Object masks from prompts with SAM and OpenVINO

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

Table of contents:

• Background

• Prerequisites

• Convert model to OpenVINO Intermediate Representation

  • Download model checkpoint and create PyTorch model

  • Image Encoder

  • Mask predictor

• Run OpenVINO model in interactive segmentation mode

  • Example Image

  • Preprocessing and visualization utilities

  • Image encoding

  • Example point input

  • Example with multiple points

  • Example box and point input with negative label

• Interactive segmentation

• Run OpenVINO model in automatic mask generation mode

• Optimize encoder using NNCF Post-training Quantization API

  • Prepare a calibration dataset

  • Run quantization and serialize OpenVINO IR model

  • Validate Quantized Model Inference

  • Compare Performance of the Original and Quantized Models

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.

Segmentation - identifying which image pixels belong to an object - is a core task in computer vision and is used in a broad array of applications, from analyzing scientific imagery to editing photos. But creating an accurate segmentation model for specific tasks typically requires highly specialized work by technical experts with access to AI training infrastructure and large volumes of carefully annotated in-domain data. Reducing the need for task-specific modeling expertise, training compute, and custom data annotation for image segmentation is the main goal of the Segment Anything project.

The Segment Anything Model (SAM) predicts object masks given prompts that indicate the desired object. SAM has learned a general notion of what objects are, and it can generate masks for any object in any image or any video, even including objects and image types that it had not encountered during training. SAM is general enough to cover a broad set of use cases and can be used out of the box on new image “domains” (e.g. underwater photos, MRI or cell microscopy) without requiring additional training (a capability often referred to as zero-shot transfer). This notebook shows an example of how to convert and use Segment Anything Model in OpenVINO format, allowing it to run on a variety of platforms that support an OpenVINO.

Background

Previously, to solve any kind of segmentation problem, there were two classes of approaches. The first, interactive segmentation, allowed for segmenting any class of object but required a person to guide the method by iterative refining a mask. The second, automatic segmentation, allowed for segmentation of specific object categories defined ahead of time (e.g., cats or chairs) but required substantial amounts of manually annotated objects to train (e.g., thousands or even tens of thousands of examples of segmented cats), along with the compute resources and technical expertise to train the segmentation model. Neither approach provided a general, fully automatic approach to segmentation.

Segment Anything Model is a generalization of these two classes of approaches. It is a single model that can easily perform both interactive segmentation and automatic segmentation. The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. The model consists of 3 parts:

• Image Encoder - Vision Transformer model (VIT) pretrained using Masked Auto Encoders approach (MAE) for encoding image to embedding space. The image encoder runs once per image and can be applied prior to prompting the model.

• Prompt Encoder - Encoder for segmentation condition. As a condition can be used:

  • points - set of points related to object which should be segmented. Prompt encoder converts points to embedding using positional encoding.

  • boxes - bounding box where object for segmentation is located. Similar to points, coordinates of bounding box encoded via positional encoding.

  • segmentation mask - provided by user segmentation mask is embedded using convolutions and summed element-wise with the image embedding.

  • text - encoded by CLIP model text representation

• Mask Decoder - The mask decoder efficiently maps the image embedding, prompt embeddings, and an output token to a mask.

The diagram below demonstrates the process of mask generation using SAM:

The model first converts the image into an image embedding that allows high quality masks to be efficiently produced from a prompt. The model returns multiple masks which fit to the provided prompt and its score. The provided masks can be overlapped areas as it shown on diagram, it is useful for complicated cases when prompt can be interpreted in different manner, e.g. segment whole object or only its specific part or when provided point at the intersection of multiple objects. The model’s promptable interface allows it to be used in flexible ways that make a wide range of segmentation tasks possible simply by engineering the right prompt for the model (clicks, boxes, text, and so on).

More details about approach can be found in the paper, original repo and Meta AI blog post

Prerequisites

%pip install -q "segment_anything" "gradio>=4.13" "openvino>=2023.1.0" "nncf>=2.7.0" "torch>=2.1" "torchvision>=0.16" Pillow opencv-python tqdm  --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "matplotlib>=3.4"

Convert model to OpenVINO Intermediate Representation

Download model checkpoint and create PyTorch model

There are several Segment Anything Model checkpoints available for downloading In this tutorial we will use model based on vit_b, but the demonstrated approach is very general and applicable to other SAM models. Set the model URL, path for saving checkpoint and model type below to a SAM model checkpoint, then load the model using sam_model_registry.

# Fetch `notebook_utils` module
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

checkpoint = "sam_vit_b_01ec64.pth"
model_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
model_type = "vit_b"

download_file(model_url)

from segment_anything import sam_model_registry

sam = sam_model_registry[model_type](checkpoint=checkpoint)

As we already discussed, Image Encoder part can be used once per image, then changing prompt, prompt encoder and mask decoder can be run multiple times to retrieve different objects from the same image. Taking into account this fact, we split model on 2 independent parts: image_encoder and mask_predictor (combination of Prompt Encoder and Mask Decoder).

