mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-11 12:31:23 +08:00
Merge branch 'master' into tb
This commit is contained in:
William Falcon
@@ -11,32 +11,52 @@ assignees: ''
|
||||
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/williamFalcon/pytorch-lightning/issues/79).
|
||||
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)
|
||||
|
||||
### Describe the bug
|
||||
A clear and concise description of what the bug is.
|
||||
## 🐛 Bug
|
||||
|
||||
<!-- A clear and concise description of what the bug is. -->
|
||||
|
||||
### To Reproduce
|
||||
|
||||
#### To Reproduce
|
||||
Steps to reproduce the behavior:
|
||||
|
||||
1. Go to '...'
|
||||
2. Run '....'
|
||||
3. Scroll down to '....'
|
||||
4. See error
|
||||
|
||||
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
|
||||
|
||||
|
||||
#### Code sample
|
||||
Ideally attach a minimal code sample to reproduce the decried issue.
|
||||
[Minimal means having the shortest code but still preserving the bug]
|
||||
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
|
||||
Minimal means having the shortest code but still preserving the bug. -->
|
||||
|
||||
#### Expected behavior
|
||||
A clear and concise description of what you expected to happen.
|
||||
### Expected behavior
|
||||
|
||||
#### Screenshots
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
<!-- A clear and concise description of what you expected to happen. -->
|
||||
|
||||
### Environment information
|
||||
### Environment
|
||||
|
||||
Desktop (please complete the following information):
|
||||
- OS: [e.g. iOS, Linux, Win]
|
||||
- Packaging [e.g. pip, conda]
|
||||
- Version [e.g. 0.5.2.1]
|
||||
Please copy and paste the output from our
|
||||
[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
|
||||
(or fill out the checklist below manually).
|
||||
|
||||
You can get the script and run it with:
|
||||
```
|
||||
wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
|
||||
# For security purposes, please check the contents of collect_env.py before running it.
|
||||
python collect_env.py
|
||||
```
|
||||
|
||||
- PyTorch Version (e.g., 1.0):
|
||||
- OS (e.g., Linux):
|
||||
- How you installed PyTorch (`conda`, `pip`, source):
|
||||
- Build command you used (if compiling from source):
|
||||
- Python version:
|
||||
- CUDA/cuDNN version:
|
||||
- GPU models and configuration:
|
||||
- Any other relevant information:
|
||||
|
||||
### Additional context
|
||||
Add any other context about the problem here.
|
||||
|
||||
<!-- Add any other context about the problem here. -->
|
||||
|
||||
+1
-1
@@ -7,7 +7,7 @@ assignees: ''
|
||||
|
||||
---
|
||||
|
||||
## Typos and docs fixes
|
||||
## 📚 Documentation
|
||||
|
||||
For typos and doc fixes, please go ahead and:
|
||||
|
||||
@@ -7,14 +7,21 @@ assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Is your feature request related to a problem? Please describe.**
|
||||
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
|
||||
## 🚀 Feature
|
||||
<!-- A clear and concise description of the feature proposal -->
|
||||
|
||||
**Describe the solution you'd like**
|
||||
A clear and concise description of what you want to happen.
|
||||
### Motivation
|
||||
|
||||
**Describe alternatives you've considered**
|
||||
A clear and concise description of any alternative solutions or features you've considered.
|
||||
<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
|
||||
|
||||
**Additional context**
|
||||
Add any other context or screenshots about the feature request here.
|
||||
### Pitch
|
||||
|
||||
<!-- A clear and concise description of what you want to happen. -->
|
||||
|
||||
### Alternatives
|
||||
|
||||
<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
|
||||
|
||||
### Additional context
|
||||
|
||||
<!-- Add any other context or screenshots about the feature request here. -->
|
||||
|
||||
@@ -7,20 +7,24 @@ assignees: ''
|
||||
|
||||
---
|
||||
|
||||
## ❓ Questions and Help
|
||||
|
||||
### Before asking:
|
||||
1. search the issues.
