FIX: Cargo check warnings and some clippy lints

Clippy lints cannot be fixed by the packages that import the library
reiimplementations directly, this is a known problem.

See rust-lang/rust-clippy#3025.

Author: Conzel

coders/src/hierarchical_coders.rs

@@ -161,8 +161,6 @@ impl Encoder<Array3<ImagePrecision>> for MeanScaleHierarchicalEncoder {
         let hyperlatents = self.hyperlatent_encoder.forward_pass(&latents);
         let latent_parameters = self.hyperlatent_decoder.forward_pass(&hyperlatents);
 
-        let latent_length = latents.len();
-
         // If we give in an uneven latent shape, then the latent parameters will
         // have even shape and the naive comparison doesn't work
         debug_assert_eq!(
@@ -256,7 +254,6 @@ impl Decoder<Array3<ImagePrecision>> for MeanScaleHierarchicalDecoder {
             make_even(side_info[4] as usize) as usize,
             make_even(side_info[5] as usize) as usize,
         );
-        let latents_len = latents_shape.0 * latents_shape.1 * latents_shape.2;
         let mut coder = DefaultAnsCoder::from_compressed(encoded_data.main_info).unwrap();
 
         let mut hyperlatents = Array::zeros(hyperlatents_shape);

ml/src/activation_functions.rs

@@ -86,11 +86,13 @@ pub fn igdn(
 }
 
 /// Leaky relu implementation
+#[allow(dead_code)]
 pub fn leaky_relu<D: Dimension>(data: &Array<ImagePrecision, D>) -> Array<ImagePrecision, D> {
     data.mapv(|x| if x > 0. { x } else { 0.01 * x })
 }
 
 /// Relu implementation
+#[allow(dead_code)]
 pub fn relu<D: Dimension>(data: &Array<ImagePrecision, D>) -> Array<ImagePrecision, D> {
     data.mapv(|x| if x > 0. { x } else { 0. })
 }

ml/src/models.rs

@@ -1,5 +1,6 @@
+//! This module provides the hierarchical models used in the encoding and decoding process.
 // This file has been automatically generated by Jinja2 via the
-// script generate_models.py.
+// script ./generate_models.py.
 // Please do not change this file by hand.
 use crate::{
     activation_functions::{GdnLayer, IgdnLayer, ReluLayer},
@@ -69,6 +70,7 @@ pub struct MinnenEncoder {
 }
 
 impl CodingModel for MinnenEncoder {
+    #[allow(clippy::let_and_return)]
     fn forward_pass(&self, input: &InternalDataRepresentation) -> InternalDataRepresentation {
         let x = input.clone();
 
@@ -197,6 +199,7 @@ pub struct JohnstonDecoder {
 }
 
 impl CodingModel for JohnstonDecoder {
+    #[allow(clippy::let_and_return)]
     fn forward_pass(&self, input: &InternalDataRepresentation) -> InternalDataRepresentation {
         let x = input.clone();
 
@@ -339,6 +342,7 @@ pub struct MinnenHyperEncoder {
 }
 
 impl CodingModel for MinnenHyperEncoder {
+    #[allow(clippy::let_and_return)]
     fn forward_pass(&self, input: &InternalDataRepresentation) -> InternalDataRepresentation {
         let x = input.clone();
 
@@ -404,6 +408,7 @@ pub struct JohnstonHyperDecoder {
 }
 
 impl CodingModel for JohnstonHyperDecoder {
+    #[allow(clippy::let_and_return)]
     fn forward_pass(&self, input: &InternalDataRepresentation) -> InternalDataRepresentation {
         let x = input.clone();
 
@@ -457,7 +462,9 @@ impl JohnstonHyperDecoder {
 }
 
 mod tests {
+    #[allow(unused_imports)]
     use super::*;
+    #[allow(unused_imports)]
     use crate::weight_loader::NpzWeightLoader;
 
     #[test]

ml/src/weight_loader.rs

@@ -74,7 +74,7 @@ impl WeightLoader for JsonWeightLoader {
         let raw_arr = self
             .content
             .get(param_name)
-            .ok_or(WeightError::WeightKeyError(param_name.to_string()))?;
+            .ok_or_else(|| WeightError::WeightKeyError(param_name.to_string()))?;
 
         let raw_value_vector = match raw_arr {
             Value::Array(v) => v,
@@ -167,7 +167,7 @@ where
             Err(_) => {
                 let arr_flat: Array1<_> = reader.by_name(param_name)?;
                 let arr_reshaped =
-                    Array::from_shape_vec(shape, arr_flat.iter().map(|x| *x).collect())?;
+                    Array::from_shape_vec(shape, arr_flat.iter().copied().collect())?;
                 arr_reshaped
             }
         })

scripts/generate_models.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 import jinja2
 import os
 import json

scripts/generate_tests.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 import jinja2
 import numpy as np
 import itertools

scripts/templates/models_template.rs

@@ -69,6 +69,8 @@ impl CodingModel for ReluLayer {
     }
 
     impl CodingModel for {{m.name}} {
+        {# Have to allow since the last let might be extraneous due to model generation #}
+        #[allow(clippy::let_and_return)]
         fn forward_pass(&self, input: &InternalDataRepresentation) -> InternalDataRepresentation {
             let x = input.clone();
             {% for l in m.layers %}
@@ -117,7 +119,9 @@ impl CodingModel for ReluLayer {
 {% endfor %}
 
 mod tests {
+    #[allow(unused_imports)]
     use crate::weight_loader::NpzWeightLoader;
+    #[allow(unused_imports)]
     use super::*;
 
     {% for m in models %}

zipnet/src/main.rs

@@ -1,21 +1,19 @@
 //! This crate ties in all the parts of the projects and provides a clean command line interface
 //! to make encoding/decoding images feasible.
 
-use bincode::serialize;
 use coders::{
     dummy_coders::DummyCoder,
     hierarchical_coders::{MeanScaleHierarchicalDecoder, MeanScaleHierarchicalEncoder},
     statistics::Statistics,
     Decoder, Encoder,
 };
+use image::io::Reader as ImageReader;
 use image::RgbImage;
-use image::{io::Reader as ImageReader, DynamicImage};
 use ndarray::{Array, Array3};
-use ndarray_npy::{read_npy, NpzReader};
+use ndarray_npy::read_npy;
 use nshare::ToNdarray3;
 use quicli::prelude::*;
 use std::{
-    array,
     ffi::OsStr,
     fs,
     io::{Read, Write},