Image Encoder

Image Encoder input is tensor with shape 1x3x1024x1024 in NCHW format, contains image for segmentation. Image Encoder output is image embeddings, tensor with shape 1x256x64x64

import warnings
from pathlib import Path
import torch
import openvino as ov

core = ov.Core()

ov_encoder_path = Path("sam_image_encoder.xml")
if not ov_encoder_path.exists():
    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
        warnings.filterwarnings("ignore", category=UserWarning)

        ov_encoder_model = ov.convert_model(
            sam.image_encoder,
            example_input=torch.zeros(1, 3, 1024, 1024),
            input=([1, 3, 1024, 1024],),
        )
    ov.save_model(ov_encoder_model, ov_encoder_path)
else:
    ov_encoder_model = core.read_model(ov_encoder_path)

device = device_widget()

device

Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')

ov_encoder = core.compile_model(ov_encoder_model, device.value)

Mask predictor

This notebook expects the model was exported with the parameter return_single_mask=True. It means that model will only return the best mask, instead of returning multiple masks. For high resolution images this can improve runtime when upscaling masks is expensive.

Combined prompt encoder and mask decoder model has following list of inputs:

• image_embeddings: The image embedding from image_encoder. Has a batch index of length 1.

• point_coords: Coordinates of sparse input prompts, corresponding to both point inputs and box inputs. Boxes are encoded using two points, one for the top-left corner and one for the bottom-right corner. Coordinates must already be transformed to long-side 1024. Has a batch index of length 1.

• point_labels: Labels for the sparse input prompts. 0 is a negative input point, 1 is a positive input point, 2 is a top-left box corner, 3 is a bottom-right box corner, and -1 is a padding point. *If there is no box input, a single padding point with label -1 and coordinates (0.0, 0.0) should be concatenated.

Model outputs:

• masks - predicted masks resized to original image size, to obtain a binary mask, should be compared with threshold (usually equal 0.0).

• iou_predictions - intersection over union predictions

• low_res_masks - predicted masks before postprocessing, can be used as mask input for model.

from typing import Tuple


class SamExportableModel(torch.nn.Module):
    def __init__(
        self,
        model,
        return_single_mask: bool,
        use_stability_score: bool = False,
        return_extra_metrics: bool = False,
    ) -> None:
        super().__init__()
        self.mask_decoder = model.mask_decoder
        self.model = model
        self.img_size = model.image_encoder.img_size
        self.return_single_mask = return_single_mask
        self.use_stability_score = use_stability_score
        self.stability_score_offset = 1.0
        self.return_extra_metrics = return_extra_metrics

    def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
        point_coords = point_coords + 0.5
        point_coords = point_coords / self.img_size
        point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
        point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)

        point_embedding = point_embedding * (point_labels != -1).to(torch.float32)
        point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1).to(torch.float32)

        for i in range(self.model.prompt_encoder.num_point_embeddings):
            point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[i].weight * (point_labels == i).to(torch.float32)

        return point_embedding

    def t_embed_masks(self, input_mask: torch.Tensor) -> torch.Tensor:
        mask_embedding = self.model.prompt_encoder.mask_downscaling(input_mask)
        return mask_embedding

    def mask_postprocessing(self, masks: torch.Tensor) -> torch.Tensor:
        masks = torch.nn.functional.interpolate(
            masks,
            size=(self.img_size, self.img_size),
            mode="bilinear",
            align_corners=False,
        )
        return masks

    def select_masks(self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int) -> Tuple[torch.Tensor, torch.Tensor]:
        # Determine if we should return the multiclick mask or not from the number of points.
        # The reweighting is used to avoid control flow.
        score_reweight = torch.tensor([[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]).to(iou_preds.device)
        score = iou_preds + (num_points - 2.5) * score_reweight
        best_idx = torch.argmax(score, dim=1)
        masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
        iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)

        return masks, iou_preds

    @torch.no_grad()
    def forward(
        self,
        image_embeddings: torch.Tensor,
        point_coords: torch.Tensor,
        point_labels: torch.Tensor,
        mask_input: torch.Tensor = None,
    ):
        sparse_embedding = self._embed_points(point_coords, point_labels)
        if mask_input is None:
            dense_embedding = self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
                point_coords.shape[0], -1, image_embeddings.shape[0], 64
            )
        else:
            dense_embedding = self._embed_masks(mask_input)

