Object segmentations with EfficientSAM and OpenVINO

This Jupyter notebook can be launched after a local installation only.

Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial, the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation, EfficientSAMs, light-weight SAM models that exhibit decent performance with largely reduced complexity, were proposed. The idea behind EfficientSAM is based on leveraging masked image pretraining, SAMI, which learns to reconstruct features from SAM image encoder for effective visual representation learning.

overview.png

More details about model can be found in paper, model web page and original repository

In this tutorial we consider how to convert and run EfficientSAM using OpenVINO. We also demonstrate how to quantize model using NNCF

Table of contents:

• Prerequisites

• Load PyTorch model

• Run PyTorch model inference

  • Prepare input data

  • Define helpers for input and output processing

• Convert model to OpenVINO IR format

• Run OpenVINO model inference

  • Select inference device from dropdown list

  • Compile OpenVINO model

  • Inference and visualize result

• Quantization

  • Prepare calibration datasets

  • Run Model Quantization

• Verify quantized model inference

  • Save quantize model on disk

  • Compare quantized model size

  • Compare inference time of the FP16 and INT8 models

• Interactive segmentation demo

Installation Instructions

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

Prerequisites

%pip install -q "openvino>=2023.3.0" "nncf>=2.7.0" opencv-python "gradio>=4.13" "matplotlib>=3.4" torch torchvision tqdm  --extra-index-url https://download.pytorch.org/whl/cpu

Note: you may need to restart the kernel to use updated packages.

from pathlib import Path

repo_dir = Path("EfficientSAM")

if not repo_dir.exists():
    !git clone https://github.com/yformer/EfficientSAM.git
%cd $repo_dir

Cloning into 'EfficientSAM'...
remote: Enumerating objects: 424, done.
remote: Counting objects: 100% (85/85), done.
remote: Compressing objects: 100% (33/33), done.
remote: Total 424 (delta 76), reused 52 (delta 52), pack-reused 339 (from 1)
Receiving objects: 100% (424/424), 262.14 MiB | 23.37 MiB/s, done.
Resolving deltas: 100% (246/246), done.
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM

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)

from notebook_utils import download_file, device_widget, quantization_widget  # noqa: F401

Load PyTorch model

There are several models available in the repository:

• efficient-sam-vitt - EfficientSAM with Vision Transformer Tiny (VIT-T) as image encoder. The smallest and fastest model from EfficientSAM family.

• efficient-sam-vits - EfficientSAM with Vision Transformer Small (VIT-S) as image encoder. Heavier than efficient-sam-vitt, but more accurate model.

EfficientSAM provides a unified interface for interaction with models. It means that all provided steps in the notebook for conversion and running the model will be the same for all models. Below, you can select one of them as example.

from efficient_sam.build_efficient_sam import (
    build_efficient_sam_vitt,
    build_efficient_sam_vits,
)
import zipfile

MODELS_LIST = {
    "efficient-sam-vitt": build_efficient_sam_vitt,
    "efficient-sam-vits": build_efficient_sam_vits,
}

# Since EfficientSAM-S checkpoint file is >100MB, we store the zip file.
with zipfile.ZipFile("weights/efficient_sam_vits.pt.zip", "r") as zip_ref:
    zip_ref.extractall("weights")

Select one from supported models:

import ipywidgets as widgets

model_ids = list(MODELS_LIST)

model_id = widgets.Dropdown(
    options=model_ids,
    value=model_ids[0],
    description="Model:",
    disabled=False,
)

model_id

Dropdown(description='Model:', options=('efficient-sam-vitt', 'efficient-sam-vits'), value='efficient-sam-vitt…

build PyTorch model

pt_model = MODELS_LIST[model_id.value]()

pt_model.eval();

Run PyTorch model inference

Now, when we selected and loaded PyTorch model, we can check its result

Prepare input data

First of all, we should prepare input data for model. Model has 3 inputs: * image tensor - tensor with normalized input image. * input points - tensor with user provided points. It maybe just some specific points on the image (e.g. provided by user clicks on the screen) or bounding box coordinates in format left-top angle point and right-bottom angle pint. * input labels - tensor with definition of point type for each provided point, 1 - for regular point, 2 - left-top point of bounding box, 3 - right-bottom point of bounding box.

