[Bug]: ONNX export failed with custom image size trained with patchcore model

Describe the bug
I trained patchcore algorithm with custom image size and want to export the model with ONNX. If I use the  patchcore algorithm--> backbone: resnet18 --> with default image size [224, 224] everything works fine without any error with onnx model to tensorrt, but if  try to transfer the onnx model that trained with custom image size to tensorrt with I got some error. The error is given into the Logs section. Is the error generated because of custom image size? How do I export the model to ONNX with custom image trained model?  Thanks in advance.
Dataset
Folder
Model
PatchCore
Steps to reproduce the behavior
[graphShapeAnalyzer.cpp::processCheck::862] Error Code 4: Internal Error ((Unnamed Layer* 46) [ElementWise]_output: tensor volume exceeds (2^31)-1, dimensions are [65536,13107,256]) In node 45 (parseGraph): INVALID_NODE: Invalid Node - /Sub [graphShapeAnalyzer.cpp::processCheck::862] Error Code 4: Internal Error ((Unnamed Layer* 46) [ElementWise]_output: tensor volume exceeds (2^31)-1, dimensions are [65536,13107,256]) Traceback (most recent call last):   File "onnx_export.py", line 39, in <module>     raise RuntimeError("ONNX parsing failed. Please check the ONNX model.") RuntimeError: ONNX parsing failed. Please check the ONNX model.
OS information
OS information:

OS: [e.g. Ubuntu 20.04]
Python version: [e.g. 3.8.10]
Anomalib version: [e.g. 0.3.6]
PyTorch version: [e.g. 1.9.0]
CUDA/cuDNN version: [e.g. 11.1]
GPU models and configuration: [e.g. 2x GeForce RTX 3090]
Any other relevant information: [e.g. I'm using a custom dataset]

Expected behavior
Default patchcore model with image size [224, 224] works file without any error from model training to onnx export--> tensorrt. How to export the patchcore model that trained with custom image size to ONNX? Do I need to change the feature extraction code section by any chance?
Screenshots
No response
Pip/GitHub
pip
What version/branch did you use?
1.0.0dev
Configuration YAML
dataset:
  name: demolayer
  format: folder

  task: segmentation # detection, segmentation, classification

  path: /demolayermodel/
  extensions: [.png]
  normal_dir: normal # name of the folder containing normal images.
  abnormal_dir: abnormal # name of the folder containing abnormal images.
  normal_test_dir: normal_test_dir
  mask: /demolayer/mask/

  category: defected
  train_batch_size: 1
  eval_batch_size: 1
  num_workers: 8
  image_size: [2048, 8192] # dimensions to which images are resized (mandatory)
  #center_crop: 256 # dimensions to which images are center-cropped after resizing (optional)
  normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
  transform_config:
    train: null
    eval: null
  test_split_mode: from_dir # options: [from_dir, synthetic]
  test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode)
  val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic]
  val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
  tiling:
    apply: false
    tile_size: 256
    stride: null
    remove_border_count: 0
    use_random_tiling: true
    random_tile_count: 192

model:
  name: demolayer
  backbone: resnet18 #wide_resnet50_2
  pre_trained: true
  layers:
#    - layer1
#    - layer2
    - layer3
  coreset_sampling_ratio: 0.1
  num_neighbors: 9
  #weight_file:  weights/model.ckpt
  normalization_method: min_max # options: [null, min_max, cdf]

metrics:
  image:
    - F1Score
    - AUROC
  pixel:
    - F1Score
    - AUROC
  threshold:
    method: adaptive #options: [adaptive, manual]
    manual_image: null
    manual_pixel:  null

visualization:
  show_images: False # show images on the screen
  save_images: True # save images to the file system
  log_images: True # log images to the available loggers (if any)
  image_save_path: null # path to which images will be saved
  mode: full # options: ["full", "simple"]

project:
  seed: 0
  path: /results/

logging:
  logger: ["tensorboard"] # options: [comet, tensorboard, wandb, csv] or combinations.
  log_graph: true #true # Logs the model graph to respective logger.

optimization:
  export_mode: onnx # options: onnx, openvino

# PL Trainer Args. Don't add extra parameter here.
trainer:
  enable_checkpointing: true
  default_root_dir: null
  gradient_clip_val: 0
  gradient_clip_algorithm: norm
  num_nodes: 1
  devices: 1 #4
  enable_progress_bar: true
  overfit_batches: 0.0
  track_grad_norm: -1
  check_val_every_n_epoch: 1 # Don't validate before extracting features.
  fast_dev_run: false
  accumulate_grad_batches: 1
  max_epochs: 1
  min_epochs: null
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  limit_predict_batches: 1.0
  val_check_interval: 1.0 # Don't validate before extracting features.
  log_every_n_steps: 1
  accelerator: gpu # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
  strategy: null # ddp, ddp_spawn
  sync_batchnorm: false
  precision: 32
  enable_model_summary: true
  num_sanity_val_steps: 0
  profiler: null
  benchmark: false
  deterministic: false
  reload_dataloaders_every_n_epochs: 0
  auto_lr_find: false
  replace_sampler_ddp: true
  detect_anomaly: false
  auto_scale_batch_size: false
  plugins: null
  move_metrics_to_cpu: false
  multiple_trainloader_mode: max_size_cycle

