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?






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







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?






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







    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?






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







      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




      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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