fastai learner requirements and batch prediction












5














I previously trained a resnet34 model using the fastai library, and have the weights.h5 file saved. With the latest version of fastai, do I still need to have non-empty train and valid folders in order to import my learner and predict on the test set?



Also, I’m currently looping through every test image and using learn.predict_array, but is there a way to predict in batches on a test folder?



Example of what I’m currently doing just to load/predict:



PATH = '/path/to/model/'
sz = 224
arch=resnet34
tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=64)
learn = ConvLearner.pretrained(arch, data, precompute=False)
learn.unfreeze()
learn.load('224_all')

imgs = sorted(glob(os.path.join(test_path, '*.jpg')))
preds =
_,val_tfms = tfms_from_model(resnet34, 224)
for n, i in enumerate(imgs):
im = val_tfms(open_image(i))[None]
preds.append(1-np.argmax(learn.predict_array(im)[0]))


There must be a cleaner way to do this by now, no?










share|improve this question





























    5














    I previously trained a resnet34 model using the fastai library, and have the weights.h5 file saved. With the latest version of fastai, do I still need to have non-empty train and valid folders in order to import my learner and predict on the test set?



    Also, I’m currently looping through every test image and using learn.predict_array, but is there a way to predict in batches on a test folder?



    Example of what I’m currently doing just to load/predict:



    PATH = '/path/to/model/'
    sz = 224
    arch=resnet34
    tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
    data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=64)
    learn = ConvLearner.pretrained(arch, data, precompute=False)
    learn.unfreeze()
    learn.load('224_all')

    imgs = sorted(glob(os.path.join(test_path, '*.jpg')))
    preds =
    _,val_tfms = tfms_from_model(resnet34, 224)
    for n, i in enumerate(imgs):
    im = val_tfms(open_image(i))[None]
    preds.append(1-np.argmax(learn.predict_array(im)[0]))


    There must be a cleaner way to do this by now, no?










    share|improve this question



























      5












      5








      5


      1





      I previously trained a resnet34 model using the fastai library, and have the weights.h5 file saved. With the latest version of fastai, do I still need to have non-empty train and valid folders in order to import my learner and predict on the test set?



      Also, I’m currently looping through every test image and using learn.predict_array, but is there a way to predict in batches on a test folder?



      Example of what I’m currently doing just to load/predict:



      PATH = '/path/to/model/'
      sz = 224
      arch=resnet34
      tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
      data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=64)
      learn = ConvLearner.pretrained(arch, data, precompute=False)
      learn.unfreeze()
      learn.load('224_all')

      imgs = sorted(glob(os.path.join(test_path, '*.jpg')))
      preds =
      _,val_tfms = tfms_from_model(resnet34, 224)
      for n, i in enumerate(imgs):
      im = val_tfms(open_image(i))[None]
      preds.append(1-np.argmax(learn.predict_array(im)[0]))


      There must be a cleaner way to do this by now, no?










      share|improve this question















      I previously trained a resnet34 model using the fastai library, and have the weights.h5 file saved. With the latest version of fastai, do I still need to have non-empty train and valid folders in order to import my learner and predict on the test set?



      Also, I’m currently looping through every test image and using learn.predict_array, but is there a way to predict in batches on a test folder?



      Example of what I’m currently doing just to load/predict:



      PATH = '/path/to/model/'
      sz = 224
      arch=resnet34
      tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
      data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=64)
      learn = ConvLearner.pretrained(arch, data, precompute=False)
      learn.unfreeze()
      learn.load('224_all')

      imgs = sorted(glob(os.path.join(test_path, '*.jpg')))
      preds =
      _,val_tfms = tfms_from_model(resnet34, 224)
      for n, i in enumerate(imgs):
      im = val_tfms(open_image(i))[None]
      preds.append(1-np.argmax(learn.predict_array(im)[0]))


      There must be a cleaner way to do this by now, no?







      python-3.x machine-learning batch-processing predict fast-ai






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Dec 12 at 22:46

























      asked Nov 11 at 21:02









      Austin

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      1,33121037





























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