leaf 0.2.1

Machine Learning Framework for Hackers
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216

extern crate leaf;
extern crate collenchyma as co;

#[cfg(test)]
mod layer_spec {
    use std::rc::Rc;
    use co::prelude::*;
    use leaf::layer::*;

    fn new_layer_config() -> LayerConfig {
        LayerConfig::new("foo", LayerType::Sigmoid)
    }

    fn native_backend() -> Rc<Backend<Native>> {
        Rc::new(Backend::<Native>::default().unwrap())
    }

    #[cfg(feature="cuda")]
    fn cuda_backend() -> Rc<Backend<Cuda>> {
        Rc::new(Backend::<Cuda>::default().unwrap())
    }

    #[cfg(all(feature="native", feature="cuda"))]
    mod native_cuda {
        use leaf::layer::*;
        use leaf::layers::*;
        use super::{native_backend, cuda_backend};

        #[test]
        fn create_layer_with_either() {
            let cfg = super::new_layer_config();
            Layer::from_config(native_backend(), &cfg);

            let cfg = super::new_layer_config();
            Layer::from_config(cuda_backend(), &cfg);
        }
    }

    #[cfg(feature="native")]
    mod native {
        use co::prelude::*;
        use leaf::layer::*;
        use leaf::layers::*;
        use super::native_backend;

        fn simple_network() -> LayerConfig {
            let mut net_cfg = SequentialConfig::default();
            net_cfg.add_input("data", &vec![1, 1, 28, 28]);
            net_cfg.add_layer(LayerConfig::new("linear", LayerType::Linear(LinearConfig { output_size: 10 })));

            LayerConfig::new("network", net_cfg)
        }

        #[test]
        fn save_and_load_layer() {
            let cfg = simple_network();
            let mut original_layer = Layer::from_config(native_backend(), &cfg);

            original_layer.save("target/testnetwork").unwrap();
            let mut loaded_layer = Layer::<Backend<Native>>::load(native_backend(), "target/testnetwork").unwrap();

            assert_eq!(original_layer.input_blob_names(), loaded_layer.input_blob_names());

            let original_weights = original_layer.learnable_weights_data();
            let original_weight_lock = original_weights[0].read().unwrap();
            let loaded_weights = loaded_layer.learnable_weights_data();
            let loaded_weight_lock = loaded_weights[0].read().unwrap();

            let original_weight = original_weight_lock.get(native_backend().device()).unwrap().as_native().unwrap().as_slice::<f32>();
            let loaded_weight = loaded_weight_lock.get(native_backend().device()).unwrap().as_native().unwrap().as_slice::<f32>();

            assert_eq!(original_weight, loaded_weight);
        }
    }

    #[cfg(feature="cuda")]
    mod cuda {
        use std::sync::{Arc, RwLock};
        use co::prelude::*;
        use leaf::layer::*;
        use leaf::layers::*;
        use leaf::util::write_to_memory;
        use super::{native_backend, cuda_backend};

        #[test]
        fn new_layer() {
            let cfg = super::new_layer_config();
            Layer::from_config(cuda_backend(), &cfg);
        }

        #[test]
        fn can_create_empty_sequential_layer() {
            let model = SequentialConfig::default();
            Layer::from_config(cuda_backend(), &LayerConfig::new("model", LayerType::Sequential(model)));
        }

        #[test]
        fn can_create_single_layer_sequential_layer() {
            let mut model = SequentialConfig::default();
            model.add_input("data", &vec![28, 28]);
            model.add_layer(LayerConfig::new("sigmoid", LayerType::Sigmoid));

            Layer::from_config(cuda_backend(), &LayerConfig::new("model", LayerType::Sequential(model)));
        }

        #[test]
        fn can_create_simple_network_sequential_layer() {
            let mut model = SequentialConfig::default();
            model.add_input("data", &vec![1, 784]);
            model.add_layer(LayerConfig::new("linear1", LinearConfig { output_size: 1568 }));
            model.add_layer(LayerConfig::new("sigmoid", LayerType::Sigmoid));
            model.add_layer(LayerConfig::new("linear2", LinearConfig { output_size: 10 }));

            let _ = Layer::from_config(cuda_backend(), &LayerConfig::new("model", LayerType::Sequential(model)));
        }