        masks, scores = self.model.mask_decoder.predict_masks(
            image_embeddings=image_embeddings,
            image_pe=self.model.prompt_encoder.get_dense_pe(),
            sparse_prompt_embeddings=sparse_embedding,
            dense_prompt_embeddings=dense_embedding,
        )

        if self.use_stability_score:
            scores = calculate_stability_score(masks, self.model.mask_threshold, self.stability_score_offset)

        if self.return_single_mask:
            masks, scores = self.select_masks(masks, scores, point_coords.shape[1])

        upscaled_masks = self.mask_postprocessing(masks)

        if self.return_extra_metrics:
            stability_scores = calculate_stability_score(upscaled_masks, self.model.mask_threshold, self.stability_score_offset)
            areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
            return upscaled_masks, scores, stability_scores, areas, masks

        return upscaled_masks, scores


ov_model_path = Path("sam_mask_predictor.xml")
if not ov_model_path.exists():
    exportable_model = SamExportableModel(sam, return_single_mask=True)
    embed_dim = sam.prompt_encoder.embed_dim
    embed_size = sam.prompt_encoder.image_embedding_size
    dummy_inputs = {
        "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
        "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
        "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
    }
    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
        warnings.filterwarnings("ignore", category=UserWarning)
        ov_model = ov.convert_model(exportable_model, example_input=dummy_inputs)
    ov.save_model(ov_model, ov_model_path)
else:
    ov_model = core.read_model(ov_model_path)

device

Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')

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

Run OpenVINO model in interactive segmentation mode

Example Image

import numpy as np
import cv2
import matplotlib.pyplot as plt

download_file("https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg")
image = cv2.imread("truck.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

'truck.jpg' already exists.

plt.figure(figsize=(10, 10))
plt.imshow(image)
plt.axis("off")
plt.show()

Preprocessing and visualization utilities

To prepare input for Image Encoder we should:

1. Convert BGR image to RGB

2. Resize image saving aspect ratio where longest size equal to Image Encoder input size - 1024.

3. Normalize image subtract mean values (123.675, 116.28, 103.53) and divide by std (58.395, 57.12, 57.375)

4. Transpose HWC data layout to CHW and add batch dimension.

5. Add zero padding to input tensor by height or width (depends on aspect ratio) according Image Encoder expected input shape.

These steps are applicable to all available models

from copy import deepcopy
from typing import Tuple
from torchvision.transforms.functional import resize, to_pil_image


class ResizeLongestSide:
    """
    Resizes images to longest side 'target_length', as well as provides
    methods for resizing coordinates and boxes. Provides methods for
    transforming numpy arrays.
    """

    def __init__(self, target_length: int) -> None:
        self.target_length = target_length

    def apply_image(self, image: np.ndarray) -> np.ndarray:
        """
        Expects a numpy array with shape HxWxC in uint8 format.
        """
        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
        return np.array(resize(to_pil_image(image), target_size))

    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
        """
        Expects a numpy array of length 2 in the final dimension. Requires the
        original image size in (H, W) format.
        """
        old_h, old_w = original_size
        new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
        coords = deepcopy(coords).astype(float)
        coords[..., 0] = coords[..., 0] * (new_w / old_w)
        coords[..., 1] = coords[..., 1] * (new_h / old_h)
        return coords

    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
        """
        Expects a numpy array shape Bx4. Requires the original image size
        in (H, W) format.
        """
        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
        return boxes.reshape(-1, 4)

    @staticmethod
    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
        """
        Compute the output size given input size and target long side length.
        """
        scale = long_side_length * 1.0 / max(oldh, oldw)
        newh, neww = oldh * scale, oldw * scale
        neww = int(neww + 0.5)
        newh = int(newh + 0.5)
        return (newh, neww)


resizer = ResizeLongestSide(1024)


def preprocess_image(image: np.ndarray):
    resized_image = resizer.apply_image(image)
    resized_image = (resized_image.astype(np.float32) - [123.675, 116.28, 103.53]) / [
        58.395,
        57.12,
        57.375,
    ]
    resized_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)).astype(np.float32), 0)

    # Pad
    h, w = resized_image.shape[-2:]
    padh = 1024 - h
    padw = 1024 - w
    x = np.pad(resized_image, ((0, 0), (0, 0), (0, padh), (0, padw)))
    return x


def postprocess_masks(masks: np.ndarray, orig_size):
    size_before_pad = resizer.get_preprocess_shape(orig_size[0], orig_size[1], masks.shape[-1])
    masks = masks[..., : int(size_before_pad[0]), : int(size_before_pad[1])]
    masks = torch.nn.functional.interpolate(torch.from_numpy(masks), size=orig_size, mode="bilinear", align_corners=False).numpy()
    return masks

def show_mask(mask, ax):
    color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)


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="green", facecolor=(0, 0, 0, 0), lw=2))

