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picie.out
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Namespace(K_test=27, K_train=27, X=80, arch='resnet18', augment=True, batch_size_cluster=256, batch_size_test=128, batch_size_train=128, blur=True, comment='', data_root='datasets/coco/', equiv=True, eval_only=False, eval_path=None, fullcoco=False, grey=True, h_flip=True, in_dim=128, jitter=True, kmeans_n_iter=20, lr=0.0001, metric_test='cosine', metric_train='cosine', min_scale=0.5, momentum=0.9, mse=False, no_balance=False, num_batches=1, num_epoch=10, num_init_batches=20, num_workers=4, optim_type='Adam', pretrain=True, random_crop=True, repeats=1, res=320, res1=320, res2=640, restart=False, restart_path=None, save_eval_path='results/picie/train/2/augmented/res1=320_res2=640/jitter=True_blur=True_grey=True/equiv/h_flip=True_v_flip=False_crop=True/min_scale\\=0.5/K_train=27_cosine/K_test=27_cosine', save_model_path='results/picie/train/2/augmented/res1=320_res2=640/jitter=True_blur=True_grey=True/equiv/h_flip=True_v_flip=False_crop=True/min_scale\\=0.5/K_train=27_cosine', save_root='results/picie/train/2/augmented/res1=320_res2=640/jitter=True_blur=True_grey=True/equiv/h_flip=True_v_flip=False_crop=True/min_scale\\=0.5', seed=1, stuff=True, thing=True, v_flip=False, val_type='train', version=7, weight_decay=0.0005)
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 18.8452
mIOU - All: 4.2436
ACC - Thing: 28.6455
mIOU - Thing: 10.1113
ACC - Stuff: 37.6319
mIOU - Stuff: 7.7557
============================= [Epoch 0] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.4601
[Saving features]: 100 / 194 | [K-Means Loss]: 0.4588
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.4479
[Saving features]: 100 / 194 | [K-Means Loss]: 0.4475
-Centroids ready. [Loss: 0.45853| 0.44700/ Time: 0:0:38:26]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:28:49]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 37.6356
mIOU - All: 14.1555
ACC - Thing: 67.0118
mIOU - Thing: 23.7457
ACC - Stuff: 50.7591
mIOU - Stuff: 23.2840
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 35.1216
mIOU - All: 13.0166
ACC - Thing: 62.3331
mIOU - Thing: 30.2290
ACC - Stuff: 49.0684
mIOU - Stuff: 17.8731
============== Epoch [0] ==============
Time: [0:1:34:22]
K-Means loss : 0.45853 | 0.44700
Training Total Loss : 3.05342
Training CE Loss (Total | Within | Across) : 3.05342 | 3.04292 | 3.06391
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 37.6356 | mIoU: 14.1555
[View 2] ACC: 35.1216 | mIoU: 13.0166
========================================
============================= [Epoch 1] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.2039
[Saving features]: 100 / 194 | [K-Means Loss]: 0.2052
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.2034
[Saving features]: 100 / 194 | [K-Means Loss]: 0.2036
-Centroids ready. [Loss: 0.20443| 0.20321/ Time: 0:0:36:22]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:28:20]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 39.0233
mIOU - All: 14.8177
ACC - Thing: 68.3996
mIOU - Thing: 26.6696
ACC - Stuff: 49.2159
mIOU - Stuff: 23.9969
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 39.1512
mIOU - All: 14.9806
ACC - Thing: 70.3425
mIOU - Thing: 34.5382
ACC - Stuff: 52.1433
mIOU - Stuff: 20.9823
============== Epoch [1] ==============
Time: [0:1:30:31]
K-Means loss : 0.20443 | 0.20321
Training Total Loss : 2.56481
Training CE Loss (Total | Within | Across) : 2.56481 | 2.53902 | 2.59061
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 39.0233 | mIoU: 14.8177
[View 2] ACC: 39.1512 | mIoU: 14.9806
========================================
============================= [Epoch 2] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.1189
[Saving features]: 100 / 194 | [K-Means Loss]: 0.1183
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.1210
[Saving features]: 100 / 194 | [K-Means Loss]: 0.1201
-Centroids ready. [Loss: 0.11824| 0.12039/ Time: 0:0:36:11]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:29:21]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 43.3283
mIOU - All: 16.1875
ACC - Thing: 59.8005
mIOU - Thing: 23.3684
ACC - Stuff: 63.3946
mIOU - Stuff: 27.9564
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 42.8166
mIOU - All: 16.1035
ACC - Thing: 48.7028
mIOU - Thing: 18.5451
ACC - Stuff: 66.8791
mIOU - Stuff: 29.7519
