diff --git a/README.md b/README.md
index a802bacecde3e1a540f7edded0de3ce691ea99d6..1368d8660f88981380db4f46b65e0ae92273bf52 100644
--- a/README.md
+++ b/README.md
@@ -12,15 +12,15 @@ as the [official repo](https://github.com/rcdaudt/fully_convolutional_change_det
```bash
# The network definition scripts are from the original repo
-git clone --recurse-submodules git@github.com:Bobholamovic/FCN-CD-PyTorch.git
+git clone --recurse-submodules git@github.com:Bobholamovic/FCN-CD-PyTorch.git
+cd FCN-CD-PyTorch
+mkdir exp
+cd src
```
For training, try
```bash
-# In the root directory of this repository
-mkdir exp
-cd src
python train.py train --exp-config ../config_base.yaml
```
diff --git a/config_base.yaml b/config_base.yaml
index f581c7126650b139df6c0f1e46ccbc823867f67a..8eee72e4f9a60e6ae029f8668c52dd3a7215b612 100644
--- a/config_base.yaml
+++ b/config_base.yaml
@@ -3,23 +3,23 @@
# Data
# Common
-dataset: OSCD
-crop_size: 256
+dataset: AC_Tiszadob
+crop_size: 112
num_workers: 1
-repeats: 1000
+repeats: 3200
# Optimizer
-optimizer: Adam
+optimizer: SGD
lr: 0.001
lr_mode: const
-weight_decay: 0.0001
+weight_decay: 0.0005
step: 2
# Training related
batch_size: 32
-num_epochs: 40
+num_epochs: 10
resume: ''
load_optim: True
anew: False
@@ -48,4 +48,4 @@ weights:
# Model
model: siamunet_conc
-num_feats_in: 13
\ No newline at end of file
+num_feats_in: 3
\ No newline at end of file
diff --git a/src/constants.py b/src/constants.py
index dfa17b4ced36f6618437d1dbdfd63bf65d329503..e41882fb592a9df376ee92e7f28b464d8944c551 100644
--- a/src/constants.py
+++ b/src/constants.py
@@ -3,7 +3,7 @@
# Dataset directories
IMDB_OSCD = '~/Datasets/OSCDDataset/'
-IMDB_AC = '~/Datasets/SZTAKI_AirChange_Benchmark/'
+IMDB_AirChange = '~/Datasets/SZTAKI_AirChange_Benchmark/'
# Checkpoint templates
CKP_LATEST = 'checkpoint_latest.pth'
diff --git a/src/core/factories.py b/src/core/factories.py
index 60e32db631bcd8b1aba7fc80618e4cf9b6a4999c..652b776bc041e413d3337da19b40ebf566cc51d4 100644
--- a/src/core/factories.py
+++ b/src/core/factories.py
@@ -109,7 +109,7 @@ def single_model_factory(model_name, C):
def single_optim_factory(optim_name, params, C):
- name = optim_name.upper()
+ name = optim_name.strip().upper()
if name == 'ADAM':
return torch.optim.Adam(
params,
@@ -117,6 +117,13 @@ def single_optim_factory(optim_name, params, C):
lr=C.lr,
weight_decay=C.weight_decay
)
+ elif name == 'SGD':
+ return torch.optim.SGD(
+ params,
+ lr=C.lr,
+ momentum=0.9,
+ weight_decay=C.weight_decay
+ )
else:
raise NotImplementedError("{} is not a supported optimizer type".format(optim_name))
@@ -137,7 +144,8 @@ def single_critn_factory(critn_name, C):
def single_train_ds_factory(ds_name, C):
from data.augmentation import Compose, Crop, Flip
- module = _import_module('data', ds_name.strip())
+ ds_name = ds_name.strip()
+ module = _import_module('data', ds_name)
dataset = getattr(module, ds_name+'Dataset')
configs = dict(
phase='train',
@@ -150,17 +158,17 @@ def single_train_ds_factory(ds_name, C):
root = constants.IMDB_OSCD
)
)
- elif ds_name == 'AC':
+ elif ds_name.startswith('AC'):
configs.update(
dict(
- root = constants.IMDB_AC
+ root = constants.IMDB_AirChange
)
