diff --git a/docs/nbdoc/consts.py b/docs/nbdoc/consts.py
index de6fc28aea1..6545e590fd0 100644
--- a/docs/nbdoc/consts.py
+++ b/docs/nbdoc/consts.py
@@ -6,7 +6,7 @@ repo_directory = "notebooks"
repo_owner = "openvinotoolkit"
repo_name = "openvino_notebooks"
repo_branch = "tree/main"
-artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20240506220807/dist/rst_files/"
+artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20240515220822/dist/rst_files/"
blacklisted_extensions = ['.xml', '.bin']
notebooks_repo = "https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/"
notebooks_binder = "https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath="
diff --git a/docs/notebooks/3D-pose-estimation-with-output.rst b/docs/notebooks/3D-pose-estimation-with-output.rst
index 69964ec3706..fda23f07b57 100644
--- a/docs/notebooks/3D-pose-estimation-with-output.rst
+++ b/docs/notebooks/3D-pose-estimation-with-output.rst
@@ -74,61 +74,61 @@ Lab instead.**
Using cached https://download.pytorch.org/whl/cpu/torch-2.3.0%2Bcpu-cp38-cp38-linux_x86_64.whl (190.4 MB)
Collecting onnx
Using cached onnx-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)
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Collecting ipydatawidgets>=1.1.1 (from pythreejs)
Using cached ipydatawidgets-4.3.5-py2.py3-none-any.whl.metadata (1.4 kB)
Collecting numpy (from pythreejs)
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Collecting openvino==2024.1.0 (from openvino-dev>=2024.0.0)
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Using cached openvino_telemetry-2024.1.0-py3-none-any.whl (23 kB)
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- Using cached fsspec-2024.3.1-py3-none-any.whl (171 kB)
+ Downloading fsspec-2024.5.0-py3-none-any.whl (316 kB)
+ [2K ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 316.1/316.1 kB 2.6 MB/s eta 0:00:00
Using cached traittypes-0.2.1-py2.py3-none-any.whl (8.6 kB)
Installing collected packages: openvino-telemetry, mpmath, traittypes, sympy, protobuf, numpy, networkx, fsspec, filelock, torch, openvino, opencv-python, onnx, openvino-dev, ipydatawidgets, pythreejs
- Successfully installed filelock-3.14.0 fsspec-2024.3.1 ipydatawidgets-4.3.5 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 onnx-1.16.0 opencv-python-4.9.0.80 openvino-2024.1.0 openvino-dev-2024.1.0 openvino-telemetry-2024.1.0 protobuf-5.26.1 pythreejs-2.4.2 sympy-1.12 torch-2.3.0+cpu traittypes-0.2.1
+ Successfully installed filelock-3.14.0 fsspec-2024.5.0 ipydatawidgets-4.3.5 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 onnx-1.16.0 opencv-python-4.9.0.80 openvino-2024.1.0 openvino-dev-2024.1.0 openvino-telemetry-2024.1.0 protobuf-5.26.1 pythreejs-2.4.2 sympy-1.12 torch-2.3.0+cpu traittypes-0.2.1
Note: you may need to restart the kernel to use updated packages.
@@ -251,18 +252,18 @@ IR format.
.. parsed-literal::
========== Converting human-pose-estimation-3d-0001 to ONNX
- Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
+ Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
ONNX check passed successfully.
========== Converting human-pose-estimation-3d-0001 to IR (FP32)
- Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
+ Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
- [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
- [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
+ [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
+ [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
diff --git a/docs/notebooks/3D-segmentation-point-clouds-with-output.rst b/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
index 55ac44430eb..6447ad00919 100644
--- a/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
+++ b/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
@@ -216,7 +216,7 @@ chair for example.
.. parsed-literal::
- /tmp/ipykernel_2840820/2434168836.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
+ /tmp/ipykernel_16799/2434168836.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label="chair")
@@ -317,7 +317,7 @@ select device from dropdown list for running inference using OpenVINO
.. parsed-literal::
- /tmp/ipykernel_2840820/2804603389.py:23: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
+ /tmp/ipykernel_16799/2804603389.py:23: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
ax.scatter(XCur, YCur, ZCur, s=5, cmap="jet", marker="o", label=classes[i])
diff --git a/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png b/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
index 05f5c2cc0e3..29fb7787324 100644
--- a/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
+++ b/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:d81ad2656ac29b01ccc926bde66d6d66d05d187c5bf95b2767317a6355ad86fb
-size 68213
+oid sha256:1f9f7a4ff050de5ac1c035ff13546573f526abbc3d4b1e157edb3a278caba746
+size 69060
diff --git a/docs/notebooks/all_notebooks_paths.txt b/docs/notebooks/all_notebooks_paths.txt
index a31daf0b9e4..ebe064c809a 100644
--- a/docs/notebooks/all_notebooks_paths.txt
+++ b/docs/notebooks/all_notebooks_paths.txt
@@ -21,6 +21,7 @@ notebooks/detectron2-to-openvino/detectron2-to-openvino.ipynb
notebooks/distilbert-sequence-classification/distilbert-sequence-classification.ipynb
notebooks/distil-whisper-asr/distil-whisper-asr.ipynb
notebooks/dolly-2-instruction-following/dolly-2-instruction-following.ipynb
+notebooks/dynamicrafter-animating-images/dynamicrafter-animating-images.ipynb
notebooks/efficient-sam/efficient-sam.ipynb
notebooks/encodec-audio-compression/encodec-audio-compression.ipynb
notebooks/fast-segment-anything/fast-segment-anything.ipynb
diff --git a/docs/notebooks/amused-lightweight-text-to-image-with-output.rst b/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
index c442118da73..d44c1c23128 100644
--- a/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
+++ b/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
@@ -78,7 +78,7 @@ Load and run the original pipeline
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
@@ -202,29 +202,29 @@ Convert the Text Encoder
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
warnings.warn(
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if input_shape[-1] > 1 or self.sliding_window is not None:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if past_key_values_length > 0:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:620: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:620: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
encoder_states = () if output_hidden_states else None
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:625: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:625: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if output_hidden_states:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:279: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:279: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:319: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:319: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:648: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:648: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if output_hidden_states:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:651: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:651: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if not return_dict:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:742: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:742: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if not return_dict:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:1227: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:1227: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if not return_dict:
@@ -333,13 +333,13 @@ suitable. This function repeats part of ``AmusedPipeline``.
.. parsed-literal::
- /tmp/ipykernel_2841622/3779428577.py:34: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /tmp/ipykernel_17572/3779428577.py:34: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
shape=shape.tolist(),
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if not force_not_quantize:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert hidden_states.shape[1] == self.channels
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if hidden_states.shape[0] >= 64:
@@ -477,7 +477,7 @@ And insert wrappers instances in the pipeline:
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
@@ -692,7 +692,7 @@ model.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1486: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1486: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
warnings.warn(
@@ -706,7 +706,7 @@ model.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
@@ -784,17 +784,17 @@ model.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
return Tensor(self.data * unwrap_tensor_data(other))
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
return Tensor(self.data * unwrap_tensor_data(other))
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
return Tensor(self.data * unwrap_tensor_data(other))
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
return Tensor(self.data * unwrap_tensor_data(other))
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
return Tensor(self.data * unwrap_tensor_data(other))
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
return Tensor(self.data * unwrap_tensor_data(other))
@@ -826,7 +826,7 @@ Demo generation with quantized pipeline
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
@@ -910,7 +910,7 @@ a rough estimate of generation quality.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: Metric `InceptionScore` will save all extracted features in buffer. For large datasets this may lead to large memory footprint.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: Metric `InceptionScore` will save all extracted features in buffer. For large datasets this may lead to large memory footprint.
warnings.warn(\*args, \*\*kwargs) # noqa: B028
@@ -922,9 +922,9 @@ a rough estimate of generation quality.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/image/inception.py:176: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1807.)
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/image/inception.py:176: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1807.)
return kl.mean(), kl.std()
@@ -941,7 +941,7 @@ a rough estimate of generation quality.
