pretrained weights error sr.py #21

raialvaro · 2021-04-05T07:21:31Z

I am testing my sentinel image (the same one with which I previously trained with your code) pre-trained weights that you have recently uploaded and I get the following error:

**python sr.py --input matched_SGtoPNOA_8bits.tif --savedmodel /SR/sr4rs_mini-mtp-2.5_savedmodel --output prueba.tif --pad 64
2021-04-05 07:10:58 (INFO) ReadImageInfo:
Image general information:
Number of bands : 3
Data type : unsigned_char
No data flags :
Band 1: 0
Band 2: 0
Band 3: 0
Start index : [0,0]
Size : [1284,1223]
Origin : [38417.6,4.70833e+06]
Spacing : [10,-10]
Estimated ground spacing (in meters): [9.95252,9.98852]

Image acquisition information:
Sensor :
Image identification number:
Image projection : PROJCS["ETRS89 / UTM zone 30N",
GEOGCS["ETRS89",
DATUM["European_Terrestrial_Reference_System_1989",
SPHEROID["GRS 1980",6378137,298.257222101,
AUTHORITY["EPSG","7019"]],
TOWGS84[0,0,0,0,0,0,0],
AUTHORITY["EPSG","6258"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4258"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",-3],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
AXIS["Easting",EAST],
AXIS["Northing",NORTH],
AUTHORITY["EPSG","25830"]]

Image default RGB composition:
[R, G, B] = [0,1,2]

Ground control points information:
Number of GCPs = 0
GCPs projection =

2021-04-05 07:10:58.706125: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-05 07:10:58 (INFO) TensorflowModelServe: Default RAM limit for OTB is 256 MB
2021-04-05 07:10:58 (INFO) TensorflowModelServe: GDAL maximum cache size is 15115 MB
2021-04-05 07:10:58 (INFO) TensorflowModelServe: OTB will use at most 32 threads
2021-04-05 07:10:58.748436: I tensorflow/cc/saved_model/reader.cc:31] Reading SavedModel from: /SR/sr4rs_mini-mtp-2.5_savedmodel
2021-04-05 07:10:59.268936: I tensorflow/cc/saved_model/reader.cc:54] Reading meta graph with tags { serve }
2021-04-05 07:10:59.269015: I tensorflow/cc/saved_model/loader.cc:264] Reading SavedModel debug info (if present) from: /SR/sr4rs_mini-mtp-2.5_savedmodel
2021-04-05 07:10:59.269182: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2021-04-05 07:10:59.288526: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz
2021-04-05 07:10:59.290216: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x204edc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-04-05 07:10:59.290255: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-04-05 07:10:59.292973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2021-04-05 07:10:59.482133: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:10:59.485225: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1fb37a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-04-05 07:10:59.485300: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Quadro RTX 8000, Compute Capability 7.5
2021-04-05 07:10:59.485993: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:10:59.492424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:14:00.0 name: Quadro RTX 8000 computeCapability: 7.5
coreClock: 1.62GHz coreCount: 72 deviceMemorySize: 44.49GiB deviceMemoryBandwidth: 581.23GiB/s
2021-04-05 07:10:59.492838: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-05 07:10:59.497695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-04-05 07:10:59.500430: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-04-05 07:10:59.500982: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-04-05 07:10:59.503614: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-04-05 07:10:59.505054: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-04-05 07:10:59.510140: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-04-05 07:10:59.510354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:10:59.513380: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:10:59.516173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-04-05 07:10:59.516328: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-05 07:10:59.998655: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-05 07:10:59.998732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2021-04-05 07:10:59.998741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2021-04-05 07:10:59.999937: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:00.002218: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:00.004378: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 42866 MB memory) -> physical GPU (device: 0, name: Quadro RTX 8000, pci bus id: 0000:14:00.0, compute capability: 7.5)
2021-04-05 07:11:01.324388: I tensorflow/cc/saved_model/loader.cc:203] Restoring SavedModel bundle.
2021-04-05 07:11:03.661090: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.
2021-04-05 07:11:03.661183: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1259] Profiler found 1 GPUs
2021-04-05 07:11:03.661775: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcupti.so.10.1'; dlerror: libcupti.so.10.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/work/otb/superbuild_install/lib/:/work/tf/installdir/lib/
2021-04-05 07:11:03.661803: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or symbol could not be found.
2021-04-05 07:11:03.661841: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI could not be loaded or symbol could not be found.
2021-04-05 07:11:04.507595: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI could not be loaded or symbol could not be found.
2021-04-05 07:11:04.507696: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88] GpuTracer has collected 0 callback api events and 0 activity events.
2021-04-05 07:11:04.527949: I tensorflow/cc/saved_model/loader.cc:333] SavedModel load for tags { serve }; Status: success: OK. Took 5779516 microseconds.
2021-04-05 07:11:04 (INFO) TensorflowModelServe: Source info :
2021-04-05 07:11:04 (INFO) TensorflowModelServe: Receptive field : [160, 160]
2021-04-05 07:11:04 (INFO) TensorflowModelServe: Placeholder name : lr_input
2021-04-05 07:11:05 (INFO) TensorflowModelServe: Output spacing ratio: 0.25
2021-04-05 07:11:05 (INFO) TensorflowModelServe: The TensorFlow model is used in fully convolutional mode
2021-04-05 07:11:05 (INFO) TensorflowModelServe: Output field of expression: [512, 512]
2021-04-05 07:11:05 (INFO) TensorflowModelServe: Tiling disabled
2021-04-05 07:11:05 (WARNING): Streaming configuration through extended filename is used. Any previous streaming configuration (ram value, streaming mode ...) will be ignored.
2021-04-05 07:11:05 (WARNING): Streaming configuration through extended filename is used. Any previous streaming configuration (ram value, streaming mode ...) will be ignored.
2021-04-05 07:11:05 (INFO): File prueba.tif will be written in 64 blocks of 512x512 pixels
Writing 1 output images ...: 0% [ ]2021-04-05 07:11:06.730618: W tensorflow/core/framework/op_kernel.cc:1655] OP_REQUIRES failed at conv_ops.cc:530 : Invalid argument: input depth must be evenly divisible by filter depth: 3 vs 4
Traceback (most recent call last):
File "sr.py", line 78, in
infer.ExecuteAndWriteOutput()
File "/work/otb/superbuild_install/lib/otb/python/otbApplication.py", line 2801, in ExecuteAndWriteOutput
return _otbApplication.Application_ExecuteAndWriteOutput(self)
RuntimeError: Exception thrown in otbApplication Application_ExecuteAndWriteOutput: /work/otb/otb/Modules/Remote/otbtf/include/otbTensorflowMultisourceModelBase.hxx:97:
itk::ERROR: TensorflowMultisourceModelFilter(0xb7cf620): Can't run the tensorflow session !
Tensorflow error message:
Invalid argument: 2 root error(s) found.
(0) Invalid argument: input depth must be evenly divisible by filter depth: 3 vs 4
[[{{node gen/encoder/conv1_9x9/Conv2D}}]]
[[output_64/_1075]]
(1) Invalid argument: input depth must be evenly divisible by filter depth: 3 vs 4
[[{{node gen/encoder/conv1_9x9/Conv2D}}]]
0 successful operations.
0 derived errors ignored.
OTB Filter debug message:
Output image buffered region: ImageRegion (0x7fff4f96e690)
Dimension: 2
Index: [0, 0]
Size: [512, 512]