Image encoding

To start work with image, we should preprocess it and obtain image embeddings using ov_encoder. We will use the same image for all experiments, so it is possible to generate image embedding once and then reuse them.

preprocessed_image = preprocess_image(image)
encoding_results = ov_encoder(preprocessed_image)

image_embeddings = encoding_results[ov_encoder.output(0)]

Now, we can try to provide different prompts for mask generation

Example point input

In this example we select one point. The green star symbol show its location on the image below.

input_point = np.array([[500, 375]])
input_label = np.array([1])

plt.figure(figsize=(10, 10))
plt.imshow(image)
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Add a batch index, concatenate a padding point, and transform it to input tensor coordinate system.

coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]
label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)
coord = resizer.apply_coords(coord, image.shape[:2]).astype(np.float32)

Package the inputs to run in the mask predictor.

inputs = {
    "image_embeddings": image_embeddings,
    "point_coords": coord,
    "point_labels": label,
}

Predict a mask and threshold it to get binary mask (0 - no object, 1 - object).

results = ov_predictor(inputs)

masks = results[ov_predictor.output(0)]
masks = postprocess_masks(masks, image.shape[:-1])
masks = masks > 0.0

plt.figure(figsize=(10, 10))
plt.imshow(image)
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Example with multiple points

in this example, we provide additional point for cover larger object area.

input_point = np.array([[500, 375], [1125, 625], [575, 750], [1405, 575]])
input_label = np.array([1, 1, 1, 1])

Now, prompt for model looks like represented on this image:

plt.figure(figsize=(10, 10))
plt.imshow(image)
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Transform the points as in the previous example.

coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]
label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)

coord = resizer.apply_coords(coord, image.shape[:2]).astype(np.float32)

Package inputs, then predict and threshold the mask.

inputs = {
    "image_embeddings": image_embeddings,
    "point_coords": coord,
    "point_labels": label,
}

results = ov_predictor(inputs)

masks = results[ov_predictor.output(0)]
masks = postprocess_masks(masks, image.shape[:-1])
masks = masks > 0.0

plt.figure(figsize=(10, 10))
plt.imshow(image)
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Great! Looks like now, predicted mask cover whole truck.

Example box and point input with negative label

In this example we define input prompt using bounding box and point inside it.The bounding box represented as set of points of its left upper corner and right lower corner. Label 0 for point speak that this point should be excluded from mask.

input_box = np.array([425, 600, 700, 875])
input_point = np.array([[575, 750]])
input_label = np.array([0])

plt.figure(figsize=(10, 10))
plt.imshow(image)
show_box(input_box, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Add a batch index, concatenate a box and point inputs, add the appropriate labels for the box corners, and transform. There is no padding point since the input includes a box input.

box_coords = input_box.reshape(2, 2)
box_labels = np.array([2, 3])

coord = np.concatenate([input_point, box_coords], axis=0)[None, :, :]
label = np.concatenate([input_label, box_labels], axis=0)[None, :].astype(np.float32)

coord = resizer.apply_coords(coord, image.shape[:2]).astype(np.float32)

Package inputs, then predict and threshold the mask.

inputs = {
    "image_embeddings": image_embeddings,
    "point_coords": coord,
    "point_labels": label,
}

results = ov_predictor(inputs)

masks = results[ov_predictor.output(0)]
masks = postprocess_masks(masks, image.shape[:-1])
masks = masks > 0.0

plt.figure(figsize=(10, 10))
plt.imshow(image)
show_mask(masks[0], plt.gca())
show_box(input_box, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Interactive segmentation

Now, you can try SAM on own image. Upload image to input window and click on desired point, model predict segment based on your image and point.

class Segmenter:
    def __init__(self, ov_encoder, ov_predictor):
        self.encoder = ov_encoder
        self.predictor = ov_predictor
        self._img_embeddings = None

    def set_image(self, img: np.ndarray):
        if self._img_embeddings is not None:
            del self._img_embeddings
        preprocessed_image = preprocess_image(img)
        encoding_results = self.encoder(preprocessed_image)
        image_embeddings = encoding_results[ov_encoder.output(0)]
        self._img_embeddings = image_embeddings
        return img

    def get_mask(self, points, img):
        coord = np.array(points)
        coord = np.concatenate([coord, np.array([[0, 0]])], axis=0)
        coord = coord[None, :, :]
        label = np.concatenate([np.ones(len(points)), np.array([-1])], axis=0)[None, :].astype(np.float32)
        coord = resizer.apply_coords(coord, img.shape[:2]).astype(np.float32)
        if self._img_embeddings is None:
            self.set_image(img)
        inputs = {
            "image_embeddings": self._img_embeddings,
            "point_coords": coord,
            "point_labels": label,
        }

        results = self.predictor(inputs)
        masks = results[ov_predictor.output(0)]
        masks = postprocess_masks(masks, img.shape[:-1])

        masks = masks > 0.0
        mask = masks[0]
        mask = np.transpose(mask, (1, 2, 0))
        return mask


segmenter = Segmenter(ov_encoder, ov_predictor)

