scirs2_io/ml_framework/converters/
tensorflow.rs

1//! TensorFlow 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/// TensorFlow format converter
12pub struct TensorFlowConverter;
13
14impl MLFrameworkConverter for TensorFlowConverter {
15    fn save_model(&self, model: &MLModel, path: &Path) -> Result<()> {
16        // TensorFlow SavedModel format
17        let model_dir = path.parent().unwrap_or(Path::new("."));
18        std::fs::create_dir_all(model_dir).map_err(IoError::Io)?;
19
20        let tf_model = serde_json::json!({
21            "saved_model_schema_version": 1,
22            "meta_graphs": [{
23                "meta_info_def": {
24                    "meta_graph_version": "v2.0.0",
25                    "tensorflow_version": "2.12.0",
26                    "tags": ["serve"]
27                },
28                "graph_def": {
29                    "versions": { "producer": 1982, "min_consumer": 12 }
30                },
31                "signature_def": {
32                    "serving_default": {
33                        "inputs": model.metadata.inputshapes,
34                        "outputs": model.metadata.outputshapes,
35                        "method_name": "tensorflow/serving/predict"
36                    }
37                }
38            }],
39            "variables": model.weights.iter().map(|(name, tensor)| {
40                serde_json::json!({
41                    "name": name,
42                    "shape": tensor.metadata.shape,
43                    "dtype": format!("{:?}", tensor.metadata.dtype),
44                    "data": tensor.data.as_slice().unwrap().to_vec()
45                })
46            }).collect::<Vec<_>>()
47        });
48
49        let file = File::create(path).map_err(IoError::Io)?;
50        serde_json::to_writer_pretty(file, &tf_model)
51            .map_err(|e| IoError::SerializationError(e.to_string()))
52    }
53
54    fn load_model(&self, path: &Path) -> Result<MLModel> {
55        let file = File::open(path).map_err(IoError::Io)?;
56        let tf_model: serde_json::Value = serde_json::from_reader(file)
57            .map_err(|e| IoError::SerializationError(e.to_string()))?;
58
59        let mut model = MLModel::new(MLFramework::TensorFlow);
60
61        // Parse TensorFlow metadata
62        if let Some(meta_graphs) = tf_model.get("meta_graphs").and_then(|v| v.as_array()) {
63            if let Some(meta_graph) = meta_graphs.first() {
64                if let Some(signature_def) = meta_graph
65                    .get("signature_def")
66                    .and_then(|v| v.get("serving_default"))
67                {
68                    if let Some(inputs) = signature_def.get("inputs").and_then(|v| v.as_object()) {
69                        for (name, input_info) in inputs {
70                            if let Some(shape) = input_info.as_array() {
71                                let shape_vec: Vec<usize> = shape
72                                    .iter()
73                                    .filter_map(|v| v.as_u64().map(|u| u as usize))
74                                    .collect();
75                                model.metadata.inputshapes.insert(name.clone(), shape_vec);
76                            }
77                        }
78                    }
79                }
80            }
81        }
82
83        // Parse variables
84        if let Some(variables) = tf_model.get("variables").and_then(|v| v.as_array()) {
85            for var in variables {
86                if let Some(var_obj) = var.as_object() {
87                    if let (Some(name), Some(shape), Some(data)) = (
88                        var_obj.get("name").and_then(|v| v.as_str()),
89                        var_obj.get("shape").and_then(|v| v.as_array()),
90                        var_obj.get("data").and_then(|v| v.as_array()),
91                    ) {
92                        let shape_vec: Vec<usize> = shape
93                            .iter()
94                            .filter_map(|v| v.as_u64().map(|u| u as usize))
95                            .collect();
96
97                        let data_vec: Vec<f32> = data
98                            .iter()
99                            .filter_map(|v| v.as_f64().map(|f| f as f32))
100                            .collect();
101
102                        if let Ok(array) = ArrayD::from_shape_vec(IxDyn(&shape_vec), data_vec) {
103                            model.weights.insert(
104                                name.to_string(),
105                                MLTensor::new(array, Some(name.to_string())),
106                            );
107                        }
108                    }
109                }
110            }
111        }
112
113        Ok(model)
114    }
115
116    fn save_tensor(&self, tensor: &MLTensor, path: &Path) -> Result<()> {
117        let tensor_data = serde_json::json!({
118            "tensor": {
119                "dtype": format!("{:?}", tensor.metadata.dtype),
120                "tensorshape": {
121                    "dim": tensor.metadata.shape.iter().map(|&d| serde_json::json!({"size": d})).collect::<Vec<_>>()
122                },
123                "tensor_content": tensor.data.as_slice().unwrap()
124                    .iter()
125                    .flat_map(|f| f.to_le_bytes().to_vec())
126                    .collect::<Vec<u8>>()
127            }
128        });
129
130        let file = File::create(path).map_err(IoError::Io)?;
131        serde_json::to_writer_pretty(file, &tensor_data)
132            .map_err(|e| IoError::SerializationError(e.to_string()))
133    }
134
135    fn load_tensor(&self, path: &Path) -> Result<MLTensor> {
136        let file = File::open(path).map_err(IoError::Io)?;
137        let tensor_data: serde_json::Value = serde_json::from_reader(file)
138            .map_err(|e| IoError::SerializationError(e.to_string()))?;
139
140        if let Some(tensor) = tensor_data.get("tensor") {
141            let shape: Vec<usize> = tensor
142                .get("tensorshape")
143                .and_then(|ts| ts.get("dim"))
144                .and_then(|dims| dims.as_array())
145                .map(|dims| {
146                    dims.iter()
147                        .filter_map(|d| d.get("size").and_then(|s| s.as_u64().map(|u| u as usize)))
148                        .collect()
149                })
150                .unwrap_or_default();
151
152            // Simplified: decode tensor_content as float array
153            let content = tensor.get("tensor_content").and_then(|c| c.as_array());
154            let data: Vec<f32> = if let Some(content_array) = content {
155                content_array
156                    .iter()
157                    .filter_map(|v| v.as_f64().map(|f| f as f32))
158                    .collect()
159            } else {
160                vec![0.0; shape.iter().product()]
161            };
162
163            let array = ArrayD::from_shape_vec(IxDyn(&shape), data)
164                .map_err(|e| IoError::Other(e.to_string()))?;
165
166            return Ok(MLTensor::new(array, None));
167        }
168
169        Err(IoError::Other(
170            "Invalid TensorFlow tensor format".to_string(),
171        ))
172    }
173}
scirs2_io/ml_framework/converters/tensorflow.rs

scirs2_io/ml_framework/converters/
tensorflow.rs