Input #0:
Requested region: ImageRegion (0x7fff4f96e6c0)
Dimension: 2
Index: [64, 64]
Size: [160, 160]

Tensor shape ("lr_input": {1, 160, 160, 3}
User placeholders:

root@2f1d9d7e2789:/SR# python sr.py --input matched_SGtoPNOA_8bits.tif --savedmodel /SR/sr4rs_mini-mtp-2.5_savedmodel --output SGx4.tif --pad 64
2021-04-05 07:11:27 (INFO) ReadImageInfo:
Image general information:
Number of bands : 3
Data type : unsigned_char
No data flags :
Band 1: 0
Band 2: 0
Band 3: 0
Start index : [0,0]
Size : [1284,1223]
Origin : [38417.6,4.70833e+06]
Spacing : [10,-10]
Estimated ground spacing (in meters): [9.95252,9.98852]

Image acquisition information:
Sensor :
Image identification number:
Image projection : PROJCS["ETRS89 / UTM zone 30N",
GEOGCS["ETRS89",
DATUM["European_Terrestrial_Reference_System_1989",
SPHEROID["GRS 1980",6378137,298.257222101,
AUTHORITY["EPSG","7019"]],
TOWGS84[0,0,0,0,0,0,0],
AUTHORITY["EPSG","6258"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4258"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",-3],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
AXIS["Easting",EAST],
AXIS["Northing",NORTH],
AUTHORITY["EPSG","25830"]]

Image default RGB composition:
[R, G, B] = [0,1,2]

