diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 86e699f17d3e6258df2119fffdae63d49faacb10..958c269fd8b39babadb477e1348dd68f0c5965b3 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -4,8 +4,8 @@ variables:
GPU_IMAGE_NAME: $CI_REGISTRY_IMAGE:gpu
DOCKER_BUILDKIT: 1
DOCKER_DRIVER: overlay2
- CPU_BASE_IMAGE: gitlab-registry.irstea.fr/remi.cresson/otbtf:3.3.2-cpu-dev
- GPU_BASE_IMAGE: gitlab-registry.irstea.fr/remi.cresson/otbtf:3.3.2-gpu-dev
+ CPU_BASE_IMAGE: gitlab-registry.irstea.fr/remi.cresson/otbtf:4.1.0-cpu-dev
+ GPU_BASE_IMAGE: gitlab-registry.irstea.fr/remi.cresson/otbtf:4.1.0-gpu-dev
workflow:
rules:
@@ -57,17 +57,17 @@ Build the docker image:
flake8:
extends: .static_analysis_base
script:
- - sudo apt update && sudo apt install -y flake8 && python -m flake8 --ignore=E402 --max-line-length=120 $PWD/decloud
+ - pip install flake8 && flake8 --ignore=E402 --max-line-length=120 $PWD/decloud
pylint:
extends: .static_analysis_base
script:
- - sudo apt update && sudo apt install -y pylint && pylint --disable=too-many-nested-blocks,too-many-locals,too-many-statements,too-few-public-methods,too-many-instance-attributes,too-many-arguments,invalid-name,cell-var-from-loop,too-many-branches,too-many-ancestors --ignored-modules=tensorflow,git,rtree,scipy,tensorboard,libamalthee,pandas --max-line-length=120 $PWD/decloud
+ - pip install pylint && pylint --disable=too-many-nested-blocks,too-many-locals,too-many-statements,too-few-public-methods,too-many-instance-attributes,too-many-arguments,invalid-name,cell-var-from-loop,too-many-branches,too-many-ancestors --ignored-modules=tensorflow,git,rtree,scipy,tensorboard,libamalthee,pandas --max-line-length=120 $PWD/decloud
codespell:
extends: .static_analysis_base
script:
- - sudo pip install codespell && codespell --skip="*.png,*.template,*.pbs,*.jpg,*git/lfs*"
+ - pip install codespell && codespell --skip="*.png,*.template,*.pbs,*.jpg,*git/lfs*"
.applications_test_base:
image: $TEST_IMAGE_NAME
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 2af0b57f382da9ece34be4f433414a341e7f334b..685c42c13c1bef5d1ee42f2fe440508ad100c5da 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,5 +1,5 @@
Contributors:
-- Rémi Cresson (remi.cresson@inrae.fr)
-- Nicolas Narçon (nicolas.narcon@inrae.fr)
-- Benjamin Commandre (benjamin.commandre@inrae.fr)
-- Raffaele Gaetano (raffaele.gaetano@cirad.fr)
+- Rémi Cresson (remi.cresson at inrae.fr)
+- Nicolas Narçon
+- Benjamin Commandre
+- Raffaele Gaetano
diff --git a/Dockerfile b/Dockerfile
index 43814a853dac96e115b205194b04b404e355a64c..31f4979df227f27eafad185c6527a646c5fdc2be 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,9 +1,9 @@
# Decloud dockerfile
# To build the docker image for cpu, do the following:
#
-# docker build --build-arg "BASE_IMAGE=mdl4eo/otbtf:3.3.2-cpu-dev" .
+# docker build --build-arg "BASE_IMAGE=mdl4eo/otbtf:4.1.0-cpu-dev" .
#
-ARG BASE_IMAGE=mdl4eo/otbtf:3.3.2-gpu-dev
+ARG BASE_IMAGE=mdl4eo/otbtf:4.1.0-gpu-dev
FROM $BASE_IMAGE
LABEL description="Decloud docker image"
LABEL maintainer="Remi Cresson [at] inrae [dot] fr"
diff --git a/LICENSE b/LICENSE
index 0e166bdc96d6f375116f2e686bf42f38b8b94da0..37862d75bd5513d69ac53834ce7b8df39b23de6f 100644
--- a/LICENSE
+++ b/LICENSE
@@ -186,7 +186,7 @@
same "printed page" as the copyright notice for easier
identification within third-party archives.