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

from gradio_helper import make_demo

demo = make_demo(segmenter)

try:
    demo.launch()
except Exception:
    demo.launch(share=True)
# If you are launching remotely, specify server_name and server_port
# EXAMPLE: `demo.launch(server_name="your server name", server_port="server port in int")`
# To learn more please refer to the Gradio docs: https://gradio.app/docs/

Run OpenVINO model in automatic mask generation mode

Since SAM can efficiently process prompts, masks for the entire image can be generated by sampling a large number of prompts over an image. automatic_mask_generation function implements this capability. It works by sampling single-point input prompts in a grid over the image, from each of which SAM can predict multiple masks. Then, masks are filtered for quality and deduplicated using non-maximal suppression. Additional options allow for further improvement of mask quality and quantity, such as running prediction on multiple crops of the image or postprocessing masks to remove small disconnected regions and holes.

from segment_anything.utils.amg import (
    MaskData,
    generate_crop_boxes,
    uncrop_boxes_xyxy,
    uncrop_masks,
    uncrop_points,
    calculate_stability_score,
    rle_to_mask,
    batched_mask_to_box,
    mask_to_rle_pytorch,
    is_box_near_crop_edge,
    batch_iterator,
    remove_small_regions,
    build_all_layer_point_grids,
    box_xyxy_to_xywh,
    area_from_rle,
)
from torchvision.ops.boxes import batched_nms, box_area
from typing import Tuple, List, Dict, Any

def process_batch(
    image_embedding: np.ndarray,
    points: np.ndarray,
    im_size: Tuple[int, ...],
    crop_box: List[int],
    orig_size: Tuple[int, ...],
    iou_thresh,
    mask_threshold,
    stability_score_offset,
    stability_score_thresh,
) -> MaskData:
    orig_h, orig_w = orig_size

    # Run model on this batch
    transformed_points = resizer.apply_coords(points, im_size)
    in_points = transformed_points
    in_labels = np.ones(in_points.shape[0], dtype=int)

    inputs = {
        "image_embeddings": image_embedding,
        "point_coords": in_points[:, None, :],
        "point_labels": in_labels[:, None],
    }
    res = ov_predictor(inputs)
    masks = postprocess_masks(res[ov_predictor.output(0)], orig_size)
    masks = torch.from_numpy(masks)
    iou_preds = torch.from_numpy(res[ov_predictor.output(1)])

    # Serialize predictions and store in MaskData
    data = MaskData(
        masks=masks.flatten(0, 1),
        iou_preds=iou_preds.flatten(0, 1),
        points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
    )
    del masks

    # Filter by predicted IoU
    if iou_thresh > 0.0:
        keep_mask = data["iou_preds"] > iou_thresh
        data.filter(keep_mask)

    # Calculate stability score
    data["stability_score"] = calculate_stability_score(data["masks"], mask_threshold, stability_score_offset)
    if stability_score_thresh > 0.0:
        keep_mask = data["stability_score"] >= stability_score_thresh
        data.filter(keep_mask)

    # Threshold masks and calculate boxes
    data["masks"] = data["masks"] > mask_threshold
    data["boxes"] = batched_mask_to_box(data["masks"])

    # Filter boxes that touch crop boundaries
    keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
    if not torch.all(keep_mask):
        data.filter(keep_mask)

    # Compress to RLE
    data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
    data["rles"] = mask_to_rle_pytorch(data["masks"])
    del data["masks"]

    return data

def process_crop(
    image: np.ndarray,
    point_grids,
    crop_box: List[int],
    crop_layer_idx: int,
    orig_size: Tuple[int, ...],
    box_nms_thresh: float = 0.7,
    mask_threshold: float = 0.0,
    points_per_batch: int = 64,
    pred_iou_thresh: float = 0.88,
    stability_score_thresh: float = 0.95,
    stability_score_offset: float = 1.0,
) -> MaskData:
    # Crop the image and calculate embeddings
    x0, y0, x1, y1 = crop_box
    cropped_im = image[y0:y1, x0:x1, :]
    cropped_im_size = cropped_im.shape[:2]
    preprocessed_cropped_im = preprocess_image(cropped_im)
    crop_embeddings = ov_encoder(preprocessed_cropped_im)[ov_encoder.output(0)]

    # Get points for this crop
    points_scale = np.array(cropped_im_size)[None, ::-1]
    points_for_image = point_grids[crop_layer_idx] * points_scale

