ValueError: (InvalidArgument) The conv2d Op's Input Variable Input contains uninitialized Tensor.

[TapendraBaduwal]

i am facing this issue when i use inference.pdiparams, inference.pdiparams.info ,inference.pdmodel models for layout analysis.

ValueError: (InvalidArgument) The conv2d Op's Input Variable Input contains uninitialized Tensor. [Hint: Expected t->IsInitialized() == true, but received t->IsInitialized():0 != true:1.] (at /paddle/paddle/fluid/framework/operator.cc:2094) [operator < conv2d > error]

[an1018]

what's your version of paddlapaddle?

[TapendraBaduwal]

@an1018 PaddlePaddle 2.4.1, compiled with with_avx: ON with_gpu: OFF with_mkl: ON with_mkldnn: ON with_python: ON

[an1018]

You can try install PaddlePaddle 2.3

[TapendraBaduwal]

@an1018 same error with paddle 2.3 --- fused 0 elementwise_mul with hardswish activation --- fused 0 elementwise_mul with sqrt activation --- fused 0 elementwise_mul with abs activation --- fused 0 elementwise_mul with clip activation --- fused 0 elementwise_mul with gelu activation --- fused 0 elementwise_mul with relu6 activation --- fused 0 elementwise_mul with sigmoid activation Traceback (most recent call last): File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/home/tapendra/Desktop/layout/layout_analysis.py", line 71, in doc_layout_recognition_result = doc_layout_analysis.layout_recognition_bbox_sorting() File "/home/tapendra/Desktop/layout/layout_analysis.py", line 29, in layout_recognition_bbox_sorting layout_result = layout_engine(numpyimg) File "/home/tapendra/.local/lib/python3.8/site-packages/paddleocr/paddleocr.py", line 645, in call res, = super().call( File "/home/tapendra/.local/lib/python3.8/site-packages/paddleocr/ppstructure/predict_system.py", line 112, in call layout_res, elapse = self.layout_predictor(img) File "/home/tapendra/.local/lib/python3.8/site-packages/paddleocr/ppstructure/layout/predict_layout.py", line 86, in call self.predictor.run() ValueError: (InvalidArgument) The conv2d Op's Input Variable Input contains uninitialized Tensor. [Hint: Expected t->IsInitialized() == true, but received t->IsInitialized():0 != true:1.] (at /paddle/paddle/fluid/framework/operator.cc:2094) [operator < conv2d > error] tapendra@tapendra:~/Desktop/layout$ paddle version /home/tapendra/.local/bin/paddle: line 150: python: command not found PaddlePaddle 2.3.2, compiled with with_avx: ON with_gpu: OFF with_mkl: ON with_mkldnn: ON with_python: ON

[TapendraBaduwal]

@an1018 my train.json file data for model training 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[TapendraBaduwal]