)
else:
pass
dataset_obj = dataset(**configs)
-
+
return data.DataLoader(
dataset_obj,
batch_size=C.batch_size,
@@ -171,11 +179,13 @@ def single_train_ds_factory(ds_name, C):
def single_val_ds_factory(ds_name, C):
- module = _import_module('data', ds_name.strip())
+ ds_name = ds_name.strip()
+ module = _import_module('data', ds_name)
dataset = getattr(module, ds_name+'Dataset')
configs = dict(
phase='val',
- transforms=(None, None, None)
+ transforms=(None, None, None),
+ repeats=1
)
if ds_name == 'OSCD':
configs.update(
@@ -183,7 +193,7 @@ def single_val_ds_factory(ds_name, C):
root = constants.IMDB_OSCD
)
)
- elif ds_name == 'AC':
+ elif ds_name.startswith('AC'):
configs.update(
dict(
root = constants.IMDB_AirChange
@@ -246,4 +256,4 @@ def data_factory(dataset_names, phase, C):
def metric_factory(metric_names, C):
from utils import metrics
name_list = _parse_input_names(metric_names)
- return [getattr(metrics, name)() for name in name_list]
+ return [getattr(metrics, name.strip())() for name in name_list]
diff --git a/src/core/trainers.py b/src/core/trainers.py
index 2c5b139b894a9262a67495d47b88ac6578e0f056..3e1381ace84c06e55652ef89cc3b5e70d7e63673 100644
--- a/src/core/trainers.py
+++ b/src/core/trainers.py
@@ -212,9 +212,9 @@ class CDTrainer(Trainer):
for i, (t1, t2, label) in enumerate(pb):
t1, t2, label = t1.to(self.device), t2.to(self.device), label.to(self.device)
-
+
prob = self.model(t1, t2)
-
+
loss = self.criterion(prob, label)
losses.update(loss.item(), n=self.batch_size)
@@ -267,6 +267,6 @@ class CDTrainer(Trainer):
self.logger.dump(desc)
if store:
- self.save_image(name[0], CM.squeeze(-1), epoch)
+ self.save_image(name[0], (CM*255).squeeze(-1), epoch)
return self.metrics[0].avg if len(self.metrics) > 0 else max(1.0 - losses.avg, self._init_max_acc)
\ No newline at end of file
diff --git a/src/data/AC_Szada.py b/src/data/AC_Szada.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b9619157a2f2dc4d892747d636fa27945a967c
--- /dev/null
+++ b/src/data/AC_Szada.py
@@ -0,0 +1,23 @@
+from ._AirChange import _AirChangeDataset
+
+
+class AC_SzadaDataset(_AirChangeDataset):
+ def __init__(
+ self,
+ root, phase='train',
+ transforms=(None, None, None),
+ repeats=1
+ ):
+ super().__init__(root, phase, transforms, repeats)
+
+ @property
+ def LOCATION(self):
+ return 'Szada'
+
+ @property
+ def TEST_SAMPLE_IDS(self):
+ return (0,)
+
+ @property
+ def N_PAIRS(self):
+ return 7
\ No newline at end of file
diff --git a/src/data/AC_Tiszadob.py b/src/data/AC_Tiszadob.py
new file mode 100644
index 0000000000000000000000000000000000000000..830450d18d88b9953c7c6740c315b20b40efdbbe
--- /dev/null
+++ b/src/data/AC_Tiszadob.py
@@ -0,0 +1,23 @@
+from ._AirChange import _AirChangeDataset
+
+
+class AC_TiszadobDataset(_AirChangeDataset):
+ def __init__(
+ self,
+ root, phase='train',
+ transforms=(None, None, None),
+ repeats=1
+ ):
+ super().__init__(root, phase, transforms, repeats)
+
+ @property
+ def LOCATION(self):
+ return 'Tiszadob'
+
+ @property
+ def TEST_SAMPLE_IDS(self):
+ return (2,)
+
+ @property
+ def N_PAIRS(self):
+ return 5
\ No newline at end of file
diff --git a/src/data/OSCD.py b/src/data/OSCD.py
