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Commit 1c136bc1 authored by André Anjos's avatar André Anjos :speech_balloon:
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[test.test_config] Fix config tests after db remodelling

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Pipeline #39846 passed
Stage: build
Stage: deploy
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bob/ip/binseg/test/test_config.py
+ 98
− 92
View file @ 1c136bc1
......@@ -34,8 +34,9 @@ def test_drive():
from ..configs.datasets.drive.default import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
_check_subset(dataset["__train__"], 20)
_check_subset(dataset["__valid__"], 20)
_check_subset(dataset["train"], 20)
_check_subset(dataset["test"], 20)
......@@ -53,7 +54,7 @@ def test_drive():
def test_drive_mtest():
from ..configs.datasets.drive.mtest import dataset
nose.tools.eq_(len(dataset), 6)
nose.tools.eq_(len(dataset), 10)
from ..configs.datasets.drive.default import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
......@@ -80,25 +81,24 @@ def test_drive_mtest():
def test_drive_covd():
from ..configs.datasets.drive.covd import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
from ..configs.datasets.drive.default import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
nose.tools.eq_(dataset["train"], dataset["__valid__"])
nose.tools.eq_(dataset["test"], baseline["test"])
# this is the only different set from the baseline
nose.tools.eq_(len(dataset["__train__"]), 53)
for sample in dataset["__train__"]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 544, 544)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 544, 544)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 544, 544)) #planes, height, width
nose.tools.eq_(sample[3].dtype, torch.float32)
for key in ("__train__", "train"):
nose.tools.eq_(len(dataset[key]), 123)
for sample in dataset["__train__"]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 544, 544)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 544, 544)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 544, 544))
nose.tools.eq_(sample[3].dtype, torch.float32)
@rc_variable_set("bob.ip.binseg.drive.datadir")
......@@ -109,15 +109,16 @@ def test_drive_covd():
def test_drive_ssl():
from ..configs.datasets.drive.ssl import dataset
nose.tools.eq_(len(dataset), 3)
from ..configs.datasets.drive.default import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
nose.tools.eq_(dataset["test"], baseline["test"])
nose.tools.eq_(len(dataset), 4)
# this is the only different set from the baseline
nose.tools.eq_(len(dataset["__train__"]), 53)
from ..configs.datasets.drive.covd import dataset as covd
nose.tools.eq_(dataset["train"], covd["train"])
nose.tools.eq_(dataset["train"], dataset["__valid__"])
nose.tools.eq_(dataset["test"], covd["test"])
nose.tools.eq_(dataset["__valid__"], covd["__valid__"])
# these are the only different from the baseline
nose.tools.eq_(len(dataset["__train__"]), 123)
for sample in dataset["__train__"]:
assert 5 <= len(sample) <= 6
assert isinstance(sample[0], str)
......@@ -172,7 +173,7 @@ def test_stare():
for protocol in "ah", "vk":
dataset = _maker(protocol, stare_dataset)
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
_check_subset(dataset["__train__"], 10)
_check_subset(dataset["train"], 10)
_check_subset(dataset["test"], 10)
......@@ -186,7 +187,7 @@ def test_stare():
def test_stare_mtest():
from ..configs.datasets.stare.mtest import dataset
nose.tools.eq_(len(dataset), 6)
nose.tools.eq_(len(dataset), 10)
from ..configs.datasets.stare.ah import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
......@@ -213,24 +214,25 @@ def test_stare_mtest():
def test_stare_covd():
from ..configs.datasets.stare.covd import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
from ..configs.datasets.stare.ah import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
nose.tools.eq_(dataset["train"], dataset["__valid__"])