- Copyright 2020 INRAE
+ Copyright 2020-2023 INRAE
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
diff --git a/README.md b/README.md
index f249fa83114b0c0e4104db8b5294e176b90d2464..f2be459f2fcb408dafe9539f9d8881fc8d5ebfb5 100644
--- a/README.md
+++ b/README.md
@@ -12,10 +12,12 @@ Representative illustrations:
## Cite
```
-@article{cresson2022clouds,
+@inproceedings{cresson2022comparison,
title={Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images},
- author={Cresson, R., Narcon, N., Gaetano, R., Dupuis A., Tanguy, Y., May, S., Commandre, B.},
- journal={arXiv preprint arXiv:2204.00424},
+ author={Cresson, R{\'e}mi and Nar{\c{c}}on, N and Gaetano, Raffaele and Dupuis, Aurore and Tanguy, Yannick and May, St{\'e}phane and Commandr{\'e}, Benjamin},
+ booktitle={XXIV ISPRS Congress (2022 edition)},
+ volume={43},
+ pages={1317--1326},
year={2022}
}
```
diff --git a/decloud/models/create_tfrecords.py b/decloud/models/create_tfrecords.py
index d9993007c7366aceff51d0a7c937cfcc7ba801f2..21b8ad547a8779f88580e891b3cd027fa7e8d706 100644
--- a/decloud/models/create_tfrecords.py
+++ b/decloud/models/create_tfrecords.py
@@ -22,7 +22,6 @@ FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
"""
"""Create some TFRecords from a decloud.dataset"""
-import os
import argparse
import sys
import logging
diff --git a/decloud/models/crga_os1_unet.py b/decloud/models/crga_os1_unet.py
index 4302cb263c022a085e594e7fad87ddb8fb505d2b..62e1ac182b295c0e7f7bd1ed1bec149aed8cda54 100644
--- a/decloud/models/crga_os1_unet.py
+++ b/decloud/models/crga_os1_unet.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
import decloud.preprocessing.constants as constants
from decloud.models.crga_os1_base import crga_os1_base
+from tensorflow import concat
class crga_os1_unet(crga_os1_base):
@@ -57,14 +58,14 @@ class crga_os1_unet(crga_os1_base):
if input_image == "current":
net = conv1_s1(input_dict[input_image]) # 256
else:
- net = layers.concatenate(input_dict[input_image], axis=-1)
+ net = concat(input_dict[input_image], axis=-1)
net = conv1_s1s2(net) # 256
features[1].append(net)
net = conv2(net) # 128
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
- net = layers.concatenate([net, net_dem], axis=-1)
+ net = concat([net, net_dem], axis=-1)
features[2].append(net)
net = conv3(net) # 64
features[4].append(net)
@@ -79,7 +80,7 @@ class crga_os1_unet(crga_os1_base):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
diff --git a/decloud/models/crga_os1_unet_all_bands.py b/decloud/models/crga_os1_unet_all_bands.py
index 252dd1a50834a569c8161a76999984ec718417c1..ac044383e016e9f94dd5ee973801d8069c74e367 100644
--- a/decloud/models/crga_os1_unet_all_bands.py
+++ b/decloud/models/crga_os1_unet_all_bands.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
import decloud.preprocessing.constants as constants
from decloud.models.crga_os1_base_all_bands import crga_os1_base_all_bands
+from tensorflow import concat
class crga_os1_unet_all_bands(crga_os1_base_all_bands):
@@ -69,7 +70,7 @@ class crga_os1_unet_all_bands(crga_os1_base_all_bands):
features[1].append(net_10m)
net = conv2(net_10m) # 128
else: # for post & ante, the is s1, s2 and s2_20m
- net_10m = layers.concatenate(input_dict[input_image][:2], axis=-1)
+ net_10m = concat(input_dict[input_image][:2], axis=-1)
net_10m = conv1_s1s2(net_10m) # 256
features[1].append(net_10m)
net_10m = conv2(net_10m) # 128
@@ -77,7 +78,7 @@ class crga_os1_unet_all_bands(crga_os1_base_all_bands):
features_20m = [net_10m, net_20m]
if self.has_dem():
features_20m.append(conv1_dem(normalized_inputs[constants.DEM_KEY]))
- net = layers.concatenate(features_20m, axis=-1)
+ net = concat(features_20m, axis=-1)
net = conv2_20m(net) # 128
features[2].append(net)
@@ -94,7 +95,7 @@ class crga_os1_unet_all_bands(crga_os1_base_all_bands):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
@@ -114,6 +115,6 @@ class crga_os1_unet_all_bands(crga_os1_base_all_bands):
# 10m-resampled stack that will be the output for inference (not used for training)
s2_20m_resampled = layers.UpSampling2D(size=(2, 2))(s2_20m_out)
- s2_all_bands = layers.concatenate([s2_out, s2_20m_resampled], axis=-1)
+ s2_all_bands = concat([s2_out, s2_20m_resampled], axis=-1)
return {"s2_t": s2_out, "s2_20m_t": s2_20m_out, 's2_all_bands_estim': s2_all_bands}