|
||||
2. search the docs.
|
||||
|
||||
If you still can't find what you need:
|
||||
#### What is your question?
|
||||
<!-- If you still can't find what you need: -->
|
||||
|
||||
#### Code
|
||||
Please paste a code snippet if your question requires it!
|
||||
#### What is your question?
|
||||
|
||||
#### What have you tried?
|
||||
#### Code
|
||||
|
||||
#### What's your environment?
|
||||
- conda version (no venv)
|
||||
- PyTorch version
|
||||
- Lightning version
|
||||
- Test-tube version
|
||||
<!-- Please paste a code snippet if your question requires it! -->
|
||||
|
||||
#### What have you tried?
|
||||
|
||||
#### What's your environment?
|
||||
|
||||
- OS: [e.g. iOS, Linux, Win]
|
||||
- Packaging [e.g. pip, conda]
|
||||
- Version [e.g. 0.5.2.1]
|
||||
|
||||
+7
-6
@@ -33,13 +33,14 @@ matrix:
|
||||
python: 3.7
|
||||
env: TOXENV=py37
|
||||
- os: osx
|
||||
osx_image: xcode9.4
|
||||
# https://blog.travis-ci.com/2019-08-07-extensive-python-testing-on-travis-ci
|
||||
osx_image: xcode10.3
|
||||
language: generic
|
||||
env: TOXENV=py36
|
||||
addons:
|
||||
homebrew:
|
||||
# update: true
|
||||
packages: python3.6
|
||||
env: TOXENV=py37
|
||||
#addons:
|
||||
# homebrew:
|
||||
# # update: true
|
||||
# packages: python3.7
|
||||
before_install:
|
||||
- pip3 install virtualenv
|
||||
- virtualenv -p python3 ~/venv
|
||||
|
||||
@@ -31,16 +31,29 @@ Simple installation from PyPI
|
||||
pip install pytorch-lightning
|
||||
```
|
||||
|
||||
[LIVE COLAB DEMO](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
|
||||
|
||||
## Docs
|
||||
**[View the docs here](https://williamfalcon.github.io/pytorch-lightning/)**
|
||||
|
||||
## Demo
|
||||
[Copy and run this COLAB!](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
|
||||
|
||||
## What is it?
|
||||
Lightning is a very lightweight wrapper on PyTorch. This means you don't have to learn a new library. To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it) format and Lightning will automate the rest. Lightning guarantees tested, correct, modern best practices for the automated parts.
|
||||
Lightning is a very lightweight wrapper on PyTorch that decouples the science code from the engineering code. It's more of a style-guide than a framework. By refactoring your code, we can automate most of the non-research code.
|
||||
|
||||
To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it) format (the science) and Lightning will automate the rest (the engineering). Lightning guarantees tested, correct, modern best practices for the automated parts.
|
||||
|
||||
- If you are a researcher, Lightning is infinitely flexible, you can modify everything down to the way .backward is called or distributed is set up.
|
||||
- If you are a scientist or production team, lightning is very simple to use with best practice defaults.
|
||||
|
||||
## What does lightning control for me?
|
||||
|
||||
Everything in Blue!
|
||||
This is how lightning separates the science (red) from the engineering (blue).
|
||||
|
||||
|
||||
|
||||
## How much effort is it to convert?
|
||||
You're probably tired of switching frameworks at this point. But it is a very quick process to refactor into the Lightning format. [Check out this tutorial](https://towardsdatascience.com/how-to-refactor-your-pytorch-code-to-get-these-42-benefits-of-pytorch-lighting-6fdd0dc97538)
|
||||
You're probably tired of switching frameworks at this point. But it is a very quick process to refactor into the Lightning format (ie: hours). [Check out this tutorial](https://towardsdatascience.com/how-to-refactor-your-pytorch-code-to-get-these-42-benefits-of-pytorch-lighting-6fdd0dc97538)
|
||||
|
||||
## Starting a new project?
|
||||
[Use our seed-project aimed at reproducibility!](https://github.com/williamFalcon/pytorch-lightning-conference-seed)
|
||||
@@ -178,29 +191,6 @@ When you're all done you can even run the test set separately.
