I think this is more likely a TensorFlow question so I would ask there.
On Saturday, January 22, 2022 at 4:30:39 AM UTC-8 [email protected] wrote:
> Hello, everyone, I'm currently trying to retrain the panoptic_deeplab
> using custom dataset. However, I got stuck due to the error shown in the
> following, could you please help me to figure out what the problem could
> be? It seems that something goes wrong in the config file.
>
> python trainer/train.py \
> --config_file=/home/caixiaoni/Desktop/project/deeplab2/configs/metal_part/panoptic_deeplab/resnet50_os32_semseg.textproto
>
> \ --mode=eval \
> --model_dir=/home/caixiaoni/Desktop/project/metal_part_retrain_1 \
> --num_gpus=0 2022-01-18 23:36:30.240123: I
> tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully
> opened dynamic library libcudart.so.11.0 I0118 23:36:31.325175
> 139693994784576 train.py:65] Reading the config file. Traceback (most
> recent call last): File "trainer/train.py", line 76, in <module>
> app.run(main) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/absl/app.py", line
> 312, in run _run_main(main, args) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/absl/app.py", line
> 258, in _run_main sys.exit(main(argv)) File "trainer/train.py", line 67, in
> main config = text_format.ParseLines(proto_file,
> config_pb2.ExperimentOptions()) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 759, in ParseLines return parser.ParseLines(lines, message) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 812, in ParseLines self._ParseOrMerge(lines, message) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 835, in _ParseOrMerge tokenizer = Tokenizer(str_lines) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 1255, in __init__ self._SkipWhitespace() File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 1283, in _SkipWhitespace self._PopLine() File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 1272, in _PopLine self._current_line = next(self._lines) File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/google/protobuf/text_format.py",
>
> line 832, in <genexpr> str_lines = ( File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/tensorflow/python/lib/io/file_io.py",
>
> line 206, in __next__ retval = self.readline() File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/tensorflow/python/lib/io/file_io.py",
>
> line 170, in readline self._preread_check() File
> "/home/caixiaoni/anaconda3/lib/python3.8/site-packages/tensorflow/python/lib/io/file_io.py",
>
> line 79, in _preread_check self._read_buf =
> _pywrap_file_io.BufferedInputStream( TypeError: __init__(): incompatible
> constructor arguments. The following argument types are supported: 1.
> tensorflow.python.lib.io._pywrap_file_io.BufferedInputStream(filename: str,
> buffer_size: int, token:
> tensorflow.python.lib.io._pywrap_file_io.TransactionToken = None) Invoked
> with: None, 524288
>
> The config file looks like:
>
> experiment_name: "coco_test"
> model_options {
> # Update the path to the initial checkpoint (e.g., ImageNet
> # pretrained checkpoint).
> initial_checkpoint:
> "/home/caixiaoni/Desktop/project/resnet50_os16_panoptic_deeplab_coco_train/ckpt-200000"
> backbone {
> name: "resnet50"
> output_stride: 16
> }
> decoder {
> feature_key: "res5"
> decoder_channels: 256
> aspp_channels: 256
> atrous_rates: 6
> atrous_rates: 12
> atrous_rates: 18
> }
> panoptic_deeplab {
> low_level {
> feature_key: "res3"
> channels_project: 64
> }
> low_level {
> feature_key: "res2"
> channels_project: 32
> }
> instance {
> low_level_override {
> feature_key: "res3"
> channels_project: 32
> }
> low_level_override {
> feature_key: "res2"
> channels_project: 16
> }
> instance_decoder_override {
> feature_key: "res5"
> decoder_channels: 128
> atrous_rates: 6
> atrous_rates: 12
> atrous_rates: 18
> }
> center_head {
> output_channels: 1
> head_channels: 32
> }
> regression_head {
> output_channels: 2
> head_channels: 32
> }
> }
> semantic_head {
> output_channels: 134
> head_channels: 256
> }
> }
> }
> trainer_options {
> save_checkpoints_steps: 1000
> save_summaries_steps: 100
> steps_per_loop: 100
> loss_options {
> semantic_loss {
> name: "softmax_cross_entropy"
> weight: 1.0
> top_k_percent: 0.2
> }
> center_loss {
> name: "mse"
> weight: 200
> }
> regression_loss {
> name: "l1"
> weight: 0.01
> }
> }
> solver_options {
> base_learning_rate: 0.0005
> training_number_of_steps: 200000
> warmup_steps: 2000
> }
> }
> train_dataset_options {
> dataset: "metal_part"
> # Update the path to training set.
> file_pattern:
> "/home/caixiaoni/Desktop/project/part-TFRecord/train-*.tfrecord"
> # Adjust the batch_size accordingly to better fit your GPU/TPU memory.
> # Also see Q1 in g3doc/faq.md.
> batch_size: 16
> crop_size: 513
> crop_size: 513
> min_resize_value: 513
> max_resize_value: 513
> augmentations {
> min_scale_factor: 0.5
> max_scale_factor: 1.5
> scale_factor_step_size: 0.1
> autoaugment_policy_name: "simple_classification_policy_magnitude_scale_0.2"
> }
> increase_small_instance_weights: true
> small_instance_weight: 3.0
> }
> eval_dataset_options {
> dataset: "metal_part"
> # Update the path to validation set.
> file_pattern:
> "/home/caixiaoni/Desktop/project/part-TFRecord/val-*.tfrecord"
> batch_size: 1
> crop_size: 513
> crop_size: 513
> min_resize_value: 513
> max_resize_value: 513
> # Add options to make the evaluation loss comparable to the training loss.
> increase_small_instance_weights: true
> small_instance_weight: 3.0
> }
> evaluator_options {
> continuous_eval_timeout: -1
> stuff_area_limit: 4096
> center_score_threshold: 0.1
> nms_kernel: 41
> save_predictions: true
> save_raw_predictions: false
> # Use pure tf functions (i.e., no CUDA kernel) to merge semantic and
> # instance maps. For faster speed, compile TensorFlow with provided kernel
> # implementation under the folder `tensorflow_ops`, and set
> # merge_semantic_and_instance_with_tf_op to true.
> merge_semantic_and_instance_with_tf_op: false
> }
>
> I currently used ubuntu-18.04 and have tried with the same code but
> different path using MacOS system, there isn't error when reading the
> config file but Ubuntu has. But Mac doesn't have Nvidia GPU such that I
> couldn't use.
>
> I assume the problem in the config file, could anyone give me some
> suggestions? I appreciate for any hints! Thanks in advance!
>
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