Logs
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::BatchedNMSDynamic_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::BatchedNMS_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::BatchTilePlugin_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::Clip_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::CoordConvAC version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::CropAndResizeDynamic version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::CropAndResize version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::DecodeBbox3DPlugin version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::DetectionLayer_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::EfficientNMS_Explicit_TF_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::EfficientNMS_Implicit_TF_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::EfficientNMS_ONNX_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::EfficientNMS_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::FlattenConcat_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::GenerateDetection_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::GridAnchor_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::GridAnchorRect_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::InstanceNormalization_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::InstanceNormalization_TRT version 2
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::LReLU_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::ModulatedDeformConv2d version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::MultilevelCropAndResize_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::MultilevelProposeROI_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::MultiscaleDeformableAttnPlugin_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::NMSDynamic_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::NMS_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::Normalize_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::PillarScatterPlugin version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::PriorBox_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::ProposalDynamic version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::ProposalLayer_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::Proposal version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::PyramidROIAlign_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::Region_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::Reorg_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::ResizeNearest_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::ROIAlign_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::RPROI_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::ScatterND version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::SpecialSlice_TRT version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::Split version 1
[08/21/2023-13:24:04] [TRT] [V] Registered plugin creator - ::VoxelGeneratorPlugin version 1
[08/21/2023-13:24:04] [TRT] [V] Adding network input: input with dtype: float32, dimensions: (1, 3, 2048, 8192)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: input for ONNX tensor: input
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: memory_bank
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: anomaly_map_generator.blur.kernel
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_322
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_323
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_325
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_326
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_328
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_329
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_331
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_332
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_334
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_335
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_337
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_338
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_340
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_341
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_343
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_344
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_346
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_347
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_349
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_350
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_352
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_353
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_355
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_356
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_358
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_359
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_361
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_362
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_364
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Conv_365
[08/21/2023-13:24:04] [TRT] [V] Importing initializer: onnx::Sub_369
[08/21/2023-13:24:04] [TRT] [V] Parsing node: Identity_0 [Identity]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Sub_369
[08/21/2023-13:24:04] [TRT] [V] Identity_0 [Identity] inputs: [onnx::Sub_369 -> (1, 13107, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: onnx::Sub_369 for ONNX node: onnx::Sub_369
[08/21/2023-13:24:04] [TRT] [V] Registering layer: Identity_0 for ONNX node: Identity_0
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: onnx::Sub_384 for ONNX tensor: onnx::Sub_384
[08/21/2023-13:24:04] [TRT] [V] Identity_0 [Identity] outputs: [onnx::Sub_384 -> (1, 13107, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: input
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_322
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_323
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/conv1/Conv [Conv] inputs: [input -> (1, 3, 2048, 8192)[FLOAT]], [onnx::Conv_322 -> (64, 3, 7, 7)[FLOAT]], [onnx::Conv_323 -> (64)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 3, 2048, 8192)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (7, 7), strides: (2, 2), prepadding: (3, 3), postpadding: (3, 3), dilations: (1, 1), numOutputs: 64
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 64, 1024, 4096)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/conv1/Conv_output_0 -> (1, 64, 1024, 4096)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/conv1/Conv_output_0 -> (1, 64, 1024, 4096)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/act1/Relu for ONNX node: /feature_extractor/feature_extractor/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/act1/Relu_output_0 -> (1, 64, 1024, 4096)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/maxpool/MaxPool [MaxPool]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/maxpool/MaxPool [MaxPool] inputs: [/feature_extractor/feature_extractor/act1/Relu_output_0 -> (1, 64, 1024, 4096)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/maxpool/MaxPool for ONNX node: /feature_extractor/feature_extractor/maxpool/MaxPool
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/maxpool/MaxPool_output_0 for ONNX tensor: /feature_extractor/feature_extractor/maxpool/MaxPool_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/maxpool/MaxPool [MaxPool] outputs: [/feature_extractor/feature_extractor/maxpool/MaxPool_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/maxpool/MaxPool_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_325
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_326
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv [Conv] inputs: [/feature_extractor/feature_extractor/maxpool/MaxPool_output_0 -> (1, 64, 512, 2048)[FLOAT]], [onnx::Conv_325 -> (64, 64, 3, 3)[FLOAT]], [onnx::Conv_326 -> (64)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer1/layer1.0/conv1/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_328
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_329
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer1/layer1.0/act1/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]], [onnx::Conv_328 -> (64, 64, 3, 3)[FLOAT]], [onnx::Conv_329 -> (64)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.0/Add [Add]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/maxpool/MaxPool_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/Add [Add] inputs: [/feature_extractor/feature_extractor/layer1/layer1.0/conv2/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]], [/feature_extractor/feature_extractor/maxpool/MaxPool_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.0/Add for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.0/Add
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.0/Add_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.0/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/Add [Add] outputs: [/feature_extractor/feature_extractor/layer1/layer1.0/Add_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.0/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer1/layer1.0/Add_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_331
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_332
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]], [onnx::Conv_331 -> (64, 64, 3, 3)[FLOAT]], [onnx::Conv_332 -> (64)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer1/layer1.1/conv1/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_334
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_335
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer1/layer1.1/act1/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]], [onnx::Conv_334 -> (64, 64, 3, 3)[FLOAT]], [onnx::Conv_335 -> (64)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.1/Add [Add]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/Add [Add] inputs: [/feature_extractor/feature_extractor/layer1/layer1.1/conv2/Conv_output_0 -> (1, 64, 512, 2048)[FLOAT]], [/feature_extractor/feature_extractor/layer1/layer1.0/act2/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.1/Add for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.1/Add
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.1/Add_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.1/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/Add [Add] outputs: [/feature_extractor/feature_extractor/layer1/layer1.1/Add_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.1/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer1/layer1.1/Add_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu for ONNX node: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_337
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_338
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]], [onnx::Conv_337 -> (128, 64, 3, 3)[FLOAT]], [onnx::Conv_338 -> (128)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer2/layer2.0/conv1/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_340
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_341
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer2/layer2.0/act1/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]], [onnx::Conv_340 -> (128, 128, 3, 3)[FLOAT]], [onnx::Conv_341 -> (128)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_343
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_344