|
||||
trainer.test()
|
||||
```
|
||||
|
||||
## What does lightning control for me?
|
||||
|
||||
Everything in gray!
|
||||
You define the blue parts using the LightningModule interface:
|
||||
|
||||
|
||||
|
||||
```python
|
||||
# what to do in the training loop
|
||||
def training_step(self, batch, batch_idx):
|
||||
|
||||
# what to do in the validation loop
|
||||
def validation_step(self, batch, batch_idx):
|
||||
|
||||
# how to aggregate validation_step outputs
|
||||
def validation_end(self, outputs):
|
||||
|
||||
# and your dataloaders
|
||||
def train_dataloader():
|
||||
def val_dataloader():
|
||||
def test_dataloader():
|
||||
```
|
||||
|
||||
**Could be as complex as seq-2-seq + attention**
|
||||
|
||||
```python
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 5.4 MiB |
@@ -1,6 +1,6 @@
|
||||
"""Package info"""
|
||||
|
||||
__version__ = '0.5.3.2'
|
||||
__version__ = '0.6.0'
|
||||
__author__ = 'William Falcon et al.'
|
||||
__author_email__ = 'waf2107@columbia.edu'
|
||||
__license__ = 'Apache-2.0'
|
||||
|
||||
@@ -48,8 +48,8 @@ Minimal example
|
||||
|
||||
def validation_end(self, outputs):
|
||||
# OPTIONAL
|
||||
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
|
||||
return {'avg_val_loss': avg_loss}
|
||||
val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean()
|
||||
return {'val_loss': val_loss_mean}
|
||||
|
||||
def test_step(self, batch, batch_idx):
|
||||
# OPTIONAL
|
||||
@@ -59,8 +59,8 @@ Minimal example
|
||||
|
||||
def test_end(self, outputs):
|
||||
# OPTIONAL
|
||||
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
|
||||
return {'avg_test_loss': avg_loss}
|
||||
test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()
|
||||
return {'test_loss': test_loss_mean}
|
||||
|
||||
def configure_optimizers(self):
|
||||
# REQUIRED
|
||||
|
||||
@@ -664,6 +664,8 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
|
||||
.. note:: If you use multiple optimizers, training_step will have an additional `optimizer_idx` parameter.
|
||||
|
||||
.. note:: If you use LBFGS lightning handles the closure function automatically for you.
|
||||
|
||||
.. note:: If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step.
|
||||
|
||||
Example
|
||||
-------
|
||||
@@ -930,7 +932,7 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def load_from_metrics(cls, weights_path, tags_csv):
|
||||
def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
|
||||
"""Primary way of loading model from csv weights path.