============== Epoch [2] ==============
Time: [0:1:32:16]
K-Means loss : 0.11824 | 0.12039
Training Total Loss : 2.50513
Training CE Loss (Total | Within | Across) : 2.50513 | 2.48279 | 2.52747
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 43.3283 | mIoU: 16.1875
[View 2] ACC: 42.8166 | mIoU: 16.1035
========================================
============================= [Epoch 3] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0917
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0928
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0908
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0910
-Centroids ready. [Loss: 0.09297| 0.09143/ Time: 0:0:36:17]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:29:25]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 45.2109
mIOU - All: 15.9478
ACC - Thing: 76.5163
mIOU - Thing: 30.3065
ACC - Stuff: 53.7017
mIOU - Stuff: 22.3206
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 45.3021
mIOU - All: 15.8186
ACC - Thing: 72.4993
mIOU - Thing: 30.6773
ACC - Stuff: 56.8843
mIOU - Stuff: 21.0298
============== Epoch [3] ==============
Time: [0:1:32:25]
K-Means loss : 0.09297 | 0.09143
Training Total Loss : 2.48141
Training CE Loss (Total | Within | Across) : 2.48141 | 2.46209 | 2.50074
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 45.2109 | mIoU: 15.9478
[View 2] ACC: 45.3021 | mIoU: 15.8186
========================================
============================= [Epoch 4] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0743
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0751
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0722
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0727
-Centroids ready. [Loss: 0.07500| 0.07269/ Time: 0:0:35:44]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:31:10]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 46.4930
mIOU - All: 16.5259
ACC - Thing: 61.1411
mIOU - Thing: 25.4212
ACC - Stuff: 65.4844
mIOU - Stuff: 26.5080
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 46.6239
mIOU - All: 16.2388
ACC - Thing: 64.1401
mIOU - Thing: 25.4810
ACC - Stuff: 64.3168
mIOU - Stuff: 24.8522
============== Epoch [4] ==============
Time: [0:1:33:49]
K-Means loss : 0.07500 | 0.07269
Training Total Loss : 2.46328
Training CE Loss (Total | Within | Across) : 2.46328 | 2.44536 | 2.48119
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 46.4930 | mIoU: 16.5259
[View 2] ACC: 46.6239 | mIoU: 16.2388
========================================
============================= [Epoch 5] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0687
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0678
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0691
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0682
-Centroids ready. [Loss: 0.06804| 0.06814/ Time: 0:0:35:57]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:29:8]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 47.8959
mIOU - All: 16.3133
ACC - Thing: 66.8713
mIOU - Thing: 23.8445
ACC - Stuff: 66.0643
mIOU - Stuff: 27.2033
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 47.8854
mIOU - All: 16.2831
ACC - Thing: 80.5183
mIOU - Thing: 25.0320
ACC - Stuff: 63.1158
mIOU - Stuff: 28.2966
============== Epoch [5] ==============
Time: [0:1:31:28]
K-Means loss : 0.06804 | 0.06814
Training Total Loss : 2.44650
Training CE Loss (Total | Within | Across) : 2.44650 | 2.42853 | 2.46447
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 47.8959 | mIoU: 16.3133
[View 2] ACC: 47.8854 | mIoU: 16.2831
========================================
============================= [Epoch 6] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0681
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0675
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0621
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0626
-Centroids ready. [Loss: 0.06752| 0.06266/ Time: 0:0:36:21]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:30:37]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 47.8187
mIOU - All: 15.3864
ACC - Thing: 67.1266
mIOU - Thing: 25.9309
ACC - Stuff: 74.6963
mIOU - Stuff: 26.6275
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 47.7087
mIOU - All: 15.3938
ACC - Thing: 73.6374
mIOU - Thing: 25.1015
ACC - Stuff: 60.9616
mIOU - Stuff: 24.1351
============== Epoch [6] ==============
Time: [0:1:33:42]
