diff --git a/bob/__init__.py b/bob/__init__.py
index 2ab1e28b150f0549def9963e9e87de3fdd6b2579..edbb4090fca046b19d22d3982711084621bff3be 100644
--- a/bob/__init__.py
+++ b/bob/__init__.py
@@ -1,3 +1,4 @@
# see https://docs.python.org/3/library/pkgutil.html
from pkgutil import extend_path
+
__path__ = extend_path(__path__, __name__)
diff --git a/bob/ip/__init__.py b/bob/ip/__init__.py
index 2ca5e07cb73f0bdddcb863ef497955964087e301..edbb4090fca046b19d22d3982711084621bff3be 100644
--- a/bob/ip/__init__.py
+++ b/bob/ip/__init__.py
@@ -1,3 +1,4 @@
# see https://docs.python.org/3/library/pkgutil.html
from pkgutil import extend_path
-__path__ = extend_path(__path__, __name__)
\ No newline at end of file
+
+__path__ = extend_path(__path__, __name__)
diff --git a/bob/ip/binseg/configs/datasets/amdrive.py b/bob/ip/binseg/configs/datasets/amdrive.py
index 6f7cc3101d158f71c7ea47f32382ce369de0552b..0b1b1639580939fde75d93f7082db7875f6aea85 100644
--- a/bob/ip/binseg/configs/datasets/amdrive.py
+++ b/bob/ip/binseg/configs/datasets/amdrive.py
@@ -11,68 +11,55 @@ import torch
# Target size: 544x544 (DRIVE)
-defaulttransforms = [RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()]
+defaulttransforms = [
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+]
-
-# CHASE_DB1
-transforms_chase = Compose([
- Resize(544)
- ,Crop(0,12,544,544)
- ,*defaulttransforms
- ])
+# CHASE_DB1
+transforms_chase = Compose([Resize(544), Crop(0, 12, 544, 544), *defaulttransforms])
# bob.db.dataset init
-bobdb_chase = CHASEDB1(protocol = 'default')
+bobdb_chase = CHASEDB1(protocol="default")
# PyTorch dataset
-torch_chase = BinSegDataset(bobdb_chase, split='train', transform=transforms_chase)
+torch_chase = BinSegDataset(bobdb_chase, split="train", transform=transforms_chase)
# IOSTAR VESSEL
-transforms_iostar = Compose([
- Resize(544)
- ,*defaulttransforms
- ])
+transforms_iostar = Compose([Resize(544), *defaulttransforms])
# bob.db.dataset init
-bobdb_iostar = IOSTAR(protocol='default_vessel')
+bobdb_iostar = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-torch_iostar = BinSegDataset(bobdb_iostar, split='train', transform=transforms_iostar)
+torch_iostar = BinSegDataset(bobdb_iostar, split="train", transform=transforms_iostar)
# STARE
-transforms = Compose([
- Resize(471)
- ,Pad((0,37,0,36))
- ,*defaulttransforms
- ])
+transforms = Compose([Resize(471), Pad((0, 37, 0, 36)), *defaulttransforms])
# bob.db.dataset init
-bobdb_stare = STARE(protocol = 'default')
+bobdb_stare = STARE(protocol="default")
# PyTorch dataset
-torch_stare = BinSegDataset(bobdb_stare, split='train', transform=transforms)
+torch_stare = BinSegDataset(bobdb_stare, split="train", transform=transforms)
# HRF
-transforms_hrf = Compose([
- Resize((363))
- ,Pad((0,90,0,91))
- ,*defaulttransforms
- ])
+transforms_hrf = Compose([Resize((363)), Pad((0, 90, 0, 91)), *defaulttransforms])
# bob.db.dataset init
-bobdb_hrf = HRF(protocol = 'default')
+bobdb_hrf = HRF(protocol="default")
# PyTorch dataset
-torch_hrf = BinSegDataset(bobdb_hrf, split='train', transform=transforms_hrf)
-
+torch_hrf = BinSegDataset(bobdb_hrf, split="train", transform=transforms_hrf)
# Merge
-dataset = torch.utils.data.ConcatDataset([torch_stare, torch_chase, torch_iostar, torch_hrf])
\ No newline at end of file
+dataset = torch.utils.data.ConcatDataset(
+ [torch_stare, torch_chase, torch_iostar, torch_hrf]
+)
diff --git a/bob/ip/binseg/configs/datasets/amdrivetest.py b/bob/ip/binseg/configs/datasets/amdrivetest.py
index 026ac236e7e9f48f5f41b6685d01369606c47321..5a6cc4af189fe226f772c258524593b6c7867fb4 100644
--- a/bob/ip/binseg/configs/datasets/amdrivetest.py
+++ b/bob/ip/binseg/configs/datasets/amdrivetest.py
@@ -14,60 +14,46 @@ import torch
defaulttransforms = [ToTensor()]
-# CHASE_DB1
-transforms_chase = Compose([
- Resize(544)
- ,Crop(0,12,544,544)
- ,*defaulttransforms
- ])
+# CHASE_DB1
+transforms_chase = Compose([Resize(544), Crop(0, 12, 544, 544), *defaulttransforms])
# bob.db.dataset init
-bobdb_chase = CHASEDB1(protocol = 'default')
+bobdb_chase = CHASEDB1(protocol="default")
# PyTorch dataset
-torch_chase = BinSegDataset(bobdb_chase, split='test', transform=transforms_chase)
+torch_chase = BinSegDataset(bobdb_chase, split="test", transform=transforms_chase)
# IOSTAR VESSEL
-transforms_iostar = Compose([
- Resize(544)
- ,*defaulttransforms
- ])
+transforms_iostar = Compose([Resize(544), *defaulttransforms])
# bob.db.dataset init
-bobdb_iostar = IOSTAR(protocol='default_vessel')
+bobdb_iostar = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-torch_iostar = BinSegDataset(bobdb_iostar, split='test', transform=transforms_iostar)
+torch_iostar = BinSegDataset(bobdb_iostar, split="test", transform=transforms_iostar)
# STARE
-transforms = Compose([
- Resize(471)
- ,Pad((0,37,0,36))
- ,*defaulttransforms
- ])
+transforms = Compose([Resize(471), Pad((0, 37, 0, 36)), *defaulttransforms])
# bob.db.dataset init
-bobdb_stare = STARE(protocol = 'default')
+bobdb_stare = STARE(protocol="default")
# PyTorch dataset
-torch_stare = BinSegDataset(bobdb_stare, split='test', transform=transforms)
+torch_stare = BinSegDataset(bobdb_stare, split="test", transform=transforms)
# HRF
-transforms_hrf = Compose([
- Resize((363))
- ,Pad((0,90,0,91))
- ,*defaulttransforms
- ])
+transforms_hrf = Compose([Resize((363)), Pad((0, 90, 0, 91)), *defaulttransforms])
# bob.db.dataset init
-bobdb_hrf = HRF(protocol = 'default')
+bobdb_hrf = HRF(protocol="default")
# PyTorch dataset
-torch_hrf = BinSegDataset(bobdb_hrf, split='test', transform=transforms_hrf)
-
+torch_hrf = BinSegDataset(bobdb_hrf, split="test", transform=transforms_hrf)
# Merge
-dataset = torch.utils.data.ConcatDataset([torch_stare, torch_chase, torch_iostar, torch_hrf])
\ No newline at end of file
+dataset = torch.utils.data.ConcatDataset(
+ [torch_stare, torch_chase, torch_iostar, torch_hrf]
+)
diff --git a/bob/ip/binseg/configs/datasets/chasedb1.py b/bob/ip/binseg/configs/datasets/chasedb1.py
index 7fa0dc096aa55a8ab19665af45c9f2e1f8368a0c..605fd0a7a3d41bd99bc0bf9cd5646368990f8b17 100644
--- a/bob/ip/binseg/configs/datasets/chasedb1.py
+++ b/bob/ip/binseg/configs/datasets/chasedb1.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Crop(0,18,960,960)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Crop(0, 18, 960, 960),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = CHASEDB1(protocol = 'default')
+bobdb = CHASEDB1(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/chasedb11024.py b/bob/ip/binseg/configs/datasets/chasedb11024.py
index 028f10fb443cd4f1c9c728203f13641cc67b95ad..27b7ab372ab537acb9590cb737ffa60346d28924 100644
--- a/bob/ip/binseg/configs/datasets/chasedb11024.py
+++ b/bob/ip/binseg/configs/datasets/chasedb11024.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Crop(0,18,960,960)
- ,Resize(1024)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Crop(0, 18, 960, 960),
+ Resize(1024),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = CHASEDB1(protocol = 'default')
+bobdb = CHASEDB1(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/chasedb11168.py b/bob/ip/binseg/configs/datasets/chasedb11168.py
index d221ea4879c4e7378e0f9ad6bcce2cd5a77bf04c..b85726e4047c4f24c12063887fca065876f7d29c 100644
--- a/bob/ip/binseg/configs/datasets/chasedb11168.py
+++ b/bob/ip/binseg/configs/datasets/chasedb11168.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Crop(140,18,680,960)
- ,Resize(1168)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Crop(140, 18, 680, 960),
+ Resize(1168),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = CHASEDB1(protocol = 'default')
+bobdb = CHASEDB1(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/chasedb1544.py b/bob/ip/binseg/configs/datasets/chasedb1544.py
index 9632d53996d343deecd0e021fb2140153df9edc0..8ea0a9c6d701ec79d9d1dd17b5b2ccb332082e8d 100644
--- a/bob/ip/binseg/configs/datasets/chasedb1544.py
+++ b/bob/ip/binseg/configs/datasets/chasedb1544.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize(544)
- ,Crop(0,12,544,544)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Resize(544),
+ Crop(0, 12, 544, 544),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = CHASEDB1(protocol = 'default')
+bobdb = CHASEDB1(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/chasedb1608.py b/bob/ip/binseg/configs/datasets/chasedb1608.py
index 9a475cae5d7fb5be49a28ae6e479dd3fe41760ed..1800574ba5c65c992e381639c2bda502f53183f0 100644
--- a/bob/ip/binseg/configs/datasets/chasedb1608.py
+++ b/bob/ip/binseg/configs/datasets/chasedb1608.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,CenterCrop((829,960))
- ,Resize(608)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ CenterCrop((829, 960)),
+ Resize(608),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = CHASEDB1(protocol = 'default')
+bobdb = CHASEDB1(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/chasedb1test.py b/bob/ip/binseg/configs/datasets/chasedb1test.py
index 4b267b0f022030d512197f89401f74e3373df3cb..17be7aa11eafe25178d55e1b44b8a87ab2e2d5c0 100644
--- a/bob/ip/binseg/configs/datasets/chasedb1test.py
+++ b/bob/ip/binseg/configs/datasets/chasedb1test.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Crop(0,18,960,960)
- ,ToTensor()
- ])
+transforms = Compose([Crop(0, 18, 960, 960), ToTensor()])
# bob.db.dataset init
-bobdb = CHASEDB1(protocol = 'default')
+bobdb = CHASEDB1(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drionsdb.py b/bob/ip/binseg/configs/datasets/drionsdb.py
index cd33c1d246755f11c217d7cbf7fcdab35b2144e6..0a03dadfcb0cfab24da924546ece9d221651b31b 100644
--- a/bob/ip/binseg/configs/datasets/drionsdb.py
+++ b/bob/ip/binseg/configs/datasets/drionsdb.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((4,8,4,8))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((4, 8, 4, 8)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRIONS(protocol = 'default')
+bobdb = DRIONS(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drionsdbtest.py b/bob/ip/binseg/configs/datasets/drionsdbtest.py
index b65100a6c21eaa0a48f2ff2c33e05e7a17c8825c..75bcbb58abf2e56539ec907f3fae5d9ad11e0d81 100644
--- a/bob/ip/binseg/configs/datasets/drionsdbtest.py
+++ b/bob/ip/binseg/configs/datasets/drionsdbtest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((4,8,4,8))
- ,ToTensor()
- ])
+transforms = Compose([Pad((4, 8, 4, 8)), ToTensor()])
# bob.db.dataset init
-bobdb = DRIONS(protocol = 'default')
+bobdb = DRIONS(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/dristhigs1cup.py b/bob/ip/binseg/configs/datasets/dristhigs1cup.py
index f7a69dadcb4010198025e7d4d44fac8aa1b06918..a1da30ad0392662cbfdaadd0ace7b011d43a7af2 100644
--- a/bob/ip/binseg/configs/datasets/dristhigs1cup.py
+++ b/bob/ip/binseg/configs/datasets/dristhigs1cup.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((1760,2048))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ CenterCrop((1760, 2048)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRISHTI(protocol = 'default_cup')
+bobdb = DRISHTI(protocol="default_cup")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/dristhigs1cuptest.py b/bob/ip/binseg/configs/datasets/dristhigs1cuptest.py
index 5c2b634e1ba402ed8af801ad7203d6585ecb6b96..e35eabf0ab2cbad1ae89b4cc4ef3685df3e3e981 100644
--- a/bob/ip/binseg/configs/datasets/dristhigs1cuptest.py
+++ b/bob/ip/binseg/configs/datasets/dristhigs1cuptest.py
@@ -6,13 +6,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((1760,2048))
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop((1760, 2048)), ToTensor()])
# bob.db.dataset init
-bobdb = DRISHTI(protocol = 'default_cup')
+bobdb = DRISHTI(protocol="default_cup")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/dristhigs1od.py b/bob/ip/binseg/configs/datasets/dristhigs1od.py
index 0bd483c1a03b00e15bd90a32b8ec369b766605ae..3421ebe63ceb25a1e3cd5243971a41ad161e8cec 100644
--- a/bob/ip/binseg/configs/datasets/dristhigs1od.py
+++ b/bob/ip/binseg/configs/datasets/dristhigs1od.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((1760,2048))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ CenterCrop((1760, 2048)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRISHTI(protocol = 'default_od')
+bobdb = DRISHTI(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/dristhigs1odtest.py b/bob/ip/binseg/configs/datasets/dristhigs1odtest.py
index ab1edd6546dac52dd235369b0126e973e2b8611a..1fdc8a28e1b6e52838be96e6601047c5295b06b5 100644
--- a/bob/ip/binseg/configs/datasets/dristhigs1odtest.py
+++ b/bob/ip/binseg/configs/datasets/dristhigs1odtest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((1760,2048))
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop((1760, 2048)), ToTensor()])
# bob.db.dataset init
-bobdb = DRISHTI(protocol = 'default_od')
+bobdb = DRISHTI(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drive.py b/bob/ip/binseg/configs/datasets/drive.py
index 5b6fa356b6f944f6e7e29e1f44b3dda3ec80d9a8..04819dc0ef1b59038a9e752dbc78003e9af78c28 100644
--- a/bob/ip/binseg/configs/datasets/drive.py
+++ b/bob/ip/binseg/configs/datasets/drive.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((544,544))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ CenterCrop((544, 544)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drive1024.py b/bob/ip/binseg/configs/datasets/drive1024.py
index dae199f50dc59c194a5ada24ff8e99f9aa4fd642..ea99feb0b0cbbd26a12c82c7507f65285257f6e6 100644
--- a/bob/ip/binseg/configs/datasets/drive1024.py
+++ b/bob/ip/binseg/configs/datasets/drive1024.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,CenterCrop((540,540))
- ,Resize(1024)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ CenterCrop((540, 540)),
+ Resize(1024),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drive1024test.py b/bob/ip/binseg/configs/datasets/drive1024test.py
index 9e9cb3e935a63ddb45bb0bba85c56c173de6912d..c409dae589a50c328736f6a339e2acaaa74a3786 100644
--- a/bob/ip/binseg/configs/datasets/drive1024test.py
+++ b/bob/ip/binseg/configs/datasets/drive1024test.py
@@ -7,14 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((540,540))
- ,Resize(1024)
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop((540, 540)), Resize(1024), ToTensor()])
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drive1168.py b/bob/ip/binseg/configs/datasets/drive1168.py
index 3f0f0537e1ba67d7beb3d77af492ba8f9fc539a2..f4a51d95e5f4cd9e6ddf94ee2341462631109c01 100644
--- a/bob/ip/binseg/configs/datasets/drive1168.py
+++ b/bob/ip/binseg/configs/datasets/drive1168.py
@@ -7,19 +7,21 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Crop(75,10,416,544)
- ,Pad((21,0,22,0))
- ,Resize(1168)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Crop(75, 10, 416, 544),
+ Pad((21, 0, 22, 0)),
+ Resize(1168),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drive608.py b/bob/ip/binseg/configs/datasets/drive608.py
index 65bc5e6521dcdd3bbbf66ea77a741c6270dcdd58..e251930b237989ecaaa98dc86448a337d9af286c 100644
--- a/bob/ip/binseg/configs/datasets/drive608.py
+++ b/bob/ip/binseg/configs/datasets/drive608.py
@@ -7,19 +7,21 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,CenterCrop((470,544))
- ,Pad((10,9,10,8))
- ,Resize(608)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ CenterCrop((470, 544)),
+ Pad((10, 9, 10, 8)),
+ Resize(608),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drive960.py b/bob/ip/binseg/configs/datasets/drive960.py
index ab3ac5a9d03916dce4bbb162f51455a5bb05bcba..1a29eeee3bda2601fad74c4016213239e48e7c67 100644
--- a/bob/ip/binseg/configs/datasets/drive960.py
+++ b/bob/ip/binseg/configs/datasets/drive960.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,CenterCrop((544,544))
- ,Resize(960)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ CenterCrop((544, 544)),
+ Resize(960),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py
index 5ab57b8ad4e34d700e4667138b6111f19b4a488a..a3410d465b1585c72d7085717edb73296baa6d60 100644
--- a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py
+++ b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py
@@ -7,4 +7,4 @@ import torch
#### Config ####
# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([drive,chase,iostar,hrf])
\ No newline at end of file
+dataset = torch.utils.data.ConcatDataset([drive, chase, iostar, hrf])
diff --git a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py
index 928452f4209d4526e66028457a3ababcd04fda8b..37c60f8a0659aa738b5377dc181e6e7586749e7e 100644
--- a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py
+++ b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py
@@ -5,30 +5,38 @@ from bob.ip.binseg.configs.datasets.hrf608 import dataset as hrf
from bob.db.stare import Database as STARE
from bob.ip.binseg.data.transforms import *
import torch
-from bob.ip.binseg.data.binsegdataset import BinSegDataset, SSLBinSegDataset, UnLabeledBinSegDataset
+from bob.ip.binseg.data.binsegdataset import (
+ BinSegDataset,
+ SSLBinSegDataset,
+ UnLabeledBinSegDataset,
+)
#### Config ####
# PyTorch dataset
-labeled_dataset = torch.utils.data.ConcatDataset([drive,chase,iostar,hrf])
+labeled_dataset = torch.utils.data.ConcatDataset([drive, chase, iostar, hrf])
#### Unlabeled STARE TRAIN ####
-unlabeled_transforms = Compose([
- Pad((2,1,2,2))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+unlabeled_transforms = Compose(
+ [
+ Pad((2, 1, 2, 2)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-starebobdb = STARE(protocol = 'default')
+starebobdb = STARE(protocol="default")
# PyTorch dataset
-unlabeled_dataset = UnLabeledBinSegDataset(starebobdb, split='train', transform=unlabeled_transforms)
+unlabeled_dataset = UnLabeledBinSegDataset(
+ starebobdb, split="train", transform=unlabeled_transforms
+)
# SSL Dataset
-dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
\ No newline at end of file
+dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
diff --git a/bob/ip/binseg/configs/datasets/drivestarechasedb11168.py b/bob/ip/binseg/configs/datasets/drivestarechasedb11168.py
index 0e36eff7195de38bd2122a0bb75942a210fba1db..1a7274ee91793f350c1819ca7726532ee3d384ab 100644
--- a/bob/ip/binseg/configs/datasets/drivestarechasedb11168.py
+++ b/bob/ip/binseg/configs/datasets/drivestarechasedb11168.py
@@ -6,4 +6,4 @@ import torch
#### Config ####
# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([drive,stare,chase])
\ No newline at end of file
+dataset = torch.utils.data.ConcatDataset([drive, stare, chase])
diff --git a/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024.py b/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024.py
index 74628abe77801ef572cd3ad2ff379e9af9287506..4bff79a5d7fda3faff10ff487d2ba176d5ffb2d8 100644
--- a/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024.py
+++ b/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024.py
@@ -7,4 +7,4 @@ import torch
#### Config ####
# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([drive,stare,hrf,chase])
+dataset = torch.utils.data.ConcatDataset([drive, stare, hrf, chase])
diff --git a/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024ssliostar.py b/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024ssliostar.py
index dd9016b7c076b08cc99e875d4f5cc9dcdd3babd0..f9a9107f22728289ed3d7cbe22a7e21a2ab56d69 100644
--- a/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024ssliostar.py
+++ b/bob/ip/binseg/configs/datasets/drivestarechasedb1hrf1024ssliostar.py
@@ -5,29 +5,31 @@ from bob.ip.binseg.configs.datasets.chasedb11024 import dataset as chasedb
from bob.db.iostar import Database as IOSTAR
from bob.ip.binseg.data.transforms import *
import torch
-from bob.ip.binseg.data.binsegdataset import BinSegDataset, SSLBinSegDataset, UnLabeledBinSegDataset
+from bob.ip.binseg.data.binsegdataset import (
+ BinSegDataset,
+ SSLBinSegDataset,
+ UnLabeledBinSegDataset,
+)
#### Config ####
# PyTorch dataset
-labeled_dataset = torch.utils.data.ConcatDataset([drive,stare,hrf,chasedb])
+labeled_dataset = torch.utils.data.ConcatDataset([drive, stare, hrf, chasedb])
#### Unlabeled IOSTAR Train ####
-unlabeled_transforms = Compose([
- RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+unlabeled_transforms = Compose(
+ [RandomHFlip(), RandomVFlip(), RandomRotation(), ColorJitter(), ToTensor()]
+)
# bob.db.dataset init
-iostarbobdb = IOSTAR(protocol='default_vessel')
+iostarbobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-unlabeled_dataset = UnLabeledBinSegDataset(iostarbobdb, split='train', transform=unlabeled_transforms)
+unlabeled_dataset = UnLabeledBinSegDataset(
+ iostarbobdb, split="train", transform=unlabeled_transforms
+)
# SSL Dataset
-dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
\ No newline at end of file
+dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
diff --git a/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168.py b/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168.py
index 62e2972d3526fd5ff138a5ea99f0855849beda12..3bb4f28e4f3b75acea6c62011527ef801d331739 100644
--- a/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168.py
+++ b/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168.py
@@ -7,4 +7,4 @@ import torch
#### Config ####
# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([drive,stare,chase,iostar])
\ No newline at end of file
+dataset = torch.utils.data.ConcatDataset([drive, stare, chase, iostar])
diff --git a/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py b/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py
index 01705e15d8752b628ed6b150cf095db08ca4eed6..3b3d2f2ef4a130d540cf956186655496f31e46cb 100644
--- a/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py
+++ b/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py
@@ -5,31 +5,39 @@ from bob.ip.binseg.configs.datasets.iostarvessel1168 import dataset as iostar
from bob.db.hrf import Database as HRF
from bob.ip.binseg.data.transforms import *
import torch
-from bob.ip.binseg.data.binsegdataset import BinSegDataset, SSLBinSegDataset, UnLabeledBinSegDataset
+from bob.ip.binseg.data.binsegdataset import (
+ BinSegDataset,
+ SSLBinSegDataset,
+ UnLabeledBinSegDataset,
+)
#### Config ####
# PyTorch dataset
-labeled_dataset = torch.utils.data.ConcatDataset([drive,stare,iostar,chasedb])
+labeled_dataset = torch.utils.data.ConcatDataset([drive, stare, iostar, chasedb])
#### Unlabeled HRF TRAIN ####
-unlabeled_transforms = Compose([
- RandomRotation()
- ,Crop(0,108,2336,3296)
- ,Resize((1168))
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+unlabeled_transforms = Compose(
+ [
+ RandomRotation(),
+ Crop(0, 108, 2336, 3296),
+ Resize((1168)),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-hrfbobdb = HRF(protocol='default')
+hrfbobdb = HRF(protocol="default")
# PyTorch dataset
-unlabeled_dataset = UnLabeledBinSegDataset(hrfbobdb, split='train', transform=unlabeled_transforms)
+unlabeled_dataset = UnLabeledBinSegDataset(
+ hrfbobdb, split="train", transform=unlabeled_transforms
+)
# SSL Dataset
-dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
\ No newline at end of file
+dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
diff --git a/bob/ip/binseg/configs/datasets/drivestareiostarhrf960.py b/bob/ip/binseg/configs/datasets/drivestareiostarhrf960.py
index 9343cf22c62a0cf9ad02d767b24ca576b00fd4d1..5ea90c4d2deefa87be678cd0d7e1f7ede8d8ac52 100644
--- a/bob/ip/binseg/configs/datasets/drivestareiostarhrf960.py
+++ b/bob/ip/binseg/configs/datasets/drivestareiostarhrf960.py
@@ -7,4 +7,4 @@ import torch
#### Config ####
# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([drive,stare,hrf,iostar])
+dataset = torch.utils.data.ConcatDataset([drive, stare, hrf, iostar])
diff --git a/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py b/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py
index a7bd4576766259e83ec633bfac15b4dc46f0da9d..46a351d72425f8c7a138553020220436cafbedad 100644
--- a/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py
+++ b/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py
@@ -6,30 +6,38 @@ from bob.db.chasedb1 import Database as CHASE
from bob.db.hrf import Database as HRF
from bob.ip.binseg.data.transforms import *
import torch
-from bob.ip.binseg.data.binsegdataset import BinSegDataset, SSLBinSegDataset, UnLabeledBinSegDataset
+from bob.ip.binseg.data.binsegdataset import (
+ BinSegDataset,
+ SSLBinSegDataset,
+ UnLabeledBinSegDataset,
+)
#### Config ####
# PyTorch dataset
-labeled_dataset = torch.utils.data.ConcatDataset([drive,stare,hrf,iostar])
+labeled_dataset = torch.utils.data.ConcatDataset([drive, stare, hrf, iostar])
#### Unlabeled CHASE TRAIN ####
-unlabeled_transforms = Compose([
- Crop(0,18,960,960)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+unlabeled_transforms = Compose(
+ [
+ Crop(0, 18, 960, 960),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-chasebobdb = CHASE(protocol = 'default')
+chasebobdb = CHASE(protocol="default")
# PyTorch dataset
-unlabeled_dataset = UnLabeledBinSegDataset(chasebobdb, split='train', transform=unlabeled_transforms)
+unlabeled_dataset = UnLabeledBinSegDataset(
+ chasebobdb, split="train", transform=unlabeled_transforms
+)
# SSL Dataset
-dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
\ No newline at end of file
+dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
diff --git a/bob/ip/binseg/configs/datasets/drivetest.py b/bob/ip/binseg/configs/datasets/drivetest.py
index 230598dce92a39276e05dd4b4f842643428546b4..c6bff8ca07bf2216a322987f04844af878f266fe 100644
--- a/bob/ip/binseg/configs/datasets/drivetest.py
+++ b/bob/ip/binseg/configs/datasets/drivetest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop((544,544))
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop((544, 544)), ToTensor()])
# bob.db.dataset init
-bobdb = DRIVE(protocol = 'default')
+bobdb = DRIVE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf.py b/bob/ip/binseg/configs/datasets/hrf.py
index cb008f7da1736ef66085ddfb4de0335695c28779..b13302096e74d5e9306b25a2c5fb7970ffeb890c 100644
--- a/bob/ip/binseg/configs/datasets/hrf.py
+++ b/bob/ip/binseg/configs/datasets/hrf.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Crop(0,108,2336,3296)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Crop(0, 108, 2336, 3296),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf1024.py b/bob/ip/binseg/configs/datasets/hrf1024.py
index 48168445f5689b95a1f55ffda0633d2acdbb619a..e07bd883ee7a4f9bc7bfeb2d196f09af03b01875 100644
--- a/bob/ip/binseg/configs/datasets/hrf1024.py
+++ b/bob/ip/binseg/configs/datasets/hrf1024.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((0,584,0,584))
- ,Resize((1024))
- ,RandomRotation()
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((0, 584, 0, 584)),
+ Resize((1024)),
+ RandomRotation(),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf1168.py b/bob/ip/binseg/configs/datasets/hrf1168.py
index 4d0c4d9eb3097a3c918a24bd2621bb3284f41215..4467c02c4f0312a72afbea385c5246f466208ea1 100644
--- a/bob/ip/binseg/configs/datasets/hrf1168.py
+++ b/bob/ip/binseg/configs/datasets/hrf1168.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Crop(0,108,2336,3296)
- ,Resize((1168))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Crop(0, 108, 2336, 3296),
+ Resize((1168)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf1168test.py b/bob/ip/binseg/configs/datasets/hrf1168test.py
index 86014b75bd7ea428a5f48f85776189d6eeccb619..86d968f46f24ca4deaacb95ff71d9e4185dd352e 100644
--- a/bob/ip/binseg/configs/datasets/hrf1168test.py
+++ b/bob/ip/binseg/configs/datasets/hrf1168test.py
@@ -7,14 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Crop(0,108,2336,3296)
- ,Resize((1168))
- ,ToTensor()
- ])
+transforms = Compose([Crop(0, 108, 2336, 3296), Resize((1168)), ToTensor()])
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf544.py b/bob/ip/binseg/configs/datasets/hrf544.py
index 0e2cc05152aa20e4c75ffcb464cd8fd90f89e7bd..6cd9ccf0ef2f7283034afcb3089a3a5618600b2c 100644
--- a/bob/ip/binseg/configs/datasets/hrf544.py
+++ b/bob/ip/binseg/configs/datasets/hrf544.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize((363))
- ,Pad((0,90,0,91))
- ,RandomRotation()
