hpanwar08/detectron2

Detectron2 for Document Layout Analysis

---

Detectron2 trained on PubLayNet dataset

This repo contains the training configurations, code and trained models trained on PubLayNet dataset using Detectron2 implementation.
PubLayNet is a very large dataset for document layout analysis (document segmentation). It can be used to trained semantic segmentation/Object detection models.

NOTE

• Models are trained on a portion of the dataset (train-0.zip, train-1.zip, train-2.zip, train-3.zip)
• Trained on total 191,832 images
• Models are evaluated on dev.zip (~11,000 images)
• Backbone pretrained on COCO dataset is used but trained from scratch on PubLayNet dataset
• Trained using Nvidia GTX 1080Ti 11GB
• Trained on Windows 10

Steps to test pretrained models locally or jump to next section for docker deployment

• Install the latest Detectron2 from https://github.com/facebookresearch/detectron2
• Copy config files (DLA_*) from this repo to the installed Detectron2
• Download the relevant model from the Benchmarking section. If you have downloaded model using wget then refer #22
• Add the below code in demo/demo.py in the mainto get confidence along with label names

from detectron2.data import MetadataCatalog
MetadataCatalog.get("dla_val").thing_classes = ['text', 'title', 'list', 'table', 'figure']

• Then run below command for prediction on single image (change the config file relevant to the model)

python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "<path to image.jpg>" --output <path to save the predicted image> --confidence-threshold 0.5 --opts MODEL.WEIGHTS <path to model_final_trimmed.pth> MODEL.DEVICE cpu

Docker Deployment

• For local docker deployment for testing use Docker DLA

Benchmarking

Architecture	No. images	AP	AP50	AP75	AP Small	AP Medium	AP Large	Model size full	Model size trimmed
MaskRCNN Resnext101_32x8d FPN 3X	191,832	90.574	97.704	95.555	39.904	76.350	95.165	816M	410M
MaskRCNN Resnet101 FPN 3X	191,832	90.335	96.900	94.609	36.588	73.672	94.533	480M	240M
MaskRCNN Resnet50 FPN 3X	191,832	87.219	96.949	94.385	38.164	72.292	94.081		168M

Configuration used for training

Architecture	Config file	Training Script
MaskRCNN Resnext101_32x8d FPN 3X	configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml	./tools/train_net_dla.py
MaskRCNN Resnet101 FPN 3X	configs/DLA_mask_rcnn_R_101_FPN_3x.yaml	./tools/train_net_dla.py
MaskRCNN Resnet50 FPN 3X	configs/DLA_mask_rcnn_R_50_FPN_3x.yaml	./tools/train_net_dla.py

Some helper code and cli commands

Add the below code in demo/demo.py to get confidence along with label names

from detectron2.data import MetadataCatalog
MetadataCatalog.get("dla_val").thing_classes = ['text', 'title', 'list', 'table', 'figure']

Then run below command for prediction on single image

python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "<path to image.jpg>" --output <path to save the predicted image> --confidence-threshold 0.5 --opts MODEL.WEIGHTS <path to model_final_trimmed.pth> MODEL.DEVICE cpu

TODOs ⏰

• Train MaskRCNN resnet50

---

Sample results from detectron2

---

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.

What's New

• It is powered by the PyTorch deep learning framework.
• Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
• Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.
• It trains much faster.

See our blog post to see more demos and learn about detectron2.

Installation

See INSTALL.md.

Quick Start

See GETTING_STARTED.md, or the Colab Notebook.

Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.

License

Detectron2 is released under the Apache 2.0 license.

Citing Detectron

If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@misc{wu2019detectron2,
  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
                  Wan-Yen Lo and Ross Girshick},
  title =        {Detectron2},
  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
  year =         {2019}
}

detectron2's Issues

Trimming Definition?

Hi there.

Thanks so much for releasing Detectron2.

I'm trying to benchmark our own extensions from the detectron2 model, and as a part of
the process I need to define what is going on with the model trimming. Can you please explain
how the model is trimmed (and if possible where in the api you would do this)?

A pointer to documentation I have missed would be perfectly fine as well.

Thanks.

Hi, Detectron 2 worked successfully for me. But the ouput images are not perfect. It is segmenting for the whole page rather than segmenting for each class like title,text,figure,etc..

Here is the output image.

Can you please tell me what is the problem and how to execute it perfectly ?

Any plan to train the Faster R-CNN model using PubLayNet data on Detectron2?

🚀 Feature

Thanks for providing the pretrained Mask R-CNN models! Just curious whether you have a plan to provide pretrained Faster R-CNN models as well? I believe it'll be helpful for fine tuning tasks where pixel-level annotation is not easy to get.

what is the order in which the predictions are recorded

Training loss does not deacrease

I have custom dataset having 8 classes for layout detection and around 3000 samples in training. I have trained model for more than 15000 iterations but loss is jumping around 1.23, 1.35 etc. I tried multiple learning rate but it's not decreasing. What changes I should do to overcome this overfitting situation or may be it something else. The configuration I am using is DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml

