parser.add_argument('--num_points',
type=int,
default=2048,
help='Num of points to use')
parser.add_argument('--model_path',
type=str,
default='',
metavar='N',
help='Path to load model')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = get_parser()
if args.eval == False:
if args.task == 'reconstruct':
reconstruction = Reconstruction(args)
reconstruction.run()
elif args.task == 'classify':
classification = Classification(args)
classification.run()
elif args.task == 'segment':
segmentation = Segmentation(args)
segmentation.run()
else:
inference = Inference(args)
feature_dir = inference.run()
svm = SVM(feature_dir)
svm.run()
str(prior["type"][idx]) + "_loc=" +
str(int(prior["location"][idx])) + "_scl=" +
str(int(prior["scale"][idx])) + ".h5")
file_chains = dir_chains + name_chains
file_csv = file_chains.replace("h5", "csv")
if not os.path.isfile(file_chains):
p1d = Inference(posterior=Posterior,
prior=prior["type"],
prior_loc=prior["location"],
prior_scale=prior["scale"],
n_walkers=n_walkers,
zero_point=zero_point)
p1d.load_data(file_data, id_name=id_name)
p1d.run(n_iter, file_chains=file_chains, tol_convergence=tolerance)
#----------------- Analysis ---------------
a1d = Analysis(
n_dim=dimension,
file_name=file_chains,
id_name=id_name,
dir_plots=dir_plots,
tol_convergence=tolerance,
statistic=statistic,
quantiles=[0.05, 0.95],
# transformation=None,
names="2",
transformation="ICRS2GAL",
)
a1d.plot_chains()
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.8,
random_state=42)
import xgboost as xgb
xgb_model = xgb.XGBClassifier()
xgb_model = xgb_model.fit(X_train, y_train)
print("Test data accuracy of the xgb classifier is {:.2f}".format(
xgb_model.score(X_test, y_test)))
from onnxmltools.convert import convert_xgboost, convert_lightgbm
from onnxconverter_common.data_types import FloatTensorType
onnx_model = convert_xgboost(xgb_model,
initial_types=[("input", FloatTensorType([1,
4]))])
with open("gbtree.onnx", "wb") as f:
f.write(onnx_model.SerializeToString())
if __name__ == '__main__':
from inference import Inference
infer = Inference("gbtree.onnx")
print(infer.run(X[:1]))
class NN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 16)
self.fc2 = nn.Linear(16, 3)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x) # F.softmax
return x
net = NN()
print(net)
# Export: X.shape
onnx.export(net,
torch.randn(128, 4),
'./iris.onnx',
verbose=True,
input_names=['input_name'],
output_names=['output_name'])
if __name__ == '__main__':
from inference import Inference
infer = Inference()
print(infer.run(X[:128])[:5])
import cv2
from inference import Inference
import flask
im1 = cv2.imread('bed (1).jpg')
infer = Inference()
#for i in range(1,9):
output = infer.run(im1)
cv2.imshow("output", output[0] / 255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
from utils import config
GPU_LIST = config.INFERENCE_GPUS
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join('{0}'.format(n) for n in GPU_LIST)
from inference import Inference
if __name__ == '__main__':
pg = Inference(data_dir='/path/to/data/', data_list='/path/to/list',
class_num=2, result_dir='./result', use_level=1)
pg.run()
