Object Detection

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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()
    python

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()
    python

The script above should produce outputs that look like this:

Last modified 4mo ago

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Semantic Segmentation

On this page
Mask RCNN code example
FBNetv3 code example
Report issue
Get help