scirs2_io/ml_framework/converters/
coreml.rs

1//! CoreML format converter
2#![allow(dead_code)]
3
4use crate::error::{IoError, Result};
5use crate::ml_framework::converters::MLFrameworkConverter;
6use crate::ml_framework::types::{MLFramework, MLModel, MLTensor};
7use scirs2_core::ndarray::{ArrayD, IxDyn};
8use std::fs::File;
9use std::path::Path;
10
11/// CoreML format converter
12pub struct CoreMLConverter;
13
14impl MLFrameworkConverter for CoreMLConverter {
15    fn save_model(&self, model: &MLModel, path: &Path) -> Result<()> {
16        // CoreML uses a specific protobuf format, simplified here
17        let coreml_model = serde_json::json!({
18            "format": "coreml",
19            "specificationVersion": 5,
20            "description": {
21                "metadata": {
22                    "userDefined": model.metadata.parameters,
23                    "author": "SciRS2",
24                    "license": "MIT",
25                    "shortDescription": model.metadata.model_name.clone().unwrap_or_default()
26                },
27                "input": model.metadata.inputshapes.iter().map(|(name, shape)| {
28                    serde_json::json!({
29                        "name": name,
30                        "type": {
31                            "multiArrayType": {
32                                "shape": shape,
33                                "dataType": "FLOAT32"
34                            }
35                        }
36                    })
37                }).collect::<Vec<_>>(),
38                "output": model.metadata.outputshapes.iter().map(|(name, shape)| {
39                    serde_json::json!({
40                        "name": name,
41                        "type": {
42                            "multiArrayType": {
43                                "shape": shape,
44                                "dataType": "FLOAT32"
45                            }
46                        }
47                    })
48                }).collect::<Vec<_>>()
49            },
50            "neuralNetwork": {
51                "layers": [],
52                "preprocessing": []
53            },
54            "weights": model.weights.iter().map(|(name, tensor)| {
55                (name.clone(), serde_json::json!({
56                    "shape": tensor.metadata.shape,
57                    "floatValue": tensor.data.as_slice().unwrap().to_vec()
58                }))
59            }).collect::<serde_json::Map<String, serde_json::Value>>()
60        });
61
62        let file = File::create(path).map_err(IoError::Io)?;
63        serde_json::to_writer_pretty(file, &coreml_model)
64            .map_err(|e| IoError::SerializationError(e.to_string()))
65    }
66
67    fn load_model(&self, path: &Path) -> Result<MLModel> {
68        let file = File::open(path).map_err(IoError::Io)?;
69        let coreml_model: serde_json::Value = serde_json::from_reader(file)
70            .map_err(|e| IoError::SerializationError(e.to_string()))?;
71
72        let mut model = MLModel::new(MLFramework::CoreML);
73
74        // Parse CoreML metadata
75        if let Some(description) = coreml_model.get("description") {
76            if let Some(metadata) = description.get("metadata") {
77                if let Some(short_desc) = metadata.get("shortDescription").and_then(|v| v.as_str())
78                {
79                    model.metadata.model_name = Some(short_desc.to_string());
80                }
81            }
82
83            // Parse inputs
84            if let Some(inputs) = description.get("input").and_then(|v| v.as_array()) {
85                for input in inputs {
86                    if let Some(input_obj) = input.as_object() {
87                        if let (Some(name), Some(shape)) = (
88                            input_obj.get("name").and_then(|v| v.as_str()),
89                            input_obj
90                                .get("type")
91                                .and_then(|t| t.get("multiArrayType"))
92                                .and_then(|mat| mat.get("shape"))
93                                .and_then(|s| s.as_array()),
94                        ) {
95                            let shape_vec: Vec<usize> = shape
96                                .iter()
97                                .filter_map(|v| v.as_u64().map(|u| u as usize))
98                                .collect();
99                            model
100                                .metadata
101                                .inputshapes
102                                .insert(name.to_string(), shape_vec);
103                        }
104                    }
105                }
106            }
107
108            // Parse outputs similarly
109            if let Some(outputs) = description.get("output").and_then(|v| v.as_array()) {
110                for output in outputs {
111                    if let Some(output_obj) = output.as_object() {
112                        if let (Some(name), Some(shape)) = (
113                            output_obj.get("name").and_then(|v| v.as_str()),
114                            output_obj
115                                .get("type")
116                                .and_then(|t| t.get("multiArrayType"))
117                                .and_then(|mat| mat.get("shape"))
118                                .and_then(|s| s.as_array()),
119                        ) {
120                            let shape_vec: Vec<usize> = shape
121                                .iter()
122                                .filter_map(|v| v.as_u64().map(|u| u as usize))
123                                .collect();
124                            model
125                                .metadata
126                                .outputshapes
127                                .insert(name.to_string(), shape_vec);
128                        }
129                    }
130                }
131            }
132        }
133
134        // Parse weights
135        if let Some(weights) = coreml_model.get("weights").and_then(|v| v.as_object()) {
136            for (name, weight_data) in weights {
137                let shape: Vec<usize> = serde_json::from_value(weight_data["shape"].clone())
138                    .map_err(|e| IoError::SerializationError(e.to_string()))?;
139
140                let data: Vec<f32> = serde_json::from_value(weight_data["floatValue"].clone())
141                    .map_err(|e| IoError::SerializationError(e.to_string()))?;
142
143                let array = ArrayD::from_shape_vec(IxDyn(&shape), data)
144                    .map_err(|e| IoError::Other(e.to_string()))?;
145
146                model
147                    .weights
148                    .insert(name.clone(), MLTensor::new(array, Some(name.clone())));
149            }
150        }
151
152        Ok(model)
153    }
154
155    fn save_tensor(&self, tensor: &MLTensor, path: &Path) -> Result<()> {
156        let tensor_data = serde_json::json!({
157            "coreml_multiarray": {
158                "shape": tensor.metadata.shape,
159                "dataType": "FLOAT32",
160                "floatValue": tensor.data.as_slice().unwrap().to_vec()
161            }
162        });
163
164        let file = File::create(path).map_err(IoError::Io)?;
165        serde_json::to_writer_pretty(file, &tensor_data)
166            .map_err(|e| IoError::SerializationError(e.to_string()))
167    }
168
169    fn load_tensor(&self, path: &Path) -> Result<MLTensor> {
170        let file = File::open(path).map_err(IoError::Io)?;
171        let tensor_data: serde_json::Value = serde_json::from_reader(file)
172            .map_err(|e| IoError::SerializationError(e.to_string()))?;
173
174        if let Some(multiarray) = tensor_data.get("coreml_multiarray") {
175            let shape: Vec<usize> = serde_json::from_value(multiarray["shape"].clone())
176                .map_err(|e| IoError::SerializationError(e.to_string()))?;
177
178            let data: Vec<f32> = serde_json::from_value(multiarray["floatValue"].clone())
179                .map_err(|e| IoError::SerializationError(e.to_string()))?;
180
181            let array = ArrayD::from_shape_vec(IxDyn(&shape), data)
182                .map_err(|e| IoError::Other(e.to_string()))?;
183
184            return Ok(MLTensor::new(array, None));
185        }
186
187        Err(IoError::Other("Invalid CoreML tensor format".to_string()))
188    }
189}
scirs2_io/ml_framework/converters/coreml.rs

scirs2_io/ml_framework/converters/
coreml.rs
