Image augmentation list is missing : How to verify whether applied augmentation worked or not ??

Instructions To Reproduce the 🐛 Bug:

1. Full runnable code or full changes you made:

If making changes to the project itself, please use output of the following command:
git rev-parse HEAD; git diff

<put code or diff here>

def custom_mapper(dataset_dict):
    # Implement a mapper, similar to the default DatasetMapper, but with your own customizations
    dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
    image = utils.read_image(dataset_dict["file_name"], format="BGR")
    augs = T.AugmentationList([
        T.RandomBrightness(0.5, 2),
        T.RandomContrast(0.5, 2),
        T.RandomSaturation(0.5, 2),
        T.RandomFlip(prob=0.5, horizontal=False, vertical=True),
        T.RandomFlip(prob=0.5, horizontal=True, vertical=False)
    ])

    auginput = T.AugInput(image)
    transform = augs(auginput)
    image = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
    dataset_dict["image"] = image

    annos = [
        utils.transform_instance_annotations(obj, [transform], image.shape[1:])
        for obj in dataset_dict.pop("annotations")
    ]
    instances = utils.annotations_to_instances(annos, image.shape[1:])
    dataset_dict["instances"] = utils.filter_empty_instances(instances)
    return dataset_dict


class BaseTrainer(engine.DefaultTrainer):

    def __init__(self, cfg):
        super().__init__(cfg)

    @classmethod
    def build_train_loader(cls, cfg):
        return build_detection_train_loader(cfg, mapper=custom_mapper)

    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        if output_folder is None:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
        return COCOEvaluator(dataset_name, cfg, True, output_folder)

    def build_hooks(self):
        hooks = super().build_hooks()

        hooks.insert(-1, Basehook(
            self.cfg.TEST.EVAL_PERIOD,
            self.model,
            build_detection_test_loader(
                self.cfg,
                self.cfg.DATASETS.TEST[0],
                DatasetMapper(self.cfg, True)
            )
        ))
        return hooks

        return hooks

1. please simplify the steps as much as possible so they do not require additional resources to run, such as a private dataset.

I applied :

augs = T.AugmentationList([ T.RandomBrightness(0.5, 2), T.RandomContrast(0.5, 2), T.RandomSaturation(0.5, 2), T.RandomFlip(prob=0.5, horizontal=False, vertical=True), T.RandomFlip(prob=0.5, horizontal=True, vertical=False) ])

But while training only [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style=‘choice’), RandomFlip()] showing. How can I verify whether augmentations applied or not ?

I have attached the screenshot for reference.

Thank you in advance

Environment:

sys.platform            linux
Python                  3.8.8 (default, Feb 24 2021, 21:46:12) [GCC 7.3.0]
numpy                   1.19.2
detectron2              0.4 @/home/sami02/anaconda3/envs/thesis/lib/python3.8/site-packages/detectron2
Compiler                GCC 9.3
CUDA compiler           not available
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.8.1 @/home/sami02/anaconda3/envs/thesis/lib/python3.8/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0                   GeForce GTX 1060 6GB (arch=6.1)
CUDA_HOME               None - invalid!
Pillow                  8.2.0
torchvision             0.9.1 @/home/sami02/anaconda3/envs/thesis/lib/python3.8/site-packages/torchvision
torchvision arch flags  /home/sami02/anaconda3/envs/thesis/lib/python3.8/site-packages/torchvision/_C.so
fvcore                  0.1.5.post20210413
iopath                  0.1.7
cv2                     4.5.1
----------------------  ---------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX
  - 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_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -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-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -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, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.1, USE_CUDA=ON, USE_CUDNN=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, 

[VGrondin]

Hi, I use this to visualize custom data augmentation before training:

# visualize DA
metadata = MetadataCatalog.get("train_set")
train_loader = Trainer.build_train_loader(cfg)
for train_image_batch in train_loader:
     for idx, train_image in enumerate(train_image_batch):       # run from here (included)
            image = train_image["image"].numpy().transpose(1, 2, 0)
            # visualize ground truth
            gt_visualizer = Visualizer(
                image[:, :, ::-1], metadata=tree_metadata, scale=1,
                instance_mode =  ColorMode.IMAGE 
            )
            gt_image = gt_visualizer.overlay_instances(
                boxes=train_image["instances"].gt_boxes, 
                labels=train_image["instances"].gt_classes,
                masks=train_image["instances"].gt_masks,
                keypoints=train_image["instances"].gt_keypoints
            )      
            
            cv2.imshow(train_image['file_name'], gt_image.get_image()[:, :, ::-1])
            k = cv2.waitKey(0)
        
            # exit loop if esc is pressed
            if k == 27:
                cv2.destroyAllWindows()
                break

Where Trainer is your custom trainer and that your dataset is already registered. Hope it helps!

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