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Resize((363)),
+ Pad((0, 90, 0, 91)),
+ RandomRotation(),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf544test.py b/bob/ip/binseg/configs/datasets/hrf544test.py
index 86da428b8f6bd0220d0b311b53ddcba098177a70..45a3e61df22e8a99c3ea199652815ccc4e3c0148 100644
--- a/bob/ip/binseg/configs/datasets/hrf544test.py
+++ b/bob/ip/binseg/configs/datasets/hrf544test.py
@@ -7,14 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize((363))
- ,Pad((0,90,0,91))
- ,ToTensor()
- ])
+transforms = Compose([Resize((363)), Pad((0, 90, 0, 91)), ToTensor()])
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf608.py b/bob/ip/binseg/configs/datasets/hrf608.py
index b26e772a0793d043af6e59f18caab5943805d929..7b232ea769872ec68247f31ac29f8235dc598bde 100644
--- a/bob/ip/binseg/configs/datasets/hrf608.py
+++ b/bob/ip/binseg/configs/datasets/hrf608.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((0,345,0,345))
- ,Resize(608)
- ,RandomRotation()
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((0, 345, 0, 345)),
+ Resize(608),
+ RandomRotation(),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrf960.py b/bob/ip/binseg/configs/datasets/hrf960.py
index dd43cf00b68e8a66e8e0ec362ec519bf3488358e..059a831c63fbccdb884b1133fde8f9d7ccc24d93 100644
--- a/bob/ip/binseg/configs/datasets/hrf960.py
+++ b/bob/ip/binseg/configs/datasets/hrf960.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((0,584,0,584))
- ,Resize((960))
- ,RandomRotation()
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((0, 584, 0, 584)),
+ Resize((960)),
+ RandomRotation(),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/hrftest.py b/bob/ip/binseg/configs/datasets/hrftest.py
index 45f952728f8e5ff4e335c8ea6a6d79762c166b8e..d7c32c2a3b5913a2f6ec3a7a19b17d9ab9599770 100644
--- a/bob/ip/binseg/configs/datasets/hrftest.py
+++ b/bob/ip/binseg/configs/datasets/hrftest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Crop(0,108,2336,3296)
- ,ToTensor()
- ])
+transforms = Compose([Crop(0, 108, 2336, 3296), ToTensor()])
# bob.db.dataset init
-bobdb = HRF(protocol = 'default')
+bobdb = HRF(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/imagefolder.py b/bob/ip/binseg/configs/datasets/imagefolder.py
index de90eeb0dd7486c9938fe1a89a704550c1ce1f17..d73de0c4f22ed11fabfe1a1f7dd73a337e511984 100644
--- a/bob/ip/binseg/configs/datasets/imagefolder.py
+++ b/bob/ip/binseg/configs/datasets/imagefolder.py
@@ -7,15 +7,17 @@ from bob.ip.binseg.data.imagefolder import ImageFolder
#### Config ####
# add your transforms below
-transforms = Compose([
- CenterCrop((544,544))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ CenterCrop((544, 544)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# PyTorch dataset
-path = '/path/to/dataset'
-dataset = ImageFolder(path,transform=transforms)
+path = "/path/to/dataset"
+dataset = ImageFolder(path, transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/imagefolderinference.py b/bob/ip/binseg/configs/datasets/imagefolderinference.py
index 634566b9c0f8c899474494651044f138e610f724..869f7e51a0fdb1fe114b6f0586bbc374647f5fdb 100644
--- a/bob/ip/binseg/configs/datasets/imagefolderinference.py
+++ b/bob/ip/binseg/configs/datasets/imagefolderinference.py
@@ -7,12 +7,8 @@ from bob.ip.binseg.data.imagefolderinference import ImageFolderInference
#### Config ####
# add your transforms below
-transforms = Compose([
- ToRGB(),
- CenterCrop((544,544))
- ,ToTensor()
- ])
+transforms = Compose([ToRGB(), CenterCrop((544, 544)), ToTensor()])
# PyTorch dataset
-path = '/path/to/folder/containing/images'
-dataset = ImageFolderInference(path,transform=transforms)
+path = "/path/to/folder/containing/images"
+dataset = ImageFolderInference(path, transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/imagefoldertest.py b/bob/ip/binseg/configs/datasets/imagefoldertest.py
index 15d9b0383163cee1f3b102783ceb3b50defe5459..474b0384b0d22822250bb1c96ed220e7f335ec65 100644
--- a/bob/ip/binseg/configs/datasets/imagefoldertest.py
+++ b/bob/ip/binseg/configs/datasets/imagefoldertest.py
@@ -7,11 +7,8 @@ from bob.ip.binseg.data.imagefolder import ImageFolder
#### Config ####
# add your transforms below
-transforms = Compose([
- CenterCrop((544,544))
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop((544, 544)), ToTensor()])
# PyTorch dataset
-path = '/path/to/testdataset'
-dataset = ImageFolder(path,transform=transforms)
+path = "/path/to/testdataset"
+dataset = ImageFolder(path, transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarod.py b/bob/ip/binseg/configs/datasets/iostarod.py
index 334df2a4ba402f879a436ce0ee3bcc07ca4ff49f..e043f4166108bfda501210a5770d5b49890406a4 100644
--- a/bob/ip/binseg/configs/datasets/iostarod.py
+++ b/bob/ip/binseg/configs/datasets/iostarod.py
@@ -7,16 +7,12 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [RandomHFlip(), RandomVFlip(), RandomRotation(), ColorJitter(), ToTensor()]
+)
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_od')
+bobdb = IOSTAR(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarodtest.py b/bob/ip/binseg/configs/datasets/iostarodtest.py
index ba06450781bc03c765504e98fa715a4b15b1e774..a4e9b4c8cb4b505a62e21000bdc37bcbf99b4a08 100644
--- a/bob/ip/binseg/configs/datasets/iostarodtest.py
+++ b/bob/ip/binseg/configs/datasets/iostarodtest.py
@@ -7,12 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- ToTensor()
- ])
+transforms = Compose([ToTensor()])
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_od')
+bobdb = IOSTAR(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvessel.py b/bob/ip/binseg/configs/datasets/iostarvessel.py
index ded01bb45820f9402fce9a5c6dc15c14908220eb..5fa8ebb66b312462cb321b0c834f354fb0e48a36 100644
--- a/bob/ip/binseg/configs/datasets/iostarvessel.py
+++ b/bob/ip/binseg/configs/datasets/iostarvessel.py
@@ -7,16 +7,12 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [RandomHFlip(), RandomVFlip(), RandomRotation(), ColorJitter(), ToTensor()]
+)
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvessel1168.py b/bob/ip/binseg/configs/datasets/iostarvessel1168.py
index 5da5ed1e912065ca4ea2a81e4bd0f8b4e8d5475d..c58bdf6679da1fc33bd5ac44ca893e42134daa10 100644
--- a/bob/ip/binseg/configs/datasets/iostarvessel1168.py
+++ b/bob/ip/binseg/configs/datasets/iostarvessel1168.py
@@ -7,19 +7,21 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Crop(144,0,768,1024)
- ,Pad((30,0,30,0))
- ,Resize(1168)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Crop(144, 0, 768, 1024),
+ Pad((30, 0, 30, 0)),
+ Resize(1168),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvessel544.py b/bob/ip/binseg/configs/datasets/iostarvessel544.py
index aa03abe2feb64a084fb76e6f60c69afc7499961f..31c513952fdfce41c2f520a197a873775a898fe5 100644
--- a/bob/ip/binseg/configs/datasets/iostarvessel544.py
+++ b/bob/ip/binseg/configs/datasets/iostarvessel544.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize(544)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Resize(544),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvessel544test.py b/bob/ip/binseg/configs/datasets/iostarvessel544test.py
index e3ccd854079e57c642669aa33f403d4ba28d4700..321579bfaa23da7efcd4e722bc92859b1c93ae99 100644
--- a/bob/ip/binseg/configs/datasets/iostarvessel544test.py
+++ b/bob/ip/binseg/configs/datasets/iostarvessel544test.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize(544)
- ,ToTensor()
- ])
+transforms = Compose([Resize(544), ToTensor()])
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvessel608.py b/bob/ip/binseg/configs/datasets/iostarvessel608.py
index 7fce4507cd090555a2e0bac7f575dfd9d9f85c3d..04280727513fa3faa548f6a2b55d9c165c14e0cc 100644
--- a/bob/ip/binseg/configs/datasets/iostarvessel608.py
+++ b/bob/ip/binseg/configs/datasets/iostarvessel608.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((81,0,81,0))
- ,Resize(608)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((81, 0, 81, 0)),
+ Resize(608),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvessel960.py b/bob/ip/binseg/configs/datasets/iostarvessel960.py
index 32feec853882cbdcadc9fea91de4a1d61e168cc0..600a8cff35d3a8993a949fda5f6e7c347a212ec0 100644
--- a/bob/ip/binseg/configs/datasets/iostarvessel960.py
+++ b/bob/ip/binseg/configs/datasets/iostarvessel960.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize(960)
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Resize(960),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/iostarvesseltest.py b/bob/ip/binseg/configs/datasets/iostarvesseltest.py
index d8fe13718be5c4517c69683696965cbbe5a9abdf..18ec9f2e1005b549eec7c921b7bbd41912f621c3 100644
--- a/bob/ip/binseg/configs/datasets/iostarvesseltest.py
+++ b/bob/ip/binseg/configs/datasets/iostarvesseltest.py
@@ -7,12 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- ToTensor()
- ])
+transforms = Compose([ToTensor()])
# bob.db.dataset init
-bobdb = IOSTAR(protocol='default_vessel')
+bobdb = IOSTAR(protocol="default_vessel")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/refugecup.py b/bob/ip/binseg/configs/datasets/refugecup.py
index 9efac5293295bc0f0eb2a03a78ebacdb4e1615c2..1100f50897b91de4624e59bb24840566fb4115f9 100644
--- a/bob/ip/binseg/configs/datasets/refugecup.py
+++ b/bob/ip/binseg/configs/datasets/refugecup.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize((1539))
- ,Pad((21,46,22,47))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Resize((1539)),
+ Pad((21, 46, 22, 47)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = REFUGE(protocol = 'default_cup')
+bobdb = REFUGE(protocol="default_cup")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/refugecuptest.py b/bob/ip/binseg/configs/datasets/refugecuptest.py
index 8ff916e30cc60e24e505ccb6c0b3455e97d7a9a9..5e6003079d226bb03ed8d4549741dca6f05ead6a 100644
--- a/bob/ip/binseg/configs/datasets/refugecuptest.py
+++ b/bob/ip/binseg/configs/datasets/refugecuptest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop(1632)
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop(1632), ToTensor()])
# bob.db.dataset init
-bobdb = REFUGE(protocol = 'default_cup')
+bobdb = REFUGE(protocol="default_cup")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/refugeod.py b/bob/ip/binseg/configs/datasets/refugeod.py
index 5faaf05a9edcd7fcb4c3353dd6f9a17478233038..4435640ea5b741bfa550d5c0a0aaad4f4e22095e 100644
--- a/bob/ip/binseg/configs/datasets/refugeod.py
+++ b/bob/ip/binseg/configs/datasets/refugeod.py
@@ -7,18 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Resize((1539))
- ,Pad((21,46,22,47))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Resize((1539)),
+ Pad((21, 46, 22, 47)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = REFUGE(protocol = 'default_od')
+bobdb = REFUGE(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/refugeodtest.py b/bob/ip/binseg/configs/datasets/refugeodtest.py
index 30085a2f5450eefb12300b27677a15bf27baa8d8..b77d3e28208a0511a02149ecb8ca006b666c17e2 100644
--- a/bob/ip/binseg/configs/datasets/refugeodtest.py
+++ b/bob/ip/binseg/configs/datasets/refugeodtest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- CenterCrop(1632)
- ,ToTensor()
- ])
+transforms = Compose([CenterCrop(1632), ToTensor()])
# bob.db.dataset init
-bobdb = REFUGE(protocol = 'default_od')
+bobdb = REFUGE(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/rimoner3cup.py b/bob/ip/binseg/configs/datasets/rimoner3cup.py
index 47b62ba0c521d4f4209fb6026c7aae184228fdb2..0fad0285ee45ba9dc53a98348a5f8401daa80215 100644
--- a/bob/ip/binseg/configs/datasets/rimoner3cup.py
+++ b/bob/ip/binseg/configs/datasets/rimoner3cup.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((8,8,8,8))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((8, 8, 8, 8)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = RIMONER3(protocol = 'default_cup')
+bobdb = RIMONER3(protocol="default_cup")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/rimoner3cuptest.py b/bob/ip/binseg/configs/datasets/rimoner3cuptest.py
index 9f227be81289e12f4cfc8e63b1a76cbc1251c614..8646533154ef6d09cb1669fefc578f1a4a97e710 100644
--- a/bob/ip/binseg/configs/datasets/rimoner3cuptest.py
+++ b/bob/ip/binseg/configs/datasets/rimoner3cuptest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((8,8,8,8))
- ,ToTensor()
- ])
+transforms = Compose([Pad((8, 8, 8, 8)), ToTensor()])
# bob.db.dataset init
-bobdb = RIMONER3(protocol = 'default_cup')
+bobdb = RIMONER3(protocol="default_cup")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/rimoner3od.py b/bob/ip/binseg/configs/datasets/rimoner3od.py
index 4905bec3cb663faed12cd85edc099f7987834657..a465342afa2470fd94b3f7aa735d67dd28126b84 100644
--- a/bob/ip/binseg/configs/datasets/rimoner3od.py
+++ b/bob/ip/binseg/configs/datasets/rimoner3od.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((8,8,8,8))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((8, 8, 8, 8)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = RIMONER3(protocol = 'default_od')
+bobdb = RIMONER3(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/rimoner3odtest.py b/bob/ip/binseg/configs/datasets/rimoner3odtest.py
index 390f20d795323082c3aca3f6f0a2c81a5b144ba1..6e4dd1a6c72ad8cfabf8d7e91f5a63ce6975a90e 100644
--- a/bob/ip/binseg/configs/datasets/rimoner3odtest.py
+++ b/bob/ip/binseg/configs/datasets/rimoner3odtest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((8,8,8,8))
- ,ToTensor()
- ])
+transforms = Compose([Pad((8, 8, 8, 8)), ToTensor()])
# bob.db.dataset init
-bobdb = RIMONER3(protocol = 'default_od')
+bobdb = RIMONER3(protocol="default_od")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/stare.py b/bob/ip/binseg/configs/datasets/stare.py
index f2c784a9ab7a75c82c11ffc5dbb32d6390fb93be..0f93cc788f307a1d4145b9a8247df0e85828f3b4 100644
--- a/bob/ip/binseg/configs/datasets/stare.py
+++ b/bob/ip/binseg/configs/datasets/stare.py
@@ -7,17 +7,19 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((2,1,2,2))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ Pad((2, 1, 2, 2)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = STARE(protocol = 'default')
+bobdb = STARE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/stare1024.py b/bob/ip/binseg/configs/datasets/stare1024.py
index 8f6df507b16aeffb485bc448c57cf8b21f47bda1..a8931ff21fa9d91355169abffd7ba24dd23bfff1 100644
--- a/bob/ip/binseg/configs/datasets/stare1024.py
+++ b/bob/ip/binseg/configs/datasets/stare1024.py
@@ -7,19 +7,21 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Pad((0,32,0,32))
- ,Resize(1024)
- ,CenterCrop(1024)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Pad((0, 32, 0, 32)),
+ Resize(1024),
+ CenterCrop(1024),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = STARE(protocol = 'default')
+bobdb = STARE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/stare1168.py b/bob/ip/binseg/configs/datasets/stare1168.py
index 77e934bf6b6f387105df08519932822bcb11cf09..516a926739387ebf4ac20a2060b9b4c8398a5863 100644
--- a/bob/ip/binseg/configs/datasets/stare1168.py
+++ b/bob/ip/binseg/configs/datasets/stare1168.py
@@ -7,19 +7,21 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Crop(50,0,500,705)
- ,Resize(1168)
- ,Pad((1,0,1,0))
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Crop(50, 0, 500, 705),
+ Resize(1168),
+ Pad((1, 0, 1, 0)),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = STARE(protocol = 'default')
+bobdb = STARE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/stare544.py b/bob/ip/binseg/configs/datasets/stare544.py
index f03fcefbe6fef80ef1e6a7ec97d2b6c1df221024..b972d1f3b7651cdd0270864f6052efbee464bc13 100644
--- a/bob/ip/binseg/configs/datasets/stare544.py
+++ b/bob/ip/binseg/configs/datasets/stare544.py
@@ -7,17 +7,20 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([ RandomRotation()
- ,Resize(471)
- ,Pad((0,37,0,36))
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Resize(471),
+ Pad((0, 37, 0, 36)),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = STARE(protocol = 'default')
+bobdb = STARE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/stare960.py b/bob/ip/binseg/configs/datasets/stare960.py
index 0d1ed7883cb746f469534ad2a29f491501e7566e..211a8448c35a99fb8204381d48f47583a7cf4b65 100644
--- a/bob/ip/binseg/configs/datasets/stare960.py
+++ b/bob/ip/binseg/configs/datasets/stare960.py
@@ -7,19 +7,21 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- RandomRotation()
- ,Pad((0,32,0,32))
- ,Resize(960)
- ,CenterCrop(960)
- ,RandomHFlip()
- ,RandomVFlip()
- ,ColorJitter()
- ,ToTensor()
- ])
+transforms = Compose(
+ [
+ RandomRotation(),
+ Pad((0, 32, 0, 32)),
+ Resize(960),
+ CenterCrop(960),
+ RandomHFlip(),
+ RandomVFlip(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-bobdb = STARE(protocol = 'default')
+bobdb = STARE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='train', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="train", transform=transforms)
diff --git a/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544.py b/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544.py
index 72349b3bd6370c0b996edf90e96403df54ec27af..200d784252028601fbaf5403332e40123c4a275e 100644
--- a/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544.py
+++ b/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544.py
@@ -7,4 +7,4 @@ import torch
#### Config ####
# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([stare,chase,hrf,iostar])
\ No newline at end of file
+dataset = torch.utils.data.ConcatDataset([stare, chase, hrf, iostar])
diff --git a/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py b/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py
index 3a5e3008f73cea8d1163030783d70736fad6ef9f..f126871ff20ce5e4b9895fbca2a841de9afcf4fb 100644
--- a/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py
+++ b/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py
@@ -5,30 +5,38 @@ from bob.ip.binseg.configs.datasets.hrf544 import dataset as hrf
from bob.db.drive import Database as DRIVE
from bob.ip.binseg.data.transforms import *
import torch
-from bob.ip.binseg.data.binsegdataset import BinSegDataset, SSLBinSegDataset, UnLabeledBinSegDataset
+from bob.ip.binseg.data.binsegdataset import (
+ BinSegDataset,
+ SSLBinSegDataset,
+ UnLabeledBinSegDataset,
+)
#### Config ####
# PyTorch dataset
-labeled_dataset = torch.utils.data.ConcatDataset([stare,chase,iostar,hrf])
+labeled_dataset = torch.utils.data.ConcatDataset([stare, chase, iostar, hrf])
#### Unlabeled STARE TRAIN ####
-unlabeled_transforms = Compose([
- CenterCrop((544,544))
- ,RandomHFlip()
- ,RandomVFlip()
- ,RandomRotation()
- ,ColorJitter()
- ,ToTensor()
- ])
+unlabeled_transforms = Compose(
+ [
+ CenterCrop((544, 544)),
+ RandomHFlip(),
+ RandomVFlip(),
+ RandomRotation(),
+ ColorJitter(),
+ ToTensor(),
+ ]
+)
# bob.db.dataset init
-drivebobdb = DRIVE(protocol = 'default')
+drivebobdb = DRIVE(protocol="default")
# PyTorch dataset
-unlabeled_dataset = UnLabeledBinSegDataset(drivebobdb, split='train', transform=unlabeled_transforms)
+unlabeled_dataset = UnLabeledBinSegDataset(
+ drivebobdb, split="train", transform=unlabeled_transforms
+)
# SSL Dataset
-dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
\ No newline at end of file
+dataset = SSLBinSegDataset(labeled_dataset, unlabeled_dataset)
diff --git a/bob/ip/binseg/configs/datasets/staretest.py b/bob/ip/binseg/configs/datasets/staretest.py
index aab80b9bea6339bff87c5cc12d7375ce6216bc60..ac03e2a7e2ba1def5994e5bbf6325f381db2be01 100644
--- a/bob/ip/binseg/configs/datasets/staretest.py
+++ b/bob/ip/binseg/configs/datasets/staretest.py
@@ -7,13 +7,10 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
#### Config ####
-transforms = Compose([
- Pad((2,1,2,2))
- ,ToTensor()
- ])
+transforms = Compose([Pad((2, 1, 2, 2)), ToTensor()])
# bob.db.dataset init
-bobdb = STARE(protocol = 'default')
+bobdb = STARE(protocol="default")
# PyTorch dataset
-dataset = BinSegDataset(bobdb, split='test', transform=transforms)
\ No newline at end of file
+dataset = BinSegDataset(bobdb, split="test", transform=transforms)
diff --git a/bob/ip/binseg/configs/models/driubn.py b/bob/ip/binseg/configs/models/driubn.py
index 0b95501d0a61053fd74b64976f6a761255944ece..aedf52ed430ac46003009e310cfdcfbd94633f4d 100644
--- a/bob/ip/binseg/configs/models/driubn.py
+++ b/bob/ip/binseg/configs/models/driubn.py
@@ -26,13 +26,23 @@ scheduler_gamma = 0.1
model = build_driu()
# pretrained backbone
-pretrained_backbone = modelurls['vgg16_bn']
+pretrained_backbone = modelurls["vgg16_bn"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
# criterion
criterion = SoftJaccardBCELogitsLoss(alpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/driubnssl.py b/bob/ip/binseg/configs/models/driubnssl.py
index 52b3a2b35272b99d5f47bae8f23d47da15990135..429a0500232aa0078633222b6dabac77603326c8 100644
--- a/bob/ip/binseg/configs/models/driubnssl.py
+++ b/bob/ip/binseg/configs/models/driubnssl.py
@@ -26,14 +26,24 @@ scheduler_gamma = 0.1
model = build_driu()
# pretrained backbone
-pretrained_backbone = modelurls['vgg16_bn']
+pretrained_backbone = modelurls["vgg16_bn"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
# criterion
criterion = MixJacLoss(lambda_u=0.05, jacalpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/driuod.py b/bob/ip/binseg/configs/models/driuod.py
index 7ad9bb836760b19c021f05d43ea9886685ce3257..b53fc751066d7a45537f848624546508dcaf68b8 100644
--- a/bob/ip/binseg/configs/models/driuod.py
+++ b/bob/ip/binseg/configs/models/driuod.py
@@ -26,13 +26,23 @@ scheduler_gamma = 0.1
model = build_driuod()
# pretrained backbone
-pretrained_backbone = modelurls['vgg16']
+pretrained_backbone = modelurls["vgg16"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
# criterion
criterion = SoftJaccardBCELogitsLoss(alpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/driussl.py b/bob/ip/binseg/configs/models/driussl.py
index 39afd4a03f956b24aeeb078c297fb026cdc369b5..a5d49950d3e3360d7bb64e810d98fb3760c1d547 100644
--- a/bob/ip/binseg/configs/models/driussl.py
+++ b/bob/ip/binseg/configs/models/driussl.py
@@ -26,14 +26,24 @@ scheduler_gamma = 0.1
model = build_driu()
# pretrained backbone
-pretrained_backbone = modelurls['vgg16']
+pretrained_backbone = modelurls["vgg16"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
# criterion
criterion = MixJacLoss(lambda_u=0.05, jacalpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/hed.py b/bob/ip/binseg/configs/models/hed.py
index eeb0e599e89c967247975456016dc35facdf805c..ee905048f388a74a6c928b9605e6a418e2e5dea8 100644
--- a/bob/ip/binseg/configs/models/hed.py
+++ b/bob/ip/binseg/configs/models/hed.py
@@ -27,13 +27,23 @@ scheduler_gamma = 0.1
model = build_hed()
# pretrained backbone
-pretrained_backbone = modelurls['vgg16']
+pretrained_backbone = modelurls["vgg16"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
# criterion
criterion = HEDSoftJaccardBCELogitsLoss(alpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/m2unet.py b/bob/ip/binseg/configs/models/m2unet.py
index b15a277966200c97b47a201ff086c7002b9655aa..4dd0da54215a23a9808963dca4d3cac738240448 100644
--- a/bob/ip/binseg/configs/models/m2unet.py
+++ b/bob/ip/binseg/configs/models/m2unet.py
@@ -26,14 +26,24 @@ scheduler_gamma = 0.1
model = build_m2unet()
# pretrained backbone
-pretrained_backbone = modelurls['mobilenetv2']
+pretrained_backbone = modelurls["mobilenetv2"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
-
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
+
# criterion
criterion = SoftJaccardBCELogitsLoss(alpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/m2unetssl.py b/bob/ip/binseg/configs/models/m2unetssl.py
index 3497cea26b03c44b682cfd3beb167afce3015e43..4eab3c6c5b447cb8a4e43923ce3081bebe0b9503 100644
--- a/bob/ip/binseg/configs/models/m2unetssl.py
+++ b/bob/ip/binseg/configs/models/m2unetssl.py
@@ -26,14 +26,24 @@ scheduler_gamma = 0.1
model = build_m2unet()
# pretrained backbone
-pretrained_backbone = modelurls['mobilenetv2']
+pretrained_backbone = modelurls["mobilenetv2"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
-
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
+
# criterion
criterion = MixJacLoss(lambda_u=0.05, jacalpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/resunet.py b/bob/ip/binseg/configs/models/resunet.py
index a1db473cc7f87b6847f57cc1200326d42cb8ab86..b2129d5e9ab32ca8a1fea2b77d9e2b5f8e3821d5 100644
--- a/bob/ip/binseg/configs/models/resunet.py
+++ b/bob/ip/binseg/configs/models/resunet.py
@@ -26,14 +26,24 @@ scheduler_gamma = 0.1
model = build_res50unet()
# pretrained backbone
-pretrained_backbone = modelurls['resnet50']
+pretrained_backbone = modelurls["resnet50"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
-
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
+
# criterion
criterion = SoftJaccardBCELogitsLoss(alpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/configs/models/unet.py b/bob/ip/binseg/configs/models/unet.py
index 8182c7fa089c77221b4434e272ae6e0d394b3912..c129c5d99813cb7089a04c4397f669cad0678217 100644
--- a/bob/ip/binseg/configs/models/unet.py
+++ b/bob/ip/binseg/configs/models/unet.py
@@ -26,14 +26,24 @@ scheduler_gamma = 0.1
model = build_unet()
# pretrained backbone
-pretrained_backbone = modelurls['vgg16']
+pretrained_backbone = modelurls["vgg16"]
# optimizer
-optimizer = AdaBound(model.parameters(), lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
-
+optimizer = AdaBound(
+ model.parameters(),
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+)
+
# criterion
criterion = SoftJaccardBCELogitsLoss(alpha=0.7)
# scheduler
-scheduler = MultiStepLR(optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma)
+scheduler = MultiStepLR(
+ optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
+)
diff --git a/bob/ip/binseg/data/binsegdataset.py b/bob/ip/binseg/data/binsegdataset.py
index 2917203c7b530bee796431e0dbe5e7af1f85a2b9..e2977d379095a4ecb77add57c27e7d7d4411a741 100644
--- a/bob/ip/binseg/data/binsegdataset.py
+++ b/bob/ip/binseg/data/binsegdataset.py
@@ -3,6 +3,7 @@
from torch.utils.data import Dataset
import random
+
class BinSegDataset(Dataset):
"""PyTorch dataset wrapper around bob.db binary segmentation datasets.
A transform object can be passed that will be applied to the image, ground truth and mask (if present).
@@ -19,18 +20,19 @@ class BinSegDataset(Dataset):
mask : bool
whether dataset contains masks or not
"""
- def __init__(self, bobdb, split = 'train', transform = None,index_to = None):
+
+ def __init__(self, bobdb, split="train", transform=None, index_to=None):
if index_to:
self.database = bobdb.samples(split)[:index_to]
else:
self.database = bobdb.samples(split)
self.transform = transform
self.split = split
-
+
@property
def mask(self):
# check if first sample contains a mask
- return hasattr(self.database[0], 'mask')
+ return hasattr(self.database[0], "mask")
def __len__(self):
"""
@@ -40,8 +42,8 @@ class BinSegDataset(Dataset):
size of the dataset
"""
return len(self.database)
-
- def __getitem__(self,index):
+
+ def __getitem__(self, index):
"""
Parameters
----------
@@ -56,12 +58,12 @@ class BinSegDataset(Dataset):
gt = self.database[index].gt.pil_image()
img_name = self.database[index].img.basename
sample = [img, gt]
-
- if self.transform :
+
+ if self.transform:
sample = self.transform(*sample)
-
- sample.insert(0,img_name)
-
+
+ sample.insert(0, img_name)
+
return sample
@@ -77,10 +79,10 @@ class SSLBinSegDataset(Dataset):
unlabeled_dataset : :py:class:`torch.utils.data.Dataset`
UnLabeledBinSegDataset with unlabeled data
"""
+
def __init__(self, labeled_dataset, unlabeled_dataset):
self.labeled_dataset = labeled_dataset
self.unlabeled_dataset = unlabeled_dataset
-
def __len__(self):
"""
@@ -90,8 +92,8 @@ class SSLBinSegDataset(Dataset):
size of the dataset
"""
return len(self.labeled_dataset)
-
- def __getitem__(self,index):
+
+ def __getitem__(self, index):
"""
Parameters
----------
@@ -123,13 +125,14 @@ class UnLabeledBinSegDataset(Dataset):
transform : :py:mod:`bob.ip.binseg.data.transforms`, optional
A transform or composition of transfroms. Defaults to ``None``.