diff --git a/decloud/models/crga_os2_david.py b/decloud/models/crga_os2_david.py
index 62efac978d57b77cdbc81763023318602a48399d..837d6121e23a7dd88d27a8aa40573f068efc22e1 100644
--- a/decloud/models/crga_os2_david.py
+++ b/decloud/models/crga_os2_david.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.crga_os2_base import crga_os2_base
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class crga_os2_david(crga_os2_base):
@@ -50,16 +51,16 @@ class crga_os2_david(crga_os2_base):
deconv2 = layers.Conv2DTranspose(64, 3, 2, activation='relu', name="deconv2_bn_relu", padding="same")
conv4 = layers.Conv2D(4, 5, 1, activation='relu', name="s2_estim", padding="same")
for input_image in input_dict:
- net = layers.concatenate(input_dict[input_image], axis=-1)
+ net = concat(input_dict[input_image], axis=-1)
net = conv1(net) # 256
net = conv2(net) # 128
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
- net = layers.concatenate([net, net_dem], axis=-1)
+ net = concat([net, net_dem], axis=-1)
net = conv3(net) # 64
features.append(net)
- net = layers.concatenate(features, axis=-1)
+ net = concat(features, axis=-1)
net = deconv1(net) # 128
net = deconv2(net) # 256
s2_out = conv4(net) # 256
diff --git a/decloud/models/crga_os2_david_all_bands.py b/decloud/models/crga_os2_david_all_bands.py
index 0b1f34b13ff4f0b597b133acf37267fe68e8bbe1..247fa1a5daa41f926fb338128d15eaac535f68fb 100644
--- a/decloud/models/crga_os2_david_all_bands.py
+++ b/decloud/models/crga_os2_david_all_bands.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.crga_os2_base_all_bands import crga_os2_base_all_bands
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class crga_os2_david_all_bands(crga_os2_base_all_bands):
@@ -56,7 +57,7 @@ class crga_os2_david_all_bands(crga_os2_base_all_bands):
conv4 = layers.Conv2D(4, 5, 1, activation='relu', name="s2_estim", padding="same")
conv4_20m = layers.Conv2D(6, 3, 1, activation='relu', name="s2_20m_estim", padding="same")
for input_image in input_dict:
- net_10m = layers.concatenate(input_dict[input_image][:2], axis=-1)
+ net_10m = concat(input_dict[input_image][:2], axis=-1)
net_10m = conv1(net_10m) # 256
net_10m = conv2(net_10m) # 128
net_20m = conv1_20m(input_dict[input_image][2]) # 128
@@ -64,11 +65,11 @@ class crga_os2_david_all_bands(crga_os2_base_all_bands):
features_20m = [net_10m, net_20m]
if self.has_dem():
features_20m.append(conv1_dem(normalized_inputs[constants.DEM_KEY]))
- net = layers.concatenate(features_20m, axis=-1) # 128
+ net = concat(features_20m, axis=-1) # 128
net = conv3(net) # 64
features.append(net)
- net = layers.concatenate(features, axis=-1)
+ net = concat(features, axis=-1)
net = deconv1(net) # 128
net_10m = deconv2(net) # 256
net_20m = deconv2_20m(net) # 128
@@ -78,6 +79,6 @@ class crga_os2_david_all_bands(crga_os2_base_all_bands):
# 10m-resampled stack that will be the output for inference (not used for training)
s2_20m_resampled = layers.UpSampling2D(size=(2, 2))(s2_20m_out)
- s2_all_bands = layers.concatenate([s2_out, s2_20m_resampled], axis=-1)
+ s2_all_bands = concat([s2_out, s2_20m_resampled], axis=-1)
return {"s2_target": s2_out, "s2_20m_target": s2_20m_out, 's2_all_bands_estim': s2_all_bands}
diff --git a/decloud/models/crga_os2_unet.py b/decloud/models/crga_os2_unet.py
index cefab53620f79149e0e06b2e9eff7cc887e9f94e..95c537b57d61e6140ec63fa7175961d6d183938e 100644
--- a/decloud/models/crga_os2_unet.py
+++ b/decloud/models/crga_os2_unet.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers, initializers
from decloud.models.crga_os2_base import crga_os2_base
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class crga_os2_unet(crga_os2_base):
@@ -67,13 +68,13 @@ class crga_os2_unet(crga_os2_base):
kernel_initializer=initializers.VarianceScaling())
for input_image in input_dict:
- net = layers.concatenate(input_dict[input_image], axis=-1)
+ net = concat(input_dict[input_image], axis=-1)
net = conv1(net) # 256
features[1].append(net)
net = conv2(net) # 128
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
- net = layers.concatenate([net, net_dem], axis=-1)
+ net = concat([net, net_dem], axis=-1)
features[2].append(net)
net = conv3(net) # 64
features[4].append(net)
@@ -88,7 +89,7 @@ class crga_os2_unet(crga_os2_base):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