|
||||
|
||||
:param str weights_path: Path to a PyTorch checkpoint
|
||||
@@ -975,9 +977,10 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
|
||||
hparams = load_hparams_from_tags_csv(tags_csv)
|
||||
hparams.__setattr__('on_gpu', False)
|
||||
|
||||
# load on CPU only to avoid OOM issues
|
||||
# then its up to user to put back on GPUs
|
||||
checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)
|
||||
if map_location is not None:
|
||||
checkpoint = torch.load(weights_path, map_location=map_location)
|
||||
else:
|
||||
checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)
|
||||
|
||||
# load the state_dict on the model automatically
|
||||
model = cls(hparams)
|
||||
@@ -989,7 +992,7 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
|
||||
return model
|
||||
|
||||
@classmethod
|
||||
def load_from_checkpoint(cls, checkpoint_path):
|
||||
def load_from_checkpoint(cls, checkpoint_path, map_location=None):
|
||||
"""
|
||||
Primary way of loading model from a checkpoint
|
||||
:param checkpoint_path:
|
||||
@@ -997,9 +1000,11 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
|
||||
:return:
|
||||
"""
|
||||
|
||||
# load on CPU only to avoid OOM issues
|
||||
# then its up to user to put back on GPUs
|
||||
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
|
||||
if map_location is not None:
|
||||
checkpoint = torch.load(checkpoint_path, map_location=map_location)
|
||||
else:
|
||||
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
|
||||
|
||||
try:
|
||||
ckpt_hparams = checkpoint['hparams']
|
||||
except KeyError:
|
||||
|
||||
@@ -50,20 +50,31 @@ class ModelSummary(object):
|
||||
input_ = self.model.example_input_array
|
||||
|
||||
if self.model.on_gpu:
|
||||
input_ = input_.cuda(0)
|
||||
device = next(self.model.parameters()).get_device()
|
||||
# test if input is a list or a tuple
|
||||
if isinstance(input_, (list, tuple)):
|
||||
input_ = [input_i.cuda(device) if torch.is_tensor(input_i) else input_i
|
||||
for input_i in input_]
|
||||
else:
|
||||
input_ = input_.cuda(device)
|
||||
|
||||
if self.model.trainer.use_amp:
|
||||
input_ = input_.half()
|
||||
# test if it is not a list or a tuple
|
||||
if isinstance(input_, (list, tuple)):
|
||||
input_ = [input_i.half() if torch.is_tensor(input_i) else input_i
|
||||
for input_i in input_]
|
||||
else:
|
||||
input_ = input_.half()
|
||||
|
||||
with torch.no_grad():
|
||||
|
||||
for _, m in mods:
|
||||
if type(input_) is list or type(input_) is tuple: # pragma: no cover
|
||||
if isinstance(input_, (list, tuple)): # pragma: no cover
|
||||
out = m(*input_)
|
||||
else:
|
||||
out = m(input_)
|
||||
|
||||
if type(input_) is tuple or type(input_) is list: # pragma: no cover
|
||||
if isinstance(input_, (list, tuple)): # pragma: no cover
|
||||
in_size = []
|
||||
for x in input_:
|
||||
if type(x) is list:
|
||||
@@ -75,7 +86,7 @@ class ModelSummary(object):
|
||||
|
||||
in_sizes.append(in_size)
|
||||
|
||||
if type(out) is tuple or type(out) is list: # pragma: no cover
|
||||
if isinstance(out, (list, tuple)): # pragma: no cover
|
||||
out_size = np.asarray([x.size() for x in out])
|
||||
else:
|
||||
out_size = np.array(out.size())
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `model_saving` module has been renamed to `saving` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `model_saving` module has been renamed to `saving` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`model_saving` module has been renamed to `saving` since v0.5.3"
|
||||
warnings.warn("`model_saving` module has been renamed to `saving` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.core.saving import ModelIO # noqa: E402
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `root_module` module has been renamed to `lightning` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `root_module` module has been renamed to `lightning` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`root_module` module has been renamed to `lightning` since v0.5.3"
|
||||
warnings.warn("`root_module` module has been renamed to `lightning` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.core.lightning import LightningModule # noqa: E402
|
||||
|
||||
@@ -168,7 +168,7 @@ Every k batches, lightning will write the new logs to disk
|
||||
from os import environ
|
||||
from .base import LightningLoggerBase, rank_zero_only
|
||||
|
||||
from .tensorboard import TensorboardLogger
|
||||
from .tensorboard import TensorBoardLogger
|
||||
|
||||
try:
|
||||
from .test_tube import TestTubeLogger
|
||||
|
||||
@@ -52,7 +52,11 @@ from logging import getLogger
|
||||
try:
|
||||
from comet_ml import Experiment as CometExperiment
|
||||
from comet_ml import OfflineExperiment as CometOfflineExperiment
|
||||
from comet_ml.papi import API
|
||||
try:
|
||||
from comet_ml.api import API
|
||||
except ImportError:
|
||||
# For more information, see: https://www.comet.ml/docs/python-sdk/releases/#release-300
|
||||
from comet_ml.papi import API
|
||||
except ImportError:
|
||||
raise ImportError('Missing comet_ml package.')
