Pusing model to hugging face

Naveen_Kumar1: <p>3]</p>
<p>0s</p>
<h1><a name="p-470973-assuming-the-previous-cell-with-googlecolabfilesupload-has-run-successfully-1" class="anchor" href="#p-470973-assuming-the-previous-cell-with-googlecolabfilesupload-has-run-successfully-1"></a>Assuming the previous cell with google.colab.files.upload() has run successfully</h1>
<h1><a name="p-470973-and-the-uploaded-dictionary-contains-your-model-file-data-2" class="anchor" href="#p-470973-and-the-uploaded-dictionary-contains-your-model-file-data-2"></a>and the ‘uploaded’ dictionary contains your model file data.</h1>
<h1><a name="p-470973-get-the-filename-from-the-uploaded-dictionary-assuming-only-one-file-was-uploaded-3" class="anchor" href="#p-470973-get-the-filename-from-the-uploaded-dictionary-assuming-only-one-file-was-uploaded-3"></a>Get the filename from the uploaded dictionary. Assuming only one file was uploaded.</h1>
<p>if uploaded:</p>
<p>uploaded_filename = list(uploaded.keys())[0]</p>
<p>print(f"Uploaded file: {uploaded_filename}")</p>
<p>else:</p>
<h1><a name="p-470973-this-should-ideally-not-happen-if-the-previous-cell-completed-without-error-4" class="anchor" href="#p-470973-this-should-ideally-not-happen-if-the-previous-cell-completed-without-error-4"></a>This should ideally not happen if the previous cell completed without error</h1>
<p>raise FileNotFoundError(“No file was uploaded. Please upload your trained YOLOv5 model.”)</p>
<h1><a name="p-470973-construct-the-full-path-to-the-uploaded-file-in-content-5" class="anchor" href="#p-470973-construct-the-full-path-to-the-uploaded-file-in-content-5"></a>Construct the full path to the uploaded file in /content/</h1>
<h1><a name="p-470973-colab-uploads-files-to-the-content-directory-by-default-6" class="anchor" href="#p-470973-colab-uploads-files-to-the-content-directory-by-default-6"></a>Colab uploads files to the /content/ directory by default.</h1>
<p>uploaded_filepath = f"/content/{uploaded_filename}"</p>
<h1><a name="p-470973-verify-that-the-uploaded-file-exists-at-the-expected-path-and-is-not-just-a-placeholder-7" class="anchor" href="#p-470973-verify-that-the-uploaded-file-exists-at-the-expected-path-and-is-not-just-a-placeholder-7"></a>Verify that the uploaded file exists at the expected path and is not just a placeholder</h1>
<h1><a name="p-470973-you-might-want-to-add-a-check-for-file-size-or-content-type-if-possible-8" class="anchor" href="#p-470973-you-might-want-to-add-a-check-for-file-size-or-content-type-if-possible-8"></a>You might want to add a check for file size or content type if possible</h1>
<p>import os</p>
<p>if not os.path.exists(uploaded_filepath):</p>
<h1><a name="p-470973-if-the-file-isnt-at-the-expected-path-list-files-in-content-to-debug-9" class="anchor" href="#p-470973-if-the-file-isnt-at-the-expected-path-list-files-in-content-to-debug-9"></a>If the file isn’t at the expected path, list files in /content/ to debug</h1>
<p>print(f"Error: Uploaded file not found at {uploaded_filepath}. Listing files in /content/:")</p>
<p>!ls -lh /content/</p>
<p>raise FileNotFoundError(f"Uploaded file not found at {uploaded_filepath}. Please check the upload process.")</p>
<p>else:</p>
<h1><a name="p-470973-optional-print-file-size-to-confirm-its-not-just-a-small-placeholder-10" class="anchor" href="#p-470973-optional-print-file-size-to-confirm-its-not-just-a-small-placeholder-10"></a>Optional: Print file size to confirm it’s not just a small placeholder</h1>
<p>print(f"Found uploaded file at: {uploaded_filepath} (Size: {os.path.getsize(uploaded_filepath)} bytes)")</p>
<h1><a name="p-470973-h-11" class="anchor" href="#p-470973-h-11"></a>%%</h1>
<h1><a name="p-470973-now-load-the-yolo-model-using-the-correct-filepath-12" class="anchor" href="#p-470973-now-load-the-yolo-model-using-the-correct-filepath-12"></a>Now, load the YOLO model using the correct filepath</h1>
<h1><a name="p-470973-make-sure-you-are-in-the-directory-where-ultralytics-is-installed-or-import-yolo-correctly-13" class="anchor" href="#p-470973-make-sure-you-are-in-the-directory-where-ultralytics-is-installed-or-import-yolo-correctly-13"></a>Make sure you are in the directory where ultralytics is installed or import YOLO correctly.</h1>