diff --git a/decloud/models/crga_os2_unet_all_bands.py b/decloud/models/crga_os2_unet_all_bands.py
index fad9e77c6d73d9f983aad40c032dc756a2302124..7e3c0543473928a6a596f35b42d88133af2babe2 100644
--- a/decloud/models/crga_os2_unet_all_bands.py
+++ b/decloud/models/crga_os2_unet_all_bands.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.crga_os2_base_all_bands import crga_os2_base_all_bands
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class crga_os2_unet_all_bands(crga_os2_base_all_bands):
@@ -65,7 +66,7 @@ class crga_os2_unet_all_bands(crga_os2_base_all_bands):
# The network
features = {factor: [] for factor in [1, 2, 4, 8, 16, 32]}
for input_image in input_dict:
- net_10m = layers.concatenate(input_dict[input_image][:2], axis=-1)
+ net_10m = concat(input_dict[input_image][:2], axis=-1)
net_10m = conv1(net_10m) # 256
features[1].append(net_10m)
net_10m = conv2(net_10m) # 128
@@ -74,7 +75,7 @@ class crga_os2_unet_all_bands(crga_os2_base_all_bands):
features_20m = [net_10m, net_20m]
if self.has_dem():
features_20m.append(conv1_dem(normalized_inputs[constants.DEM_KEY]))
- net = layers.concatenate(features_20m, axis=-1) # 128
+ net = concat(features_20m, axis=-1) # 128
features[2].append(net)
net = conv3(net) # 64
features[4].append(net)
@@ -89,7 +90,7 @@ class crga_os2_unet_all_bands(crga_os2_base_all_bands):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
@@ -109,6 +110,6 @@ class crga_os2_unet_all_bands(crga_os2_base_all_bands):
# 10m-resampled stack that will be the output for inference (not used for training)
s2_20m_resampled = layers.UpSampling2D(size=(2, 2))(s2_20m_out)
- s2_all_bands = layers.concatenate([s2_out, s2_20m_resampled], axis=-1)
+ s2_all_bands = concat([s2_out, s2_20m_resampled], axis=-1)
return {"s2_target": s2_out, "s2_20m_target": s2_20m_out, 's2_all_bands_estim': s2_all_bands}
diff --git a/decloud/models/meraner_original.py b/decloud/models/meraner_original.py
index 3aee97a342274d857e7b5dcfaad3821f5f431d97..a4302195f6a30fb510724e51a5c543397f80a236 100644
--- a/decloud/models/meraner_original.py
+++ b/decloud/models/meraner_original.py
@@ -23,6 +23,7 @@ DEALINGS IN THE SOFTWARE.
"""Implementation of the Meraner et al. original network"""
from tensorflow.keras import layers
from decloud.models.model import Model
+from tensorflow import concat
class meraner_original(Model):
@@ -50,7 +51,7 @@ class meraner_original(Model):
# The network
conv1 = layers.Conv2D(resblocks_dim, 3, 1, activation='relu', name="conv1_relu", padding="same")
- net = layers.concatenate([normalized_inputs["s1_t"], normalized_inputs["s2_t"]], axis=-1)
+ net = concat([normalized_inputs["s1_t"], normalized_inputs["s2_t"]], axis=-1)
net = conv1(net)
for i in range(n_resblocks):
net = _resblock(net, i)
diff --git a/decloud/models/meraner_unet.py b/decloud/models/meraner_unet.py
index c2b6141f5cb5995e9386a08c56f34faf9c966395..1696869e21cf08ec93d039f91617cf7c295f1fd9 100644
--- a/decloud/models/meraner_unet.py
+++ b/decloud/models/meraner_unet.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.model import Model
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class meraner_unet(Model):
@@ -56,13 +57,13 @@ class meraner_unet(Model):
deconv5 = layers.Conv2DTranspose(64, 3, 2, activation='relu', name="deconv5_bn_relu", padding="same")
conv_final = layers.Conv2D(4, 5, 1, name="s2_estim", padding="same")
- net = layers.concatenate([normalized_inputs["s1_t"], normalized_inputs["s2_t"]], axis=-1)
+ net = concat([normalized_inputs["s1_t"], normalized_inputs["s2_t"]], axis=-1)
net = conv1(net) # 256
features[1].append(net)
net = conv2(net) # 128
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
- net = layers.concatenate([net, net_dem], axis=-1)
+ net = concat([net, net_dem], axis=-1)
features[2].append(net)
net = conv3(net) # 64
features[4].append(net)
@@ -76,7 +77,7 @@ class meraner_unet(Model):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = deconv1(net) # 16
net = _combine(factor=16, x=net)
diff --git a/decloud/models/meraner_unet_all_bands.py b/decloud/models/meraner_unet_all_bands.py
index cbee50a9b46fa3e5854e6b19e75ab87d48a79adf..76e2389ea0033188bf7d7733af0b80a29b79b515 100644
--- a/decloud/models/meraner_unet_all_bands.py
+++ b/decloud/models/meraner_unet_all_bands.py
@@ -24,18 +24,32 @@ DEALINGS IN THE SOFTWARE.