My config file is-

CUDNN_BENCHMARK: false
DATALOADER:
  ASPECT_RATIO_GROUPING: true
  FILTER_EMPTY_ANNOTATIONS: true
  NUM_WORKERS: 4
  REPEAT_THRESHOLD: 0.0
  SAMPLER_TRAIN: TrainingSampler
DATASETS:
  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
  PROPOSAL_FILES_TEST: []
  PROPOSAL_FILES_TRAIN: []
  TEST:
  - layout_valid
  TRAIN:
  - layout_train
GLOBAL:
  HACK: 1.0
INPUT:
  CROP:
    ENABLED: false
    SIZE:
    - 0.9
    - 0.9
    TYPE: relative_range
  FORMAT: BGR
  MASK_FORMAT: polygon
  MAX_SIZE_TEST: 1333
  MAX_SIZE_TRAIN: 1333
  MIN_SIZE_TEST: 800
  MIN_SIZE_TRAIN:
  - 640
  - 672
  - 704
  - 736
  - 768
  - 800
  MIN_SIZE_TRAIN_SAMPLING: choice
  RANDOM_FLIP: horizontal
MODEL:
  ANCHOR_GENERATOR:
    ANGLES:
    - - -90
      - 0
      - 90
    ASPECT_RATIOS:
    - - 0.5
      - 1.0
      - 2.0
    NAME: DefaultAnchorGenerator
    OFFSET: 0.0
    SIZES:
    - - 32
    - - 64
    - - 128
    - - 256
    - - 512
  BACKBONE:
    FREEZE_AT: 2
    NAME: build_resnet_fpn_backbone
  DEVICE: cuda
  FPN:
    FUSE_TYPE: sum
    IN_FEATURES:
    - res2
    - res3
    - res4
    - res5
    NORM: ''
    OUT_CHANNELS: 256
  KEYPOINT_ON: false
  LOAD_PROPOSALS: false
  MASK_ON: true
  META_ARCHITECTURE: GeneralizedRCNN
  PANOPTIC_FPN:
    COMBINE:
      ENABLED: true
      INSTANCES_CONFIDENCE_THRESH: 0.5
      OVERLAP_THRESH: 0.5
      STUFF_AREA_LIMIT: 4096
    INSTANCE_LOSS_WEIGHT: 1.0
  PIXEL_MEAN:
  - 103.53
  - 116.28
  - 123.675
  PIXEL_STD:
  - 57.375
  - 57.12
  - 58.395
  PROPOSAL_GENERATOR:
    MIN_SIZE: 0
    NAME: RPN
  RESNETS:
    DEFORM_MODULATED: false
    DEFORM_NUM_GROUPS: 1
    DEFORM_ON_PER_STAGE:
    - false
    - false
    - false
    - false
    DEPTH: 101
    NORM: FrozenBN
    NUM_GROUPS: 32
    OUT_FEATURES:
    - res2
    - res3
    - res4
    - res5
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: false
    WIDTH_PER_GROUP: 8
  RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: &id001
    - 1.0
    - 1.0
    - 1.0
    - 1.0
    FOCAL_LOSS_ALPHA: 0.25
    FOCAL_LOSS_GAMMA: 2.0
    IN_FEATURES:
    - p3
    - p4
    - p5
    - p6
    - p7
    IOU_LABELS:
    - 0
    - -1
    - 1
    IOU_THRESHOLDS:
    - 0.4
    - 0.5
    NMS_THRESH_TEST: 0.5
    NORM: ''
    NUM_CLASSES: 80
    NUM_CONVS: 4
    PRIOR_PROB: 0.01
    SCORE_THRESH_TEST: 0.05
    SMOOTH_L1_LOSS_BETA: 0.1
    TOPK_CANDIDATES_TEST: 1000
  ROI_BOX_CASCADE_HEAD:
    BBOX_REG_WEIGHTS:
    - - 10.0
      - 10.0
      - 5.0
      - 5.0
    - - 20.0
      - 20.0
      - 10.0
      - 10.0
    - - 30.0
      - 30.0
      - 15.0
      - 15.0
    IOUS:
    - 0.5
    - 0.6
    - 0.7
  ROI_BOX_HEAD:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS:
    - 10.0
    - 10.0
    - 5.0
    - 5.0
    CLS_AGNOSTIC_BBOX_REG: false
    CONV_DIM: 256
    FC_DIM: 1024
    NAME: FastRCNNConvFCHead
    NORM: ''
    NUM_CONV: 0
    NUM_FC: 2
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: false
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 512
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    IOU_LABELS:
    - 0
    - 1
    IOU_THRESHOLDS:
    - 0.5
    NAME: StandardROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 8
    POSITIVE_FRACTION: 0.25
    PROPOSAL_APPEND_GT: true
    SCORE_THRESH_TEST: 0.05
  ROI_KEYPOINT_HEAD:
    CONV_DIMS:
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    LOSS_WEIGHT: 1.0
    MIN_KEYPOINTS_PER_IMAGE: 1
    NAME: KRCNNConvDeconvUpsampleHead
    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
    NUM_KEYPOINTS: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  ROI_MASK_HEAD:
    CLS_AGNOSTIC_MASK: false
    CONV_DIM: 256
    NAME: MaskRCNNConvUpsampleHead
    NORM: ''
    NUM_CONV: 4
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  RPN:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: *id001
    BOUNDARY_THRESH: -1
    CONV_DIMS:
    - -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    - p6
    IOU_LABELS:
    - 0
    - -1
    - 1
    IOU_THRESHOLDS:
    - 0.3
    - 0.7
    LOSS_WEIGHT: 1.0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TRAIN: 1000
    PRE_NMS_TOPK_TEST: 1000
    PRE_NMS_TOPK_TRAIN: 2000
    SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
    COMMON_STRIDE: 4
    CONVS_DIM: 128
    IGNORE_VALUE: 255
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    LOSS_WEIGHT: 1.0
    NAME: SemSegFPNHead
    NORM: GN
    NUM_CLASSES: 54
  WEIGHTS: /content/drive/MyDrive/layout/resnext101.pth
OUTPUT_DIR: ./output
SEED: -1
SOLVER:
  AMP:
    ENABLED: false
  BASE_LR: 8.0e-06
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: 2000
  CLIP_GRADIENTS:
    CLIP_TYPE: value
    CLIP_VALUE: 1.0
    ENABLED: false
    NORM_TYPE: 2.0
  GAMMA: 0.1
  IMS_PER_BATCH: 2
  LR_SCHEDULER_NAME: WarmupMultiStepLR
  MAX_ITER: 25000
  MOMENTUM: 0.9
  NESTEROV: false
  REFERENCE_WORLD_SIZE: 0
  STEPS:
  - 210000
  - 250000
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 1000
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  WEIGHT_DECAY_BIAS: 0.0001
  WEIGHT_DECAY_NORM: 0.0
TEST:
  AUG:
    ENABLED: false
    FLIP: true
    MAX_SIZE: 4000
    MIN_SIZES:
    - 400
    - 500
    - 600
    - 700
    - 800
    - 900
    - 1000
    - 1100
    - 1200
  DETECTIONS_PER_IMAGE: 100
  EVAL_PERIOD: 0
  EXPECTED_RESULTS: []
  KEYPOINT_OKS_SIGMAS: []
  PRECISE_BN:
    ENABLED: false
    NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0