from PIL import Image

image_path = "figs/examples/dogs.jpg"

image = Image.open(image_path)
image

Define helpers for input and output processing

The code below defines helpers for preparing model input and postprocess inference results. The input format is accepted by the model described above. The model predicts mask logits for each pixel on the image and intersection over union score for each area, how close it is to provided points. We also provided some helper function for results visualization.

import torch
import matplotlib.pyplot as plt
import numpy as np


def prepare_input(input_image, points, labels, torch_tensor=True):
    img_tensor = np.ascontiguousarray(input_image)[None, ...].astype(np.float32) / 255
    img_tensor = np.transpose(img_tensor, (0, 3, 1, 2))
    pts_sampled = np.reshape(np.ascontiguousarray(points), [1, 1, -1, 2])
    pts_labels = np.reshape(np.ascontiguousarray(labels), [1, 1, -1])
    if torch_tensor:
        img_tensor = torch.from_numpy(img_tensor)
        pts_sampled = torch.from_numpy(pts_sampled)
        pts_labels = torch.from_numpy(pts_labels)
    return img_tensor, pts_sampled, pts_labels


def postprocess_results(predicted_iou, predicted_logits):
    sorted_ids = np.argsort(-predicted_iou, axis=-1)
    predicted_iou = np.take_along_axis(predicted_iou, sorted_ids, axis=2)
    predicted_logits = np.take_along_axis(predicted_logits, sorted_ids[..., None, None], axis=2)

    return predicted_logits[0, 0, 0, :, :] >= 0


def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels == 1]
    neg_points = coords[labels == 0]
    ax.scatter(
        pos_points[:, 0],
        pos_points[:, 1],
        color="green",
        marker="*",
        s=marker_size,
        edgecolor="white",
        linewidth=1.25,
    )
    ax.scatter(
        neg_points[:, 0],
        neg_points[:, 1],
        color="red",
        marker="*",
        s=marker_size,
        edgecolor="white",
        linewidth=1.25,
    )


def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="yellow", facecolor=(0, 0, 0, 0), lw=5))


def show_anns(mask, ax):
    ax.set_autoscale_on(False)
    img = np.ones((mask.shape[0], mask.shape[1], 4))
    img[:, :, 3] = 0
    # for ann in mask:
    #     m = ann
    color_mask = np.concatenate([np.random.random(3), [0.5]])
    img[mask] = color_mask
    ax.imshow(img)

The complete model inference example demonstrated below

input_points = [[580, 350], [650, 350]]
input_labels = [1, 1]

example_input = prepare_input(image, input_points, input_labels)

predicted_logits, predicted_iou = pt_model(*example_input)

predicted_mask = postprocess_results(predicted_iou.detach().numpy(), predicted_logits.detach().numpy())

image = Image.open(image_path)

plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(image)
show_points(np.array(input_points), np.array(input_labels), plt.gca())
plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(image)
show_anns(predicted_mask, plt.gca())
plt.title(f"PyTorch {model_id.value}", fontsize=18)
plt.show()

Convert model to OpenVINO IR format

OpenVINO supports PyTorch models via conversion in Intermediate Representation (IR) format using OpenVINO Model Conversion API. openvino.convert_model function accepts instance of PyTorch model and example input (that helps in correct model operation tracing and shape inference) and returns openvino.Model object that represents model in OpenVINO framework. This openvino.Model is ready for loading on the device using ov.Core.compile_model or can be saved on disk using openvino.save_model.

import openvino as ov

core = ov.Core()

ov_model_path = Path(f"{model_id.value}.xml")

if not ov_model_path.exists():
    ov_model = ov.convert_model(pt_model, example_input=example_input)
    ov.save_model(ov_model, ov_model_path)
else:
    ov_model = core.read_model(ov_model_path)