"""
- def __init__(self, db, split = 'train', transform = None,index_from= None):
+
+ def __init__(self, db, split="train", transform=None, index_from=None):
if index_from:
self.database = db.samples(split)[index_from:]
else:
self.database = db.samples(split)
self.transform = transform
- self.split = split
+ self.split = split
def __len__(self):
"""
@@ -139,8 +142,8 @@ class UnLabeledBinSegDataset(Dataset):
size of the dataset
"""
return len(self.database)
-
- def __getitem__(self,index):
+
+ def __getitem__(self, index):
"""
Parameters
----------
@@ -155,9 +158,9 @@ class UnLabeledBinSegDataset(Dataset):
img = self.database[index].img.pil_image()
img_name = self.database[index].img.basename
sample = [img]
- if self.transform :
+ if self.transform:
sample = self.transform(img)
-
- sample.insert(0,img_name)
-
- return sample
\ No newline at end of file
+
+ sample.insert(0, img_name)
+
+ return sample
diff --git a/bob/ip/binseg/data/imagefolder.py b/bob/ip/binseg/data/imagefolder.py
index 7ec9dd9dbd558b8a1b405076946dca0919df5990..9794f5c11a564f99f54baa00aa19ce6547882ff4 100644
--- a/bob/ip/binseg/data/imagefolder.py
+++ b/bob/ip/binseg/data/imagefolder.py
@@ -8,16 +8,18 @@ import torch
import torchvision.transforms.functional as VF
import bob.io.base
+
def get_file_lists(data_path):
data_path = Path(data_path)
-
- image_path = data_path.joinpath('images')
- image_file_names = np.array(sorted(list(image_path.glob('*'))))
- gt_path = data_path.joinpath('gt')
- gt_file_names = np.array(sorted(list(gt_path.glob('*'))))
+ image_path = data_path.joinpath("images")
+ image_file_names = np.array(sorted(list(image_path.glob("*"))))
+
+ gt_path = data_path.joinpath("gt")
+ gt_file_names = np.array(sorted(list(gt_path.glob("*"))))
return image_file_names, gt_file_names
+
class ImageFolder(Dataset):
"""
Generic ImageFolder dataset, that contains two folders:
@@ -32,7 +34,8 @@ class ImageFolder(Dataset):
full path to root of dataset
"""
- def __init__(self, path, transform = None):
+
+ def __init__(self, path, transform=None):
self.transform = transform
self.img_file_list, self.gt_file_list = get_file_lists(path)
@@ -44,8 +47,8 @@ class ImageFolder(Dataset):
size of the dataset
"""
return len(self.img_file_list)
-
- def __getitem__(self,index):
+
+ def __getitem__(self, index):
"""
Parameters
----------
@@ -58,22 +61,22 @@ class ImageFolder(Dataset):
"""
img_path = self.img_file_list[index]
img_name = img_path.name
- img = Image.open(img_path).convert(mode='RGB')
-
+ img = Image.open(img_path).convert(mode="RGB")
+
gt_path = self.gt_file_list[index]
- if gt_path.suffix == '.hdf5':
- gt = bob.io.base.load(str(gt_path)).astype('float32')
+ if gt_path.suffix == ".hdf5":
+ gt = bob.io.base.load(str(gt_path)).astype("float32")
# not elegant but since transforms require PIL images we do this hacky workaround here
gt = torch.from_numpy(gt)
- gt = VF.to_pil_image(gt).convert(mode='1', dither=None)
+ gt = VF.to_pil_image(gt).convert(mode="1", dither=None)
else:
- gt = Image.open(gt_path).convert(mode='1', dither=None)
-
+ gt = Image.open(gt_path).convert(mode="1", dither=None)
+
sample = [img, gt]
-
- if self.transform :
+
+ if self.transform:
sample = self.transform(*sample)
-
- sample.insert(0,img_name)
-
+
+ sample.insert(0, img_name)
+
return sample
diff --git a/bob/ip/binseg/data/imagefolderinference.py b/bob/ip/binseg/data/imagefolderinference.py
index c4218755f099e5455e474b67fcaa004f99f46ca4..5a3fdfa0b336595c7ca52092c6fe456a08a1910f 100644
--- a/bob/ip/binseg/data/imagefolderinference.py
+++ b/bob/ip/binseg/data/imagefolderinference.py
@@ -8,6 +8,7 @@ import torch
import torchvision.transforms.functional as VF
import bob.io.base
+
def get_file_lists(data_path, glob):
"""
Recursively retrieves file lists from a given path, matching a given glob
@@ -20,6 +21,7 @@ def get_file_lists(data_path, glob):
image_file_names = np.array(sorted(list(data_path.rglob(glob))))
return image_file_names
+
class ImageFolderInference(Dataset):
"""
Generic ImageFolder containing images for inference
@@ -43,7 +45,8 @@ class ImageFolderInference(Dataset):
List of transformations to apply to every input sample
"""
- def __init__(self, path, glob='*', transform = None):
+
+ def __init__(self, path, glob="*", transform=None):
self.transform = transform
self.path = path
self.img_file_list = get_file_lists(path, glob)
@@ -57,7 +60,7 @@ class ImageFolderInference(Dataset):
"""
return len(self.img_file_list)
- def __getitem__(self,index):
+ def __getitem__(self, index):
"""
Parameters
----------
@@ -74,9 +77,9 @@ class ImageFolderInference(Dataset):
sample = [img]
- if self.transform :
+ if self.transform:
sample = self.transform(*sample)
- sample.insert(0,img_name)
+ sample.insert(0, img_name)
return sample
diff --git a/bob/ip/binseg/data/transforms.py b/bob/ip/binseg/data/transforms.py
index 659a090e28e45c94b22ef31a7e3ddc995543aa45..05040e4697c1cc3835b76fd513f65b662d1bb7e3 100644
--- a/bob/ip/binseg/data/transforms.py
+++ b/bob/ip/binseg/data/transforms.py
@@ -22,17 +22,18 @@ import collections
import bob.core
_pil_interpolation_to_str = {
- Image.NEAREST: 'PIL.Image.NEAREST',
- Image.BILINEAR: 'PIL.Image.BILINEAR',
- Image.BICUBIC: 'PIL.Image.BICUBIC',
- Image.LANCZOS: 'PIL.Image.LANCZOS',
- Image.HAMMING: 'PIL.Image.HAMMING',
- Image.BOX: 'PIL.Image.BOX',
+ Image.NEAREST: "PIL.Image.NEAREST",
+ Image.BILINEAR: "PIL.Image.BILINEAR",
+ Image.BICUBIC: "PIL.Image.BICUBIC",
+ Image.LANCZOS: "PIL.Image.LANCZOS",
+ Image.HAMMING: "PIL.Image.HAMMING",
+ Image.BOX: "PIL.Image.BOX",
}
Iterable = collections.abc.Iterable
# Compose
+
class Compose:
"""Composes several transforms.
@@ -51,15 +52,17 @@ class Compose:
return args
def __repr__(self):
- format_string = self.__class__.__name__ + '('
+ format_string = self.__class__.__name__ + "("
for t in self.transforms:
- format_string += '\n'
- format_string += ' {0}'.format(t)
- format_string += '\n)'
+ format_string += "\n"
+ format_string += " {0}".format(t)
+ format_string += "\n)"
return format_string
+
# Preprocessing
+
class CenterCrop:
"""
Crop at the center.
@@ -69,6 +72,7 @@ class CenterCrop:
size : int
target size
"""
+
def __init__(self, size):
self.size = size
@@ -91,6 +95,7 @@ class Crop:
w : int
width of the cropped image.
"""
+
def __init__(self, i, j, h, w):
self.i = i
self.j = j
@@ -98,7 +103,10 @@ class Crop:
self.w = w
def __call__(self, *args):
- return [img.crop((self.j, self.i, self.j + self.w, self.i + self.h)) for img in args]
+ return [
+ img.crop((self.j, self.i, self.j + self.w, self.i + self.h)) for img in args
+ ]
+
class Pad:
"""
@@ -115,12 +123,17 @@ class Pad:
pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant
"""
+
def __init__(self, padding, fill=0):
self.padding = padding
self.fill = fill
def __call__(self, *args):
- return [VF.pad(img, self.padding, self.fill, padding_mode='constant') for img in args]
+ return [
+ VF.pad(img, self.padding, self.fill, padding_mode="constant")
+ for img in args
+ ]
+
class AutoLevel16to8:
"""Converts a 16-bit image to 8-bit representation using "auto-level"
@@ -131,13 +144,16 @@ class AutoLevel16to8:
consider such a range should be mapped to the [0,255] range of the
destination image.
"""
+
def _process_one(self, img):
- return Image.fromarray(bob.core.convert(img, 'uint8', (0,255),
- img.getextrema()))
+ return Image.fromarray(
+ bob.core.convert(img, "uint8", (0, 255), img.getextrema())
+ )
def __call__(self, *args):
return [self._process_one(img) for img in args]
+
class ToRGB:
"""Converts from any input format to RGB, using an ADAPTIVE conversion.
@@ -146,17 +162,21 @@ class ToRGB:
defaults. This may be aggressive if applied to 16-bit images without
further considerations.
"""
+
def __call__(self, *args):
return [img.convert(mode="RGB") for img in args]
+
class ToTensor:
"""Converts :py:class:`PIL.Image.Image` to :py:class:`torch.Tensor` """
+
def __call__(self, *args):
return [VF.to_tensor(img) for img in args]
# Augmentations
+
class RandomHFlip:
"""
Flips horizontally
@@ -166,7 +186,8 @@ class RandomHFlip:
prob : float
probability at which imgage is flipped. Defaults to ``0.5``
"""
- def __init__(self, prob = 0.5):
+
+ def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, *args):
@@ -186,7 +207,8 @@ class RandomVFlip:
prob : float
probability at which imgage is flipped. Defaults to ``0.5``
"""
- def __init__(self, prob = 0.5):
+
+ def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, *args):
@@ -208,17 +230,19 @@ class RandomRotation:
prob : float
probability at which imgage is rotated. Defaults to ``0.5``
"""
- def __init__(self, degree_range = (-15, +15), prob = 0.5):
+
+ def __init__(self, degree_range=(-15, +15), prob=0.5):
self.prob = prob
self.degree_range = degree_range
def __call__(self, *args):
if random.random() < self.prob:
degree = random.randint(*self.degree_range)
- return [VF.rotate(img, degree, resample = Image.BILINEAR) for img in args]
+ return [VF.rotate(img, degree, resample=Image.BILINEAR) for img in args]
else:
return args
+
class ColorJitter(object):
"""
Randomly change the brightness, contrast, saturation and hue
@@ -240,7 +264,10 @@ class ColorJitter(object):
prob : float
probability at which the operation is applied
"""
- def __init__(self, brightness=0.3, contrast=0.3, saturation=0.02, hue=0.02, prob=0.5):
+
+ def __init__(
+ self, brightness=0.3, contrast=0.3, saturation=0.02, hue=0.02, prob=0.5
+ ):
self.brightness = brightness
self.contrast = contrast
self.saturation = saturation
@@ -252,15 +279,21 @@ class ColorJitter(object):
transforms = []
if brightness > 0:
brightness_factor = random.uniform(max(0, 1 - brightness), 1 + brightness)
- transforms.append(Lambda(lambda img: VF.adjust_brightness(img, brightness_factor)))
+ transforms.append(
+ Lambda(lambda img: VF.adjust_brightness(img, brightness_factor))
+ )
if contrast > 0:
contrast_factor = random.uniform(max(0, 1 - contrast), 1 + contrast)
- transforms.append(Lambda(lambda img: VF.adjust_contrast(img, contrast_factor)))
+ transforms.append(
+ Lambda(lambda img: VF.adjust_contrast(img, contrast_factor))
+ )
if saturation > 0:
saturation_factor = random.uniform(max(0, 1 - saturation), 1 + saturation)
- transforms.append(Lambda(lambda img: VF.adjust_saturation(img, saturation_factor)))
+ transforms.append(
+ Lambda(lambda img: VF.adjust_saturation(img, saturation_factor))
+ )
if hue > 0:
hue_factor = random.uniform(-hue, hue)
@@ -273,8 +306,9 @@ class ColorJitter(object):
def __call__(self, *args):
if random.random() < self.prob:
- transform = self.get_params(self.brightness, self.contrast,
- self.saturation, self.hue)
+ transform = self.get_params(
+ self.brightness, self.contrast, self.saturation, self.hue
+ )
trans_img = transform(args[0])
return [trans_img, *args[1:]]
else:
@@ -301,7 +335,14 @@ class RandomResizedCrop:
probability at which the operation is applied. Defaults to ``0.5``
"""
- def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR, prob = 0.5):
+ def __init__(
+ self,
+ size,
+ scale=(0.08, 1.0),
+ ratio=(3.0 / 4.0, 4.0 / 3.0),
+ interpolation=Image.BILINEAR,
+ prob=0.5,
+ ):
if isinstance(size, tuple):
self.size = size
else:
@@ -333,10 +374,10 @@ class RandomResizedCrop:
# Fallback to central crop
in_ratio = img.size[0] / img.size[1]
- if (in_ratio < min(ratio)):
+ if in_ratio < min(ratio):
w = img.size[0]
h = w / min(ratio)
- elif (in_ratio > max(ratio)):
+ elif in_ratio > max(ratio):
h = img.size[1]
w = h * max(ratio)
else: # whole image
@@ -359,10 +400,10 @@ class RandomResizedCrop:
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
- format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
- format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
- format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio))
- format_string += ', interpolation={0})'.format(interpolate_str)
+ format_string = self.__class__.__name__ + "(size={0}".format(self.size)
+ format_string += ", scale={0}".format(tuple(round(s, 4) for s in self.scale))
+ format_string += ", ratio={0}".format(tuple(round(r, 4) for r in self.ratio))
+ format_string += ", interpolation={0})".format(interpolate_str)
return format_string
@@ -391,4 +432,6 @@ class Resize:
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
- return self.__class__.__name__ + '(size={0}, interpolation={1})'.format(self.size, interpolate_str)
+ return self.__class__.__name__ + "(size={0}, interpolation={1})".format(
+ self.size, interpolate_str
+ )
diff --git a/bob/ip/binseg/engine/adabound.py b/bob/ip/binseg/engine/adabound.py
index 9e658b32fff24ec10a143f597d87ee10cfc11456..683bd76f4cf11412780bc5b78a2fa6d687707534 100644
--- a/bob/ip/binseg/engine/adabound.py
+++ b/bob/ip/binseg/engine/adabound.py
@@ -54,8 +54,17 @@ class AdaBound(torch.optim.Optimizer):
"""
- def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1,
- gamma=1e-3, eps=1e-8, weight_decay=0, amsbound=False):
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.9, 0.999),
+ final_lr=0.1,
+ gamma=1e-3,
+ eps=1e-8,
+ weight_decay=0,
+ amsbound=False,
+ ):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
@@ -68,16 +77,23 @@ class AdaBound(torch.optim.Optimizer):
raise ValueError("Invalid final learning rate: {}".format(final_lr))
if not 0.0 <= gamma < 1.0:
raise ValueError("Invalid gamma parameter: {}".format(gamma))
- defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
- weight_decay=weight_decay, amsbound=amsbound)
+ defaults = dict(
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+ )
super(AdaBound, self).__init__(params, defaults)
- self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
+ self.base_lrs = list(map(lambda group: group["lr"], self.param_groups))
def __setstate__(self, state):
super(AdaBound, self).__setstate__(state)
for group in self.param_groups:
- group.setdefault('amsbound', False)
+ group.setdefault("amsbound", False)
def step(self, closure=None):
"""Performs a single optimization step.
@@ -94,37 +110,38 @@ class AdaBound(torch.optim.Optimizer):
loss = closure()
for group, base_lr in zip(self.param_groups, self.base_lrs):
- for p in group['params']:
+ for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
- 'Adam does not support sparse gradients, please consider SparseAdam instead')
- amsbound = group['amsbound']
+ "Adam does not support sparse gradients, please consider SparseAdam instead"
+ )
+ amsbound = group["amsbound"]
state = self.state[p]
# State initialization
if len(state) == 0:
- state['step'] = 0
+ state["step"] = 0
# Exponential moving average of gradient values
- state['exp_avg'] = torch.zeros_like(p.data)
+ state["exp_avg"] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
- state['exp_avg_sq'] = torch.zeros_like(p.data)
+ state["exp_avg_sq"] = torch.zeros_like(p.data)
if amsbound:
# Maintains max of all exp. moving avg. of sq. grad. values
- state['max_exp_avg_sq'] = torch.zeros_like(p.data)
+ state["max_exp_avg_sq"] = torch.zeros_like(p.data)
- exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
if amsbound:
- max_exp_avg_sq = state['max_exp_avg_sq']
- beta1, beta2 = group['betas']
+ max_exp_avg_sq = state["max_exp_avg_sq"]
+ beta1, beta2 = group["betas"]
- state['step'] += 1
+ state["step"] += 1
- if group['weight_decay'] != 0:
- grad = grad.add(group['weight_decay'], p.data)
+ if group["weight_decay"] != 0:
+ grad = grad.add(group["weight_decay"], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
@@ -133,19 +150,19 @@ class AdaBound(torch.optim.Optimizer):
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
- denom = max_exp_avg_sq.sqrt().add_(group['eps'])
+ denom = max_exp_avg_sq.sqrt().add_(group["eps"])
else:
- denom = exp_avg_sq.sqrt().add_(group['eps'])
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
- bias_correction1 = 1 - beta1 ** state['step']
- bias_correction2 = 1 - beta2 ** state['step']
- step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
+ bias_correction1 = 1 - beta1 ** state["step"]
+ bias_correction2 = 1 - beta2 ** state["step"]
+ step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
# Applies bounds on actual learning rate
# lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
- final_lr = group['final_lr'] * group['lr'] / base_lr
- lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
- upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
+ final_lr = group["final_lr"] * group["lr"] / base_lr
+ lower_bound = final_lr * (1 - 1 / (group["gamma"] * state["step"] + 1))
+ upper_bound = final_lr * (1 + 1 / (group["gamma"] * state["step"]))
step_size = torch.full_like(denom, step_size)
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
@@ -153,6 +170,7 @@ class AdaBound(torch.optim.Optimizer):
return loss
+
class AdaBoundW(torch.optim.Optimizer):
"""Implements AdaBound algorithm with Decoupled Weight Decay
(See https://arxiv.org/abs/1711.05101)
@@ -187,8 +205,17 @@ class AdaBoundW(torch.optim.Optimizer):
"""
- def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1,
- gamma=1e-3, eps=1e-8, weight_decay=0, amsbound=False):
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.9, 0.999),
+ final_lr=0.1,
+ gamma=1e-3,
+ eps=1e-8,
+ weight_decay=0,
+ amsbound=False,
+ ):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
@@ -202,16 +229,23 @@ class AdaBoundW(torch.optim.Optimizer):
raise ValueError("Invalid final learning rate: {}".format(final_lr))
if not 0.0 <= gamma < 1.0:
raise ValueError("Invalid gamma parameter: {}".format(gamma))
- defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma,
- eps=eps, weight_decay=weight_decay, amsbound=amsbound)
+ defaults = dict(
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+ )
super(AdaBoundW, self).__init__(params, defaults)
- self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
+ self.base_lrs = list(map(lambda group: group["lr"], self.param_groups))
def __setstate__(self, state):
super(AdaBoundW, self).__setstate__(state)
for group in self.param_groups:
- group.setdefault('amsbound', False)
+ group.setdefault("amsbound", False)
def step(self, closure=None):
"""Performs a single optimization step.
@@ -229,34 +263,35 @@ class AdaBoundW(torch.optim.Optimizer):
loss = closure()
for group, base_lr in zip(self.param_groups, self.base_lrs):
- for p in group['params']:
+ for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
- 'Adam does not support sparse gradients, please consider SparseAdam instead')
- amsbound = group['amsbound']
+ "Adam does not support sparse gradients, please consider SparseAdam instead"
+ )
+ amsbound = group["amsbound"]
state = self.state[p]
# State initialization
if len(state) == 0:
- state['step'] = 0
+ state["step"] = 0
# Exponential moving average of gradient values
- state['exp_avg'] = torch.zeros_like(p.data)
+ state["exp_avg"] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
- state['exp_avg_sq'] = torch.zeros_like(p.data)
+ state["exp_avg_sq"] = torch.zeros_like(p.data)
if amsbound:
# Maintains max of all exp. moving avg. of sq. grad. values
- state['max_exp_avg_sq'] = torch.zeros_like(p.data)
+ state["max_exp_avg_sq"] = torch.zeros_like(p.data)
- exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
if amsbound:
- max_exp_avg_sq = state['max_exp_avg_sq']
- beta1, beta2 = group['betas']
+ max_exp_avg_sq = state["max_exp_avg_sq"]
+ beta1, beta2 = group["betas"]
- state['step'] += 1
+ state["step"] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
@@ -265,25 +300,25 @@ class AdaBoundW(torch.optim.Optimizer):
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
- denom = max_exp_avg_sq.sqrt().add_(group['eps'])
+ denom = max_exp_avg_sq.sqrt().add_(group["eps"])
else:
- denom = exp_avg_sq.sqrt().add_(group['eps'])
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
- bias_correction1 = 1 - beta1 ** state['step']
- bias_correction2 = 1 - beta2 ** state['step']
- step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
+ bias_correction1 = 1 - beta1 ** state["step"]
+ bias_correction2 = 1 - beta2 ** state["step"]
+ step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
# Applies bounds on actual learning rate
# lr_scheduler cannot affect final_lr, this is a workaround to
# apply lr decay
- final_lr = group['final_lr'] * group['lr'] / base_lr
- lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
- upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
+ final_lr = group["final_lr"] * group["lr"] / base_lr
+ lower_bound = final_lr * (1 - 1 / (group["gamma"] * state["step"] + 1))
+ upper_bound = final_lr * (1 + 1 / (group["gamma"] * state["step"]))
step_size = torch.full_like(denom, step_size)
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
- if group['weight_decay'] != 0:
- decayed_weights = torch.mul(p.data, group['weight_decay'])
+ if group["weight_decay"] != 0:
+ decayed_weights = torch.mul(p.data, group["weight_decay"])
p.data.add_(-step_size)
p.data.sub_(decayed_weights)
else:
diff --git a/bob/ip/binseg/engine/inferencer.py b/bob/ip/binseg/engine/inferencer.py
index e76153c045fc69cabffa1e5ea7d70efacbd510a6..43f2dd7ecca2344f39e66fb549de42ff4da9a904 100644
--- a/bob/ip/binseg/engine/inferencer.py
+++ b/bob/ip/binseg/engine/inferencer.py
@@ -18,7 +18,6 @@ from bob.ip.binseg.utils.plot import precision_recall_f1iso_confintval
from bob.ip.binseg.utils.summary import summary
-
def batch_metrics(predictions, ground_truths, names, output_folder, logger):
"""
Calculates metrics on the batch and saves it to disc
@@ -51,21 +50,23 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
file_name = "{}.csv".format(names[j])
logger.info("saving {}".format(file_name))
- with open (os.path.join(output_folder,file_name), "w+") as outfile:
+ with open(os.path.join(output_folder, file_name), "w+") as outfile:
- outfile.write("threshold, precision, recall, specificity, accuracy, jaccard, f1_score\n")
+ outfile.write(
+ "threshold, precision, recall, specificity, accuracy, jaccard, f1_score\n"
+ )
- for threshold in np.arange(0.0,1.0,step_size):
+ for threshold in np.arange(0.0, 1.0, step_size):
# threshold
binary_pred = torch.gt(predictions[j], threshold).byte()
# equals and not-equals
- equals = torch.eq(binary_pred, gts).type(torch.uint8) # tensor
- notequals = torch.ne(binary_pred, gts).type(torch.uint8) # tensor
+ equals = torch.eq(binary_pred, gts).type(torch.uint8) # tensor
+ notequals = torch.ne(binary_pred, gts).type(torch.uint8) # tensor
# true positives
- tp_tensor = (gts * binary_pred ) # tensor
- tp_count = torch.sum(tp_tensor).item() # scalar
+ tp_tensor = gts * binary_pred # tensor
+ tp_count = torch.sum(tp_tensor).item() # scalar
# false positives
fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
@@ -83,10 +84,13 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
metrics = base_metrics(tp_count, fp_count, tn_count, fn_count)
# write to disk
- outfile.write("{:.2f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f} \n".format(threshold, *metrics))
-
- batch_metrics.append([names[j],threshold, *metrics ])
+ outfile.write(
+ "{:.2f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f} \n".format(
+ threshold, *metrics
+ )
+ )
+ batch_metrics.append([names[j], threshold, *metrics])
return batch_metrics
@@ -106,16 +110,18 @@ def save_probability_images(predictions, names, output_folder, logger):
logger : :py:class:`logging.Logger`
python logger
"""
- images_subfolder = os.path.join(output_folder,'images')
+ images_subfolder = os.path.join(output_folder, "images")
for j in range(predictions.size()[0]):
img = VF.to_pil_image(predictions.cpu().data[j])
- filename = '{}.png'.format(names[j].split(".")[0])
+ filename = "{}.png".format(names[j].split(".")[0])
fullpath = os.path.join(images_subfolder, filename)
logger.info("saving {}".format(fullpath))
fulldir = os.path.dirname(fullpath)
- if not os.path.exists(fulldir): os.makedirs(fulldir)
+ if not os.path.exists(fulldir):
+ os.makedirs(fulldir)
img.save(fullpath)
+
def save_hdf(predictions, names, output_folder, logger):
"""
Saves probability maps as image in the same format as the test image
@@ -131,23 +137,21 @@ def save_hdf(predictions, names, output_folder, logger):
logger : :py:class:`logging.Logger`
python logger
"""
- hdf5_subfolder = os.path.join(output_folder,'hdf5')
- if not os.path.exists(hdf5_subfolder): os.makedirs(hdf5_subfolder)
+ hdf5_subfolder = os.path.join(output_folder, "hdf5")
+ if not os.path.exists(hdf5_subfolder):
+ os.makedirs(hdf5_subfolder)
for j in range(predictions.size()[0]):
img = predictions.cpu().data[j].squeeze(0).numpy()
- filename = '{}.hdf5'.format(names[j].split(".")[0])
+ filename = "{}.hdf5".format(names[j].split(".")[0])
fullpath = os.path.join(hdf5_subfolder, filename)
logger.info("saving {}".format(filename))
fulldir = os.path.dirname(fullpath)
- if not os.path.exists(fulldir): os.makedirs(fulldir)
+ if not os.path.exists(fulldir):
+ os.makedirs(fulldir)
bob.io.base.save(img, fullpath)
-def do_inference(
- model,
- data_loader,
- device,
- output_folder = None
-):
+
+def do_inference(model, data_loader, device, output_folder=None):
"""
Run inference and calculate metrics
@@ -164,8 +168,8 @@ def do_inference(
logger = logging.getLogger("bob.ip.binseg.engine.inference")
logger.info("Start evaluation")
logger.info("Output folder: {}, Device: {}".format(output_folder, device))
- results_subfolder = os.path.join(output_folder,'results')
- os.makedirs(results_subfolder,exist_ok=True)
+ results_subfolder = os.path.join(output_folder, "results")
+ os.makedirs(results_subfolder, exist_ok=True)
model.eval().to(device)
# Sigmoid for probabilities
@@ -189,7 +193,7 @@ def do_inference(
# necessary check for hed architecture that uses several outputs
# for loss calculation instead of just the last concatfuse block
- if isinstance(outputs,list):
+ if isinstance(outputs, list):
outputs = outputs[-1]
probabilities = sigmoid(outputs)
@@ -198,7 +202,9 @@ def do_inference(
times.append(batch_time)
logger.info("Batch time: {:.5f} s".format(batch_time))
- b_metrics = batch_metrics(probabilities, ground_truths, names,results_subfolder, logger)
+ b_metrics = batch_metrics(
+ probabilities, ground_truths, names, results_subfolder, logger
+ )
metrics.extend(b_metrics)
# Create probability images
@@ -207,74 +213,94 @@ def do_inference(
save_hdf(probabilities, names, output_folder, logger)
# DataFrame
- df_metrics = pd.DataFrame(metrics,columns= \
- ["name",
- "threshold",
- "precision",
- "recall",
- "specificity",
- "accuracy",
- "jaccard",
- "f1_score"])
+ df_metrics = pd.DataFrame(
+ metrics,
+ columns=[
+ "name",
+ "threshold",
+ "precision",
+ "recall",
+ "specificity",
+ "accuracy",
+ "jaccard",
+ "f1_score",
+ ],
+ )
# Report and Averages
metrics_file = "Metrics.csv".format(model.name)
metrics_path = os.path.join(results_subfolder, metrics_file)
logger.info("Saving average over all input images: {}".format(metrics_file))
- avg_metrics = df_metrics.groupby('threshold').mean()
- std_metrics = df_metrics.groupby('threshold').std()
+ avg_metrics = df_metrics.groupby("threshold").mean()
+ std_metrics = df_metrics.groupby("threshold").std()
# Uncomment below for F1-score calculation based on average precision and metrics instead of
# F1-scores of individual images. This method is in line with Maninis et. al. (2016)
- #avg_metrics["f1_score"] = (2* avg_metrics["precision"]*avg_metrics["recall"])/ \
+ # avg_metrics["f1_score"] = (2* avg_metrics["precision"]*avg_metrics["recall"])/ \
# (avg_metrics["precision"]+avg_metrics["recall"])
avg_metrics["std_pr"] = std_metrics["precision"]
- avg_metrics["pr_upper"] = avg_metrics['precision'] + avg_metrics["std_pr"]
- avg_metrics["pr_lower"] = avg_metrics['precision'] - avg_metrics["std_pr"]
+ avg_metrics["pr_upper"] = avg_metrics["precision"] + avg_metrics["std_pr"]
+ avg_metrics["pr_lower"] = avg_metrics["precision"] - avg_metrics["std_pr"]
avg_metrics["std_re"] = std_metrics["recall"]
- avg_metrics["re_upper"] = avg_metrics['recall'] + avg_metrics["std_re"]
- avg_metrics["re_lower"] = avg_metrics['recall'] - avg_metrics["std_re"]
+ avg_metrics["re_upper"] = avg_metrics["recall"] + avg_metrics["std_re"]
+ avg_metrics["re_lower"] = avg_metrics["recall"] - avg_metrics["std_re"]
avg_metrics["std_f1"] = std_metrics["f1_score"]
avg_metrics.to_csv(metrics_path)
- maxf1 = avg_metrics['f1_score'].max()
- optimal_f1_threshold = avg_metrics['f1_score'].idxmax()
+ maxf1 = avg_metrics["f1_score"].max()
+ optimal_f1_threshold = avg_metrics["f1_score"].idxmax()
- logger.info("Highest F1-score of {:.5f}, achieved at threshold {}".format(maxf1, optimal_f1_threshold))
+ logger.info(
+ "Highest F1-score of {:.5f}, achieved at threshold {}".format(
+ maxf1, optimal_f1_threshold
+ )
+ )
# Plotting
np_avg_metrics = avg_metrics.to_numpy().T
fig_name = "precision_recall.pdf"
logger.info("saving {}".format(fig_name))
- fig = precision_recall_f1iso_confintval([np_avg_metrics[0]],[np_avg_metrics[1]],[np_avg_metrics[7]],[np_avg_metrics[8]],[np_avg_metrics[10]],[np_avg_metrics[11]], [model.name,None], title=output_folder)
+ fig = precision_recall_f1iso_confintval(
+ [np_avg_metrics[0]],
+ [np_avg_metrics[1]],
+ [np_avg_metrics[7]],
+ [np_avg_metrics[8]],
+ [np_avg_metrics[10]],
+ [np_avg_metrics[11]],
+ [model.name, None],
+ title=output_folder,
+ )
fig_filename = os.path.join(results_subfolder, fig_name)
fig.savefig(fig_filename)
# Report times
total_inference_time = str(datetime.timedelta(seconds=int(sum(times))))
average_batch_inference_time = np.mean(times)
- total_evalution_time = str(datetime.timedelta(seconds=int(time.time() - start_total_time )))
+ total_evalution_time = str(
+ datetime.timedelta(seconds=int(time.time() - start_total_time))
+ )
- logger.info("Average batch inference time: {:.5f}s".format(average_batch_inference_time))
+ logger.info(
+ "Average batch inference time: {:.5f}s".format(average_batch_inference_time)
+ )
times_file = "Times.txt"
logger.info("saving {}".format(times_file))
- with open (os.path.join(results_subfolder,times_file), "w+") as outfile:
+ with open(os.path.join(results_subfolder, times_file), "w+") as outfile:
date = datetime.datetime.now()
outfile.write("Date: {} \n".format(date.strftime("%Y-%m-%d %H:%M:%S")))
outfile.write("Total evaluation run-time: {} \n".format(total_evalution_time))
- outfile.write("Average batch inference time: {} \n".format(average_batch_inference_time))
+ outfile.write(
+ "Average batch inference time: {} \n".format(average_batch_inference_time)
+ )
outfile.write("Total inference time: {} \n".format(total_inference_time))
# Save model summary
- summary_file = 'ModelSummary.txt'
+ summary_file = "ModelSummary.txt"
logger.info("saving {}".format(summary_file))
- with open (os.path.join(results_subfolder,summary_file), "w+") as outfile:
- summary(model,outfile)
-
-
-
+ with open(os.path.join(results_subfolder, summary_file), "w+") as outfile:
+ summary(model, outfile)
diff --git a/bob/ip/binseg/engine/predicter.py b/bob/ip/binseg/engine/predicter.py
index ebd09ac5e84d4f9a81a20f72c3919f42071fb73d..d8fb2de3515cb963f633151db779fdc5813c53e9 100644
--- a/bob/ip/binseg/engine/predicter.py
+++ b/bob/ip/binseg/engine/predicter.py
@@ -15,12 +15,7 @@ from bob.ip.binseg.engine.inferencer import save_probability_images
from bob.ip.binseg.engine.inferencer import save_hdf
-def do_predict(
- model,
- data_loader,
- device,
- output_folder = None
-):
+def do_predict(model, data_loader, device, output_folder=None):
"""
Run inference and calculate metrics
@@ -37,8 +32,8 @@ def do_predict(
logger = logging.getLogger("bob.ip.binseg.engine.inference")
logger.info("Start evaluation")
logger.info("Output folder: {}, Device: {}".format(output_folder, device))
- results_subfolder = os.path.join(output_folder,'results')
- os.makedirs(results_subfolder,exist_ok=True)
+ results_subfolder = os.path.join(output_folder, "results")
+ os.makedirs(results_subfolder, exist_ok=True)
model.eval().to(device)
# Sigmoid for probabilities
@@ -58,7 +53,7 @@ def do_predict(
# necessary check for hed architecture that uses several outputs
# for loss calculation instead of just the last concatfuse block
- if isinstance(outputs,list):
+ if isinstance(outputs, list):
outputs = outputs[-1]
probabilities = sigmoid(outputs)
@@ -72,22 +67,25 @@ def do_predict(
# Save hdf5
save_hdf(probabilities, names, output_folder, logger)
-
# Report times
total_inference_time = str(datetime.timedelta(seconds=int(sum(times))))
average_batch_inference_time = np.mean(times)
- total_evalution_time = str(datetime.timedelta(seconds=int(time.time() - start_total_time )))
+ total_evalution_time = str(
+ datetime.timedelta(seconds=int(time.time() - start_total_time))
+ )
- logger.info("Average batch inference time: {:.5f}s".format(average_batch_inference_time))
+ logger.info(
+ "Average batch inference time: {:.5f}s".format(average_batch_inference_time)
+ )
times_file = "Times.txt"
logger.info("saving {}".format(times_file))
- with open (os.path.join(results_subfolder,times_file), "w+") as outfile:
+ with open(os.path.join(results_subfolder, times_file), "w+") as outfile:
date = datetime.datetime.now()
outfile.write("Date: {} \n".format(date.strftime("%Y-%m-%d %H:%M:%S")))
outfile.write("Total evaluation run-time: {} \n".format(total_evalution_time))
- outfile.write("Average batch inference time: {} \n".format(average_batch_inference_time))
+ outfile.write(
+ "Average batch inference time: {} \n".format(average_batch_inference_time)
+ )
outfile.write("Total inference time: {} \n".format(total_inference_time))
-
-
diff --git a/bob/ip/binseg/engine/ssltrainer.py b/bob/ip/binseg/engine/ssltrainer.py
index dfc73c86587559417831f1a7a3e5f7fad89db743..4608354461b83fff9678eb86db144a0cabbd54c1 100644
--- a/bob/ip/binseg/engine/ssltrainer.py
+++ b/bob/ip/binseg/engine/ssltrainer.py
@@ -13,10 +13,12 @@ import numpy as np
from bob.ip.binseg.utils.metric import SmoothedValue
from bob.ip.binseg.utils.plot import loss_curve
+
def sharpen(x, T):
- temp = x**(1/T)
+ temp = x ** (1 / T)
return temp / temp.sum(dim=1, keepdim=True)
+
def mix_up(alpha, input, target, unlabeled_input, unlabled_target):
"""Applies mix up as described in [MIXMATCH_19].
