Use the Mapping-Challenge-weights to predict on my own data #231
Comments
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Hi, I tried using predict on dir command on test data placed in an inference directory and still have score : 0 however when I did the same with a random picture of val data it had a score of : 0.90. And since I had a ground truth for this image I could use the notebook and print my results and I can confirm it was fine. Code in 168 still gave me empty predictions though. @ZYT1122 : Dear sir you should remove a bit of your .json, having it all doesn't really help that much and remove some visibility on this thread. Hope there will be an answer soon :). Best regards |
I also use the #168 !! I also get empty predictions !!!!!!!!!!! |
Now my problem is that I can’t determine if there is a problem with my prediction . I’m not sure if my prediction.json is reasonable. |
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Hi @ZYT1122 @Dante9566, I am so sorry but I cannot get to this right now. From experience working on this project. |
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Hi @jakubczakon , I really hope that the code can add a visualization module, I want to visualize my prediction results |
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Hi @ZYT1122 , I just tried doing the test again, the code in #168 works fine, I maybe did some changes but I don't see where. The only difference I can see is our score, for instance I had 0.9 in my first annotation, while when I use a "test_images" in the inference directory I have a score of 0, which don't apply the masks since the score is too low. So their is a problem with our predictions when using predict_on_dir outside of "train" or "val" sets... Here-below the code I used for visualizing (I think its the same as issue 168 but I'll post it here just in case" : Here is the result when using the image "00000001" from "val" set (I had to change in the predictions.json "image_id :0" to "image_id:1" but you can do it easily using the text editor) :
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That is great @Dante9566 ! |
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Thanks @jakubczakon, however I still don't understand why I can't have a good prediction on images outside of the train / val sets... I don't know what to do haha |
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The competition score on test was pretty much exactly what it was on valid so it must be the problem with how Maybe it assigns some wrong image ids and later the predictions are not connected to the correct image or something? |
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@Dante9566 I think it must be it. I think |
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Thanks for your reply！ For example: my test pic named: 234234.png、242324.png、11214.png......... |
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Likely enumerate in here causes the problem. Simple, way to check it would be to run If so, then changing what is assigned here: df_dict['ImageId'].append(image_id)to df_dict['ImageId'].append(filename.split('.')[0])or something like that will solve it. Remember that |
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@jakubczakon , seriously, thank you for taking the time to answer, really. @ZYT1122 you just have to go in src/utils.py and change the "generate_inference_metadata" as follow : |
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@Dante9566 maybe you'd like to submit a PR with a fix? |
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@jakubczakon |
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Thanks @Dante9566 @jakubczakon ！！ I love you !!! |
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@jakubczakon @ZYT1122 @Dante9566 I would like to get prediction and visualisation for an image out of this dataset (my own image) . I did predict_on_dir but it gives empty prediction. |
Hi
When I use the Mapping-Challenge-weights（scoring_model）to predict on my own data ，I want to visual my prediction results， but I'm not ！ I’m not sure if there is a problem in the predicition, I feel wrong. Could you please help me to see if I successfully completed the prediction.
I have three images need to predict
！！！！There is my terminal output：_
/home/zyt/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
from numpy.core.umath_tests import inner1d
2020-09-15 16-11-17 mapping-challenge >>> creating metadata
3it [00:00, 24013.19it/s]
2020-09-15 16-11-17 mapping-challenge >>> predicting
0%| | 0/1 [00:00<?, ?it/s]2020-09-15 16-11-18 steps >>> step xy_inference adapting inputs
2020-09-15 16-11-18 steps >>> step xy_inference transforming...
2020-09-15 16-11-18 steps >>> step xy_inference adapting inputs
2020-09-15 16-11-18 steps >>> step xy_inference transforming...
2020-09-15 16-11-18 steps >>> step tta_generator adapting inputs
2020-09-15 16-11-18 steps >>> step tta_generator transforming...
2020-09-15 16-11-18 steps >>> step loader adapting inputs
2020-09-15 16-11-18 steps >>> step loader transforming...
2020-09-15 16-11-18 steps >>> step unet unpacking inputs
2020-09-15 16-11-18 steps >>> step unet loading transformer...
2020-09-15 16-11-25 steps >>> step unet transforming...
/home/zyt/house/open-solution-mapping-challenge/src/steps/pytorch/models.py:127: UserWarning: volatile was removed and now has no effect. Use
with torch.no_grad():instead.X = Variable(X, volatile=True).cuda()
2020-09-15 16-11-37 steps >>> step xy_inference adapting inputs
2020-09-15 16-11-37 steps >>> step xy_inference transforming...