.. parsed-literal::
- Quantized pipeline Inception Score: 9.630990028381348
+ Quantized pipeline Inception Score: 9.630992889404297
Quantization speed-up: 2.10x
diff --git a/docs/notebooks/animate-anyone-with-output.rst b/docs/notebooks/animate-anyone-with-output.rst
index 0af941f8997..c07b1321d80 100644
--- a/docs/notebooks/animate-anyone-with-output.rst
+++ b/docs/notebooks/animate-anyone-with-output.rst
@@ -64,7 +64,7 @@ Table of contents:
- `Video post-processing <#video-post-processing>`__
- `Interactive inference <#interactive-inference>`__
-.. |image0| image:: animate-anyone-with-output_files/animate-anyone.gif
+.. |image0| image:: https://github.com/openvinotoolkit/openvino_notebooks/raw/latest/notebooks/animate-anyone/animate-anyone.gif
Prerequisites
-------------
@@ -153,11 +153,11 @@ Note that we clone a fork of original repo with tweaked forward methods.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
torch.utils._pytree._register_pytree_node(
@@ -284,7 +284,7 @@ Download weights
.. parsed-literal::
- README.md: 0%| | 0.00/6.84k [00:00`__.
-.. |image01| image:: https://humanaigc.github.io/animate-anyone/static/images/f2_img.png
+.. |image1| image:: https://humanaigc.github.io/animate-anyone/static/images/f2_img.png
.. code:: ipython3
@@ -839,7 +839,7 @@ required for both reference and denoising UNets.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
warnings.warn(
@@ -1210,7 +1210,7 @@ Video post-processing
.. raw:: html
diff --git a/docs/notebooks/animate-anyone-with-output_files/animate-anyone.gif b/docs/notebooks/animate-anyone-with-output_files/animate-anyone.gif
deleted file mode 100644
index 5cd205f4af3..00000000000
--- a/docs/notebooks/animate-anyone-with-output_files/animate-anyone.gif
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3d53c86b99c2ec0b3b3cfc9f9acc9e1a44643013b4cfdf7ba27231bb70150be7
-size 1456471
diff --git a/docs/notebooks/async-api-with-output.rst b/docs/notebooks/async-api-with-output.rst
new file mode 100644
index 00000000000..b7d53ad0cd1
--- /dev/null
+++ b/docs/notebooks/async-api-with-output.rst
@@ -0,0 +1,638 @@
+Asynchronous Inference with OpenVINO™
+=====================================
+
+This notebook demonstrates how to use the `Async
+API `__
+for asynchronous execution with OpenVINO.
+
+OpenVINO Runtime supports inference in either synchronous or
+asynchronous mode. The key advantage of the Async API is that when a
+device is busy with inference, the application can perform other tasks
+in parallel (for example, populating inputs or scheduling other
+requests) rather than wait for the current inference to complete first.
+
+Table of contents:
+^^^^^^^^^^^^^^^^^^
+
+- `Imports <#imports>`__
+- `Prepare model and data
+ processing <#prepare-model-and-data-processing>`__
+
+ - `Download test model <#download-test-model>`__
+ - `Load the model <#load-the-model>`__
+ - `Create functions for data
+ processing <#create-functions-for-data-processing>`__
+ - `Get the test video <#get-the-test-video>`__
+
+- `How to improve the throughput of video
+ processing <#how-to-improve-the-throughput-of-video-processing>`__
+
+ - `Sync Mode (default) <#sync-mode-default>`__
+ - `Test performance in Sync Mode <#test-performance-in-sync-mode>`__
+ - `Async Mode <#async-mode>`__
+ - `Test the performance in Async
+ Mode <#test-the-performance-in-async-mode>`__
+ - `Compare the performance <#compare-the-performance>`__
+
+- `AsyncInferQueue <#asyncinferqueue>`__
+
+ - `Setting Callback <#setting-callback>`__
+ - `Test the performance with
+ AsyncInferQueue <#test-the-performance-with-asyncinferqueue>`__
+
+Imports
+-------
+
+
+
+.. code:: ipython3
+
+ import platform
+
+ %pip install -q "openvino>=2023.1.0"
+ %pip install -q opencv-python
+ if platform.system() != "windows":
+ %pip install -q "matplotlib>=3.4"
+ else:
+ %pip install -q "matplotlib>=3.4,<3.7"
+
+
+.. parsed-literal::
+
+ Note: you may need to restart the kernel to use updated packages.
+ Note: you may need to restart the kernel to use updated packages.
+ Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+ import cv2
+ import time
+ import numpy as np
+ import openvino as ov
+ from IPython import display
+ import matplotlib.pyplot as plt
+
+ # Fetch the notebook utils script from the openvino_notebooks repo
+ import requests
+
+ r = requests.get(
+ url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+ )
+ open("notebook_utils.py", "w").write(r.text)
+
+ import notebook_utils as utils
+
+Prepare model and data processing
+---------------------------------
+
+
+
+Download test model
+~~~~~~~~~~~~~~~~~~~
+
+
+
+We use a pre-trained model from OpenVINO’s `Open Model
+Zoo `__
+to start the test. In this case, the model will be executed to detect
+the person in each frame of the video.
+
+.. code:: ipython3
+
+ # directory where model will be downloaded
+ base_model_dir = "model"
+
+ # model name as named in Open Model Zoo
+ model_name = "person-detection-0202"
+ precision = "FP16"
+ model_path = f"model/intel/{model_name}/{precision}/{model_name}.xml"
+ download_command = f"omz_downloader " f"--name {model_name} " f"--precision {precision} " f"--output_dir {base_model_dir} " f"--cache_dir {base_model_dir}"
+ ! $download_command
+
+
+.. parsed-literal::
+
+ ################|| Downloading person-detection-0202 ||################
+
+ ========== Downloading model/intel/person-detection-0202/FP16/person-detection-0202.xml
+
+
+ ========== Downloading model/intel/person-detection-0202/FP16/person-detection-0202.bin
+
+
+
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ import ipywidgets as widgets
+
+ core = ov.Core()
+ device = widgets.Dropdown(
+ options=core.available_devices + ["AUTO"],
+ value="CPU",
+ description="Device:",
+ disabled=False,
+ )
+
+ device
+
+
+
+
+.. parsed-literal::
+
+ Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
+
+
+
+Load the model
+~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ # initialize OpenVINO runtime
+ core = ov.Core()
+
+ # read the network and corresponding weights from file
+ model = core.read_model(model=model_path)
+
+ # compile the model for the CPU (you can choose manually CPU, GPU etc.)
