neural-pipeline
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toodef
Neural networks training pipeline based on PyTorch
### Describe the bug I was wondering when saw strange results of my metrics. I have custom metrics-wrappers around sklearn metrics: ```python3 from neural_pipeline.train_config import AbstractMetric, MetricsProcessor, MetricsGroup from sklearn.metrics...
Separate `Predictor` to `Predictor` and `DataProducerPredictor`. `Predictor` just init model and provide an interface to predict one item, `DataProducerPredictor` - provides an interface to predict all data from `DataProducer` object....
Now in np user do Trainer(..., device=torch.device('cuda)). It's good but bad when we try to pass unexpected data to device. Need to make possible use callback for this + create...
* Make possible to share calculation between metrics * Make plots grouping more intuitive * Add function to restore metrics from log * Make possible to disable/enable histogram
Frequently we need to extract part of dataset for test network for example. We need to implement this feature in DataProducer, cause it'll make possible to not change dataset class....
It's really useful to manage and customize console output by separated class. Also, this makes possible to include some info to console: * Real and model forward fps * Some...
### Environment - OS: Ubuntu 18.04 - Python version: 3.6 - PyTorch version: 0.4.1 - Neural Pipeline version: 0.1.0 ### Describe the bug There is traceback on screenshot, which happen...
