Open SamPIngram opened 4 years ago
hi @singram12 can you post the exact error you're having?
INFO:fnet.cli.train_model: Started training at: 2020-03-13 15:17:10.692903
INFO:fnet.cli.train_model: Training options
INFO:fnet.cli.train_model: {'batch_size': 28,
INFO:fnet.cli.train_model: 'bpds_kwargs': {'buffer_size': 16,
INFO:fnet.cli.train_model: 'buffer_switch_interval': 2800,
INFO:fnet.cli.train_model: 'patch_shape': [32, 64, 64]},
INFO:fnet.cli.train_model: 'dataset_train': 'fnet.data.MultiChTiffDataset',
INFO:fnet.cli.train_model: 'dataset_train_kwargs': {'path_csv': '/content/pytorch_fnet/examples//image_list_train.csv'},
INFO:fnet.cli.train_model: 'dataset_val': 'fnet.data.MultiChTiffDataset',
INFO:fnet.cli.train_model: 'dataset_val_kwargs': {'path_csv': '/content/pytorch_fnet/examples//image_list_test.csv'},
INFO:fnet.cli.train_model: 'fnet_model_class': 'fnet.fnet_model.Model',
INFO:fnet.cli.train_model: 'fnet_model_kwargs': {'betas': [0.9, 0.999],
INFO:fnet.cli.train_model: 'criterion_class': 'fnet.losses.WeightedMSE',
INFO:fnet.cli.train_model: 'init_weights': False,
INFO:fnet.cli.train_model: 'lr': 0.001,
INFO:fnet.cli.train_model: 'nn_class': 'fnet.nn_modules.fnet_nn_3d.Net',
INFO:fnet.cli.train_model: 'scheduler': None},
INFO:fnet.cli.train_model: 'gpu_ids': [0],
INFO:fnet.cli.train_model: 'interval_checkpoint': 1000,
INFO:fnet.cli.train_model: 'interval_save': 1000,
INFO:fnet.cli.train_model: 'iter_checkpoint': [],
INFO:fnet.cli.train_model: 'json': PosixPath('/content/pytorch_fnet/examples/model/prefs.json'),
INFO:fnet.cli.train_model: 'n_iter': 5000,
INFO:fnet.cli.train_model: 'path_json': PosixPath('/content/pytorch_fnet/examples/model/prefs.json'),
INFO:fnet.cli.train_model: 'path_save_dir': '/content/pytorch_fnet/examples/model',
INFO:fnet.cli.train_model: 'seed': None}
INFO:fnet.cli.train_model: Model
fnet.nn_modules.fnet_nn_3d.Net({})
iter: 3000
gpu: [0]
INFO:fnet.cli.train_model: History loaded from: /content/pytorch_fnet/examples/model/losses.csv
Traceback (most recent call last):
File "/usr/local/bin/fnet", line 8, in
Issues corresponding to trying to use the examples/download_and_train.py. This has been specifically tested when trying to run this example in a google colab notebook, so some issues may be specific to internal versions of python being used in colab.
Image_list CSV files are being created with no channel signal or channel target values leading list comprehension issues of trying to turn NaN into int during training start-up. This was being caused as the method in which "channel_signal" and "channel_target" are being added to the data frame is not working correctly and is resulting in NaN being parsed into those columns instead.
To fix this you can convert the numpy arrays to lists when assigning to the dataframe: df["channel_signal"] = data_manifest["ChannelNumberBrightfield"].tolist() df["channel_target"] = data_manifest["ChannelNumber405"].tolist()