import decloud.preprocessing.constants as constants
from decloud.models.model import Model
from tensorflow.keras import layers
+from tensorflow import concat
class meraner_unet_all_bands(Model):
"""
Implementation of a variant of the Meraner et al. network (all bands)
"""
- def __init__(self, dataset_shapes,
- dataset_input_keys=["s1_t", "s2_t", "s2_20m_t",
- constants.DEM_KEY],
- model_output_keys=["s2_target"]):
- super().__init__(dataset_input_keys=dataset_input_keys, model_output_keys=model_output_keys,
- dataset_shapes=dataset_shapes)
+ def __init__(
+ self,
+ dataset_shapes,
+ dataset_input_keys=[
+ "s1_t",
+ "s2_t",
+ "s2_20m_t",
+ constants.DEM_KEY
+ ],
+ model_output_keys=[
+ "s2_target",
+ "s2_20m_target"
+ ]
+ ):
+ super().__init__(
+ dataset_input_keys=dataset_input_keys,
+ model_output_keys=model_output_keys,
+ dataset_shapes=dataset_shapes
+ )
def get_outputs(self, normalized_inputs):
# The network
@@ -58,7 +72,7 @@ class meraner_unet_all_bands(Model):
conv_final = layers.Conv2D(4, 5, 1, name="s2_estim", padding="same")
conv_20m_final = layers.Conv2D(6, 3, 1, name="s2_20m_estim", padding="same")
- net_10m = layers.concatenate([normalized_inputs["s1_t"], normalized_inputs["s2_t"]], axis=-1)
+ net_10m = concat([normalized_inputs["s1_t"], normalized_inputs["s2_t"]], axis=-1)
net_10m = conv1(net_10m) # 256
features[1].append(net_10m)
net_10m = conv2(net_10m) # 128
@@ -67,7 +81,7 @@ class meraner_unet_all_bands(Model):
features_20m = [net_10m, net_20m]
if self.has_dem():
features_20m.append(conv1_dem(normalized_inputs[constants.DEM_KEY]))
- net = layers.concatenate(features_20m, axis=-1) # 128
+ net = concat(features_20m, axis=-1) # 128
features[2].append(net)
net = conv3(net) # 64
@@ -83,7 +97,7 @@ class meraner_unet_all_bands(Model):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
@@ -102,6 +116,10 @@ class meraner_unet_all_bands(Model):
# 10m-resampled stack that will be the output for inference (not used for training)
s2_20m_resampled = layers.UpSampling2D(size=(2, 2))(s2_20m_out)
- s2_all_bands = layers.concatenate([s2_out, s2_20m_resampled], axis=-1)
+ s2_all_bands = concat([s2_out, s2_20m_resampled], axis=-1)
- return {"s2_target": s2_out, "s2_20m_target": s2_20m_out, 's2_all_bands_estim': s2_all_bands}
+ return {
+ "s2_target": s2_out,
+ "s2_20m_target": s2_20m_out,
+ "s2_all_bands_estim": s2_all_bands
+ }
diff --git a/decloud/models/monthly_synthesis_6_s2_images.py b/decloud/models/monthly_synthesis_6_s2_images.py
index 5ed1a8ad65b616f628f184e1dc576433a9d49697..fc04ed33d1807c0ccd19d1b7c9fa9bde1e1467af 100644
--- a/decloud/models/monthly_synthesis_6_s2_images.py
+++ b/decloud/models/monthly_synthesis_6_s2_images.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.model import Model
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class monthly_synthesis_6_s2_images(Model):
@@ -55,7 +56,7 @@ class monthly_synthesis_6_s2_images(Model):
features[1].append(net)
net = conv2(net) # 128
if self.has_dem():
- net = layers.concatenate([net, normalized_inputs[constants.DEM_KEY]], axis=-1)
+ net = concat([net, normalized_inputs[constants.DEM_KEY]], axis=-1)
features[2].append(net)
net = conv3(net) # 64
features[4].append(net)
@@ -70,7 +71,7 @@ class monthly_synthesis_6_s2_images(Model):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
diff --git a/decloud/models/monthly_synthesis_6_s2_images_david.py b/decloud/models/monthly_synthesis_6_s2_images_david.py
index 3f10ca65b00b91b7c47626653920f06316ca283b..5d1e744186f4c8a843e3ef6da1539feaa3dce8db 100644
--- a/decloud/models/monthly_synthesis_6_s2_images_david.py
+++ b/decloud/models/monthly_synthesis_6_s2_images_david.py
@@ -23,6 +23,7 @@ DEALINGS IN THE SOFTWARE.