+ # or let the engine choose the best available device (AUTO)
+ compiled_model = core.compile_model(model=model, device_name=device.value)
+
+ # get input node
+ input_layer_ir = model.input(0)
+ N, C, H, W = input_layer_ir.shape
+ shape = (H, W)
+
+Create functions for data processing
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ def preprocess(image):
+ """
+ Define the preprocess function for input data
+
+ :param: image: the orignal input frame
+ :returns:
+ resized_image: the image processed
+ """
+ resized_image = cv2.resize(image, shape)
+ resized_image = cv2.cvtColor(np.array(resized_image), cv2.COLOR_BGR2RGB)
+ resized_image = resized_image.transpose((2, 0, 1))
+ resized_image = np.expand_dims(resized_image, axis=0).astype(np.float32)
+ return resized_image
+
+
+ def postprocess(result, image, fps):
+ """
+ Define the postprocess function for output data
+
+ :param: result: the inference results
+ image: the orignal input frame
+ fps: average throughput calculated for each frame
+ :returns:
+ image: the image with bounding box and fps message
+ """
+ detections = result.reshape(-1, 7)
+ for i, detection in enumerate(detections):
+ _, image_id, confidence, xmin, ymin, xmax, ymax = detection
+ if confidence > 0.5:
+ xmin = int(max((xmin * image.shape[1]), 10))
+ ymin = int(max((ymin * image.shape[0]), 10))
+ xmax = int(min((xmax * image.shape[1]), image.shape[1] - 10))
+ ymax = int(min((ymax * image.shape[0]), image.shape[0] - 10))
+ cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+ cv2.putText(
+ image,
+ str(round(fps, 2)) + " fps",
+ (5, 20),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.7,
+ (0, 255, 0),
+ 3,
+ )
+ return image
+
+Get the test video
+~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ video_path = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/CEO%20Pat%20Gelsinger%20on%20Leading%20Intel.mp4"
+
+How to improve the throughput of video processing
+-------------------------------------------------
+
+
+
+Below, we compare the performance of the synchronous and async-based
+approaches:
+
+Sync Mode (default)
+~~~~~~~~~~~~~~~~~~~
+
+
+
+Let us see how video processing works with the default approach. Using
+the synchronous approach, the frame is captured with OpenCV and then
+immediately processed:
+
+.. figure:: https://user-images.githubusercontent.com/91237924/168452573-d354ea5b-7966-44e5-813d-f9053be4338a.png
+ :alt: drawing
+
+ drawing
+
+::
+
+ while(true) {
+ // capture frame
+ // populate CURRENT InferRequest
+ // Infer CURRENT InferRequest
+ //this call is synchronous
+ // display CURRENT result
+ }
+
+\``\`
+
+.. code:: ipython3
+
+ def sync_api(source, flip, fps, use_popup, skip_first_frames):
+ """
+ Define the main function for video processing in sync mode
+
+ :param: source: the video path or the ID of your webcam
+ :returns:
+ sync_fps: the inference throughput in sync mode
+ """
+ frame_number = 0
+ infer_request = compiled_model.create_infer_request()
+ player = None
+ try:
+ # Create a video player
+ player = utils.VideoPlayer(source, flip=flip, fps=fps, skip_first_frames=skip_first_frames)
+ # Start capturing
+ start_time = time.time()
+ player.start()
+ if use_popup:
+ title = "Press ESC to Exit"
+ cv2.namedWindow(title, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
+ while True:
+ frame = player.next()
+ if frame is None:
+ print("Source ended")
+ break
+ resized_frame = preprocess(frame)
+ infer_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))
+ # Start the inference request in synchronous mode
+ infer_request.infer()
+ res = infer_request.get_output_tensor(0).data
+ stop_time = time.time()
+ total_time = stop_time - start_time
+ frame_number = frame_number + 1
+ sync_fps = frame_number / total_time
+ frame = postprocess(res, frame, sync_fps)
+ # Display the results
+ if use_popup:
+ cv2.imshow(title, frame)
+ key = cv2.waitKey(1)
+ # escape = 27
+ if key == 27:
+ break
+ else:
+ # Encode numpy array to jpg
+ _, encoded_img = cv2.imencode(".jpg", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])
+ # Create IPython image
+ i = display.Image(data=encoded_img)
+ # Display the image in this notebook
+ display.clear_output(wait=True)
+ display.display(i)
+ # ctrl-c
+ except KeyboardInterrupt:
+ print("Interrupted")
+ # Any different error
+ except RuntimeError as e:
+ print(e)
+ finally:
+ if use_popup:
+ cv2.destroyAllWindows()
+ if player is not None:
+ # stop capturing
+ player.stop()
+ return sync_fps
+
+Test performance in Sync Mode
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ sync_fps = sync_api(source=video_path, flip=False, fps=30, use_popup=False, skip_first_frames=800)
+ print(f"average throuput in sync mode: {sync_fps:.2f} fps")
+
+
+
+.. image:: async-api-with-output_files/async-api-with-output_17_0.png
+
+
+.. parsed-literal::
+
+ Source ended
+ average throuput in sync mode: 43.35 fps
+
+
+Async Mode
+~~~~~~~~~~
+
+
+
+Let us see how the OpenVINO Async API can improve the overall frame rate
+of an application. The key advantage of the Async approach is as
+follows: while a device is busy with the inference, the application can
+do other things in parallel (for example, populating inputs or
+scheduling other requests) rather than wait for the current inference to
+complete first.
+
+.. figure:: https://user-images.githubusercontent.com/91237924/168452572-c2ff1c59-d470-4b85-b1f6-b6e1dac9540e.png
+ :alt: drawing
+
+ drawing
+
+In the example below, inference is applied to the results of the video
+decoding. So it is possible to keep multiple infer requests, and while
+the current request is processed, the input frame for the next is being
+captured. This essentially hides the latency of capturing, so that the
+overall frame rate is rather determined only by the slowest part of the
+pipeline (decoding vs inference) and not by the sum of the stages.
+
+::
+
+ while(true) {
+ // capture frame
+ // populate NEXT InferRequest
+ // start NEXT InferRequest
+ // this call is async and returns immediately
+ // wait for the CURRENT InferRequest
+ // display CURRENT result
+ // swap CURRENT and NEXT InferRequests
+ }
+
+.. code:: ipython3
+
+ def async_api(source, flip, fps, use_popup, skip_first_frames):
+ """
+ Define the main function for video processing in async mode
+
+ :param: source: the video path or the ID of your webcam
+ :returns:
+ async_fps: the inference throughput in async mode
+ """
+ frame_number = 0
+ # Create 2 infer requests
+ curr_request = compiled_model.create_infer_request()
+ next_request = compiled_model.create_infer_request()
+ player = None
+ async_fps = 0
+ try:
+ # Create a video player
+ player = utils.VideoPlayer(source, flip=flip, fps=fps, skip_first_frames=skip_first_frames)
+ # Start capturing
+ start_time = time.time()
+ player.start()
+ if use_popup:
+ title = "Press ESC to Exit"
+ cv2.namedWindow(title, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
+ # Capture CURRENT frame
+ frame = player.next()
+ resized_frame = preprocess(frame)
+ curr_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))
+ # Start the CURRENT inference request
+ curr_request.start_async()
+ while True:
+ # Capture NEXT frame
+ next_frame = player.next()
+ if next_frame is None:
+ print("Source ended")
+ break
+ resized_frame = preprocess(next_frame)
+ next_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))
+ # Start the NEXT inference request
+ next_request.start_async()
+ # Waiting for CURRENT inference result
+ curr_request.wait()
+ res = curr_request.get_output_tensor(0).data
+ stop_time = time.time()
+ total_time = stop_time - start_time
+ frame_number = frame_number + 1
+ async_fps = frame_number / total_time
+ frame = postprocess(res, frame, async_fps)
+ # Display the results
+ if use_popup:
+ cv2.imshow(title, frame)
+ key = cv2.waitKey(1)
+ # escape = 27
+ if key == 27:
+ break
+ else:
+ # Encode numpy array to jpg
+ _, encoded_img = cv2.imencode(".jpg", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])
+ # Create IPython image
+ i = display.Image(data=encoded_img)
+ # Display the image in this notebook
+ display.clear_output(wait=True)
+ display.display(i)
+ # Swap CURRENT and NEXT frames
+ frame = next_frame
+ # Swap CURRENT and NEXT infer requests
+ curr_request, next_request = next_request, curr_request
+ # ctrl-c
+ except KeyboardInterrupt:
+ print("Interrupted")
+ # Any different error
+ except RuntimeError as e:
+ print(e)
+ finally:
+ if use_popup:
+ cv2.destroyAllWindows()
+ if player is not None:
+ # stop capturing
+ player.stop()
+ return async_fps
+
+Test the performance in Async Mode
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ async_fps = async_api(source=video_path, flip=False, fps=30, use_popup=False, skip_first_frames=800)
+ print(f"average throuput in async mode: {async_fps:.2f} fps")
+
+
+
+.. image:: async-api-with-output_files/async-api-with-output_21_0.png
+
+
+.. parsed-literal::
+
+ Source ended
+ average throuput in async mode: 73.97 fps
+
+
+Compare the performance
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ width = 0.4
+ fontsize = 14
+
+ plt.rc("font", size=fontsize)
+ fig, ax = plt.subplots(1, 1, figsize=(10, 8))
+
+ rects1 = ax.bar([0], sync_fps, width, color="#557f2d")
+ rects2 = ax.bar([width], async_fps, width)
+ ax.set_ylabel("frames per second")
+ ax.set_xticks([0, width])
+ ax.set_xticklabels(["Sync mode", "Async mode"])
+ ax.set_xlabel("Higher is better")
+
+ fig.suptitle("Sync mode VS Async mode")
+ fig.tight_layout()
+
+ plt.show()
+
+
+
+.. image:: async-api-with-output_files/async-api-with-output_23_0.png
+
+
+``AsyncInferQueue``
+-------------------
+
+
+
+Asynchronous mode pipelines can be supported with the
+`AsyncInferQueue `__
+wrapper class. This class automatically spawns the pool of
+``InferRequest`` objects (also called “jobs”) and provides
+synchronization mechanisms to control the flow of the pipeline. It is a
+simpler way to manage the infer request queue in Asynchronous mode.
