I struggle to manually reproduce the model output. More precisely:

- I have a vision model (called
`learn`

) trained on MNIST_TINY data -
`learn`

got the data from a default ImageDataLoaders.from_folder DataLoader

Given a new image I get the predicted class probabilities by calling `learn.predict(image)`

I like to reproduce these probabilities by hand. I guess my main issue is that I struggle to preprocess the source image the same way as is done by `learn`

. Is there an easy way to extract all preprocessing steps from a trained model and apply those to the source image?

Here is a reproducible example:

```
!pip install -Uqq fastai
!pip install nbdev
from fastai.vision.all import *
from fastai import metrics, learner
import pathlib
import torch
import torchvision.transforms as transforms
path = untar_data(URLs.MNIST_TINY)
dls = ImageDataLoaders.from_folder(
path,
seed = 11,
train="train",
valid="valid"
)
dls.show_batch()
learn = vision_learner(dls, models.resnet18, metrics=metrics.error_rate)
learn.fine_tune(1)
# reproduce the probabilities for this guy
myimg = PILImage.create(get_image_files(path)[0])
myimg
# by the model
learn.predict(myimg)
# manually: this fails
# how do I get all transformations?
print(learn.dls.tfms)
print(learn.dls.before_iter)
print(learn.dls.before_batch)
# this is not correct
input_tensor = Pipeline(ToTensor)(myimg).float().cuda()
input_tensor = input_tensor.unsqueeze(0)
input_tensor.shape
from functools import reduce
def manually(model, input_tensor):
input_tensor = input_tensor.clone()
for l in model:
input_tensor = l(input_tensor)
return nn.Softmax(dim=1)(input_tensor)
model = learn.model
model = model.cuda()
# not the same as in learn.predict(myimg)
manually(model, input_tensor)
```