K-Means loss : 0.06752 | 0.06266
Training Total Loss : 2.47982
Training CE Loss (Total | Within | Across) : 2.47982 | 2.46186 | 2.49778
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 47.8187 | mIoU: 15.3864
[View 2] ACC: 47.7087 | mIoU: 15.3938
========================================
============================= [Epoch 7] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0644
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0627
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0580
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0568
-Centroids ready. [Loss: 0.06287| 0.05667/ Time: 0:0:37:15]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:30:29]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 49.3254
mIOU - All: 15.0569
ACC - Thing: 69.1581
mIOU - Thing: 21.2520
ACC - Stuff: 60.3569
mIOU - Stuff: 21.6412
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 49.5130
mIOU - All: 15.0085
ACC - Thing: 71.1429
mIOU - Thing: 24.1204
ACC - Stuff: 67.0931
mIOU - Stuff: 25.2922
============== Epoch [7] ==============
Time: [0:1:34:48]
K-Means loss : 0.06287 | 0.05667
Training Total Loss : 2.44805
Training CE Loss (Total | Within | Across) : 2.44805 | 2.43068 | 2.46543
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 49.3254 | mIoU: 15.0569
[View 2] ACC: 49.5130 | mIoU: 15.0085
========================================
============================= [Epoch 8] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0533
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0528
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0509
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0507
-Centroids ready. [Loss: 0.05263| 0.05073/ Time: 0:0:36:18]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:30:16]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 50.2814
mIOU - All: 15.5417
ACC - Thing: 71.5066
mIOU - Thing: 26.9288
ACC - Stuff: 65.4421
mIOU - Stuff: 23.6686
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 50.1504
mIOU - All: 14.9099
ACC - Thing: 86.4098
mIOU - Thing: 22.2009
ACC - Stuff: 56.7916
mIOU - Stuff: 22.6426
============== Epoch [8] ==============
Time: [0:1:32:52]
K-Means loss : 0.05263 | 0.05073
Training Total Loss : 2.45938
Training CE Loss (Total | Within | Across) : 2.45938 | 2.44209 | 2.47667
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 50.2814 | mIoU: 15.5417
[View 2] ACC: 50.1504 | mIoU: 14.9099
========================================
============================= [Epoch 9] =============================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0543
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0545
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0556
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0543
-Centroids ready. [Loss: 0.05454| 0.05451/ Time: 0:0:36:50]
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
Centroid size : [27, 128]
Batch input size : [256, 3, 320, 320]
Batch feature size : [256, 128, 80, 80]
[Assigning labels] 0 / 194
-Cluster labels ready. [0:0:30:15]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 388
200 / 388
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 50.3443
mIOU - All: 14.9282
ACC - Thing: 74.4732
mIOU - Thing: 25.2948
ACC - Stuff: 66.9904
mIOU - Stuff: 23.5130
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 50.7423
mIOU - All: 15.3916
ACC - Thing: 69.2927
mIOU - Thing: 21.3143
ACC - Stuff: 68.5654
mIOU - Stuff: 25.2316
============== Epoch [9] ==============
Time: [0:1:33:3]
K-Means loss : 0.05454 | 0.05451
Training Total Loss : 2.46851
Training CE Loss (Total | Within | Across) : 2.46851 | 2.45140 | 2.48562
Training MSE Loss (Total) : 0.00000
[View 1] ACC: 50.3443 | mIoU: 14.9282
[View 2] ACC: 50.7423 | mIoU: 15.3916
========================================
Start computing centroids.
Batch input size : [256, 3, 320, 320]
Batch feature : [256, 128, 80, 80]
[Saving features]: 0 / 194 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0433
[Saving features]: 100 / 194 | [K-Means Loss]: 0.0430
-Centroids ready. [Loss: 0.04318/ Time: 0:0:15:8]
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/17
ACC - All: 50.3493
mIOU - All: 14.9896
ACC - Thing: 57.9217
mIOU - Thing: 22.0514
ACC - Stuff: 73.5671
mIOU - Stuff: 24.8037
Average overall pixel accuracy [NEW] : 50.349 +/- 0.000.
Average mIoU [NEW] : 14.990 +/- 0.000.
Experiment done. [0:15:19:53]