+
+Setting Callback
+~~~~~~~~~~~~~~~~
+
+
+
+When ``callback`` is set, any job that ends inference calls upon the
+Python function. The ``callback`` function must have two arguments: one
+is the request that calls the ``callback``, which provides the
+``InferRequest`` API; the other is called “user data”, which provides
+the possibility of passing runtime values.
+
+.. code:: ipython3
+
+ def callback(infer_request, info) -> None:
+ """
+ Define the callback function for postprocessing
+
+ :param: infer_request: the infer_request object
+ info: a tuple includes original frame and starts time
+ :returns:
+ None
+ """
+ global frame_number
+ global total_time
+ global inferqueue_fps
+ stop_time = time.time()
+ frame, start_time = info
+ total_time = stop_time - start_time
+ frame_number = frame_number + 1
+ inferqueue_fps = frame_number / total_time
+
+ res = infer_request.get_output_tensor(0).data[0]
+ frame = postprocess(res, frame, inferqueue_fps)
+ # Encode numpy array to jpg
+ _, encoded_img = cv2.imencode(".jpg", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])
+ # Create IPython image
+ i = display.Image(data=encoded_img)
+ # Display the image in this notebook
+ display.clear_output(wait=True)
+ display.display(i)
+
+.. code:: ipython3
+
+ def inferqueue(source, flip, fps, skip_first_frames) -> None:
+ """
+ Define the main function for video processing with async infer queue
+
+ :param: source: the video path or the ID of your webcam
+ :retuns:
+ None
+ """
+ # Create infer requests queue
+ infer_queue = ov.AsyncInferQueue(compiled_model, 2)
+ infer_queue.set_callback(callback)
+ player = None
+ try:
+ # Create a video player
+ player = utils.VideoPlayer(source, flip=flip, fps=fps, skip_first_frames=skip_first_frames)
+ # Start capturing
+ start_time = time.time()
+ player.start()
+ while True:
+ # Capture frame
+ frame = player.next()
+ if frame is None:
+ print("Source ended")
+ break
+ resized_frame = preprocess(frame)
+ # Start the inference request with async infer queue
+ infer_queue.start_async({input_layer_ir.any_name: resized_frame}, (frame, start_time))
+ except KeyboardInterrupt:
+ print("Interrupted")
+ # Any different error
+ except RuntimeError as e:
+ print(e)
+ finally:
+ infer_queue.wait_all()
+ player.stop()
+
+Test the performance with ``AsyncInferQueue``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+ frame_number = 0
+ total_time = 0
+ inferqueue(source=video_path, flip=False, fps=30, skip_first_frames=800)
+ print(f"average throughput in async mode with async infer queue: {inferqueue_fps:.2f} fps")
+
+
+
+.. image:: async-api-with-output_files/async-api-with-output_29_0.png
+
+
+.. parsed-literal::
+
+ average throughput in async mode with async infer queue: 111.33 fps
+
diff --git a/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png b/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
index 2aad811bb6b..07fc6bb0553 100644
--- a/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
+++ b/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
@@ -1,3 +1,3 @@
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-size 30483
+oid sha256:c6eb6b07a2e43cfab480087829f6babef1e7050550997c85a7a6824f8c308cc3
+size 30403
diff --git a/docs/notebooks/auto-device-with-output.rst b/docs/notebooks/auto-device-with-output.rst
index 6790703a654..06c0ef2defb 100644
--- a/docs/notebooks/auto-device-with-output.rst
+++ b/docs/notebooks/auto-device-with-output.rst
@@ -186,15 +186,15 @@ By default, ``compile_model`` API will select **AUTO** as
.. parsed-literal::
- [23:25:36.8706]I[plugin.cpp:418][AUTO] device:CPU, config:LOG_LEVEL=LOG_INFO
- [23:25:36.8707]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
- [23:25:36.8707]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
- [23:25:36.8707]I[plugin.cpp:418][AUTO] device:CPU, config:PERF_COUNT=NO
- [23:25:36.8707]I[plugin.cpp:423][AUTO] device:CPU, priority:0
- [23:25:36.8707]I[schedule.cpp:17][AUTO] scheduler starting
- [23:25:36.8707]I[auto_schedule.cpp:131][AUTO] select device:CPU
- [23:25:37.0101]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
- [23:25:37.0103]I[plugin.cpp:451][AUTO] underlying hardware does not support hardware context
+ [23:28:01.3129]I[plugin.cpp:418][AUTO] device:CPU, config:LOG_LEVEL=LOG_INFO
+ [23:28:01.3129]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
+ [23:28:01.3130]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
+ [23:28:01.3130]I[plugin.cpp:418][AUTO] device:CPU, config:PERF_COUNT=NO
+ [23:28:01.3130]I[plugin.cpp:423][AUTO] device:CPU, priority:0
+ [23:28:01.3130]I[schedule.cpp:17][AUTO] scheduler starting
+ [23:28:01.3130]I[auto_schedule.cpp:131][AUTO] select device:CPU
+ [23:28:01.4657]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
+ [23:28:01.4659]I[plugin.cpp:451][AUTO] underlying hardware does not support hardware context
Successfully compiled model without a device_name.
@@ -208,7 +208,7 @@ By default, ``compile_model`` API will select **AUTO** as
.. parsed-literal::
Deleted compiled_model
- [23:25:37.0205]I[schedule.cpp:303][AUTO] scheduler ending
+ [23:28:01.4767]I[schedule.cpp:303][AUTO] scheduler ending
Explicitly pass AUTO as device_name to Core::compile_model API
@@ -366,7 +366,7 @@ executed on CPU until GPU is ready.
.. parsed-literal::
- Time to load model using AUTO device and get first inference: 0.19 seconds.
+ Time to load model using AUTO device and get first inference: 0.18 seconds.