    # Generate masks for this crop in batches
    data = MaskData()
    for (points,) in batch_iterator(points_per_batch, points_for_image):
        batch_data = process_batch(
            crop_embeddings,
            points,
            cropped_im_size,
            crop_box,
            orig_size,
            pred_iou_thresh,
            mask_threshold,
            stability_score_offset,
            stability_score_thresh,
        )
        data.cat(batch_data)
        del batch_data

    # Remove duplicates within this crop.
    keep_by_nms = batched_nms(
        data["boxes"].float(),
        data["iou_preds"],
        torch.zeros(len(data["boxes"])),  # categories
        iou_threshold=box_nms_thresh,
    )
    data.filter(keep_by_nms)

    # Return to the original image frame
    data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
    data["points"] = uncrop_points(data["points"], crop_box)
    data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])

    return data

def generate_masks(image: np.ndarray, point_grids, crop_n_layers, crop_overlap_ratio, crop_nms_thresh) -> MaskData:
    orig_size = image.shape[:2]
    crop_boxes, layer_idxs = generate_crop_boxes(orig_size, crop_n_layers, crop_overlap_ratio)

    # Iterate over image crops
    data = MaskData()
    for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
        crop_data = process_crop(image, point_grids, crop_box, layer_idx, orig_size)
        data.cat(crop_data)

    # Remove duplicate masks between crops
    if len(crop_boxes) > 1:
        # Prefer masks from smaller crops
        scores = 1 / box_area(data["crop_boxes"])
        scores = scores.to(data["boxes"].device)
        keep_by_nms = batched_nms(
            data["boxes"].float(),
            scores,
            torch.zeros(len(data["boxes"])),  # categories
            iou_threshold=crop_nms_thresh,
        )
        data.filter(keep_by_nms)

    data.to_numpy()
    return data

def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
    """
    Removes small disconnected regions and holes in masks, then reruns
    box NMS to remove any new duplicates.

    Edits mask_data in place.

    Requires open-cv as a dependency.
    """
    if len(mask_data["rles"]) == 0:
        return mask_data

    # Filter small disconnected regions and holes
    new_masks = []
    scores = []
    for rle in mask_data["rles"]:
        mask = rle_to_mask(rle)

        mask, changed = remove_small_regions(mask, min_area, mode="holes")
        unchanged = not changed
        mask, changed = remove_small_regions(mask, min_area, mode="islands")
        unchanged = unchanged and not changed

        new_masks.append(torch.as_tensor(mask).unsqueeze(0))
        # Give score=0 to changed masks and score=1 to unchanged masks
        # so NMS will prefer ones that didn't need postprocessing
        scores.append(float(unchanged))

    # Recalculate boxes and remove any new duplicates
    masks = torch.cat(new_masks, dim=0)
    boxes = batched_mask_to_box(masks)
    keep_by_nms = batched_nms(
        boxes.float(),
        torch.as_tensor(scores),
        torch.zeros(len(boxes)),  # categories
        iou_threshold=nms_thresh,
    )

    # Only recalculate RLEs for masks that have changed
    for i_mask in keep_by_nms:
        if scores[i_mask] == 0.0:
            mask_torch = masks[i_mask].unsqueeze(0)
            mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
            # update res directly
            mask_data["boxes"][i_mask] = boxes[i_mask]
    mask_data.filter(keep_by_nms)

    return mask_data

There are several tunable parameters in automatic mask generation that control how densely points are sampled and what the thresholds are for removing low quality or duplicate masks. Additionally, generation can be automatically run on crops of the image to get improved performance on smaller objects, and post-processing can remove stray pixels and holes

def automatic_mask_generation(
    image: np.ndarray,
    min_mask_region_area: int = 0,
    points_per_side: int = 32,
    crop_n_layers: int = 0,
    crop_n_points_downscale_factor: int = 1,
    crop_overlap_ratio: float = 512 / 1500,
    box_nms_thresh: float = 0.7,
    crop_nms_thresh: float = 0.7,
) -> List[Dict[str, Any]]:
    """
    Generates masks for the given image.

    Arguments:
      image (np.ndarray): The image to generate masks for, in HWC uint8 format.