How to use configs

I think "--num-gpus 8" config is not working with train_net_dla.py when I tryed to training from scratch.
How can I set the config?

model shape don't match, no boxes was produced

I just run

python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "test001.jpg" --output  "results/abc.jpg" --confidence-threshold 0.5 --opts MODEL.WEIGHTS  "models/model_final_trimmed.pth"  MODEL.DEVICE cp

What I get :

[05/28 17:14:21 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml', input=['test001.jpg'], opts=['MODEL.WEIGHTS', 'models/model_final_trimmed.pth', 'MODEL.DEVICE', 'cpu'], output='results/abc.jpg', video_input=None, webcam=False)
WARNING [05/28 17:14:21 d2.config.compat]: Config 'configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
'backbone.bottom_up.res2.0.conv1.weight' has shape (64, 64, 1, 1) in the checkpoint but (256, 64, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.weight' has shape (64, 256, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.

At last, no box was produced.

I don't know what's happened.
Hoping get some suggestions.

Info on LR

Hi @hpanwar08

Can you please confirm, what is the LR (learning rate you used), also did you used same learning rate through out your training process or was it scheduled at different learning rates ?

Correct way of mapping labels with category_ids

According to the documentation of Detectron2, when registering custom dataset, you should set 'category_id' for each annotation in range [0, num_of_categories) where the value for 'num_of_categories' is reserved for the background class. link

However, the original paper states that the background's id is 0.

Please tell me how should I map the labels with the correct category_ids.

Add additional categories

Is it possible to start training with additional categories such as: heading2, heading3, ..., image description?

What Image size to use and do I need to normalize the images

1. What size are the images resized to while training on document images. While testing do you suggest to use any specific sizes
2. Do I need to do any sort of normalization on the input images. I see that in case of object detection models in case of natural images, it is a common practice to normalize images using mean and SD computed from imagenet dataset

How to trim model weights after fine-tuning ?

Hey!

How can I trim model weights ?

Unable to see predicted labels

❓ How to use Detectron2

I've setup everything and unable to see labels as text,figure,table on the output image. please help....

model is not predicting anything

the output i got is shown below;
00:44 detectron2]: Arguments: Namespace(confidence_threshold=0.2, config_file='configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml', input=['/content/gdrive/My Drive/Untitled folder/PMC4527132_00004.jpg'], opts=['MODEL.WEIGHTS', '/content/gdrive/My Drive/Unt/model_final.pth', 'MODEL.DEVICE', 'cpu'], output='/content/gdrive/My Drive/Unt', video_input=None, webcam=False)
WARNING [03/27 12:00:44 d2.config.compat]: Config 'configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
'backbone.bottom_up.res2.0.conv1.weight' has shape (64, 64, 1, 1) in the checkpoint but (256, 64, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.weight' has shape (64, 256, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.weight' has shape (64, 256, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.weight' has shape (128, 256, 1, 1) in the checkpoint but (512, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.weight' has shape (128, 512, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.weight' has shape (128, 512, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.weight' has shape (128, 512, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.weight' has shape (256, 512, 1, 1) in the checkpoint but (1024, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.weight' has shape (512, 1024, 1, 1) in the checkpoint but (2048, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.weight' has shape (512, 512, 3, 3) in the checkpoint but (2048, 64, 3, 3) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv3.weight' has shape (2048, 512, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.weight' has shape (512, 2048, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.weight' has shape (512, 512, 3, 3) in the checkpoint but (2048, 64, 3, 3) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv3.weight' has shape (2048, 512, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.weight' has shape (512, 2048, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.weight' has shape (512, 512, 3, 3) in the checkpoint but (2048, 64, 3, 3) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv3.weight' has shape (2048, 512, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
0% 0/1 [00:00<?, ?it/s][03/27 12:01:02 detectron2]: /content/gdrive/My Drive/Untitled folder/PMC4527132_00004.jpg: detected 0 instances in 15.44s
100% 1/1 [00:15<00:00, 15.52s/it]
i use google colab and i use the pre trained weights given in repository

How long did it take for you to train the model ?

Hi, I'm currently using the pre-trained model weights supplied in the repo. But, I need to fine-tune the model further to suit my requirements. So, I ran a sample training with only one image and one instance/bbox in that image, but the training script says it needs 1 day 18 hrs to complete training on this minuscule training set. Is this an issue with ETA estimation or does the model really take that long to train on one image ??

Facing Error while Fine Tuning on my dataset

Hi,

I prepared a custom dataset in COCO format having 3 classes like below:
But there is no samples related to 'Resumes' class. Only 'heading' and 'text' classes are present there in my sample.

