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Some better error messages #189

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Some better error messages #189

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d945d7a
Some better error messages
opfromthestart Jan 31, 2023
264f38c
Slightly more clear
opfromthestart Jan 31, 2023
996d5c6
Errors to clarify current batch size limitations.
opfromthestart Jan 31, 2023
71da871
More info on input shape mismatch
opfromthestart Feb 11, 2023
632a2bc
Merge branch 'spearow:master' into better-error
opfromthestart Feb 22, 2023
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20 changes: 16 additions & 4 deletions juice/src/layer.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -537,10 +537,22 @@ impl<B: IBackend> Layer<B> {
// reshape input tensor to the reshaped shape
let old_shape = self.input_blobs_data[input_i].read().unwrap().desc().clone();
if old_shape.size() != reshaped_shape.size() {
panic!(
"Input Shape Mismatch\nExpected {:?}\nActual {:?}",
reshaped_shape, old_shape
);
if reshaped_shape[1..] == old_shape[1..] {
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I think it'd make more sense to add an assert per dimension, and just fail with the first.

for dim in 0..5 {
     let old = old_shape[dim];
     let reshaped = reshaped__shape[dim];
     assert_eq!(old, reshpaed, "Dimension {dim} mismatches {old} != {reshaped}"); 
}
unreachable!("If the total size is not equal, one dimension mismatch must exist. qed");

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I did it this way to catch some errors with mismatched batch sizes to make it a bit more clear.

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I think it should always show the full dimensions as well. That way it stays clear where it could originate from your code. These errors can arise from rather deep inside the code.

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I believe that my recent commit should have done this.

eprintln!("Expected batch size {} but got batch size {}.", reshaped_shape[0], old_shape[0]);
}
else if reshaped_shape[1..] == old_shape {
eprintln!("Expected batch size {} but got batch size {}.", reshaped_shape[0], 1);
}
else {
eprintln!(
"Input Shape Mismatch at layer {}\nLayer has input shape {:?}\nInput given has shape {:?}",
self.name, reshaped_shape, old_shape
);
}
if reshaped_shape == old_shape[1..] {
eprintln!("You may have forgotten to specify a batch size in your model input.");
}
panic!();
}
self.input_blobs_data[input_i]
.write()
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3 changes: 3 additions & 0 deletions juice/src/layers/common/linear.rs
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Expand Up @@ -86,6 +86,9 @@ impl<B: IBackend + LayerOps<f32>> ILayer<B> for Linear {
output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
) {
if input_data.len() == 0 {
panic!("Linear layer expected input, but none was given.");
}
let input = input_data[0].read().unwrap();
let batch_size = input.desc()[0];
// reshape top
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3 changes: 3 additions & 0 deletions juice/src/layers/container/sequential.rs
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Expand Up @@ -293,6 +293,9 @@ impl<B: IBackend + LayerOps<f32> + 'static> ILayer<B> for Sequential<B> {
output_data: &mut [ArcLock<SharedTensor<f32>>],
) {
for layer in &self.layers {
if layer.borrow().input_blob_names.len() < input_data.len() {
panic!("Layer {} expected {} input(s) but got {}.", layer.borrow().name, layer.borrow().input_blob_names.len(), input_data.len());
}
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👍

for (i, (input, input_name)) in input_data.iter().zip(self.input_tensor_names.iter()).enumerate() {
if &layer.borrow().input_blob_names[i] == input_name {
layer.borrow_mut().input_blobs_data[i] = input.clone();
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