/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:220: 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 (
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:241: 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 (
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:163: TracerWarning: Converting a tensor to a Python float 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!
  size = int(math.sqrt(xy_num))
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:164: 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 size * size == xy_num
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:166: 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 size != h or size != w:
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:251: 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 x.shape[2] == num_patches
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:85: 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 num_pts > self.decoder_max_num_input_points:
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:92: 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!
  elif num_pts < self.decoder_max_num_input_points:
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:126: 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_w > 0 and output_h > 0:

Run OpenVINO model inference

Select inference device from dropdown list

device = device_widget()

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

Compile OpenVINO model

compiled_model = core.compile_model(ov_model, device.value)

Inference and visualize result

Now, we can take a look on OpenVINO model prediction

example_input = prepare_input(image, input_points, input_labels, torch_tensor=False)
result = compiled_model(example_input)

predicted_logits, predicted_iou = result[0], result[1]

predicted_mask = postprocess_results(predicted_iou, predicted_logits)

plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(image)
show_points(np.array(input_points), np.array(input_labels), plt.gca())
plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(image)
show_anns(predicted_mask, plt.gca())
plt.title(f"OpenVINO {model_id.value}", fontsize=18)
plt.show()

Quantization

NNCF enables post-training quantization by adding the quantization layers into the model graph and then using a subset of the training dataset to initialize the parameters of these additional quantization layers. The framework is designed so that modifications to your original training code are minor.

The optimization process contains the following steps:

1. Create a calibration dataset for quantization.

2. Run nncf.quantize to obtain quantized encoder and decoder models.

3. Serialize the INT8 model using openvino.save_model function.

Note: Quantization is time and memory consuming operation. Running quantization code below may take some time.

Please select below whether you would like to run EfficientSAM quantization.

to_quantize = quantization_widget()

to_quantize

Checkbox(value=True, description='Quantization')

# Fetch `skip_kernel_extension` module
import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
)
open("skip_kernel_extension.py", "w").write(r.text)

%load_ext skip_kernel_extension

Prepare calibration datasets

The first step is to prepare calibration datasets for quantization. We will use coco128 dataset for quantization. Usually, this dataset used for solving object detection task and its annotation provides box coordinates for images. In our case, box coordinates will serve as input points for object segmentation, the code below downloads dataset and creates DataLoader for preparing inputs for EfficientSAM model.

%%skip not $to_quantize.value

from zipfile import ZipFile

DATA_URL = "https://ultralytics.com/assets/coco128.zip"
OUT_DIR = Path('.')

download_file(DATA_URL, directory=OUT_DIR, show_progress=True)

if not (OUT_DIR / "coco128/images/train2017").exists():
    with ZipFile('coco128.zip' , "r") as zip_ref:
        zip_ref.extractall(OUT_DIR)

coco128.zip:   0%|          | 0.00/6.66M [00:00<?, ?B/s]

%%skip not $to_quantize.value

import torch.utils.data as data

class COCOLoader(data.Dataset):
    def __init__(self, images_path):
        self.images = list(Path(images_path).iterdir())
        self.labels_dir = images_path.parents[1] / 'labels' / images_path.name

    def get_points(self, image_path, image_width, image_height):
        file_name = image_path.name.replace('.jpg', '.txt')
        label_file =  self.labels_dir / file_name
        if not label_file.exists():
            x1, x2 = np.random.randint(low=0, high=image_width, size=(2, ))
            y1, y2 = np.random.randint(low=0, high=image_height, size=(2, ))
        else:
            with label_file.open("r") as f:
                box_line = f.readline()
            _, x1, y1, x2, y2 = box_line.split()
            x1 = int(float(x1) * image_width)
            y1 = int(float(y1) * image_height)
            x2 = int(float(x2) * image_width)
            y2 = int(float(y2) * image_height)
        return [[x1, y1], [x2, y2]]

    def __getitem__(self, index):
        image_path = self.images[index]
        image = Image.open(image_path)
        image = image.convert('RGB')
        w, h = image.size
        points = self.get_points(image_path, w, h)
        labels = [1, 1] if index % 2 == 0 else [2, 3]
        batched_images, batched_points, batched_point_labels = prepare_input(image, points, labels, torch_tensor=False)
        return {'batched_images': np.ascontiguousarray(batched_images)[0], 'batched_points': np.ascontiguousarray(batched_points)[0], 'batched_point_labels': np.ascontiguousarray(batched_point_labels)[0]}

    def __len__(self):
        return len(self.images)

