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README.md

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+---
+pipeline_tag: text-to-image
+inference: false
+---
+# Model summary
+This Segment Anything Model has been optimized to work with WebNN. This model is licensed under the [Apache-2.0](https://github.com/facebookresearch/segment-anything?tab=Apache-2.0-1-ov-file#readme) License. For terms of use, please visit the [Code of Conduct](https://github.com/facebookresearch/segment-anything/blob/main/CODE_OF_CONDUCT.md). If you comply with the license and terms of use, you have the rights described therin. By using this Model, you accept the terms.
+
+Segment-Anything-WebNN is meant to be used with the corresponding sample [here](https://microsoft.github.io/webnn-developer-preview/).
+
+# Model changes
+Segment-Anything-Model-WebNN is an ONNX version of the Segment Anything Model, and is optimized for WebNN by using static input shapes and eliminates operators that are not in use.
+
+Please find the original Segment Anything Model [here](https://github.com/facebookresearch/segment-anything).

optimize_sam_decoder.py

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+import onnx
+from onnx import helper, TensorProto
+import numpy as np
+
+# Optimize the SAM decoder model to accept dynamic original image size input
+# to enable constant folding with freeDimensionOverride option in onnxruntime.
+def optimize_sa_model(model_path: str, out_path: str, is_fp16: bool = False):
+    model = onnx.load(model_path)
+
+    graph = model.graph
+
+    old_input_name="orig_im_size"
+    new_input_name="orig_im_size_shape"
+    
+    # 1) Find old input
+    old_inputs = {vi.name: vi for vi in graph.input}
+    assert old_input_name in old_inputs, f"Input {old_input_name} not found"
+    old_vi = old_inputs[old_input_name]
+
+    # 2) Remove old input and add new input
+    graph.input.remove(old_vi)
+    # new input with shape: [height, width]
+    new_input_vi = helper.make_tensor_value_info(new_input_name, TensorProto.FLOAT, ["height", "width"])
+    graph.input.extend([new_input_vi])
+
+    # Check if new_input_name exists in the graph inputs
+    if new_input_name not in [input.name for input in graph.input]:
+        raise ValueError(f"Input '{new_input_name}' does not exist in the graph inputs.")
+
+    # 3) Insert Shape node: Shape(X) -> shape_X(INT64 1D tensor [H, W])
+    shape_output_name = old_input_name # keep the same name as old input
+    shape_node = helper.make_node(
+        "Shape",
+        inputs=[new_input_name],
+        outputs=[shape_output_name],
+        name="shape_of_orig_im_size_shape"
+    )
+    # Insert the Shape node
+    graph.node.insert(0, shape_node)
+
+    # 4) The origin input dtype is not INT64, need to add Cast node
+    #    But the original model has already a Cast node after the input, ignore it
+
+    if is_fp16:
+        # For fp16 model, since CPU kernel doesn't support constant folding
+        # for fp16 data type, we need to convert some fp16 constants and input/output info to fp32
+        fp16_constants = ["/Constant_85", "/Constant_86"]
+        
+        # Convert fp16 constants in fp16_constants to fp32
+        for node in graph.node:
+            if node.op_type == "Constant" and node.name in fp16_constants:
+                print(node.name)
+                # Locate the "value" attribute of the Constant node
+                for attr in node.attribute:
+                    if attr.name == "value":
+                        # Extract the tensor value
+                        tensor = onnx.numpy_helper.to_array(attr.t)
+                        
+                        # Convert the tensor to the target data type
+                        new_tensor = tensor.astype(np.float32)
+                        
+                        # Create a new ONNX tensor with the updated data type
+                        attr.t.CopyFrom(onnx.numpy_helper.from_array(new_tensor))
+                        break
+                    else:
+                        raise ValueError(f"Constant node '{node.name}' does not have a 'value' attribute.")
+        
+        fp16_nodes = ["/ReduceMax", "/Reciprocal", "/Mul_19", "/Mul_20", "/Add_11", "/Floor"]
+        # Change fp16 nodes in fp16_nodes to fp32
+        for node in graph.node:
+            if node.name in fp16_nodes:
+                print(f"Processing node: {node.name}")
+                for input_name in node.input:
+                    for value_info in graph.value_info:
+                        if value_info.name == input_name:
+                            value_info.type.tensor_type.elem_type = TensorProto.FLOAT
+                            print(f" - Change input: {input_name} to fp32")
+
+                for output_name in node.output:
+                    for value_info in graph.value_info:
+                        if value_info.name == output_name:
+                            value_info.type.tensor_type.elem_type = TensorProto.FLOAT
+                            print(f" - Change output: {output_name} to fp32")
+
+        # Change /Cast_9 to fp32
+        for node in graph.node:
+            if node.name == "/Cast_9":
+                node.attribute[0].i = TensorProto.FLOAT
+                print(f"Changed /Cast_9 to fp32")
+                break
+    onnx.checker.check_model(model)
+    onnx.save(model, out_path)
+    print(f"Saved to {out_path}")
+
+# the original int8 decoder model: https://huggingface.co/schmuell/sam-b-fp16/blob/main/sam_vit_b-decoder-int8.onnx
+# optimize_sa_model("sam_vit_b-decoder-int8.onnx", "sam_vit_b-decoder-int8-orig-img-size-dynamic.onnx", False)
+# the original fp32 decoder model: https://huggingface.co/schmuell/sam-b-fp16/blob/main/sam_vit_b_01ec64.decoder.onnx
+optimize_sa_model("sam_vit_b_01ec64.decoder-fp16.onnx", "sam_vit_b_01ec64.decoder-orig-img-size-dynamic-fp16.onnx", True)