File size: 2,962 Bytes
0ad74ed |
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 |
from pathlib import Path
import numpy as np
import pytest
import gradio as gr
from gradio import FileData
class TestLabel:
def test_component_functions(self):
"""
Process, postprocess, deserialize
"""
y = "happy"
label_output = gr.Label()
label = label_output.postprocess(y).model_dump() # type: ignore
assert label == {"label": "happy", "confidences": None}
y = {3: 0.7, 1: 0.2, 0: 0.1}
label = label_output.postprocess(y).model_dump() # type: ignore
assert label == {
"label": 3,
"confidences": [
{"label": 3, "confidence": 0.7},
{"label": 1, "confidence": 0.2},
{"label": 0, "confidence": 0.1},
],
}
label_output = gr.Label(num_top_classes=2)
label = label_output.postprocess(y).model_dump() # type: ignore
assert label == {
"label": 3,
"confidences": [
{"label": 3, "confidence": 0.7},
{"label": 1, "confidence": 0.2},
],
}
with pytest.raises(ValueError):
label_output.postprocess([1, 2, 3]).model_dump() # type: ignore
test_file_dir = Path(__file__).parent.parent / "test_files"
path = str(test_file_dir / "test_label_json.json")
label_dict = label_output.postprocess(path).model_dump() # type: ignore
assert label_dict["label"] == "web site"
assert label_output.get_config() == {
"name": "label",
"show_label": True,
"num_top_classes": 2,
"value": {},
"label": None,
"container": True,
"min_width": 160,
"scale": None,
"elem_id": None,
"elem_classes": [],
"visible": True,
"proxy_url": None,
"color": None,
"_selectable": False,
"key": None,
}
def test_color_argument(self):
label = gr.Label(value=-10, color="red")
assert label.get_config()["color"] == "red"
def test_in_interface(self):
"""
Interface, process
"""
x_img = FileData(path="test/test_files/bus.png")
def rgb_distribution(img):
rgb_dist = np.mean(img, axis=(0, 1))
rgb_dist /= np.sum(rgb_dist)
rgb_dist = np.round(rgb_dist, decimals=2)
return {
"red": rgb_dist[0],
"green": rgb_dist[1],
"blue": rgb_dist[2],
}
iface = gr.Interface(rgb_distribution, "image", "label")
output = iface(x_img)
assert output == {
"label": "red",
"confidences": [
{"label": "red", "confidence": 0.44},
{"label": "green", "confidence": 0.28},
{"label": "blue", "confidence": 0.28},
],
}
|