%%skip not $to_quantize.value

coco_dataset = COCOLoader(OUT_DIR / 'coco128/images/train2017')
calibration_loader = torch.utils.data.DataLoader(coco_dataset)

Run Model Quantization

The nncf.quantize function provides an interface for model quantization. It requires an instance of the OpenVINO Model and quantization dataset. Optionally, some additional parameters for the configuration quantization process (number of samples for quantization, preset, ignored scope, etc.) can be provided. EfficientSAM contains non-ReLU activation functions, which require asymmetric quantization of activations. To achieve a better result, we will use a mixed quantization preset. Model encoder part is based on Vision Transformer architecture for activating special optimizations for this architecture type, we should specify transformer in model_type.

%%skip not $to_quantize.value

import nncf

calibration_dataset = nncf.Dataset(calibration_loader)

model = core.read_model(ov_model_path)
quantized_model = nncf.quantize(model,
                                calibration_dataset,
                                model_type=nncf.parameters.ModelType.TRANSFORMER,
                                subset_size=128)
print("model quantization finished")

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino

2024-11-05 01:15:40.935673: 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.
2024-11-05 01:15:40.968460: 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.
2024-11-05 01:15:41.606156: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Output()

Output()

Output()

Output()

model quantization finished

Verify quantized model inference

%%skip not $to_quantize.value

compiled_model = core.compile_model(quantized_model, device.value)

result = compiled_model(example_input)

predicted_logits, predicted_iou = result[0], result[1]

predicted_mask = postprocess_results(predicted_iou, predicted_logits)

plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(image)
show_points(np.array(input_points), np.array(input_labels), plt.gca())
plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(image)
show_anns(predicted_mask, plt.gca())
plt.title(f"OpenVINO INT8 {model_id.value}", fontsize=18)
plt.show()

Save quantize model on disk

%%skip not $to_quantize.value

quantized_model_path = Path(f"{model_id.value}_int8.xml")
ov.save_model(quantized_model, quantized_model_path)

Compare quantized model size

%%skip not $to_quantize.value

fp16_weights = ov_model_path.with_suffix('.bin')
quantized_weights = quantized_model_path.with_suffix('.bin')

print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
print(f"Size of INT8 quantized model is {quantized_weights.stat().st_size / 1024 / 1024:.2f} MB")
print(f"Compression rate for INT8 model: {fp16_weights.stat().st_size / quantized_weights.stat().st_size:.3f}")

Size of FP16 model is 21.50 MB
Size of INT8 quantized model is 11.08 MB
Compression rate for INT8 model: 1.941

Compare inference time of the FP16 and INT8 models

To measure the inference performance of the FP16 and INT8 models, we use bencmark_app.

NOTE: For the most accurate performance estimation, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications.

!benchmark_app -m $ov_model_path -d $device.value -data_shape "batched_images[1,3,512,512],batched_points[1,1,2,2],batched_point_labels[1,1,2]" -t 15

