Adjusts on evaluation bechmark 'now'

BOVIFOCR · Apr 19, 2023 · 5bb1f85 · 5bb1f85
1 parent 9cf4d84
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diff --git a/...cal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=0.0_lamb2=1.0.yml b/...cal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=0.0_lamb2=1.0.yml
@@ -0,0 +1,85 @@
+# Mica config 
+
+pretrained_model_path: '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/data/pretrained/mica.tar'
+
+dataset:
+  # root: '/home/bjgbiesseck/datasets/'
+  root: '/datasets1/bjgbiesseck/MICA'
+
+  # training_data: [ 'LYHM', 'D3DFACS', 'BU3DFE', 'FRGC', 'Stirling', 'FaceWarehouse', 'BP4D' ]    # original
+  # training_data: [ 'FRGC' ]                          # BERNARDO
+  # training_data: [ 'Stirling' ]                          # BERNARDO
+  # training_data: [ 'FRGC', 'Stirling', 'LYHM', 'FLORENCE' ]      # BERNARDO
+  # training_data: [ 'FRGC', 'LYHM', 'Stirling', 'FACEWAREHOUSE' ]   # BERNARDO
+  training_data: [ 'FRGC', 'LYHM', 'Stirling', 'FACEWAREHOUSE', 'FLORENCE' ]   # BERNARDO
+  # training_data: [ 'FLORENCE' ]   # BERNARDO
+  # training_data: [ 'FRGC_10classes' ]                          # BERNARDO
+
+  # eval_data: [ 'FRGC' ]          # original
+  # eval_data: [ 'FACEWAREHOUSE' ]   # Bernardo
+  # eval_data: [ 'Stirling' ]        # Bernardo
+  eval_data: [ 'FRGC', 'LYHM', 'Stirling', 'FACEWAREHOUSE', 'FLORENCE' ]   # BERNARDO
+  # eval_data: [ 'FLORENCE' ]   # BERNARDO
+  # eval_data: [ 'FRGC_10classes' ]          # original
+
+  train_prop: 0.8
+  eval_prop: 0.2
+
+  num_workers: 4      # original
+  # num_workers: 1    # BERNARDO
+
+  batch_size: 8
+  # batch_size: 10
+
+  # K: 2    # original
+  K: 1      # BERNARDO
+
+# Bernardo
+output_dir_annotation: ''
+
+train:
+  lr: 1e-5
+  arcface_lr: 1e-5
+  face_recog_lr: 1e-5
+  lambda1: 0.0    # reconstruction
+  lambda2: 1.0    # recognition
+  weight_decay: 1e-5
+  use_mask: True
+  reset_optimizer: True
+  max_steps: 160000
+  log_steps: 50         # original
+  # log_steps: 20           # Bernardo
+  val_steps: 300        # original
+  # val_steps:   100        # Bernardo
+  vis_steps: 1200       # original
+  # vis_steps: 100          # Bernardo
+  val_save_img: 1200    # original
+  # val_save_img: 100       # Bernardo
+  checkpoint_steps: 1000
+  checkpoint_epochs_steps: 10000
+  # loss: 'cross-entropy' # Bernardo
+  loss: 'arcface'         # Bernardo
+  arcface_margin1: 0.5    # Bernardo
+  arcface_scale1: 32.0    # Bernardo
+  compute_confusion_matrix: True   # Bernardo
+  compute_affinity_score: False    # Bernardo
+  compute_gradient_angles: False   # Bernardo
+  early_stop_tolerance: 0.1        # Bernardo
+  early_stop_patience: 10          # Bernardo
+  use_masked_faces: True           # Bernardo
+
+model:
+  use_pretrained: False   # original
+  # use_pretrained: True  # Bernardo
+  n_shape: 300
+  # name: 'mica'          # original
+  name: 'micamultitaskfacerecognition1'
+
+  # face_embed: '3dmm'    # Bernardo
+  face_embed: 'arcface'   # Bernardo
+
+  # num_classes: 1945     # Bernardo
+  num_classes: 2040       # Bernardo
+  # num_classes: 531      # Bernardo
+  # num_classes: 53       # Bernardo
+  # num_classes: 10       # Bernardo
diff --git a/reconstruct_faces_3D_MICA-multitask-facerecognition.py b/reconstruct_faces_3D_MICA-multitask-facerecognition.py
@@ -224,20 +224,22 @@ def main(cfg, args):
     # parser.add_argument('-i', default='demo/input_TESTE', type=str, help='Input folder with images')                                          # BERNARDO
     # parser.add_argument('-i', default='demo/input/lfw', type=str, help='Input folder with images')                                            # BERNARDO
     # parser.add_argument('-i', default='demo/input/CelebA/Img/img_align_celeba', type=str, help='Input folder with images')                    # BERNARDO
-    # parser.add_argument('-i', default='demo/input/MS-Celeb-1M/ms1m-retinaface-t1/images', type=str, help='Input folder with images')          # BERNARDO
+    parser.add_argument('-i', default='demo/input/MS-Celeb-1M/ms1m-retinaface-t1/images', type=str, help='Input folder with images')          # BERNARDO
     # parser.add_argument('-i', default='demo/input/MS-Celeb-1M/ms1m-retinaface-t1/images_reduced', type=str, help='Input folder with images')  # BERNARDO
     # parser.add_argument('-i', default='demo/input/MLFW_small', type=str, help='Input folder with images')                                     # BERNARDO
-    parser.add_argument('-i', default='demo/input/MLFW/origin', type=str, help='Input folder with images')                                      # BERNARDO
+    # parser.add_argument('-i', default='demo/input/MLFW/origin', type=str, help='Input folder with images')                                      # BERNARDO
 