.. code:: ipython3
@@ -538,12 +538,12 @@ Loop for inference and update the FPS/Latency every
Compiling Model for AUTO device with THROUGHPUT hint
Start inference, 6 groups of FPS/latency will be measured over 10s intervals
- throughput: 177.50fps, latency: 32.10ms, time interval: 10.00s
- throughput: 179.46fps, latency: 32.64ms, time interval: 10.01s
- throughput: 179.28fps, latency: 32.70ms, time interval: 10.00s
- throughput: 177.92fps, latency: 32.86ms, time interval: 10.01s
- throughput: 178.98fps, latency: 32.68ms, time interval: 10.02s
- throughput: 178.91fps, latency: 32.77ms, time interval: 10.01s
+ throughput: 177.51fps, latency: 32.04ms, time interval: 10.01s
+ throughput: 179.73fps, latency: 32.54ms, time interval: 10.01s
+ throughput: 178.74fps, latency: 32.73ms, time interval: 10.01s
+ throughput: 179.46fps, latency: 32.59ms, time interval: 10.01s
+ throughput: 178.98fps, latency: 32.74ms, time interval: 10.02s
+ throughput: 178.58fps, latency: 32.79ms, time interval: 10.01s
Done
@@ -589,12 +589,12 @@ Loop for inference and update the FPS/Latency for each
Compiling Model for AUTO Device with LATENCY hint
Start inference, 6 groups fps/latency will be out with 10s interval
- throughput: 135.86fps, latency: 6.81ms, time interval: 10.00s
- throughput: 138.93fps, latency: 6.82ms, time interval: 10.01s
- throughput: 138.89fps, latency: 6.82ms, time interval: 10.00s
- throughput: 138.82fps, latency: 6.81ms, time interval: 10.01s
- throughput: 138.99fps, latency: 6.82ms, time interval: 10.00s
- throughput: 139.01fps, latency: 6.82ms, time interval: 10.01s
+ throughput: 136.40fps, latency: 6.83ms, time interval: 10.01s
+ throughput: 137.96fps, latency: 6.81ms, time interval: 10.00s
+ throughput: 137.97fps, latency: 6.80ms, time interval: 10.00s
+ throughput: 137.97fps, latency: 6.80ms, time interval: 10.00s
+ throughput: 138.06fps, latency: 6.80ms, time interval: 10.00s
+ throughput: 133.29fps, latency: 7.06ms, time interval: 10.01s
Done
diff --git a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
index 4b5d482c7b3..dbe3a0edb38 100644
--- a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
+++ b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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-size 27041
+oid sha256:d644b71f335dd26763dfad14f93ba1ff32ffe30cfdbe3ac06d1c7346aaba3985
+size 27550
diff --git a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
index 31d8b7b761c..0e617d97958 100644
--- a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
+++ b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
@@ -1,3 +1,3 @@
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-size 39972
+oid sha256:b3e6b932de9fd81a384cccede0ed571c920f68e6a9176ce2d84cee626bb03f05
+size 40115
diff --git a/docs/notebooks/blip-visual-language-processing-with-output.rst b/docs/notebooks/blip-visual-language-processing-with-output.rst
index 29884c54209..8a00c7cdf6d 100644
--- a/docs/notebooks/blip-visual-language-processing-with-output.rst
+++ b/docs/notebooks/blip-visual-language-processing-with-output.rst
@@ -559,7 +559,7 @@ As discussed before, the model consists of several blocks which can be
reused for building pipelines for different tasks. In the diagram below,
you can see how image captioning works:
-|image6|
+|image01|
The visual model accepts the image preprocessed by ``BlipProcessor`` as
input and produces image embeddings, which are directly passed to the
@@ -573,12 +573,12 @@ tokenized by ``BlipProcessor`` are provided to the text encoder and then
multimodal question embedding is passed to the text decoder for
performing generation of answers.
-|image7|
+|image11|
The next step is implementing both pipelines using OpenVINO models.
-.. |image6| image:: https://user-images.githubusercontent.com/29454499/221865836-a56da06e-196d-449c-a5dc-4136da6ab5d5.png
-.. |image7| image:: https://user-images.githubusercontent.com/29454499/221868167-d0081add-d9f3-4591-80e7-4753c88c1d0a.png
+.. |image01| image:: https://user-images.githubusercontent.com/29454499/221865836-a56da06e-196d-449c-a5dc-4136da6ab5d5.png
+.. |image11| image:: https://user-images.githubusercontent.com/29454499/221868167-d0081add-d9f3-4591-80e7-4753c88c1d0a.png
.. code:: ipython3
diff --git a/docs/notebooks/convert-to-openvino-with-output.rst b/docs/notebooks/convert-to-openvino-with-output.rst
index 4e38d7b6c19..60b730e4f49 100644
--- a/docs/notebooks/convert-to-openvino-with-output.rst
+++ b/docs/notebooks/convert-to-openvino-with-output.rst
@@ -35,7 +35,7 @@ Table of contents:
.. parsed-literal::
- Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
+ Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.
@@ -181,13 +181,13 @@ NLP model from Hugging Face and export it in ONNX format:
.. parsed-literal::
- 2024-05-06 23:46:51.110172: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
- 2024-05-06 23:46:51.145296: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+ 2024-05-15 23:49:16.064636: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+ 2024-05-15 23:49:16.099980: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
- 2024-05-06 23:46:51.660347: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+ 2024-05-15 23:49:16.617080: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:234: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:234: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
mask, torch.tensor(torch.finfo(scores.dtype).min)
@@ -664,12 +664,12 @@ frameworks conversion guides.
.. parsed-literal::
- 2024-05-06 23:47:11.917183: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
- 2024-05-06 23:47:11.917219: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
- 2024-05-06 23:47:11.917224: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
- 2024-05-06 23:47:11.917431: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
- 2024-05-06 23:47:11.917454: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
- 2024-05-06 23:47:11.917459: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+ 2024-05-15 23:49:36.583572: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+ 2024-05-15 23:49:36.583606: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+ 2024-05-15 23:49:36.583610: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+ 2024-05-15 23:49:36.583842: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+ 2024-05-15 23:49:36.583866: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+ 2024-05-15 23:49:36.583871: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
Migration from Legacy conversion API
diff --git a/docs/notebooks/convnext-classification-with-output.rst b/docs/notebooks/convnext-classification-with-output.rst
index b6b2d1166a1..692982069bd 100644
--- a/docs/notebooks/convnext-classification-with-output.rst
+++ b/docs/notebooks/convnext-classification-with-output.rst
@@ -183,7 +183,7 @@ And print results
Predicted Class: 281
Predicted Label: n02123045 tabby, tabby cat
- Predicted Probability: 0.5510364174842834
+ Predicted Probability: 0.4661690592765808
Convert the model to OpenVINO Intermediate representation format
diff --git a/docs/notebooks/cross-lingual-books-alignment-with-output.rst b/docs/notebooks/cross-lingual-books-alignment-with-output.rst
index 2df720172b1..fb79d3ab196 100644
--- a/docs/notebooks/cross-lingual-books-alignment-with-output.rst
+++ b/docs/notebooks/cross-lingual-books-alignment-with-output.rst
@@ -197,7 +197,7 @@ which in a raw format looks like this:
.. parsed-literal::
- '\ufeffThe Project Gutenberg eBook of Anna Karenina\r\n \r\nThis ebook is for the use of anyone anywhere in the United States and\r\nmost other parts of the world at no cost and with almost no restrictions\r\nwhatsoever. You may copy it, give it away or re-use it under the terms\r\nof the Project Gutenberg License included with this ebook or online\r\nat www.gutenberg.org. If you are not located in the United States,\r\nyou will have to check the laws of the country where you are located\r\nbefore using this eBook.\r\n\r\nTitle: Anna Karenina\r\n\r\n\r\nAuthor: graf Leo Tolstoy\r\n\r\nTranslator: Constance Garnett\r\n\r\nRelease date: July 1, 1998 [eBook #1399]\r\n Most recently updated: April 9, 2023\r\n\r\nLanguage: English\r\n\r\n\r\n\r\n\*\*\* START OF THE PROJECT GUTENBERG EBOOK ANNA KARENINA \*\*\*\r\n[Illustration]\r\n\r\n\r\n\r\n\r\n ANNA KARENINA \r\n\r\n by Leo Tolstoy \r\n\r\n Translated by Constance Garnett \r\n\r\nContents\r\n\r\n\r\n PART ONE\r\n PART TWO\r\n PART THREE\r\n PART FOUR\r\n PART FIVE\r\n PART SIX\r\n PART SEVEN\r\n PART EIGHT\r\n\r\n\r\n\r\n\r\nPART ONE\r\n\r\nChapter 1\r\n\r\n\r\nHappy families are all alike; every unhappy family is unhappy in its\r\nown way.\r\n\r\nEverything was in confusion in the Oblonskys’ house. The wife had\r\ndiscovered that the husband was carrying on an intrigue with a French\r\ngirl, who had been a governess in their family, and she had announced\r\nto her husband that she could not go on living in the same house with\r\nhim. This position of affairs had now lasted three days, and not only\r\nthe husband and wife themselves, but all the me'