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ 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.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 30.24 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
[ INFO ]     batched_points (node: batched_points) : i64 / [...] / [?,?,?,?]
[ INFO ]     batched_point_labels (node: batched_point_labels) : i64 / [...] / [?,?,?]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,?,?,?]
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_2) : f32 / [...] / [?,?,?]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
[ INFO ]     batched_points (node: batched_points) : i64 / [...] / [?,?,?,?]
[ INFO ]     batched_point_labels (node: batched_point_labels) : i64 / [...] / [?,?,?]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,?,?,?]
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_2) : f32 / [...] / [?,?,?]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 1388.43 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 24
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 6
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]     PERFORMANCE_HINT: THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'batched_images'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'batched_points'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'batched_point_labels'!. This input will be filled with random values!
[ INFO ] Fill input 'batched_images' with random values
[ INFO ] Fill input 'batched_points' with random values
[ INFO ] Fill input 'batched_point_labels' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in full mode (inputs filling are included in measurement loop).
[ INFO ] First inference took 798.46 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            49 iterations
[ INFO ] Duration:         16827.30 ms
[ INFO ] Latency:
[ INFO ]    Median:        2025.54 ms
[ INFO ]    Average:       1991.09 ms
[ INFO ]    Min:           816.09 ms
[ INFO ]    Max:           2176.67 ms
[ INFO ] Throughput:   2.91 FPS

if to_quantize.value:
    !benchmark_app -m $quantized_model_path -d $device.value -data_shape "batched_images[1,3,512,512],batched_points[1,1,2,2],batched_point_labels[1,1,2]" -t 15

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ 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.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 43.95 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
[ INFO ]     batched_points (node: batched_points) : i64 / [...] / [?,?,?,?]
[ INFO ]     batched_point_labels (node: batched_point_labels) : i64 / [...] / [?,?,?]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,?,?,?]
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_2) : f32 / [...] / [?,?,?]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
[ INFO ]     batched_points (node: batched_points) : i64 / [...] / [?,?,?,?]
[ INFO ]     batched_point_labels (node: batched_point_labels) : i64 / [...] / [?,?,?]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,?,?,?]
[ INFO ]     *NO_NAME* (node: aten::reshape/Reshape_2) : f32 / [...] / [?,?,?]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 1607.96 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 24
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 6
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]     PERFORMANCE_HINT: THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'batched_images'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'batched_points'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'batched_point_labels'!. This input will be filled with random values!
[ INFO ] Fill input 'batched_images' with random values
[ INFO ] Fill input 'batched_points' with random values
[ INFO ] Fill input 'batched_point_labels' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in full mode (inputs filling are included in measurement loop).
[ INFO ] First inference took 596.94 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            55 iterations
[ INFO ] Duration:         15959.69 ms
[ INFO ] Latency:
[ INFO ]    Median:        1701.74 ms
[ INFO ]    Average:       1692.86 ms
[ INFO ]    Min:           653.76 ms
[ INFO ]    Max:           1817.85 ms
[ INFO ] Throughput:   3.45 FPS

Interactive segmentation demo

import numpy as np
from PIL import Image
import cv2
import matplotlib.pyplot as plt


def sigmoid(x):
    return 1 / (1 + np.exp(-x))


def format_results(masks, scores, logits, filter=0):
    annotations = []
    n = len(scores)
    for i in range(n):
        annotation = {}

        mask = masks[i]
        tmp = np.where(mask != 0)
        if np.sum(mask) < filter:
            continue
        annotation["id"] = i
        annotation["segmentation"] = mask
        annotation["bbox"] = [
            np.min(tmp[0]),
            np.min(tmp[1]),
            np.max(tmp[1]),
            np.max(tmp[0]),
        ]
        annotation["score"] = scores[i]
        annotation["area"] = annotation["segmentation"].sum()
        annotations.append(annotation)
    return annotations


def point_prompt(masks, points, point_label, target_height, target_width):  # numpy
    h = masks[0]["segmentation"].shape[0]
    w = masks[0]["segmentation"].shape[1]
    if h != target_height or w != target_width:
        points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points]
    onemask = np.zeros((h, w))
    for i, annotation in enumerate(masks):
        if isinstance(annotation, dict):
            mask = annotation["segmentation"]
        else:
            mask = annotation
        for i, point in enumerate(points):
            if point[1] < mask.shape[0] and point[0] < mask.shape[1]:
                if mask[point[1], point[0]] == 1 and point_label[i] == 1:
                    onemask += mask
                if mask[point[1], point[0]] == 1 and point_label[i] == 0:
                    onemask -= mask
    onemask = onemask >= 1
    return onemask, 0


