Sample inference script for torchscript exported semantic segmentation model
Hello, thank you for using the code provided by CloudFactory. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add it to your system to complement it.
python
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)
h, w = image.shape[:2]
# 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).unsqueeze(dim=0).float()
y = model(x)
mask = torch.argmax(y, dim=1).squeeze()
# Overlay predicted mask on image and display
plt.imshow(image)
plt.imshow(mask, alpha=0.5)
plt.show()
The script above should produce outputs that look like this:
Example output from the semseg inference script, yellow highlights the present class.
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