@@ -41,21 +43,30 @@ def mix_up(alpha, input, target, unlabeled_input, unlabled_target):
"""
# TODO:
with torch.no_grad():
- l = np.random.beta(alpha, alpha) # Eq (8)
- l = max(l, 1 - l) # Eq (9)
+ l = np.random.beta(alpha, alpha) # Eq (8)
+ l = max(l, 1 - l) # Eq (9)
# Shuffle and concat. Alg. 1 Line: 12
- w_inputs = torch.cat([input,unlabeled_input],0)
- w_targets = torch.cat([target,unlabled_target],0)
- idx = torch.randperm(w_inputs.size(0)) # get random index
+ w_inputs = torch.cat([input, unlabeled_input], 0)
+ w_targets = torch.cat([target, unlabled_target], 0)
+ idx = torch.randperm(w_inputs.size(0)) # get random index
# Apply MixUp to labeled data and entries from W. Alg. 1 Line: 13
- input_mixedup = l * input + (1 - l) * w_inputs[idx[len(input):]]
- target_mixedup = l * target + (1 - l) * w_targets[idx[len(target):]]
+ input_mixedup = l * input + (1 - l) * w_inputs[idx[len(input) :]]
+ target_mixedup = l * target + (1 - l) * w_targets[idx[len(target) :]]
# Apply MixUp to unlabeled data and entries from W. Alg. 1 Line: 14
- unlabeled_input_mixedup = l * unlabeled_input + (1 - l) * w_inputs[idx[:len(unlabeled_input)]]
- unlabled_target_mixedup = l * unlabled_target + (1 - l) * w_targets[idx[:len(unlabled_target)]]
- return input_mixedup, target_mixedup, unlabeled_input_mixedup, unlabled_target_mixedup
+ unlabeled_input_mixedup = (
+ l * unlabeled_input + (1 - l) * w_inputs[idx[: len(unlabeled_input)]]
+ )
+ unlabled_target_mixedup = (
+ l * unlabled_target + (1 - l) * w_targets[idx[: len(unlabled_target)]]
+ )
+ return (
+ input_mixedup,
+ target_mixedup,
+ unlabeled_input_mixedup,
+ unlabled_target_mixedup,
+ )
def square_rampup(current, rampup_length=16):
@@ -80,9 +91,10 @@ def square_rampup(current, rampup_length=16):
if rampup_length == 0:
return 1.0
else:
- current = np.clip((current/ float(rampup_length))**2, 0.0, 1.0)
+ current = np.clip((current / float(rampup_length)) ** 2, 0.0, 1.0)
return float(current)
+
def linear_rampup(current, rampup_length=16):
"""slowly ramp-up ``lambda_u``
@@ -107,6 +119,7 @@ def linear_rampup(current, rampup_length=16):
current = np.clip(current / rampup_length, 0.0, 1.0)
return float(current)
+
def guess_labels(unlabeled_images, model):
"""
Calculate the average predictions by 2 augmentations: horizontal and vertical flips
@@ -130,15 +143,16 @@ def guess_labels(unlabeled_images, model):
guess1 = torch.sigmoid(model(unlabeled_images)).unsqueeze(0)
# Horizontal flip and unsqueeze to work with batches (increase flip dimension by 1)
hflip = torch.sigmoid(model(unlabeled_images.flip(2))).unsqueeze(0)
- guess2 = hflip.flip(3)
+ guess2 = hflip.flip(3)
# Vertical flip and unsqueeze to work with batches (increase flip dimension by 1)
vflip = torch.sigmoid(model(unlabeled_images.flip(3))).unsqueeze(0)
guess3 = vflip.flip(4)
# Concat
- concat = torch.cat([guess1,guess2,guess3],0)
- avg_guess = torch.mean(concat,0)
+ concat = torch.cat([guess1, guess2, guess3], 0)
+ avg_guess = torch.mean(concat, 0)
return avg_guess
+
def do_ssltrain(
model,
data_loader,
@@ -150,7 +164,7 @@ def do_ssltrain(
device,
arguments,
output_folder,
- rampup_length
+ rampup_length,
):
"""
Train model and save to disk.
@@ -196,7 +210,9 @@ def do_ssltrain(
max_epoch = arguments["max_epoch"]
# Logg to file
- with open (os.path.join(output_folder,"{}_trainlog.csv".format(model.name)), "a+",1) as outfile:
+ with open(
+ os.path.join(output_folder, "{}_trainlog.csv".format(model.name)), "a+", 1
+ ) as outfile:
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
@@ -226,11 +242,17 @@ def do_ssltrain(
unlabeled_outputs = model(unlabeled_images)
# guessed unlabeled outputs
unlabeled_ground_truths = guess_labels(unlabeled_images, model)
- #unlabeled_ground_truths = sharpen(unlabeled_ground_truths,0.5)
- #images, ground_truths, unlabeled_images, unlabeled_ground_truths = mix_up(0.75, images, ground_truths, unlabeled_images, unlabeled_ground_truths)
- ramp_up_factor = square_rampup(epoch,rampup_length=rampup_length)
-
- loss, ll, ul = criterion(outputs, ground_truths, unlabeled_outputs, unlabeled_ground_truths, ramp_up_factor)
+ # unlabeled_ground_truths = sharpen(unlabeled_ground_truths,0.5)
+ # images, ground_truths, unlabeled_images, unlabeled_ground_truths = mix_up(0.75, images, ground_truths, unlabeled_images, unlabeled_ground_truths)
+ ramp_up_factor = square_rampup(epoch, rampup_length=rampup_length)
+
+ loss, ll, ul = criterion(
+ outputs,
+ ground_truths,
+ unlabeled_outputs,
+ unlabeled_ground_truths,
+ ramp_up_factor,
+ )
optimizer.zero_grad()
loss.backward()
optimizer.step()
@@ -247,60 +269,77 @@ def do_ssltrain(
epoch_time = time.time() - start_epoch_time
-
eta_seconds = epoch_time * (max_epoch - epoch)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
- outfile.write(("{epoch}, "
- "{avg_loss:.6f}, "
- "{median_loss:.6f}, "
- "{median_labeled_loss},"
- "{median_unlabeled_loss},"
- "{lr:.6f}, "
- "{memory:.0f}"
- "\n"
- ).format(
+ outfile.write(
+ (
+ "{epoch}, "
+ "{avg_loss:.6f}, "
+ "{median_loss:.6f}, "
+ "{median_labeled_loss},"
+ "{median_unlabeled_loss},"
+ "{lr:.6f}, "
+ "{memory:.0f}"
+ "\n"
+ ).format(
eta=eta_string,
epoch=epoch,
avg_loss=losses.avg,
median_loss=losses.median,
- median_labeled_loss = labeled_loss.median,
- median_unlabeled_loss = unlabeled_loss.median,
+ median_labeled_loss=labeled_loss.median,
+ median_unlabeled_loss=unlabeled_loss.median,
lr=optimizer.param_groups[0]["lr"],
- memory = (torch.cuda.max_memory_allocated() / 1024.0 / 1024.0) if torch.cuda.is_available() else .0,
- )
+ memory=(torch.cuda.max_memory_allocated() / 1024.0 / 1024.0)
+ if torch.cuda.is_available()
+ else 0.0,
)
- logger.info(("eta: {eta}, "
- "epoch: {epoch}, "
- "avg. loss: {avg_loss:.6f}, "
- "median loss: {median_loss:.6f}, "
- "labeled loss: {median_labeled_loss}, "
- "unlabeled loss: {median_unlabeled_loss}, "
- "lr: {lr:.6f}, "
- "max mem: {memory:.0f}"
- ).format(
+ )
+ logger.info(
+ (
+ "eta: {eta}, "
+ "epoch: {epoch}, "
+ "avg. loss: {avg_loss:.6f}, "
+ "median loss: {median_loss:.6f}, "
+ "labeled loss: {median_labeled_loss}, "
+ "unlabeled loss: {median_unlabeled_loss}, "
+ "lr: {lr:.6f}, "
+ "max mem: {memory:.0f}"
+ ).format(
eta=eta_string,
epoch=epoch,
avg_loss=losses.avg,
median_loss=losses.median,
- median_labeled_loss = labeled_loss.median,
- median_unlabeled_loss = unlabeled_loss.median,
+ median_labeled_loss=labeled_loss.median,
+ median_unlabeled_loss=unlabeled_loss.median,
lr=optimizer.param_groups[0]["lr"],
- memory = (torch.cuda.max_memory_allocated() / 1024.0 / 1024.0) if torch.cuda.is_available() else .0
- )
+ memory=(torch.cuda.max_memory_allocated() / 1024.0 / 1024.0)
+ if torch.cuda.is_available()
+ else 0.0,
)
-
+ )
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info(
"Total training time: {} ({:.4f} s / epoch)".format(
total_time_str, total_training_time / (max_epoch)
- ))
-
- log_plot_file = os.path.join(output_folder,"{}_trainlog.pdf".format(model.name))
- logdf = pd.read_csv(os.path.join(output_folder,"{}_trainlog.csv".format(model.name)),header=None, names=["avg. loss", "median loss", "labeled loss", "unlabeled loss", "lr","max memory"])
- fig = loss_curve(logdf,output_folder)
+ )
+ )
+
+ log_plot_file = os.path.join(output_folder, "{}_trainlog.pdf".format(model.name))
+ logdf = pd.read_csv(
+ os.path.join(output_folder, "{}_trainlog.csv".format(model.name)),
+ header=None,
+ names=[
+ "avg. loss",
+ "median loss",
+ "labeled loss",
+ "unlabeled loss",
+ "lr",
+ "max memory",
+ ],
+ )
+ fig = loss_curve(logdf, output_folder)
logger.info("saving {}".format(log_plot_file))
fig.savefig(log_plot_file)
-
diff --git a/bob/ip/binseg/engine/trainer.py b/bob/ip/binseg/engine/trainer.py
index e083ec852491e82d2abee84421e1f14d20966a2a..2b35528e2f1a759728ed90952dbd22f5700e381e 100644
--- a/bob/ip/binseg/engine/trainer.py
+++ b/bob/ip/binseg/engine/trainer.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-import os
+import os
import logging
import time
import datetime
@@ -23,7 +23,7 @@ def do_train(
checkpoint_period,
device,
arguments,
- output_folder
+ output_folder,
):
"""
Train model and save to disk.
@@ -55,8 +55,10 @@ def do_train(
max_epoch = arguments["max_epoch"]
# Logg to file
- with open (os.path.join(output_folder,"{}_trainlog.csv".format(model.name)), "a+") as outfile:
-
+ with open(
+ os.path.join(output_folder, "{}_trainlog.csv".format(model.name)), "a+"
+ ) as outfile:
+
model.train().to(device)
for state in optimizer.state.values():
for k, v in state.items():
@@ -70,7 +72,7 @@ def do_train(
losses = SmoothedValue(len(data_loader))
epoch = epoch + 1
arguments["epoch"] = epoch
-
+
# Epoch time
start_epoch_time = time.time()
@@ -81,9 +83,9 @@ def do_train(
masks = None
if len(samples) == 4:
masks = samples[-1].to(device)
-
+
outputs = model(images)
-
+
loss = criterion(outputs, ground_truths, masks)
optimizer.zero_grad()
loss.backward()
@@ -100,51 +102,62 @@ def do_train(
epoch_time = time.time() - start_epoch_time
-
eta_seconds = epoch_time * (max_epoch - epoch)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
- outfile.write(("{epoch}, "
- "{avg_loss:.6f}, "
- "{median_loss:.6f}, "
- "{lr:.6f}, "
- "{memory:.0f}"
- "\n"
- ).format(
+ outfile.write(
+ (
+ "{epoch}, "
+ "{avg_loss:.6f}, "
+ "{median_loss:.6f}, "
+ "{lr:.6f}, "
+ "{memory:.0f}"
+ "\n"
+ ).format(
eta=eta_string,
epoch=epoch,
avg_loss=losses.avg,
median_loss=losses.median,
lr=optimizer.param_groups[0]["lr"],
- memory = (torch.cuda.max_memory_allocated() / 1024.0 / 1024.0) if torch.cuda.is_available() else .0,
- )
- )
- logger.info(("eta: {eta}, "
- "epoch: {epoch}, "
- "avg. loss: {avg_loss:.6f}, "
- "median loss: {median_loss:.6f}, "
- "lr: {lr:.6f}, "
- "max mem: {memory:.0f}"
- ).format(
+ memory=(torch.cuda.max_memory_allocated() / 1024.0 / 1024.0)
+ if torch.cuda.is_available()
+ else 0.0,
+ )
+ )
+ logger.info(
+ (
+ "eta: {eta}, "
+ "epoch: {epoch}, "
+ "avg. loss: {avg_loss:.6f}, "
+ "median loss: {median_loss:.6f}, "
+ "lr: {lr:.6f}, "
+ "max mem: {memory:.0f}"
+ ).format(
eta=eta_string,
epoch=epoch,
avg_loss=losses.avg,
median_loss=losses.median,
lr=optimizer.param_groups[0]["lr"],
- memory = (torch.cuda.max_memory_allocated() / 1024.0 / 1024.0) if torch.cuda.is_available() else .0
- )
+ memory=(torch.cuda.max_memory_allocated() / 1024.0 / 1024.0)
+ if torch.cuda.is_available()
+ else 0.0,
)
-
+ )
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info(
"Total training time: {} ({:.4f} s / epoch)".format(
total_time_str, total_training_time / (max_epoch)
- ))
-
- log_plot_file = os.path.join(output_folder,"{}_trainlog.pdf".format(model.name))
- logdf = pd.read_csv(os.path.join(output_folder,"{}_trainlog.csv".format(model.name)),header=None, names=["avg. loss", "median loss","lr","max memory"])
- fig = loss_curve(logdf,output_folder)
+ )
+ )
+
+ log_plot_file = os.path.join(output_folder, "{}_trainlog.pdf".format(model.name))
+ logdf = pd.read_csv(
+ os.path.join(output_folder, "{}_trainlog.csv".format(model.name)),
+ header=None,
+ names=["avg. loss", "median loss", "lr", "max memory"],
+ )
+ fig = loss_curve(logdf, output_folder)
logger.info("saving {}".format(log_plot_file))
fig.savefig(log_plot_file)
diff --git a/bob/ip/binseg/modeling/backbones/mobilenetv2.py b/bob/ip/binseg/modeling/backbones/mobilenetv2.py
index 5d87f49633051ccc3b1570d83464f3a26057e939..2f4b88519a961115c06b096330a213800b3bcb70 100644
--- a/bob/ip/binseg/modeling/backbones/mobilenetv2.py
+++ b/bob/ip/binseg/modeling/backbones/mobilenetv2.py
@@ -12,7 +12,7 @@ def conv_bn(inp, oup, stride):
return torch.nn.Sequential(
torch.nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
torch.nn.BatchNorm2d(oup),
- torch.nn.ReLU6(inplace=True)
+ torch.nn.ReLU6(inplace=True),
)
@@ -20,7 +20,7 @@ def conv_1x1_bn(inp, oup):
return torch.nn.Sequential(
torch.nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
torch.nn.BatchNorm2d(oup),
- torch.nn.ReLU6(inplace=True)
+ torch.nn.ReLU6(inplace=True),
)
@@ -36,7 +36,9 @@ class InvertedResidual(torch.nn.Module):
if expand_ratio == 1:
self.conv = torch.nn.Sequential(
# dw
- torch.nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+ torch.nn.Conv2d(
+ hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False
+ ),
torch.nn.BatchNorm2d(hidden_dim),
torch.nn.ReLU6(inplace=True),
# pw-linear
@@ -50,7 +52,9 @@ class InvertedResidual(torch.nn.Module):
torch.nn.BatchNorm2d(hidden_dim),
torch.nn.ReLU6(inplace=True),
# dw
- torch.nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+ torch.nn.Conv2d(
+ hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False
+ ),
torch.nn.BatchNorm2d(hidden_dim),
torch.nn.ReLU6(inplace=True),
# pw-linear
@@ -66,7 +70,14 @@ class InvertedResidual(torch.nn.Module):
class MobileNetV2(torch.nn.Module):
- def __init__(self, n_class=1000, input_size=224, width_mult=1., return_features = None, m2u=True):
+ def __init__(
+ self,
+ n_class=1000,
+ input_size=224,
+ width_mult=1.0,
+ return_features=None,
+ m2u=True,
+ ):
super(MobileNetV2, self).__init__()
self.return_features = return_features
self.m2u = m2u
@@ -80,34 +91,38 @@ class MobileNetV2(torch.nn.Module):
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
- #[6, 160, 3, 2],
- #[6, 320, 1, 1],
+ # [6, 160, 3, 2],
+ # [6, 320, 1, 1],
]
# building first layer
assert input_size % 32 == 0
input_channel = int(input_channel * width_mult)
- #self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
+ # self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
self.features = [conv_bn(3, input_channel, 2)]
# building inverted residual blocks
for t, c, n, s in interverted_residual_setting:
output_channel = int(c * width_mult)
for i in range(n):
if i == 0:
- self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
+ self.features.append(
+ block(input_channel, output_channel, s, expand_ratio=t)
+ )
else:
- self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
+ self.features.append(
+ block(input_channel, output_channel, 1, expand_ratio=t)
+ )
input_channel = output_channel
# building last several layers
- #self.features.append(conv_1x1_bn(input_channel, self.last_channel))
+ # self.features.append(conv_1x1_bn(input_channel, self.last_channel))
# make it torch.nn.Sequential
self.features = torch.nn.Sequential(*self.features)
# building classifier
- #self.classifier = torch.nn.Sequential(
+ # self.classifier = torch.nn.Sequential(
# torch.nn.Dropout(0.2),
# torch.nn.Linear(self.last_channel, n_class),
- #)
+ # )
self._initialize_weights()
@@ -117,7 +132,7 @@ class MobileNetV2(torch.nn.Module):
outputs.append(x.shape[2:4])
if self.m2u:
outputs.append(x)
- for index,m in enumerate(self.features):
+ for index, m in enumerate(self.features):
x = m(x)
# extract layers
if index in self.return_features:
@@ -128,7 +143,7 @@ class MobileNetV2(torch.nn.Module):
for m in self.modules():
if isinstance(m, torch.nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
+ m.weight.data.normal_(0, math.sqrt(2.0 / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, torch.nn.BatchNorm2d):
diff --git a/bob/ip/binseg/modeling/backbones/resnet.py b/bob/ip/binseg/modeling/backbones/resnet.py
index 285a5a15e57932c2458030759f75b99d1ee8470b..f26c5d57721752c8133eab11f7239f1f4fd5630b 100644
--- a/bob/ip/binseg/modeling/backbones/resnet.py
+++ b/bob/ip/binseg/modeling/backbones/resnet.py
@@ -18,20 +18,13 @@ model_urls = {
def _conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
- in_planes,
- out_planes,
- kernel_size=3,
- stride=stride,
- padding=1,
- bias=False,
+ in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False,
)
def _conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
- return nn.Conv2d(
- in_planes, out_planes, kernel_size=1, stride=stride, bias=False
- )
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class _BasicBlock(nn.Module):
@@ -105,9 +98,7 @@ class _Bottleneck(nn.Module):
class ResNet(nn.Module):
- def __init__(
- self, block, layers, return_features, zero_init_residual=False
- ):
+ def __init__(self, block, layers, return_features, zero_init_residual=False):
"""
Generic ResNet network with layer return.