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer1/layer1.1/act2/Relu_output_0 -> (1, 64, 512, 2048)[FLOAT]], [onnx::Conv_343 -> (128, 64, 1, 1)[FLOAT]], [onnx::Conv_344 -> (128)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 64, 512, 2048)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (1, 1), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.0/Add [Add]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/Add [Add] inputs: [/feature_extractor/feature_extractor/layer2/layer2.0/conv2/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]], [/feature_extractor/feature_extractor/layer2/layer2.0/downsample/downsample.0/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.0/Add for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.0/Add
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.0/Add_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.0/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/Add [Add] outputs: [/feature_extractor/feature_extractor/layer2/layer2.0/Add_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer2/layer2.0/Add_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_346
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_347
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]], [onnx::Conv_346 -> (128, 128, 3, 3)[FLOAT]], [onnx::Conv_347 -> (128)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer2/layer2.1/conv1/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_349
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_350
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer2/layer2.1/act1/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]], [onnx::Conv_349 -> (128, 128, 3, 3)[FLOAT]], [onnx::Conv_350 -> (128)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.1/Add [Add]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/Add [Add] inputs: [/feature_extractor/feature_extractor/layer2/layer2.1/conv2/Conv_output_0 -> (1, 128, 256, 1024)[FLOAT]], [/feature_extractor/feature_extractor/layer2/layer2.0/act2/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.1/Add for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.1/Add
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.1/Add_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.1/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/Add [Add] outputs: [/feature_extractor/feature_extractor/layer2/layer2.1/Add_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.1/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer2/layer2.1/Add_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu for ONNX node: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_352
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_353
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]], [onnx::Conv_352 -> (256, 128, 3, 3)[FLOAT]], [onnx::Conv_353 -> (256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer3/layer3.0/conv1/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_355
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_356
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer3/layer3.0/act1/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]], [onnx::Conv_355 -> (256, 256, 3, 3)[FLOAT]], [onnx::Conv_356 -> (256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_358
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_359
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer2/layer2.1/act2/Relu_output_0 -> (1, 128, 256, 1024)[FLOAT]], [onnx::Conv_358 -> (256, 128, 1, 1)[FLOAT]], [onnx::Conv_359 -> (256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 128, 256, 1024)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (1, 1), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.0/Add [Add]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/Add [Add] inputs: [/feature_extractor/feature_extractor/layer3/layer3.0/conv2/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]], [/feature_extractor/feature_extractor/layer3/layer3.0/downsample/downsample.0/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.0/Add for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.0/Add
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.0/Add_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.0/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/Add [Add] outputs: [/feature_extractor/feature_extractor/layer3/layer3.0/Add_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer3/layer3.0/Add_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_361
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_362
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]], [onnx::Conv_361 -> (256, 256, 3, 3)[FLOAT]], [onnx::Conv_362 -> (256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer3/layer3.1/conv1/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv [Conv]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_364
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Conv_365
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv [Conv] inputs: [/feature_extractor/feature_extractor/layer3/layer3.1/act1/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]], [onnx::Conv_364 -> (256, 256, 3, 3)[FLOAT]], [onnx::Conv_365 -> (256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Convolution input dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv
[08/21/2023-13:24:04] [TRT] [V] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256
[08/21/2023-13:24:04] [TRT] [V] Convolution output dimensions: (1, 256, 128, 512)
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv [Conv] outputs: [/feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.1/Add [Add]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/Add [Add] inputs: [/feature_extractor/feature_extractor/layer3/layer3.1/conv2/Conv_output_0 -> (1, 256, 128, 512)[FLOAT]], [/feature_extractor/feature_extractor/layer3/layer3.0/act2/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.1/Add for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.1/Add
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.1/Add_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.1/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/Add [Add] outputs: [/feature_extractor/feature_extractor/layer3/layer3.1/Add_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu [Relu]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.1/Add_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu [Relu] inputs: [/feature_extractor/feature_extractor/layer3/layer3.1/Add_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu for ONNX node: /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu_output_0 for ONNX tensor: /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu [Relu] outputs: [/feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_pooler/Constant [Constant]
[08/21/2023-13:24:04] [TRT] [V] /feature_pooler/Constant [Constant] inputs:
[08/21/2023-13:24:04] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[08/21/2023-13:24:04] [TRT] [V] /feature_pooler/Constant [Constant] outputs: [/feature_pooler/Constant_output_0 -> (8)[INT32]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_pooler/Pad [Pad]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_pooler/Constant_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_pooler/Pad [Pad] inputs: [/feature_extractor/feature_extractor/layer3/layer3.1/act2/Relu_output_0 -> (1, 256, 128, 512)[FLOAT]], [/feature_pooler/Constant_output_0 -> (8)[INT32]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_pooler/Pad for ONNX node: /feature_pooler/Pad
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_pooler/Pad_output_0 for ONNX tensor: /feature_pooler/Pad_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_pooler/Pad [Pad] outputs: [/feature_pooler/Pad_output_0 -> (1, 256, 130, 514)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /feature_pooler/AveragePool [AveragePool]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_pooler/Pad_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_pooler/AveragePool [AveragePool] inputs: [/feature_pooler/Pad_output_0 -> (1, 256, 130, 514)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /feature_pooler/AveragePool for ONNX node: /feature_pooler/AveragePool
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /feature_pooler/AveragePool_output_0 for ONNX tensor: /feature_pooler/AveragePool_output_0
[08/21/2023-13:24:04] [TRT] [V] /feature_pooler/AveragePool [AveragePool] outputs: [/feature_pooler/AveragePool_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Constant [Constant]
[08/21/2023-13:24:04] [TRT] [V] /Constant [Constant] inputs:
[08/21/2023-13:24:04] [TRT] [V] /Constant [Constant] outputs: [/Constant_output_0 -> ()[INT32]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Transpose [Transpose]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /feature_pooler/AveragePool_output_0
[08/21/2023-13:24:04] [TRT] [V] /Transpose [Transpose] inputs: [/feature_pooler/AveragePool_output_0 -> (1, 256, 128, 512)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /Transpose for ONNX node: /Transpose
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /Transpose_output_0 for ONNX tensor: /Transpose_output_0
[08/21/2023-13:24:04] [TRT] [V] /Transpose [Transpose] outputs: [/Transpose_output_0 -> (1, 128, 512, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Constant_1 [Constant]
[08/21/2023-13:24:04] [TRT] [V] /Constant_1 [Constant] inputs:
[08/21/2023-13:24:04] [TRT] [V] /Constant_1 [Constant] outputs: [/Constant_1_output_0 -> (2)[INT32]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Reshape [Reshape]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /Transpose_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /Constant_1_output_0
[08/21/2023-13:24:04] [TRT] [V] /Reshape [Reshape] inputs: [/Transpose_output_0 -> (1, 128, 512, 256)[FLOAT]], [/Constant_1_output_0 -> (2)[INT32]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /Reshape for ONNX node: /Reshape
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /Reshape_output_0 for ONNX tensor: /Reshape_output_0
[08/21/2023-13:24:04] [TRT] [V] /Reshape [Reshape] outputs: [/Reshape_output_0 -> (65536, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Unsqueeze [Unsqueeze]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /Reshape_output_0
[08/21/2023-13:24:04] [TRT] [V] /Unsqueeze [Unsqueeze] inputs: [/Reshape_output_0 -> (65536, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Original shape: (65536, 256), unsqueezing to: (65536, 1, 256)
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /Unsqueeze for ONNX node: /Unsqueeze
[08/21/2023-13:24:04] [TRT] [V] Registering tensor: /Unsqueeze_output_0 for ONNX tensor: /Unsqueeze_output_0
[08/21/2023-13:24:04] [TRT] [V] /Unsqueeze [Unsqueeze] outputs: [/Unsqueeze_output_0 -> (65536, 1, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Constant_2 [Constant]
[08/21/2023-13:24:04] [TRT] [V] /Constant_2 [Constant] inputs:
[08/21/2023-13:24:04] [TRT] [V] /Constant_2 [Constant] outputs: [/Constant_2_output_0 -> (1)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Parsing node: /Sub [Sub]
[08/21/2023-13:24:04] [TRT] [V] Searching for input: /Unsqueeze_output_0
[08/21/2023-13:24:04] [TRT] [V] Searching for input: onnx::Sub_369
[08/21/2023-13:24:04] [TRT] [V] /Sub [Sub] inputs: [/Unsqueeze_output_0 -> (65536, 1, 256)[FLOAT]], [onnx::Sub_369 -> (1, 13107, 256)[FLOAT]],
[08/21/2023-13:24:04] [TRT] [V] Registering layer: /Sub for ONNX node: /Sub
[08/21/2023-13:24:04] [TRT] [E] [graphShapeAnalyzer.cpp::processCheck::862] Error Code 4: Internal Error ((Unnamed Layer* 46) [ElementWise]_output: tensor volume exceeds (2^31)-1, dimensions are [65536,13107,256])
In node 45 (parseGraph): INVALID_NODE: Invalid Node - /Sub
[graphShapeAnalyzer.cpp::processCheck::862] Error Code 4: Internal Error ((Unnamed Layer* 46) [ElementWise]_output: tensor volume exceeds (2^31)-1, dimensions are [65536,13107,256])
Traceback (most recent call last):
  File "onnx_export.py", line 39, in <module>
    raise RuntimeError("ONNX parsing failed. Please check the ONNX model.")
RuntimeError: ONNX parsing failed. Please check the ONNX model.

