Created by: zachary-zheng

笔记本配置：GeForce GTX 1660Ti with Max-Q Design 显存6G Python 版本： 3.7.7 PaddleDetection: V0.4 我在跑水果的例子出现以下问题及疑问： 报错内容：

2020-09-26 23:23:06,817-INFO: If regularizer of a Parameter has been set by 'fluid.ParamAttr' or 'fluid.WeightNormParamAttr' already. The Regularization[L2Decay, regularization_coeff=0.000500] in Optimizer will not take effect, and it will only be applied to other Parameters!
2020-09-26 23:23:07,418-INFO: places would be ommited when DataLoader is not iterable
2020-09-26 23:23:08,959-WARNING: C:\Users\DELL/.cache/paddle/weights\yolov3_mobilenet_v1.pdparams not found, try to load model file saved with [ save_params, save_persistables, save_vars ]
2020-09-26 23:23:09,331-WARNING: variable yolo_output.1.conv.weights not used
2020-09-26 23:23:09,331-WARNING: variable yolo_output.2.conv.weights not used
2020-09-26 23:23:09,331-WARNING: variable yolo_output.0.conv.weights not used
2020-09-26 23:23:09,331-WARNING: variable yolo_output.1.conv.bias not used
2020-09-26 23:23:09,331-WARNING: variable yolo_output.0.conv.bias not used
2020-09-26 23:23:09,331-WARNING: variable yolo_output.2.conv.bias not used
2020-09-26 23:23:09,534-INFO: places would be ommited when DataLoader is not iterable
*** Check failure stack trace: ***

Process finished with exit code -1073740791 (0xC0000409)

yml文件为PaddleDetection 内的yolov3_mobilenet_v1_fruit.yml配置：

architecture: YOLOv3
use_gpu: true
max_iters: 20000
log_smooth_window: 20
save_dir: output
snapshot_iter: 200
metric: VOC
map_type: 11point
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
weights: output/yolov3_mobilenet_v1_fruit/best_model
num_classes: 3
finetune_exclude_pretrained_params: ['yolo_output']
use_fine_grained_loss: false

YOLOv3:
  backbone: MobileNet
  yolo_head: YOLOv3Head

MobileNet:
  norm_type: sync_bn
  norm_decay: 0.
  conv_group_scale: 1
  with_extra_blocks: false

YOLOv3Head:
  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  anchors: [[10, 13], [16, 30], [33, 23],
            [30, 61], [62, 45], [59, 119],
            [116, 90], [156, 198], [373, 326]]
  norm_decay: 0.
  yolo_loss: YOLOv3Loss
  nms:
    background_label: -1
    keep_top_k: 100
    nms_threshold: 0.45
    nms_top_k: 1000
    normalized: false
    score_threshold: 0.01

YOLOv3Loss:
  # batch_size here is only used for fine grained loss, not used
  # for training batch_size setting, training batch_size setting
  # is in configs/yolov3_reader.yml TrainReader.batch_size, batch
  # size here should be set as same value as TrainReader.batch_size
  batch_size: 8
  ignore_thresh: 0.7
  label_smooth: true

LearningRate:
  base_lr: 0.00001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 15000
    - 18000
  - !LinearWarmup
    start_factor: 0.
    steps: 100

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0005
    type: L2

_READER_: 'yolov3_reader.yml'
# will merge TrainReader into yolov3_reader.yml
TrainReader:
  inputs_def:
    image_shape: [3, 608, 608]
    fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
    num_max_boxes: 50
  dataset:
    !VOCDataSet
    dataset_dir: ../dataset/fruit
    anno_path: train.txt
    with_background: false
    use_default_label: false
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeBox {}
  - !ExpandImage
    max_ratio: 4.0
    mean: [123.675, 116.28, 103.53]
    prob: 0.5
  - !RandomInterpImage
    max_size: 0
    target_size: 608
  - !RandomFlipImage
    is_normalized: true
    prob: 0.5
  - !NormalizeImage
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
    is_scale: true
    is_channel_first: false
  - !PadBox
    num_max_boxes: 50
  - !BboxXYXY2XYWH {}
  batch_transforms:
  - !RandomShape
    sizes: [608]
  - !Permute
    channel_first: true
    to_bgr: false
  batch_size: 1
  shuffle: true
  mixup_epoch: -1

EvalReader:
  batch_size: 1
  inputs_def:
    image_shape: [3, 608, 608]
    fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
    num_max_boxes: 50
  dataset:
    !VOCDataSet
    dataset_dir: ../dataset/fruit
    anno_path: val.txt
    use_default_label: false
    with_background: false

TestReader:
  batch_size: 1
  dataset:
    !ImageFolder
    anno_path: ../dataset/fruit/label_list.txt
    use_default_label: false
    with_background: false

请问 （1）我用pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar，为什么会报错yolov3_mobilenet_v1.pdparams not found， 去哪里搞到.pdparams文件呢？ （2）为什么提示yolo_output.1.conv.weights not used？ （3）*** Check failure stack trace: ***这是显存不够吗？ batch_size=1 了还有其他方法吗？