Attributes
@@ -118,9 +109,7 @@ class ResNet(nn.Module):
super(ResNet, self).__init__()
self.inplanes = 64
self.return_features = return_features
- self.conv1 = nn.Conv2d(
- 3, 64, kernel_size=7, stride=2, padding=3, bias=False
- )
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@@ -142,9 +131,7 @@ class ResNet(nn.Module):
for m in self.modules():
if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(
- m.weight, mode="fan_out", nonlinearity="relu"
- )
+ nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@@ -229,9 +216,7 @@ def shaperesnet50(pretrained=False, **kwargs):
if pretrained:
model.load_state_dict(
model_zoo.load_url(
- model_urls[
- "resnet50_trained_on_SIN_and_IN_then_finetuned_on_IN"
- ]
+ model_urls["resnet50_trained_on_SIN_and_IN_then_finetuned_on_IN"]
)
)
return model
diff --git a/bob/ip/binseg/modeling/backbones/vgg.py b/bob/ip/binseg/modeling/backbones/vgg.py
index 7736a4c1ecef832c914552398907b5b0d0f29cf5..390f6ef44105b6d4aa6fccf938bbfd28394b8b76 100644
--- a/bob/ip/binseg/modeling/backbones/vgg.py
+++ b/bob/ip/binseg/modeling/backbones/vgg.py
@@ -8,19 +8,18 @@ import torch.utils.model_zoo as model_zoo
model_urls = {
- 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
- 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
- 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
- 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
- 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
- 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
- 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
- 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
+ "vgg11": "https://download.pytorch.org/models/vgg11-bbd30ac9.pth",
+ "vgg13": "https://download.pytorch.org/models/vgg13-c768596a.pth",
+ "vgg16": "https://download.pytorch.org/models/vgg16-397923af.pth",
+ "vgg19": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
+ "vgg11_bn": "https://download.pytorch.org/models/vgg11_bn-6002323d.pth",
+ "vgg13_bn": "https://download.pytorch.org/models/vgg13_bn-abd245e5.pth",
+ "vgg16_bn": "https://download.pytorch.org/models/vgg16_bn-6c64b313.pth",
+ "vgg19_bn": "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth",
}
class VGG(nn.Module):
-
def __init__(self, features, return_features, init_weights=True):
super(VGG, self).__init__()
self.features = features
@@ -32,7 +31,7 @@ class VGG(nn.Module):
outputs = []
# hw of input, needed for DRIU and HED
outputs.append(x.shape[2:4])
- for index,m in enumerate(self.features):
+ for index, m in enumerate(self.features):
x = m(x)
# extract layers
if index in self.return_features:
@@ -42,7 +41,7 @@ class VGG(nn.Module):
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
@@ -57,7 +56,7 @@ def _make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
- if v == 'M':
+ if v == "M":
layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
@@ -70,10 +69,51 @@ def _make_layers(cfg, batch_norm=False):
_cfg = {
- 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
- 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
- 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
- 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
+ "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
+ "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
+ "D": [
+ 64,
+ 64,
+ "M",
+ 128,
+ 128,
+ "M",
+ 256,
+ 256,
+ 256,
+ "M",
+ 512,
+ 512,
+ 512,
+ "M",
+ 512,
+ 512,
+ 512,
+ "M",
+ ],
+ "E": [
+ 64,
+ 64,
+ "M",
+ 128,
+ 128,
+ "M",
+ 256,
+ 256,
+ 256,
+ 256,
+ "M",
+ 512,
+ 512,
+ 512,
+ 512,
+ "M",
+ 512,
+ 512,
+ 512,
+ 512,
+ "M",
+ ],
}
@@ -83,10 +123,10 @@ def vgg11(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['A']), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["A"]), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg11']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg11"]))
return model
@@ -96,10 +136,10 @@ def vgg11_bn(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['A'], batch_norm=True), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["A"], batch_norm=True), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg11_bn"]))
return model
@@ -109,10 +149,10 @@ def vgg13(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['B']), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["B"]), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg13']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg13"]))
return model
@@ -122,10 +162,10 @@ def vgg13_bn(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['B'], batch_norm=True), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["B"], batch_norm=True), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg13_bn"]))
return model
@@ -135,10 +175,10 @@ def vgg16(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['D']), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["D"]), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg16']),strict=False)
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg16"]), strict=False)
return model
@@ -148,10 +188,10 @@ def vgg16_bn(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['D'], batch_norm=True), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["D"], batch_norm=True), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg16_bn']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg16_bn"]))
return model
@@ -161,10 +201,10 @@ def vgg19(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['E']), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["E"]), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg19"]))
return model
@@ -174,8 +214,8 @@ def vgg19_bn(pretrained=False, **kwargs):
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
- kwargs['init_weights'] = False
- model = VGG(_make_layers(_cfg['E'], batch_norm=True), **kwargs)
+ kwargs["init_weights"] = False
+ model = VGG(_make_layers(_cfg["E"], batch_norm=True), **kwargs)
if pretrained:
- model.load_state_dict(model_zoo.load_url(model_urls['vgg19_bn']))
+ model.load_state_dict(model_zoo.load_url(model_urls["vgg19_bn"]))
return model
diff --git a/bob/ip/binseg/modeling/driu.py b/bob/ip/binseg/modeling/driu.py
index 06454a17fe7b0daf870520badbcc457b2623c957..5b4425c22938327aabed4e796d482b7e524d0dfe 100644
--- a/bob/ip/binseg/modeling/driu.py
+++ b/bob/ip/binseg/modeling/driu.py
@@ -43,12 +43,7 @@ class DRIU(torch.nn.Module):
def __init__(self, in_channels_list=None):
super(DRIU, self).__init__()
- (
- in_conv_1_2_16,
- in_upsample2,
- in_upsample_4,
- in_upsample_8,
- ) = in_channels_list
+ (in_conv_1_2_16, in_upsample2, in_upsample_4, in_upsample_8,) = in_channels_list
self.conv1_2_16 = torch.nn.Conv2d(in_conv_1_2_16, 16, 3, 1, 1)
# Upsample layers
diff --git a/bob/ip/binseg/modeling/driubn.py b/bob/ip/binseg/modeling/driubn.py
index f914501156bbfa7e593593e862a9819444d80939..245fdf17e2bb4f8a475563820453e26f4aca6c21 100644
--- a/bob/ip/binseg/modeling/driubn.py
+++ b/bob/ip/binseg/modeling/driubn.py
@@ -5,24 +5,31 @@ import torch
import torch.nn
from collections import OrderedDict
from bob.ip.binseg.modeling.backbones.vgg import vgg16_bn
-from bob.ip.binseg.modeling.make_layers import conv_with_kaiming_uniform,convtrans_with_kaiming_uniform, UpsampleCropBlock
+from bob.ip.binseg.modeling.make_layers import (
+ conv_with_kaiming_uniform,
+ convtrans_with_kaiming_uniform,
+ UpsampleCropBlock,
+)
+
class ConcatFuseBlock(torch.nn.Module):
"""
Takes in four feature maps with 16 channels each, concatenates them
and applies a 1x1 convolution with 1 output channel.
"""
+
def __init__(self):
super().__init__()
self.conv = torch.nn.Sequential(
- conv_with_kaiming_uniform(4*16,1,1,1,0)
- ,torch.nn.BatchNorm2d(1)
+ conv_with_kaiming_uniform(4 * 16, 1, 1, 1, 0), torch.nn.BatchNorm2d(1)
)
- def forward(self,x1,x2,x3,x4):
- x_cat = torch.cat([x1,x2,x3,x4],dim=1)
+
+ def forward(self, x1, x2, x3, x4):
+ x_cat = torch.cat([x1, x2, x3, x4], dim=1)
x = self.conv(x_cat)
return x
+
class DRIU(torch.nn.Module):
"""
DRIU head module
@@ -34,6 +41,7 @@ class DRIU(torch.nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
+
def __init__(self, in_channels_list=None):
super(DRIU, self).__init__()
in_conv_1_2_16, in_upsample2, in_upsample_4, in_upsample_8 = in_channels_list
@@ -47,7 +55,7 @@ class DRIU(torch.nn.Module):
# Concat and Fuse
self.concatfuse = ConcatFuseBlock()
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -62,12 +70,13 @@ class DRIU(torch.nn.Module):
"""
hw = x[0]
conv1_2_16 = self.conv1_2_16(x[1]) # conv1_2_16
- upsample2 = self.upsample2(x[2], hw) # side-multi2-up
- upsample4 = self.upsample4(x[3], hw) # side-multi3-up
- upsample8 = self.upsample8(x[4], hw) # side-multi4-up
+ upsample2 = self.upsample2(x[2], hw) # side-multi2-up
+ upsample4 = self.upsample4(x[3], hw) # side-multi3-up
+ upsample8 = self.upsample8(x[4], hw) # side-multi4-up
out = self.concatfuse(conv1_2_16, upsample2, upsample4, upsample8)
return out
+
def build_driu():
"""
Adds backbone and head together
@@ -78,9 +87,11 @@ def build_driu():
module : :py:class:`torch.nn.Module`
"""
- backbone = vgg16_bn(pretrained=False, return_features = [5, 12, 19, 29])
+ backbone = vgg16_bn(pretrained=False, return_features=[5, 12, 19, 29])
driu_head = DRIU([64, 128, 256, 512])
- model = torch.nn.Sequential(OrderedDict([("backbone", backbone), ("head", driu_head)]))
+ model = torch.nn.Sequential(
+ OrderedDict([("backbone", backbone), ("head", driu_head)])
+ )
model.name = "DRIUBN"
return model
diff --git a/bob/ip/binseg/modeling/driuod.py b/bob/ip/binseg/modeling/driuod.py
index ab543e9e9b13b6cea1728198afbc56f03a4f36db..25e5b82dca52b681bee3288f620aecfef763a0be 100644
--- a/bob/ip/binseg/modeling/driuod.py
+++ b/bob/ip/binseg/modeling/driuod.py
@@ -5,22 +5,29 @@ import torch
import torch.nn
from collections import OrderedDict
from bob.ip.binseg.modeling.backbones.vgg import vgg16
-from bob.ip.binseg.modeling.make_layers import conv_with_kaiming_uniform,convtrans_with_kaiming_uniform, UpsampleCropBlock
+from bob.ip.binseg.modeling.make_layers import (
+ conv_with_kaiming_uniform,
+ convtrans_with_kaiming_uniform,
+ UpsampleCropBlock,
+)
+
class ConcatFuseBlock(torch.nn.Module):
"""
Takes in four feature maps with 16 channels each, concatenates them
and applies a 1x1 convolution with 1 output channel.
"""
+
def __init__(self):
super().__init__()
- self.conv = conv_with_kaiming_uniform(4*16,1,1,1,0)
+ self.conv = conv_with_kaiming_uniform(4 * 16, 1, 1, 1, 0)
- def forward(self,x1,x2,x3,x4):
- x_cat = torch.cat([x1,x2,x3,x4],dim=1)
+ def forward(self, x1, x2, x3, x4):
+ x_cat = torch.cat([x1, x2, x3, x4], dim=1)
x = self.conv(x_cat)
return x
+
class DRIUOD(torch.nn.Module):
"""
DRIU head module
@@ -30,6 +37,7 @@ class DRIUOD(torch.nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
+
def __init__(self, in_channels_list=None):
super(DRIUOD, self).__init__()
in_upsample2, in_upsample_4, in_upsample_8, in_upsample_16 = in_channels_list
@@ -40,11 +48,10 @@ class DRIUOD(torch.nn.Module):
self.upsample8 = UpsampleCropBlock(in_upsample_8, 16, 16, 8, 0)
self.upsample16 = UpsampleCropBlock(in_upsample_16, 16, 32, 16, 0)
-
# Concat and Fuse
self.concatfuse = ConcatFuseBlock()
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -59,12 +66,13 @@ class DRIUOD(torch.nn.Module):
"""
hw = x[0]
upsample2 = self.upsample2(x[1], hw) # side-multi2-up
- upsample4 = self.upsample4(x[2], hw) # side-multi3-up
- upsample8 = self.upsample8(x[3], hw) # side-multi4-up
+ upsample4 = self.upsample4(x[2], hw) # side-multi3-up
+ upsample8 = self.upsample8(x[3], hw) # side-multi4-up
upsample16 = self.upsample16(x[4], hw) # side-multi5-up
- out = self.concatfuse(upsample2, upsample4, upsample8,upsample16)
+ out = self.concatfuse(upsample2, upsample4, upsample8, upsample16)
return out
+
def build_driuod():
"""
Adds backbone and head together
@@ -74,9 +82,11 @@ def build_driuod():
module : :py:class:`torch.nn.Module`
"""
- backbone = vgg16(pretrained=False, return_features = [8, 14, 22,29])
- driu_head = DRIUOD([128, 256, 512,512])
+ backbone = vgg16(pretrained=False, return_features=[8, 14, 22, 29])
+ driu_head = DRIUOD([128, 256, 512, 512])
- model = torch.nn.Sequential(OrderedDict([("backbone", backbone), ("head", driu_head)]))
+ model = torch.nn.Sequential(
+ OrderedDict([("backbone", backbone), ("head", driu_head)])
+ )
model.name = "DRIUOD"
return model
diff --git a/bob/ip/binseg/modeling/driupix.py b/bob/ip/binseg/modeling/driupix.py
index e38768eab1ab71a4d2f6683fba626062a1d23361..3ad10aa7fabd23089c811b88d3433efd6326fb44 100644
--- a/bob/ip/binseg/modeling/driupix.py
+++ b/bob/ip/binseg/modeling/driupix.py
@@ -5,22 +5,29 @@ import torch
import torch.nn
from collections import OrderedDict
from bob.ip.binseg.modeling.backbones.vgg import vgg16
-from bob.ip.binseg.modeling.make_layers import conv_with_kaiming_uniform,convtrans_with_kaiming_uniform, UpsampleCropBlock
+from bob.ip.binseg.modeling.make_layers import (
+ conv_with_kaiming_uniform,
+ convtrans_with_kaiming_uniform,
+ UpsampleCropBlock,
+)
+
class ConcatFuseBlock(torch.nn.Module):
"""
Takes in four feature maps with 16 channels each, concatenates them
and applies a 1x1 convolution with 1 output channel.
"""
+
def __init__(self):
super().__init__()
- self.conv = conv_with_kaiming_uniform(4*16,1,1,1,0)
+ self.conv = conv_with_kaiming_uniform(4 * 16, 1, 1, 1, 0)
- def forward(self,x1,x2,x3,x4):
- x_cat = torch.cat([x1,x2,x3,x4],dim=1)
+ def forward(self, x1, x2, x3, x4):
+ x_cat = torch.cat([x1, x2, x3, x4], dim=1)
x = self.conv(x_cat)
return x
+
class DRIUPIX(torch.nn.Module):
"""
DRIUPIX head module. DRIU with pixelshuffle instead of ConvTrans2D
@@ -30,6 +37,7 @@ class DRIUPIX(torch.nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
+
def __init__(self, in_channels_list=None):
super(DRIUPIX, self).__init__()
in_conv_1_2_16, in_upsample2, in_upsample_4, in_upsample_8 = in_channels_list
@@ -37,13 +45,17 @@ class DRIUPIX(torch.nn.Module):
self.conv1_2_16 = torch.nn.Conv2d(in_conv_1_2_16, 16, 3, 1, 1)
# Upsample layers
self.upsample2 = UpsampleCropBlock(in_upsample2, 16, 4, 2, 0, pixelshuffle=True)
- self.upsample4 = UpsampleCropBlock(in_upsample_4, 16, 8, 4, 0, pixelshuffle=True)
- self.upsample8 = UpsampleCropBlock(in_upsample_8, 16, 16, 8, 0, pixelshuffle=True)
+ self.upsample4 = UpsampleCropBlock(
+ in_upsample_4, 16, 8, 4, 0, pixelshuffle=True
+ )
+ self.upsample8 = UpsampleCropBlock(
+ in_upsample_8, 16, 16, 8, 0, pixelshuffle=True
+ )
# Concat and Fuse
self.concatfuse = ConcatFuseBlock()
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -58,12 +70,13 @@ class DRIUPIX(torch.nn.Module):
"""
hw = x[0]
conv1_2_16 = self.conv1_2_16(x[1]) # conv1_2_16
- upsample2 = self.upsample2(x[2], hw) # side-multi2-up
- upsample4 = self.upsample4(x[3], hw) # side-multi3-up
- upsample8 = self.upsample8(x[4], hw) # side-multi4-up
+ upsample2 = self.upsample2(x[2], hw) # side-multi2-up
+ upsample4 = self.upsample4(x[3], hw) # side-multi3-up
+ upsample8 = self.upsample8(x[4], hw) # side-multi4-up
out = self.concatfuse(conv1_2_16, upsample2, upsample4, upsample8)
return out
+
def build_driupix():
"""
Adds backbone and head together
@@ -73,9 +86,11 @@ def build_driupix():
module : :py:class:`torch.nn.Module`
"""
- backbone = vgg16(pretrained=False, return_features = [3, 8, 14, 22])
+ backbone = vgg16(pretrained=False, return_features=[3, 8, 14, 22])
driu_head = DRIUPIX([64, 128, 256, 512])
- model = torch.nn.Sequential(OrderedDict([("backbone", backbone), ("head", driu_head)]))
+ model = torch.nn.Sequential(
+ OrderedDict([("backbone", backbone), ("head", driu_head)])
+ )
model.name = "DRIUPIX"
return model
diff --git a/bob/ip/binseg/modeling/hed.py b/bob/ip/binseg/modeling/hed.py
index 9be7fc86104002e07399959d044428383b9fe325..02c2b9574fba9e43e06220aefd8866ceee69a803 100644
--- a/bob/ip/binseg/modeling/hed.py
+++ b/bob/ip/binseg/modeling/hed.py
@@ -5,22 +5,29 @@ import torch
import torch.nn
from collections import OrderedDict
from bob.ip.binseg.modeling.backbones.vgg import vgg16
-from bob.ip.binseg.modeling.make_layers import conv_with_kaiming_uniform, convtrans_with_kaiming_uniform, UpsampleCropBlock
+from bob.ip.binseg.modeling.make_layers import (
+ conv_with_kaiming_uniform,
+ convtrans_with_kaiming_uniform,
+ UpsampleCropBlock,
+)
+
class ConcatFuseBlock(torch.nn.Module):
"""
Takes in five feature maps with one channel each, concatenates thems
and applies a 1x1 convolution with 1 output channel.
"""
+
def __init__(self):
super().__init__()
- self.conv = conv_with_kaiming_uniform(5,1,1,1,0)
+ self.conv = conv_with_kaiming_uniform(5, 1, 1, 1, 0)
- def forward(self,x1,x2,x3,x4,x5):
- x_cat = torch.cat([x1,x2,x3,x4,x5],dim=1)
+ def forward(self, x1, x2, x3, x4, x5):
+ x_cat = torch.cat([x1, x2, x3, x4, x5], dim=1)
x = self.conv(x_cat)
return x
+
class HED(torch.nn.Module):
"""
HED head module
@@ -30,20 +37,27 @@ class HED(torch.nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
+
def __init__(self, in_channels_list=None):
super(HED, self).__init__()
- in_conv_1_2_16, in_upsample2, in_upsample_4, in_upsample_8, in_upsample_16 = in_channels_list
+ (
+ in_conv_1_2_16,
+ in_upsample2,
+ in_upsample_4,
+ in_upsample_8,
+ in_upsample_16,
+ ) = in_channels_list
- self.conv1_2_16 = torch.nn.Conv2d(in_conv_1_2_16,1,3,1,1)
+ self.conv1_2_16 = torch.nn.Conv2d(in_conv_1_2_16, 1, 3, 1, 1)
# Upsample
- self.upsample2 = UpsampleCropBlock(in_upsample2,1,4,2,0)
- self.upsample4 = UpsampleCropBlock(in_upsample_4,1,8,4,0)
- self.upsample8 = UpsampleCropBlock(in_upsample_8,1,16,8,0)
- self.upsample16 = UpsampleCropBlock(in_upsample_16,1,32,16,0)
+ self.upsample2 = UpsampleCropBlock(in_upsample2, 1, 4, 2, 0)
+ self.upsample4 = UpsampleCropBlock(in_upsample_4, 1, 8, 4, 0)
+ self.upsample8 = UpsampleCropBlock(in_upsample_8, 1, 16, 8, 0)
+ self.upsample16 = UpsampleCropBlock(in_upsample_16, 1, 32, 16, 0)
# Concat and Fuse
self.concatfuse = ConcatFuseBlock()
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -58,15 +72,18 @@ class HED(torch.nn.Module):
"""
hw = x[0]
conv1_2_16 = self.conv1_2_16(x[1])
- upsample2 = self.upsample2(x[2],hw)
- upsample4 = self.upsample4(x[3],hw)
- upsample8 = self.upsample8(x[4],hw)
- upsample16 = self.upsample16(x[5],hw)
- concatfuse = self.concatfuse(conv1_2_16,upsample2,upsample4,upsample8,upsample16)
+ upsample2 = self.upsample2(x[2], hw)
+ upsample4 = self.upsample4(x[3], hw)
+ upsample8 = self.upsample8(x[4], hw)
+ upsample16 = self.upsample16(x[5], hw)
+ concatfuse = self.concatfuse(
+ conv1_2_16, upsample2, upsample4, upsample8, upsample16
+ )
- out = [upsample2,upsample4,upsample8,upsample16,concatfuse]
+ out = [upsample2, upsample4, upsample8, upsample16, concatfuse]
return out
+
def build_hed():
"""
Adds backbone and head together
@@ -75,9 +92,11 @@ def build_hed():
-------
module : :py:class:`torch.nn.Module`
"""
- backbone = vgg16(pretrained=False, return_features = [3, 8, 14, 22, 29])
+ backbone = vgg16(pretrained=False, return_features=[3, 8, 14, 22, 29])
hed_head = HED([64, 128, 256, 512, 512])
- model = torch.nn.Sequential(OrderedDict([("backbone", backbone), ("head", hed_head)]))
+ model = torch.nn.Sequential(
+ OrderedDict([("backbone", backbone), ("head", hed_head)])
+ )
model.name = "HED"
return model
diff --git a/bob/ip/binseg/modeling/losses.py b/bob/ip/binseg/modeling/losses.py
index 93235d039177433c1b4d3fce78f4248e598cce41..e2aa27b892d84ce6a3762ca998d27ce2391395ff 100644
--- a/bob/ip/binseg/modeling/losses.py
+++ b/bob/ip/binseg/modeling/losses.py
@@ -32,9 +32,7 @@ class WeightedBCELogitsLoss(_Loss):
reduction="mean",
pos_weight=None,
):
- super(WeightedBCELogitsLoss, self).__init__(
- size_average, reduce, reduction
- )
+ super(WeightedBCELogitsLoss, self).__init__(size_average, reduce, reduction)
self.register_buffer("weight", weight)
self.register_buffer("pos_weight", pos_weight)
@@ -56,9 +54,7 @@ class WeightedBCELogitsLoss(_Loss):
torch.sum(target, dim=[1, 2, 3]).float().reshape(n, 1)
) # torch.Size([n, 1])
if hasattr(masks, "dtype"):
- num_mask_neg = c * h * w - torch.sum(
- masks, dim=[1, 2, 3]
- ).float().reshape(
+ num_mask_neg = c * h * w - torch.sum(masks, dim=[1, 2, 3]).float().reshape(
n, 1
) # torch.Size([n, 1])
num_neg = c * h * w - num_pos - num_mask_neg
@@ -97,9 +93,7 @@ class SoftJaccardBCELogitsLoss(_Loss):
reduction="mean",
pos_weight=None,
):
- super(SoftJaccardBCELogitsLoss, self).__init__(
- size_average, reduce, reduction
- )
+ super(SoftJaccardBCELogitsLoss, self).__init__(size_average, reduce, reduction)
self.alpha = alpha
@weak_script_method
@@ -145,9 +139,7 @@ class HEDWeightedBCELogitsLoss(_Loss):
reduction="mean",
pos_weight=None,
):
- super(HEDWeightedBCELogitsLoss, self).__init__(
- size_average, reduce, reduction
- )
+ super(HEDWeightedBCELogitsLoss, self).__init__(size_average, reduce, reduction)
self.register_buffer("weight", weight)
self.register_buffer("pos_weight", pos_weight)
@@ -185,9 +177,7 @@ class HEDWeightedBCELogitsLoss(_Loss):
numnegnumtotal = torch.ones_like(target) * (
num_neg / (num_pos + num_neg)
).unsqueeze(1).unsqueeze(2)
- weight = torch.where(
- (target <= 0.5), numposnumtotal, numnegnumtotal
- )
+ weight = torch.where((target <= 0.5), numposnumtotal, numnegnumtotal)
loss = torch.nn.functional.binary_cross_entropy_with_logits(
input, target, weight=weight, reduction=self.reduction
)
@@ -278,9 +268,7 @@ class MixJacLoss(_Loss):
self.unlabeled_loss = torch.nn.BCEWithLogitsLoss()
@weak_script_method
- def forward(
- self, input, target, unlabeled_input, unlabeled_traget, ramp_up_factor
- ):
+ def forward(self, input, target, unlabeled_input, unlabeled_traget, ramp_up_factor):
"""
Parameters
----------
diff --git a/bob/ip/binseg/modeling/m2u.py b/bob/ip/binseg/modeling/m2u.py
index fa34c579aff77d7bf6474e9767f496fa27a9473f..25bc0515313a20fbeefc2b61df56f97eaa751c4f 100644
--- a/bob/ip/binseg/modeling/m2u.py
+++ b/bob/ip/binseg/modeling/m2u.py
@@ -8,35 +8,46 @@ import torch
import torch.nn
from bob.ip.binseg.modeling.backbones.mobilenetv2 import MobileNetV2, InvertedResidual
+
class DecoderBlock(torch.nn.Module):
"""
Decoder block: upsample and concatenate with features maps from the encoder part
"""
- def __init__(self,up_in_c,x_in_c,upsamplemode='bilinear',expand_ratio=0.15):
- super().__init__()
- self.upsample = torch.nn.Upsample(scale_factor=2,mode=upsamplemode,align_corners=False) # H, W -> 2H, 2W
- self.ir1 = InvertedResidual(up_in_c+x_in_c,(x_in_c + up_in_c) // 2,stride=1,expand_ratio=expand_ratio)
- def forward(self,up_in,x_in):
+ def __init__(self, up_in_c, x_in_c, upsamplemode="bilinear", expand_ratio=0.15):
+ super().__init__()
+ self.upsample = torch.nn.Upsample(
+ scale_factor=2, mode=upsamplemode, align_corners=False
+ ) # H, W -> 2H, 2W
+ self.ir1 = InvertedResidual(
+ up_in_c + x_in_c,
+ (x_in_c + up_in_c) // 2,
+ stride=1,
+ expand_ratio=expand_ratio,
+ )
+
+ def forward(self, up_in, x_in):
up_out = self.upsample(up_in)
- cat_x = torch.cat([up_out, x_in] , dim=1)
+ cat_x = torch.cat([up_out, x_in], dim=1)
x = self.ir1(cat_x)
return x
+
class LastDecoderBlock(torch.nn.Module):
- def __init__(self,x_in_c,upsamplemode='bilinear',expand_ratio=0.15):
+ def __init__(self, x_in_c, upsamplemode="bilinear", expand_ratio=0.15):
super().__init__()
- self.upsample = torch.nn.Upsample(scale_factor=2,mode=upsamplemode,align_corners=False) # H, W -> 2H, 2W
- self.ir1 = InvertedResidual(x_in_c,1,stride=1,expand_ratio=expand_ratio)
+ self.upsample = torch.nn.Upsample(
+ scale_factor=2, mode=upsamplemode, align_corners=False
+ ) # H, W -> 2H, 2W
+ self.ir1 = InvertedResidual(x_in_c, 1, stride=1, expand_ratio=expand_ratio)
- def forward(self,up_in,x_in):
+ def forward(self, up_in, x_in):
up_out = self.upsample(up_in)
- cat_x = torch.cat([up_out, x_in] , dim=1)
+ cat_x = torch.cat([up_out, x_in], dim=1)
x = self.ir1(cat_x)
return x
-
class M2U(torch.nn.Module):
"""
M2U-Net head module
@@ -46,14 +57,17 @@ class M2U(torch.nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
- def __init__(self, in_channels_list=None,upsamplemode='bilinear',expand_ratio=0.15):
+
+ def __init__(
+ self, in_channels_list=None, upsamplemode="bilinear", expand_ratio=0.15
+ ):
super(M2U, self).__init__()
# Decoder
- self.decode4 = DecoderBlock(96,32,upsamplemode,expand_ratio)
- self.decode3 = DecoderBlock(64,24,upsamplemode,expand_ratio)
- self.decode2 = DecoderBlock(44,16,upsamplemode,expand_ratio)
- self.decode1 = LastDecoderBlock(33,upsamplemode,expand_ratio)
+ self.decode4 = DecoderBlock(96, 32, upsamplemode, expand_ratio)
+ self.decode3 = DecoderBlock(64, 24, upsamplemode, expand_ratio)
+ self.decode2 = DecoderBlock(44, 16, upsamplemode, expand_ratio)
+ self.decode1 = LastDecoderBlock(33, upsamplemode, expand_ratio)
# initilaize weights
self._initialize_weights()
@@ -68,7 +82,7 @@ class M2U(torch.nn.Module):
m.weight.data.fill_(1)
m.bias.data.zero_()
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -80,13 +94,14 @@ class M2U(torch.nn.Module):
-------
tensor : :py:class:`torch.Tensor`
"""
- decode4 = self.decode4(x[5],x[4]) # 96, 32
- decode3 = self.decode3(decode4,x[3]) # 64, 24
- decode2 = self.decode2(decode3,x[2]) # 44, 16
- decode1 = self.decode1(decode2,x[1]) # 30, 3
+ decode4 = self.decode4(x[5], x[4]) # 96, 32
+ decode3 = self.decode3(decode4, x[3]) # 64, 24
+ decode2 = self.decode2(decode3, x[2]) # 44, 16
+ decode1 = self.decode1(decode2, x[1]) # 30, 3
return decode1
+
def build_m2unet():
"""
Adds backbone and head together
@@ -95,9 +110,11 @@ def build_m2unet():
-------
module : :py:class:`torch.nn.Module`
"""
- backbone = MobileNetV2(return_features = [1, 3, 6, 13], m2u=True)
+ backbone = MobileNetV2(return_features=[1, 3, 6, 13], m2u=True)
m2u_head = M2U(in_channels_list=[16, 24, 32, 96])
- model = torch.nn.Sequential(OrderedDict([("backbone", backbone), ("head", m2u_head)]))
+ model = torch.nn.Sequential(
+ OrderedDict([("backbone", backbone), ("head", m2u_head)])
+ )
model.name = "M2UNet"
return model
diff --git a/bob/ip/binseg/modeling/make_layers.py b/bob/ip/binseg/modeling/make_layers.py
index 88103048602c42e19eb83bd21cacfcfac14c847a..23704eae10913ad9235a19cde4f024587333ddac 100644
--- a/bob/ip/binseg/modeling/make_layers.py
+++ b/bob/ip/binseg/modeling/make_layers.py
@@ -6,7 +6,10 @@ import torch.nn
from torch.nn import Conv2d
from torch.nn import ConvTranspose2d
-def conv_with_kaiming_uniform(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1):
+
+def conv_with_kaiming_uniform(
+ in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1
+):
conv = Conv2d(
in_channels,
out_channels,
@@ -14,16 +17,18 @@ def conv_with_kaiming_uniform(in_channels, out_channels, kernel_size, stride=1,
stride=stride,
padding=padding,
dilation=dilation,
- bias= True
- )
- # Caffe2 implementation uses XavierFill, which in fact
- # corresponds to kaiming_uniform_ in PyTorch
+ bias=True,
+ )
+ # Caffe2 implementation uses XavierFill, which in fact
+ # corresponds to kaiming_uniform_ in PyTorch
torch.nn.init.kaiming_uniform_(conv.weight, a=1)
torch.nn.init.constant_(conv.bias, 0)
return conv
-def convtrans_with_kaiming_uniform(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1):
+def convtrans_with_kaiming_uniform(
+ in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1
+):
conv = ConvTranspose2d(
in_channels,
out_channels,
@@ -31,10 +36,10 @@ def convtrans_with_kaiming_uniform(in_channels, out_channels, kernel_size, strid
stride=stride,
padding=padding,
dilation=dilation,
- bias= True
- )
- # Caffe2 implementation uses XavierFill, which in fact
- # corresponds to kaiming_uniform_ in PyTorch
+ bias=True,
+ )
+ # Caffe2 implementation uses XavierFill, which in fact
+ # corresponds to kaiming_uniform_ in PyTorch
torch.nn.init.kaiming_uniform_(conv.weight, a=1)
torch.nn.init.constant_(conv.bias, 0)
return conv
@@ -63,15 +68,24 @@ class UpsampleCropBlock(torch.nn.Module):
"""
- def __init__(self, in_channels, out_channels, up_kernel_size, up_stride, up_padding, pixelshuffle=False):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ up_kernel_size,
+ up_stride,
+ up_padding,
+ pixelshuffle=False,
+ ):
super().__init__()
# NOTE: Kaiming init, replace with torch.nn.Conv2d and torch.nn.ConvTranspose2d to get original DRIU impl.
self.conv = conv_with_kaiming_uniform(in_channels, out_channels, 3, 1, 1)
if pixelshuffle:
- self.upconv = PixelShuffle_ICNR( out_channels, out_channels, scale = up_stride)
+ self.upconv = PixelShuffle_ICNR(out_channels, out_channels, scale=up_stride)
else:
- self.upconv = convtrans_with_kaiming_uniform(out_channels, out_channels, up_kernel_size, up_stride, up_padding)
-
+ self.upconv = convtrans_with_kaiming_uniform(
+ out_channels, out_channels, up_kernel_size, up_stride, up_padding
+ )
def forward(self, x, input_res):
"""Forward pass of UpsampleBlock.