+ '\ufeffThe Project Gutenberg eBook of Anna Karenina\r\n \r\nThis ebook is for the use of anyone anywhere in the United States and\r\nmost other parts of the world at no cost and with almost no restrictions\r\nwhatsoever. You may copy it, give it away or re-use it under the terms\r\nof the Project Gutenberg License included with this ebook or online\r\nat www.gutenberg.org. If you are not located in the United States,\r\nyou will have to check the laws of the country where you are located\r\nbefore using this eBook.\r\n\r\nTitle: Anna Karenina\r\n\r\n\r\nAuthor: graf Leo Tolstoy\r\n\r\nTranslator: Constance Garnett\r\n\r\nRelease date: July 1, 1998 [eBook #1399]\r\n Most recently updated: April 9, 2023\r\n\r\nLanguage: English\r\n\r\n\r\n\r\n*** START OF THE PROJECT GUTENBERG EBOOK ANNA KARENINA \*\*\*\r\n[Illustration]\r\n\r\n\r\n\r\n\r\n ANNA KARENINA \r\n\r\n by Leo Tolstoy \r\n\r\n Translated by Constance Garnett \r\n\r\nContents\r\n\r\n\r\n PART ONE\r\n PART TWO\r\n PART THREE\r\n PART FOUR\r\n PART FIVE\r\n PART SIX\r\n PART SEVEN\r\n PART EIGHT\r\n\r\n\r\n\r\n\r\nPART ONE\r\n\r\nChapter 1\r\n\r\n\r\nHappy families are all alike; every unhappy family is unhappy in its\r\nown way.\r\n\r\nEverything was in confusion in the Oblonskys’ house. The wife had\r\ndiscovered that the husband was carrying on an intrigue with a French\r\ngirl, who had been a governess in their family, and she had announced\r\nto her husband that she could not go on living in the same house with\r\nhim. This position of affairs had now lasted three days, and not only\r\nthe husband and wife themselves, but all the me'
@@ -210,7 +210,7 @@ which in a raw format looks like this:
.. parsed-literal::
- 'The Project Gutenberg EBook of Anna Karenina, 1. Band, by Leo N. Tolstoi\r\n\r\nThis eBook is for the use of anyone anywhere at no cost and with\r\nalmost no restrictions whatsoever. You may copy it, give it away or\r\nre-use it under the terms of the Project Gutenberg License included\r\nwith this eBook or online at www.gutenberg.org\r\n\r\n\r\nTitle: Anna Karenina, 1. Band\r\n\r\nAuthor: Leo N. Tolstoi\r\n\r\nRelease Date: February 18, 2014 [EBook #44956]\r\n\r\nLanguage: German\r\n\r\nCharacter set encoding: ISO-8859-1\r\n\r\n\*\*\* START OF THIS PROJECT GUTENBERG EBOOK ANNA KARENINA, 1. BAND \*\*\*\r\n\r\n\r\n\r\n\r\nProduced by Norbert H. Langkau, Jens Nordmann and the\r\nOnline Distributed Proofreading Team at http://www.pgdp.net\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n Anna Karenina.\r\n\r\n\r\n Roman aus dem Russischen\r\n\r\n des\r\n\r\n Grafen Leo N. Tolstoi.\r\n\r\n\r\n\r\n Nach der siebenten Auflage übersetzt\r\n\r\n von\r\n\r\n Hans Moser.\r\n\r\n\r\n Erster Band.\r\n\r\n\r\n\r\n Leipzig\r\n\r\n Druck und Verlag von Philipp Reclam jun.\r\n\r\n \* \* \* \* \*\r\n\r\n\r\n\r\n\r\n Erster Teil.\r\n\r\n »Die Rache ist mein, ich will vergelten.«\r\n\r\n 1.\r\n\r\n\r\nAlle glücklichen Familien sind einander ähnlich; jede unglücklich'
+ 'The Project Gutenberg EBook of Anna Karenina, 1. Band, by Leo N. Tolstoi\r\n\r\nThis eBook is for the use of anyone anywhere at no cost and with\r\nalmost no restrictions whatsoever. You may copy it, give it away or\r\nre-use it under the terms of the Project Gutenberg License included\r\nwith this eBook or online at www.gutenberg.org\r\n\r\n\r\nTitle: Anna Karenina, 1. Band\r\n\r\nAuthor: Leo N. Tolstoi\r\n\r\nRelease Date: February 18, 2014 [EBook #44956]\r\n\r\nLanguage: German\r\n\r\nCharacter set encoding: ISO-8859-1\r\n\r\n\*\*\* START OF THIS PROJECT GUTENBERG EBOOK ANNA KARENINA, 1. BAND \*\*\*\r\n\r\n\r\n\r\n\r\nProduced by Norbert H. Langkau, Jens Nordmann and the\r\nOnline Distributed Proofreading Team at http://www.pgdp.net\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n Anna Karenina.\r\n\r\n\r\n Roman aus dem Russischen\r\n\r\n des\r\n\r\n Grafen Leo N. Tolstoi.\r\n\r\n\r\n\r\n Nach der siebenten Auflage übersetzt\r\n\r\n von\r\n\r\n Hans Moser.\r\n\r\n\r\n Erster Band.\r\n\r\n\r\n\r\n Leipzig\r\n\r\n Druck und Verlag von Philipp Reclam jun.\r\n\r\n \* \* \* \* *\r\n\r\n\r\n\r\n\r\n Erster Teil.\r\n\r\n »Die Rache ist mein, ich will vergelten.«\r\n\r\n 1.\r\n\r\n\r\nAlle glücklichen Familien sind einander ähnlich; jede unglücklich'
diff --git a/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst b/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
index 99629c15f18..c2991dc95a3 100644
--- a/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
+++ b/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
@@ -39,7 +39,7 @@ This notebook needs a trained UNet model. We provide a pre-trained
model, trained for 20 epochs with the full
`Kits-19 `__ frames dataset, which
has an F1 score on the validation set of 0.9. The training code is
-available in `this notebook `__.
+available in `this notebook `__.
NNCF for PyTorch models requires a C++ compiler. On Windows, install
`Microsoft Visual Studio
@@ -88,9 +88,9 @@ Table of contents:
.. code:: ipython3
import platform
-
+
%pip install -q "openvino>=2023.3.0" "monai>=0.9.1" "torchmetrics>=0.11.0" "nncf>=2.8.0" "opencv-python" torch tqdm --extra-index-url https://download.pytorch.org/whl/cpu
-
+
if platform.system() != "Windows":
%pip install -q "matplotlib>=3.4"
else:
@@ -118,9 +118,9 @@ Imports
import zipfile
from pathlib import Path
from typing import Union
-
+
warnings.filterwarnings("ignore", category=UserWarning)
-
+
import cv2
import matplotlib.pyplot as plt
import monai
@@ -132,19 +132,19 @@ Imports
from nncf.common.logging.logger import set_log_level
from torchmetrics import F1Score as F1
import requests
-
-
+
+
set_log_level(logging.ERROR) # Disables all NNCF info and warning messages
-
+
# Fetch `notebook_utils` module
r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
open("notebook_utils.py", "w").write(r.text)
from notebook_utils import download_file
-
+
if not Path("./custom_segmentation.py").exists():
download_file(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/ct-segmentation-quantize/custom_segmentation.py")
from custom_segmentation import SegmentationModel
-
+
if not Path("./async_pipeline.py").exists():
download_file(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/ct-segmentation-quantize/async_pipeline.py")
from async_pipeline import show_live_inference
@@ -152,10 +152,10 @@ Imports
.. parsed-literal::
- 2024-05-06 23:48:33.144412: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
- 2024-05-06 23:48:33.181396: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+ 2024-05-15 23:50:56.536543: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+ 2024-05-15 23:50:56.573924: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
- 2024-05-06 23:48:33.764576: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+ 2024-05-15 23:50:57.174238: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
.. parsed-literal::
@@ -197,13 +197,13 @@ notebook `__.
state_dict_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/kidney-segmentation-kits19/unet_kits19_state_dict.pth"
state_dict_file = download_file(state_dict_url, directory="pretrained_model")
state_dict = torch.load(state_dict_file, map_location=torch.device("cpu"))
-
+
new_state_dict = {}
for k, v in state_dict.items():
new_key = k.replace("_model.", "")
new_state_dict[new_key] = v
new_state_dict.pop("loss_function.pos_weight")
-
+
model = monai.networks.nets.BasicUNet(spatial_dims=2, in_channels=1, out_channels=1).eval()
model.load_state_dict(new_state_dict)
@@ -285,8 +285,8 @@ method to display the images in the expected orientation:
def rotate_and_flip(image):
"""Rotate `image` by 90 degrees and flip horizontally"""
return cv2.flip(cv2.rotate(image, rotateCode=cv2.ROTATE_90_CLOCKWISE), flipCode=1)
-
-
+
+
class KitsDataset:
def __init__(self, basedir: str):
"""
@@ -295,35 +295,35 @@ method to display the images in the expected orientation:
with each subdirectory containing directories imaging_frames, with jpg images, and
segmentation_frames with segmentation masks as png files.