Ground control points information:
Number of GCPs = 0
GCPs projection =

2021-04-05 07:11:27.754635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-05 07:11:27 (INFO) TensorflowModelServe: Default RAM limit for OTB is 256 MB
2021-04-05 07:11:27 (INFO) TensorflowModelServe: GDAL maximum cache size is 15115 MB
2021-04-05 07:11:27 (INFO) TensorflowModelServe: OTB will use at most 32 threads
2021-04-05 07:11:27.796086: I tensorflow/cc/saved_model/reader.cc:31] Reading SavedModel from: /SR/sr4rs_mini-mtp-2.5_savedmodel
2021-04-05 07:11:28.319189: I tensorflow/cc/saved_model/reader.cc:54] Reading meta graph with tags { serve }
2021-04-05 07:11:28.319256: I tensorflow/cc/saved_model/loader.cc:264] Reading SavedModel debug info (if present) from: /SR/sr4rs_mini-mtp-2.5_savedmodel
2021-04-05 07:11:28.319368: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2021-04-05 07:11:28.338710: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz
2021-04-05 07:11:28.340415: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x277bce0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-04-05 07:11:28.340449: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-04-05 07:11:28.342910: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2021-04-05 07:11:28.541756: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:28.580486: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x26e08b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-04-05 07:11:28.580578: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Quadro RTX 8000, Compute Capability 7.5
2021-04-05 07:11:28.580943: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:28.584156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:14:00.0 name: Quadro RTX 8000 computeCapability: 7.5
coreClock: 1.62GHz coreCount: 72 deviceMemorySize: 44.49GiB deviceMemoryBandwidth: 581.23GiB/s
2021-04-05 07:11:28.584355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-05 07:11:28.587459: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-04-05 07:11:28.589788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-04-05 07:11:28.590268: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-04-05 07:11:28.592864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-04-05 07:11:28.594259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-04-05 07:11:28.599444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-04-05 07:11:28.599624: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:28.602641: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:28.605493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-04-05 07:11:28.605622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-05 07:11:29.078497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-05 07:11:29.078561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2021-04-05 07:11:29.078581: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2021-04-05 07:11:29.078960: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:29.081111: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 07:11:29.083328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 42866 MB memory) -> physical GPU (device: 0, name: Quadro RTX 8000, pci bus id: 0000:14:00.0, compute capability: 7.5)
2021-04-05 07:11:30.376168: I tensorflow/cc/saved_model/loader.cc:203] Restoring SavedModel bundle.
2021-04-05 07:11:32.615952: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.
2021-04-05 07:11:32.616028: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1259] Profiler found 1 GPUs
2021-04-05 07:11:32.616584: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcupti.so.10.1'; dlerror: libcupti.so.10.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/work/otb/superbuild_install/lib/:/work/tf/installdir/lib/
2021-04-05 07:11:32.616612: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or symbol could not be found.
2021-04-05 07:11:32.616626: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI could not be loaded or symbol could not be found.
2021-04-05 07:11:33.403085: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI could not be loaded or symbol could not be found.
2021-04-05 07:11:33.403168: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88] GpuTracer has collected 0 callback api events and 0 activity events.
2021-04-05 07:11:33.420199: I tensorflow/cc/saved_model/loader.cc:333] SavedModel load for tags { serve }; Status: success: OK. Took 5624117 microseconds.
2021-04-05 07:11:33 (INFO) TensorflowModelServe: Source info :
2021-04-05 07:11:33 (INFO) TensorflowModelServe: Receptive field : [160, 160]
2021-04-05 07:11:33 (INFO) TensorflowModelServe: Placeholder name : lr_input
2021-04-05 07:11:33 (INFO) TensorflowModelServe: Output spacing ratio: 0.25
2021-04-05 07:11:33 (INFO) TensorflowModelServe: The TensorFlow model is used in fully convolutional mode
2021-04-05 07:11:33 (INFO) TensorflowModelServe: Output field of expression: [512, 512]
2021-04-05 07:11:33 (INFO) TensorflowModelServe: Tiling disabled
2021-04-05 07:11:34 (WARNING): Streaming configuration through extended filename is used. Any previous streaming configuration (ram value, streaming mode ...) will be ignored.
2021-04-05 07:11:34 (WARNING): Streaming configuration through extended filename is used. Any previous streaming configuration (ram value, streaming mode ...) will be ignored.
2021-04-05 07:11:34 (INFO): File SGx4.tif will be written in 64 blocks of 512x512 pixels
Writing 1 output images ...: 0% [ ]2021-04-05 07:11:35.582924: W tensorflow/core/framework/op_kernel.cc:1655] OP_REQUIRES failed at conv_ops.cc:530 : Invalid argument: input depth must be evenly divisible by filter depth: 3 vs 4
Traceback (most recent call last):
File "sr.py", line 78, in
infer.ExecuteAndWriteOutput()
File "/work/otb/superbuild_install/lib/otb/python/otbApplication.py", line 2801, in ExecuteAndWriteOutput
return _otbApplication.Application_ExecuteAndWriteOutput(self)
RuntimeError: Exception thrown in otbApplication Application_ExecuteAndWriteOutput: /work/otb/otb/Modules/Remote/otbtf/include/otbTensorflowMultisourceModelBase.hxx:97:
itk::ERROR: TensorflowMultisourceModelFilter(0x135a98e0): Can't run the tensorflow session !
Tensorflow error message:
Invalid argument: 2 root error(s) found.
(0) Invalid argument: input depth must be evenly divisible by filter depth: 3 vs 4
[[{{node gen/encoder/conv1_9x9/Conv2D}}]]
(1) Invalid argument: input depth must be evenly divisible by filter depth: 3 vs 4
[[{{node gen/encoder/conv1_9x9/Conv2D}}]]
[[output_64/_1075]]
0 successful operations.
0 derived errors ignored.
OTB Filter debug message:
Output image buffered region: ImageRegion (0x7ffc4d6f8a40)
Dimension: 2
Index: [0, 0]
Size: [512, 512]