<h1><a name="p-470973-assuming-you-have-already-cloned-and-installed-ultralytics-and-are-in-the-correct-directory-14" class="anchor" href="#p-470973-assuming-you-have-already-cloned-and-installed-ultralytics-and-are-in-the-correct-directory-14"></a>Assuming you have already cloned and installed ultralytics and are in the correct directory,</h1>
<h1><a name="p-470973-eg-if-you-cloned-to-contentultralytics-and-did-cd-contentultralytics-15" class="anchor" href="#p-470973-eg-if-you-cloned-to-contentultralytics-and-did-cd-contentultralytics-15"></a>e.g., if you cloned to /content/ultralytics and did %cd /content/ultralytics</h1>
<h1><a name="p-470973-if-you-are-running-this-cell-after-cloning-and-changing-directory-to-contentultralytics-16" class="anchor" href="#p-470973-if-you-are-running-this-cell-after-cloning-and-changing-directory-to-contentultralytics-16"></a>If you are running this cell after cloning and changing directory to /content/ultralytics</h1>
<h1><a name="p-470973-and-you-want-to-load-a-model-from-content-use-the-full-path-17" class="anchor" href="#p-470973-and-you-want-to-load-a-model-from-content-use-the-full-path-17"></a>and you want to load a model from /content/, use the full path.</h1>
<h1><a name="p-470973-assuming-you-want-to-load-a-yolov8-model-as-per-the-original-code-attempt-18" class="anchor" href="#p-470973-assuming-you-want-to-load-a-yolov8-model-as-per-the-original-code-attempt-18"></a>Assuming you want to load a YOLOv8 model (as per the original code attempt)</h1>
<p>from ultralytics import YOLO</p>
<p>from huggingface_hub import HfApi, upload_file # Import necessary modules for upload</p>
<p>import _pickle # Import _pickle to catch the specific error</p>
<h1><a name="p-470973-assuming-model_name-is-defined-in-a-previous-cell-19" class="anchor" href="#p-470973-assuming-model_name-is-defined-in-a-previous-cell-19"></a>Assuming MODEL_NAME is defined in a previous cell</h1>
<h1><a name="p-470973-model_name-yolov8n-onnx-export-20" class="anchor" href="#p-470973-model_name-yolov8n-onnx-export-20"></a>MODEL_NAME = “Yolov8n-onnx-export”</h1>
<h1><a name="p-470973-define-hugging-face-repo-details-21" class="anchor" href="#p-470973-define-hugging-face-repo-details-21"></a>Define Hugging Face repo details</h1>
<p>api = HfApi()</p>
<p>hf_username = api.whoami()[“name”]</p>
<p>repo_id = f"{hf_username}/{MODEL_NAME}"</p>
<h1><a name="p-470973-attempt-to-load-and-export-the-model-22" class="anchor" href="#p-470973-attempt-to-load-and-export-the-model-22"></a>Attempt to load and export the model</h1>
<p>print(f"Attempting to load model from: {uploaded_filepath}")</p>
<p>try:</p>
<p>model = YOLO(uploaded_filepath)</p>
<p>print(“Model loaded successfully.”)</p>
<h1><a name="p-470973-export-the-model-to-onnx-format-23" class="anchor" href="#p-470973-export-the-model-to-onnx-format-23"></a>Export the model to ONNX format</h1>
<h1><a name="p-470973-modelexport-returns-the-path-to-the-exported-file-24" class="anchor" href="#p-470973-modelexport-returns-the-path-to-the-exported-file-24"></a>model.export() returns the path to the exported file</h1>
<p>results = model.export(format=‘onnx’)</p>
<p>print(“Model exported to ONNX successfully.”)</p>
<p>print(f"Exported file: {results}")</p>
<h1><a name="p-470973-assuming-the-export-was-successful-and-results-contains-the-onnx-file-path-25" class="anchor" href="#p-470973-assuming-the-export-was-successful-and-results-contains-the-onnx-file-path-25"></a>Assuming the export was successful and ‘results’ contains the ONNX file path</h1>
<p>onnx_filepath_after_export = results</p>
<h1><a name="p-470973-now-use-onnx_filepath_after_export-for-uploading-to-hugging-face-26" class="anchor" href="#p-470973-now-use-onnx_filepath_after_export-for-uploading-to-hugging-face-26"></a>Now use onnx_filepath_after_export for uploading to Hugging Face</h1>
<p>print(f"Attempting to upload ONNX file: {onnx_filepath_after_export}")</p>
<p>if os.path.exists(onnx_filepath_after_export):</p>
<p>upload_file(</p>
<p>path_or_fileobj=onnx_filepath_after_export,</p>
<p>path_in_repo=f"{MODEL_NAME}.onnx", # Upload with the model name as filename in repo</p>
<p>repo_id=repo_id,</p>
<p>repo_type=“model”</p>
<p>)</p>
