def test_task():
dataset = Task.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
train_data, _, test_data = dataset.random_split()
detector = Task()
detector.fit(train_data, num_trials=1, hyperparameters={'batch_size': 4, 'epochs': 5, 'early_stop_max_value': 0.2})
test_result = detector.predict(test_data)
print('test result', test_result)
detector.save('detector.ag')
detector2 = Task.load('detector.ag')
fit_summary = detector2.fit_summary()
test_map = detector2.evaluate(test_data)
test_result2 = detector2.predict(test_data)
assert test_result2.equals(test_result)
def test_task():
dataset = Task.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
train_data, _, test_data = dataset.random_split()
detector = Task()
detector.fit(train_data, hyperparameters={'batch_size': 4, 'epochs': 5, 'early_stop_max_value': 0.2}, hyperparameter_tune_kwargs={'num_trials': 1})
test_result = detector.predict(test_data)
detector.save('detector.ag')
detector2 = Task.load('detector.ag')
fit_summary = detector2.fit_summary()
test_map = detector2.evaluate(test_data)
test_result2 = detector2.predict(test_data)
assert test_result2.equals(test_result), f'{test_result2} != \n {test_result}'
# to numpy
test_result2 = detector2.predict(test_data, as_pandas=False)
import numpy as np
from matplotlib import pyplot as plt
from mxnet import image
from gluoncv import utils
from autogluon.vision import ObjectDetector
detector = ObjectDetector.load('enemy_detector.ag')
image_array = image.imread('test.jpg')
result = detector.predict(image_array)
selected_result = result.query('predict_score > 0.85')
class_ids , class_names = selected_result['predict_class'].factorize()
bounding_boxes = np.array([[x[i] for i in x.keys()] for x in list(selected_result['predict_rois'])])
scores = np.array(selected_result['predict_score'])
utils.viz.plot_bbox(image_array, bounding_boxes, scores=scores,
labels=class_ids, class_names = class_names, absolute_coordinates=False)
plt.savefig('result.jpg')