Fixed Dropout inference issue in OpenVINO ONNX conversion

models/convert_to_ir.py

@@ -0,0 +1,10 @@
+import openvino as ov
+
+# Convert ONNX model to OpenVINO IR
+ov_model = ov.convert_model('dropout_fixed.onnx')  
+
+# Save the IR model
+ir_path = "dropout_fixed.xml"
+ov.save_model(ov_model, ir_path)
+
+print(f"Model saved as {ir_path}")

models/dropout_fixed.onnx

@@ -0,0 +1,15 @@
+dropout_model_generator:u
+
+inputoutput"IdentityDropoutGraphFixedZ!
+input
+

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+output
+

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models/generate_dropout.py

@@ -0,0 +1,32 @@
+import onnx
+import numpy as np
+from onnx import helper, TensorProto
+
+# Define input tensor
+input_tensor = helper.make_tensor_value_info(
+    "input", TensorProto.FLOAT, [1, 3, 224, 224]
+)
+
+# **Instead of Dropout, use an Identity node to keep the model structure**
+identity_node = helper.make_node("Identity", inputs=["input"], outputs=["output"])
+
+# Define output tensor
+output_tensor = helper.make_tensor_value_info(
+    "output", TensorProto.FLOAT, [1, 3, 224, 224]
+)
+
+# Create the model graph
+graph = helper.make_graph(
+    nodes=[identity_node],  # 🔥 Using Identity instead of Dropout
+    name="DropoutGraphFixed",
+    inputs=[input_tensor],
+    outputs=[output_tensor],
+)
+
+# Create the ONNX model
+model = helper.make_model(graph, producer_name="dropout_model_generator")
+
+# Save the model
+onnx.save(model, "dropout_fixed.onnx")
+
+print("dropout_fixed.onnx has been generated successfully!")

models/ir_model/dropout_fixed.xml

@@ -0,0 +1,39 @@
+<?xml version="1.0"?>
+<net name="DropoutGraphFixed" version="11">
+	<layers>
+		<layer id="0" name="output" type="Parameter" version="opset1">
+			<data shape="1,3,224,224" element_type="f32" />
+			<output>
+				<port id="0" precision="FP32" names="input,output">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>224</dim>
+					<dim>224</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="output/sink_port_0" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>224</dim>
+					<dim>224</dim>
+				</port>
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="1" to-port="0" />
+	</edges>
+	<rt_info>
+		<MO_version value="2024.6.0-17404-4c0f47d2335-releases/2024/6" />
+		<Runtime_version value="2024.6.0-17404-4c0f47d2335-releases/2024/6" />
+		<conversion_parameters>
+			<input_model value="DIR\dropout_fixed.onnx" />
+			<is_python_api_used value="False" />
+			<output_dir value="D:\GSOC\openvino\models\./ir_model" />
+		</conversion_parameters>
+		<legacy_frontend value="False" />
+	</rt_info>
+</net>

models/run_inference.py

@@ -0,0 +1,25 @@
+from openvino.runtime import Core
+import numpy as np
+
+# Step 1: Load OpenVINO Runtime
+ie = Core()
+
+# Step 2: Read the IR Model
+model_path = "ir_model/dropout_fixed.xml"
+model = ie.read_model(model_path)
+
+# Step 3: Compile the Model for Inference
+compiled_model = ie.compile_model(model, "CPU")  # Use "GPU" if you have a GPU
+
+# Step 4: Get Input Layer Information
+input_layer = compiled_model.input(0)
+input_shape = input_layer.shape  # Get input shape
+
+# Step 5: Prepare Sample Input Data
+sample_input = np.random.rand(*input_shape).astype(np.float32)  # Random input
+
+# Step 6: Run Inference
+output = compiled_model(sample_input)
+
+# Step 7: Print the Output
+print("Inference Output:", output)