Input #0:
Requested region: ImageRegion (0x7ffc4d6f8a70)
Dimension: 2
Index: [64, 64]
Size: [160, 160]

Tensor shape ("lr_input": {1, 160, 160, 3}
User placeholders:**

I have tried changing some parameter like the pad and it has not worked.

Any idea what is happening?

Thank so much!

remicres · 2021-04-05T08:59:57Z

Hi @raialvaro ,

The input depth must be evenly divisible by filter depth: 3 vs 4 thing looks like you have trained a net on 3 bands, and you input some 4 channels image (or the other way).
Else, without your code I can't tell 😉 Could you paste some diff here?

raialvaro · 2021-04-05T11:42:25Z

Here I attach your original sr.py code. I have only added the choice of the GPU device 1, Quadro RTX 8000, 48 GB

import argparse
import otbApplication
import constants
import logging

import os

os.environ['CUDA_VISIBLE_DEVICES'] = '1'

logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.WARNING,
datefmt='%Y-%m-%d %H:%M:%S')

parser = argparse.ArgumentParser()

def get_encoding():
"""
Get the encoding of input image pixels
"""
infos = otbApplication.Registry.CreateApplication('ReadImageInfo')
infos.SetParameterString("in", params.input)
infos.Execute()
return infos.GetImageBasePixelType("in")

encodings = {"auto": get_encoding,
"uint8": lambda x: otbApplication.ImagePixelType_uint8,
"uint16": lambda x: otbApplication.ImagePixelType_uint16,
"int16": lambda x: otbApplication.ImagePixelType_int16,
"float": lambda x: otbApplication.ImagePixelType_float}

parser.add_argument("--input", help="Input LR image ", required=True)
parser.add_argument("--savedmodel", help="Input SavedModel ", required=True)
parser.add_argument("--output", help="Output HR image", required=True)
parser.add_argument('--encoding', type=str, default="auto", const="auto", nargs="?", choices=encodings.keys(),
help="Output HR image encoding")
parser.add_argument('--pad', type=int, default=64, const=64, nargs="?", choices=constants.pads,
help="Margin size for blocking artefacts removal")
parser.add_argument('--ts', default=512, type=int, help="Tile size")
params = parser.parse_args()

if name == "main":

gen_fcn = params.pad
efield = params.ts  # OTBTF expression field
if efield % min(constants.factors) != 0:
    logging.fatal("Please chose a tile size that is consistent with the network.")
    quit()
ratio = 1.0 / float(max(constants.factors))  # OTBTF Spacing ratio
rfield = int((efield + 2 * gen_fcn) * ratio)  # OTBTF receptive field