See [data-preparation-ct-scan](./data-preparation-ct-scan.ipynb)
-
+
:param basedir: Directory that contains the prepared CT scans
"""
masks = sorted(BASEDIR.glob("case_*/segmentation_frames/*png"))
-
+
self.basedir = basedir
self.dataset = masks
print(f"Created dataset with {len(self.dataset)} items. " f"Base directory for data: {basedir}")
-
+
def __getitem__(self, index):
"""
Get an item from the dataset at the specified index.
-
+
:return: (image, segmentation_mask)
"""
mask_path = self.dataset[index]
image_path = str(mask_path.with_suffix(".jpg")).replace("segmentation_frames", "imaging_frames")
-
+
# Load images with MONAI's LoadImage to match data loading in training notebook
mask = LoadImage(image_only=True, dtype=np.uint8)(str(mask_path)).numpy()
img = LoadImage(image_only=True, dtype=np.float32)(str(image_path)).numpy()
-
+
if img.shape[:2] != (512, 512):
img = cv2.resize(img.astype(np.uint8), (512, 512)).astype(np.float32)
mask = cv2.resize(mask, (512, 512))
-
+
input_image = np.expand_dims(img, axis=0)
return input_image, mask
-
+
def __len__(self):
return len(self.dataset)
@@ -341,10 +341,10 @@ kidney pixels to verify that the annotations look correct:
image_data, mask = next(item for item in dataset if np.count_nonzero(item[1]) > 5000)
# Remove extra image dimension and rotate and flip the image for visualization
image = rotate_and_flip(image_data.squeeze())
-
+
# The data loader returns annotations as (index, mask) and mask in shape (H,W)
mask = rotate_and_flip(mask)
-
+
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].imshow(image, cmap="gray")
ax[1].imshow(mask, cmap="gray");
@@ -422,7 +422,7 @@ this notebook.
.. code:: ipython3
fp32_ir_path = MODEL_DIR / Path("unet_kits19_fp32.xml")
-
+
fp32_ir_model = ov.convert_model(model, example_input=torch.ones(1, 1, 512, 512, dtype=torch.float32))
ov.save_model(fp32_ir_model, str(fp32_ir_path))
@@ -435,7 +435,7 @@ this notebook.
.. parsed-literal::
[ WARNING ] Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
@@ -467,8 +467,8 @@ steps:
"""
images, _ = data_item
return images
-
-
+
+
data_loader = torch.utils.data.DataLoader(dataset)
calibration_dataset = nncf.Dataset(data_loader, transform_fn)
quantized_model = nncf.quantize(
@@ -534,18 +534,18 @@ Convert quantized model to OpenVINO IR model and save it.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:337: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:337: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
return self._level_low.item()
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:345: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:345: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
return self._level_high.item()
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Tensor-likes are not close!
-
- Mismatched elements: 249823 / 262144 (95.3%)
- Greatest absolute difference: 4.744992733001709 at index (0, 0, 242, 231) (up to 1e-05 allowed)
- Greatest relative difference: 26823.613314473136 at index (0, 0, 124, 22) (up to 1e-05 allowed)
+
+ Mismatched elements: 247888 / 262144 (94.6%)
+ Greatest absolute difference: 6.055658936500549 at index (0, 0, 158, 273) (up to 1e-05 allowed)
+ Greatest relative difference: 13834.454856259192 at index (0, 0, 343, 273) (up to 1e-05 allowed)
_check_trace(
@@ -570,7 +570,7 @@ Compare File Size
fp32_ir_model_size = fp32_ir_path.with_suffix(".bin").stat().st_size / 1024
quantized_model_size = int8_ir_path.with_suffix(".bin").stat().st_size / 1024
-
+
print(f"FP32 IR model size: {fp32_ir_model_size:.2f} KB")
print(f"INT8 model size: {quantized_model_size:.2f} KB")
@@ -591,16 +591,16 @@ Select Inference Device
core = ov.Core()
# By default, benchmark on MULTI:CPU,GPU if a GPU is available, otherwise on CPU.
device_list = ["MULTI:CPU,GPU" if "GPU" in core.available_devices else "AUTO"]
-
+
import ipywidgets as widgets
-
+
device = widgets.Dropdown(
options=core.available_devices + device_list,
value=device_list[0],
description="Device:",
disabled=False,
)
-
+
device
@@ -621,7 +621,7 @@ Compare Metrics for the original model and the quantized model to be sure that t
int8_compiled_model = core.compile_model(int8_ir_model, device.value)
int8_f1 = compute_f1(int8_compiled_model, dataset)
-
+
print(f"FP32 F1: {fp32_f1:.3f}")
print(f"INT8 F1: {int8_f1:.3f}")
@@ -669,17 +669,17 @@ be run in the notebook with ``! benchmark_app`` or
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
- [ INFO ]
+ [ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
- [ INFO ]
- [ INFO ]
+ [ INFO ]
+ [ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
[Step 4/11] Reading model files
[ INFO ] Loading model files
- [ INFO ] Read model took 8.87 ms
+ [ INFO ] Read model took 8.68 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] x (node: x) : f32 / [...] / [?,?,?,?]
@@ -693,7 +693,7 @@ be run in the notebook with ``! benchmark_app`` or
[ INFO ] Model outputs:
[ INFO ] ***NO_NAME*** (node: __module.final_conv/aten::_convolution/Add) : f32 / [...] / [?,1,16..,16..]
[Step 7/11] Loading the model to the device
- [ INFO ] Compile model took 148.60 ms
+ [ INFO ] Compile model took 149.48 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ] NETWORK_NAME: Model0
@@ -728,9 +728,9 @@ be run in the notebook with ``! benchmark_app`` or
[Step 9/11] Creating infer requests and preparing input tensors
[ ERROR ] Input x is dynamic. Provide data shapes!
Traceback (most recent call last):
- File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 486, in main
+ File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 486, in main
data_queue = get_input_data(paths_to_input, app_inputs_info)
- File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/utils/inputs_filling.py", line 123, in get_input_data
+ File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/utils/inputs_filling.py", line 123, in get_input_data
raise Exception(f"Input {info.name} is dynamic. Provide data shapes!")
Exception: Input x is dynamic. Provide data shapes!
@@ -748,17 +748,17 @@ be run in the notebook with ``! benchmark_app`` or
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
- [ INFO ]
+ [ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
- [ INFO ]
- [ INFO ]
+ [ INFO ]
+ [ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
[Step 4/11] Reading model files
[ INFO ] Loading model files
- [ INFO ] Read model took 10.46 ms
+ [ INFO ] Read model took 10.82 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] x (node: x) : f32 / [...] / [1,1,512,512]
@@ -772,7 +772,7 @@ be run in the notebook with ``! benchmark_app`` or
[ INFO ] Model outputs:
[ INFO ] ***NO_NAME*** (node: __module.final_conv/aten::_convolution/Add) : f32 / [...] / [1,1,512,512]
[Step 7/11] Loading the model to the device
- [ INFO ] Compile model took 253.55 ms
+ [ INFO ] Compile model took 257.40 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ] NETWORK_NAME: Model49
@@ -806,20 +806,20 @@ be run in the notebook with ``! benchmark_app`` or
[ INFO ] PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
- [ INFO ] Fill input 'x' with random values
+ [ INFO ] Fill input 'x' with random values
[Step 10/11] Measuring performance (Start inference synchronously, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
- [ INFO ] First inference took 28.04 ms
+ [ INFO ] First inference took 27.38 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
- [ INFO ] Count: 969 iterations
- [ INFO ] Duration: 15000.48 ms
+ [ INFO ] Count: 959 iterations
+ [ INFO ] Duration: 15011.96 ms
[ INFO ] Latency:
- [ INFO ] Median: 15.24 ms
- [ INFO ] Average: 15.28 ms
- [ INFO ] Min: 14.97 ms
- [ INFO ] Max: 17.08 ms
- [ INFO ] Throughput: 64.60 FPS
+ [ INFO ] Median: 15.40 ms
+ [ INFO ] Average: 15.45 ms
+ [ INFO ] Min: 15.18 ms
+ [ INFO ] Max: 17.18 ms
+ [ INFO ] Throughput: 63.88 FPS
Visually Compare Inference Results
@@ -853,11 +853,11 @@ seed is displayed to enable reproducing specific runs of this cell.
