Row 37480

Row ID: 37480 | Dataset Entry | Axioma AXP Content Repository

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I do not know if this is at all helpful, but here is what Gemini 1.5 Pro says. Let me know if it helps:

Let's troubleshoot this ONNX conversion and inference issue. Here's a breakdown of the problem and a revised approach:

**The Issue**

The error you're encountering, "Got invalid dimensions for input: input.1...", means there's a mismatch between the output shape of the `BACKBONE` ONNX model and the input shape expected by the `QUALITY` ONNX model. Let's dissect why and how to fix it.

**Debugging & Conversion Improvements**

1. **Shape Mismatch Analysis:** The key is understanding the transformations your original PyTorch model performed. The `QUALITY` model seems to expect an input of shape (1, 512 * 7 * 7), but the `BACKBONE` model's output might be different after conversion to ONNX.

2. **Reshape with Dynamic Axes in ONNX:** ONNX provides a way to handle this using dynamic axes during export. This lets you specify that certain dimensions can change during inference.

3. **Updated Export with Dynamic Axes:** Here's the improved `torch.onnx.export` call for your `BACKBONE` model:

```python # Define dynamic axes for the input and output of the BACKBONE model dynamic_axes = { 'input': {0: 'batch_size'}, # Batch size can change 'output': {0: 'batch_size'} # Batch size can change } torch.onnx.export(BACKBONE, dummy_input, 'backbone.onnx', opset_version=11, dynamic_axes=dynamic_axes) ```

4. **Inference Code:** Adapt your inference code accordingly:

```python # Load the ONNX models backbone_session = onnxruntime.InferenceSession('backbone.onnx') quality_session = onnxruntime.InferenceSession('quality.onnx')

# ... (rest of your code to get the face image)

# Inference with the backbone model backbone_output = backbone_session.run(None, {'input': np.expand_dims(face_image, axis=0)}) # Wrap in an array to match dynamic batch size

# Reshape the backbone output to match the expected input of the quality model # (You might not need this if your export is now correct) backbone_output = backbone_output[0].reshape(1, -1) # Reshape to (1, 512*7*7)

# Inference with the quality model quality_output = quality_session.run(None, {'input.1': backbone_output}) ```

**Key Points**

* **Opset Version:** While not the root cause here, I've kept `opset_version=11` as a best practice. Newer versions may offer additional operators or optimizations. * **Data Types:** Double-check that your `face_image` is of the correct data type (e.g., float32) and normalized to the range expected by your model. * **Debugging Tools:** Use ONNX tools like `onnx.checker.check_model('backbone.onnx')` to verify the model structure and use `print(backbone_output.shape)` in your inference code to examine the shape of the backbone's output.

**Complete Example:**

```python # ... (your existing imports and model loading) ...

# Export with dynamic axes (as shown above)

# Inference backbone_session = onnxruntime.InferenceSession('backbone.onnx') quality_session = onnxruntime.InferenceSession('quality.onnx')

# ... (rest of your code to get the face image)

# Inference with backbone model backbone_output = backbone_session.run(None, {'input': np.expand_dims(face_image, axis=0)})

# Reshape if necessary # backbone_output = backbone_output[0].reshape(1, -1)

# Inference with quality model quality_output = quality_session.run(None, {'input.1': backbone_output}) ```

FieldValue
text I do not know if this is at all helpful, but here is what Gemini 1.5 Pro says. Let me know if it helps: Let's troubleshoot this ONNX conversion and inference issue. Here's a breakdown of the problem and a revised approach: **The Issue** The error you're encountering, "Got invalid dimensions for input: input.1...", means there's a mismatch between the output shape of the `BACKBONE` ONNX model and the input shape expected by the `QUALITY` ONNX model. Let's dissect why and how to fix it. **Deb…
label r/pytorch
dataType comment
communityName r/pytorch
datetime 2024-05-21
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Raw Record

{
  "text": "I do not know if this is at all helpful, but here is what Gemini 1.5 Pro says. Let me know if it helps: \n\nLet's troubleshoot this ONNX conversion and inference issue. Here's a breakdown of the problem and a revised approach:\n\n**The Issue**\n\nThe error you're encountering, \"Got invalid dimensions for input: input.1...\", means there's a mismatch between the output shape of the `BACKBONE` ONNX model and the input shape expected by the `QUALITY` ONNX model. Let's dissect why and how to fix it.\n\n**Debugging & Conversion Improvements**\n\n1. **Shape Mismatch Analysis:** The key is understanding the transformations your original PyTorch model performed. The `QUALITY` model seems to expect an input of shape (1, 512 * 7 * 7), but the `BACKBONE` model's output might be different after conversion to ONNX.\n\n2. **Reshape with Dynamic Axes in ONNX:** ONNX provides a way to handle this using dynamic axes during export. This lets you specify that certain dimensions can change during inference.  \n\n3. **Updated Export with Dynamic Axes:** Here's the improved `torch.onnx.export` call for your `BACKBONE` model:\n\n```python\n# Define dynamic axes for the input and output of the BACKBONE model\ndynamic_axes = {\n    'input': {0: 'batch_size'},  # Batch size can change\n    'output': {0: 'batch_size'}  # Batch size can change\n}\ntorch.onnx.export(BACKBONE, dummy_input, 'backbone.onnx', opset_version=11, dynamic_axes=dynamic_axes)\n```\n\n4. **Inference Code:** Adapt your inference code accordingly:\n\n```python\n# Load the ONNX models\nbackbone_session = onnxruntime.InferenceSession('backbone.onnx')\nquality_session = onnxruntime.InferenceSession('quality.onnx')\n\n# ... (rest of your code to get the face image)\n\n# Inference with the backbone model\nbackbone_output = backbone_session.run(None, {'input': np.expand_dims(face_image, axis=0)})  # Wrap in an array to match dynamic batch size\n\n# Reshape the backbone output to match the expected input of the quality model\n# (You might not need this if your export is now correct)\nbackbone_output = backbone_output[0].reshape(1, -1)  # Reshape to (1, 512*7*7)\n\n# Inference with the quality model\nquality_output = quality_session.run(None, {'input.1': backbone_output})\n```\n\n\n\n**Key Points**\n\n* **Opset Version:** While not the root cause here, I've kept `opset_version=11` as a best practice. Newer versions may offer additional operators or optimizations.\n* **Data Types:** Double-check that your `face_image` is of the correct data type (e.g., float32) and normalized to the range expected by your model.\n* **Debugging Tools:**  Use ONNX tools like `onnx.checker.check_model('backbone.onnx')` to verify the model structure and use `print(backbone_output.shape)` in your inference code to examine the shape of the backbone's output.\n\n\n\n**Complete Example:**\n\n```python\n# ... (your existing imports and model loading) ...\n\n# Export with dynamic axes (as shown above)\n\n# Inference\nbackbone_session = onnxruntime.InferenceSession('backbone.onnx')\nquality_session = onnxruntime.InferenceSession('quality.onnx')\n\n# ... (rest of your code to get the face image)\n\n# Inference with backbone model\nbackbone_output = backbone_session.run(None, {'input': np.expand_dims(face_image, axis=0)})  \n\n# Reshape if necessary\n# backbone_output = backbone_output[0].reshape(1, -1)\n\n# Inference with quality model\nquality_output = quality_session.run(None, {'input.1': backbone_output})\n```",
  "label": "r/pytorch",
  "dataType": "comment",
  "communityName": "r/pytorch",
  "datetime": "2024-05-21",
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}
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