2020-09-15 16-11-37 steps >>> step tta_generator adapting inputs
2020-09-15 16-11-37 steps >>> step tta_generator transforming...
2020-09-15 16-11-37 steps >>> step tta_aggregator adapting inputs
2020-09-15 16-11-37 steps >>> step tta_aggregator transforming...
2020-09-15 16-11-37 steps >>> step prediction_renamed adapting inputs
2020-09-15 16-11-37 steps >>> step prediction_renamed transforming...
2020-09-15 16-11-37 steps >>> step mask_resize adapting inputs
2020-09-15 16-11-37 steps >>> step mask_resize transforming...
2020-09-15 16-11-37 steps >>> step mask_resize caching outputs...███████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 37.35it/s]
2020-09-15 16-11-37 steps >>> step category_mapper adapting inputs
2020-09-15 16-11-37 steps >>> step category_mapper transforming...
2020-09-15 16-11-37 steps >>> step mask_erosion adapting inputs███████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1078.78it/s]
2020-09-15 16-11-37 steps >>> step mask_erosion transforming...
2020-09-15 16-11-37 steps >>> step labeler adapting inputs███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 91846.07it/s]
2020-09-15 16-11-37 steps >>> step labeler transforming...
2020-09-15 16-11-38 steps >>> step mask_dilation adapting inputs████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 57.11it/s]
2020-09-15 16-11-38 steps >>> step mask_dilation transforming...
2020-09-15 16-11-38 steps >>> step mask_dilation caching outputs...█████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 34.97it/s]
2020-09-15 16-11-38 steps >>> step mask_resize loading output...
2020-09-15 16-11-38 steps >>> step feature_extractor adapting inputs
2020-09-15 16-11-38 steps >>> step feature_extractor transforming...
2020-09-15 16-11-38 steps >>> step scoring_model unpacking inputs
2020-09-15 16-11-38 steps >>> step scoring_model loading transformer...
2020-09-15 16-11-42 steps >>> step scoring_model transforming...
2020-09-15 16-11-44 steps >>> step mask_dilation loading output...
2020-09-15 16-11-44 steps >>> step score_builder adapting inputs
2020-09-15 16-11-44 steps >>> step score_builder transforming...
2020-09-15 16-11-44 steps >>> step nms unpacking inputs
2020-09-15 16-11-44 steps >>> step nms transforming...
2020-09-15 16-11-46 steps >>> step output adapting inputs
2020-09-15 16-11-46 steps >>> step output transforming...
2020-09-15 16-11-46 mapping-challenge >>> Creating annotations
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:27<00:00, 27.60s/it]
2020-09-15 16-11-46 mapping-challenge >>> submission saved to ./prediction1.json
2020-09-15 16-11-46 mapping-challenge >>> submission head