@@ -98,39 +112,40 @@ class UpsampleCropBlock(torch.nn.Module):
# height
up_h = x.shape[2]
h_crop = up_h - img_h
- h_s = h_crop//2
+ h_s = h_crop // 2
h_e = up_h - (h_crop - h_s)
# width
up_w = x.shape[3]
- w_crop = up_w-img_w
- w_s = w_crop//2
+ w_crop = up_w - img_w
+ w_s = w_crop // 2
w_e = up_w - (w_crop - w_s)
# perform crop
# needs explicit ranges for onnx export
- x = x[:,:,h_s:h_e,w_s:w_e] # crop to input size
+ x = x[:, :, h_s:h_e, w_s:w_e] # crop to input size
return x
-
def ifnone(a, b):
"``a`` if ``a`` is not None, otherwise ``b``."
return b if a is None else a
+
def icnr(x, scale=2, init=torch.nn.init.kaiming_normal_):
"""https://docs.fast.ai/layers.html#PixelShuffle_ICNR
ICNR init of ``x``, with ``scale`` and ``init`` function.
"""
- ni,nf,h,w = x.shape
- ni2 = int(ni/(scale**2))
- k = init(torch.zeros([ni2,nf,h,w])).transpose(0, 1)
+ ni, nf, h, w = x.shape
+ ni2 = int(ni / (scale ** 2))
+ k = init(torch.zeros([ni2, nf, h, w])).transpose(0, 1)
k = k.contiguous().view(ni2, nf, -1)
- k = k.repeat(1, 1, scale**2)
- k = k.contiguous().view([nf,ni,h,w]).transpose(0, 1)
+ k = k.repeat(1, 1, scale ** 2)
+ k = k.contiguous().view([nf, ni, h, w]).transpose(0, 1)
x.data.copy_(k)
+
class PixelShuffle_ICNR(torch.nn.Module):
"""https://docs.fast.ai/layers.html#PixelShuffle_ICNR
@@ -138,47 +153,52 @@ class PixelShuffle_ICNR(torch.nn.Module):
``torch.nn.PixelShuffle``, ``icnr`` init, and ``weight_norm``.
"""
- def __init__(self, ni:int, nf:int=None, scale:int=2):
+ def __init__(self, ni: int, nf: int = None, scale: int = 2):
super().__init__()
nf = ifnone(nf, ni)
- self.conv = conv_with_kaiming_uniform(ni, nf*(scale**2), 1)
+ self.conv = conv_with_kaiming_uniform(ni, nf * (scale ** 2), 1)
icnr(self.conv.weight)
self.shuf = torch.nn.PixelShuffle(scale)
# Blurring over (h*w) kernel
# "Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts"
# - https://arxiv.org/abs/1806.02658
- self.pad = torch.nn.ReplicationPad2d((1,0,1,0))
+ self.pad = torch.nn.ReplicationPad2d((1, 0, 1, 0))
self.blur = torch.nn.AvgPool2d(2, stride=1)
self.relu = torch.nn.ReLU(inplace=True)
- def forward(self,x):
+ def forward(self, x):
x = self.shuf(self.relu(self.conv(x)))
x = self.blur(self.pad(x))
return x
+
class UnetBlock(torch.nn.Module):
def __init__(self, up_in_c, x_in_c, pixel_shuffle=False, middle_block=False):
super().__init__()
# middle block for VGG based U-Net
if middle_block:
- up_out_c = up_in_c
+ up_out_c = up_in_c
else:
- up_out_c = up_in_c // 2
+ up_out_c = up_in_c // 2
cat_channels = x_in_c + up_out_c
inner_channels = cat_channels // 2
if pixel_shuffle:
- self.upsample = PixelShuffle_ICNR( up_in_c, up_out_c )
+ self.upsample = PixelShuffle_ICNR(up_in_c, up_out_c)
else:
- self.upsample = convtrans_with_kaiming_uniform( up_in_c, up_out_c, 2, 2)
- self.convtrans1 = convtrans_with_kaiming_uniform( cat_channels, inner_channels, 3, 1, 1)
- self.convtrans2 = convtrans_with_kaiming_uniform( inner_channels, inner_channels, 3, 1, 1)
+ self.upsample = convtrans_with_kaiming_uniform(up_in_c, up_out_c, 2, 2)
+ self.convtrans1 = convtrans_with_kaiming_uniform(
+ cat_channels, inner_channels, 3, 1, 1
+ )
+ self.convtrans2 = convtrans_with_kaiming_uniform(
+ inner_channels, inner_channels, 3, 1, 1
+ )
self.relu = torch.nn.ReLU(inplace=True)
def forward(self, up_in, x_in):
up_out = self.upsample(up_in)
- cat_x = torch.cat([up_out, x_in] , dim=1)
+ cat_x = torch.cat([up_out, x_in], dim=1)
x = self.relu(self.convtrans1(cat_x))
x = self.relu(self.convtrans2(x))
return x
diff --git a/bob/ip/binseg/modeling/resunet.py b/bob/ip/binseg/modeling/resunet.py
index 5256bd720131c6fcf3006b45eab8217741d11465..7b262c7a95680f5972cb17374ac0e4aa12d6efc9 100644
--- a/bob/ip/binseg/modeling/resunet.py
+++ b/bob/ip/binseg/modeling/resunet.py
@@ -4,11 +4,15 @@
import torch.nn as nn
import torch
from collections import OrderedDict
-from bob.ip.binseg.modeling.make_layers import conv_with_kaiming_uniform, convtrans_with_kaiming_uniform, PixelShuffle_ICNR, UnetBlock
+from bob.ip.binseg.modeling.make_layers import (
+ conv_with_kaiming_uniform,
+ convtrans_with_kaiming_uniform,
+ PixelShuffle_ICNR,
+ UnetBlock,
+)
from bob.ip.binseg.modeling.backbones.resnet import resnet50
-
class ResUNet(nn.Module):
"""
UNet head module for ResNet backbones
@@ -18,12 +22,13 @@ class ResUNet(nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
+
def __init__(self, in_channels_list=None, pixel_shuffle=False):
super(ResUNet, self).__init__()
# number of channels
c_decode1, c_decode2, c_decode3, c_decode4, c_decode5 = in_channels_list
# number of channels for last upsampling operation
- c_decode0 = (c_decode1 + c_decode2//2)//2
+ c_decode0 = (c_decode1 + c_decode2 // 2) // 2
# build layers
self.decode4 = UnetBlock(c_decode5, c_decode4, pixel_shuffle)
@@ -36,7 +41,7 @@ class ResUNet(nn.Module):
self.decode0 = convtrans_with_kaiming_uniform(c_decode0, c_decode0, 2, 2)
self.final = conv_with_kaiming_uniform(c_decode0, 1, 1)
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -54,6 +59,7 @@ class ResUNet(nn.Module):
out = self.final(decode0)
return out
+
def build_res50unet():
"""
Adds backbone and head together
@@ -62,8 +68,8 @@ def build_res50unet():
-------
model : :py:class:`torch.nn.Module`
"""
- backbone = resnet50(pretrained=False, return_features = [2, 4, 5, 6, 7])
- unet_head = ResUNet([64, 256, 512, 1024, 2048],pixel_shuffle=False)
+ backbone = resnet50(pretrained=False, return_features=[2, 4, 5, 6, 7])
+ unet_head = ResUNet([64, 256, 512, 1024, 2048], pixel_shuffle=False)
model = nn.Sequential(OrderedDict([("backbone", backbone), ("head", unet_head)]))
model.name = "ResUNet"
return model
diff --git a/bob/ip/binseg/modeling/unet.py b/bob/ip/binseg/modeling/unet.py
index b89ec5f17a1ae7a4def2dbb7cfd097c941fab958..7602d63aae183936a66f5d1986ce454ba6e32516 100644
--- a/bob/ip/binseg/modeling/unet.py
+++ b/bob/ip/binseg/modeling/unet.py
@@ -4,11 +4,15 @@
import torch.nn as nn
import torch
from collections import OrderedDict
-from bob.ip.binseg.modeling.make_layers import conv_with_kaiming_uniform, convtrans_with_kaiming_uniform, PixelShuffle_ICNR, UnetBlock
+from bob.ip.binseg.modeling.make_layers import (
+ conv_with_kaiming_uniform,
+ convtrans_with_kaiming_uniform,
+ PixelShuffle_ICNR,
+ UnetBlock,
+)
from bob.ip.binseg.modeling.backbones.vgg import vgg16
-
class UNet(nn.Module):
"""
UNet head module
@@ -18,6 +22,7 @@ class UNet(nn.Module):
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
+
def __init__(self, in_channels_list=None, pixel_shuffle=False):
super(UNet, self).__init__()
# number of channels
@@ -30,7 +35,7 @@ class UNet(nn.Module):
self.decode1 = UnetBlock(c_decode2, c_decode1, pixel_shuffle)
self.final = conv_with_kaiming_uniform(c_decode1, 1, 1)
- def forward(self,x):
+ def forward(self, x):
"""
Parameters
----------
@@ -47,6 +52,7 @@ class UNet(nn.Module):
out = self.final(decode1)
return out
+
def build_unet():
"""
Adds backbone and head together
@@ -56,7 +62,7 @@ def build_unet():
module : :py:class:`torch.nn.Module`
"""
- backbone = vgg16(pretrained=False, return_features = [3, 8, 14, 22, 29])
+ backbone = vgg16(pretrained=False, return_features=[3, 8, 14, 22, 29])
unet_head = UNet([64, 128, 256, 512, 512], pixel_shuffle=False)
model = nn.Sequential(OrderedDict([("backbone", backbone), ("head", unet_head)]))
diff --git a/bob/ip/binseg/script/binseg.py b/bob/ip/binseg/script/binseg.py
index 944a99953a40ed2fc4c3a0a9d07e28e8f0f21c5e..9fc05e9ae1744ade6dca5c2041691efae92e13be 100644
--- a/bob/ip/binseg/script/binseg.py
+++ b/bob/ip/binseg/script/binseg.py
@@ -18,8 +18,12 @@ import logging
import torch
import bob.extension
-from bob.extension.scripts.click_helper import (verbosity_option,
- ConfigCommand, ResourceOption, AliasedGroup)
+from bob.extension.scripts.click_helper import (
+ verbosity_option,
+ ConfigCommand,
+ ResourceOption,
+ AliasedGroup,
+)
from bob.ip.binseg.utils.checkpointer import DetectronCheckpointer
from torch.utils.data import DataLoader
@@ -29,7 +33,7 @@ from bob.ip.binseg.engine.inferencer import do_inference
from bob.ip.binseg.utils.plot import plot_overview
from bob.ip.binseg.utils.click import OptionEatAll
from bob.ip.binseg.utils.rsttable import create_overview_grid
-from bob.ip.binseg.utils.plot import metricsviz, overlay,savetransformedtest
+from bob.ip.binseg.utils.plot import metricsviz, overlay, savetransformedtest
from bob.ip.binseg.utils.transformfolder import transformfolder as transfld
from bob.ip.binseg.utils.evaluate import do_eval
from bob.ip.binseg.engine.predicter import do_predict
@@ -37,121 +41,94 @@ from bob.ip.binseg.engine.predicter import do_predict
logger = logging.getLogger(__name__)
-@with_plugins(pkg_resources.iter_entry_points('bob.ip.binseg.cli'))
+@with_plugins(pkg_resources.iter_entry_points("bob.ip.binseg.cli"))
@click.group(cls=AliasedGroup)
def binseg():
"""Binary 2D Fundus Image Segmentation Benchmark commands."""
pass
+
# Train
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
-@click.option(
- '--output-path',
- '-o',
- required=True,
- default="output",
- cls=ResourceOption
- )
-@click.option(
- '--model',
- '-m',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--dataset',
- '-d',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--optimizer',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--criterion',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--scheduler',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--pretrained-backbone',
- '-t',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--batch-size',
- '-b',
- required=True,
- default=2,
- cls=ResourceOption)
-@click.option(
- '--epochs',
- '-e',
- help='Number of epochs used for training',
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
+@click.option(
+ "--output-path", "-o", required=True, default="output", cls=ResourceOption
+)
+@click.option("--model", "-m", required=True, cls=ResourceOption)
+@click.option("--dataset", "-d", required=True, cls=ResourceOption)
+@click.option("--optimizer", required=True, cls=ResourceOption)
+@click.option("--criterion", required=True, cls=ResourceOption)
+@click.option("--scheduler", required=True, cls=ResourceOption)
+@click.option("--pretrained-backbone", "-t", required=True, cls=ResourceOption)
+@click.option("--batch-size", "-b", required=True, default=2, cls=ResourceOption)
+@click.option(
+ "--epochs",
+ "-e",
+ help="Number of epochs used for training",
show_default=True,
required=True,
default=1000,
- cls=ResourceOption)
+ cls=ResourceOption,
+)
@click.option(
- '--checkpoint-period',
- '-p',
- help='Number of epochs after which a checkpoint is saved',
+ "--checkpoint-period",
+ "-p",
+ help="Number of epochs after which a checkpoint is saved",
show_default=True,
required=True,
default=100,
- cls=ResourceOption)
+ cls=ResourceOption,
+)
@click.option(
- '--device',
- '-d',
+ "--device",
+ "-d",
help='A string indicating the device to use (e.g. "cpu" or "cuda:0"',
show_default=True,
required=True,
- default='cpu',
- cls=ResourceOption)
+ default="cpu",
+ cls=ResourceOption,
+)
@click.option(
- '--seed',
- '-s',
- help='torch random seed',
+ "--seed",
+ "-s",
+ help="torch random seed",
show_default=True,
required=False,
default=42,
- cls=ResourceOption)
-
+ cls=ResourceOption,
+)
@verbosity_option(cls=ResourceOption)
-def train(model
- ,optimizer
- ,scheduler
- ,output_path
- ,epochs
- ,pretrained_backbone
- ,batch_size
- ,criterion
- ,dataset
- ,checkpoint_period
- ,device
- ,seed
- ,**kwargs):
+def train(
+ model,
+ optimizer,
+ scheduler,
+ output_path,
+ epochs,
+ pretrained_backbone,
+ batch_size,
+ criterion,
+ dataset,
+ checkpoint_period,
+ device,
+ seed,
+ **kwargs
+):
""" Train a model """
- if not os.path.exists(output_path): os.makedirs(output_path)
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
torch.manual_seed(seed)
# PyTorch dataloader
data_loader = DataLoader(
- dataset = dataset
- ,batch_size = batch_size
- ,shuffle= True
- ,pin_memory = torch.cuda.is_available()
- )
+ dataset=dataset,
+ batch_size=batch_size,
+ shuffle=True,
+ pin_memory=torch.cuda.is_available(),
+ )
# Checkpointer
- checkpointer = DetectronCheckpointer(model, optimizer, scheduler,save_dir = output_path, save_to_disk=True)
+ checkpointer = DetectronCheckpointer(
+ model, optimizer, scheduler, save_dir=output_path, save_to_disk=True
+ )
arguments = {}
arguments["epoch"] = 0
extra_checkpoint_data = checkpointer.load(pretrained_backbone)
@@ -161,125 +138,98 @@ def train(model
# Train
logger.info("Training for {} epochs".format(arguments["max_epoch"]))
logger.info("Continuing from epoch {}".format(arguments["epoch"]))
- do_train(model
- , data_loader
- , optimizer
- , criterion
- , scheduler
- , checkpointer
- , checkpoint_period
- , device
- , arguments
- , output_path
- )
+ do_train(
+ model,
+ data_loader,
+ optimizer,
+ criterion,
+ scheduler,
+ checkpointer,
+ checkpoint_period,
+ device,
+ arguments,
+ output_path,
+ )
# Inference
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
@click.option(
- '--output-path',
- '-o',
- required=True,
- default="output",
- cls=ResourceOption
- )
+ "--output-path", "-o", required=True, default="output", cls=ResourceOption
+)
+@click.option("--model", "-m", required=True, cls=ResourceOption)
+@click.option("--dataset", "-d", required=True, cls=ResourceOption)
+@click.option("--batch-size", "-b", required=True, default=2, cls=ResourceOption)
@click.option(
- '--model',
- '-m',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--dataset',
- '-d',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--batch-size',
- '-b',
- required=True,
- default=2,
- cls=ResourceOption)
-@click.option(
- '--device',
- '-d',
+ "--device",
+ "-d",
help='A string indicating the device to use (e.g. "cpu" or "cuda:0"',
show_default=True,
required=True,
- default='cpu',
- cls=ResourceOption)
+ default="cpu",
+ cls=ResourceOption,
+)
@click.option(
- '--weight',
- '-w',
- help='Path or URL to pretrained model',
+ "--weight",
+ "-w",
+ help="Path or URL to pretrained model",
required=False,
default=None,
- cls=ResourceOption
- )
+ cls=ResourceOption,
+)
@verbosity_option(cls=ResourceOption)
-def test(model
- ,output_path
- ,device
- ,batch_size
- ,dataset
- ,weight
- , **kwargs):
+def test(model, output_path, device, batch_size, dataset, weight, **kwargs):
""" Run inference and evalaute the model performance """
# PyTorch dataloader
data_loader = DataLoader(
- dataset = dataset
- ,batch_size = batch_size
- ,shuffle= False
- ,pin_memory = torch.cuda.is_available()
- )
+ dataset=dataset,
+ batch_size=batch_size,
+ shuffle=False,
+ pin_memory=torch.cuda.is_available(),
+ )
# checkpointer, load last model in dir
- checkpointer = DetectronCheckpointer(model, save_dir = output_path, save_to_disk=False)
+ checkpointer = DetectronCheckpointer(
+ model, save_dir=output_path, save_to_disk=False
+ )
checkpointer.load(weight)
do_inference(model, data_loader, device, output_path)
-
# Plot comparison
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
@click.option(
- '--output-path-list',
- '-l',
+ "--output-path-list",
+ "-l",
required=True,
- help='Pass all output paths as arguments',
+ help="Pass all output paths as arguments",
cls=OptionEatAll,
- )
+)
@click.option(
- '--output-path',
- '-o',
- required=True,
- )
+ "--output-path", "-o", required=True,
+)
@click.option(
- '--title',
- '-t',
- required=False,
- )
+ "--title", "-t", required=False,
+)
@verbosity_option(cls=ResourceOption)
def compare(output_path_list, output_path, title, **kwargs):
""" Compares multiple metrics files that are stored in the format mymodel/results/Metrics.csv """
logger.debug("Output paths: {}".format(output_path_list))
- logger.info('Plotting precision vs recall curves for {}'.format(output_path_list))
- fig = plot_overview(output_path_list,title)
- if not os.path.exists(output_path): os.makedirs(output_path)
- fig_filename = os.path.join(output_path, 'precision_recall_comparison.pdf')
- logger.info('saving {}'.format(fig_filename))
+ logger.info("Plotting precision vs recall curves for {}".format(output_path_list))
+ fig = plot_overview(output_path_list, title)
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ fig_filename = os.path.join(output_path, "precision_recall_comparison.pdf")
+ logger.info("saving {}".format(fig_filename))
fig.savefig(fig_filename)
# Create grid table with results
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
@click.option(
- '--output-path',
- '-o',
- required=True,
- )
+ "--output-path", "-o", required=True,
+)
@verbosity_option(cls=ResourceOption)
def gridtable(output_path, **kwargs):
""" Creates an overview table in grid rst format for all Metrics.csv in the output_path
@@ -289,23 +239,16 @@ def gridtable(output_path, **kwargs):
├── images
└── results
"""
- logger.info('Creating grid for all results in {}'.format(output_path))
+ logger.info("Creating grid for all results in {}".format(output_path))
create_overview_grid(output_path)
# Create metrics viz
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
-@click.option(
- '--dataset',
- '-d',
- required=True,
- cls=ResourceOption
- )
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
+@click.option("--dataset", "-d", required=True, cls=ResourceOption)
@click.option(
- '--output-path',
- '-o',
- required=True,
- )
+ "--output-path", "-o", required=True,
+)
@verbosity_option(cls=ResourceOption)
def visualize(dataset, output_path, **kwargs):
""" Creates the following visualizations of the probabilties output maps:
@@ -318,132 +261,105 @@ def visualize(dataset, output_path, **kwargs):
├── images
└── results
"""
- logger.info('Creating TP, FP, FN visualizations for {}'.format(output_path))
+ logger.info("Creating TP, FP, FN visualizations for {}".format(output_path))
metricsviz(dataset=dataset, output_path=output_path)
- logger.info('Creating overlay visualizations for {}'.format(output_path))
+ logger.info("Creating overlay visualizations for {}".format(output_path))
overlay(dataset=dataset, output_path=output_path)
- logger.info('Saving transformed test images {}'.format(output_path))
+ logger.info("Saving transformed test images {}".format(output_path))
savetransformedtest(dataset=dataset, output_path=output_path)
# SSLTrain
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
-@click.option(
- '--output-path',
- '-o',
- required=True,
- default="output",
- cls=ResourceOption
- )
-@click.option(
- '--model',
- '-m',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--dataset',
- '-d',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--optimizer',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--criterion',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--scheduler',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--pretrained-backbone',
- '-t',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--batch-size',
- '-b',
- required=True,
- default=2,
- cls=ResourceOption)
-@click.option(
- '--epochs',
- '-e',
- help='Number of epochs used for training',
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
+@click.option(
+ "--output-path", "-o", required=True, default="output", cls=ResourceOption
+)
+@click.option("--model", "-m", required=True, cls=ResourceOption)
+@click.option("--dataset", "-d", required=True, cls=ResourceOption)
+@click.option("--optimizer", required=True, cls=ResourceOption)
+@click.option("--criterion", required=True, cls=ResourceOption)
+@click.option("--scheduler", required=True, cls=ResourceOption)
+@click.option("--pretrained-backbone", "-t", required=True, cls=ResourceOption)
+@click.option("--batch-size", "-b", required=True, default=2, cls=ResourceOption)
+@click.option(
+ "--epochs",
+ "-e",
+ help="Number of epochs used for training",
show_default=True,
required=True,
default=1000,
- cls=ResourceOption)
+ cls=ResourceOption,
+)
@click.option(
- '--checkpoint-period',
- '-p',
- help='Number of epochs after which a checkpoint is saved',
+ "--checkpoint-period",
+ "-p",
+ help="Number of epochs after which a checkpoint is saved",
show_default=True,
required=True,
default=100,
- cls=ResourceOption)
+ cls=ResourceOption,
+)
@click.option(
- '--device',
- '-d',
+ "--device",
+ "-d",
help='A string indicating the device to use (e.g. "cpu" or "cuda:0"',
show_default=True,
required=True,
- default='cpu',
- cls=ResourceOption)
+ default="cpu",
+ cls=ResourceOption,
+)
@click.option(
- '--rampup',
- '-r',
- help='Ramp-up length in epochs',
+ "--rampup",
+ "-r",
+ help="Ramp-up length in epochs",
show_default=True,
required=True,
- default='900',
- cls=ResourceOption)
+ default="900",
+ cls=ResourceOption,
+)
@click.option(
- '--seed',
- '-s',
- help='torch random seed',
+ "--seed",
+ "-s",
+ help="torch random seed",
show_default=True,
required=False,
default=42,
- cls=ResourceOption)
-
+ cls=ResourceOption,
+)
@verbosity_option(cls=ResourceOption)
-def ssltrain(model
- ,optimizer
- ,scheduler
- ,output_path
- ,epochs
- ,pretrained_backbone
- ,batch_size
- ,criterion
- ,dataset
- ,checkpoint_period
- ,device
- ,rampup
- ,seed
- ,**kwargs):
+def ssltrain(
+ model,
+ optimizer,
+ scheduler,
+ output_path,
+ epochs,
+ pretrained_backbone,
+ batch_size,
+ criterion,
+ dataset,
+ checkpoint_period,
+ device,
+ rampup,
+ seed,
+ **kwargs
+):
""" Train a model """
- if not os.path.exists(output_path): os.makedirs(output_path)
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
torch.manual_seed(seed)
# PyTorch dataloader
data_loader = DataLoader(
- dataset = dataset
- ,batch_size = batch_size
- ,shuffle= True
- ,pin_memory = torch.cuda.is_available()
- )
+ dataset=dataset,
+ batch_size=batch_size,
+ shuffle=True,
+ pin_memory=torch.cuda.is_available(),
+ )
# Checkpointer
- checkpointer = DetectronCheckpointer(model, optimizer, scheduler,save_dir = output_path, save_to_disk=True)
+ checkpointer = DetectronCheckpointer(
+ model, optimizer, scheduler, save_dir=output_path, save_to_disk=True
+ )
arguments = {}
arguments["epoch"] = 0
extra_checkpoint_data = checkpointer.load(pretrained_backbone)
@@ -453,109 +369,77 @@ def ssltrain(model
# Train
logger.info("Training for {} epochs".format(arguments["max_epoch"]))
logger.info("Continuing from epoch {}".format(arguments["epoch"]))
- do_ssltrain(model
- , data_loader
- , optimizer
- , criterion
- , scheduler
- , checkpointer
- , checkpoint_period
- , device
- , arguments
- , output_path
- , rampup
- )
-
-# Apply image transforms to a folder containing images
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
-@click.option(
- '--source-path',
- '-s',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--target-path',
- '-t',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--transforms',
- '-a',
- required=True,
- cls=ResourceOption
+ do_ssltrain(
+ model,
+ data_loader,
+ optimizer,
+ criterion,
+ scheduler,
+ checkpointer,
+ checkpoint_period,
+ device,
+ arguments,
+ output_path,
+ rampup,
)
+
+# Apply image transforms to a folder containing images
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
+@click.option("--source-path", "-s", required=True, cls=ResourceOption)
+@click.option("--target-path", "-t", required=True, cls=ResourceOption)
+@click.option("--transforms", "-a", required=True, cls=ResourceOption)
@verbosity_option(cls=ResourceOption)
-def transformfolder(source_path ,target_path,transforms,**kwargs):
- logger.info('Applying transforms to images in {} and saving them to {}'.format(source_path, target_path))
- transfld(source_path,target_path,transforms)
+def transformfolder(source_path, target_path, transforms, **kwargs):
+ logger.info(
+ "Applying transforms to images in {} and saving them to {}".format(
+ source_path, target_path
+ )
+ )
+ transfld(source_path, target_path, transforms)
# Run inference and create predictions only (no ground truth available)
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
-@click.option(
- '--output-path',
- '-o',
- required=True,
- default="output",
- cls=ResourceOption
- )
-@click.option(
- '--model',
- '-m',
- required=True,
- cls=ResourceOption
- )
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
@click.option(
- '--dataset',
- '-d',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--batch-size',
- '-b',
- required=True,
- default=2,
- cls=ResourceOption)
+ "--output-path", "-o", required=True, default="output", cls=ResourceOption