     # parser.add_argument('-exp', default='', type=str, help='Processed images for MICA input')
-    parser.add_argument('-exp', default='4_mica_duo_TESTS_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100', type=str, help='Processed images for MICA input')
+    # parser.add_argument('-exp', default='4_mica_duo_TESTS_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100', type=str, help='Processed images for MICA input')
     # parser.add_argument('-exp', default='6_mica_duo_TESTS_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100', type=str, help='Processed images for MICA input')
     # parser.add_argument('-exp', default='11_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_lamb1=0.5_lamb2=1.0', type=str, help='Processed images for MICA input')
     # parser.add_argument('-exp', default='11_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_lamb1=0.5_lamb2=1.0', type=str, help='Processed images for MICA input')
     # parser.add_argument('-exp', default='12_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,Stirling,FACEWAREHOUSE_eval=FLORENCE_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-6_lamb1=0.5_lamb2=1.0', type=str, help='Processed images for MICA input')
     # parser.add_argument('-exp', default='12_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,Stirling,FACEWAREHOUSE_eval=FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_lamb1=0.5_lamb2=1.0', type=str, help='Processed images for MICA input')
     # parser.add_argument('-exp', default='12_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,Stirling,FACEWAREHOUSE_eval=FLORENCE_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_lamb1=0.5_lamb2=1.0', type=str, help='Processed images for MICA input')
 
+    parser.add_argument('-exp', default='26_SANITY-CHECK_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_lamb1=1.0_lamb2=1.0', type=str, help='Processed images for MICA input')
+
     # parser.add_argument('-o', default='output' +'_'+exp, type=str, help='Output folder')
     # parser.add_argument('-a', default='arcface'+'_'+exp, type=str, help='Processed images for MICA input')
 
@@ -249,9 +251,14 @@ def main(cfg, args):
     parser.add_argument('-str_end', default='', type=str, help='Substring to find and stop processing')
 
     args = parser.parse_args()
-    args.o = 'output'  + '_' + args.exp
+
+    # args.o = 'output'  + '_' + args.exp
+    args.o = 'output/MS-Celeb-1M/ms1m-retinaface-t1/3D_reconstruction/' + args.exp + '/'
+
     args.a = 'arcface' + '_' + args.exp
-    args.m = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/' + args.exp + '/model.tar'
+
+    # args.m = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/' + args.exp + '/model.tar'
+    args.m = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/' + args.exp + '/model_290000.tar'
 
     cfg = get_cfg_defaults()
 

diff --git a/test_late_fusion_models.py b/test_late_fusion_models.py
@@ -47,15 +47,15 @@ def find_best_treshold(pair_labels, cos_sims):
 
     # # LFW Dataset
     # file_model1 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/19_mica_duo_pretrainedARCFACE=ms1mv3-r100_fr-feat=original-arcface_ORIGINAL-ARCFACE/cos-sims_checkpoint=_dataset=LFW.npy'
-    # file_model2 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0/cos-sims_checkpoint=model_30000.tar_dataset=LFW.npy'
-    
-    # # MLFW Dataset
+    # file_model2 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0/cos-sims_checkpoint=model_60000.tar_dataset=LFW.npy'
+
+    # MLFW Dataset
     file_model1 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/19_mica_duo_pretrainedARCFACE=ms1mv3-r100_fr-feat=original-arcface_ORIGINAL-ARCFACE/cos-sims_checkpoint=_dataset=MLFW.npy'
-    file_model2 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0/cos-sims_checkpoint=model_30000.tar_dataset=MLFW.npy'
-    
+    file_model2 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0/cos-sims_checkpoint=model_60000.tar_dataset=MLFW.npy'
+
     # TALFW Dataset
     # file_model1 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/19_mica_duo_pretrainedARCFACE=ms1mv3-r100_fr-feat=original-arcface_ORIGINAL-ARCFACE/cos-sims_checkpoint=_dataset=TALFW.npy'
-    # file_model2 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0/cos-sims_checkpoint=model_30000.tar_dataset=TALFW.npy'
+    # file_model2 = '/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/output/27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0/cos-sims_checkpoint=model_60000.tar_dataset=TALFW.npy'
 