@an1018 my single image json file formate from labelme tool is

{ "version": "3.16.7", "flags": {}, "shapes": [ { "label": "text", "line_color": null, "fill_color": null, "points": [ [ 35.38461538461536, 105.07692307692307 ], [ 35.38461538461536, 264.05128205128204 ], [ 514.8717948717949, 261.4871794871795 ], [ 514.8717948717949, 105.07692307692307 ] ], "shape_type": "polygon", "flags": {} }, { "label": "table", "line_color": null, "fill_color": null, "points": [ [ 514.8717948717949, 105.07692307692307 ], [ 1130.2564102564102, 110.2051282051282 ], [ 1122.5641025641025, 284.56410256410254 ], [ 520.0, 264.05128205128204 ] ], "shape_type": "polygon", "flags": {} }, { "label": "text", "line_color": null, "fill_color": null, "points": [ [ 35.38461538461536, 294.8205128205128 ], [ 1114.871794871795, 307.64102564102564 ], [ 1109.7435897435896, 830.7179487179487 ], [ 30.256410256410277, 825.5897435897435 ] ], "shape_type": "polygon", "flags": {} }, { "label": "figure", "line_color": null, "fill_color": null, "points": [ [ 258.46153846153845, 840.9743589743589 ], [ 268.71794871794873, 1192.2564102564102 ], [ 894.3589743589744, 1199.948717948718 ], [ 899.4871794871794, 843.5384615384615 ] ], "shape_type": "polygon", "flags": {} }, { "label": "text", "line_color": null, "fill_color": null, "points": [ [ 86.66666666666663, 1207.6410256410256 ], [ 89.23076923076917, 1302.5128205128206 ], [ 1089.2307692307693, 1305.076923076923 ], [ 1084.102564102564, 1223.0256410256409 ] ], "shape_type": "polygon", "flags": {} }, { "label": "figure", "line_color": null, "fill_color": null, "points": [ [ 158.46153846153845, 1282.0 ], [ 171.28205128205127, 1548.6666666666665 ], [ 925.1282051282051, 1569.179487179487 ], [ 922.5641025641025, 1312.7692307692307 ] ], "shape_type": "polygon", "flags": {} }, { "label": "text", "line_color": null, "fill_color": null, "points": [ [ 89.23076923076917, 1548.6666666666665 ], [ 89.23076923076917, 1620.4615384615383 ], [ 1091.7948717948718, 1630.7179487179487 ], [ 1089.2307692307693, 1553.7948717948718 ] ], "shape_type": "polygon", "flags": {} } ], "lineColor": [ 0, 255, 0, 128 ], "fillColor": [ 255, 0, 0, 128 ], "imagePath": "Tu_page-0001.jpg", "Someting long key here" "imageHeight": 1650, "imageWidth": 1275 }

[an1018]

Is it normal to train with your own dataset? and did you test your model before export?

[TapendraBaduwal]

@an1018 in output dir below image save while infer

[TapendraBaduwal]

@an1018 while training in last i got

Accumulating evaluation results... DONE (t=0.02s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 [01/16 12:33:12] ppdet.engine INFO: Total sample number: 3, average FPS: 1.5615825421222556 [01/16 12:33:12] ppdet.engine INFO: Best test bbox ap is 0.000. [01/16 12:33:12] ppdet.utils.checkpoint INFO: Save checkpoint: output/picodet_lcnet_x1_0_layout

[1061302569]

您可以尝试安装 PaddlePaddle 2.3

我也是报这个错误，版本2.4换成2.3还是不行

[1061302569]

用自己的数据集训练正常吗？你在出口前测试过你的模型吗？

#测试版面分析结果
!python3 PaddleDetection/tools/infer.py \
    -c ./picodet_lcnet_x1_0_layout.yml \
    -o weights='output/picodet_lcnet_x1_0_layout/best_model.pdparams' \
    --infer_img='data/images/123.png' \
    --output_dir=output_dir/ \
    --draw_threshold=0.5 

[1061302569]

用自己的数据集训练正常吗？你在出门前测试过你的模型吗？

#生成训练集合测试集
!python label_studio.py \
    --label_studio_file ./datasets/label.json \
    --save_dir ./datasets \
    --splits 0.8 0.2 0\
    --task_type ext \
    --layout_analysis True

我在layout_analysis True用我自己训练版面模型时报了 ValueError: (InvalidArgument) The conv2d Op's Input Variable Input contains uninitialized Tensor. [Hint: Expected t->IsInitialized() == true, but received t->IsInitialized():0 != true:1.] (at /paddle/paddle/fluid/framework/operator.cc:2411) [operator < conv2d > error]

[TapendraBaduwal]

@1061302569 yes the version 2.4 is changed to 2.3 still not solved what is the minimum number of data required to train a layout model currently i am doing finetune on 10 dataset "Is it a problem due to less number of of data? in how many data you train your model?

[1061302569]

@1061302569 yes the version 2.4 is changed to 2.3 still not solved what is the minimum number of data required to train a layout model currently i am doing finetune on 10 dataset "Is it a problem due to less number of of data? in how many data you train your model?

It should not be the reason for the small data set. At the beginning, I also used 10 pictures for training. Now I increase the data set to 100 pictures. This error is still the same.

[TapendraBaduwal]

@1061302569 @an1018 "Is it a problem due to image size? my images size is like "imageHeight": 1650, "imageWidth": 1275 ? In .yml file image_shape: [1, 3, 800, 608] is mention. Shall we maintain this size while training?

[TapendraBaduwal]

@1061302569 Did you solve this issue?

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