Code of Conduct

[X] I agree to follow this project's Code of Conduct

I believe this comes from distance calculation that is done as part of model, where the result is entire matrix of distances. From the error it seems like this exceeds the size that is supported (2^31)-1. I am not familiar with TensorRT so I can't help much in that regard. One thing is though: image size of 2048x8192 is quite huge for patchcore, so I'd say that you try something between the default 224x224 and this large one.

@blaz-r thanks for your reply. It's working with default image size 224*224, but failed with custom image size because of dimensionality, [graphShapeAnalyzer.cpp::processCheck::862] Error Code 4: Internal Error ((Unnamed Layer* 46) [ElementWise]_output: tensor volume exceeds (2^31)-1, dimensions are [65536,13107,256]). Could you please explain what do you mean by try something between the default 224x224 and this large one?

Let's convert this to a Q&A in discussions. We could continue from there. Thanks!


Kicad simulation results do not match LTSpice

Kicad simulation uses a different component for now, since the lm358 generic spice models do not work as well
compared to LM358 ltspice model.
Kicad simulation does not work with the pspice lib for lm358 at the moment.

For this project, LTSpice is primarily used for simulation. KiCAD is primarily used for producing the pcb, but KiCAD simulation is helpful for checking that the correct circuit is printed.


Error installing pyautogui

Hi,
Thanks for the hard work you've done creating this tool.
I am on a mac. I installed python 3.6 & PyPDF2 with no issues. However when I try installing pyautogui I get the following error:
"python setup.py egg_info" failed with error code 1 in /private/var/folders/bm/k674gvpd5x3f0p05v4gxm7b80000gn/T/pip-build-u9_d7dd8/pyautogui/
I have tried installing Pillow then the pyautogui but I still get the error.
I am not good at coding. Can you please tell me how to fix this?
thanks

Hi, let's see if this works. Try running "pip3 install --upgrade pip" then running "pip3 install Pillow" (it may already be added from last step), and then "pip3 install pyautogui".
If the above doesn't work, try "sudo easy_install Pillow" then "pip3 install pyautogui"

The above didn't work. After some searching, I installed pyobjc prior to installing pyautogui. Once done I was able to install pyautogui. Now I need to find a pdfprinter for mac
Process was:
pip3 install --upgrade pip
pip3 install PyPDF2
pip3 install Pillow
pip3 install pyobjc --core
pip3 install pyobjc
pip3 install pyautogui


Crash due to TextureRect and RichTextLabel causing a size-fitting infinite loop with certain Containers

Godot version
4.1.1
System information
Godot v4.1.1.stable - Windows 10.0.22621 - Vulkan (Mobile) - dedicated NVIDIA GeForce GTX 1660 Ti (NVIDIA; <IP_ADDRESS>67) - AMD Ryzen 7 4800H with Radeon Graphics (16 Threads)
Issue description
While experimenting with Control nodes as a new user, I encountered a crash that occurs if a Margin or Panel Container has a TextureRect with Expand Mode set to "Fit Width" and a RichTextLabel with "Fit Content" turned on as child nodes.
I am not 100% sure, but my guess is that if enough text is entered into the RichTextLabel, it will try to wrap the text around and consequently increase the Container's height. The TextureRect increases in height to fill the new Container's height, but also increases width due to its expansion settings. The RichTextLabel fills to the Container's resulting new width and, while fitting the content, ends up decreasing its height. At this point, the interaction between the two nodes is enough to cause an infinite loop, causing the editor to crash.
The following is the error generated in the logs, which seems indicative of an infinite loop:
USER ERROR: Failed method: CanvasItem::_redraw_callback. Message queue out of memory. Message queue out of memory. Try increasing 'memory/limits/message_queue/max_size_mb' in project settings.
   at: push_callablep (core/object/message_queue.cpp:96)

Further testing suggests that the loop relies on specific conditions that allows this size-fitting behavior to occur. It doesn't seem to happen for other Containers such as Box Containers, where child and parent size are not equally related as in Margin or Panel Containers. It also doesn't happen if fitting or filling behaviors are set in a way that logically leaves the loop incomplete (ex. it runs perfectly fine if TextureRect's Expand Mode is set to "Fit Height" instead).
Steps to reproduce

Create a MarginContainer or PanelContainer.
Add a TextureRect with Expand Mode set to "Fit Width" as a child to the container, and assign a texture to it (the default icon.svg is enough for this)
Add a RichTextLabel with "Fit Content" checked/turned on as a child to the container.
Start writing text into the RichTextLabel until it causes the Container to expand. This should trigger the loop, causing the editor to hang and eventually crash.

Minimal reproduction project
N/A.

Reproduced this issue on Godot 4.3-dev3
OpenGL API 4.6 (Core Profile) Mesa 23.3.6 - Compatibility - Using Device: AMD - AMD Radeon RX 6750 XT (radeonsi, navi22, LLVM 17.0.6, DRM 3.57, 6.7.4-200.fc39.x86_64)

Failed method: CanvasItem::_redraw_callback. Message queue out of memory. Message queue out of memory. Try increasing 'memory/limits/message_queue/max_size_mb' in project settings.

================================================================
Failed method: EditorNode::_print_handler_impl. Message queue out of memory. Message queue out of memory. Try increasing 'memory/limits/message_queue/max_size_mb' in project settings.

