File size: 2,741 Bytes
c18b721 5877e39 183bd3e c18b721 fc0b387 c18b721 5877e39 c18b721 183bd3e c18b721 183bd3e c18b721 183bd3e c18b721 08b815a c18b721 37db9a5 c18b721 183bd3e c18b721 183bd3e c18b721 183bd3e c18b721 183bd3e c18b721 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
import argparse
from . import *
from .classes import *
from .funcs import *
from abcli import file
import abcli.logging
import logging
logger = logging.getLogger(__name__)
parser = argparse.ArgumentParser(name, description=f"{name}-{version}")
parser.add_argument(
"task",
type=str,
default="",
help="describe,eval,ingest,predict,preprocess,train",
)
parser.add_argument(
"--objects",
type=str,
default="",
)
parser.add_argument(
"--color",
type=int,
default=0,
help="0/1",
)
parser.add_argument(
"--convnet",
type=int,
default=1,
help="0/1",
)
parser.add_argument(
"--count",
type=int,
default=-1,
)
parser.add_argument(
"--data_path",
type=str,
default="",
)
parser.add_argument(
"--epochs",
default=10,
type=int,
help="",
)
parser.add_argument(
"--infer_annotation",
type=int,
default=1,
help="0/1",
)
parser.add_argument(
"--input_path",
type=str,
default="",
)
parser.add_argument(
"--model_path",
type=str,
default="",
)
parser.add_argument(
"--output_path",
type=str,
default="",
)
parser.add_argument(
"--purpose",
type=str,
default="",
help="predict/train",
)
parser.add_argument(
"--window_size",
type=int,
default=default_window_size,
)
args = parser.parse_args()
success = False
if args.task == "describe":
Image_Classifier().load(args.model_path)
success = True
elif args.task == "eval":
success = eval(args.input_path, args.output_path)
elif args.task == "predict":
classifier = Image_Classifier()
if classifier.load(args.model_path):
success, test_images = file.load(f"{args.data_path}/test_images.pyndarray")
if success:
logger.info(f"test_images: {string.pretty_shape_of_matrix(test_images)}")
_, test_labels = file.load(
f"{args.data_path}/test_labels.pyndarray",
civilized=True,
default=None,
)
test_images = test_images / 255.0
success = classifier.predict(
test_images,
test_labels,
args.output_path,
)
elif args.task == "preprocess":
success = preprocess(
args.output_path,
objects=args.objects,
infer_annotation=args.infer_annotation,
purpose=args.purpose,
window_size=args.window_size,
)
elif args.task == "train":
success = Image_Classifier.train(
args.data_path,
args.model_path,
color=args.color,
convnet=args.convnet,
epochs=args.epochs,
)
else:
logger.error(f"-{name}: {args.task}: command not found.")
if not success:
logger.error(f"-{name}: {args.task}: failed.")
|