        #[test]
        fn reshape_does_not_affect_output() {
            let native_backend = native_backend();
            let cuda_backend = cuda_backend();

            let mut normal_model = SequentialConfig::default();
            normal_model.add_input("data", &vec![3]);
            normal_model.add_layer(LayerConfig::new("sigmoid", LayerType::Sigmoid));
            let mut normal_network = Layer::from_config(cuda_backend.clone(), &LayerConfig::new("normal_model", LayerType::Sequential(normal_model)));

            let mut reshape_model = SequentialConfig::default();
            reshape_model.add_input("data", &vec![3]);
            reshape_model.add_layer(LayerConfig::new("reshape", ReshapeConfig { shape: vec![1, 1, 3] }));
            reshape_model.add_layer(LayerConfig::new("sigmoid", LayerType::Sigmoid));
            let mut reshape_network = Layer::from_config(cuda_backend.clone(), &LayerConfig::new("reshape_model", LayerType::Sequential(reshape_model)));

            let input = vec![1f32, 1f32, 2f32];
            let mut normal_tensor = SharedTensor::<f32>::new(native_backend.device(), &(3)).unwrap();
            // let mut normal_tensor_output = SharedTensor::<f32>::new(native_backend.device(), &(3)).unwrap();
            let mut reshape_tensor = SharedTensor::<f32>::new(native_backend.device(), &(3)).unwrap();
            // let mut reshape_tensor_output = SharedTensor::<f32>::new(native_backend.device(), &(3)).unwrap();
            write_to_memory(normal_tensor.get_mut(native_backend.device()).unwrap(), &input);
            write_to_memory(reshape_tensor.get_mut(native_backend.device()).unwrap(), &input);

            let normal_tensor_output = normal_network.forward(&[Arc::new(RwLock::new(normal_tensor))])[0].clone();
            let _ = normal_tensor_output.write().unwrap().add_device(native_backend.device());
            normal_tensor_output.write().unwrap().sync(native_backend.device()).unwrap();
            let normal_tensor_output_native_ = normal_tensor_output.read().unwrap();
            let normal_tensor_output_native = normal_tensor_output_native_.get(native_backend.device()).unwrap().as_native().unwrap();
            assert_eq!(&[0.7310585786f32, 0.7310586f32, 0.880797f32], normal_tensor_output_native.as_slice::<f32>());

            let reshape_tensor_output = reshape_network.forward(&[Arc::new(RwLock::new(reshape_tensor))])[0].clone();
            let _ = reshape_tensor_output.write().unwrap().add_device(native_backend.device());
            reshape_tensor_output.write().unwrap().sync(native_backend.device()).unwrap();
            let reshape_tensor_output_native_ = reshape_tensor_output.read().unwrap();
            let reshape_tensor_output_native = reshape_tensor_output_native_.get(native_backend.device()).unwrap().as_native().unwrap();
            assert_eq!(&[0.7310585786f32, 0.7310586f32, 0.880797f32], reshape_tensor_output_native.as_slice::<f32>());
            assert_eq!(normal_tensor_output_native.as_slice::<f32>(), reshape_tensor_output_native.as_slice::<f32>());
        }
    }

    // #[test]
    // fn dim_check_strict() {
    //     let cfg = WeightConfig { share_mode: DimCheckMode::Strict, ..WeightConfig::default() };
    //     let blob_one = SharedTensor::<f32>::new(backend().device(), &vec![2, 3, 3]);
    //     let blob_two = SharedTensor::<f32>::new(backend().device(), &vec![3, 2, 3]);
    //     let param_name = "foo".to_owned();
    //     let owner_name = "owner".to_owned();
    //     let layer_name = "layer".to_owned();
    //
    //     assert!(cfg.check_dimensions(&blob_one,
    //                                  &blob_one,
    //                                  param_name.clone(),
    //                                  owner_name.clone(),
    //                                  layer_name.clone())
    //                .is_ok());
    //     assert!(cfg.check_dimensions(&blob_one,
    //                                  &blob_two,
    //                                  param_name.clone(),
    //                                  owner_name.clone(),
    //                                  layer_name.clone())
    //                .is_err());
    // }

    // #[test]
    // fn dim_check_permissive() {
    //     let cfg = WeightConfig { share_mode: DimCheckMode::Permissive, ..WeightConfig::default() };
    //     let blob_one = SharedTensor::<f32>::new(backend().device(), &vec![2, 3, 3]);
    //     let blob_two = SharedTensor::<f32>::new(backend().device(), &vec![3, 2, 3]);
    //     let blob_three = SharedTensor::<f32>::new(backend().device(), &vec![3, 10, 3]);
    //     let param_name = "foo".to_owned();
    //     let owner_name = "owner".to_owned();
    //     let layer_name = "layer".to_owned();
    //
    //     assert!(cfg.check_dimensions(&blob_one,
    //                                  &blob_one,
    //                                  param_name.clone(),
    //                                  owner_name.clone(),
    //                                  layer_name.clone())
    //                .is_ok());
    //     assert!(cfg.check_dimensions(&blob_one,
    //                                  &blob_two,
    //                                  param_name.clone(),
    //                                  owner_name.clone(),
    //                                  layer_name.clone())
    //                .is_ok());
    //     assert!(cfg.check_dimensions(&blob_one,
    //                                  &blob_three,
    //                                  param_name.clone(),
    //                                  owner_name.clone(),
    //                                  layer_name.clone())
    //                .is_err());
    //     assert!(cfg.check_dimensions(&blob_two,
    //                                  &blob_three,
    //                                  param_name.clone(),
    //                                  owner_name.clone(),
    //                                  layer_name.clone())
    //                .is_err());
    // }
}