Open Repro project -> main.tscn -> Resize Panel Node for a bit -> Godot Engine disappears.
Screencast from 2024-02-27 09-28-32.webm
Repro82033_CallbackQueue_TextureRect.zip

I have a repro without RichTextLabel, just TextureRects and Containers.

Setting the selected TextureRect to "Fit Height" or "Fit Height Proportional" crashes the editor with the same "Message queue" error. Using Godot_v4.2.2-stable_linux.x86_64.
Repro project: test.zip

@KoBeWi, who wrote https://github.com/godotengine/godot/pull/58517


add: zoom-deb

One package per PR.

One package per PR.

I have added 4 pkgs together! Can u help me please, adding one by one pr?

Just close this PR, create 4 new branches, then add the packages.


How to handle fields in abstract object?

Description:
 An abstract object type must not declare an __init object.
According to the spec, it is not clear how to handle fields in an abstract object.

Moved to ballerina-spec issues.
https://github.com/ballerina-platform/ballerina-spec/issues/380


Can't select newly created video or photo

When creating a new video or photo, the media appears in the collection view grid but the new media is not selectable.
Notes:

This is using the demo code
collectionView:didSelectItemAtIndexPath is not being called in this circumstance.
I can confirm this on iOS 8.3 for sure.

Has anyone seen this or can lead me in the right direction to getting it fixed?

today I checked Latest version 0.9.7.
it works well. could you explain more ?

I will check again.  My config is the latest Xcode and swift 1.2

On Apr 15, 2015, at 10:28 PM, Jaehoon Jung<EMAIL_ADDRESS>wrote:
today I checked Latest version 0.9.7.
it works well. could you explain more ?
—
Reply to this email directly or view it on GitHub.

Please check the delegate name. Didfinish...

thanks for the reporting . bug fixed.


Security of the CLI download step

In the CLI setup instructions: https://docs.rocketpool.net/guides/node/docker.html#downloading-the-rocket-pool-cli
It was the user to wget a url, give it execution permissions and run it.
The file downloaded is an 18MB binary that does who knows what. There doesn't seem to be a way that I can verify this binary corresponds to the code in: https://github.com/rocket-pool/smartnode
So the only encouragement that running this code is in any way trustworthy is that it in an https url.
There are several mitigations that could be done to increase the security posture of this critical step:

Sign the binaries (e.g. with gpg keys from trusted community members).
Release expected sha256 / sha512 hashes of the binaries so users can verify they have something expected.
Release the binaries on IPFS in addition to github
Add a section to the docs that provides instructions on how to build the CLI from source.

The first 3 options make a modified binary attack much harder. Signing the binaries gives as much confidence as the chain of trust provides, which can be a little or a lot. Publishing hashes makes it much easier to automate setting up nodes that pin to a specific version of the CLI. Publishing on IPFS has similar benefits to the hashes in addition to providing a distributed means of accessing the binaries rather than relying on a centralized github server.
The last option is the most impactful. Users can verify the code themselves and use the builtin security that comes with git to verify the code they are building is the code they expect to build.

Hi! Thanks for your concerns here. Just so you're aware, the first point is already being done; the Smartnode is released on GitHub signed by a GPG key. For example, take a look at the release artifacts here:
https://github.com/rocket-pool/smartnode-install/releases/tag/v1.6.5
You'll see each file (including the install script, the CLI binaries, and the daemons) is accompanied by a GPG signature. The GPG public key itself is also included there. Users are free to verify that the binaries match what is signed by that key. That process should account for your second point as well, but if you'd like to see sha hashes of the files as well, we can certainly provide those. I think it's fair that this process could be included in the documentation for people who don't know how to verify GPG signatures, so we'll work on that.
Regarding point 3, I think we could discuss putting the binaries up on IPFS as well. Let me talk about that with the team, because we'd likely need to use a pinning service for it.
For point 4, building from source would probably require its own section to cover things like setting up a Go environment, testing, comparing your version against the canonical version, etc. I think it makes sense to do, but maybe in the dev section of the docs instead of the user guides.

the first point is already being done

Excellent, thank you for the pointer. I'll take a look at using it and I may submit a PR that adds a little bit of text to the docs that point at it too.

Regarding point 3, I think we could discuss putting the binaries up on IPFS as well. Let me talk about that with the team, because we'd likely need to use a pinning service for it.

A pinning service probably is the best way to go. Not sure what your current server for publishing binaries looks like, but it might not be too hard to run an IPFS node on that as well. Not sure, thanks for considering it.

For point 4, building from source would probably require its own section to cover things like setting up a Go environment, testing, comparing your version against the canonical version, etc. I think it makes sense to do, but maybe in the dev section of the docs instead of the user guides

Yeah, I think just adding a quick link like: advanced: if you want to build from source see . Most people wont use it, but some will, and those people will be more likely to help make those build instructions even better.

I am working on making a small PR to add some text about GPG verification. For reference my process for choosing the arch looks like this:
    ARCH=$(uname -m)
    declare -A ARCH_LUT=(
        ["x86"]="amd64"
        ["x86_64"]="amd64"
        ["aarch64"]="arm64"
    )
    ARCH="${ARCH_LUT[${ARCH}]}"
    echo "ARCH = $ARCH"

    PREFIX=$HOME/.local
    mkdir -p "$PREFIX/bin"
    wget https://github.com/rocket-pool/smartnode-install/releases/latest/download/rocketpool-cli-linux-"${ARCH}" -O ./rocketpool
    wget https://github.com/rocket-pool/smartnode-install/releases/latest/download/rocketpool-cli-linux-"${ARCH}".sig -O ./rocketpool.sig
    wget https://github.com/rocket-pool/smartnode-install/releases/latest/download/smartnode-signing-key-v3.asc -O ./smartnode-signing-key-v3.asc

    # Sign key belongs to:
    # https://github.com/jclapis
    gpg --import ./smartnode-signing-key-v3.asc
    gpg --verify rocketpool.sig rocketpool
    mv ./rocketpool "$PREFIX/bin/rocketpool"

    chmod +x "$HOME"/.local/bin/rocketpool
    rocketpool --version


Bump various dependencies

On top of #377.
Including jsdom, which had been blocked on https://github.com/jsdom/jsdom/issues/2968. I finally gave up waiting and just reached into the internals to patch it, which is gross, but what can you do.

jsdom should be better in v19, apparently

Yup, I've included it and dropped the hack.


Can't get tox + pdm to work

I'm clearly doing something wrong, although I've tried both methods of integrating tox + pdm that are described here.

