Merge branch 'master' into dist_part

examples/pytorch/hilander/PSS/Smooth_AP/src/evaluate_model.py

+import os, torch, argparse
+import netlib as netlib
+import auxiliaries as aux
+import datasets as data
+import evaluate as eval
+
+if __name__ == '__main__':
+
+    ################## INPUT ARGUMENTS ###################
+    parser = argparse.ArgumentParser()
+    ####### Main Parameter: Dataset to use for Training
+    parser.add_argument('--dataset', default='vehicle_id', type=str, help='Dataset to use.',
+                        choices=['Inaturalist', 'vehicle_id'])
+    parser.add_argument('--source_path', default='/scratch/shared/beegfs/abrown/datasets', type=str,
+                        help='Path to training data.')
+    parser.add_argument('--save_path', default=os.getcwd() + '/Training_Results', type=str,
+                        help='Where to save everything.')
+    parser.add_argument('--savename', default='', type=str,
+                        help='Save folder name if any special information is to be included.')
+
+    ### General Training Parameters
+    parser.add_argument('--kernels', default=8, type=int, help='Number of workers for pytorch dataloader.')
+    parser.add_argument('--bs', default=112, type=int, help='Mini-Batchsize to use.')
+    parser.add_argument('--samples_per_class', default=4, type=int,help='Number of samples in one class drawn before choosing the next class. Set to >1 for losses other than ProxyNCA.')
+    parser.add_argument('--loss', default='smoothap', type=str)
+
+    ##### Evaluation Settings
+    parser.add_argument('--k_vals', nargs='+', default=[1, 2, 4, 8], type=int, help='Recall @ Values.')
+    ##### Network parameters
+    parser.add_argument('--embed_dim', default=512, type=int,
+                        help='Embedding dimensionality of the network. Note: in literature, dim=128 is used for ResNet50 and dim=512 for GoogLeNet.')
+    parser.add_argument('--arch', default='resnet50', type=str,
+                        help='Network backend choice: resnet50, googlenet, BNinception')
+    parser.add_argument('--gpu', default=0, type=int, help='GPU-id for GPU to use.')
+    parser.add_argument('--resume', default='', type=str, help='path to where weights to be evaluated are saved.')
+    parser.add_argument('--not_pretrained', action='store_true',
+                        help='If added, the network will be trained WITHOUT ImageNet-pretrained weights.')
+
+    parser.add_argument('--trainset', default="lin_train_set1.txt", type=str)
+    parser.add_argument('--testset', default="Inaturalist_test_set1.txt", type=str)
+    parser.add_argument('--cluster_path', default="", type=str)
+    parser.add_argument('--finetune', default="false", type=str)
+    parser.add_argument('--class_num', default=948, type=int)
+    parser.add_argument('--get_features', default="false", type=str)
+    parser.add_argument('--patch_size', default=16, type=int, help='vit patch size')
+    parser.add_argument('--pretrained_weights', default="", type=str, help='pretrained weight path')
+    parser.add_argument('--use_bn_in_head', default=False, type=aux.bool_flag,
+                        help="Whether to use batch normalizations in projection head (Default: False)")
+    parser.add_argument("--checkpoint_key", default="teacher", type=str,
+                        help='Key to use in the checkpoint (example: "teacher")')
+    parser.add_argument('--drop_path_rate', default=0.1, type=float, help="stochastic depth rate")
+    parser.add_argument('--norm_last_layer', default=True, type=aux.bool_flag,
+                        help="""Whether or not to weight normalize the last layer of the DINO head.
+        Not normalizing leads to better performance but can make the training unstable.
+        In our experiments, we typically set this paramater to False with vit_small and True with vit_base.""")
+    parser.add_argument('--linsize', default=29011, type=int, help="Lin data size.")
+    parser.add_argument('--uinsize', default=18403, type=int, help="Uin data size.")
+    opt = parser.parse_args()
+
+    """============================================================================"""
+    opt.source_path += '/' + opt.dataset
+
+    if opt.dataset == 'Inaturalist':
+        opt.n_epochs = 90
+        opt.tau = [40, 70]
+        opt.k_vals = [1, 4, 16, 32]
+
+    if opt.dataset == 'vehicle_id':
+        opt.k_vals = [1, 5]
+
+    if opt.finetune == 'true':
+        opt.finetune = True
+    elif opt.finetune == 'false':
+        opt.finetune = False
+
+    if opt.get_features == 'true':
+        opt.get_features = True
+    elif opt.get_features == 'false':
+        opt.get_features = False
+
+    metrics_to_log = aux.metrics_to_examine(opt.dataset, opt.k_vals)
+    LOG = aux.LOGGER(opt, metrics_to_log, name='Base', start_new=True)
+
+    """============================================================================"""
+    ##################### NETWORK SETUP ##################
+
+    opt.device = torch.device('cuda')
+    model = netlib.networkselect(opt)
+
+    # Push to Device
+    _ = model.to(opt.device)
+
+    """============================================================================"""
+    #################### DATALOADER SETUPS ##################
+    # Returns a dictionary containing 'training', 'testing', and 'evaluation' dataloaders.
+    # The 'testing'-dataloader corresponds to the validation set, and the 'evaluation'-dataloader
+    # Is simply using the training set, however running under the same rules as 'testing' dataloader,
+    # i.e. no shuffling and no random cropping.
+    dataloaders = data.give_dataloaders(opt.dataset, opt.trainset, opt.testset, opt)
+    # Because the number of supervised classes is dataset dependent, we store them after
+    # initializing the dataloader
+    opt.num_classes = len(dataloaders['training'].dataset.avail_classes)
+
+    if opt.dataset == 'Inaturalist':
+        eval_params = {'dataloader': dataloaders['testing'], 'model': model, 'opt': opt, 'epoch': 0}
+
+    elif opt.dataset == 'vehicle_id':
+        eval_params = {
+            'dataloaders': [dataloaders['testing_set1'], dataloaders['testing_set2'], dataloaders['testing_set3']],
+            'model': model, 'opt': opt, 'epoch': 0}
+
+    """============================================================================"""
+    ####################evaluation ##################
+
+    results = eval.evaluate(opt.dataset, LOG, save=True, **eval_params)