"categories": [
        {
            "id": 0,
            "name": "Resumes",
            "supercategory": "none"
        },
        {
            "id": 1,
            "name": "heading",
            "supercategory": "Resumes"
        },
        {
            "id": 2,
            "name": "text",
            "supercategory": "Resumes"
        }
    ],
"images":[{
            "id": 159,
            "license": 1,
            "file_name": "outfile_Lavanya-4_10---2--docx-pdf_1.rf.bfcd3081e0b5399829ae9b1bdc1e67d4.jpg",
            "height": 842,
            "width": 596,
            "date_captured": "2021-09-09T13:55:03+00:00"
        },
    ],
    "annotations": [
        {
            "id": 0,
            "image_id": 0,
            "category_id": 2,
            "bbox": [
                7,
                6,
                266.66666666666663,
                101.28205128205127
            ],
            "area": 27008.547008547,
            "segmentation": [
                [
                    7,
                    6,
                    266.66666666666663,
                    6,
                    266.66666666666663,
                    101.28205128205127,
                    7,
                    101.28205128205127
                ]
            ],
            "iscrowd": 0
        },
    ]

I'm using this config file for finetuning

_BASE_: "Base-RCNN-FPN.yaml"
MODEL:
  MASK_ON: True
#  WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
  #WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl"
  PIXEL_STD: [57.375, 57.120, 58.395]
  ROI_HEADS:
    NUM_CLASSES: 5
  RESNETS:
    STRIDE_IN_1X1: False  # this is a C2 model
    NUM_GROUPS: 32
    WIDTH_PER_GROUP: 8
    DEPTH: 101
DATASETS:
  TRAIN: ("dla_train",)
  TEST: ("dla_val",)
SOLVER:
  STEPS: (210000, 250000)
  MAX_ITER: 125500
  IMS_PER_BATCH: 2
  #BASE_LR: 0.0009
  BASE_LR: 0.00005
DATALOADER:
  NUM_WORKERS: 1

Got following error:-

WARNING [09/09 20:18:55 d2.data.datasets.coco]: 
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[09/09 20:18:55 d2.data.datasets.coco]: Loaded 638 images in COCO format from ./data/train/annotations.json
[09/09 20:18:55 d2.data.build]: Removed 320 images with no usable annotations. 318 images left.
[09/09 20:18:55 d2.data.build]: Distribution of instances among all 3 categories:
|  category  | #instances   |  category  | #instances   |  category  | #instances   |
|:----------:|:-------------|:----------:|:-------------|:----------:|:-------------|
|  Resumes   | 0            |  heading   | 1028         |    text    | 1951         |
|            |              |            |              |            |              |
|   total    | 2979         |            |              |            |              |
[09/09 20:18:55 d2.data.detection_utils]: TransformGens used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[09/09 20:18:55 d2.data.build]: Using training sampler TrainingSampler
[09/09 20:18:56 d2.engine.train_loop]: Starting training from iteration 75500
ERROR [09/09 20:18:57 d2.engine.train_loop]: Exception during training:
Traceback (most recent call last):
  File "/home/ujjawal/miniconda2/envs/caffe2/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 132, in train
    self.run_step()
  File "/home/ujjawal/miniconda2/envs/caffe2/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 216, in run_step
    self._detect_anomaly(losses, loss_dict)
  File "/home/ujjawal/miniconda2/envs/caffe2/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 239, in _detect_anomaly
    self.iter, loss_dict
FloatingPointError: Loss became infinite or NaN at iteration=75501!
loss_dict = {'loss_cls': tensor(nan, device='cuda:0', grad_fn=<NllLossBackward>), 'loss_box_reg': tensor(nan, device='cuda:0', grad_fn=<DivBackward0>), 'loss_mask': tensor(0.7118, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), 'loss_rpn_cls': tensor(0.6949, device='cuda:0', grad_fn=<MulBackward0>), 'loss_rpn_loc': tensor(0.4812, device='cuda:0', grad_fn=<MulBackward0>)}

I tried to change the NUM_CLASSES: 5 to 3 but no luck.
Some suggested to reduce the LR still no luck.
Can anyone please suggest a way to tackle this issue?

How to further fine-tune on custom data ?

I've gone through https://github.com/hpanwar08/detectron2/blob/master/tools/train_net_dla.py training script. But, I'm bit confused on how to structure my custom data (both JSON and IMG-data).

If you can share a sample JSON and folder structure for placing training images, it would be great !!

Yolo v4

@hpanwar08 hi there,

1. have you tested yolo v4?
2. do you think yolov4 is suitable for detecting text?
3. are you available for freelancing?

Text Appearing with Table/Figures

Hello,

In PDF documents am currently working on text is coming along with tables and figures. I am only concerned with tables and figures (each one essential). Is there any parameter that I could set to avoid appearance of text?
Thanks

How did you do training?

I did the normal training from scratch with the LR at .0009 for 125k iterations like is shown in the config file for DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml. and am only getting a 77.22 AP score. This learning rate is the last one that IBM had used so confused as to what your training process looked like, and if you changed learning rates when getting a total of 190K iterations that it looks like you did.

So the question is, what exactly did your training run look like on the DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml to get a ~90 AP score.

Thanks!

Problems in using pre-trained models

Hi @hpanwar08 ,
I was trying to use your pre-trained models for prediction by following your instructions and using the below command:

python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "<path to image.jpg>" --output <path to save the predicted image> --confidence-threshold 0.5 --opts MODEL.WEIGHTS <path to model_final_trimmed.pth> MODEL.DEVICE cpu

I am using Google Colab for the work and I am getting this error:

Traceback (most recent call last):
  File "demo/demo.py", line 73, in <module>
    demo = VisualizationDemo(cfg)
  File "/content/drive/My Drive/PublayNet/detectron2/demo/predictor.py", line 35, in __init__
    self.predictor = DefaultPredictor(cfg)
  File "/usr/local/lib/python3.6/dist-packages/detectron2/engine/defaults.py", line 187, in __init__
    checkpointer.load(cfg.MODEL.WEIGHTS)
  File "/usr/local/lib/python3.6/dist-packages/fvcore/common/checkpoint.py", line 117, in load
    checkpoint = self._load_file(path)
  File "/usr/local/lib/python3.6/dist-packages/detectron2/checkpoint/detection_checkpoint.py", line 42, in _load_file
    loaded = super()._load_file(filename)  # load native pth checkpoint
  File "/usr/local/lib/python3.6/dist-packages/fvcore/common/checkpoint.py", line 213, in _load_file
    return torch.load(f, map_location=torch.device("cpu"))
  File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 586, in load
    with _open_zipfile_reader(f) as opened_zipfile:
  File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 246, in __init__
    super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))
RuntimeError: [enforce fail at inline_container.cc:208] . file not found: /version

How to finetune this model?