{'image_id': 0, 'category_id': 100, 'score': 0, 'segmentation': {'size': [300, 300], 'counts': 'cR6:l87G9L4L3O1K6N2M2M3O2N100N2M3N2\OaNSIg1j6?M3O1K5K5L4I8M2\KoLk2S3SMoLk2S3TMnLi2U3RMPMk2T3oLQMn2R3mLSMn2R3gLYMX3k2_L[Ma3X40000000000\O_J^M
5^2fJMZ5]2jJbMV5Y2oJgMQ5U2SKkMm4j1_KVN4g1cKYN]4d1fK]NY4a1iKNV4\1nKeNQ4W1SLiNm3R1XLnNh3i0bLWO]3d0hL\OX3b0jL_OU3=oLDP37VMIi22\M1a2JdM6\2^OQNa0o1]OSNd0l1\OTNe0k1[OUNf0j1ZOWNh0f1XOZNi0e1WO\Nh0d1XO\Nh0d1XO]Ng0c1YO]Ng0c1YO]Ng0c1YO]Ng0c1YO^Ne0c1[O]Ne0c1[O]Ne0c1[O]Nf0b1ZO^Nf0b1ZO^Ng0a1YO_Ng0a1YONf01ZONf01ZONg0_1YObNf0^1ZObNf0^1ZObNg0]1YOcNg0]1YOcNg0]1YOdNf0\\1ZOdNf0\\1ZOeNf0Z1ZOfNg0Y1YOhNg0W1YOiNg0W1YOjNf0V1ZOjNg0U1YOlNf0T1ZOlNf0T1ZOlNf0T1ZOlNg0S1YOmNg0S1YOmNg0S1YOnNf0R1ZOnNf0R1ZOnNe0S1[OmNd0T1\\OlN0AA[NOU28HHSN0U23MMnM0P60PJ0o51QJOo52PJ1m5OSJ8f5HZJ9dNFh60dJe0NZOf61jJl0T5YOgJl0T5WOIGU1R1[5[OeJe0[5\OdJd0[5^OdJb0\5^OdJb0[5_OeJc0V5AiJe0k2SOhLN]OI0g0o0a0i2YO`LU1HVNN0g0_1o2j1PMWNP3h1PMXNP3h1oLZNP3f1oL\NP3d1eLgN[3[1bLfN_3Z1^LiNa3a30O1J6M3L4N2K5K5K5G;J4M3N2N3O00001O00000000001O001O000000000000N2O1M3N20000O1J6L4K5J6M3N2N2I7K5L4TObJgMa5Y2_JcMe5]2^J^Md5b2]J[Me5e2a0O1O1O1M3M3E;I7L4J6N2M3K5H8L4K5J6N2M3M3F:LdXf0'}, 'bbox': [20.0, 0.0, 204.0, 299.0]}！！！！And there is my prediction.json：
[{"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": "cR6:l87G9L4L3O1K6N2M2M3O2N100N2M3N2\OaNSIg1j6?M3O1K5K5L4I8M2\KoLk2S3SMoLk2S3TMnLi2U3RMPMk2T3oLQMn2R3mLSMn2R3gLYMX3k2_L[Ma3X40000000000\O_J^M
5^2fJMZ5]2jJbMV5Y2oJgMQ5U2SKkMm4j1_KVN4g1cKYN]4d1fK]NY4a1iKNV4\1nKeNQ4W1SLiNm3R1XLnNh3i0bLWO]3d0hL\OX3b0jL_OU3=oLDP37VMIi22\M1a2JdM6\2^OQNa0o1]OSNd0l1\OTNe0k1[OUNf0j1ZOWNh0f1XOZNi0e1WO\Nh0d1XO\Nh0d1XO]Ng0c1YO]Ng0c1YO]Ng0c1YO]Ng0c1YO^Ne0c1[O]Ne0c1[O]Ne0c1[O]Nf0b1ZO^Nf0b1ZO^Ng0a1YO_Ng0a1YONf01ZONf01ZONg0_1YObNf0^1ZObNf0^1ZObNg0]1YOcNg0]1YOcNg0]1YOdNf0\\1ZOdNf0\\1ZOeNf0Z1ZOfNg0Y1YOhNg0W1YOiNg0W1YOjNf0V1ZOjNg0U1YOlNf0T1ZOlNf0T1ZOlNf0T1ZOlNg0S1YOmNg0S1YOmNg0S1YOnNf0R1ZOnNf0R1ZOnNe0S1[OmNd0T1\\OlN0AA[NOU28HHSN0U23MMnM0P60PJ0o51QJOo52PJ1m5OSJ8f5HZJ9dNFh60dJe0NZOf61jJl0T5YOgJl0T5WOIGU1R1[5[OeJe0[5\OdJd0[5^OdJb0\5^OdJb0[5_OeJc0V5AiJe0k2SOhLN]OI0g0o0a0i2YOLU1HVNN0g0_1o2j1PMWNP3h1PMXNP3h1oLZNP3f1oL\\NP3d1eLgN[3[1bLfN_3Z1^LiNa3a30O1J6M3L4N2K5K5K5G;J4M3N2N3O00001O00000000001O001O000000000000N2O1M3N20000O1J6L4K5J6M3N2N2I7K5L4TObJgMa5Y2_JcMe5]2^J^Md5b2]J[Me5e2a0O1O1O1M3M3E;I7L4J6N2M3K5H8L4K5J6N2M3M3F:LdXf0"}, "bbox": [20.0, 0.0, 204.0, 299.0]}, {"image_id": 0, "category_id": 100, "score": 0.0015902000448486198, "segmentation": {"size": [300, 300], "counts": "SSf02Z90eVQ2"}, "bbox": [75.0, 127.0, 2.0, 