"""David model implementation (monthly synthesis of 6 optical images)"""
from tensorflow.keras import layers
from decloud.models.model import Model
+from tensorflow import concat
class monthly_synthesis_6_s2_images_david(Model):
@@ -48,7 +49,7 @@ class monthly_synthesis_6_s2_images_david(Model):
net = conv3(net) # 64
features.append(net)
- net = layers.concatenate(features, axis=-1)
+ net = concat(features, axis=-1)
net = deconv1(net) # 128
net = deconv2(net) # 256
s2_out = conv4(net) # 256
diff --git a/decloud/models/monthly_synthesis_6_s2s1_images.py b/decloud/models/monthly_synthesis_6_s2s1_images.py
index 8b1f8fb292fa9c92d7a3c8d2a85072160fe0fe72..838a9fe338b1f3e2351f655e722c290d72191b57 100644
--- a/decloud/models/monthly_synthesis_6_s2s1_images.py
+++ b/decloud/models/monthly_synthesis_6_s2s1_images.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.model import Model
import decloud.preprocessing.constants as constants
+from tensorflow import concat
class monthly_synthesis_6_s2s1_images(Model):
@@ -64,7 +65,7 @@ class monthly_synthesis_6_s2s1_images(Model):
features[1].append(net)
net = conv2_s2(net)
if self.has_dem():
- net = layers.concatenate([net, normalized_inputs[constants.DEM_KEY]], axis=-1)
+ net = concat([net, normalized_inputs[constants.DEM_KEY]], axis=-1)
features[2].append(net)
net = conv3(net) # 64
features[4].append(net)
@@ -79,7 +80,7 @@ class monthly_synthesis_6_s2s1_images(Model):
def _combine(factor, x=None):
if x is not None:
features[factor].append(x)
- return layers.concatenate(features[factor], axis=-1)
+ return concat(features[factor], axis=-1)
net = _combine(factor=32)
net = deconv1(net) # 16
diff --git a/decloud/models/monthly_synthesis_6_s2s1_images_david.py b/decloud/models/monthly_synthesis_6_s2s1_images_david.py
index 1c3ec0239a3b51d83462da9640c1889bef35013b..123053352edf00ca3b432ed56eb66c2ce8386e4c 100644
--- a/decloud/models/monthly_synthesis_6_s2s1_images_david.py
+++ b/decloud/models/monthly_synthesis_6_s2s1_images_david.py
@@ -24,6 +24,7 @@ DEALINGS IN THE SOFTWARE.
from tensorflow.keras import layers
from decloud.models.model import Model
from decloud.preprocessing import constants
+from tensorflow import concat
class monthly_synthesis_6_s2s1_images_david(Model):
@@ -56,11 +57,11 @@ class monthly_synthesis_6_s2s1_images_david(Model):
net = conv2(net) # 128
if self.has_dem():
net_dem = conv1_dem(normalized_inputs[constants.DEM_KEY])
- net = layers.concatenate([net, net_dem], axis=-1)
+ net = concat([net, net_dem], axis=-1)
net = conv3(net) # 64
features.append(net)
- net = layers.concatenate(features, axis=-1)
+ net = concat(features, axis=-1)
net = deconv1(net) # 128
net = deconv2(net) # 256
s2_out = conv4(net) # 256
diff --git a/decloud/models/train_from_tfrecords.py b/decloud/models/train_from_tfrecords.py
index eb58d4e449e841342867a547296a92d4cd4e11dc..75ef766905e5bb695107aab6a2b263d55bc1e662 100644
--- a/decloud/models/train_from_tfrecords.py
+++ b/decloud/models/train_from_tfrecords.py
@@ -146,117 +146,122 @@ def main(args):
expe_name += "_e{}".format(params.epochs)
expe_name += suffix
- if True: # TODO: detete, just used for review
- # Date tag
- date_tag = time.strftime("%d-%m-%y-%H%M%S")
-
- # adding the info to the SavedModel path
- out_savedmodel = None if params.out_savedmodel is None else \
- os.path.join(params.out_savedmodel, expe_name + date_tag)
-
- # Scaling batch size and learning rate accordingly to number of workers
- batch_size_train = params.batch_size_train * n_workers
- batch_size_valid = params.batch_size_valid * n_workers
- learning_rate = params.learning_rate * n_workers
-
- logging.info("Learning rate was scaled to %s, effective batch size is %s (%s workers)",
- learning_rate, batch_size_train, n_workers)
-
- # Datasets
- tfrecord_train = TFRecords(params.training_record) if params.training_record else None
- tfrecord_valid_array = [TFRecords(rep) for rep in params.valid_records]
-
- # Model instantiation
- model = ModelFactory.get_model(params.model, dataset_shapes=tfrecord_train.output_shape)
-
- # TF.dataset-s instantiation
- tf_ds_train = tfrecord_train.read(batch_size=batch_size_train,
- target_keys=model.model_output_keys,
- n_workers=n_workers,
- shuffle_buffer_size=params.shuffle_buffer_size) if tfrecord_train else None
