class_docstr: Currently, the models implemented in PaddleSeg is mostly on CityScapes with resoulution 1024x512.
  Thus, this class is used to plot model's performance.All resolution is unified to `1024x512`.
  
  However: 
          1. Some models do not have `model.pdparams` yet,
          2. Some models already have `model.pdparams`, but its resolution is not equal to 1024x512.
  Thus, we set some status flags to note them. Please refer to the function `get_model_status()`.
  
  This class include several funcitons:
          get_model_names()
          get_model_status()
          set_detailed_info()
          do_plot()
  
  If needs, please modify them synchronously.
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docstr: Define the detailed information of each implemented models.
  The mIoU is evaluated with the resulotion: 1024x512.  
  The whole processing time includes preprocess_time, Inference Time (ms) and postprocess_time.(ms)
docstr_tok: ["Define","the","detailed","information","of","each","implemented","models",".","The","mIoU","is","evaluated","with","the","resulotion",":","1024x512",".","The","whole","processing","time","includes","preprocess_time",",","Inference","Time","(","ms",")","and","postprocess_time",".","(","ms",")"]
code: def set_model_info(self):
      """
      Define the detailed information of each implemented models.
      The mIoU is evaluated with the resulotion: 1024x512.
      The whole processing time includes preprocess_time, Inference Time (ms) and postprocess_time.(ms)
      """
      model_infos = {}
      # large model
      model_info = {}
      model_info["mIoU"] = 79.50
      model_info["preprocess_time"] = 143.3464
      model_info["Inference Time (ms)"] = 94.9185
      model_info["postprocess_time"] = 0.0133
      model_info["FLOPs (G)"] = 564.43
      model_info["Params (M)"] = 67.70
      model_infos["ANN_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 80.85
      model_info["preprocess_time"] = 141.65
      model_info["Inference Time (ms)"] = 114.00
      model_info["postprocess_time"] = 0.014
      model_info["FLOPs (G)"] = 481.00
      model_info["Params (M)"] = 58.17
      model_infos["DeeplabV3_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 81.10
      model_info["preprocess_time"] = 147.2358
      model_info["Inference Time (ms)"] = 69.7831
      model_info["postprocess_time"] = 0.016
      model_info["FLOPs (G)"] = 228.44
      model_info["Params (M)"] = 26.79
      model_infos["DeeplabV3p_ResNet50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 80.70
      model_info["preprocess_time"] = 130.5809
      model_info["Inference Time (ms)"] = 45.4565
      model_info["postprocess_time"] = 0.012
      model_info["FLOPs (G)"] = 187.50
      model_info["Params (M)"] = 65.94
      model_infos["FCN_HRNetw18"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 81.01
      model_info["preprocess_time"] = 119.3773
      model_info["Inference Time (ms)"] = 90.2779
      model_info["postprocess_time"] = 0.0127
      model_info["FLOPs (G)"] = 570.74
      model_info["Params (M)"] = 68.73
      model_infos["GCNet_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 82.15
      model_info["preprocess_time"] = 138.4849
      model_info["Inference Time (ms)"] = 61.8837
      model_info["postprocess_time"] = 0.0143
      model_info["FLOPs (G)"] = 324.66
      model_info["Params (M)"] = 70.47
      model_infos["OCRNet_HRNetw48"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 80.48
      model_info["preprocess_time"] = 134.2316
      model_info["Inference Time (ms)"] = 115.9394
      model_info["postprocess_time"] = 0.0123
      model_info["FLOPs (G)"] = 686.89
      model_info["Params (M)"] = 86.97
      model_infos["PSPNet_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 81.26
      model_info["preprocess_time"] = 136.2757
      model_info["Inference Time (ms)"] = 66.8982
      model_info["postprocess_time"] = 0.0125
      model_info["FLOPs (G)"] = 395.10
      model_info["Params (M)"] = 41.71
      model_infos["DecoupledSegnet_ResNet50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 80.00
      model_info["preprocess_time"] = 140.4741
      model_info["Inference Time (ms)"] = 80.0515
      model_info["postprocess_time"] = 0.0133
      model_info["FLOPs (G)"] = 512.18
      model_info["Params (M)"] = 61.45
      model_infos["EMANet_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 81.03
      model_info["preprocess_time"] = 138.9493
      model_info["Inference Time (ms)"] = 97.8105
      model_info["postprocess_time"] = 0.0142
      model_info["FLOPs (G)"] = 575.04
      model_info["Params (M)"] = 69.13
      model_infos["DNLnet_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 80.27
      model_info["preprocess_time"] = 134.7776
      model_info["Inference Time (ms)"] = 95.0819
      model_info["postprocess_time"] = 0.0146
      model_info["FLOPs (G)"] = 398.48
      model_info["Params (M)"] = 47.52
      model_infos["DANet_ResNet50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 80.10
      model_info["preprocess_time"] = 132.4482
      model_info["Inference Time (ms)"] = 81.2459
      model_info["postprocess_time"] = 0.0143
      model_info["FLOPs (G)"] = 474.13
      model_info["Params (M)"] = 56.81
      model_infos["ISANet_ResNet101"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 78.72
      model_info["preprocess_time"] = 131.6711
      model_info["Inference Time (ms)"] = 69.5135
      model_info["postprocess_time"] = 0.0153
      model_info["FLOPs (G)"] = 136.80
      model_info["Params (M)"] = 13.81
      model_infos["SFNet_ResNet18"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 81.49
      model_info["Inference Time (ms)"] = 121.35
      model_info["FLOPs (G)"] = 394.37
      model_info["Params (M)"] = 42.03
      model_infos["SFNet_ResNet50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 76.54
      model_info["Inference Time (ms)"] = 70.35
      model_info["FLOPs (G)"] = 363.17
      model_info["Params (M)"] = 28.18
      model_infos["PointRend_ResNet50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 81.60
      model_info["Inference Time (ms)"] = 47.08
      model_info["FLOPs (G)"] = 113.71
      model_info["Params (M)"] = 27.36
      model_infos["SegFormer_B2"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 82.47
      model_info["Inference Time (ms)"] = 62.70
      model_info["FLOPs (G)"] = 142.97
      model_info["Params (M)"] = 47.24
      model_infos["SegFormer_B3"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 82.38
      model_info["Inference Time (ms)"] = 73.26
      model_info["FLOPs (G)"] = 171.05
      model_info["Params (M)"] = 64.01
      model_infos["SegFormer_B4"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 82.58
      model_info["Inference Time (ms)"] = 84.34
      model_info["FLOPs (G)"] = 199.68
      model_info["Params (M)"] = 84.61
      model_infos["SegFormer_B5"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 77.29
      model_info["Inference Time (ms)"] = 201.26
      model_info["FLOPs (G)"] = 620.94
      model_info["Params (M)"] = 303.37
      model_infos["SETR-Naive"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 78.08
      model_info["Inference Time (ms)"] = 212.22
      model_info["FLOPs (G)"] = 727.46
      model_info["Params (M)"] = 307.24
      model_infos["SETR-PUP"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 76.52
      model_info["Inference Time (ms)"] = 204.87
      model_info["FLOPs (G)"] = 633.88
      model_info["Params (M)"] = 307.05
      model_infos["SETR-MLA"] = model_info
  