2.0]}, {"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": "idd19l89N000O2K4O2J6M3N1N2M3M3L5L3L4O100L4N2M3I7M3O2O0M3M3O2O000001O01O001O0100O1O01O001O000O1O1O10000011N00001O01O0010O0010O00001000O3M2N010O1O1O2N0000010O00100O00000001002M0010O00001KdMWI]2f6600L4O10001O000000001O00010O01O00100O01O00010O000010O0000W4"}, "bbox": [179.0, 145.0, 120.0, 111.0]}, {"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": "oaV235OL1l81UG6j8600000001lGCW7=aHK_76[HOe71ZH0f70XH2h7ORH6n7d0000000001O00000000001O1O001O00001O00001O0000001O0000000000001O000000001O001O001O00001O000000R1"}, "bbox": [240.0, 254.0, 59.0, 45.0]}, {"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": "hR64R98F9J5N2O1J7L3N3N2N1N200N2M3M4[ONUIKLh1m6NTIf1j6\\NUIe1h6^NXIb1d6e0K5K5I7N2]KhLbN2Y4X3SMoLi2S3UMoLh2T3TMPMi2T3PMRMn2P3oLSMk2T3hLXMW3i2cL]M]3e2L\M3d2]L_Mc3U4000000\\O^J^Mb5_2bJM^5\2hJbMX5Y2nJgMQ5U2SKkMm4j1_KUNa4h1bKYN]4d1fK]NY4a1iKNV4]1mKcNS4X1RLhNn3S1WLnNh3i0aLWO_3d0gL\\OX3b0jL@iMDR5i0XMMg2N^M2b2IdM8Z2CkM=U2ZOTNe0m1TO[Nk0e1UO[Nk0e1UO[Nl0d1TO\\Nm0c1SO^Nl0b1TO^Nl0b1TO_Nk0a1UO_Nj0b1VO_Ni0a1WO_Ni0a1WO_Ni0a1WO_Nh0b1XO_Ng0a1YO_Ng0a1YO_Ng0a1YO_Nh01XONh01XONh01XONh01XOaNg0_1YOaNg0_1YOaNh0^1XObNh0^1XOcNh0\1XOdNh0\1XOdNh0\1XOeNg0[1YOeNh0Z1XOfNi0Y1WOhNh0X1XOhNi0W1WOjNh0V1XOjNh0V1XOkNg0U1YOkNh0T1XOmNg0S1YOmNg0S1YOmNh0ZOYOdNOR2h0[OXOcN0R2h0[OXOcN0R2h0[OXOcN0S2g0ZOZObNOT2f0[O[OaNOT2e0\O\ONOT20ABZNNU2:GHTNNV25JMPNNP62PJNP62PJN^N010Y73XJ7e5I[Jh0T5YOkJi0S5ZOjJg0V5ZOhJj0T5XOaIFU1S1Y5]OeJd0Z5\OfJe0Y5\OfJc0Z5^OfJb0Z5^OfJc0nNkNU6e0lJe0P3iNQLMk03@j0FSOd0_1P3VOcL1<[Oo2]O[L\\1e0WOP3k1oLVNP3j1mLYNS3g1mLZNR3f1dLdN]3\\1aLfN^3\\1]LgNc3b3O00O1J6M4L3M3K4L5K5H8K6L3N2M2100001O3M0000001O00001O0000000000O1O1M3N2O10000M3L4L4I7J6N2O1M3J6lNmJkMW5T2jJgM[5Y2fJcM]5]2cJ_Ma5a2_J]Mc5c2^JXMf5h2?00O1N2M3M3E;J6K5J6M3N2K5H8K5L4J6N2L4L4H8LdXf0"}, "bbox": [20.0, 0.0, 204.0, 299.0]}, {"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": "Snd1<o82O0L5M2L4M3M4N2N1L4M4L3L5K4O1O1N2M3M3I7M3O100M4M2N2O2O01O1O0001O0011N0001O0010O01O000O100N2002N000001O00010O01O010O0001O1O04M1N1O0010O03M001O0000010O1O00010O00002O000O1O01O001O0O1O1I7M3O10001O0000000010O0010O02N0001O010O00010O001O01O00V4"}, "bbox": [180.0, 145.0, 119.0, 111.0]}, {"image_id": 0, "category_id": 100, "score": -4.450376142550545e-05, "segmentation": {"size": [300, 300], "counts": "\\We12Z90\\RR1"}, "bbox": [181.0, 208.0, 2.0, 2.0]}, {"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": "gTW2340M0m80UG5k8KUG5j8LTG7k85nGCU7>bHJ^76_HMa73\\H0d70[H1f7OTH6k7f000001O0000000000001O1O1O0000001O00001O0000001O0000000000001O00001O0000001O001O001O0000000000R1"}, "bbox": [242.0, 254.0, 57.0, 45.0]}, {"image_id": 0, "category_id": 100, "score": 0, "segmentation": {"size": [300, 300], "counts": 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