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En prévision de l'arrivée de la forge institutionnelle INRAE, nous vous invitons à créer vos nouveaux projets sur la forge MIA.

  • umr-tetisumr-tetis
  • rt_decloudrt_decloud
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  • !10
An error occurred while fetching the assigned milestone of the selected merge_request.

Update Dockerfile

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Merged Cresson Remi requested to merge update_dockerfile into master 1 year ago
  • Overview 0
  • Commits 9
  • Pipelines 5
  • Changes 21
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  • version 4
    fa5d0141
    1 year ago

  • version 3
    70a70fee
    1 year ago

  • version 2
    3c13b9d7
    1 year ago

  • version 1
    dae952dc
    1 year ago

  • master (base)

and
  • latest version
    671879f3
    9 commits, 1 year ago

  • version 4
    fa5d0141
    6 commits, 1 year ago

  • version 3
    70a70fee
    3 commits, 1 year ago

  • version 2
    3c13b9d7
    2 commits, 1 year ago

  • version 1
    dae952dc
    1 commit, 1 year ago

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decloud/models/create_tfrecords.py
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− 1
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@@ -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
decloud/models/crga_os1_unet.py
+ 4
− 3
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@@ -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
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@@ -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
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@@ -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
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@@ -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}
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Source branch: update_dockerfile

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