- tf_ds_valid = [tfrecord.read(batch_size=batch_size_valid,
- target_keys=model.model_output_keys,
- n_workers=n_workers) for tfrecord in tfrecord_valid_array]
-
- with strategy.scope():
- # Creating the Keras network corresponding to the model
- model.create_network()
-
- # Metrics
- metrics_list = [metrics.MeanSquaredError(), metrics.PSNR()]
- if params.all_metrics:
- metrics_list += [metrics.StructuralSimilarity(), metrics.SpectralAngle()] # A bit slow to compute
-
- # Creating the model or loading it from checkpoints
- logging.info("Loading model \"%s\"", params.model)
- model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
- loss=model.get_loss(),
- metrics={out_key: metrics_list for out_key in model.model_output_keys})
- model.summary(strategy)
-
- if params.plot_model:
- model.plot('/tmp/model_architecture_{}.png'.format(model.__class__.__name__), strategy)
-
- callbacks = []
- # Define the checkpoint callback
- if params.ckpt_dir:
- if params.strategy == 'singlecpu':
- logging.warning('Checkpoints can not be saved while using singlecpu option. Discarding checkpoints')
- else:
- backup_dir = os.path.join(params.ckpt_dir, params.model)
- # Backup (deleted once the model is trained the specified number of epochs)
- callbacks.append(keras.callbacks.BackupAndRestore(backup_dir=backup_dir))
- # Persistent save (still here after the model is trained)
- callbacks.append(ArchiveCheckpoint(backup_dir, strategy))
-
- # Define the Keras TensorBoard callback.
- logdir = None
- if params.logdir:
- logdir = os.path.join(params.logdir, f"{date_tag}_{expe_name}")
- tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir,
- profile_batch=params.profiling)
- callbacks.append(tensorboard_callback)
-
- # Define the previews callback
- if params.previews:
- # We run the preview on an arbitrary sample of the validation dataset
- sample = tfrecord_valid_array[0].read_one_sample(target_keys=model.model_output_keys)
- previews_callback = PreviewsCallback(sample, logdir, input_keys=model.dataset_input_keys,
- target_keys=model.model_output_keys)
- callbacks.append(previews_callback)
-
- # Validation on multiple datasets
- if tf_ds_valid:
- additional_validation_callback = AdditionalValidationSets(tf_ds_valid[1:], logdir)
- callbacks.append(additional_validation_callback)
-
- # Save best checkpoint only
- if params.save_best:
- callbacks.append(keras.callbacks.ModelCheckpoint(params.out_savedmodel, save_best_only=True,
- monitor=params.save_best_ref, mode='min'))
-
- # Early stopping if the training stops improving
- if params.early_stopping:
- callbacks.append(keras.callbacks.EarlyStopping(monitor=params.save_best_ref, min_delta=0.0001,
- patience=10, mode='min'))
-
- # Training
- model.fit(tf_ds_train,
- epochs=params.epochs,
- validation_data=tf_ds_valid[0] if tf_ds_valid else None,
- callbacks=callbacks,
- verbose=1 if params.verbose else 2)
-
- # Multiworker training tries to save the model multiple times and this can create corrupted models
- # Thus we save the model at the final path only for the 'chief' worker
- if params.strategy != 'singlecpu':
- if not _is_chief(strategy):
- out_savedmodel = None
-
- # Export SavedModel
- if out_savedmodel and not params.save_best:
- logging.info("Saving SavedModel in %s", out_savedmodel)
- model.save(out_savedmodel)
+ # Date tag
+ date_tag = time.strftime("%d-%m-%y-%H%M%S")
+
+ # adding the info to the SavedModel path
+ out_savedmodel = None if params.out_savedmodel is None else \
+ os.path.join(params.out_savedmodel, expe_name + date_tag)
+
+ # Scaling batch size and learning rate accordingly to number of workers
+ batch_size_train = params.batch_size_train * n_workers
+ batch_size_valid = params.batch_size_valid * n_workers
+ learning_rate = params.learning_rate * n_workers
+
+ logging.info("Learning rate was scaled to %s, effective batch size is %s (%s workers)",
+ learning_rate, batch_size_train, n_workers)
+
+ # Datasets
+ tfrecord_train = TFRecords(params.training_record) if params.training_record else None
+ tfrecord_valid_array = [TFRecords(rep) for rep in params.valid_records]
+
+ # Model instantiation
+ model = ModelFactory.get_model(params.model, dataset_shapes=tfrecord_train.output_shape)
