What are image rules and how to test fastai's processes?

I had submitted this post and got no feedback so I added color to the B&W images in a small subset of the training images and found that I now get 0.000000 train_loss, valid_loss and error_rate. My code is as shown below with the B&W centric code commented out and the additional code for color below those comments.

the shape of pix is:  (224, 224, 3)
image format:  PNG
image size:  (224, 224)
image mode:  RGB

I train using the following code:

from fastai.vision.all import *
if __name__ == '__main__':
    path = 'C:/training'
    #the following line was added because of Microsoft Windows and computer
    silence = DataBlock(
        #blocks=(ImageBlock(cls=PILImageBW), CategoryBlock),
        blocks=(ImageBlock, CategoryBlock),
        splitter=RandomSplitter(valid_pct=0.2, seed=42),
        item_tfms=Resize(224, ResizeMethod.Pad, pad_mode='zeros'))
    dls = silence.dataloaders(path)
    learn = cnn_learner(dls, resnet18,
        #metrics=error_rate, loss_func=BCEWithLogitsLossFlat())
    dls.valid.show_batch(max_n=4, nrows=1)

When I used the model trained with the B&W images for “silence” I got the following result:
pred is: [] , pred_idx is: tensor([False]) and probs is: tensor([0.0536])

When I used the model trained with the color images for “silence” I now get the following result:
pred is: silence , pred_idx is: tensor(0) and probs is: tensor([1.])

Now, the prediction is not empty. However when I test a noisy sample, both B&W and colorized, the result is the same as that for the “silent” image shown above. Also, when I run:

pred,pred_idx,probs = learn_inf.predict('C:/HOME/.fastai/data/oxford-iiit-pet/images/Abyssinian_1.jpg')

I also get pred is: silence , pred_idx is: tensor(0) and probs is: tensor([1.]), which should not happen as it’s a cat.

  1. What might be going wrong?

  2. How can I get logs of what fastai is doing for troubleshooting?

The fastai lesson samples work using essentially the same process shown here.