Comments (13)
@xinyuh No idea. Have you made any change to the code?
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no any changes. just downloaded the model and source codes and run the command
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one possible reason is that the model parameters are not loaded correctly. Any idea to check loaded parameters?
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I just checked the image before running the mod.forward(...). The input image has no problem.
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@xinyuh Check the md5sum of your downloaded model.
1faf29850bfa194678f0b8e1cbbffa98
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The two checksum values are matched
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yes, that is the link I used. "Data: train/val (922MB)"
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here is another test of two images (ILSVRC2012_val_00000005.JPEG and ILSVRC2012_val/ILSVRC2012_val_00000006.JPEG) based on the models for ILSVRC 2012 classification. It seems results are good. I guess the models or codes for ADE20K may have problems.
c:\ademxapp>python iclass/ilsvrc.py --data-root data\ilsvrc12 --output output --batch-images 2 --phase val --weight models\ilsvrc-cls_rna-a1_cls1000_ep-0001.params --split val --test-scales 320 --gpus 0 --no-choose-interp-method
2017-01-13 14:47:58,598 Host Run with arguments: Namespace(backward_do_mirror=False, base_lr=None, batch_images=2, choose_interpolation_method=False, crop_size=None, data_root='data\ilsvrc12', dataset=None, debug=False, from_epoch=0, gpus='0', kvstore='device', log_file='ilsvrc-cls_rna-a1_cls1000_ep-0001.log', lr_steps=None, lr_type=None, model='ilsvrc-cls_rna-a1_cls1000', output='output', phase='val', pool_top_infer_style=None, prefetch_threads=1, prefetcher='thread', split='val', stop_epoch=None, test_3crops=False, test_flipping=False, test_scales='320', to_epoch=None, weights='models\ilsvrc-cls_rna-a1_cls1000_ep-0001.params')
2017-01-13 14:47:58,601 Host and model specs: {'batch_images': 2, 'classes': 1000, 'net_type': 'rna', 'net_name': 'a1', 'feat_stride': 32, 'crop_size': 320, 'lr_params': {'base': 0.1, 'type': 'fixed', 'args': None}, 'dataset': 'ilsvrc-cls'}
Level 0
[(64L, 3L, 224L, 224L)]
Level 1
[(64L, 64L, 224L, 224L)]
Level 2
First block on level 2, stride: 2, dilate: 1
[(64L, 128L, 112L, 112L)]
Level 3
First block on level 3, stride: 2, dilate: 1
[(64L, 256L, 56L, 56L)]
Level 4
First block on level 4, stride: 2, dilate: 1
[(64L, 512L, 28L, 28L)]
Level 5
First block on level 5, stride: 2, dilate: 1
[(64L, 1024L, 14L, 14L)]
Level 6
First block on level 6, stride: 2, dilate: 1
[(64L, 2048L, 7L, 7L)]
Level 7
[(64L, 4096L, 7L, 7L)]
Waited for 0.0 seconds
(2L, 1000L)
2017-01-13 14:48:08,240 Host Crop size 320, done 1/1 at speed: 0.50/s, Top1 0.0000%
2017-01-13 14:48:08,240 Host Done crop size 320 in 4.0040s.
2017-01-13 14:48:08,267 Host Done testing crop_size [320]
2017-01-13 14:48:08,269 Host Top1 0.0000%, Top5 0.0000%
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Not get IoU as the paper too,
2017-01-12 17:13:48,914 Host Done 1211/2000 with speed: 0.14/s
2017-01-12 17:13:48,914 Host pixel acc: 80.91%, mean acc: 53.63%, mean iou: 43.45%
2017-01-12 17:13:48,916 Host
[89.13 90.79 96.97 92.09 88.10 89.96 89.40 93.84 72.54 85.78 64.34 71.98
91.42 50.39 51.66 70.88 68.93 59.27 79.49 65.94 93.08 66.44 81.88 78.39
60.07 53.94 90.06 55.35 51.99 31.98 52.17 77.18 51.92 69.19 68.75 57.84
70.34 71.09 44.50 58.00 20.52 27.25 53.66 43.65 66.91 30.10 68.89 83.49