Can I use your custom class model to finetune for object detection or segmentation?
Basically, I should for register Detectron2 dataset?

Training from scratch

I want to train a model from scratch. It looks like the tools directory is where I should dive into. I have the PubLayNet data, but I refined the data I want to use, and don't want to use the pre-trained models or weights(All the labels are the same format). How would I go about training a fresh model?

Thanks in advance.

Extracting the text ?

1. Do you have any idea of how we can extract text from predicted BBOX ? Like Is there any inbuilt OCR ?

no VERSION error while following the readme instruction

Hi, when I got the error by calling
python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input “/home/jupyter/layout/detectron2/input.jpg” --output “/home/jupyter/layout/” --confidence-threshold 0.5 --opts MODEL.WEIGHTS /home/jupyter/layout/detectron2/model_final_trimmed.pth MODEL.DEVICE cpu
Here is the error, not sure why the .yaml doesn't have VERSION. Thank you

WARNING [03/26 04:43:12 d2.config.compat]: Config 'configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
Traceback (most recent call last):
  File "demo/demo.py", line 76, in <module>
    demo = VisualizationDemo(cfg)
  File "/home/jupyter/layout/detectron2/demo/predictor.py", line 35, in __init__
    self.predictor = DefaultPredictor(cfg)
  File "/opt/conda/lib/python3.7/site-packages/detectron2/engine/defaults.py", line 168, in __init__
    checkpointer.load(cfg.MODEL.WEIGHTS)
  File "/opt/conda/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 103, in load
    checkpoint = self._load_file(path)
  File "/opt/conda/lib/python3.7/site-packages/detectron2/checkpoint/detection_checkpoint.py", line 42, in _load_file
    loaded = super()._load_file(filename)  # load native pth checkpoint
  File "/opt/conda/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 189, in _load_file
    return torch.load(f, map_location=torch.device("cpu"))
  File "/opt/conda/lib/python3.7/site-packages/torch/serialization.py", line 529, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "/opt/conda/lib/python3.7/site-packages/torch/serialization.py", line 709, in _legacy_load
    deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)
RuntimeError: unexpected EOF, expected 16489904 more bytes. The file might be corrupted.
terminate called after throwing an instance of 'c10::Error'
  what():  owning_ptr == NullType::singleton() || owning_ptr->refcount_.load() > 0 INTERNAL ASSERT FAILED at /pytorch/c10/util/intrusive_ptr.h:348, please report a bug to PyTorch. intrusive_ptr: Can only intrusive_ptr::reclaim() owning pointers that were created using intrusive_ptr::release(). (reclaim at /pytorch/c10/util/intrusive_ptr.h:348)
frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x33 (0x7f545aa70193 in /opt/conda/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x186b6af (0x7f545c72e6af in /opt/conda/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: THStorage_free + 0x17 (0x7f545cef6cb7 in /opt/conda/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: <unknown function> + 0x55d23d (0x7f54a377223d in /opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #22: __libc_start_main + 0xf1 (0x7f54c6f032e1 in /lib/x86_64-linux-gnu/libc.so.6)

Aborted

Re: detectron2 not predicting results

Hi,

I am trying to predict titles, tables and text in an input image.

I used the command :

python demo/demo.py --config-file configs/DLA_mask_rcnn_R_101_FPN_3x.yaml --input "1.png" --output "./testt.png" --confidence-threshold 0.5 --opts MODEL.WEIGHTS "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl" MODEL.DEVICE cpu

The command runs with the following logs and returns true at the end.

Config './configs/DLA_mask_rcnn_R_101_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
'roi_heads.box_predictor.cls_score.weight' has shape (81, 1024) in the checkpoint but (6, 1024) in the model! Skipped.
'roi_heads.box_predictor.cls_score.bias' has shape (81,) in the checkpoint but (6,) in the model! Skipped.
'roi_heads.box_predictor.bbox_pred.weight' has shape (320, 1024) in the checkpoint but (20, 1024) in the model! Skipped.
'roi_heads.box_predictor.bbox_pred.bias' has shape (320,) in the checkpoint but (20,) in the model! Skipped.
'roi_heads.mask_head.predictor.weight' has shape (80, 256, 1, 1) in the checkpoint but (5, 256, 1, 1) in the model! Skipped.
'roi_heads.mask_head.predictor.bias' has shape (80,) in the checkpoint but (5,) in the model! Skipped.

However, the output file remains unchanged with no bounding boxes or labels getting predicted. Can anyone guide why it's happening so.

Kindly help.

Thanks!

Remove duplicate entities

Some elements are being detected twice:
first time as a different class and second time as a different class with slight variation in bounding box or addition of some extra texts.
Also, many smaller elements(like titles or small paragraphs) are being detected inside a bigger element(like a list). Mean to say, overlapping is also there in some cases.

So, it is possible to eliminate those issues or at least the overlapping issue?

Thanks in advance

Labels not displaying while visualization of Results

How to do we display labels with detectron2 on the output?

I checked the visualizer.py file, the logic seems to be fine within. But then again there are no labels displayed on the results, although the confidence score is displayed.
It uses the labels from the training dataset which is not available. But while training, the labels must have been encoded or dumped somewhere, can someone please point to the dump of the labels. Or if there is a way around to display the labels.

