_argument('--test_dataset', choices=['val','test','trainval'], default='val', help="""Dataset to use for testing. Run on val until final runs. The training dataset is inferred (val->train; test->trainval; trainval->trainval).""") parser.add_argument('--first_n', type=int, help='only take the first N images in the test dataset') parser.add_argument('--first_n_train', type=int, help='only take the first N images in the train dataset') parser.add_argument('--config', help="""Config file name that specifies the experiments to run. Give name such that the file is configs/#{name}.json or configs/#{name}/ In the latter case, all files within the directory will be loaded.""") parser.add_argument('--suffix', help="Overwrites the suffix in the config(s).") parser.add_argument('--bounds10', action='store_true', default=False, help='set bounds to [0,10]') parser.add_argument('--bounds515', action='store_true', default=False, help='set bounds to [5,15]') parser.add_argument('--force', action='store_true', default=False, help='force overwrite') parser.add_argument('--wholeset_prs', action='store_true', default=False, help='evaluate in the final p-r regime') parser.add_argument('--no_apvst', action='store_true', default=False, help='do NOT evaluate in the ap vs. time regime') parser.add_argument('--det_configs', action='store_true', default=False, help='output detector statistics to det_configs') parser.add_argument('--inverse_prior', action='store_true', default=False, help='use inverse prior class values') args = parser.parse_args() print(args) # If config file is not given, just run one experiment using default config if not args.config: configs = [DatasetPolicy.default_config] else: configs = load_configs(args.config) # Load the dataset dataset = Dataset('full_pascal_'+args.test_dataset) if args.first_n: dataset.images = dataset.images[:args.first_n] # Infer train_dataset if args.test_dataset=='test': train_dataset = Dataset('full_pascal_trainval') elif args.test_dataset=='val': train_dataset = Dataset('full_pascal_train') elif args.test_dataset=='trainval': train_dataset = Dataset('full_pascal_trainval') else: None # impossible by argparse settings # Only need to set training dataset values; evaluation gets it from there if args.inverse_prior: train_dataset.set_values('inverse_prior') # TODO: hack if args.first_n_train: train_dataset.images = train_dataset.images[:args.first_n_train] # In both the above cases, we use the val dataset for weights weights_dataset_name = 'full_pascal_val' dets_tables = [] dets_tables_whole = [] clses_tables_whole = [] all_bounds = [] plot_infos = [] for config_f in configs: if args.suffix: config_f['suffix'] = args.suffix if args.bounds10: config_f['bounds'] = [0,10] if args.bounds515: config_f['bounds'] = [5,15] assert(not (args.bounds10 and args.bounds515)) if args.inverse_prior: config_f['suffix'] += '_inverse_prior' config_f['values'] = 'inverse_prior' dp = DatasetPolicy(dataset, train_dataset, weights_dataset_name, **config_f) ev = Evaluation(dp) all_bounds.append(dp.bounds) plot_infos.append(dict((k,config_f[k]) for k in ('label','line','color') if k in config_f)) # output the det configs first if args.det_configs: dp.output_det_statistics() # evaluate in the AP vs. Time regime, unless told not to if not args.no_apvst: dets_table = ev.evaluate_vs_t(None,None,force=args.force) #dets_table_whole,clses_table_whole = ev.evaluate_vs_t_whole(None,None,force=args.force) if comm_rank==0: dets_tables.append(dets_table) #dets_tables_whole.append(dets_table_whole) #clses_tables_whole.append(clses_table_whole) # optionally, evaluate in the standard PR regime if args.wholeset_prs: ev.evaluate_detections_whole(None,force=args.force) # and plot the comparison if multiple config files were given if not args.no_apvst and len(configs)>1 and comm_rank==0: # filename of the final plot is the config file name dirname = config.get_evals_dir(dataset.get_name()) filename = args.config if args.inverse_prior: filename += '_inverse_prior' # det avg ff = opjoin(dirname, '%s_det_avg.png'%filename) ff_nl = opjoin(dirname, '%s_det_avg_nl.png'%filename) # make sure directory exists ut.makedirs(os.path.dirname(ff)) Evaluation.plot_ap_vs_t(dets_tables, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos) Evaluation.plot_ap_vs_t(dets_tables, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos) if False: # det whole ff = opjoin(dirname, '%s_det_whole.png'%filename) ff_nl = opjoin(dirname, '%s_det_whole_nl.png'%filename) Evaluation.plot_ap_vs_t(dets_tables_whole, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos) Evaluation.plot_ap_vs_t(dets_tables_whole, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos) # cls whole ff = opjoin(dirname, '%s_cls_whole.png'%filename) ff_nl = opjoin(dirname, '%s_cls_whole_nl.png'%filename) Evaluation.plot_ap_vs_t(clses_tables_whole, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos) Evaluation.plot_ap_vs_t(clses_tables_whole, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos)
class TestDatasetJson: def setup(self): self.d = Dataset('test_data1',force=True) self.classes = ["A","B","C"] def test_load(self): assert(self.d.num_images() == 4) assert(self.d.classes == self.classes) def test_get_det_gt(self): gt = self.d.get_det_gt(with_diff=True,with_trun=False) df = Table( np.array([[ 0., 0., 0., 0., 0., 0, 0, 0.], [ 1., 1., 1., 1., 1., 0, 0, 0.], [ 1., 1., 1., 0., 0., 0, 0, 1.], [ 0., 0., 0., 0., 1., 0, 0, 2.], [ 0., 0., 0., 0., 2., 0, 0, 3.], [ 1., 1., 1., 1., 2., 0, 0, 3.]]), ['x','y','w','h','cls_ind','diff','trun','img_ind']) print(gt) print(df) assert(gt == df) def test_get_cls_counts_json(self): arr = np.array( [ [ 1, 1, 0], [ 1, 0, 0], [ 0, 1, 0], [ 0, 0, 2]]) print(self.d.get_cls_counts()) assert(np.all(self.d.get_cls_counts() == arr)) def test_get_cls_ground_truth_json(self): table = Table( np.array([ [ True, True, False], [ True, False, False], [ False, True, False], [ False, False, True] ]), ["A","B","C"]) assert(self.d.get_cls_ground_truth()==table) def test_det_ground_truth_for_class_json(self): gt = self.d.get_det_gt_for_class("A",with_diff=True,with_trun=True) arr = np.array( [[ 0., 0., 0., 0., 0., 0., 0, 0.], [ 1., 1., 1., 0., 0., 0., 0., 1.]]) cols = ['x','y','w','h','cls_ind','diff','trun','img_ind'] print(gt.arr) assert(np.all(gt.arr == arr)) assert(gt.cols == cols) # no diff or trun gt = self.d.get_det_gt_for_class("A",with_diff=False,with_trun=False) arr = np.array( [[ 0., 0., 0., 0., 0., 0., 0, 0.], [ 1., 1., 1., 0., 0., 0., 0., 1.]]) cols = ['x','y','w','h','cls_ind','diff','trun','img_ind'] print(gt.arr) assert(np.all(gt.arr == arr)) assert(gt.cols == cols) def test_set_values(self): assert(np.all(self.d.values == 1/3. * np.ones(len(self.classes)))) self.d.set_values('uniform') assert(np.all(self.d.values == 1/3. * np.ones(len(self.classes)))) self.d.set_values('inverse_prior') print(self.d.values) assert(np.all(self.d.values == np.array([0.25,0.25,0.5])))