Draft PR without tox-pdm
Draft PR using tox-pdm

A few things I've discovered, rightly or wrongly.

When attempting to integrate with tox-pdm, I find that I need to use the keyword groups rather than sections in my tox.ini.  That seems to jive with what the tox-pdm README implies, but not what the pdm docs say.
When attempting to integrate with tox-pdm, I also have to invoke this via pdm run tox since tox-pdm is not installed into my global tox.
When attempting to integrate without tox-pdm I find that the pdm install -G <group> doesn't seem to install the dependencies into the venv.  It's not clear that it should, but even with use_venv set, I don't have to have to repeat myself if I can avoid it.

I would love to get this to work!

The without tox-pdm branch can work with a little tweak, the ini file should be:
[tox]
envlist = {py37,py38,py39,py310},qa,docs
skip_missing_interpreters = true
isolated_build = true

[testenv]
setenv =
    PDM_IGNORE_SAVED_PYTHON="1"
    PYTHONPATH = ''
...

I've updated the doc with the correct content.
The with tox-pdm branch might have some conflicts with setuptools and can't work locally if the global python is Python 2. Not sure if CI would be fine. This should be a bug though.

@frostming Yay!  Thank you.  The no-tox-pdm version works with the changes just as you suggest.  I'll try the with-tox-pdm version once you release a version with that fix.  I think I prefer the no-tox-pdm version in general though because --unless I'm mistaken-- the with-tox-pdm version requires you to run it via pdm run tox or install tox-pdm in the global tox environment.
Did you also see the question about groups vs sections?  That's only relevant for the with-tox-pdm version, but it still appears to be a conflict in the documentation.

Oh also, you're right.  I'm on a Mac so python is /usr/bin/python a.k.a. Python 2.7.16

Yes, the doc is out-of-date, I've updated it. In fact for the no-tox-pdm branch, if you have pdm installed globally, it doesn't need to appear in deps as well.
The release is on the way.

Yes, the doc is out-of-date, I've updated it.

Thanks.

In fact for the no-tox-pdm branch, if you have pdm installed globally, it doesn't need to appear in deps as well.

I thought about that, but I also need to get pdm installed into my CI images, which should be easy though I haven't gotten to that yet.

The release is on the way.

Thanks again.


[SIL Optimization][OSLogOptimization] Improve the logic for discovering the beginning of the string interpolation passed to os log APIs.

This PR updates the beginOfInterpolation function of the OSLogOptimization that extracts the first instruction generated for an interpolated string literal from which the constant evaluation must begin. The old implementation assumed a certain pattern of SIL generation for interpolated literals. The new implementation eliminates this assumption and follows the chain of dependencies starting
from an initializer call to OSLogMessage until the first instruction of interpolation is discovered. This is more robust towards changes to the SIL generation of string interpolation literals.
This change will also fix the errors in this PR: https://github.com/apple/swift/pull/27391 created by  @brentdax

@swift-ci Please test

I think that this may have regressed the Windows build: https://dev.azure.com/compnerd/windows-swift/_build/results?buildId=10356&view=logs&j=b9e62f99-1a98-5ed7-01d2-f4794231ed79&t=3083c4c7-eb7b-5b23-2f1f-ad33b3556855&l=1029


Allow requests from all client origins to obtain access cookie

The origins table is a vestige of misunderstanding IIIF Auth 1.0; since the info.json is the carrier of auth info, access mode is determined on a per-item basis by the access_mode field in the items table.

ugh, forgot to set commit.gpgsign 🤦🏻

Cool that GitHub didn't dismiss approvals in this case; I didn't realize it did that (assuming because the only diff is the commit sigs)


Scrolling Up: Dropped frames + flicker + rendering artifacts

@jordwalke noticed that there is some weirdness when scrolling up:

This isn't just with fast scrolling - it reproduces deterministically if you just scroll up at the boundary:

If you scroll down when the line number doesn't update - there are some rendering artifacts and the cursor position is off by a bit.

This seems resolved (master at: f5241034796) for me

@bryphe yeah seems fixed, could you verify

It seems pretty good to me!

Sure, I'll close this out! I believe this was addressed. We do have some more perf issues to fix relate to scrolling - namely #139 and #174 . Interested to see how things perform / what bottlenecks are present once we switch to Skia

Will close this out, but we can continue to track specific perf issues in separate bugs 👍


Action "summary" of arguments is invalid when configuration is passed

identified in https://github.com/bids-standard/bids-specification/pull/1322#issuecomment-1280914648
How to reproduce:

run a basic codespell GH action (without passing with: arguments)
add a .codespellrc configuration file to the repo root
the config file is being observed BUT
the "argument summary" in the GH actions log is not reflecting that

Pedantically I think that's accurate. If you run the same command locally, without any options, you'd get the same behaviour.
For me as a user that was the thinking too, if I want to recreate it on the command line locally, that's all I should need to know.

For consistency and clarity it would be good to say what we're using out of setup.cfg, .codespellrc, pyproject.toml both from the command-line and this action. @sappelhoff up for a PR to codespell first to add it there (probably via some verbosity level if not already present), then the action could just inherit the behavior?

I'll put it on my list and will look into it :heavy_check_mark:


Largest Piece of the Cake

Is your feature request related to a problem? Please describe.
Topic: Graph
It's Gary's birthday today and he has ordered his favorite square cake consisting of '0's and '1's.  A piece of cake is defined as a part that consists of only '1's, and all '1's share an edge with each other on the cake. Given the size of cake N and the cake, can you find the count of '1's in the biggest piece of '1's for Gary?
Input:
The first line of input contains an integer, that denotes the value of N.
Each of the following N lines contains N space-separated integers.
Sample Case:
2
1 1
0 1
output: 3
Describe the solution you'd like
The solution will be done using dfs approach.
We can go in 4 directions from the position in which we are i.e. If we are at the i,j position .we can go i+1,j/i-1,j/i,j+1/i,j-1.and we basically need to calculate the max length of the  connected 1's.
Do you want to work on it?
Yes, I am a GSSOC'21 participant I would like to solve it in C++.

@tarun26091999  Sir please assign this issue to me

Hi,
I am a GSSOC'21 participant I can solve this in python. Please assign this issue to me.

thanks, @yasharth291  sir for assigning

Hi,
I am a GSSOC'21 participant I can solve this in java. Please assign this issue to me.

@tarun26091999 Sir please assign this issue to me I can code it in JAVA

Hello, @aaadddiii @anubhavitis @veenit1802 @ssaksham sir, I think this is an important question and this should definitely be added in the Algo-Tree and I will start working on it as soon as I get assigned!! as I am a part of GSSoC'21, so I would like to implement this in c++.