     # file_model1 = file_model2
     # file_model2 = file_model1
@@ -66,8 +66,19 @@ def find_best_treshold(pair_labels, cos_sims):
 
     sims_model1, pair_labels_model1 = data_model1['cos-sims'], data_model1['pair_labels']
     sims_model2, pair_labels_model2 = data_model2['cos-sims'], data_model2['pair_labels']
-
-    final_sims = (sims_model1 + sims_model2) / 2
+
+    # AVERAGE DISTANCE
+    # final_sims = (sims_model1 + sims_model2) / 2
+
+    # # MINIMUM DISTANCE
+    # final_sims = np.zeros(shape=sims_model1.shape, dtype=float)
+    # for i in range(len(final_sims)):
+    #     final_sims[i] = np.minimum(sims_model1[i], sims_model2[i])
+
+    # MAXIMUM DISTANCE
+    final_sims = np.zeros(shape=sims_model1.shape, dtype=float)
+    for i in range(len(final_sims)):
+        final_sims[i] = np.maximum(sims_model1[i], sims_model2[i])
 
     print('\nFindind best treshold...')
     best_tresh, best_acc = find_best_treshold(pair_labels_model1, final_sims)

diff --git a/test_multitask_facerecognition1.py b/test_multitask_facerecognition1.py
@@ -22,7 +22,7 @@
 import torch.backends.cudnn as cudnn
 import torch.multiprocessing as mp
 
-# from jobs import test                            # original
+from jobs import test                              # original
 from jobs import test_multitask_facerecognition1   # Bernardo
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
@@ -49,8 +49,8 @@
     # checkpoint = 'model_190000.tar'      # LFW: 95.3%,  MLFW: 68.5%,  TALFW: 75.2%
     # model = '20_SINGLE-TASK-ARCFACE-ACC-CONFMAT_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_eval=20perc_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_lamb1=0.0_lamb2=1.0'
     # checkpoint = 'model_10000.tar'       # LFW: 98.5%,  MLFW: 81.9%,  TALFW: 70.0%
-    # model = '26_SANITY-CHECK_SINGLE-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_lamb1=0.0_lamb2=1.0'
-    # checkpoint = 'model_20000.tar'       # LFW: ????%,  MLFW: ????%,  TALFW: ????%
+    model = '26_SANITY-CHECK_SINGLE-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_lamb1=0.0_lamb2=1.0'
+    checkpoint = 'model_20000.tar'       # LFW: 99.5%,  MLFW: 84.0%,  TALFW: 71.9%
     # model = '26_SANITY-CHECK_SINGLE-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_lamb1=0.0_lamb2=1.0'
     # checkpoint = 'model_190000.tar'       # LFW: 97.3%,  MLFW: 72.2%,  TALFW: 76.0%
 
@@ -79,8 +79,6 @@
     # checkpoint = 'model_80000.tar'     # LFW: 98.6%,  MLFW: 77.2%,  TALFW: 74.4%
     # model = '26_SANITY-CHECK_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-7_loss=arcface_marg1=1.0_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_lamb1=1.0_lamb2=1.0'
     # checkpoint = 'model_80000.tar'     # LFW: 98.5%,  MLFW: 76.8%,  TALFW: 73.9%
-
-    # checkpoint = 'model_??????.tar'   # LFW: ????%,  MLFW: ????%,  TALFW: ????%
 