nose.tools.eq_(dataset["test"], baseline["test"])
# this is the only different set from the baseline
nose.tools.eq_(len(dataset["__train__"]), 63)
for sample in dataset["__train__"]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 608, 704)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 608, 704)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 608, 704)) #planes, height, width
nose.tools.eq_(sample[3].dtype, torch.float32)
# these are the only different sets from the baseline
for key in ("__train__", "train"):
nose.tools.eq_(len(dataset[key]), 143)
for sample in dataset[key]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 608, 704)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 608, 704)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 608, 704))
nose.tools.eq_(sample[3].dtype, torch.float32)
@rc_variable_set("bob.ip.binseg.chasedb1.datadir")
......@@ -249,8 +251,9 @@ def test_chasedb1():
for m in ("first_annotator", "second_annotator"):
d = importlib.import_module(f"...configs.datasets.chasedb1.{m}",
package=__name__).dataset
nose.tools.eq_(len(d), 3)
nose.tools.eq_(len(d), 4)
_check_subset(d["__train__"], 8)
_check_subset(d["__valid__"], 8)
_check_subset(d["train"], 8)
_check_subset(d["test"], 20)
......@@ -263,7 +266,7 @@ def test_chasedb1():
def test_chasedb1_mtest():
from ..configs.datasets.chasedb1.mtest import dataset
nose.tools.eq_(len(dataset), 6)
nose.tools.eq_(len(dataset), 10)
from ..configs.datasets.chasedb1.first_annotator import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
......@@ -290,24 +293,25 @@ def test_chasedb1_mtest():
def test_chasedb1_covd():
from ..configs.datasets.chasedb1.covd import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
from ..configs.datasets.chasedb1.first_annotator import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
nose.tools.eq_(dataset["train"], dataset["__valid__"])
nose.tools.eq_(dataset["test"], baseline["test"])
# this is the only different set from the baseline
nose.tools.eq_(len(dataset["__train__"]), 65)
for sample in dataset["__train__"]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 960, 960)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 960, 960)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 960, 960)) #planes, height, width
nose.tools.eq_(sample[3].dtype, torch.float32)
# these are the only different sets from the baseline
for key in ("__train__", "train"):
nose.tools.eq_(len(dataset[key]), 135)
for sample in dataset[key]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 960, 960)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 960, 960)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 960, 960))
nose.tools.eq_(sample[3].dtype, torch.float32)
@rc_variable_set("bob.ip.binseg.hrf.datadir")
......@@ -326,7 +330,7 @@ def test_hrf():
nose.tools.eq_(s[3].dtype, torch.float32)
from ..configs.datasets.hrf.default import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
_check_subset(dataset["__train__"], 15)
_check_subset(dataset["train"], 15)
_check_subset(dataset["test"], 30)
......@@ -340,7 +344,7 @@ def test_hrf():
def test_hrf_mtest():
from ..configs.datasets.hrf.mtest import dataset
nose.tools.eq_(len(dataset), 6)
nose.tools.eq_(len(dataset), 10)
from ..configs.datasets.hrf.default import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
......@@ -367,24 +371,25 @@ def test_hrf_mtest():
def test_hrf_covd():
from ..configs.datasets.hrf.covd import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
from ..configs.datasets.hrf.default import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
nose.tools.eq_(dataset["train"], dataset["__valid__"])
nose.tools.eq_(dataset["test"], baseline["test"])
# this is the only different set from the baseline
nose.tools.eq_(len(dataset["__train__"]), 58)
for sample in dataset["__train__"]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 1168, 1648)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 1168, 1648)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 1168, 1648))
