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Commit 70a70fee authored by Cresson Remi's avatar Cresson Remi
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FIX: replace keras.layers.Concatenate() with tf.concat

parent 3c13b9d7
master otbtf_4.3.1 1.4
1 merge request!10Update Dockerfile
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decloud/models/crga_os1_unet.py
+ 4
− 3
View file @ 70a70fee
......@@ -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
......
decloud/models/crga_os1_unet_all_bands.py
+ 5
− 4
View file @ 70a70fee
......@@ -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}
decloud/models/crga_os2_david.py
+ 4
− 3
View file @ 70a70fee
......@@ -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
......
decloud/models/crga_os2_david_all_bands.py
+ 5
− 4
View file @ 70a70fee
......@@ -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}
decloud/models/crga_os2_unet.py
+ 4
− 3
View file @ 70a70fee
......@@ -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
......
decloud/models/crga_os2_unet_all_bands.py
+ 5
− 4
View file @ 70a70fee
......@@ -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}
decloud/models/meraner_original.py
+ 2
− 1
View file @ 70a70fee
......@@ -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)
......
decloud/models/meraner_unet.py
+ 4
− 3
View file @ 70a70fee
......@@ -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)
......
decloud/models/meraner_unet_all_bands.py
+ 5
− 4
View file @ 70a70fee
......@@ -24,6 +24,7 @@ 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):
......@@ -58,7 +59,7 @@ 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 +68,7 @@ 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 +84,7 @@ 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 +103,6 @@ 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}
decloud/models/monthly_synthesis_6_s2_images.py
+ 3
− 2
View file @ 70a70fee
......@@ -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
......
decloud/models/monthly_synthesis_6_s2_images_david.py
+ 2
− 1
View file @ 70a70fee
......@@ -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
......
decloud/models/monthly_synthesis_6_s2s1_images.py
+ 3
− 2
View file @ 70a70fee
......@@ -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
......
decloud/models/monthly_synthesis_6_s2s1_images_david.py
+ 3
− 2
View file @ 70a70fee
......@@ -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
......
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