np.argmax() returns the index of an array. Since the array has two elements, the two possibilities for the index are 0 or 1. See: https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.argmax.html
Here is the post with the code that worked best for me (after sifting through this entire thread). probs_single is the array with the probabilities for each of the two classes: http://forums.fast.ai/t/how-do-we-use-our-model-against-a-specific-image/7661/99
I’ve been trying old working examples that no longer work for about an hour now. Where is the most recent code that works to do this?
I have been trying to get single image classification to work on the dogs/cats example from lesson 1, but passing in the images from the validation set is yielding some unexpected results. I have the following code:
PATH = "data/dogscats"
arch = resnet34
sz = 224
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1))
learn = ConvLearner.pretrained(arch, data, precompute=True)
learn.fit(0.01, 3)
learn.precompute = False
trn_tfms, val_tfms = tfms_from_model(arch, sz)
imgs = np.array([val_tfms(open_image(PATH+'/valid/cats/' + fn)) for fn in os.listdir(PATH+"/valid/cats/")])
log_preds = learn.predict_array(imgs)
preds = np.argmax(log_preds, axis=1)
I can see from the data.classes variable that a 0 indicates a cat and a 1 indicates a dog, but the values in the preds array are almost half and half 1’s and 0’s.
Shouldn’t the values in the preds array be almost entirely 0’s (cats) since these images are from the cats portion of the validation set (aka, the set that the training process used to determine that the model has ~99% accuracy). OR perhaps I have a bug in my code or a fundamental misunderstanding of how the training process works / how the validation set is used. Can anyone set me straight?
Hi, I’m trying to use the saved model against an image, with the following code
#using existing model
# Make transformation model & data
tfmModel = tfms_from_model(resnet34, imSize, aug_tfms = transforms_side_on, max_zoom = 1.1)
data = ImageClassifierData.from_paths(PATH, tfms = tfmModel, bs = batchSize)
myModel = ConvLearner.pretrained(resnet34, data)
myModel.load('180123_resnet34_AllLayer01')
#try to predict the image
trn_tfms, val_tfms = tfms_from_model(resnet34, imSize, aug_tfms = transforms_side_on, max_zoom = 1.1)
url_response = urllib.request.urlopen(url)
im = val_tfms(np.array(Image.open(url_response))/255)
url_log_pred = myModel.predict_array(im[None])
However i encountered the following error:
ValueError: Expected more than 1 value per channel when training, got input size [1, 1024]
However, if I run:
logPred,y = myModel.TTA()
then perform:
url_log_pred = myModel.predict_array(im[None])
then it will be able to run as expected.
Does anyone know why?
If possible I don’t want to run myModel.TTA()
I’m trying to predict a single image and have gotten the code to run without any errors; however, I’m not exactly sure what the “np.argmax(preds)” is supposed to output. I added the print(preds[:2]) to see what preds was actually equal to. If the “np.argmax(preds)” outputs which type of image it thinks it is which is what I thought it did then it seems to get most of them wrong. There are also dramatic swings in what the preds is equal to when you retrain the model. Could someone try to explain this to me? Thanks. Here is the code.
How long ago did you update the fastai library? Seems like your predict_array does not have model.eval() in it.
np.argmax(preds) returns an index of predicted class (from learn.data.classes)
I git pulled and didn’t get any noticeable change. I also haven’t gotten any positive numbers from print(preds).
Sorry, for confusion, but my first post was related to @arifoyong question.
Try running this:
learn.data.classes[np.argmax(preds)]
Do you get a correct class?
Suppose I’m using a saved model for prediction. What is the correct way to create the learn object ? How to initialize it ?
tfmModel = tfms_from_model(resnet34, imSize, aug_tfms = transforms_side_on, max_zoom = 1.1)
data = ImageClassifierData.from_paths(PATH, tfms = tfmModel, bs = batchSize)
learn = ConvLearner.pretrained(resnet34, data)
I am doing it like above, but if I have to take my saved model to someplace else and make predictions out of it, do I need to take the training data with it too ? as I understand from
ImageClassifierData.from_paths(PATH, ...)
Hi @ace139 you just need to serialize the learner to disk like using a serialization library like pickle.
Reload the serialized file using pickle.load
@binarypoet This is a very useful code! Could you suggest a similar dataset that could run as your shopstyle one?
Hi Nick,
You are right.
After I updated the fastai library it works fine.
Thanks a lot
Quick question regarding this approach - how are you making sure that the models from pytorch and fast ai have the excact same architecture? I tried this approach using Resnet18 but got a mismatch of names when trying to load the weights from fast ai in raw pytorch 
If one needs to load the model architecture from learn.models.model it is still dependent on fast ai
Has anyone got a working example how to load a fastai model into pytorch, to use it for prediction? I would like to be able to train my model in fastai, but then just load it with pytorch for prediction without having to install fastai.
How can apply that object detection model [lesson (9)] on new image data set?
I tried
fp = str(PATH) + “/” + “person.jpg” ; fp
trn_tfms, val_tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_side_on, max_zoom=1.05) # get transformations
im = val_tfms(open_image(fp))
learn.precompute=False
preds = learn.predict_array(im[None])
but I got array! and the shape of array are : print(im[None].shape) , print(im.shape)
(1, 3, 224, 224)
(3, 224, 224)
Is there any suggestion?
hi yes i think i can
Hi,
Can you explain?
How do I view the preds file?