I would like to contribute to the above problem in java as I am a participant of GSSOC'21. Kindly assign this to me.

@rish-singhal can you please add the level of the question too.


Prank in the code ?

I am using the android device to take a picture. However, when I pressed the save the photo is discarded and when I press the discard the photo is saved!(Not Funny)
How can I fix this issue?

And when you take a picture without using this pickage did you get the same issue ?

I can assure you that my library with 1000+ stars is not a "prank"...lol

I know it sounds crazy! But that is my problem. When u want to take a picture without the camera, I just press the button and it takes the picture.(there is no save and discard button)
But in the app, after I take the picture , discard save the picture and save discard the picture! Can I somehow change the caption for them?

It's an issue with your native library not this package


Revert "Fix API integration tests"

Reverts google/osv.dev#1138
A bug on GHSA's end caused those records to be recreated without a last_affected date. It should now be fixed.

Actually, GHSA-r88r-gmrh-7j83 hasn't been reprocessed yet...

Yep - I'll close this. We'll need to make a new PR in the near future to remove GHSA-r88r-gmrh-7j83 when that's fixed as well.


REQUEST-921-PROTOCOL-ATTACK

Issue: WAF/Prevention Mode is not detecting CR/LF in the form of: GET /%0d%0aMy-Custom-Header:My-Custom-Value%0d%0a

Expectation is that since these requests contain “/%0d%0a” the request should be blocked by the WAF. Request is only visible in ApplicationGatewayAccessLog but no corresponding Match/Block in the ApplicationGatewayFirewallLog.
Category: ApplicationGatewayAccessLog
requestUri_s: /
Set-Cookie:SRT=AnInsiderThreat
httpMethod_s: GET
WAFEvaluationTime_s: 0.000
WAFMode_s: Prevention
details_matches_s: -
details_msg_s: -
ruleSetType_s: -
ruleSetVersion_s: -
ruleId_s: -
action_s: -
transactionId_g: 94061d4e-ed9b-bdb8-c02c-687d36ab2183
originalRequestUriWithArgs_s: /%0d%0aSet-Cookie:SRT=AnInsiderThreat%0d%0a
serverStatus_s: 303
https://learn.microsoft.com/en-us/azure/web-application-firewall/ag/application-gateway-crs-rulegroups-rules?tabs=owasp32#crs921-32
Does rule evaluate URI/URL with ARGs for CR/LF?

Hey @astew-msft, sorry for the inconvenience. But it seems this works on native CRS. Could it be an Azure problem?
$ curl -H "x-crs-paranoia-level: 1" -H "x-format-output: txt-matched-rules"  "https://sandbox.coreruleset.org/%0d%0aMy-Custom-Header:My-Custom-Value%0d%0a"
921190 PL1 HTTP Splitting (CR/LF in request filename detected)
...

If my call is wrong and I did not get your message, please let me know.

Hey Christian,
the OWASP rule set for rule 921151 applies to ARGS_GET.
If I put %0a in the ARGS then the request is blocked, if I put it outside of the request it is let through.
[image]
Is this by design, as In the OWASP rule set it only looks for CR/LF in the ARGS.
Does OWASP rule that blocks CR/LF in the URL/URI path.
@.***
@.***
Adrian Stewart
Azure Customer Engineer Gov
Phone: +1<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Working Hours: Mon-Fri 8am-5pm EST
OOF:
@.@.https://www.credly.com/badges/10fbfdd6-5aa8-4363-bc27-b0d7a0c76584/public_url
We are always interested to hear you feedback. Please feel free to email my manager/Myriam Johnson regarding the level of service you have received, at<EMAIL_ADDRESS>If you need immediate attention for any of the ongoing case while I am Out of Facility:

email Gov escalations at<EMAIL_ADDRESS>email Gov backup support at<EMAIL_ADDRESS>
From: Christian Folini @.>
Sent: Monday, August 28, 2023 12:01 PM
To: coreruleset/coreruleset @.>
Cc: Adrian Stewart @.>; Mention @.>
Subject: Re: [coreruleset/coreruleset] REQUEST-921-PROTOCOL-ATTACK (Issue #3286)
Hey @astew-msfthttps://github.com/astew-msft, sorry for the inconvenience. But it seems this works on native CRS. Could it be an Azure problem?
$ curl -H "x-crs-paranoia-level: 1" -H "x-format-output: txt-matched-rules"  "https://sandbox.coreruleset.org/
My-Custom-Header:My-Custom-Value
"
921190 PL1 HTTP Splitting (CR/LF in request filename detected)
...
If my call is wrong and I did not get your message, please let me know.

Reply to this email directly, view it on GitHubhttps://github.com/coreruleset/coreruleset/issues/3286#issuecomment-1695949056, or unsubscribehttps://github.com/notifications/unsubscribe-auth/BCDIK4TESK4DSCAW2KLEOKDXXS6EJANCNFSM6AAAAAA35TWY5Y.
You are receiving this because you were mentioned.Message ID<EMAIL_ADDRESS>

Sorry, but I do not follow you.
"If I put %0a in the ARGS then the request is blocked, if I put it outside of the request it is let through."
No, that's not the case. As I showed above, there is 921190 that checks the request filename. And there is also a header rule that does the same.

921140 PL1 : REQUEST_HEADERS_NAMES|REQUEST_HEADERS "@rx [\n\r]"
921150 PL1 : ARGS_NAMES "@rx [\n\r]"
921151 PL2 : ARGS_GET "@rx [\n\r]"
921160 PL1 : ARGS_GET_NAMES|ARGS_GET "@rx [\n\r]+... (list of certain HTTP headers)
921190 PL1 : REQUEST_FILENAME "@rx [\n\r]"

Where do you think the detection is lacking in this regard?
And then there is also 921160

Sorry for the inconvenience. Think detection is lacking URI.
Mitigation: Create a custom rule with block action based on URI.

confirm CRS 3.2 is being used.
@.***
@.***
Adrian Stewart
Azure Customer Engineer Gov
Phone: +1<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Working Hours: Mon-Fri 8am-5pm EST
OOF:
@.https://www.credly.com/badges/dfc17e6e-63e8-4618-8842-062a3e89ab55/public_url @. https://www.credly.com/badges/10fbfdd6-5aa8-4363-bc27-b0d7a0c76584/public_url
We are always interested to hear you feedback. Please feel free to email my manager/Myriam Johnson regarding the level of service you have received, at<EMAIL_ADDRESS>If you need immediate attention for any of the ongoing case while I am Out of Facility:

email Gov escalations at<EMAIL_ADDRESS>email Gov backup support at<EMAIL_ADDRESS>
From: Andrew Howe @.>
Sent: Wednesday, September 6, 2023 1:11 PM
To: coreruleset/coreruleset @.>
Cc: Adrian Stewart @.>; Mention @.>
Subject: Re: [coreruleset/coreruleset] REQUEST-921-PROTOCOL-ATTACK (Issue #3286)
Hi @astew-msfthttps://github.com/astew-msft,
I can confirm @dune73https://github.com/dune73's findings: rule 921190 definitely catches the behaviour you're looking at.
To also confirm: this rule in question (921190) first appeared in CRS v3.3.0.
Are you using CRS v3.2.0? The Microsoft documentation for Azure WAF suggests that v3.2.0 is the latest CRS release currently supported. This is quite a legacy release.