    Returns:
       list(dict(str, any)): A list over records for masks. Each record is
         a dict containing the following keys:
           segmentation (dict(str, any) or np.ndarray): The mask. If
             output_mode='binary_mask', is an array of shape HW. Otherwise,
             is a dictionary containing the RLE.
           bbox (list(float)): The box around the mask, in XYWH format.
           area (int): The area in pixels of the mask.
           predicted_iou (float): The model's own prediction of the mask's
             quality. This is filtered by the pred_iou_thresh parameter.
           point_coords (list(list(float))): The point coordinates input
             to the model to generate this mask.
           stability_score (float): A measure of the mask's quality. This
             is filtered on using the stability_score_thresh parameter.
           crop_box (list(float)): The crop of the image used to generate
             the mask, given in XYWH format.
    """
    point_grids = build_all_layer_point_grids(
        points_per_side,
        crop_n_layers,
        crop_n_points_downscale_factor,
    )
    mask_data = generate_masks(image, point_grids, crop_n_layers, crop_overlap_ratio, crop_nms_thresh)

    # Filter small disconnected regions and holes in masks
    if min_mask_region_area > 0:
        mask_data = postprocess_small_regions(
            mask_data,
            min_mask_region_area,
            max(box_nms_thresh, crop_nms_thresh),
        )

    mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]

    # Write mask records
    curr_anns = []
    for idx in range(len(mask_data["segmentations"])):
        ann = {
            "segmentation": mask_data["segmentations"][idx],
            "area": area_from_rle(mask_data["rles"][idx]),
            "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
            "predicted_iou": mask_data["iou_preds"][idx].item(),
            "point_coords": [mask_data["points"][idx].tolist()],
            "stability_score": mask_data["stability_score"][idx].item(),
            "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
        }
        curr_anns.append(ann)

    return curr_anns

prediction = automatic_mask_generation(image)

automatic_mask_generation returns a list over masks, where each mask is a dictionary containing various data about the mask. These keys are:

• segmentation : the mask

• area : the area of the mask in pixels

• bbox : the boundary box of the mask in XYWH format

• predicted_iou : the model’s own prediction for the quality of the mask

• point_coords : the sampled input point that generated this mask

• stability_score : an additional measure of mask quality

• crop_box : the crop of the image used to generate this mask in XYWH format

print(f"Number of detected masks: {len(prediction)}")
print(f"Annotation keys: {prediction[0].keys()}")

Number of detected masks: 48
Annotation keys: dict_keys(['segmentation', 'area', 'bbox', 'predicted_iou', 'point_coords', 'stability_score', 'crop_box'])

from tqdm.notebook import tqdm


def draw_anns(image, anns):
    if len(anns) == 0:
        return
    segments_image = image.copy()
    sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
    for ann in tqdm(sorted_anns):
        mask = ann["segmentation"]
        mask_color = np.random.randint(0, 255, size=(1, 1, 3)).astype(np.uint8)
        segments_image[mask] = mask_color
    return cv2.addWeighted(image.astype(np.float32), 0.7, segments_image.astype(np.float32), 0.3, 0.0)

import PIL

out = draw_anns(image, prediction)
cv2.imwrite("result.png", out[:, :, ::-1])

PIL.Image.open("result.png")

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Optimize encoder using NNCF Post-training Quantization API

NNCF provides a suite of advanced algorithms for Neural Networks inference optimization in OpenVINO with minimal accuracy drop.

Since encoder costing much more time than other parts in SAM inference pipeline, we will use 8-bit quantization in post-training mode (without the fine-tuning pipeline) to optimize encoder of SAM.

The optimization process contains the following steps:

1. Create a Dataset for quantization.

2. Run nncf.quantize for getting an optimized model.

3. Serialize OpenVINO IR model, using the openvino.save_model function.

Prepare a calibration dataset

Download COCO dataset. Since the dataset is used to calibrate the model’s parameter instead of fine-tuning it, we don’t need to download the label files.

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' already exists.

Create an instance of the nncf.Dataset class that represents the calibration dataset. For PyTorch, we can pass an instance of the torch.utils.data.DataLoader object.

import torch.utils.data as data


class COCOLoader(data.Dataset):
    def __init__(self, images_path):
        self.images = list(Path(images_path).iterdir())

    def __getitem__(self, index):
        image_path = self.images[index]
        image = cv2.imread(str(image_path))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        return image

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


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

The transformation function is a function that takes a sample from the dataset and returns data that can be passed to the model for inference.

import nncf


def transform_fn(image_data):
    """
    Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
    Parameters:
        image_data: image data produced by DataLoader during iteration
    Returns:
        input_tensor: input data in Dict format for model quantization
    """
    image = image_data.numpy()
    processed_image = preprocess_image(np.squeeze(image))
    return processed_image


calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)

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

Run quantization and serialize OpenVINO IR model

The nncf.quantize function provides an interface for model quantization. It requires an instance of the OpenVINO Model and quantization dataset. It is available for models in the following frameworks: PyTorch, TensorFlow 2.x, ONNX, and OpenVINO IR.

Optionally, some additional parameters for the configuration quantization process (number of samples for quantization, preset, model type, etc.) can be provided. model_type can be used to specify quantization scheme required for specific type of the model. For example, Transformer models such as SAM require a special quantization scheme to preserve accuracy after quantization. To achieve a better result, we will use a mixed quantization preset. It provides symmetric quantization of weights and asymmetric quantization of activations.