+
+ # TF.dataset-s instantiation
+ tf_ds_train = tfrecord_train.read(batch_size=batch_size_train,
+ target_keys=model.model_output_keys,
+ n_workers=n_workers,
+ shuffle_buffer_size=params.shuffle_buffer_size) if tfrecord_train else None
+ tf_ds_valid = [tfrecord.read(batch_size=batch_size_valid,
+ target_keys=model.model_output_keys,
+ n_workers=n_workers) for tfrecord in tfrecord_valid_array]
+
+ with strategy.scope():
+ # Creating the Keras network corresponding to the model
+ model.create_network()
+
+ # Metrics
+ metrics_list = [metrics.MeanSquaredError, metrics.PSNR]
+ if params.all_metrics:
+ metrics_list += [metrics.StructuralSimilarity, metrics.SpectralAngle] # A bit slow to compute
+
+ # Creating the model or loading it from checkpoints
+ logging.info("Loading model \"%s\"", params.model)
+ model.compile(
+ optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
+ loss=model.get_loss(),
+ metrics={
+ out_key: metric()
+ for out_key in model.model_output_keys
+ for metric in metrics_list
+ }
+ )
+ model.summary(strategy)
+
+ if params.plot_model:
+ model.plot('/tmp/model_architecture_{}.png'.format(model.__class__.__name__), strategy)
+
+ callbacks = []
+ # Define the checkpoint callback
+ if params.ckpt_dir:
+ if params.strategy == 'singlecpu':
+ logging.warning('Checkpoints can not be saved while using singlecpu option. Discarding checkpoints')
+ else:
+ backup_dir = os.path.join(params.ckpt_dir, params.model)
+ # Backup (deleted once the model is trained the specified number of epochs)
+ callbacks.append(keras.callbacks.BackupAndRestore(backup_dir=backup_dir))
+ # Persistent save (still here after the model is trained)
+ callbacks.append(ArchiveCheckpoint(backup_dir, strategy))
+
+ # Define the Keras TensorBoard callback.
+ logdir = None
+ if params.logdir:
+ logdir = os.path.join(params.logdir, f"{date_tag}_{expe_name}")
+ tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir,
+ profile_batch=params.profiling)
+ callbacks.append(tensorboard_callback)
+
+ # Define the previews callback
+ if params.previews:
+ # We run the preview on an arbitrary sample of the validation dataset
+ sample = tfrecord_valid_array[0].read_one_sample(target_keys=model.model_output_keys)
+ previews_callback = PreviewsCallback(sample, logdir, input_keys=model.dataset_input_keys,
+ target_keys=model.model_output_keys)
+ callbacks.append(previews_callback)
+
+ # Validation on multiple datasets
+ if tf_ds_valid:
+ additional_validation_callback = AdditionalValidationSets(tf_ds_valid[1:], logdir)
+ callbacks.append(additional_validation_callback)
+
+ # Save best checkpoint only
+ if params.save_best:
+ callbacks.append(keras.callbacks.ModelCheckpoint(params.out_savedmodel, save_best_only=True,
+ monitor=params.save_best_ref, mode='min'))
+
+ # Early stopping if the training stops improving
+ if params.early_stopping:
+ callbacks.append(keras.callbacks.EarlyStopping(monitor=params.save_best_ref, min_delta=0.0001,
+ patience=10, mode='min'))
+
+ # Training
+ model.fit(tf_ds_train,
+ epochs=params.epochs,
+ validation_data=tf_ds_valid[0] if tf_ds_valid else None,
+ callbacks=callbacks,
+ verbose=1 if params.verbose else 2)
+
+ # Multiworker training tries to save the model multiple times and this can create corrupted models
+ # Thus we save the model at the final path only for the 'chief' worker
+ if params.strategy != 'singlecpu':
+ if not _is_chief(strategy):
+ out_savedmodel = None
+
+ # Export SavedModel
+ if out_savedmodel and not params.save_best:
+ logging.info("Saving SavedModel in %s", out_savedmodel)
+ model.save(out_savedmodel)
if __name__ == "__main__":
diff --git a/setup.py b/setup.py
index ec9843b927c4ad69034de0b3a72905f199fd3038..83446ec85865532a4da1a06579378ed5c2b4fad1 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
-Copyright (c) 2020-2022 INRAE
+Copyright (c) 2020-2023 INRAE
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
@@ -28,7 +28,7 @@ with open("README.md", "r", encoding="utf-8") as fh:
setuptools.setup(
name="decloud",
- version="1.3",
+ version="1.4",
author="Remi Cresson, Nicolas Narçon, Benjamin Commandre",
author_email="remi.cresson@inrae.fr",
description="Deep learning based reconstruction of optical time series using SAR imagery",