Datasets
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preprocessr ┌copy)r Zin_datar r r ┌_┌H┌Wr r r ┌__call__" s zTransform.__call__N)┌__name__┌
__module__┌ __qualname__r r& r r r r r s r c C s× | j \ } } } | d } t j | | | | f d d â} | j t j â } t j | d | | f d d â} t j ré t } n t } | | â } | | f S)Ng Óo@┌mode┌reflectr )
r r ┌resize┌astyper ┌float32r r ┌caffe_normalize┌pytorch_normalze)r r r ┌Cr$ r% ┌ normalizer r r r! * s
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https://discuss.pytorch.org/t/how-to-preprocess-input-for-pre-trained-networks/683
https://github.com/pytorch/vision/issues/223
return appr -1~1 RGB
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return appr -125-125 BGR
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Args:
:param root_dir (string): Directory with all the images
:param img_id (list): lists of image id
:param train: if equals true, then read training set, so the output is image, mask and imgId
if equals false, then read testing set, so the output is image and imgId
:param transform (callable, optional): Optional transform to be applied on a sample
N)┌root_dirr ┌mask_idr r r r ┌tsf)r r: r r; r r r r r R s
zRSNADataset.__init__c C s
t | j â S)N)┌lenr )r r r r ┌__len__a s zRSNADataset.__len__c C s┼ t j j | j d | j | j d â d d â } t j j | j d | j | â } t | t j d â } t | t j
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Args:
:param root_dir (string): Directory with all the images
:param img_id (list): lists of image id
:param train: if equals true, then read training set, so the output is image, mask and imgId
if equals false, then read testing set, so the output is image and imgId
:param transform (callable, optional): Optional transform to be applied on a sample
N) r: rB ┌listdirr r r r r r< )r r: r r r r r s s zRSNADatasetTest.__init__c C s
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C s\ t j | â } | j â t d | d | d d â } t | d | d | d | d | â} | S) a* Utility function for loading and returning training and validation Dataloader
:param root_dir: the root directory of data set
:param batch_size: batch size of training and validation set
:param split: if split data set to training set and validation set
:param shuffle: if shuffle the image in training and validation set
:param num_workers: number of workers loading the data, when using CUDA, set to 1
:param val_ratio: ratio of validation set size
:param pin_memory: store data in CPU pin buffer rather than memory. when using CUDA, set to True
:return:
if split the data set then returns:
- train_loader: Dataloader for training
- valid_loader: Dataloader for validation
else returns:
- dataloader: Dataloader of all the data set
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| d | d | d | d | â} | | f S)a* Utility function for loading and returning training and validation Dataloader
:param root_dir: the root directory of data set
:param batch_size: batch size of training and validation set
:param split: if split data set to training set and validation set
:param shuffle: if shuffle the image in training and validation set
:param num_workers: number of workers loading the data, when using CUDA, set to 1
:param val_ratio: ratio of validation set size
:param pin_memory: store data in CPU pin buffer rather than memory. when using CUDA, set to True
:return:
if split the data set then returns:
- train_loader: Dataloader for training
- valid_loader: Dataloader for validation
else returns:
- dataloader: Dataloader of all the data set
z train.csvr ┌labelZ random_stateÚ* Z test_sizerP Fr: r r; r TrO rQ rR ) ┌pdZread_csvrB rC rD r r: ┌listr r9 r )r: rO ┌ val_ratiorP rQ rR ┌dfr r; Z train_img_idZ
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val_loaderr r r ┌get_train_val_loader░ s !!-!!r_ c
C s: t d t j â } t | d | d | d | d | â} | S)a Utility function for loading and returning training and validation Dataloader
:param root_dir: the root directory of data set
:param batch_size: batch size of training and validation set
:param shuffle: if shuffle the image in training and validation set
:param num_workers: number of workers loading the data, when using CUDA, set to 1
:param pin_memory: store data in CPU pin buffer rather than memory. when using CUDA, set to True
:return:
- testloader: Dataloader of all the test set
r: rO rP rQ rR )rK r Z test_rootr )rO rP rQ rR rU Z
testloaderr r r ┌get_test_loader╬ s r` c C s▀ | d | d | d } } } t j t j | â â t j t j | â â } } t | â } t j | | â } t j d â } | j | d â | j | d â t j d â } | j | d â | j | d â t j
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Visualize one training image and its corresponding bbox
r r r Úy g Óo@r Úz N) r ┌squeeze┌at┌tonumpyr ┌multiply┌pltZsubplot┌imshow┌ set_title┌show)┌sample_batchedr r r ZcombinedZax1Zax2r r r ┌show_batch_trainŮ s 1 rl c C s` | d | d } } t t j t j | d â â â } t j â t j | d â t j â d S)Nr r r r ) r r rc rd re rg ┌figurerh rj )Z sample_batchr r r r r ┌show_batch_test s
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array_toolrd Zmatplotlib.patchesZpatchesZdata.data_utilsr ZDSB_BBOX_LABEL_NAMESr ┌objectr r! r0 r/ r9 rK rV r_ r` rl rn r' rQ rR ┌ test_loader┌ enumerateZi_batchrI ┌printr r r r r ┌<module> s@
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