Note: Model post-training quantization is time-consuming process. Be patient, it can take several minutes depending on your hardware.

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

2023-09-11 20:39:36.145499: 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.
2023-09-11 20:39:36.181406: 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.
2023-09-11 20:39:36.769588: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Statistics collection: 100%|██████████████████| 128/128 [02:12<00:00,  1.03s/it]
Applying Smooth Quant: 100%|████████████████████| 48/48 [00:01<00:00, 32.29it/s]

INFO:nncf:36 ignored nodes was found by name in the NNCFGraph

Statistics collection: 100%|██████████████████| 128/128 [04:36<00:00,  2.16s/it]
Applying Fast Bias correction: 100%|████████████| 49/49 [00:28<00:00,  1.72it/s]

model quantization finished

ov_encoder_path_int8 = "sam_image_encoder_int8.xml"
ov.save_model(quantized_model, ov_encoder_path_int8)

Validate Quantized Model Inference

We can reuse the previous code to validate the output of INT8 model.

# Load INT8 model and run pipeline again
ov_encoder_model_int8 = core.read_model(ov_encoder_path_int8)
ov_encoder_int8 = core.compile_model(ov_encoder_model_int8, device.value)
encoding_results = ov_encoder_int8(preprocessed_image)
image_embeddings = encoding_results[ov_encoder_int8.output(0)]

input_point = np.array([[500, 375]])
input_label = np.array([1])
coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]
label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)

coord = resizer.apply_coords(coord, image.shape[:2]).astype(np.float32)
inputs = {
    "image_embeddings": image_embeddings,
    "point_coords": coord,
    "point_labels": label,
}
results = ov_predictor(inputs)

masks = results[ov_predictor.output(0)]
masks = postprocess_masks(masks, image.shape[:-1])
masks = masks > 0.0
plt.figure(figsize=(10, 10))
plt.imshow(image)
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis("off")
plt.show()

Run INT8 model in automatic mask generation mode

ov_encoder = ov_encoder_int8
prediction = automatic_mask_generation(image)
out = draw_anns(image, prediction)
cv2.imwrite("result_int8.png", out[:, :, ::-1])
PIL.Image.open("result_int8.png")

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Compare Performance of the Original and Quantized Models

Finally, use the OpenVINO Benchmark Tool to measure the inference performance of the FP32 and INT8 models.

# Inference FP32 model (OpenVINO IR)
!benchmark_app -m $ov_encoder_path -d $device.value

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ WARNING ] Default duration 120 seconds is used for unknown device AUTO
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.1.0-12050-e33de350633
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2023.1.0-12050-e33de350633
[ 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 31.21 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     x (node: x) : f32 / [...] / [1,3,1024,1024]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.neck.3/aten::add/Add_2933) : f32 / [...] / [1,256,64,64]
[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 ]     x (node: x) : u8 / [N,C,H,W] / [1,3,1024,1024]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.neck.3/aten::add/Add_2933) : f32 / [...] / [1,256,64,64]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 956.62 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model474
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ 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 ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 36
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     NETWORK_NAME: Model474
[ INFO ]     NUM_STREAMS: 12
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: False
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: 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
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 3347.39 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            132 iterations
[ INFO ] Duration:         135907.17 ms
[ INFO ] Latency:
[ INFO ]    Median:        12159.63 ms
[ INFO ]    Average:       12098.43 ms
[ INFO ]    Min:           7652.77 ms
[ INFO ]    Max:           13027.98 ms
[ INFO ] Throughput:   0.97 FPS

# Inference INT8 model (OpenVINO IR)
!benchmark_app -m $ov_encoder_path_int8 -d $device.value

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ WARNING ] Default duration 120 seconds is used for unknown device AUTO
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.1.0-12050-e33de350633
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2023.1.0-12050-e33de350633
[ 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 40.67 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     x (node: x) : f32 / [...] / [1,3,1024,1024]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.neck.3/aten::add/Add_2933) : f32 / [...] / [1,256,64,64]
[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 ]     x (node: x) : u8 / [N,C,H,W] / [1,3,1024,1024]
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: __module.neck.3/aten::add/Add_2933) : f32 / [...] / [1,256,64,64]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 1151.47 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model474
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ 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 ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 36
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     NETWORK_NAME: Model474
[ INFO ]     NUM_STREAMS: 12
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]     PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: False
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: 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
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 1951.78 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            216 iterations
[ INFO ] Duration:         130123.96 ms
[ INFO ] Latency:
[ INFO ]    Median:        7192.03 ms
[ INFO ]    Average:       7197.18 ms
[ INFO ]    Min:           6134.35 ms
[ INFO ]    Max:           7888.28 ms
[ INFO ] Throughput:   1.66 FPS