<p>print(f"Successfully uploaded {onnx_filepath_after_export} to {repo_id}/{MODEL_NAME}.onnx")</p>
<p>else:</p>
<h1><a name="p-470973-this-else-block-should-ideally-not-be-reached-if-export-was-successful-27" class="anchor" href="#p-470973-this-else-block-should-ideally-not-be-reached-if-export-was-successful-27"></a>This else block should ideally not be reached if export was successful</h1>
<p>print(f"Error: ONNX file not found after export at {onnx_filepath_after_export}.")</p>
<p>print(“Please check the export process.”)</p>
<h1><a name="p-470973-catch-the-specific-unpickling-error-28" class="anchor" href="#p-470973-catch-the-specific-unpickling-error-28"></a>Catch the specific unpickling error</h1>
<p>except _pickle.UnpicklingError as e:</p>
<p>print(f"Error loading model: {e}")</p>
<p>print(“-” * 50)</p>
<p>print(“This error usually means the uploaded model file is corrupted or not a valid PyTorch model (.pt).”)</p>
<p>print(“Please ensure you uploaded the correct, uncorrupted .pt file.”)</p>
<p>print(“You may need to re-download the model from its source or re-upload it.”)</p>
<p>print(“-” * 50)</p>
<p>import traceback # Import traceback to print detailed error info</p>
<p>traceback.print_exc() # Print the full traceback</p>
<h1><a name="p-470973-catch-any-other-exceptions-during-loading-or-exporting-29" class="anchor" href="#p-470973-catch-any-other-exceptions-during-loading-or-exporting-29"></a>Catch any other exceptions during loading or exporting</h1>
<p>except Exception as e:</p>
<p>print(f"An unexpected error occurred during model loading or exporting: {e}")</p>
<p>import traceback # Import traceback to print detailed error info</p>
<p>traceback.print_exc() # Print the full traceback</p>
<h1><a name="p-470973-h-30" class="anchor" href="#p-470973-h-30"></a>%%</h1>
<h1><a name="p-470973-continue-with-the-rest-of-your-notebook-if-any-cells-follow-31" class="anchor" href="#p-470973-continue-with-the-rest-of-your-notebook-if-any-cells-follow-31"></a>Continue with the rest of your notebook if any cells follow</h1>
<p>Uploaded file: Yolov8n train.pt Found uploaded file at: /content/Yolov8n train.pt (Size: 844 bytes) Attempting to load model from: /content/Yolov8n train.pt Error loading model: unpickling stack underflow -------------------------------------------------- This error usually means the uploaded model file is corrupted or not a valid PyTorch model (.pt). Please ensure you uploaded the correct, uncorrupted .pt file. You may need to re-download the model from its source or re-upload it. --------------------------------------------------</p>
<p>Traceback (most recent call last): File “”, line 55, in &lt;cell line: 0&gt; model = YOLO(uploaded_filepath) ^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.11/dist-packages/ultralytics/models/yolo/model.py”, line 79, in <strong>init</strong> super().<strong>init</strong>(model=model, task=task, verbose=verbose) File “/usr/local/lib/python3.11/dist-packages/ultralytics/engine/model.py”, line 151, in <strong>init</strong> self._load(model, task=task) File “/usr/local/lib/python3.11/dist-packages/ultralytics/engine/model.py”, line 295, in _load self.model, self.ckpt = attempt_load_one_weight(weights) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.11/dist-packages/ultralytics/nn/tasks.py”, line 1540, in attempt_load_one_weight ckpt, weight = torch_safe_load(weight) # load ckpt ^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.11/dist-packages/ultralytics/nn/tasks.py”, line 1444, in torch_safe_load ckpt = torch.load(file, map_location=“cpu”) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.11/dist-packages/ultralytics/utils/patches.py”, line 117, in torch_load return _torch_load(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.11/dist-packages/torch/serialization.py”, line 1495, in load return _legacy_load( ^^^^^^^^^^^^^ File “/usr/local/lib/python3.11/dist-packages/torch/serialization.py”, line 1744, in _legacy_load magic_number = pickle_module.load(f, **pickle_load_args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ _pickle.UnpicklingError: unpickling stack underflow</p>

ptrblck: <p>It seems you just posted a notebook so could you describe what your question is?</p>