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Object Detection

Inference sample for exported torchscript object detector model.

Mask RCNN code example

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

with open('class_mapping.json') as data:
    mappings = json.load(data)

class_mapping = {item['model_idx']: item['class_name'] for item in mappings}

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = torch.jit.load('model.pt').to(device)

image_path = '/path/to/your/image'
image = Image.open(image_path)
\# Transform your image if the config.yaml shows
\# you used any image transforms for validation data
image = np.array(image)
\# Convert to torch tensor
x = torch.from_numpy(image).to(device)
with torch.no_grad():
    \# Convert to channels first, convert to float datatype
    x = x.permute(2, 0, 1).float()
    y = model(x)

    \# Some optional postprocessing, you can change the 0.5 iou
    \# overlap as needed
    to_keep = torchvision.ops.nms(y['pred_boxes'], y['scores'], 0.5)
    y['pred_boxes'] = y['pred_boxes'][to_keep]
    y['pred_classes'] = y['pred_classes'][to_keep]

    \# Draw you box predictions:
    for bbox, label in zip(y['pred_boxes'], y['pred_classes']):
        bbox = list(map(int, bbox))
        x1, y1, x2, y2 = bbox
        class_idx = label.item()
        class_name = class_mapping[class_idx]
        cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 4)
        cv2.putText(
            image,
            class_name,
            (x1, y1),
            cv2.FONT_HERSHEY_SIMPLEX,
            4,
            (255, 0, 0)
        )
\# Display predicted boxes and classes on your image
plt.imshow(image)
plt.show()

FBNetv3 code example

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

with open('class_mapping.json') as data:
    mappings = json.load(data)

class_mapping = {item['model_idx']: item['class_name'] for item in mappings}

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = torch.jit.load('model.pt').to(device)

image_path = '/path/to/your/image'
image = Image.open(image_path)
\# Transform your image if the config.yaml shows
\# you used any image transforms for validation data
image = np.array(image)
\# Convert to torch tensor
x = torch.from_numpy(image).to(device)
with torch.no_grad():
    \# Convert to channels first, convert to float datatype
    x = x.permute(2, 0, 1).float()
    pred_boxes, pred_classes, scores, _ = model(x)

    \# Some optional postprocessing, you can change the 0.5 iou
    \# overlap as needed
    to_keep = torchvision.ops.nms(pred_boxes, scores, 0.5)
    pred_boxes = pred_boxes[to_keep]
    pred_classes = pred_classes[to_keep]

    \# Draw you box predictions:
    for bbox, label in zip(pred_boxes, pred_classes):
        bbox = list(map(int, bbox))
        x1, y1, x2, y2 = bbox
        class_idx = label.item()
        class_name = class_mapping[class_idx]
        cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 4)
        cv2.putText(
            image,
            class_name,
            (x1, y1),
            cv2.FONT_HERSHEY_SIMPLEX,
            4,
            (255, 0, 0)
        )
\# Display predicted boxes and classes on your image
plt.imshow(image)
plt.show()

The script above should produce outputs that look like this:

Last updated on Jun 01, 2022

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