index 53c1225161d89fa7c55e1cf78474fcb0ff32cd09..e6ba2a77c7b99e375b4d394ed67b41f60008a0b1 100644
--- a/src/data/OSCD.py
+++ b/src/data/OSCD.py
@@ -33,7 +33,7 @@ class OSCDDataset(CDDataset):
with open(txt_file, 'r') as f:
cities = [city.strip() for city in f.read().strip().split(',')]
if self.phase == 'train':
- # For training, use the first 10 pairs
+ # For training, use the first 11 pairs
cities = cities[:-3]
else:
# For validation, use the remaining 3 pairs
diff --git a/src/data/_AirChange.py b/src/data/_AirChange.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d8555d843bc8b5933e166a4c9aa1612b252cf49
--- /dev/null
+++ b/src/data/_AirChange.py
@@ -0,0 +1,79 @@
+import abc
+from os.path import join, basename
+from multiprocessing import Manager
+
+import numpy as np
+
+from . import CDDataset
+from .common import default_loader
+from .augmentation import Crop
+
+
+class _AirChangeDataset(CDDataset):
+ def __init__(
+ self,
+ root, phase='train',
+ transforms=(None, None, None),
+ repeats=1
+ ):
+ super().__init__(root, phase, transforms, repeats)
+ self.cropper = Crop(bounds=(0, 0, 748, 448))
+
+ self._manager = Manager()
+ sync_list = self._manager.list
+ self.images = sync_list([sync_list([None]*self.N_PAIRS), sync_list([None]*self.N_PAIRS)])
+ self.labels = sync_list([None]*self.N_PAIRS)
+
+ @property
+ @abc.abstractmethod
+ def LOCATION(self):
+ return ''
+
+ @property
+ @abc.abstractmethod
+ def TEST_SAMPLE_IDS(self):
+ return ()
+
+ @property
+ @abc.abstractmethod
+ def N_PAIRS(self):
+ return 0
+
+ def _read_file_paths(self):
+ if self.phase == 'train':
+ sample_ids = range(self.N_PAIRS)
+ t1_list = ['-'.join([self.LOCATION,str(i),'0.bmp']) for i in sample_ids if i not in self.TEST_SAMPLE_IDS]
+ t2_list = ['-'.join([self.LOCATION,str(i),'1.bmp']) for i in sample_ids if i not in self.TEST_SAMPLE_IDS]
+ label_list = ['-'.join([self.LOCATION,str(i),'cm.bmp']) for i in sample_ids if i not in self.TEST_SAMPLE_IDS]
+ else:
+ t1_list = ['-'.join([self.LOCATION,str(i),'0.bmp']) for i in self.TEST_SAMPLE_IDS]
+ t2_list = ['-'.join([self.LOCATION,str(i),'1.bmp']) for i in self.TEST_SAMPLE_IDS]
+ label_list = ['-'.join([self.LOCATION,str(i),'cm.bmp']) for i in self.TEST_SAMPLE_IDS]
+
+ return t1_list, t2_list, label_list
+
+ def fetch_image(self, image_name):
+ _, i, t = image_name.split('-')
+ i, t = int(i), int(t[:-4])
+ if self.images[t][i] is None:
+ image = self._bmp_loader(join(self.root, self.LOCATION, str(i+1), 'im'+str(t+1)))
+ self.images[t][i] = image if self.phase == 'train' else self.cropper(image)
+ return self.images[t][i]
+
+ def fetch_label(self, label_name):
+ index = int(label_name.split('-')[1])
+ if self.labels[index] is None:
+ label = self._bmp_loader(join(self.root, self.LOCATION, str(index+1), 'gt'))
+ label = (label / 255.0).astype(np.uint8) # To 0,1
+ self.labels[index] = label if self.phase == 'train' else self.cropper(label)
+ return self.labels[index]
+
+ @staticmethod
+ def _bmp_loader(bmp_path_wo_ext):
+ # Case insensitive .bmp loader
+ try:
+ return default_loader(bmp_path_wo_ext+'.bmp')
+ except FileNotFoundError:
+ return default_loader(bmp_path_wo_ext+'.BMP')
+
+
\ No newline at end of file