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mdc_city.out
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mdc_city.out
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/scratch/cluster/jc88964/anaconda3/envs/picie/lib/python3.7/site-packages/sklearn/utils/linear_assignment_.py:22: FutureWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.
FutureWarning)
Namespace(K_test=27, K_train=27, X=80, arch='resnet18', augment=True, batch_size_cluster=128, batch_size_test=128, batch_size_train=128, blur=True, cityscapes=True, comment='', data_root='datasets/cityscapes/', equiv=True, eval_only=False, eval_path=None, fullcoco=False, grey=True, h_flip=True, in_dim=128, jitter=True, kmeans_n_iter=30, label_mode='gtFine', long_image=False, lr=0.001, metric_test='cosine', metric_train='cosine', min_scale=0.5, momentum=0.9, nonparametric=False, num_batches=1, num_epoch=10, num_init_batches=30, 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/reproduce/cityscapes/mdc/train/1/km_30_1_train_bn/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/reproduce/cityscapes/mdc/train/1/km_30_1_train_bn/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/reproduce/cityscapes/mdc/train/1/km_30_1_train_bn/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=False, thing=False, v_flip=False, val_type='val', version=7, weight_decay=0.0005)
Adam optimizer is used.
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 29.2446
mIOU - All: 3.1794
============================= [Epoch 0] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.5171
[Saving features]: 100 / 169 | [K-Means Loss]: 0.5175
-Centroids ready. [0:0:28:13]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:23:50]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 31.2881
mIOU - All: 8.1299
========== Epoch [0] ==========
Time total : [0:1:15:16].
K-Means loss : 0.51776.
Training loss : 2.71490.
ACC: 31.2881 | mIoU: 8.1299
================================
============================= [Epoch 1] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.1882
[Saving features]: 100 / 169 | [K-Means Loss]: 0.1873
-Centroids ready. [0:0:23:36]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:19:7]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 37.4753
mIOU - All: 8.3659
========== Epoch [1] ==========
Time total : [0:1:7:36].
K-Means loss : 0.18738.
Training loss : 2.51994.
ACC: 37.4753 | mIoU: 8.3659
================================
============================= [Epoch 2] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.1203
[Saving features]: 100 / 169 | [K-Means Loss]: 0.1202
-Centroids ready. [0:0:29:0]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:21:44]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 39.4089
mIOU - All: 8.5133
========== Epoch [2] ==========
Time total : [0:1:13:8].
K-Means loss : 0.12036.
Training loss : 2.43685.
ACC: 39.4089 | mIoU: 8.5133
================================
============================= [Epoch 3] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.1130
[Saving features]: 100 / 169 | [K-Means Loss]: 0.1130
-Centroids ready. [0:0:24:0]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:20:9]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 40.2728
mIOU - All: 7.9877
========== Epoch [3] ==========
Time total : [0:1:11:6].
K-Means loss : 0.11319.
Training loss : 2.37918.
ACC: 40.2728 | mIoU: 7.9877
================================
============================= [Epoch 4] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0881
[Saving features]: 100 / 169 | [K-Means Loss]: 0.0880
-Centroids ready. [0:0:27:9]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:19:40]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 40.4841
mIOU - All: 8.2279
========== Epoch [4] ==========
Time total : [0:1:8:55].
K-Means loss : 0.08787.
Training loss : 2.32256.
ACC: 40.4841 | mIoU: 8.2279
================================
============================= [Epoch 5] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0956
[Saving features]: 100 / 169 | [K-Means Loss]: 0.0953
-Centroids ready. [0:0:24:12]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:22:23]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 42.4989
mIOU - All: 8.3647
========== Epoch [5] ==========
Time total : [0:1:12:40].
K-Means loss : 0.09521.
Training loss : 2.25190.
ACC: 42.4989 | mIoU: 8.3647
================================
============================= [Epoch 6] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0636
[Saving features]: 100 / 169 | [K-Means Loss]: 0.0639
-Centroids ready. [0:0:25:24]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:19:25]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 42.9933
mIOU - All: 7.9496
========== Epoch [6] ==========
Time total : [0:1:7:0].
K-Means loss : 0.06389.
Training loss : 2.22499.
ACC: 42.9933 | mIoU: 7.9496
================================
============================= [Epoch 7] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0556
[Saving features]: 100 / 169 | [K-Means Loss]: 0.0557
-Centroids ready. [0:0:25:57]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:23:40]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 42.4900
mIOU - All: 7.4535
========== Epoch [7] ==========
Time total : [0:1:14:18].
K-Means loss : 0.05577.
Training loss : 2.19134.
ACC: 42.4900 | mIoU: 7.4535
================================
============================= [Epoch 8] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0454
[Saving features]: 100 / 169 | [K-Means Loss]: 0.0449
-Centroids ready. [0:0:24:5]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:19:29]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 41.8911
mIOU - All: 6.9310
========== Epoch [8] ==========
Time total : [0:1:6:48].
K-Means loss : 0.04488.
Training loss : 2.15638.
ACC: 41.8911 | mIoU: 6.9310
================================
============================= [Epoch 9] =============================
Start computing centroids.
Batch input size : [128, 3, 320, 320]
Batch feature : [128, 128, 80, 80]
[Saving features]: 0 / 169 | [K-Means Loss]: 0.0000
Initial k-means loss: 0.0379
[Saving features]: 100 / 169 | [K-Means Loss]: 0.0378
-Centroids ready. [0:0:27:8]
Centroid size : [27, 128]
Batch input size : [128, 3, 320, 320]
Batch feature size : [128, 128, 80, 80]
[Assigning labels] 0 / 169
-Cluster labels ready. [0:0:22:7]
Start training ...
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 80, 80]
Batch feature size : [128, 128, 80, 80]
0 / 169
====== METRIC TEST : cosine ======
Batch input size : [128, 3, 320, 320]
Batch label size : [128, 320, 320]
Batch feature size : [128, 128, 80, 80]
0/28
20/28
ACC - All: 44.3811
mIOU - All: 6.6877
========== Epoch [9] ==========
Time total : [0:1:12:52].
K-Means loss : 0.03783.
Training loss : 2.12418.
ACC: 44.3811 | mIoU: 6.6877
================================