+)
+@click.option("--model", "-m", required=True, cls=ResourceOption)
+@click.option("--dataset", "-d", required=True, cls=ResourceOption)
+@click.option("--batch-size", "-b", required=True, default=2, cls=ResourceOption)
@click.option(
- '--device',
- '-d',
+ "--device",
+ "-d",
help='A string indicating the device to use (e.g. "cpu" or "cuda:0"',
show_default=True,
required=True,
- default='cpu',
- cls=ResourceOption)
+ default="cpu",
+ cls=ResourceOption,
+)
@click.option(
- '--weight',
- '-w',
- help='Path or URL to pretrained model',
+ "--weight",
+ "-w",
+ help="Path or URL to pretrained model",
required=False,
default=None,
- cls=ResourceOption
- )
+ cls=ResourceOption,
+)
@verbosity_option(cls=ResourceOption)
-def predict(model
- ,output_path
- ,device
- ,batch_size
- ,dataset
- ,weight
- , **kwargs):
+def predict(model, output_path, device, batch_size, dataset, weight, **kwargs):
""" Run inference and evalaute the model performance """
# PyTorch dataloader
data_loader = DataLoader(
- dataset = dataset
- ,batch_size = batch_size
- ,shuffle= False
- ,pin_memory = torch.cuda.is_available()
- )
+ dataset=dataset,
+ batch_size=batch_size,
+ shuffle=False,
+ pin_memory=torch.cuda.is_available(),
+ )
# checkpointer, load last model in dir
- checkpointer = DetectronCheckpointer(model, save_dir = output_path, save_to_disk=False)
+ checkpointer = DetectronCheckpointer(
+ model, save_dir=output_path, save_to_disk=False
+ )
checkpointer.load(weight)
do_predict(model, data_loader, device, output_path)
@@ -563,68 +447,55 @@ def predict(model
overlay(dataset=dataset, output_path=output_path)
-
# Evaluate only. Runs evaluation on predicted probability maps (--prediction-folder)
-@binseg.command(entry_point_group='bob.ip.binseg.config', cls=ConfigCommand)
+@binseg.command(entry_point_group="bob.ip.binseg.config", cls=ConfigCommand)
@click.option(
- '--output-path',
- '-o',
- required=True,
- default="output",
- cls=ResourceOption
- )
+ "--output-path", "-o", required=True, default="output", cls=ResourceOption
+)
@click.option(
- '--prediction-folder',
- '-p',
- help = 'Path containing output probability maps',
+ "--prediction-folder",
+ "-p",
+ help="Path containing output probability maps",
required=True,
- cls=ResourceOption
- )
+ cls=ResourceOption,
+)
@click.option(
- '--prediction-extension',
- '-x',
- help = 'Extension (e.g. ".png") for the prediction files',
+ "--prediction-extension",
+ "-x",
+ help='Extension (e.g. ".png") for the prediction files',
default=".png",
required=False,
- cls=ResourceOption
- )
-@click.option(
- '--dataset',
- '-d',
- required=True,
- cls=ResourceOption
- )
-@click.option(
- '--title',
- required=False,
- cls=ResourceOption
- )
-@click.option(
- '--legend',
- cls=ResourceOption
- )
-
+ cls=ResourceOption,
+)
+@click.option("--dataset", "-d", required=True, cls=ResourceOption)
+@click.option("--title", required=False, cls=ResourceOption)
+@click.option("--legend", cls=ResourceOption)
@verbosity_option(cls=ResourceOption)
def evalpred(
- output_path
- ,prediction_folder
- ,prediction_extension
- ,dataset
- ,title
- ,legend
- , **kwargs):
+ output_path,
+ prediction_folder,
+ prediction_extension,
+ dataset,
+ title,
+ legend,
+ **kwargs
+):
""" Run inference and evalaute the model performance """
# PyTorch dataloader
data_loader = DataLoader(
- dataset = dataset
- ,batch_size = 1
- ,shuffle= False
- ,pin_memory = torch.cuda.is_available()
- )
+ dataset=dataset,
+ batch_size=1,
+ shuffle=False,
+ pin_memory=torch.cuda.is_available(),
+ )
# Run eval
- do_eval(prediction_folder, data_loader, output_folder = output_path, title=title, legend=legend, prediction_extension=prediction_extension)
-
-
-
+ do_eval(
+ prediction_folder,
+ data_loader,
+ output_folder=output_path,
+ title=title,
+ legend=legend,
+ prediction_extension=prediction_extension,
+ )
diff --git a/bob/ip/binseg/test/test_basemetrics.py b/bob/ip/binseg/test/test_basemetrics.py
index bf478ac788d038dd9038ca5d5b5cf7aa1ac7a83a..6cd716149e9b421b3a5bca7acd64e325076804c7 100644
--- a/bob/ip/binseg/test/test_basemetrics.py
+++ b/bob/ip/binseg/test/test_basemetrics.py
@@ -6,39 +6,44 @@ import numpy as np
from bob.ip.binseg.utils.metric import base_metrics
import random
+
class Tester(unittest.TestCase):
"""
Unit test for base metrics
"""
+
def setUp(self):
self.tp = random.randint(1, 100)
self.fp = random.randint(1, 100)
self.tn = random.randint(1, 100)
self.fn = random.randint(1, 100)
-
+
def test_precision(self):
precision = base_metrics(self.tp, self.fp, self.tn, self.fn)[0]
- self.assertEqual((self.tp)/(self.tp + self.fp),precision)
+ self.assertEqual((self.tp) / (self.tp + self.fp), precision)
def test_recall(self):
recall = base_metrics(self.tp, self.fp, self.tn, self.fn)[1]
- self.assertEqual((self.tp)/(self.tp + self.fn),recall)
+ self.assertEqual((self.tp) / (self.tp + self.fn), recall)
def test_specificity(self):
specificity = base_metrics(self.tp, self.fp, self.tn, self.fn)[2]
- self.assertEqual((self.tn)/(self.tn + self.fp),specificity)
-
+ self.assertEqual((self.tn) / (self.tn + self.fp), specificity)
+
def test_accuracy(self):
accuracy = base_metrics(self.tp, self.fp, self.tn, self.fn)[3]
- self.assertEqual((self.tp + self.tn)/(self.tp + self.tn + self.fp + self.fn), accuracy)
+ self.assertEqual(
+ (self.tp + self.tn) / (self.tp + self.tn + self.fp + self.fn), accuracy
+ )
def test_jaccard(self):
jaccard = base_metrics(self.tp, self.fp, self.tn, self.fn)[4]
- self.assertEqual(self.tp / (self.tp+self.fp+self.fn), jaccard)
+ self.assertEqual(self.tp / (self.tp + self.fp + self.fn), jaccard)
def test_f1(self):
f1 = base_metrics(self.tp, self.fp, self.tn, self.fn)[5]
- self.assertEqual((2.0 * self.tp ) / (2.0 * self.tp + self.fp + self.fn ),f1)
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
+ self.assertEqual((2.0 * self.tp) / (2.0 * self.tp + self.fp + self.fn), f1)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/bob/ip/binseg/test/test_batchmetrics.py b/bob/ip/binseg/test/test_batchmetrics.py
index 4988cab6ea8a7ffbc907f2e580ebe46b48ad9611..25868691d9c4fd13c73a31b4bcf7d671f2e0404b 100644
--- a/bob/ip/binseg/test/test_batchmetrics.py
+++ b/bob/ip/binseg/test/test_batchmetrics.py
@@ -9,31 +9,42 @@ import shutil, tempfile
import logging
import torch
+
class Tester(unittest.TestCase):
"""
Unit test for batch metrics
"""
+
def setUp(self):
self.tp = random.randint(1, 100)
self.fp = random.randint(1, 100)
self.tn = random.randint(1, 100)
self.fn = random.randint(1, 100)
- self.predictions = torch.rand(size=(2,1,420,420))
- self.ground_truths = torch.randint(low=0, high=2, size=(2,1,420,420))
- self.names = ['Bob','Tim']
+ self.predictions = torch.rand(size=(2, 1, 420, 420))
+ self.ground_truths = torch.randint(low=0, high=2, size=(2, 1, 420, 420))
+ self.names = ["Bob", "Tim"]
self.output_folder = tempfile.mkdtemp()
self.logger = logging.getLogger(__name__)
def tearDown(self):
# Remove the temporary folder after the test
shutil.rmtree(self.output_folder)
-
+
def test_batch_metrics(self):
- bm = batch_metrics(self.predictions, self.ground_truths, self.names, self.output_folder, self.logger)
- self.assertEqual(len(bm),2*100)
+ bm = batch_metrics(
+ self.predictions,
+ self.ground_truths,
+ self.names,
+ self.output_folder,
+ self.logger,
+ )
+ self.assertEqual(len(bm), 2 * 100)
for metric in bm:
# check whether f1 score agree
- self.assertAlmostEqual(metric[-1],2*(metric[-6]*metric[-5])/(metric[-6]+metric[-5]))
+ self.assertAlmostEqual(
+ metric[-1], 2 * (metric[-6] * metric[-5]) / (metric[-6] + metric[-5])
+ )
+
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
+if __name__ == "__main__":
+ unittest.main()
diff --git a/bob/ip/binseg/test/test_checkpointer.py b/bob/ip/binseg/test/test_checkpointer.py
index 976e9d942b0796cf3a535f76024e3821e40abb0b..b8fb615996a11f3f572023b4bfb1bed7a592e892 100644
--- a/bob/ip/binseg/test/test_checkpointer.py
+++ b/bob/ip/binseg/test/test_checkpointer.py
@@ -39,9 +39,7 @@ class TestCheckpointer(unittest.TestCase):
trained_model = self.create_model()
fresh_model = self.create_model()
with TemporaryDirectory() as f:
- checkpointer = Checkpointer(
- trained_model, save_dir=f, save_to_disk=True
- )
+ checkpointer = Checkpointer(trained_model, save_dir=f, save_to_disk=True)
checkpointer.save("checkpoint_file")
# in the same folder
@@ -66,9 +64,7 @@ class TestCheckpointer(unittest.TestCase):
trained_model = self.create_model()
fresh_model = self.create_model()
with TemporaryDirectory() as f:
- checkpointer = Checkpointer(
- trained_model, save_dir=f, save_to_disk=True
- )
+ checkpointer = Checkpointer(trained_model, save_dir=f, save_to_disk=True)
checkpointer.save("checkpoint_file")
# on different folders
diff --git a/bob/ip/binseg/test/test_summary.py b/bob/ip/binseg/test/test_summary.py
index 7faabf796674db6b7914d631ba41f9160c08a623..aebcaace81971f00d15443dc946ccf11905bdfc0 100644
--- a/bob/ip/binseg/test/test_summary.py
+++ b/bob/ip/binseg/test/test_summary.py
@@ -11,36 +11,37 @@ from bob.ip.binseg.modeling.unet import build_unet
from bob.ip.binseg.modeling.resunet import build_res50unet
from bob.ip.binseg.utils.summary import summary
+
class Tester(unittest.TestCase):
"""
Unit test for model architectures
- """
+ """
+
def test_summary_driu(self):
model = build_driu()
param = summary(model)
- self.assertIsInstance(param,int)
-
+ self.assertIsInstance(param, int)
def test__summary_driuod(self):
model = build_driuod()
param = summary(model)
- self.assertIsInstance(param,int)
-
+ self.assertIsInstance(param, int)
def test_summary_hed(self):
model = build_hed()
param = summary(model)
- self.assertIsInstance(param,int)
+ self.assertIsInstance(param, int)
def test_summary_unet(self):
model = build_unet()
param = summary(model)
- self.assertIsInstance(param,int)
+ self.assertIsInstance(param, int)
def test_summary_resunet(self):
model = build_res50unet()
param = summary(model)
- self.assertIsInstance(param,int)
+ self.assertIsInstance(param, int)
+
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
+if __name__ == "__main__":
+ unittest.main()
diff --git a/bob/ip/binseg/test/test_transforms.py b/bob/ip/binseg/test/test_transforms.py
index 479cd79c063e8059c6048dfd0ead1f3a7062e006..e71716a0bc927186ad2121340e7e0b230b5adfae 100644
--- a/bob/ip/binseg/test/test_transforms.py
+++ b/bob/ip/binseg/test/test_transforms.py
@@ -6,15 +6,13 @@ import unittest
import numpy as np
from bob.ip.binseg.data.transforms import *
-transforms = Compose([
- RandomHFlip(prob=1)
- ,RandomHFlip(prob=1)
- ,RandomVFlip(prob=1)
- ,RandomVFlip(prob=1)
- ])
+transforms = Compose(
+ [RandomHFlip(prob=1), RandomHFlip(prob=1), RandomVFlip(prob=1), RandomVFlip(prob=1)]
+)
+
def create_img():
- t = torch.randn((3,42,24))
+ t = torch.randn((3, 42, 24))
pil = VF.to_pil_image(t)
return pil
@@ -23,14 +21,16 @@ class Tester(unittest.TestCase):
"""
Unit test for random flips
"""
-
+
def test_flips(self):
- transforms = Compose([
- RandomHFlip(prob=1)
- ,RandomHFlip(prob=1)
- ,RandomVFlip(prob=1)
- ,RandomVFlip(prob=1)
- ])
+ transforms = Compose(
+ [
+ RandomHFlip(prob=1),
+ RandomHFlip(prob=1),
+ RandomVFlip(prob=1),
+ RandomVFlip(prob=1),
+ ]
+ )
img, gt, mask = [create_img() for i in range(3)]
img_t, gt_t, mask_t = transforms(img, gt, mask)
self.assertTrue(np.all(np.array(img_t) == np.array(img)))
@@ -41,9 +41,10 @@ class Tester(unittest.TestCase):
transforms = ToTensor()
img, gt, mask = [create_img() for i in range(3)]
img_t, gt_t, mask_t = transforms(img, gt, mask)
- self.assertEqual(str(img_t.dtype),"torch.float32")
- self.assertEqual(str(gt_t.dtype),"torch.float32")
- self.assertEqual(str(mask_t.dtype),"torch.float32")
+ self.assertEqual(str(img_t.dtype), "torch.float32")
+ self.assertEqual(str(gt_t.dtype), "torch.float32")
+ self.assertEqual(str(mask_t.dtype), "torch.float32")
+
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
+if __name__ == "__main__":
+ unittest.main()
diff --git a/bob/ip/binseg/utils/checkpointer.py b/bob/ip/binseg/utils/checkpointer.py
index 1a79d908ef8ad7f2b74dc7d7e341b4a6f16b1bb1..4b375e9f6cd2454706842a4ee9e5e1b76e31a234 100644
--- a/bob/ip/binseg/utils/checkpointer.py
+++ b/bob/ip/binseg/utils/checkpointer.py
@@ -59,9 +59,7 @@ class Checkpointer:
f = self.get_checkpoint_file()
if not f:
# no checkpoint could be found
- self.logger.warn(
- "No checkpoint found. Initializing model from scratch"
- )
+ self.logger.warn("No checkpoint found. Initializing model from scratch")
return {}
self.logger.info("Loading checkpoint from {}".format(f))
checkpoint = self._load_file(f)
diff --git a/bob/ip/binseg/utils/click.py b/bob/ip/binseg/utils/click.py
index 03fd5d30e7df650d1ae40d392fecd261f1a17bc2..792cebfd34f50d3fbe2a08e6d829fb17aaba91e3 100644
--- a/bob/ip/binseg/utils/click.py
+++ b/bob/ip/binseg/utils/click.py
@@ -3,6 +3,7 @@
import click
+
class OptionEatAll(click.Option):
"""
Allows for ``*args`` and ``**kwargs`` to be passed to click
@@ -11,15 +12,14 @@ class OptionEatAll(click.Option):
"""
def __init__(self, *args, **kwargs):
- self.save_other_options = kwargs.pop('save_other_options', True)
- nargs = kwargs.pop('nargs', -1)
- assert nargs == -1, 'nargs, if set, must be -1 not {}'.format(nargs)
+ self.save_other_options = kwargs.pop("save_other_options", True)
+ nargs = kwargs.pop("nargs", -1)
+ assert nargs == -1, "nargs, if set, must be -1 not {}".format(nargs)
super(OptionEatAll, self).__init__(*args, **kwargs)
self._previous_parser_process = None
self._eat_all_parser = None
def add_to_parser(self, parser, ctx):
-
def parser_process(value, state):
# method to hook to the parser.process
done = False
diff --git a/bob/ip/binseg/utils/evaluate.py b/bob/ip/binseg/utils/evaluate.py
index 99259f412be8f00e7b2ea2572ba654689a4cf92d..d891a1ce71d8aa85b4c4777c570fc4544c22e728 100644
--- a/bob/ip/binseg/utils/evaluate.py
+++ b/bob/ip/binseg/utils/evaluate.py
@@ -13,7 +13,10 @@ import torchvision.transforms.functional as VF
from tqdm import tqdm
from bob.ip.binseg.utils.metric import SmoothedValue, base_metrics
-from bob.ip.binseg.utils.plot import precision_recall_f1iso, precision_recall_f1iso_confintval
+from bob.ip.binseg.utils.plot import (
+ precision_recall_f1iso,
+ precision_recall_f1iso_confintval,
+)
from bob.ip.binseg.utils.summary import summary
from PIL import Image
from torchvision.transforms.functional import to_tensor
@@ -51,21 +54,23 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
file_name = "{}.csv".format(names[j])
logger.info("saving {}".format(file_name))
- with open (os.path.join(output_folder,file_name), "w+") as outfile:
+ with open(os.path.join(output_folder, file_name), "w+") as outfile:
- outfile.write("threshold, precision, recall, specificity, accuracy, jaccard, f1_score\n")
+ outfile.write(
+ "threshold, precision, recall, specificity, accuracy, jaccard, f1_score\n"
+ )
- for threshold in np.arange(0.0,1.0,step_size):
+ for threshold in np.arange(0.0, 1.0, step_size):
# threshold
binary_pred = torch.gt(predictions[j], threshold).byte()
# equals and not-equals
- equals = torch.eq(binary_pred, gts) # tensor
- notequals = torch.ne(binary_pred, gts) # tensor
+ equals = torch.eq(binary_pred, gts) # tensor
+ notequals = torch.ne(binary_pred, gts) # tensor
# true positives
- tp_tensor = (gts * binary_pred ) # tensor
- tp_count = torch.sum(tp_tensor).item() # scalar
+ tp_tensor = gts * binary_pred # tensor
+ tp_count = torch.sum(tp_tensor).item() # scalar
# false positives
fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
@@ -83,22 +88,24 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
metrics = base_metrics(tp_count, fp_count, tn_count, fn_count)
# write to disk
- outfile.write("{:.2f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f} \n".format(threshold, *metrics))
-
- batch_metrics.append([names[j],threshold, *metrics ])
+ outfile.write(
+ "{:.2f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f} \n".format(
+ threshold, *metrics
+ )
+ )
+ batch_metrics.append([names[j], threshold, *metrics])
return batch_metrics
-
def do_eval(
prediction_folder,
data_loader,
- output_folder = None,
- title = '2nd human',
- legend = '2nd human',
- prediction_extension = None,
+ output_folder=None,
+ title="2nd human",
+ legend="2nd human",
+ prediction_extension=None,
):
"""
@@ -116,9 +123,8 @@ def do_eval(
logger = logging.getLogger("bob.ip.binseg.engine.evaluate")
logger.info("Start evaluation")
logger.info("Prediction folder {}".format(prediction_folder))
- results_subfolder = os.path.join(output_folder,'results')
- os.makedirs(results_subfolder,exist_ok=True)
-
+ results_subfolder = os.path.join(output_folder, "results")
+ os.makedirs(results_subfolder, exist_ok=True)
# Collect overall metrics
metrics = []
@@ -129,66 +135,80 @@ def do_eval(
ground_truths = samples[2]
if prediction_extension is None:
- pred_file = os.path.join(prediction_folder,names[0])
+ pred_file = os.path.join(prediction_folder, names[0])
else:
- pred_file = os.path.join(prediction_folder,os.path.splitext(names[0])[0] + '.png')
+ pred_file = os.path.join(
+ prediction_folder, os.path.splitext(names[0])[0] + ".png"
+ )
probabilities = Image.open(pred_file)
- probabilities = probabilities.convert(mode='L')
+ probabilities = probabilities.convert(mode="L")
probabilities = to_tensor(probabilities)
-
- b_metrics = batch_metrics(probabilities, ground_truths, names,results_subfolder, logger)
+ b_metrics = batch_metrics(
+ probabilities, ground_truths, names, results_subfolder, logger
+ )
metrics.extend(b_metrics)
-
-
# DataFrame
- df_metrics = pd.DataFrame(metrics,columns= \
- ["name",
- "threshold",
- "precision",
- "recall",
- "specificity",
- "accuracy",
- "jaccard",
- "f1_score"])
+ df_metrics = pd.DataFrame(
+ metrics,
+ columns=[
+ "name",
+ "threshold",
+ "precision",
+ "recall",
+ "specificity",
+ "accuracy",
+ "jaccard",
+ "f1_score",
+ ],
+ )
# Report and Averages
metrics_file = "Metrics.csv"
metrics_path = os.path.join(results_subfolder, metrics_file)
logger.info("Saving average over all input images: {}".format(metrics_file))
- avg_metrics = df_metrics.groupby('threshold').mean()
- std_metrics = df_metrics.groupby('threshold').std()
+ avg_metrics = df_metrics.groupby("threshold").mean()
+ std_metrics = df_metrics.groupby("threshold").std()
# Uncomment below for F1-score calculation based on average precision and metrics instead of
# F1-scores of individual images. This method is in line with Maninis et. al. (2016)
- #avg_metrics["f1_score"] = (2* avg_metrics["precision"]*avg_metrics["recall"])/ \
+ # avg_metrics["f1_score"] = (2* avg_metrics["precision"]*avg_metrics["recall"])/ \
# (avg_metrics["precision"]+avg_metrics["recall"])
-
avg_metrics["std_pr"] = std_metrics["precision"]
- avg_metrics["pr_upper"] = avg_metrics['precision'] + avg_metrics["std_pr"]
- avg_metrics["pr_lower"] = avg_metrics['precision'] - avg_metrics["std_pr"]
+ avg_metrics["pr_upper"] = avg_metrics["precision"] + avg_metrics["std_pr"]
+ avg_metrics["pr_lower"] = avg_metrics["precision"] - avg_metrics["std_pr"]
avg_metrics["std_re"] = std_metrics["recall"]
- avg_metrics["re_upper"] = avg_metrics['recall'] + avg_metrics["std_re"]
- avg_metrics["re_lower"] = avg_metrics['recall'] - avg_metrics["std_re"]
+ avg_metrics["re_upper"] = avg_metrics["recall"] + avg_metrics["std_re"]
+ avg_metrics["re_lower"] = avg_metrics["recall"] - avg_metrics["std_re"]
avg_metrics["std_f1"] = std_metrics["f1_score"]
avg_metrics.to_csv(metrics_path)
- maxf1 = avg_metrics['f1_score'].max()
- optimal_f1_threshold = avg_metrics['f1_score'].idxmax()
+ maxf1 = avg_metrics["f1_score"].max()
+ optimal_f1_threshold = avg_metrics["f1_score"].idxmax()
- logger.info("Highest F1-score of {:.5f}, achieved at threshold {}".format(maxf1, optimal_f1_threshold))
+ logger.info(
+ "Highest F1-score of {:.5f}, achieved at threshold {}".format(
+ maxf1, optimal_f1_threshold
+ )
+ )
# Plotting
- #print(avg_metrics)
+ # print(avg_metrics)
np_avg_metrics = avg_metrics.to_numpy().T
fig_name = "precision_recall.pdf"
logger.info("saving {}".format(fig_name))
- fig = precision_recall_f1iso_confintval([np_avg_metrics[0]],[np_avg_metrics[1]],[np_avg_metrics[7]],[np_avg_metrics[8]],[np_avg_metrics[10]],[np_avg_metrics[11]], [legend ,None], title=title)
+ fig = precision_recall_f1iso_confintval(
+ [np_avg_metrics[0]],
+ [np_avg_metrics[1]],
+ [np_avg_metrics[7]],
+ [np_avg_metrics[8]],
+ [np_avg_metrics[10]],
+ [np_avg_metrics[11]],
+ [legend, None],
+ title=title,
+ )
fig_filename = os.path.join(results_subfolder, fig_name)
fig.savefig(fig_filename)
-
-
-
diff --git a/bob/ip/binseg/utils/metric.py b/bob/ip/binseg/utils/metric.py
index bcb91511f533a8ed69843487ef8cbe793e42a925..471cac63e3c383295732eeefa4a5f7e6a899a0d0 100644
--- a/bob/ip/binseg/utils/metric.py
+++ b/bob/ip/binseg/utils/metric.py
@@ -27,6 +27,7 @@ class SmoothedValue:
d = torch.tensor(list(self.deque))
return d.mean().item()
+
def base_metrics(tp, fp, tn, fn):
"""
Calculates Precision, Recall (=Sensitivity), Specificity, Accuracy, Jaccard and F1-score (Dice)
@@ -54,11 +55,11 @@ def base_metrics(tp, fp, tn, fn):
metrics : list
"""
- precision = tp / (tp + fp + ( (tp+fp) == 0) )
- recall = tp / (tp + fn + ( (tp+fn) == 0) )
- specificity = tn / (fp + tn + ( (fp+tn) == 0) )
- accuracy = (tp + tn) / (tp+fp+fn+tn)
- jaccard = tp / (tp+fp+fn + ( (tp+fp+fn) == 0) )
- f1_score = (2.0 * tp ) / (2.0 * tp + fp + fn + ( (2.0 * tp + fp + fn) == 0) )
- #f1_score = (2.0 * precision * recall) / (precision + recall)
- return [precision, recall, specificity, accuracy, jaccard, f1_score]
\ No newline at end of file
+ precision = tp / (tp + fp + ((tp + fp) == 0))
+ recall = tp / (tp + fn + ((tp + fn) == 0))
+ specificity = tn / (fp + tn + ((fp + tn) == 0))
+ accuracy = (tp + tn) / (tp + fp + fn + tn)
+ jaccard = tp / (tp + fp + fn + ((tp + fp + fn) == 0))
+ f1_score = (2.0 * tp) / (2.0 * tp + fp + fn + ((2.0 * tp + fp + fn) == 0))
+ # f1_score = (2.0 * precision * recall) / (precision + recall)
+ return [precision, recall, specificity, accuracy, jaccard, f1_score]
diff --git a/bob/ip/binseg/utils/model_serialization.py b/bob/ip/binseg/utils/model_serialization.py
index 84ff2491ea85751cc0b0910d278f035e9571f0eb..016f085e481a28648dd3c6dfcef64b021a34b01f 100644
--- a/bob/ip/binseg/utils/model_serialization.py
+++ b/bob/ip/binseg/utils/model_serialization.py
@@ -5,6 +5,7 @@ import logging
import torch
+
def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that we will create will have prefixes appended
@@ -75,4 +76,4 @@ def load_state_dict(model, loaded_state_dict):
align_and_update_state_dicts(model_state_dict, loaded_state_dict)
# use strict loading
- model.load_state_dict(model_state_dict)
\ No newline at end of file
+ model.load_state_dict(model_state_dict)
diff --git a/bob/ip/binseg/utils/model_zoo.py b/bob/ip/binseg/utils/model_zoo.py
index 00c7f7c5758383150884c40daf4598b2cb05bf6c..18052744b49c5f884ab02da78a5ebf208894f5a3 100644
--- a/bob/ip/binseg/utils/model_zoo.py
+++ b/bob/ip/binseg/utils/model_zoo.py
@@ -35,13 +35,14 @@ modelurls = {
"resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
"resnet50_SIN_IN": "https://bitbucket.org/robert_geirhos/texture-vs-shape-pretrained-models/raw/60b770e128fffcbd8562a3ab3546c1a735432d03/resnet50_finetune_60_epochs_lr_decay_after_30_start_resnet50_train_45_epochs_combined_IN_SF-ca06340c.pth.tar",
"mobilenetv2": "https://dl.dropboxusercontent.com/s/4nie4ygivq04p8y/mobilenet_v2.pth.tar",
- }
+}
+
def _download_url_to_file(url, dst, hash_prefix, progress):
file_size = None
u = urlopen(url)
meta = u.info()
- if hasattr(meta, 'getheaders'):
+ if hasattr(meta, "getheaders"):
content_length = meta.getheaders("Content-Length")
else:
content_length = meta.get_all("Content-Length")
@@ -65,16 +66,21 @@ def _download_url_to_file(url, dst, hash_prefix, progress):
f.close()
if hash_prefix is not None:
digest = sha256.hexdigest()
- if digest[:len(hash_prefix)] != hash_prefix:
- raise RuntimeError('invalid hash value (expected "{}", got "{}")'
- .format(hash_prefix, digest))
+ if digest[: len(hash_prefix)] != hash_prefix:
+ raise RuntimeError(
+ 'invalid hash value (expected "{}", got "{}")'.format(
+ hash_prefix, digest
+ )
+ )
shutil.move(f.name, dst)
finally:
f.close()
if os.path.exists(f.name):
os.remove(f.name)
-HASH_REGEX = re.compile(r'-([a-f0-9]*)\.')
