Hi,
Here are the error logs:
2024-01-12T20:29:49.652000+00:00 2024-01-12 20:29:49.646225: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0
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2024-01-12T20:29:49.652000+00:00 2024-01-12 20:29:49.650080: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2024-01-12T20:29:50.652000+00:00 2024-01-12 20:29:49.688535: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-12T20:29:50.653000+00:00 2024-01-12 20:29:49.688568: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-12T20:29:50.653000+00:00 2024-01-12 20:29:49.689644: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-12T20:29:50.653000+00:00 2024-01-12 20:29:49.695716: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2024-01-12T20:29:50.653000+00:00 2024-01-12 20:29:49.695935: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
2024-01-12T20:29:50.653000+00:00 To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-01-12T20:29:51.653000+00:00 2024-01-12 20:29:50.759736: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-12T20:29:57.656000+00:00 /opt/app/infer_universeg.py:111: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
2024-01-12T20:29:57.656000+00:00 tensor_batch_low_image = torch.Tensor(tensor_batch_low_image)
In case it helps, these are the execution logs:
2024-01-12T20:29:56.654000+00:00 Model running on cpu
2024-01-12T20:29:56.654000+00:00 Loaded 189 low images and 186 high images.
2024-01-12T20:29:56.654000+00:00 Loaded 189 low masks and 186 high masks.
2024-01-12T20:29:56.654000+00:00 Generating Image Tensors
2024-01-12T20:29:58.656000+00:00 Generated Tensors of shape torch.Size([189, 1, 128, 128]) for low and torch.Size([186, 1, 128, 128]) for high
2024-01-12T20:29:58.656000+00:00 Generating Mask Tensors
2024-01-12T20:29:58.656000+00:00 Generated Tensors of shape torch.Size([189, 1, 128, 128]) for low and torch.Size([186, 1, 128, 128]) for high
2024-01-12T20:29:58.656000+00:00 Combined support set image shape torch.Size([375, 1, 128, 128])
2024-01-12T20:29:58.656000+00:00 Combined support set mask shape torch.Size([375, 1, 128, 128])
2024-01-12T20:29:58.656000+00:00 Shape of case_mri_torch 24