def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]:
"Factory method to convert a batch of model images to a list of ModelImageSet."
image_sets = []
x,y = batch[0],batch[1]
preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True)
for orig_px, real_px, gen in zip(x,y,preds):
orig, real = Image(px=orig_px), Image(px=real_px)
image_set = ModelImageSet(orig=orig, real=real, gen=gen)
image_sets.append(image_set)
return image_sets
def show_prediction_vs_actual(sample_idx: int, learn: Learner) -> ImageSegment:
"""Return predicted mask, additionally print input image and tile-level label"""
sample = learn.data.valid_ds[sample_idx]
image, label = sample
print("Label: " + str(label.__repr__()))
image.show()
batch = learn.data.one_item(image)
pred = learn.pred_batch(batch=batch).squeeze(dim=0)
img = pred.argmax(dim=0, keepdim=True)
predicted_colors = torch.zeros(len(ALL_CLASSES))
for i in img.unique():
predicted_colors[i] = 1
print("Predicted colors: " + str(predicted_colors))
image_segment = ImageSegment(img)
return image_segment