# pixel encoding
encoding_fn = encodings[params.encoding]
encoding = encoding_fn()

# call otbtf
logging.info("Receptive field: {}, Expression field: {}".format(rfield, efield))
ph = "{}{}".format(constants.outputs_prefix, params.pad)
infer = otbApplication.Registry.CreateApplication("TensorflowModelServe")
infer.SetParameterStringList("source1.il", [params.input])
infer.SetParameterInt("source1.rfieldx", rfield)
infer.SetParameterInt("source1.rfieldy", rfield)
infer.SetParameterString("source1.placeholder", constants.lr_input_name)
infer.SetParameterString("model.dir", params.savedmodel)
infer.SetParameterString("model.fullyconv", "on")
infer.SetParameterStringList("output.names", [ph])
infer.SetParameterInt("output.efieldx", efield)
infer.SetParameterInt("output.efieldy", efield)
infer.SetParameterFloat("output.spcscale", ratio)
infer.SetParameterInt("optim.tilesizex", efield)
infer.SetParameterInt("optim.tilesizey", efield)
infer.SetParameterInt("optim.disabletiling", 1)
out_fn = "{}{}&gdal:co:COMPRESS=DEFLATE".format(params.output, "?" if "?" not in params.output else "")
out_fn += "&streaming:type=tiled&streaming:sizemode=height&streaming:sizevalue={}".format(efield)
infer.SetParameterString("out", out_fn)
infer.SetParameterOutputImagePixelType("out", encoding)
infer.ExecuteAndWriteOutput()*

Below, it shows the image and its characteristics

any other information you need, I will be happy to help you.

Thanks so much!

remicres · 2021-04-05T12:06:26Z

Hi @raialvaro ,
I need the diff with the original code to see the changes you made.

cd /path/to/sr4rs
git diff

raialvaro · 2021-04-05T22:06:23Z

that's correct?

I'm sorry for my low experience 😞

remicres · 2021-04-06T02:30:10Z

Thank you. I don't see anything in the code (by the way, you are right to set the CUDA_VISIBLE_DEVICES!).

Can you check that you input image has the same properties as your lr_patches? (i.e. same dynamic, number of channels, physical spacing, etc.)

raialvaro · 2021-04-06T13:00:46Z

Maybe this interests @remicres .

I think your code is written in a version of Tensorflow below 2.X while your docker OTBTF version has version 2.1.1.

I adapted the code to this version and got it not to give sintasix error.

Can you check that you input image has the same properties as your lr_patches? (i.e. same dynamic, number of channels, physical spacing, etc.)

At the moment I am testing the image that I have indicated above for the weights of yours that you have recently shared sr4rs_mini-mtp-2.5_savedmodel and it is the error that has arisen in the first section #21 (comment)

That is why I ask how the input image should be so that I can obtain a super resolution image with the sr.py script

Thanks for your help and your time!

remicres · 2021-04-06T13:49:31Z

Channels

The pre-trained model applies to a 4-channels image.
The input image must be Sentinel-2 (or Landsat) 10m spacing channels stacked in the following order:

Red (Sentinel-2 channel 4)
Green (Sentinel-2 channel 3)
Blue (Sentinel-2 channel 2)
Near infra-red (Sentinel-2 channel 8)

To perform such layer stack you can use the OTB application named ConcatenateImages.

Dynamic

Keep the original Sentinel-2 images dynamic, no transformation needed!
It should be something like [0, 10000] approximately (I believe images are encoded using int16)

Example

Here is some script you should be able to follow, from the docker image pull, to the HR image generation.

# 1. Get latest OTBTF docker image and enter the docker image.
# Use the nice and lightweight builds done by Vincent@LaTelescop.
# Here is an example with the otbtf gpu:2.3-dev image, using NVIDIA runtime 
docker run -ti --runtime=nvidia registry.gitlab.com/latelescop/docker/otbtf/gpu:2.3-dev bash

# Download and unzip the pre-trained SavedModel1
wget https://nextcloud.inrae.fr/s/JLsak68H2KYzPyG/download
unzip download

# Clone SR4RS
git clone https://github.com/remicres/sr4rs.git

# Use SR4RS to create an HR image
cd sr4rs/code
python sr4rs/code/sr.py \
--savedmodel sr4rs_mini-mtp-2.5_savedmodel \
--input /path/to/some/S2_image/stacked_channels_4328_10m.tif \
--output test.tif

raialvaro · 2021-04-07T16:27:58Z

thank you very much, @remicres . It has been very helpful!!!

I had not realized that my sentinel image had only the RGB bands.

remicres · 2021-04-07T16:29:51Z

Thanks. I've added the example in the README !

remicres added the help wanted Extra attention is needed label Apr 6, 2021

remicres pinned this issue Apr 6, 2021

raialvaro closed this as completed Apr 7, 2021

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pretrained weights error sr.py #21

pretrained weights error sr.py #21

raialvaro commented Apr 5, 2021

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raialvaro commented Apr 7, 2021

remicres commented Apr 7, 2021

pretrained weights error sr.py #21

pretrained weights error sr.py #21

Comments

raialvaro commented Apr 5, 2021

remicres commented Apr 5, 2021

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remicres commented Apr 6, 2021

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Channels

Dynamic

Example

raialvaro commented Apr 7, 2021

remicres commented Apr 7, 2021

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