48.52 83.06 67.36 60.36 12.76 38.98 90.17 30.39 95.54 66.77 76.11 33.85
26.80 49.28 48.50 36.97 77.09 92.37 46.67 60.33 8.84 45.29 74.47 80.28
63.33 32.64 80.91 55.31 79.33 14.37 26.42 27.53 94.89 61.33 53.52 17.31
41.93 69.03 12.02 38.61 22.29 78.53 94.78 12.77 52.62 29.82 0.57 8.40
20.07 53.04 31.97 64.17 27.54 11.41 29.07 81.82 23.37 86.00 17.25 65.89
40.50 48.90 53.53 74.11 25.51 87.45 0.00 7.25 70.62 96.90 48.92 63.57
47.89 12.33 42.48 18.90 75.75 39.63 53.14 71.38 3.64 61.14 79.14 1.77
57.55 70.77 45.15 57.78 49.66 10.34 58.71 60.86 40.26 24.45 53.56 37.48
58.44 4.41 43.71 12.41 45.24 17.73]
2017-01-12 17:13:48,917 Host
[73.70 79.89 93.31 78.06 71.39 79.92 81.00 85.78 55.50 68.71 53.58 56.45
76.89 37.12 38.64 52.83 55.32 46.69 67.58 51.38 85.02 53.58 68.48 62.52
41.53 37.03 57.45 49.98 46.38 24.13 36.83 61.75 40.82 48.98 44.46 36.61
58.48 67.81 33.10 44.63 16.67 21.94 45.86 32.82 47.39 21.89 43.00 68.36
40.06 72.37 61.35 27.45 9.61 32.86 86.89 24.35 90.48 56.08 66.53 26.32
15.90 45.37 26.92 34.48 61.23 83.81 35.40 41.80 7.98 38.56 55.58 73.04
44.04 21.81 64.43 45.69 72.61 11.48 25.87 23.55 84.84 50.06 44.91 13.84
31.64 57.13 9.55 30.65 22.03 69.81 78.32 6.46 39.47 21.36 0.44 6.35
19.13 46.45 25.49 57.96 23.92 9.36 22.81 37.47 22.56 79.41 15.45 64.55
28.27 27.19 40.78 65.96 23.29 57.44 0.00 6.63 64.38 89.96 39.44 44.53
37.75 10.13 37.90 15.67 62.78 34.54 51.01 52.00 3.58 56.03 64.71 1.65
46.29 64.30 36.80 36.43 35.90 5.42 42.95 50.06 33.49 17.40 37.17 27.28
44.42 3.27 39.77 11.52 35.53 16.81]
2017-01-12 17:13:55,935 Host Done 1212/2000 with speed: 0.14/s
2017-01-12 17:13:55,935 Host pixel acc: 80.90%, mean acc: 53.62%, mean iou: 43.45%
2017-01-12 17:13:55,937 Host
[89.12 90.79 96.97 92.10 88.10 89.98 89.40 93.84 72.48 85.78 64.34 71.98
91.32 50.39 51.63 70.88 68.93 59.27 79.49 65.94 93.08 66.44 81.88 78.39
60.07 53.94 90.06 55.35 51.99 31.98 52.17 77.18 51.92 69.19 68.75 57.84
70.34 71.09 44.50 58.00 20.52 27.25 53.66 43.65 66.91 30.10 68.89 83.49
48.52 83.06 67.36 59.32 12.76 38.98 90.17 30.39 95.54 66.77 76.11 33.85
26.80 49.28 48.50 36.97 77.09 92.37 46.67 60.33 8.84 45.29 74.47 80.28
63.33 32.64 80.91 55.31 79.33 14.37 26.42 27.53 94.89 61.33 53.52 17.31
41.93 69.03 12.02 38.61 22.29 78.53 94.78 12.77 52.62 29.82 0.57 8.40
20.07 53.04 31.97 64.17 27.54 11.41 29.07 81.82 23.37 86.00 17.25 65.89
40.50 48.90 53.53 74.11 25.51 87.45 0.00 7.25 70.62 96.90 48.92 63.29
47.89 12.33 42.48 18.90 75.75 39.63 53.14 71.38 3.64 61.14 79.14 1.77
57.55 70.77 45.15 57.78 49.66 10.34 58.71 60.86 40.26 24.45 53.56 37.48
58.44 4.41 43.71 12.41 45.24 17.73]
2017-01-12 17:13:55,939 Host
[73.69 79.89 93.31 78.07 71.39 79.88 81.00 85.78 55.46 68.71 53.58 56.45
76.81 37.12 38.63 52.83 55.32 46.69 67.58 51.38 85.02 53.58 68.48 62.52
41.53 37.03 57.45 49.98 46.38 24.13 36.83 61.75 40.82 48.98 44.46 36.61
58.48 67.81 33.10 44.63 16.67 21.94 45.86 32.82 47.39 21.89 43.00 68.36
40.06 72.37 61.35 27.82 9.61 32.86 86.89 24.35 90.48 56.08 66.53 26.32
15.90 45.37 26.92 34.48 61.23 83.81 35.40 41.80 7.98 38.56 55.58 73.04
44.04 21.81 64.43 45.69 72.61 11.48 25.87 23.55 84.84 50.06 44.91 13.84
31.64 57.13 9.55 30.65 22.03 69.81 78.32 6.46 39.47 21.36 0.44 6.35
19.13 46.45 25.49 57.96 23.92 9.36 22.81 37.47 22.56 79.41 15.45 64.55
28.27 27.19 40.78 65.96 23.29 57.44 0.00 6.63 64.38 89.96 39.44 44.40
37.75 10.13 37.90 15.67 62.78 34.54 51.01 52.00 3.58 56.03 64.71 1.65
46.29 64.30 36.80 36.43 35.90 5.42 42.95 50.06 33.49 17.40 37.17 27.28
44.42 3.27 39.77 11.52 35.53 16.81]
IoU changes from low(2.0%) to high(about 43.%). The segmented image result not so smooth as paper.