 
     # # 3DMM (3D only)
@@ -141,13 +139,15 @@
     # checkpoint = 'model_20000.tar'
 
     # TRAINED WITH MASKED FACES
+    # model = '27_SINGLE-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=0.0_lamb2=1.0'
+    # checkpoint = 'model_20000.tar'   # LFW: 98.4%,  MLFW: 85.1%,   TALFW: 73.3%
     # model = '27_SINGLE-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=0.0_lamb2=1.0'
     # checkpoint = 'model_200000.tar'   # LFW: 97.0%,  MLFW: 77.4%,   TALFW: 75.0%
     # model = '27_SINGLE-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=0.0_lamb2=1.0'
     # checkpoint = 'model_200000.tar'   # LFW: 94.0%,  MLFW: 72.7%,   TALFW: 74.7%
-    model = '27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0'
+    # model = '27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0'
     # checkpoint = 'model_10000.tar'    # LFW: 98.5%,  MLFW: 85.0%,   TALFW: 73.5%
-    checkpoint = 'model_30000.tar'    # LFW: 98.3%,  MLFW: 83.6%,   TALFW: 73.8%
+    # checkpoint = 'model_30000.tar'    # LFW: 98.3%,  MLFW: 83.6%,   TALFW: 73.8%
     # checkpoint = 'model_70000.tar'    # LFW: 98.0%,  MLFW: 82.5%,   TALFW: 73.4%
     # model = '27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0'
     # checkpoint = 'model_60000.tar'    # LFW: 96.6%,  MLFW: 79.3%,   TALFW: 72.8%
@@ -185,7 +185,11 @@
     if num_gpus == 0:
         num_gpus = 1    # cpu
 
-    # mp.spawn(test, args=(num_gpus, cfg, args), nprocs=num_gpus, join=True)                            # original
-    mp.spawn(test_multitask_facerecognition1, args=(num_gpus, cfg, args), nprocs=num_gpus, join=True)   # Bernardo
+    # Bernardo
+    if args.test_dataset.upper() == 'NOW' or args.test_dataset.upper() == 'STIRLING':
+        mp.spawn(test, args=(num_gpus, cfg, args), nprocs=num_gpus, join=True)
+    else:
+        # mp.spawn(test, args=(num_gpus, cfg, args), nprocs=num_gpus, join=True)                            # original
+        mp.spawn(test_multitask_facerecognition1, args=(num_gpus, cfg, args), nprocs=num_gpus, join=True)   # Bernardo
 
     exit(0)
diff --git a/testing/now/now_multitask_facerecognition1.py b/testing/now/now_multitask_facerecognition1.py
@@ -32,7 +32,8 @@
 # experiments = ['5_mica_duo_TESTS_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedCOSFACE=glint360k-r100']   # Bernardo
 # experiments = ['6_mica_duo_TESTS_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=True_pretrainedARCFACE=ms1mv3-r100']   # Bernardo
 # experiments = ['10_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5']   # Bernardo
-experiments = ['11_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_lamb1=0.5_lamb2=1.0']   # Bernardo
+# experiments = ['11_mica_duo_MULTITASK-VALIDATION-WORKING_train=FRGC,LYHM,FLORENCE,FACEWAREHOUSE_eval=Stirling_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=3dmm_fr-lr=1e-5_lamb1=0.5_lamb2=1.0']   # Bernardo
+experiments = ['27_MULTI-TASK_train=FRGC,LYHM,Stirling,FACEWAREHOUSE,FLORENCE_pretrainedMICA=False_pretrainedARCFACE=ms1mv3-r100_fr-feat=arcface_fr-lr=1e-5_loss=arcface_marg1=0.5_scal1=32_wd=1e-5_opt=SGD_sched=CosAnn_reset-opt=True_maskface=True_lamb1=1.0_lamb2=1.0']   # Bernardo
 
 
 def test():
@@ -82,6 +83,8 @@ def test():
             print('predicted_meshes:', predicted_meshes)
             print('run:', run)
 
+            '''
+            # original
             with open(f'{jobs}/{run}.sub', 'w') as fid:
                 fid.write('executable = /bin/bash\n')
 
@@ -97,6 +100,12 @@ def test():
                 fid.write(f'requirements = (TARGET.CUDAGlobalMemoryMb > 5000) && (TARGET.CUDAGlobalMemoryMb < 33000)\n')
                 fid.write(f'request_memory = 8192\n')
                 fid.write(f'queue\n')
+            '''
+
+            # Bernardo
+            # arguments = f'/home/wzielonka/projects/MICA/testing/now/template.sh {experiment} {checkpoint} now {predicted_meshes}'    # original
+            # arguments = f'/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/testing/now/template.sh {experiment} {checkpoint} now {predicted_meshes}'      # BERNARDO
+            arguments = f'/home/bjgbiesseck/GitHub/BOVIFOCR_MICA_3Dreconstruction/testing/now/template_multitask_facerecognition1.sh {experiment} {checkpoint} now {predicted_meshes}'      # BERNARDO
 
             # os.system(f'condor_submit_bid 512 {jobs}/{run}.sub')   # original
             # os.system(f'condor_submit {jobs}/{run}.sub')             # Bernardo