Historical Newspaper Segmentation

Hi,

I would like to use this implementation to segment historical newspaper pages. Should I simply use the existing model for prediction, or train the model further with additional historical newspaper ground truth?

All the best

Julian

How to use Dilated with FPN

Hi @hpanwar08
Thank you very much for your contributions.

Currently, I trained detectron2 (use RCNN-FPN network) with PubLayNet. However, I want to use dilated convolution for this network (RCNN-FPN network, not DC5 network).

Could you please give me the guideline to training?

Thank you very much.

Download links for pre-trained models not working

Hi. I wanted to download the pre-trained models, but the download links for all of them give a 404 error. Could you check this please ?

Fine-tuning: AttributeError: Cannot find field 'gt_masks' in DataLoader with COCO format

Unfortunately, I always run into an error when fine-tuning on my own dataset (in coco format).
The detectron2 documentation on datasets was of no help either (maybe I overlooked something though).

After registering the data via register_coco_instances (as seen in train_net_dla.py) the following code

train data (train_data = DatasetCatalog.get("dla_train")
train data[0]

gives me:

{'file_name': 'cyber_train/public_imgs_sampled/file01.png',
 'height': 1754,
 'width': 1241,
 'image_id': 0,
 'annotations': [{'iscrowd': 0,
   'bbox': [78, 600, 977, 33],
   'category_id': 1,
   'segmentation': [[83, 606, 83, 633, 1050, 633, 1050, 606]],
   'bbox_mode': <BoxMode.XYWH_ABS: 1>}
...

which does look good to me telling from the COCO formatting style.

I can even visualize the data using Visualizer:

for d in random.sample(train_data, 1):
    img = cv2.imread(d["file_name"])
    visualizer = Visualizer(img[:, :, :], scale=0.5)
    out = visualizer.draw_dataset_dict(d)
    plt.imshow(out.get_image()[:,:,::-1])

which gives me the expected output:

Creating the trainer is also possible using trainer = DefaultTrainer(cfg) , but calling trainer.train() gives me a stack trace for the AttributeError: Cannot find field 'gt_masks' in the given Instances!' in DataLoader.
I am very confused about the error, because 'segmentation' is present in the train_data and the specified polygons are also valid telling from the output of Visualizer.

Anyway, thank you very much for your great work @hpanwar08 !

RuntimeError: Not compiled with GPU support

Hi, I tried the train_net_dla.py script in the repo to finetune on custom data. Data is all fine, but I'm getting this error just before training starts: RuntimeError: Not compiled with GPU support. I double checked everything !! not able to figure out this issue ??

Can not build with CUDA 10.2

Instructions To Reproduce the Issue:

1. Have not made any changes in code
2. Command: pip install -e detectron2_repo
3. Log:

Obtaining file:///root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo
Requirement already satisfied: termcolor>=1.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (1.1.0)
Requirement already satisfied: Pillow>=6.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (7.1.2)
Requirement already satisfied: yacs>=0.1.6 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (0.1.7)
Requirement already satisfied: tabulate in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (0.8.7)
Requirement already satisfied: cloudpickle in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (1.4.1)
Requirement already satisfied: matplotlib in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (3.2.1)
Requirement already satisfied: tqdm>4.29.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (4.46.0)
Requirement already satisfied: tensorboard in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (2.2.1)
Requirement already satisfied: fvcore in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (0.1.dev200506)
Requirement already satisfied: PyYAML in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from yacs>=0.1.6->detectron2==0.1) (5.3.1)
Requirement already satisfied: python-dateutil>=2.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (2.8.1)
Requirement already satisfied: numpy>=1.11 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (1.18.1)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (2.4.7)
Requirement already satisfied: kiwisolver>=1.0.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (1.2.0)
Requirement already satisfied: cycler>=0.10 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (0.10.0)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (0.4.1)
Requirement already satisfied: six>=1.10.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.14.0)
Requirement already satisfied: grpcio>=1.24.3 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.28.1)
Requirement already satisfied: google-auth<2,>=1.6.3 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.14.2)
Requirement already satisfied: wheel>=0.26; python_version >= "3" in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (0.34.2)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.6.0.post3)
Requirement already satisfied: requests<3,>=2.21.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (2.23.0)
Requirement already satisfied: werkzeug>=0.11.15 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.0.1)
Requirement already satisfied: setuptools>=41.0.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (46.1.3.post20200325)
Requirement already satisfied: absl-py>=0.4 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (0.9.0)
Requirement already satisfied: protobuf>=3.6.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (3.11.3)
Requirement already satisfied: markdown>=2.6.8 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (3.2.2)
Requirement already satisfied: portalocker in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from fvcore->detectron2==0.1) (1.7.0)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->detectron2==0.1) (1.3.0)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (0.2.8)
Requirement already satisfied: rsa<4.1,>=3.1.4 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (4.0)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (4.1.0)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (1.25.9)
Requirement already satisfied: chardet<4,>=3.0.2 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (2.9)
Requirement already satisfied: certifi>=2017.4.17 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (2020.4.5.1)
Requirement already satisfied: importlib-metadata; python_version < "3.8" in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from markdown>=2.6.8->tensorboard->detectron2==0.1) (1.6.0)
Requirement already satisfied: oauthlib>=3.0.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->detectron2==0.1) (3.1.0)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (0.4.8)
Requirement already satisfied: zipp>=0.5 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from importlib-metadata; python_version < "3.8"->markdown>=2.6.8->tensorboard->detectron2==0.1) (3.1.0)
Installing collected packages: detectron2
  Running setup.py develop for detectron2
    ERROR: Command errored out with exit status 1:
     command: /root/miniconda3/envs/dla/bin/python3.6 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"'; __file__='"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps
         cwd: /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/
    Complete output (581 lines):
    running develop
    running egg_info
    writing detectron2.egg-info/PKG-INFO
    writing dependency_links to detectron2.egg-info/dependency_links.txt
    writing requirements to detectron2.egg-info/requires.txt
    writing top-level names to detectron2.egg-info/top_level.txt
    reading manifest file 'detectron2.egg-info/SOURCES.txt'
    writing manifest file 'detectron2.egg-info/SOURCES.txt'
    running build_ext
    building 'detectron2._C' extension
    Emitting ninja build file /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/build.ninja...
    Compiling objects...
    Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
    [1/4] /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/cuda_version.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/cuda_version.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
    [2/4] /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
    FAILED: /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.o
    /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(136): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(184): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(234): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(284): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(341): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(155): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(338): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(503): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(696): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(823): error: identifier "AT_CHECK" is undefined
    