def show_mask(
    annotation,
    ax,
    random_color=False,
    bbox=None,
    retinamask=True,
    target_height=960,
    target_width=960,
):
    mask_sum = annotation.shape[0]
    height = annotation.shape[1]
    weight = annotation.shape[2]
    # annotation is sorted by area
    areas = np.sum(annotation, axis=(1, 2))
    sorted_indices = np.argsort(areas)[::1]
    annotation = annotation[sorted_indices]

    index = (annotation != 0).argmax(axis=0)
    if random_color:
        color = np.random.random((mask_sum, 1, 1, 3))
    else:
        color = np.ones((mask_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 255 / 255])
    transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6
    visual = np.concatenate([color, transparency], axis=-1)
    mask_image = np.expand_dims(annotation, -1) * visual

    mask = np.zeros((height, weight, 4))

    h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing="ij")
    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))

    mask[h_indices, w_indices, :] = mask_image[indices]
    if bbox is not None:
        x1, y1, x2, y2 = bbox
        ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1))

    if not retinamask:
        mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)

    return mask


def process(
    annotations,
    image,
    scale,
    better_quality=False,
    mask_random_color=True,
    bbox=None,
    points=None,
    use_retina=True,
    withContours=True,
):
    if isinstance(annotations[0], dict):
        annotations = [annotation["segmentation"] for annotation in annotations]

    original_h = image.height
    original_w = image.width
    if better_quality:
        if isinstance(annotations[0], torch.Tensor):
            annotations = np.array(annotations)
        for i, mask in enumerate(annotations):
            mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
            annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
    annotations = np.array(annotations)
    inner_mask = show_mask(
        annotations,
        plt.gca(),
        random_color=mask_random_color,
        bbox=bbox,
        retinamask=use_retina,
        target_height=original_h,
        target_width=original_w,
    )

    if isinstance(annotations, torch.Tensor):
        annotations = annotations.cpu().numpy()

    if withContours:
        contour_all = []
        temp = np.zeros((original_h, original_w, 1))
        for i, mask in enumerate(annotations):
            if isinstance(mask, dict):
                mask = mask["segmentation"]
            annotation = mask.astype(np.uint8)
            if not use_retina:
                annotation = cv2.resize(
                    annotation,
                    (original_w, original_h),
                    interpolation=cv2.INTER_NEAREST,
                )
            contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
            for contour in contours:
                contour_all.append(contour)
        cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
        color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
        contour_mask = temp / 255 * color.reshape(1, 1, -1)

    image = image.convert("RGBA")
    overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), "RGBA")
    image.paste(overlay_inner, (0, 0), overlay_inner)

    if withContours:
        overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), "RGBA")
        image.paste(overlay_contour, (0, 0), overlay_contour)

    return image


def segment_with_boxs(
    image,
    seg_image,
    global_points,
    global_point_label,
    input_size=1024,
    better_quality=False,
    withContours=True,
    use_retina=True,
    mask_random_color=True,
):
    if global_points is None or len(global_points) < 2 or global_points[0] is None:
        return image, global_points, global_point_label

    input_size = int(input_size)
    w, h = image.size
    scale = input_size / max(w, h)
    new_w = int(w * scale)
    new_h = int(h * scale)
    image = image.resize((new_w, new_h))

    scaled_points = np.array([[int(x * scale) for x in point] for point in global_points])
    scaled_points = scaled_points[:2]
    scaled_point_label = np.array(global_point_label)[:2]

    if scaled_points.size == 0 and scaled_point_label.size == 0:
        return image, global_points, global_point_label

    nd_image = np.array(image)
    img_tensor = nd_image.astype(np.float32) / 255
    img_tensor = np.transpose(img_tensor, (2, 0, 1))

    pts_sampled = np.reshape(scaled_points, [1, 1, -1, 2])
    pts_sampled = pts_sampled[:, :, :2, :]
    pts_labels = np.reshape(np.array([2, 3]), [1, 1, 2])

    results = compiled_model([img_tensor[None, ...], pts_sampled, pts_labels])
    predicted_logits = results[0]
    predicted_iou = results[1]
    all_masks = sigmoid(predicted_logits[0, 0, :, :, :]) >= 0.5
    predicted_iou = predicted_iou[0, 0, ...]