      # lightweight
      model_info = {}
      model_info["mIoU"] = 76.73
      model_info["Inference Time (ms)"] = 15.66
      model_info["FLOPs (G)"] = 13.63
      model_info["Params (M)"] = 3.72
      model_infos["SegFormer_B0"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 78.35
      model_info["Inference Time (ms)"] = 21.48
      model_info["FLOPs (G)"] = 26.55
      model_info["Params (M)"] = 13.68
      model_infos["SegFormer_B1"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 74.74
      model_info["Inference Time (ms)"] = 9.10
      model_info["FLOPs (G)"] = 24.83
      model_info["Params (M)"] = 8.29
      model_infos["STDC1-Seg50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 77.60
      model_info["Inference Time (ms)"] = 10.88
      model_info["FLOPs (G)"] = 38.05
      model_info["Params (M)"] = 12.33
      model_infos["STDC2-Seg50"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 73.19
      model_info["preprocess_time"] = 145.7413
      model_info["Inference Time (ms)"] = 7.5558
      model_info["postprocess_time"] = 0.0132
      model_info["FLOPs (G)"] = 16.14
      model_info["Params (M)"] = 2.33
      model_infos["BiseNetV2"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 69.31
      model_info["preprocess_time"] = 142.109
      model_info["Inference Time (ms)"] = 8.7706
      model_info["postprocess_time"] = 0.0134
      model_info["FLOPs (G)"] = 2.04
      model_info["Params (M)"] = 1.44
      model_infos["Fast-SCNN"] = model_info
  
      model_info = {}
      model_info["mIoU"] = 79.03
      model_info["preprocess_time"] = 135.8398
      model_info["Inference Time (ms)"] = 12.8848
      model_info["postprocess_time"] = 0.0127
      model_info["FLOPs (G)"] = 35.40
      model_info["Params (M)"] = 4.13
      model_infos["HarDNet"] = model_info
  
      # model_info = {}
      # model_info['mIoU'] = 65.00
      # model_info['preprocess_time'] = 137.7497
      # model_info['Inference Time (ms)'] = 29.1146
      # model_info['postprocess_time'] = 0.0123
      # model_info['FLOPs (G)'] = 253.75
      # model_info['Params (M)'] = 13.41
      # model_infos['U-Net'] = model_info
  
      return model_infos
  
imports: import random
  import numpy
  from matplotlib import pyplot
label_desc: Use image node_id 1 for calling the ModelPerformance obj's underlying member method code with example usage: obj.set_model_info() and returns: model_infos