And 0.14/s means 0.14 images per seconds. if thus, it seems the model may too slow for application.
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Yes, I have done it,
2017-01-23 00:09:03,391 Host Done 2000/2000 with speed: 0.13/s
59996 2017-01-23 00:09:03,391 Host pixel acc: 80.56%, mean acc: 53.51%, mean iou: 43.35%
59997 2017-01-23 00:09:03,393 Host
59998 [88.35 90.89 97.22 91.05 88.17 89.73 89.76 92.96 75.16 84.42 67.58 76.52
59999 91.54 48.22 52.73 71.40 68.42 60.73 81.58 64.77 89.77 65.26 82.83 72.33
60000 55.09 60.22 72.67 65.47 59.10 39.82 55.46 74.25 46.77 62.72 71.11 62.56
60001 72.27 72.11 37.07 61.37 20.70 24.73 49.68 43.52 63.84 28.29 49.68 78.34
60002 51.60 81.32 72.32 63.67 34.56 31.58 91.14 55.87 95.45 62.98 69.03 31.08
60003 28.88 51.55 50.28 31.45 76.63 89.45 45.26 59.34 8.49 49.39 67.25 76.92
60004 63.46 40.11 67.50 56.36 71.44 24.92 25.10 26.44 90.89 63.24 56.49 23.17
60005 41.79 72.22 23.49 35.65 42.53 78.36 68.38 17.70 51.44 30.31 8.20 9.23
60006 18.01 50.91 32.45 43.55 33.36 8.13 51.54 8.21 19.92 68.98 16.30 66.59
60007 42.89 40.56 55.50 74.12 33.56 54.94 97.54 12.58 66.85 95.17 54.59 66.52
60008 58.16 11.10 25.76 25.41 38.68 45.25 62.01 70.37 54.60 69.70 80.92 5.25
60009 54.33 51.13 47.65 52.59 44.64 14.49 52.82 62.58 20.76 17.08 53.17 54.42
60010 44.54 4.39 47.19 13.36 29.94 29.38]
60011 2017-01-23 00:09:03,394 Host
60012 [73.07 79.78 93.75 76.35 70.95 80.62 80.83 83.85 57.84 68.10 56.20 61.65
60013 76.20 34.57 41.20 53.20 54.55 48.52 69.40 51.92 81.45 50.46 69.64 58.09
60014 37.62 47.88 50.36 58.77 53.30 30.79 36.27 57.64 35.13 44.55 46.74 45.08
60015 58.89 67.18 27.79 48.51 16.81 19.47 41.31 32.17 44.74 21.66 34.45 65.09
60016 41.85 67.57 66.15 35.63 24.72 25.53 70.69 40.21 90.61 50.32 60.27 22.76
60017 16.02 45.28 35.26 29.07 59.97 82.79 32.94 40.58 6.91 41.58 52.18 70.14
60018 47.73 24.67 59.24 42.11 61.66 21.62 23.04 22.69 83.01 52.08 48.08 18.65
60019 30.23 60.74 20.34 27.51 41.63 65.68 52.86 10.04 37.64 21.89 6.15 6.76
60020 17.19 41.53 25.24 38.45 26.77 6.62 40.22 7.57 19.12 55.13 14.72 64.49
60021 32.15 26.62 41.16 53.13 28.58 46.24 75.86 11.27 60.17 79.15 40.42 42.81
60022 48.38 9.34 24.02 21.47 35.34 39.10 59.01 51.34 46.58 62.74 61.09 4.85
60023 44.73 47.76 38.83 35.10 31.67 7.71 41.58 51.51 15.22 10.39 39.97 46.64
60024 34.71 2.90 43.00 12.09 23.53 27.37]
60025 2017-01-23 00:09:03,394 Host Done in 15368.70 s.
And it seems forward is very slow on my K40.
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Related Issues (20)
- Scale rates of multiscale test in cityscapes HOT 2
- How to run the semantic segmentation on my own images? HOT 11
- Simple_bind error HOT 3
- Pretrained models for cityscape needed HOT 3
- Training command required to train semantic segmentation on cityscape dataset...
- How can I train a model with 2 classes?
- Training script for semantic segmentation VOC2012 HOT 1
- how to get coco20 png annotations
- Only got 76.90% over Pascal VOC2012 val set HOT 5
- What's the difference of the model a2 and a
- Experimental setting for training ADE20K?
- MxNet, Python, CUDA, CUDNN versions HOT 2
- multi-steps testing~
- Where to get pretrained city scape model (cityscapes_rna-a1_cls19_s8_ep-0001.params)? HOT 4
- The parameter for training VOC 2012 dataset
- pre-trained model in ade20k HOT 1
- Training on VOC from Scratch HOT 2
- the predicted image is all black using trained model on VOC
- pre-training files for resnet38? HOT 1
- The download link for the pretrained model is no longer available.
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