+
+HASH_REGEX = re.compile(r"-([a-f0-9]*)\.")
+
def cache_url(url, model_dir=None, progress=True):
r"""Loads the Torch serialized object at the given URL.
diff --git a/bob/ip/binseg/utils/plot.py b/bob/ip/binseg/utils/plot.py
index de1d531d8bdab9db1f2263f4221157a3cca24912..ba85ba3dd1679a257a033055ae46a2a3d0ac18b1 100644
--- a/bob/ip/binseg/utils/plot.py
+++ b/bob/ip/binseg/utils/plot.py
@@ -6,10 +6,11 @@ import os
import csv
import pandas as pd
import PIL
-from PIL import Image,ImageFont, ImageDraw
+from PIL import Image, ImageFont, ImageDraw
import torchvision.transforms.functional as VF
import torch
+
def precision_recall_f1iso(precision, recall, names, title=None):
"""
Author: Andre Anjos (andre.anjos@idiap.ch).
@@ -40,11 +41,13 @@ def precision_recall_f1iso(precision, recall, names, title=None):
A matplotlib figure you can save or display
"""
import matplotlib
- matplotlib.use('agg')
+
+ matplotlib.use("agg")
import matplotlib.pyplot as plt
from itertools import cycle
+
fig, ax1 = plt.subplots(1)
- lines = ["-","--","-.",":"]
+ lines = ["-", "--", "-.", ":"]
linecycler = cycle(lines)
for p, r, n in zip(precision, recall, names):
# Plots only from the point where recall reaches its maximum, otherwise, we
@@ -52,23 +55,29 @@ def precision_recall_f1iso(precision, recall, names, title=None):
i = r.argmax()
pi = p[i:]
ri = r[i:]
- valid = (pi+ri) > 0
- f1 = 2 * (pi[valid]*ri[valid]) / (pi[valid]+ri[valid])
+ valid = (pi + ri) > 0
+ f1 = 2 * (pi[valid] * ri[valid]) / (pi[valid] + ri[valid])
# optimal point along the curve
argmax = f1.argmax()
opi = pi[argmax]
ori = ri[argmax]
# Plot Recall/Precision as threshold changes
- ax1.plot(ri[pi>0], pi[pi>0], next(linecycler), label='[F={:.4f}] {}'.format(f1.max(), n),)
- ax1.plot(ori,opi, marker='o', linestyle=None, markersize=3, color='black')
- ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
+ ax1.plot(
+ ri[pi > 0],
+ pi[pi > 0],
+ next(linecycler),
+ label="[F={:.4f}] {}".format(f1.max(), n),
+ )
+ ax1.plot(ori, opi, marker="o", linestyle=None, markersize=3, color="black")
+ ax1.grid(linestyle="--", linewidth=1, color="gray", alpha=0.2)
if len(names) > 1:
- plt.legend(loc='lower left', framealpha=0.5)
- ax1.set_xlabel('Recall')
- ax1.set_ylabel('Precision')
+ plt.legend(loc="lower left", framealpha=0.5)
+ ax1.set_xlabel("Recall")
+ ax1.set_ylabel("Precision")
ax1.set_xlim([0.0, 1.0])
ax1.set_ylim([0.0, 1.0])
- if title is not None: ax1.set_title(title)
+ if title is not None:
+ ax1.set_title(title)
# Annotates plot with F1-score iso-lines
ax2 = ax1.twinx()
f_scores = np.linspace(0.1, 0.9, num=9)
@@ -77,32 +86,35 @@ def precision_recall_f1iso(precision, recall, names, title=None):
for f_score in f_scores:
x = np.linspace(0.01, 1)
y = f_score * x / (2 * x - f_score)
- l, = plt.plot(x[y >= 0], y[y >= 0], color='green', alpha=0.1)
+ (l,) = plt.plot(x[y >= 0], y[y >= 0], color="green", alpha=0.1)
tick_locs.append(y[-1])
- tick_labels.append('%.1f' % f_score)
- ax2.tick_params(axis='y', which='both', pad=0, right=False, left=False)
- ax2.set_ylabel('iso-F', color='green', alpha=0.3)
+ tick_labels.append("%.1f" % f_score)
+ ax2.tick_params(axis="y", which="both", pad=0, right=False, left=False)
+ ax2.set_ylabel("iso-F", color="green", alpha=0.3)
ax2.set_ylim([0.0, 1.0])
ax2.yaxis.set_label_coords(1.015, 0.97)
- ax2.set_yticks(tick_locs) #notice these are invisible
+ ax2.set_yticks(tick_locs) # notice these are invisible
for k in ax2.set_yticklabels(tick_labels):
- k.set_color('green')
+ k.set_color("green")
k.set_alpha(0.3)
k.set_size(8)
# we should see some of axes 1 axes
- ax1.spines['right'].set_visible(False)
- ax1.spines['top'].set_visible(False)
- ax1.spines['left'].set_position(('data', -0.015))
- ax1.spines['bottom'].set_position(('data', -0.015))
+ ax1.spines["right"].set_visible(False)
+ ax1.spines["top"].set_visible(False)
+ ax1.spines["left"].set_position(("data", -0.015))
+ ax1.spines["bottom"].set_position(("data", -0.015))
# we shouldn't see any of axes 2 axes
- ax2.spines['right'].set_visible(False)
- ax2.spines['top'].set_visible(False)
- ax2.spines['left'].set_visible(False)
- ax2.spines['bottom'].set_visible(False)
+ ax2.spines["right"].set_visible(False)
+ ax2.spines["top"].set_visible(False)
+ ax2.spines["left"].set_visible(False)
+ ax2.spines["bottom"].set_visible(False)
plt.tight_layout()
return fig
-def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_upper, re_lower, names, title=None):
+
+def precision_recall_f1iso_confintval(
+ precision, recall, pr_upper, pr_lower, re_upper, re_lower, names, title=None
+):
"""
Author: Andre Anjos (andre.anjos@idiap.ch).
@@ -132,17 +144,30 @@ def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_
A matplotlib figure you can save or display
"""
import matplotlib
- matplotlib.use('agg')
+
+ matplotlib.use("agg")
import matplotlib.pyplot as plt
from itertools import cycle
+
fig, ax1 = plt.subplots(1)
- lines = ["-","--","-.",":"]
- colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
- '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
- '#bcbd22', '#17becf']
+ lines = ["-", "--", "-.", ":"]
+ colors = [
+ "#1f77b4",
+ "#ff7f0e",
+ "#2ca02c",
+ "#d62728",
+ "#9467bd",
+ "#8c564b",
+ "#e377c2",
+ "#7f7f7f",
+ "#bcbd22",
+ "#17becf",
+ ]
colorcycler = cycle(colors)
linecycler = cycle(lines)
- for p, r, pu, pl, ru, rl, n in zip(precision, recall, pr_upper, pr_lower, re_upper, re_lower, names):
+ for p, r, pu, pl, ru, rl, n in zip(
+ precision, recall, pr_upper, pr_lower, re_upper, re_lower, names
+ ):
# Plots only from the point where recall reaches its maximum, otherwise, we
# don't see a curve...
i = r.argmax()
@@ -152,39 +177,57 @@ def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_
pli = pl[i:]
rui = ru[i:]
rli = rl[i:]
- valid = (pi+ri) > 0
- f1 = 2 * (pi[valid]*ri[valid]) / (pi[valid]+ri[valid])
+ valid = (pi + ri) > 0
+ f1 = 2 * (pi[valid] * ri[valid]) / (pi[valid] + ri[valid])
# optimal point along the curve
argmax = f1.argmax()
opi = pi[argmax]
ori = ri[argmax]
# Plot Recall/Precision as threshold changes
- ax1.plot(ri[pi>0], pi[pi>0], next(linecycler), label='[F={:.4f}] {}'.format(f1.max(), n),)
- ax1.plot(ori,opi, marker='o', linestyle=None, markersize=3, color='black')
+ ax1.plot(
+ ri[pi > 0],
+ pi[pi > 0],
+ next(linecycler),
+ label="[F={:.4f}] {}".format(f1.max(), n),
+ )
+ ax1.plot(ori, opi, marker="o", linestyle=None, markersize=3, color="black")
# Plot confidence
# Upper bound
- #ax1.plot(r95ui[p95ui>0], p95ui[p95ui>0])
+ # ax1.plot(r95ui[p95ui>0], p95ui[p95ui>0])
# Lower bound
- #ax1.plot(r95li[p95li>0], p95li[p95li>0])
+ # ax1.plot(r95li[p95li>0], p95li[p95li>0])
# create the limiting polygon
- vert_x = np.concatenate((rui[pui>0], rli[pli>0][::-1]))
- vert_y = np.concatenate((pui[pui>0], pli[pli>0][::-1]))
+ vert_x = np.concatenate((rui[pui > 0], rli[pli > 0][::-1]))
+ vert_y = np.concatenate((pui[pui > 0], pli[pli > 0][::-1]))
# hacky workaround to plot 2nd human
if np.isclose(np.mean(rui), rui[1], rtol=1e-05):
- print('found human')
- p = plt.Polygon(np.column_stack((vert_x, vert_y)), facecolor='none', alpha=.2, edgecolor=next(colorcycler),lw=2)
+ print("found human")
+ p = plt.Polygon(
+ np.column_stack((vert_x, vert_y)),
+ facecolor="none",
+ alpha=0.2,
+ edgecolor=next(colorcycler),
+ lw=2,
+ )
else:
- p = plt.Polygon(np.column_stack((vert_x, vert_y)), facecolor=next(colorcycler), alpha=.2, edgecolor='none',lw=.2)
+ p = plt.Polygon(
+ np.column_stack((vert_x, vert_y)),
+ facecolor=next(colorcycler),
+ alpha=0.2,
+ edgecolor="none",
+ lw=0.2,
+ )
ax1.add_artist(p)
- ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
+ ax1.grid(linestyle="--", linewidth=1, color="gray", alpha=0.2)
if len(names) > 1:
- plt.legend(loc='lower left', framealpha=0.5)
- ax1.set_xlabel('Recall')
- ax1.set_ylabel('Precision')
+ plt.legend(loc="lower left", framealpha=0.5)
+ ax1.set_xlabel("Recall")
+ ax1.set_ylabel("Precision")
ax1.set_xlim([0.0, 1.0])
ax1.set_ylim([0.0, 1.0])
- if title is not None: ax1.set_title(title)
+ if title is not None:
+ ax1.set_title(title)
# Annotates plot with F1-score iso-lines
ax2 = ax1.twinx()
f_scores = np.linspace(0.1, 0.9, num=9)
@@ -193,31 +236,32 @@ def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_
for f_score in f_scores:
x = np.linspace(0.01, 1)
y = f_score * x / (2 * x - f_score)
- l, = plt.plot(x[y >= 0], y[y >= 0], color='green', alpha=0.1)
+ (l,) = plt.plot(x[y >= 0], y[y >= 0], color="green", alpha=0.1)
tick_locs.append(y[-1])
- tick_labels.append('%.1f' % f_score)
- ax2.tick_params(axis='y', which='both', pad=0, right=False, left=False)
- ax2.set_ylabel('iso-F', color='green', alpha=0.3)
+ tick_labels.append("%.1f" % f_score)
+ ax2.tick_params(axis="y", which="both", pad=0, right=False, left=False)
+ ax2.set_ylabel("iso-F", color="green", alpha=0.3)
ax2.set_ylim([0.0, 1.0])
ax2.yaxis.set_label_coords(1.015, 0.97)
- ax2.set_yticks(tick_locs) #notice these are invisible
+ ax2.set_yticks(tick_locs) # notice these are invisible
for k in ax2.set_yticklabels(tick_labels):
- k.set_color('green')
+ k.set_color("green")
k.set_alpha(0.3)
k.set_size(8)
# we should see some of axes 1 axes
- ax1.spines['right'].set_visible(False)
- ax1.spines['top'].set_visible(False)
- ax1.spines['left'].set_position(('data', -0.015))
- ax1.spines['bottom'].set_position(('data', -0.015))
+ ax1.spines["right"].set_visible(False)
+ ax1.spines["top"].set_visible(False)
+ ax1.spines["left"].set_position(("data", -0.015))
+ ax1.spines["bottom"].set_position(("data", -0.015))
# we shouldn't see any of axes 2 axes
- ax2.spines['right'].set_visible(False)
- ax2.spines['top'].set_visible(False)
- ax2.spines['left'].set_visible(False)
- ax2.spines['bottom'].set_visible(False)
+ ax2.spines["right"].set_visible(False)
+ ax2.spines["top"].set_visible(False)
+ ax2.spines["left"].set_visible(False)
+ ax2.spines["bottom"].set_visible(False)
plt.tight_layout()
return fig
+
def loss_curve(df, title):
""" Creates a loss curve given a Dataframe with column names:
@@ -232,15 +276,17 @@ def loss_curve(df, title):
matplotlib.figure.Figure
"""
import matplotlib
- matplotlib.use('agg')
+
+ matplotlib.use("agg")
import matplotlib.pyplot as plt
+
ax1 = df.plot(y="median loss", grid=True)
ax1.set_title(title)
- ax1.set_ylabel('median loss')
- ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
- ax2 = df['lr'].plot(secondary_y=True,legend=True,grid=True,)
- ax2.set_ylabel('lr')
- ax1.set_xlabel('epoch')
+ ax1.set_ylabel("median loss")
+ ax1.grid(linestyle="--", linewidth=1, color="gray", alpha=0.2)
+ ax2 = df["lr"].plot(secondary_y=True, legend=True, grid=True,)
+ ax2.set_ylabel("lr")
+ ax1.set_xlabel("epoch")
plt.tight_layout()
fig = ax1.get_figure()
return fig
@@ -260,7 +306,7 @@ def read_metricscsv(file):
:py:class:`numpy.ndarray`
:py:class:`numpy.ndarray`
"""
- with open (file, "r") as infile:
+ with open(file, "r") as infile:
metricsreader = csv.reader(infile)
# skip header row
next(metricsreader)
@@ -277,10 +323,17 @@ def read_metricscsv(file):
pr_lower.append(float(row[9]))
re_upper.append(float(row[11]))
re_lower.append(float(row[12]))
- return np.array(precision), np.array(recall), np.array(pr_upper), np.array(pr_lower), np.array(re_upper), np.array(re_lower)
+ return (
+ np.array(precision),
+ np.array(recall),
+ np.array(pr_upper),
+ np.array(pr_lower),
+ np.array(re_upper),
+ np.array(re_lower),
+ )
-def plot_overview(outputfolders,title):
+def plot_overview(outputfolders, title):
"""
Plots comparison chart of all trained models
@@ -304,7 +357,7 @@ def plot_overview(outputfolders,title):
params = []
for folder in outputfolders:
# metrics
- metrics_path = os.path.join(folder,'results/Metrics.csv')
+ metrics_path = os.path.join(folder, "results/Metrics.csv")
pr, re, pr_upper, pr_lower, re_upper, re_lower = read_metricscsv(metrics_path)
precisions.append(pr)
recalls.append(re)
@@ -312,19 +365,24 @@ def plot_overview(outputfolders,title):
pr_lows.append(pr_lower)
re_ups.append(re_upper)
re_lows.append(re_lower)
- modelname = folder.split('/')[-1]
- name = '{} '.format(modelname)
+ modelname = folder.split("/")[-1]
+ name = "{} ".format(modelname)
names.append(name)
- #title = folder.split('/')[-4]
- fig = precision_recall_f1iso_confintval(precisions,recalls, pr_ups, pr_lows, re_ups, re_lows, names,title)
+ # title = folder.split('/')[-4]
+ fig = precision_recall_f1iso_confintval(
+ precisions, recalls, pr_ups, pr_lows, re_ups, re_lows, names, title
+ )
return fig
-def metricsviz(dataset
- ,output_path
- ,tp_color= (0,255,0) # (128,128,128) Gray
- ,fp_color = (0, 0, 255) # (70, 240, 240) Cyan
- ,fn_color = (255, 0, 0) # (245, 130, 48) Orange
- ,overlayed=True):
+
+def metricsviz(
+ dataset,
+ output_path,
+ tp_color=(0, 255, 0), # (128,128,128) Gray
+ fp_color=(0, 0, 255), # (70, 240, 240) Cyan
+ fn_color=(255, 0, 0), # (245, 130, 48) Orange
+ overlayed=True,
+):
""" Visualizes true positives, false positives and false negatives
Default colors TP: Gray, FP: Cyan, FN: Orange
@@ -343,56 +401,59 @@ def metricsviz(dataset
for sample in dataset:
# get sample
- name = sample[0]
- img = VF.to_pil_image(sample[1]) # PIL Image
- gt = sample[2].byte() # byte tensor
+ name = sample[0]
+ img = VF.to_pil_image(sample[1]) # PIL Image
+ gt = sample[2].byte() # byte tensor
# read metrics
- metrics = pd.read_csv(os.path.join(output_path,'results','Metrics.csv'))
- optimal_threshold = metrics['threshold'][metrics['f1_score'].idxmax()]
+ metrics = pd.read_csv(os.path.join(output_path, "results", "Metrics.csv"))
+ optimal_threshold = metrics["threshold"][metrics["f1_score"].idxmax()]
# read probability output
- pred = Image.open(os.path.join(output_path,'images',name))
- pred = pred.convert(mode='L')
+ pred = Image.open(os.path.join(output_path, "images", name))
+ pred = pred.convert(mode="L")
pred = VF.to_tensor(pred)
binary_pred = torch.gt(pred, optimal_threshold).byte()
# calc metrics
# equals and not-equals
- equals = torch.eq(binary_pred, gt) # tensor
- notequals = torch.ne(binary_pred, gt) # tensor
+ equals = torch.eq(binary_pred, gt) # tensor
+ notequals = torch.ne(binary_pred, gt) # tensor
# true positives
- tp_tensor = (gt * binary_pred ) # tensor
+ tp_tensor = gt * binary_pred # tensor
tp_pil = VF.to_pil_image(tp_tensor.float())
- tp_pil_colored = PIL.ImageOps.colorize(tp_pil, (0,0,0), tp_color)
+ tp_pil_colored = PIL.ImageOps.colorize(tp_pil, (0, 0, 0), tp_color)
# false positives
fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
fp_pil = VF.to_pil_image(fp_tensor.float())
- fp_pil_colored = PIL.ImageOps.colorize(fp_pil, (0,0,0), fp_color)
+ fp_pil_colored = PIL.ImageOps.colorize(fp_pil, (0, 0, 0), fp_color)
# false negatives
fn_tensor = notequals - fp_tensor
fn_pil = VF.to_pil_image(fn_tensor.float())
- fn_pil_colored = PIL.ImageOps.colorize(fn_pil, (0,0,0), fn_color)
+ fn_pil_colored = PIL.ImageOps.colorize(fn_pil, (0, 0, 0), fn_color)
# paste together
- tp_pil_colored.paste(fp_pil_colored,mask=fp_pil)
- tp_pil_colored.paste(fn_pil_colored,mask=fn_pil)
+ tp_pil_colored.paste(fp_pil_colored, mask=fp_pil)
+ tp_pil_colored.paste(fn_pil_colored, mask=fn_pil)
if overlayed:
tp_pil_colored = PIL.Image.blend(img, tp_pil_colored, 0.4)
- img_metrics = pd.read_csv(os.path.join(output_path,'results',name+'.csv'))
- f1 = img_metrics[' f1_score'].max()
+ img_metrics = pd.read_csv(
+ os.path.join(output_path, "results", name + ".csv")
+ )
+ f1 = img_metrics[" f1_score"].max()
# add f1-score
- fnt_size = tp_pil_colored.size[1]//25
+ fnt_size = tp_pil_colored.size[1] // 25
draw = ImageDraw.Draw(tp_pil_colored)
- fnt = ImageFont.truetype('FreeMono.ttf', fnt_size)
- draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
+ fnt = ImageFont.truetype("FreeMono.ttf", fnt_size)
+ draw.text((0, 0), "F1: {:.4f}".format(f1), (255, 255, 255), font=fnt)
# save to disk
- overlayed_path = os.path.join(output_path,'tpfnfpviz')
+ overlayed_path = os.path.join(output_path, "tpfnfpviz")
fullpath = os.path.join(overlayed_path, name)
fulldir = os.path.dirname(fullpath)
- if not os.path.exists(fulldir): os.makedirs(fulldir)
+ if not os.path.exists(fulldir):
+ os.makedirs(fulldir)
tp_pil_colored.save(fullpath)
@@ -408,25 +469,26 @@ def overlay(dataset, output_path):
for sample in dataset:
# get sample
- name = sample[0]
- img = VF.to_pil_image(sample[1]) # PIL Image
+ name = sample[0]
+ img = VF.to_pil_image(sample[1]) # PIL Image
# read probability output
- pred = Image.open(os.path.join(output_path,'images',name)).convert(mode='L')
+ pred = Image.open(os.path.join(output_path, "images", name)).convert(mode="L")
# color and overlay
- pred_green = PIL.ImageOps.colorize(pred, (0,0,0), (0,255,0))
+ pred_green = PIL.ImageOps.colorize(pred, (0, 0, 0), (0, 255, 0))
overlayed = PIL.Image.blend(img, pred_green, 0.4)
# add f1-score
- #fnt_size = overlayed.size[1]//25
- #draw = ImageDraw.Draw(overlayed)
- #fnt = ImageFont.truetype('FreeMono.ttf', fnt_size)
- #draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
+ # fnt_size = overlayed.size[1]//25
+ # draw = ImageDraw.Draw(overlayed)
+ # fnt = ImageFont.truetype('FreeMono.ttf', fnt_size)
+ # draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
# save to disk
- overlayed_path = os.path.join(output_path,'overlayed')
+ overlayed_path = os.path.join(output_path, "overlayed")
fullpath = os.path.join(overlayed_path, name)
fulldir = os.path.dirname(fullpath)
- if not os.path.exists(fulldir): os.makedirs(fulldir)
+ if not os.path.exists(fulldir):
+ os.makedirs(fulldir)
overlayed.save(fullpath)
@@ -443,12 +505,13 @@ def savetransformedtest(dataset, output_path):
for sample in dataset:
# get sample
- name = sample[0]
- img = VF.to_pil_image(sample[1]) # PIL Image
+ name = sample[0]
+ img = VF.to_pil_image(sample[1]) # PIL Image
# save to disk
- testimg_path = os.path.join(output_path,'transformedtestimages')
+ testimg_path = os.path.join(output_path, "transformedtestimages")
fullpath = os.path.join(testimg_path, name)
fulldir = os.path.dirname(fullpath)
- if not os.path.exists(fulldir): os.makedirs(fulldir)
+ if not os.path.exists(fulldir):
+ os.makedirs(fulldir)
img.save(fullpath)
diff --git a/bob/ip/binseg/utils/rsttable.py b/bob/ip/binseg/utils/rsttable.py
index fdc17982f8bfcdb1643baf3aff57a988352f3561..c5329d8aee9ea28fd202fb057076d0f6a11eca24 100644
--- a/bob/ip/binseg/utils/rsttable.py
+++ b/bob/ip/binseg/utils/rsttable.py
@@ -3,6 +3,7 @@ from tabulate import tabulate
import os
from pathlib import Path
+
def get_paths(output_path, filename):
"""
Parameters
@@ -17,39 +18,39 @@ def get_paths(output_path, filename):
list of file paths
"""
datadir = Path(output_path)
- file_paths = sorted(list(datadir.glob('**/{}'.format(filename))))
+ file_paths = sorted(list(datadir.glob("**/{}".format(filename))))
file_paths = [f.as_posix() for f in file_paths]
return file_paths
def create_overview_grid(output_path):
""" Reads all Metrics.csv in a certain output path and pivots them to a rst grid table"""
- filename = 'Metrics.csv'
- metrics = get_paths(output_path,filename)
+ filename = "Metrics.csv"
+ metrics = get_paths(output_path, filename)
f1s = []
stds = []
models = []
databases = []
for m in metrics:
metrics = pd.read_csv(m)
- maxf1 = metrics['f1_score'].max()
- idmaxf1 = metrics['f1_score'].idxmax()
- std = metrics['std_f1'][idmaxf1]
+ maxf1 = metrics["f1_score"].max()
+ idmaxf1 = metrics["f1_score"].idxmax()
+ std = metrics["std_f1"][idmaxf1]
stds.append(std)
f1s.append(maxf1)
- model = m.split('/')[-3]
+ model = m.split("/")[-3]
models.append(model)
- database = m.split('/')[-4]
+ database = m.split("/")[-4]
databases.append(database)
df = pd.DataFrame()
- df['database'] = databases
- df['model'] = models
- df['f1'] = f1s
- df['std'] = stds
- pivot = df.pivot(index='database',columns='model',values='f1')
- pivot2 = df.pivot(index='database',columns='model',values='std')
+ df["database"] = databases
+ df["model"] = models
+ df["f1"] = f1s
+ df["std"] = stds
+ pivot = df.pivot(index="database", columns="model", values="f1")
+ pivot2 = df.pivot(index="database", columns="model", values="std")
- with open (os.path.join(output_path,'Metrics_overview.rst'), "w+") as outfile:
- outfile.write(tabulate(pivot,headers=pivot.columns, tablefmt="grid"))
- with open (os.path.join(output_path,'Metrics_overview_std.rst'), "w+") as outfile:
- outfile.write(tabulate(pivot2,headers=pivot2.columns, tablefmt="grid"))
\ No newline at end of file
+ with open(os.path.join(output_path, "Metrics_overview.rst"), "w+") as outfile:
+ outfile.write(tabulate(pivot, headers=pivot.columns, tablefmt="grid"))
+ with open(os.path.join(output_path, "Metrics_overview_std.rst"), "w+") as outfile:
+ outfile.write(tabulate(pivot2, headers=pivot2.columns, tablefmt="grid"))
diff --git a/bob/ip/binseg/utils/summary.py b/bob/ip/binseg/utils/summary.py
index 127c5e66a3d7c97629ef95acebccb4f138ba43b6..17cf07c3cb6e70246b5b7fba523fcf75978611ae 100644
--- a/bob/ip/binseg/utils/summary.py
+++ b/bob/ip/binseg/utils/summary.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-# Adapted from https://github.com/pytorch/pytorch/issues/2001#issuecomment-405675488
+# Adapted from https://github.com/pytorch/pytorch/issues/2001#issuecomment-405675488
import sys
import logging
from functools import reduce
@@ -22,42 +22,43 @@ def summary(model, file=sys.stderr):
int
number of parameters
"""
+
def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty string will be split into list ['']
if extra_repr:
- extra_lines = extra_repr.split('\n')
+ extra_lines = extra_repr.split("\n")
child_lines = []
total_params = 0
for key, module in model._modules.items():
mod_str, num_params = repr(module)
mod_str = _addindent(mod_str, 2)
- child_lines.append('(' + key + '): ' + mod_str)
+ child_lines.append("(" + key + "): " + mod_str)
total_params += num_params
lines = extra_lines + child_lines
for name, p in model._parameters.items():
- if hasattr(p,'dtype'):
+ if hasattr(p, "dtype"):
total_params += reduce(lambda x, y: x * y, p.shape)
- main_str = model._get_name() + '('
+ main_str = model._get_name() + "("
if lines:
# simple one-liner info, which most builtin Modules will use
if len(extra_lines) == 1 and not child_lines:
main_str += extra_lines[0]
else:
- main_str += '\n ' + '\n '.join(lines) + '\n'
+ main_str += "\n " + "\n ".join(lines) + "\n"
- main_str += ')'
+ main_str += ")"
if file is sys.stderr:
- main_str += ', \033[92m{:,}\033[0m params'.format(total_params)
+ main_str += ", \033[92m{:,}\033[0m params".format(total_params)
else:
- main_str += ', {:,} params'.format(total_params)
+ main_str += ", {:,} params".format(total_params)
return main_str, total_params
string, count = repr(model)
if file is not None:
print(string, file=file)
- return count
\ No newline at end of file
+ return count
diff --git a/bob/ip/binseg/utils/transformfolder.py b/bob/ip/binseg/utils/transformfolder.py
index 9308d64705adeba36ae0edefc7f07b3f56b8069f..95c3353947530e6ff33423db6adf3fc222a481b8 100644
--- a/bob/ip/binseg/utils/transformfolder.py
+++ b/bob/ip/binseg/utils/transformfolder.py
@@ -1,9 +1,10 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-from pathlib import Path,PurePosixPath
+from pathlib import Path, PurePosixPath
from PIL import Image
from torchvision.transforms.functional import to_pil_image
+
def transformfolder(source_path, target_path, transforms):
"""Applies a set of transfroms on an image folder
@@ -18,10 +19,10 @@ def transformfolder(source_path, target_path, transforms):
"""
source_path = Path(source_path)
target_path = Path(target_path)
- file_paths = sorted(list(source_path.glob('*?.*')))
+ file_paths = sorted(list(source_path.glob("*?.*")))
for f in file_paths:
timg_path = PurePosixPath(target_path).joinpath(f.name)
- img = Image.open(f).convert(mode='1', dither=None)
- img, _ = transforms(img,img)
+ img = Image.open(f).convert(mode="1", dither=None)
+ img, _ = transforms(img, img)
img = to_pil_image(img)
- img.save(str(timg_path))
\ No newline at end of file
+ img.save(str(timg_path))