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initial commit adding support for from_lmm and specifically for Pal… #1221

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merged 9 commits into from
May 24, 2024
Merged

initial commit adding support for from_lmm and specifically for Pal… #1221

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a2195aa
initial commit adding support for `from_lmm` and specifically for Pal…
SkalskiP May 22, 2024
36a73f7
clean up
SkalskiP May 22, 2024
7eb9182
fix(pre_commit): 🎨 auto format pre-commit hooks
pre-commit-ci[bot] May 22, 2024
b81c5e8
update to allow multi-word class names
SkalskiP May 22, 2024
b59d17f
Merge remote-tracking branch 'origin/from_paligemma_support' into fro…
SkalskiP May 22, 2024
bf43d65
fix(pre_commit): 🎨 auto format pre-commit hooks
pre-commit-ci[bot] May 22, 2024
07c36c6
make linter happy
SkalskiP May 22, 2024
0115ef8
small fix when `mask` is empty
SkalskiP May 23, 2024
ad2220b
fix(pre_commit): 🎨 auto format pre-commit hooks
pre-commit-ci[bot] May 23, 2024
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fix(pre_commit): 🎨 auto format pre-commit hooks
  • Loading branch information
@pre-commit-ci
pre-commit-ci[bot] committed May 22, 2024
commit bf43d6566b8bfa9ac6c317bceb312c414268a60b
22 changes: 9 additions & 13 deletions supervision/detection/lmm.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,17 @@
import re
import numpy as np
from enum import Enum
from typing import Dict, List, Tuple, Optional, Union, Any
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np


class LMM(Enum):
PALIGEMMA = 'paligemma'
PALIGEMMA = "paligemma"


REQUIRED_ARGUMENTS: Dict[LMM, List[str]] = {
LMM.PALIGEMMA: ['resolution_wh']
}
REQUIRED_ARGUMENTS: Dict[LMM, List[str]] = {LMM.PALIGEMMA: ["resolution_wh"]}

ALLOWED_ARGUMENTS: Dict[LMM, List[str]] = {
LMM.PALIGEMMA: ['resolution_wh', 'classes']
}
ALLOWED_ARGUMENTS: Dict[LMM, List[str]] = {LMM.PALIGEMMA: ["resolution_wh", "classes"]}


def validate_lmm_and_kwargs(lmm: Union[LMM, str], kwargs: Dict[str, Any]) -> LMM:
Expand All @@ -40,13 +37,12 @@ def validate_lmm_and_kwargs(lmm: Union[LMM, str], kwargs: Dict[str, Any]) -> LMM


def from_paligemma(
result: str,
resolution_wh: Tuple[int, int],
classes: Optional[List[str]] = None
result: str, resolution_wh: Tuple[int, int], classes: Optional[List[str]] = None
) -> Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]:
w, h = resolution_wh
pattern = re.compile(
r'(?<!<loc\d{4}>)<loc(\d{4})><loc(\d{4})><loc(\d{4})><loc(\d{4})> ([\w\s]+)')
r"(?<!<loc\d{4}>)<loc(\d{4})><loc(\d{4})><loc(\d{4})><loc(\d{4})> ([\w\s]+)"
)
matches = pattern.findall(result)
matches = np.array(matches) if matches else np.empty((0, 5))

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71 changes: 34 additions & 37 deletions test/detection/test_lmm.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import numpy as np
from typing import Tuple, Optional, List
from typing import List, Optional, Tuple

import numpy as np
import pytest

from supervision.detection.lmm import from_paligemma
Expand All @@ -13,114 +13,111 @@
"",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str))
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # empty response
(
"\n",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str))
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # new line response
(
"the quick brown fox jumps over the lazy dog.",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str))
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with no location
(
"<loc0256><loc0768><loc0768> cat",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str))
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with missing location
(
"<loc0256><loc0256><loc0768><loc0768><loc0768> cat",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str))
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with extra location
(
"<loc0256><loc0256><loc0768><loc0768>",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str))
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with no class
(
"<loc0256><loc0256><loc0768><loc0768> catt",
(1000, 1000),
['cat', 'dog'],
(np.empty((0, 4)), np.empty(0), np.empty(0).astype(str))
["cat", "dog"],
(np.empty((0, 4)), np.empty(0), np.empty(0).astype(str)),
), # response with invalid class
(
"<loc0256><loc0256><loc0768><loc0768> cat",
(1000, 1000),
None,
(
np.array([[250., 250., 750., 750.]]),
np.array([[250.0, 250.0, 750.0, 750.0]]),
None,
np.array(['cat']).astype(str)
)
np.array(["cat"]).astype(str),
),
), # correct response; no classes
(
"<loc0256><loc0256><loc0768><loc0768> black cat",
(1000, 1000),
None,
(
np.array([[250., 250., 750., 750.]]),
np.array([[250.0, 250.0, 750.0, 750.0]]),
None,
np.array(['black cat']).astype(np.dtype('U'))
)
np.array(["black cat"]).astype(np.dtype("U")),
),
), # correct response; no classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ;",
(1000, 1000),
['cat', 'dog'],
["cat", "dog"],
(
np.array([[250., 250., 750., 750.]]),
np.array([[250.0, 250.0, 750.0, 750.0]]),
np.array([0]),
np.array(['cat']).astype(str)
)
np.array(["cat"]).astype(str),
),
), # correct response; with classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768><loc0768> dog",
(1000, 1000),
['cat', 'dog'],
["cat", "dog"],
(
np.array([
[250., 250., 750., 750.],
[250., 250., 750., 750.]
]),
np.array([[250.0, 250.0, 750.0, 750.0], [250.0, 250.0, 750.0, 750.0]]),
np.array([0, 1]),
np.array(['cat', 'dog']).astype(np.dtype('U'))
)
np.array(["cat", "dog"]).astype(np.dtype("U")),
),
), # correct response; with classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768> cat",
(1000, 1000),
['cat', 'dog'],
["cat", "dog"],
(
np.array([[250., 250., 750., 750.]]),
np.array([[250.0, 250.0, 750.0, 750.0]]),
np.array([0]),
np.array(['cat']).astype(str)
)
np.array(["cat"]).astype(str),
),
), # partially correct response; with classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768><loc0768><loc0768> cat",
(1000, 1000),
['cat', 'dog'],
["cat", "dog"],
(
np.array([[250., 250., 750., 750.]]),
np.array([[250.0, 250.0, 750.0, 750.0]]),
np.array([0]),
np.array(['cat']).astype(str)
)
np.array(["cat"]).astype(str),
),
), # partially correct response; with classes
]
],
)
def test_from_paligemma(
result: str,
resolution_wh: Tuple[int, int],
classes: Optional[List[str]],
expected_results: Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]
expected_results: Tuple[np.ndarray, Optional[np.ndarray], np.ndarray],
) -> None:
result = from_paligemma(result=result, resolution_wh=resolution_wh, classes=classes)
np.testing.assert_array_equal(result[0], expected_results[0])
Expand Down