# to binary segmentation masks
def sigmoid(x):
return np.exp(-np.logaddexp(0, -x))
-
-
+
+
num_images = 4
colormap = "gray"
-
+
# Load FP32 and INT8 models
core = ov.Core()
fp_model = core.read_model(fp32_ir_path)
@@ -866,18 +866,18 @@ seed is displayed to enable reproducing specific runs of this cell.
compiled_model_int8 = core.compile_model(int8_model, device_name=device.value)
output_layer_fp = compiled_model_fp.output(0)
output_layer_int8 = compiled_model_int8.output(0)
-
+
# Create subset of dataset
background_slices = (item for item in dataset if np.count_nonzero(item[1]) == 0)
kidney_slices = (item for item in dataset if np.count_nonzero(item[1]) > 50)
data_subset = random.sample(list(background_slices), 2) + random.sample(list(kidney_slices), 2)
-
+
# Set seed to current time. To reproduce specific results, copy the printed seed
# and manually set `seed` to that value.
seed = int(time.time())
random.seed(seed)
print(f"Visualizing results with seed {seed}")
-
+
fig, ax = plt.subplots(nrows=num_images, ncols=4, figsize=(24, num_images * 4))
for i, (image, mask) in enumerate(data_subset):
display_image = rotate_and_flip(image.squeeze())
@@ -886,13 +886,13 @@ seed is displayed to enable reproducing specific runs of this cell.
input_image = np.expand_dims(image, 0)
res_fp = compiled_model_fp([input_image])
res_int8 = compiled_model_int8([input_image])
-
+
# Process inference outputs and convert to binary segementation masks
result_mask_fp = sigmoid(res_fp[output_layer_fp]).squeeze().round().astype(np.uint8)
result_mask_int8 = sigmoid(res_int8[output_layer_int8]).squeeze().round().astype(np.uint8)
result_mask_fp = rotate_and_flip(result_mask_fp)
result_mask_int8 = rotate_and_flip(result_mask_int8)
-
+
# Display images, annotations, FP32 result and INT8 result
ax[i, 0].imshow(display_image, cmap=colormap)
ax[i, 1].imshow(target_mask, cmap=colormap)
@@ -904,7 +904,7 @@ seed is displayed to enable reproducing specific runs of this cell.
.. parsed-literal::
- Visualizing results with seed 1715032183
+ Visualizing results with seed 1715809926
@@ -946,7 +946,7 @@ overlay of the segmentation mask on the original image/frame.
.. code:: ipython3
CASE = 117
-
+
segmentation_model = SegmentationModel(ie=core, model_path=int8_ir_path, sigmoid=True, rotate_and_flip=True)
case_path = BASEDIR / f"case_{CASE:05d}"
image_paths = sorted(case_path.glob("imaging_frames/*jpg"))
@@ -987,8 +987,8 @@ performs inference, and displays the results on the frames loaded in
.. parsed-literal::
- Loaded model to AUTO in 0.23 seconds.
- Total time for 68 frames: 2.67 seconds, fps:25.86
+ Loaded model to AUTO in 0.21 seconds.
+ Total time for 68 frames: 2.72 seconds, fps:25.33
References
diff --git a/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
index 51147de15b1..b3af95598c6 100644
--- a/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
+++ b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:16b3bd0c7d257596831f1d01b363b5f52edfd6db56c147651414f6ac0f77d958
-size 382301
+oid sha256:28010020834c2072a301b1a4eab4743fe594249fd6868e415af89e0dbc74892e
+size 383860
diff --git a/docs/notebooks/depth-anything-with-output.rst b/docs/notebooks/depth-anything-with-output.rst
index 1003219a81a..d583ea67720 100644
--- a/docs/notebooks/depth-anything-with-output.rst
+++ b/docs/notebooks/depth-anything-with-output.rst
@@ -79,9 +79,9 @@ Prerequisites
remote: Counting objects: 100% (144/144), done.[K
remote: Compressing objects: 100% (105/105), done.[K
remote: Total 421 (delta 101), reused 43 (delta 39), pack-reused 277[K
- Receiving objects: 100% (421/421), 237.89 MiB | 26.48 MiB/s, done.
+ Receiving objects: 100% (421/421), 237.89 MiB | 27.94 MiB/s, done.
Resolving deltas: 100% (144/144), done.
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.
WARNING: typer 0.12.3 does not provide the extra 'all'
@@ -273,13 +273,13 @@ loading on device using ``core.complie_model``.
.. parsed-literal::
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if npatch == N and w == h:
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
@@ -571,7 +571,7 @@ Run inference on video
.. parsed-literal::
- Processed 60 frames in 13.33 seconds. Total FPS (including video processing): 4.50.Inference FPS: 10.52
+ Processed 60 frames in 13.28 seconds. Total FPS (including video processing): 4.52.Inference FPS: 10.54
Video saved to 'output/Coco Walking in Berkeley_depth_anything.mp4'.
@@ -598,7 +598,7 @@ Run inference on video
.. parsed-literal::
Showing video saved at
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
If you cannot see the video in your browser, please click on the following link to download the video
@@ -735,10 +735,10 @@ quantization code below may take some time.
.. parsed-literal::
- 2024-05-06 23:52:17.005825: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
- 2024-05-06 23:52:17.039208: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+ 2024-05-15 23:54:08.057348: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+ 2024-05-15 23:54:08.091208: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
- 2024-05-06 23:52:17.604619: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+ 2024-05-15 23:54:08.654880: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
@@ -902,10 +902,10 @@ data.
.. parsed-literal::
- Processed 60 frames in 12.84 seconds. Total FPS (including video processing): 4.67.Inference FPS: 12.79
+ Processed 60 frames in 12.70 seconds. Total FPS (including video processing): 4.73.Inference FPS: 12.89
Video saved to 'output/Coco Walking in Berkeley_depth_anything_int8.mp4'.
Showing video saved at
- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
+ /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-681/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
If you cannot see the video in your browser, please click on the following link to download the video
@@ -985,8 +985,8 @@ Tool
Your browser does not support the audio element.
@@ -270,10 +270,10 @@ OpenVINO model. It means that we can reuse initialized early processor.
from pathlib import Path
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
-
+
model_path = Path(model_id.value.replace("/", "_"))
ov_config = {"CACHE_DIR": ""}
-
+
if not model_path.exists():
ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
model_id.value,
@@ -302,16 +302,16 @@ Select Inference device
import openvino as ov
import ipywidgets as widgets
-
+
core = ov.Core()
-
+
device = widgets.Dropdown(
options=core.available_devices + ["AUTO"],
value="AUTO",
description="Device:",
disabled=False,
)
-
+
device
@@ -350,7 +350,7 @@ Run OpenVINO model inference
predicted_ids = ov_model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-
+
display(ipd.Audio(sample["audio"]["array"], rate=sample["audio"]["sampling_rate"]))
print(f"Reference: {sample['text']}")
print(f"Result: {transcription[0]}")
@@ -367,7 +367,7 @@ Run OpenVINO model inference
.. raw:: html
-
+