    max_predicted_iou = -1
    selected_mask_using_predicted_iou = None
    selected_predicted_iou = None

    for m in range(all_masks.shape[0]):
        curr_predicted_iou = predicted_iou[m]
        if curr_predicted_iou > max_predicted_iou or selected_mask_using_predicted_iou is None:
            max_predicted_iou = curr_predicted_iou
            selected_mask_using_predicted_iou = all_masks[m : m + 1]
            selected_predicted_iou = predicted_iou[m : m + 1]

    results = format_results(selected_mask_using_predicted_iou, selected_predicted_iou, predicted_logits, 0)

    annotations = results[0]["segmentation"]
    annotations = np.array([annotations])
    fig = process(
        annotations=annotations,
        image=image,
        scale=(1024 // input_size),
        better_quality=better_quality,
        mask_random_color=mask_random_color,
        use_retina=use_retina,
        bbox=scaled_points.reshape([4]),
        withContours=withContours,
    )

    global_points = []
    global_point_label = []
    return fig, global_points, global_point_label


def segment_with_points(
    image,
    global_points,
    global_point_label,
    input_size=1024,
    better_quality=False,
    withContours=True,
    use_retina=True,
    mask_random_color=True,
):
    input_size = int(input_size)
    w, h = image.size
    scale = input_size / max(w, h)
    new_w = int(w * scale)
    new_h = int(h * scale)
    image = image.resize((new_w, new_h))

    if global_points is None or len(global_points) < 1 or global_points[0] is None:
        return image, global_points, global_point_label
    scaled_points = np.array([[int(x * scale) for x in point] for point in global_points])
    scaled_point_label = np.array(global_point_label)

    if scaled_points.size == 0 and scaled_point_label.size == 0:
        return image, global_points, global_point_label

    nd_image = np.array(image)
    img_tensor = (nd_image).astype(np.float32) / 255
    img_tensor = np.transpose(img_tensor, (2, 0, 1))

    pts_sampled = np.reshape(scaled_points, [1, 1, -1, 2])
    pts_labels = np.reshape(np.array(global_point_label), [1, 1, -1])

    results = compiled_model([img_tensor[None, ...], pts_sampled, pts_labels])
    predicted_logits = results[0]
    predicted_iou = results[1]
    all_masks = sigmoid(predicted_logits[0, 0, :, :, :]) >= 0.5
    predicted_iou = predicted_iou[0, 0, ...]

    results = format_results(all_masks, predicted_iou, predicted_logits, 0)
    annotations, _ = point_prompt(results, scaled_points, scaled_point_label, new_h, new_w)
    annotations = np.array([annotations])

    fig = process(
        annotations=annotations,
        image=image,
        scale=(1024 // input_size),
        better_quality=better_quality,
        mask_random_color=mask_random_color,
        points=scaled_points,
        bbox=None,
        use_retina=use_retina,
        withContours=withContours,
    )

    global_points = []
    global_point_label = []
    # return fig, None
    return fig, global_points, global_point_label

# Go back to the efficient-sam notebook directory
%cd ..

if not Path("gradio_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/efficient-sam/gradio_helper.py")
    open("gradio_helper.py", "w").write(r.text)

from gradio_helper import make_demo

demo = make_demo(segment_with_point_fn=segment_with_points, segment_with_box_fn=segment_with_boxs)

try:
    demo.queue().launch(debug=False)
except Exception:
    demo.queue().launch(share=True, debug=False)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/

/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/efficient-sam
Running on local URL:  http://127.0.0.1:7860

To create a public link, set share=True in launch().