What’s the reason of the error ValueError: Expected more than 1 value per channel?

github repository of fast.ai (as the code elevates the library which is built on top of PyTorch)

Please scroll the discussion a bit

I am running the following code, and get an error while trying to pass the data to the predict_array function

The code is failing when i am trying to use it to predict directly on a single image but it run’s perfectly when that same image is in a test folder

from fastai.conv_learner import *
from planet import f2

PATH = 'data/shopstyle/'

metrics=[f2]
f_model = resnet34

def get_data(sz):
    tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_side_on, max_zoom=1.05)
    return ImageClassifierData.from_csv(PATH, 'train', label_csv, tfms=tfms, suffix='.jpg', val_idxs=val_idxs, test_name='test')

def print_list(list_or_iterator):
        return "[" + ", ".join( str(x) for x in list_or_iterator) + "]"

label_csv = f'{PATH}prod_train.csv'
n = len(list(open(label_csv)))-1
val_idxs = get_cv_idxs(n)

sz = 64
data = get_data(sz)

print("Loading model...")
learn = ConvLearner.pretrained(f_model, data, metrics=metrics)
learn.load(f'{sz}')
#learn.load("tmp")

print("Predicting...")
learn.precompute=False
trn_tfms, val_tfrms = tfms_from_model(f_model, sz)
#im = val_tfrms(open_image(f'{PATH}valid/4500132.jpg'))
im = val_tfrms(np.array(PIL.Image.open(f'{PATH}valid/4500132.jpg')))
preds = learn.predict_array(im[None])
p=list(zip(data.classes, preds))
print("predictions = " + print_list(p))

Here’s the Traceback I am Getting

  Traceback (most recent call last):
  File "predict.py", line 34, in <module>
    preds = learn.predict_array(im[None])
  File "/home/ubuntu/fastai/courses/dl1/fastai/learner.py", line 266, in predict_array
    def predict_array(self, arr): return to_np(self.model(V(T(arr).cuda())))
  File "/home/ubuntu/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/container.py", line 67, in forward
    input = module(input)
  File "/home/ubuntu/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py", line 37, in forward
    self.training, self.momentum, self.eps)
  File "/home/ubuntu/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/functional.py", line 1011, in batch_norm
    raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size [1, 1024]

Things I have Tried

np.expand_dims(IMG,axis=0) or image = image[..., np.newaxis]

Tried a different way of reading the image

img = cv2.imread(img_path)
img = cv2.resize(img, dsize = (200,200))
img = np.einsum('ijk->kij', img)
img = np.expand_dims(img, axis =0) 
img = torch.from_numpy(img) 
learn.model(Variable(img.float()).cuda())

BTW the error still remains

ValueError: Expected more than 1 value per channel when training, got input size [1, 1024]

Can’t find any reference in The Google search also..

Answer

It will fail on batches of size 1 if we use feature-wise batch normalization.

As Batch normalization computes:

y = (x - mean(x)) / (std(x) + eps)

If we have one sample per batch then mean(x) = x, and the output will be entirely zero (ignoring the bias). We can’t use that for learning…

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Answer