generated from roboflow/template-python
-
Notifications
You must be signed in to change notification settings - Fork 2k
/
Copy pathtest_vlm_florence_2.py
291 lines (287 loc) · 10.1 KB
/
test_vlm_florence_2.py
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
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
from contextlib import ExitStack as DoesNotRaise
from typing import Optional, Tuple
import numpy as np
import pytest
from supervision.detection.vlm import from_florence_2
@pytest.mark.parametrize(
"florence_result, resolution_wh, expected_results, exception",
[
( # Object detection: empty
{"<OD>": {"bboxes": [], "labels": []}},
(10, 10),
(np.array([], dtype=np.float32), np.array([]), None, None),
DoesNotRaise(),
),
( # Object detection: two detections
{
"<OD>": {
"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
"labels": ["car", "door"],
}
},
(10, 10),
(
np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
np.array(["car", "door"]),
None,
None,
),
DoesNotRaise(),
),
( # Caption: unsupported
{"<CAPTION>": "A green car parked in front of a yellow building."},
(10, 10),
None,
pytest.raises(ValueError),
),
( # Detailed Caption: unsupported
{
"<DETAILED_CAPTION>": "The image shows a blue Volkswagen Beetle parked "
"in front of a yellow building with two brown doors, surrounded by "
"trees and a clear blue sky."
},
(10, 10),
None,
pytest.raises(ValueError),
),
( # More Detailed Caption: unsupported
{
"<MORE_DETAILED_CAPTION>": "The image shows a vintage Volkswagen "
"Beetle car parked on a "
"cobblestone street in front of a yellow building with two wooden "
"doors. The car is painted in a bright turquoise color and has a "
"white stripe running along the side. It has two doors on either side "
"of the car, one on top of the other, and a small window on the "
"front. The building appears to be old and dilapidated, with peeling "
"paint and crumbling walls. The sky is blue and there are trees in "
"the background."
},
(10, 10),
None,
pytest.raises(ValueError),
),
( # Caption to Phrase Grounding: empty
{"<CAPTION_TO_PHRASE_GROUNDING>": {"bboxes": [], "labels": []}},
(10, 10),
(np.array([], dtype=np.float32), np.array([]), None, None),
DoesNotRaise(),
),
( # Caption to Phrase Grounding: two detections
{
"<CAPTION_TO_PHRASE_GROUNDING>": {
"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
"labels": ["a green car", "a yellow building"],
}
},
(10, 10),
(
np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
np.array(["a green car", "a yellow building"]),
None,
None,
),
DoesNotRaise(),
),
( # Dense Region caption: empty
{"<DENSE_REGION_CAPTION>": {"bboxes": [], "labels": []}},
(10, 10),
(np.array([], dtype=np.float32), np.array([]), None, None),
DoesNotRaise(),
),
( # Caption to Phrase Grounding: two detections
{
"<DENSE_REGION_CAPTION>": {
"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
"labels": ["a green car", "a yellow building"],
}
},
(10, 10),
(
np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
np.array(["a green car", "a yellow building"]),
None,
None,
),
DoesNotRaise(),
),
( # Region proposal
{
"<REGION_PROPOSAL>": {
"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
"labels": ["", ""],
}
},
(10, 10),
(
np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
None,
None,
None,
),
DoesNotRaise(),
),
( # Referring Expression Segmentation
{
"<REFERRING_EXPRESSION_SEGMENTATION>": {
"polygons": [[[1, 1, 2, 1, 2, 2, 1, 2]]],
"labels": [""],
}
},
(10, 10),
(
np.array([[1.0, 1.0, 2.0, 2.0]], dtype=np.float32),
None,
np.array(
[
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
],
dtype=bool,
),
None,
),
DoesNotRaise(),
),
( # Referring Expression Segmentation
{
"<REFERRING_EXPRESSION_SEGMENTATION>": {
"polygons": [[[1, 1, 2, 1, 2, 2, 1, 2]]],
"labels": [""],
}
},
(10, 10),
(
np.array([[1.0, 1.0, 2.0, 2.0]], dtype=np.float32),
None,
np.array(
[
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
],
dtype=bool,
),
None,
),
DoesNotRaise(),
),
( # OCR: unsupported
{"<OCR>": "A"},
(10, 10),
None,
pytest.raises(ValueError),
),
( # OCR with Region: obb boxes
{
"<OCR_WITH_REGION>": {
"quad_boxes": [[2, 2, 6, 4, 5, 6, 1, 5], [4, 4, 5, 5, 4, 6, 3, 5]],
"labels": ["some text", "other text"],
}
},
(10, 10),
(
np.array([[1, 2, 6, 6], [3, 4, 5, 6]], dtype=np.float32),
np.array(["some text", "other text"]),
None,
np.array(
[[[2, 2], [6, 4], [5, 6], [1, 5]], [[4, 4], [5, 5], [4, 6], [3, 5]]]
),
),
DoesNotRaise(),
),
( # Open Vocabulary Detection
{
"<OPEN_VOCABULARY_DETECTION>": {
"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
"bboxes_labels": ["cat", "cat"],
"polygon": [],
"polygons_labels": [],
}
},
(10, 10),
(
np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
np.array(["cat", "cat"]),
None,
None,
),
DoesNotRaise(),
),
( # Region to Category: empty
{"<REGION_TO_CATEGORY>": "No object detected."},
(10, 10),
(np.empty((0, 4), dtype=np.float32), np.array([]), None, None),
DoesNotRaise(),
),
( # Region to Category: detected
{"<REGION_TO_CATEGORY>": "some object<loc_300><loc_400><loc_500><loc_600>"},
(10, 10),
(
np.array([[3, 4, 5, 6]], dtype=np.float32),
np.array(["some object"]),
None,
None,
),
DoesNotRaise(),
),
( # Region to Description: empty
{"<REGION_TO_DESCRIPTION>": "No object detected."},
(10, 10),
(np.empty((0, 4), dtype=np.float32), np.array([]), None, None),
DoesNotRaise(),
),
( # Region to Description: detected
{"<REGION_TO_DESCRIPTION>": "descr<loc_300><loc_400><loc_500><loc_600>"},
(10, 10),
(
np.array([[3, 4, 5, 6]], dtype=np.float32),
np.array(["descr"]),
None,
None,
),
DoesNotRaise(),
),
],
)
def test_florence_2(
florence_result: dict,
resolution_wh: Tuple[int, int],
expected_results: Tuple[
np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]
],
exception: Exception,
) -> None:
with exception:
result = from_florence_2(florence_result, resolution_wh)
np.testing.assert_array_equal(result[0], expected_results[0])
if expected_results[1] is None:
assert result[1] is None
else:
np.testing.assert_array_equal(result[1], expected_results[1])
if expected_results[2] is None:
assert result[2] is None
else:
np.testing.assert_array_equal(result[2], expected_results[2])
if expected_results[3] is None:
assert result[3] is None
else:
np.testing.assert_array_equal(result[3], expected_results[3])