Reply to this email directly, view it on GitHubhttps://github.com/coreruleset/coreruleset/issues/3286#issuecomment-1708785468, or unsubscribehttps://github.com/notifications/unsubscribe-auth/BCDIK4QAUPJB23TQ6XU3UZLXZCVANANCNFSM6AAAAAA35TWY5Y.
You are receiving this because you were mentioned.Message ID<EMAIL_ADDRESS>

@astew-msft Considering that your issue is with 3.2 and that you had no further inquiries, I'm going to close this issue. Please reopen it if you need more help.


Faster Rust SHA3 Implementations

[x] aarch64
[x] avx2
[x] Portable

Coding is done. Now it is cleanup/verification etc.

Closing this for now. Follow-ups will be filed for further tasks like verification.


remove endian-specific logic from str*cmp

Problem
profiler doesn't show string comparison done in parser as a significant source of CPU usage, and the extra code can be distraction. End goal is to remove endianness-logic completely, which depends on removing the lookup3 hasher.
Solution
Keep endianness oblivious implementation of str*cmp. Add notes for furthehr cleanup.

have you profiled this versus just using strncmp?

@kevyang good question. I have not, will do shortly.

Oops dropped this thread, so as we discussed in private, in the most extreme case, the original implementation is about twice as fast as the proposed implementation here. However, the absolute cost remains really low- on my 2017 MBP, -O0 (no optimization), 1 billion execution, it comes down to <5ns (new) vs <3ns (old) for string comparison up to 12 characters on little endian machines.


Combo ports

Hi !Would it be possible to add combo ports as a feature request to the interface selection for a device ?
Some ports are either SFP or Copper.
thanks for a great  project.

The data model requires each interface to have exactly one form factor. You can edit an interface and toggle its form factor based on which of the two physical ports is in use. Most people tend to stick with one or the other anyway. Trying to support multiple form factors would add undue complexity.

Yea this came up on irc.  The best way to handle it is via the SFP 1G form factor.  Technically that can do copper or fiber sfps.

Ok thanks sounds resonable.


Add typescript-modern target

Move flow/typescript modern targets into a javascript folder to clean
things up.
Make minimal modifications to cp/pasted flow target to create valid
typescript code.
This is just a starting point -- as the babel 7 ts types mature, and we
start to get complete type definitions for babel-types w/ babel7, we
will need to adjust the nodes to use proper ts nodes.

As I was working through this, I noticed some bugs in the flow-modern target so i fixed those up too!
ptal @martijnwalraven


Popups with just images don't look good on IE and Edge

Related to https://github.com/CartoDB/cartodb/issues/13808
[WIP] This is the simplest solution: disable css clip-path when using IE or Edge, as it is not supported (https://caniuse.com/#feat=css-clip-path).
The result is displayed below (notice the triangle, that was previously hidden):

This will be resolved at https://github.com/CartoDB/carto.js/pull/2105


Implementation of non-dec number parsing, trying to match clojure.edn/read-string as much as possible.

Resolves #100, both the mentioned non-dec parsing but also throwing errors when encountering invalid numbers instead of giving a bad result.
Using rust's from_str_radix, supporting all radix from 2 to 36, just like clojure.edn/read-string ended up not being too painful.
I don't have a super strong opinion other than needing 0x support before picking up this crate for my projects. That being said, I can only imagine future problems/compatibility by adding rust's additional 0b and 0o patterns.

Codecov Report
Patch coverage: 100.00% and project coverage change: +1.62% :tada:

Comparison is base (e74e564) 69.02% compared to head (ab73a61) 70.65%.

:exclamation: Your organization is not using the GitHub App Integration. As a result you may experience degraded service beginning May 15th. Please install the Github App Integration for your organization. Read more.
Additional details and impacted files
@@            Coverage Diff             @@
##           master     #103      +/-   ##
==========================================
+ Coverage   69.02%   70.65%   +1.62%
==========================================
  Files           8        8
  Lines         946      978      +32
==========================================
+ Hits          653      691      +38
+ Misses        293      287       -6

Files Changed
Coverage Δ

src/deserialize/parse.rs
93.81% <100.00%> (+0.93%)
:arrow_up:

... and 3 files with indirect coverage changes

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.

Instead of commenting on a lot of the repeated areas, I was trying to match clojure.edn/read-string as much as possible. Here's some input/outputs
(clojure.edn/read-string "16r2a")
42

(clojure.edn/read-string "16r2a")
42

(clojure.edn/read-string "-16r2a")
-42

(clojure.edn/read-string "42invalid123")
java.lang.NumberFormatException: Invalid number: 42invalid123 [at <repl>:5:1]

(clojure.edn/read-string "0xxyz123")
java.lang.NumberFormatException: Invalid number: 0xxyz123 [at <repl>:1:1]

(clojure.edn/read-string "42rabcxzy")
java.lang.NumberFormatException: Radix out of range [at <repl>:2:1]

(clojure.edn/read-string "42crazyrabcxzy")
java.lang.NumberFormatException: Invalid number: 42crazyrabcxzy [at <repl>:3:1]

(clojure.edn/read-string "42crazyrabcxzy")
java.lang.NumberFormatException: Invalid number: 42crazyrabcxzy [at <repl>:4:1]

https://github.com/edn-format/edn#symbols

Symbols begin with a non-numeric character

Sorry for the delay, crazy times. I agree with you point, will merge


BlockId removal: refactor: ProofProvider

It changes the arguments of methods of ProofProvider trait from:
block: BlockId<Block> to: hash: &Block::Hash
This PR is part of BlockId::Number refactoring analysis (paritytech/substrate#11292)

bot merge