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(953): error: identifier "AT_CHECK" is undefined
    
    11 errors detected in the compilation of "/tmp/tmpxft_00002fc3_00000000-6_deform_conv_cuda.cpp1.ii".
    [3/4] c++ -MMD -MF /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o.d -pthread -B /root/miniconda3/envs/dla/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14
    FAILED: /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o
    c++ -MMD -MF /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o.d -pthread -B /root/miniconda3/envs/dla/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14
    cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:4:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h: In function ‘at::Tensor detectron2::ROIAlign_forward(const at::Tensor&, const at::Tensor&, float, int, int, int, bool)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h:62:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (input.type().is_cuda()) {
                      ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:4:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h: In function ‘at::Tensor detectron2::ROIAlign_backward(const at::Tensor&, const at::Tensor&, float, int, int, int, int, int, int, int, bool)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h:98:17: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (grad.type().is_cuda()) {
                     ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:5:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h: In function ‘at::Tensor detectron2::ROIAlignRotated_forward(const at::Tensor&, const at::Tensor&, float, int, int, int)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h:57:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (input.type().is_cuda()) {
                      ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:5:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h: In function ‘at::Tensor detectron2::ROIAlignRotated_backward(const at::Tensor&, const at::Tensor&, float, int, int, int, int, int, int, int)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h:85:17: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (grad.type().is_cuda()) {
                     ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘int detectron2::deform_conv_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, int)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:134:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (input.type().is_cuda()) {
                      ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:136:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:136:5: error: ‘AT_CHECK’ was not declared in this scope
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
         ^~~~~~~~
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:136:5: note: suggested alternative: ‘DCHECK’
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
         ^~~~~~~~
         DCHECK
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:137:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘int detectron2::deform_conv_backward_input(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, int)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:182:23: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (gradOutput.type().is_cuda()) {
                           ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:184:25: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
                             ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:184:5: error: ‘AT_CHECK’ was not declared in this scope
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
         ^~~~~~~~
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:184:5: note: suggested alternative: ‘DCHECK’
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
         ^~~~~~~~
         DCHECK
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:185:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:186:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘int detectron2::deform_conv_backward_filter(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, float, int)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:232:23: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (gradOutput.type().is_cuda()) {
                           ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:234:25: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
                             ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:234:5: error: ‘AT_CHECK’ was not declared in this scope
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
         ^~~~~~~~
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:234:5: note: suggested alternative: ‘DCHECK’
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
         ^~~~~~~~
         DCHECK
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:235:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘void detectron2::modulated_deform_conv_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, bool)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:282:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (input.type().is_cuda()) {
                      ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:284:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:284:5: error: ‘AT_CHECK’ was not declared in this scope
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
         ^~~~~~~~
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:284:5: note: suggested alternative: ‘DCHECK’
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
         ^~~~~~~~
         DCHECK
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:285:24: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
                            ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:286:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘void detectron2::modulated_deform_conv_backward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, bool)’:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:339:24: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
       if (grad_output.type().is_cuda()) {
                            ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:341:25: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
                             ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:341:5: error: ‘AT_CHECK’ was not declared in this scope
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
         ^~~~~~~~
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:341:5: note: suggested alternative: ‘DCHECK’
         AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
         ^~~~~~~~
         DCHECK
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:342:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:343:24: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
                            ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:344:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
         AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
                              ^
    In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
                     from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
    /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
       DeprecatedTypeProperties & type() const {
                                  ^~~~
    [4/4] /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
    ninja: build stopped: subcommand failed.
    Traceback (most recent call last):
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1400, in _run_ninja_build
        check=True)
      File "/root/miniconda3/envs/dla/lib/python3.6/subprocess.py", line 438, in run
        output=stdout, stderr=stderr)
    subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
    
    During handling of the above exception, another exception occurred:
    
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py", line 138, in <module>
        cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/__init__.py", line 144, in setup
        return distutils.core.setup(**attrs)
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/core.py", line 148, in setup
        dist.run_commands()
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/dist.py", line 955, in run_commands
        self.run_command(cmd)
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/dist.py", line 974, in run_command
        cmd_obj.run()
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/develop.py", line 38, in run
        self.install_for_development()
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/develop.py", line 140, in install_for_development
        self.run_command('build_ext')
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/cmd.py", line 313, in run_command
        self.distribution.run_command(command)
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/dist.py", line 974, in run_command
        cmd_obj.run()
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 87, in run
        _build_ext.run(self)
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/Cython/Distutils/old_build_ext.py", line 186, in run
        _build_ext.build_ext.run(self)
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 339, in run
        self.build_extensions()
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 580, in build_extensions
        build_ext.build_extensions(self)
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/Cython/Distutils/old_build_ext.py", line 195, in build_extensions
        _build_ext.build_ext.build_extensions(self)
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 448, in build_extensions
        self._build_extensions_serial()
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 473, in _build_extensions_serial
        self.build_extension(ext)
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 208, in build_extension
        _build_ext.build_extension(self, ext)
      File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 533, in build_extension
        depends=ext.depends)
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 423, in unix_wrap_ninja_compile
        with_cuda=with_cuda)
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1140, in _write_ninja_file_and_compile_objects
        error_prefix='Error compiling objects for extension')
      File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1413, in _run_ninja_build
        raise RuntimeError(message)
    RuntimeError: Error compiling objects for extension
    ----------------------------------------
ERROR: Command errored out with exit status 1: /root/miniconda3/envs/dla/bin/python3.6 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"'; __file__='"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.

