def test_chicken(self):
chicken_img = Image.open(
pathlib.Path(__file__).parent / 'efficientnet/Chicken.jpg')
model = EfficientNet(number=0)
model.load_from_pretrained()
label = _infer(model, chicken_img)
self.assertEqual(label, "hen",
f"Expected hen but got {label} for number=0")
# run the net
out = model.forward(Tensor(img)).cpu()
# if you want to look at the outputs
"""
import matplotlib.pyplot as plt
plt.plot(out.data[0])
plt.show()
"""
return out, retimg
if __name__ == "__main__":
# instantiate my net
model = EfficientNet(int(os.getenv("NUM", "0")))
model.load_from_pretrained()
# category labels
import ast
lbls = fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
lbls = ast.literal_eval(lbls.decode('utf-8'))
# load image and preprocess
from PIL import Image
url = sys.argv[1]
if url == 'webcam':
import cv2
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
while 1:
_ = cap.grab() # discard one frame to circumvent capture buffering