Merge branch 'chore/dual-license'

Author: pixelspark

LICENSE-APACHE

@@ -0,0 +1,13 @@
+Copyright (c) 2021 Xavier Tao, Tommy van der Vorst & WONNX contributors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

LICENSE renamed to LICENSE-MIT

@@ -434,3 +434,27 @@ supported in the following cases:
 Constant folding is performed as part of shape inference, unless disabled (from the CLI pass `--no-fold-constants` to disable). This
 is done in order to support models that dynamically calculate shapes using operators such as `Shape`/`Squeeze`/`Unsqueeze` depending
 on dynamically set dimension parameters (e.g. batch size).
+
+## License
+
+Licensed under either of
+ * Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
+ * MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)
+at your option.
+
+Except for the following files:
+
+* `data/models`: 
+  * `mobilenetv2-7.onnx`: [source](https://github.com/onnx/models/blob/main/vision/classification/mobilenet/model/mobilenetv2-7.onnx), Apache-2.0 license only.
+  * `squeezenet-labels.txt`: [source](https://github.com/onnx/models/blob/main/vision/classification/synset.txt), Apache-2.0 license only.
+
+* `data/images`:
+  * `pelican.jpeg`: [source](https://en.wikipedia.org/wiki/Pelican#/media/File:Pelikan_Walvis_Bay.jpg), (C) Rui Ornelas, [CC-BY 2.0](https://creativecommons.org/licenses/by/2.0/).
+  * `bald_eagle.jpeg`: [source](https://en.wikipedia.org/wiki/Bald_eagle#/media/File:Bald-Eagle-9114-cropped.jpg), (C) David R. Tribble, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+
+### Contribution
+
+Unless you explicitly state otherwise, any contribution intentionally submitted
+for inclusion in the work by you shall be dual licensed as above, without any
+additional terms or conditions.

README.md

@@ -1,4 +1,4 @@
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: MIT OR Apache-2.0
 
 import itertools
 import os

data/images/bald_eagle.jpeg

@@ -1,4 +1,4 @@
-# SPDX-License-Identifier: Apache-2.0
+# SPDX-License-Identifier: MIT OR Apache-2.0
 import onnx.backend.test
 
 pytest_plugins = ("onnx.backend.test.report",)

data/images/italian_greyhound.jpeg

@@ -83,11 +83,6 @@ lazy_static! {
             include_str!("../templates/matrix/transpose.wgsl"),
         )
         .unwrap();
-        tera.add_raw_template(
-            "matrix/lrn.wgsl",
-            include_str!("../templates/matrix/lrn.wgsl"),
-        )
-        .unwrap();
         tera.add_raw_template(
             "pool/aggregate.wgsl",
             include_str!("../templates/pool/aggregate.wgsl"),
@@ -1326,38 +1321,6 @@ pub fn compile(
                 threads: (ceil(output_lengths[0], 256) as _, 1, 1),
             }
         }
-        "LRN" => {
-            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#lrn
-            let alpha = node.get_attribute_value("alpha", Some(0.0001))?;
-            let beta = node.get_attribute_value("beta", Some(0.75))?;
-            let bias = node.get_attribute_value("bias", Some(1.0))?;
-            let size = node.get_attribute_value("size", Some(1))?;
-
-            context.insert("alpha", &alpha);
-            context.insert("beta", &beta);
-            context.insert("bias", &bias);
-            context.insert("size", &size);
-
-            let left_size = f64::floor((size - 1) as f64 / 2.0) as u32;
-            let right_size = f64::ceil((size - 1) as f64 / 2.0) as u32;
-
-            context.insert("left_size", &left_size);
-            context.insert("right_size", &right_size);
-
-            let (x_threads, workgroup_size_x) = workgroup_size(
-                output_lengths[0],
-                MAX_COMPUTE_WORKGROUPS_PER_DIMENSION,
-                MAX_WORKGROUP_SIZE_X,
-            )?;
-            context.insert("workgroup_size_x", &workgroup_size_x);
-            context.insert("i_chunks", &input_chunks);
-
-            NodeTemplate {
-                scalar_type: agreed_type(input_shapes, output_shapes)?,
-                template: "matrix/lrn.wgsl",
-                threads: (x_threads, 1, 1),
-            }
-        }
         op => return Err(CompileError::UnimplementedOp(op.to_string())),
     };