Environment:

---

sys.platform linux
Python 3.6.10 | packaged by conda-forge | (default, Apr 24 2020, 16:44:11) [GCC 7.3.0]
Numpy 1.18.1
detectron2._C failed to import
DETECTRON2_ENV_MODULE
PyTorch 1.5.0
PyTorch Debug Build False
torchvision 0.6.0a0+82fd1c8
CUDA available True
GPU 0 GeForce GTX 1080
CUDA_HOME /usr/local/cuda-10.2
NVCC Cuda compilation tools, release 10.2, V10.2.89
Pillow 7.1.2

---

PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.2
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
• CuDNN 7.6.5
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,

How to Use Detectron2

❓ How to use Detectron2

This is my colab file: https://gist.github.com/alex-2201/56c9cb561fb6d5e4626ba7abcfc95471
but it doesn't work :(
I can't setup and run your code, could you give me guideline please.
Thank you!

How to deal with tables?

Firstly, thank you so much @hpanwar08 for your contributions!
I was trying to use Mask_RCNN but I observed that whenever data is a little bit spreaded like in a table, then it fails to record it. There is no bbox in that case.
Consider the following picture as a reference.

Can you help me with this?
Thank you

Are the models fine tune-able?

❓ How to Finetune these new models

1. Are the three models provided work in same way as pre-trained models?
2. How to finetune them?

It would be great if you can provide script for fine-tuning.

Segmenting and saving each class as image to allow OCR

🚀 Feature

Hi, is there an internal feature which lets each classed be saved as a seperate segment, or image? I am trying to identify tables, seperate and then run through a tabular data analyzer and ocr - so far am able to get the image predictions with your code, but not the actual annotations/segmented fields for further analysis/ocr.

Motivation

Pitch

Note that you can implement many features by extending detectron2.
See projects for some examples.
We would only consider adding new features if they are relevant to many users.

Has anyone trained a model on more general pdf documents?

Does a more general model already exist?

Hi, I'm planning to apply Detectron2 on more general pdf documents from all sorts of companies. Is there a feature or model already present or is my best option to start fine tuning the existing model by making my own test dataset? Thanks in advance

TensorRT

🚀 Feature

whether to consider using TensorRT to accelerate the MaskRCNN model?

Motivation

Pitch

Note that you can implement many features by extending detectron2.
See projects for some examples.
We would only consider adding new features if they are relevant to many users.

What is the process of finetuning in colab?

Hi, while I am trying to finetune the publaynet model in colab I keep running into errors. What would the process of this be? Should I start with this package or the normal detectron2 from facebookreserch? I have tried to manually add the publaynet model to my google drive but it does not seem to work that way. Some steps to achieve this would be of great help.

Thanks in advance

Resume training after certain iterations

I am using Colab for training my model and as the time limit gets over, training stops. I have checkpoints saved after 500 iterations. I want to resume my training given a checkpoint in the output folder but every time it starts training from iteration 0. I have opened the train_net_dla.py file and training the model, below is configurations and steps for training

cfg = get_cfg()
# mask rcnn resnet101
# mask rcnn resnext
cfg.merge_from_file("./configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml")
cfg.MODEL.WEIGHTS = "/content/drive/MyDrive/layout/model_final_trimmed.pth"
output_dir = "./output"
cfg.OUTPUT_DIR = output_dir
#os.makedirs(output_dir, exist_ok=True)
cfg.TEST.EVAL_PERIOD = 200
cfg.SOLVER.CHECKPOINT_PERIOD= 500
#logger.info(cfg)

# serialize the training config
cfg_str = cfg.dump()
with open(os.path.join(cfg.OUTPUT_DIR, "train_config.yaml"), "w") as f:
    f.write(cfg_str)
    f.close()
model = build_model(cfg)
checkpointer = DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
checkpointer = DetectionCheckpointer(model, save_dir=output_dir)
checkpointer = PeriodicCheckpointer(checkpointer, period=200,max_iter = 500)
 checkpointer.save("model")
trainer = COCOTrainer(cfg)
trainer.resume_or_load(resume=True)
trainer.train()

mask_rcnn_R_50_FPN_3x.yaml

@hpanwar08

Why did you train using x101 fpn, have you tested mask_rcnn_R_50_FPN_3x.yaml?

How to set category id when fine tuning with an existing class

Hi,

I want to fine-tune the model with an existing class (table in my case). How should I set the category id of the new data? What should be its value, in a sense should it be the same as in the pre-trained model?

Thanks in advance!

Mask R-CNN with ResNet-101-FPN-3x trimmed weights

Can you please provide trimmed version of the weights for the model mentioned in the title as there is trimmed version of the other model (Mask R-CNN with ResNeXt-101-32x8-FPN)

How to create Evaluation metrics for custom PDF dataset

Hi ,
I wanted to Know how to calculate the evaluation metrics for custom dataset which has only pdf, no xml as told by the PublayNet paper anyone tried this?

I ran this on my documents got the labels and their predictions but now I need to calculate the accuracy with ground truth but I don't have the labelled data so is there any way to calculate metrics for the results.

How to get region features for a bounding box specified by the user

Thank you @hpanwar08 for providing with detectron2 trained publaynet models.
I wanted help with using this model to extract region features of the objects (the layout elements detected) detected