nose.tools.eq_(sample[3].dtype, torch.float32)
# these are the only different sets from the baseline
for key in ("__train__", "train"):
nose.tools.eq_(len(dataset[key]), 118)
for sample in dataset[key]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 1168, 1648))
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 1168, 1648))
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 1168, 1648))
nose.tools.eq_(sample[3].dtype, torch.float32)
@rc_variable_set("bob.ip.binseg.iostar.datadir")
......@@ -405,7 +410,7 @@ def test_iostar():
for m in ("vessel", "optic_disc"):
d = importlib.import_module(f"...configs.datasets.iostar.{m}",
package=__name__).dataset
nose.tools.eq_(len(d), 3)
nose.tools.eq_(len(d), 4)
_check_subset(d["__train__"], 20)
_check_subset(d["train"], 20)
_check_subset(d["test"], 10)
......@@ -419,7 +424,7 @@ def test_iostar():
def test_iostar_mtest():
from ..configs.datasets.iostar.vessel_mtest import dataset
nose.tools.eq_(len(dataset), 6)
nose.tools.eq_(len(dataset), 10)
from ..configs.datasets.iostar.vessel import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
......@@ -446,24 +451,25 @@ def test_iostar_mtest():
def test_iostar_covd():
from ..configs.datasets.iostar.covd import dataset
nose.tools.eq_(len(dataset), 3)
nose.tools.eq_(len(dataset), 4)
from ..configs.datasets.iostar.vessel import dataset as baseline
nose.tools.eq_(dataset["train"], baseline["train"])
nose.tools.eq_(dataset["train"], dataset["__valid__"])
nose.tools.eq_(dataset["test"], baseline["test"])
# this is the only different set from the baseline
nose.tools.eq_(len(dataset["__train__"]), 53)
for sample in dataset["__train__"]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 1024, 1024)) #planes, height, width
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 1024, 1024)) #planes, height, width
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 1024, 1024))
nose.tools.eq_(sample[3].dtype, torch.float32)
# these are the only different sets from the baseline
for key in ("__train__", "train"):
nose.tools.eq_(len(dataset[key]), 133)
for sample in dataset[key]:
assert 3 <= len(sample) <= 4
assert isinstance(sample[0], str)
nose.tools.eq_(sample[1].shape, (3, 1024, 1024))
nose.tools.eq_(sample[1].dtype, torch.float32)
nose.tools.eq_(sample[2].shape, (1, 1024, 1024))
nose.tools.eq_(sample[2].dtype, torch.float32)
if len(sample) == 4:
nose.tools.eq_(sample[3].shape, (1, 1024, 1024))
nose.tools.eq_(sample[3].dtype, torch.float32)
@rc_variable_set("bob.ip.binseg.refuge.datadir")
......@@ -482,7 +488,7 @@ def test_refuge():
for m in ("disc", "cup"):
d = importlib.import_module(f"...configs.datasets.refuge.{m}",
package=__name__).dataset
nose.tools.eq_(len(d), 4)
nose.tools.eq_(len(d), 5)
_check_subset(d["__train__"], 400)
_check_subset(d["train"], 400)
_check_subset(d["validation"], 400)
......@@ -505,7 +511,7 @@ def test_drishtigs1():
for m in ("disc_all", "cup_all", "disc_any", "cup_any"):
d = importlib.import_module(f"...configs.datasets.drishtigs1.{m}",
package=__name__).dataset
nose.tools.eq_(len(d), 3)
nose.tools.eq_(len(d), 4)
_check_subset(d["__train__"], 50)
_check_subset(d["train"], 50)
_check_subset(d["test"], 51)
......@@ -527,7 +533,7 @@ def test_rimoner3():
for m in ("disc_exp1", "cup_exp1", "disc_exp2", "cup_exp2"):
d = importlib.import_module(f"...configs.datasets.rimoner3.{m}",
package=__name__).dataset
nose.tools.eq_(len(d), 3)
nose.tools.eq_(len(d), 4)
_check_subset(d["__train__"], 99)
_check_subset(d["train"], 99)
_check_subset(d["test"], 60)
......@@ -549,7 +555,7 @@ def test_drionsdb():
for m in ("expert1", "expert2"):
d = importlib.import_module(f"...configs.datasets.drionsdb.{m}",
package=__name__).dataset
nose.tools.eq_(len(d), 3)
nose.tools.eq_(len(d), 4)
_check_subset(d["__train__"], 60)
_check_subset(d["train"], 60)
_check_subset(d["test"], 50)
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