|
||||
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `comet_logger` module has been renamed to `comet` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `comet_logger` module has been renamed to `comet` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`comet_logger` module has been renamed to `comet` since v0.5.3"
|
||||
warnings.warn("`comet_logger` module has been renamed to `comet` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.logging.comet import CometLogger # noqa: E402
|
||||
|
||||
@@ -59,7 +59,7 @@ class MLFlowLogger(LightningLoggerBase):
|
||||
if expt:
|
||||
self._expt_id = expt.experiment_id
|
||||
else:
|
||||
logger.warning(f"Experiment with name f{self.experiment_name} not found. Creating it.")
|
||||
logger.warning(f"Experiment with name {self.experiment_name} not found. Creating it.")
|
||||
self._expt_id = self._mlflow_client.create_experiment(name=self.experiment_name)
|
||||
|
||||
run = self._mlflow_client.create_run(experiment_id=self._expt_id, tags=self.tags)
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `mlflow_logger` module has been renamed to `mlflow` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `mlflow_logger` module has been renamed to `mlflow` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`mlflow_logger` module has been renamed to `mlflow` since v0.5.3"
|
||||
warnings.warn("`mlflow_logger` module has been renamed to `mlflow` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.logging.mlflow import MLFlowLogger # noqa: E402
|
||||
|
||||
@@ -8,8 +8,8 @@ from torch.utils.tensorboard import SummaryWriter
|
||||
from .base import LightningLoggerBase, rank_zero_only
|
||||
|
||||
|
||||
class TensorboardLogger(LightningLoggerBase):
|
||||
r"""Log to local file system in Tensorboard format
|
||||
class TensorBoardLogger(LightningLoggerBase):
|
||||
r"""Log to local file system in TensorBoard format
|
||||
|
||||
Implemented using :class:`torch.utils.tensorboard.SummaryWriter`. Logs are saved to
|
||||
`os.path.join(save_dir, name, version)`
|
||||
@@ -18,7 +18,7 @@ class TensorboardLogger(LightningLoggerBase):
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
logger = TensorboardLogger("tb_logs", name="my_model")
|
||||
logger = TensorBoardLogger("tb_logs", name="my_model")
|
||||
trainer = Trainer(logger=logger)
|
||||
trainer.train(model)
|
||||
|
||||
@@ -35,7 +35,7 @@ class TensorboardLogger(LightningLoggerBase):
|
||||
super().__init__()
|
||||
self.save_dir = save_dir
|
||||
self._name = name
|
||||
self._version = version if version is not None else None
|
||||
self._version = version
|
||||
|
||||
self._experiment = None
|
||||
self.kwargs = kwargs
|
||||
@@ -59,23 +59,35 @@ class TensorboardLogger(LightningLoggerBase):
|
||||
def log_hyperparams(self, params):
|
||||
if parse_version(torch.__version__) < parse_version("1.3.0"):
|
||||
warn(
|
||||
f"Hyperparameter logging is not available for Torch version {torch.__version__}. "
|
||||
"Skipping log_hyperparams. Upgrade to Torch 1.3.0 or above to enable "
|
||||
"hyperparameter logging"
|
||||
f"Hyperparameter logging is not available for Torch version {torch.__version__}."
|
||||
" Skipping log_hyperparams. Upgrade to Torch 1.3.0 or above to enable"
|
||||
" hyperparameter logging."
|
||||
)
|
||||
# TODO: some alternative should be added
|
||||
return
|
||||
self.experiment.add_hparams(hparam_dict=vars(params))
|
||||
try:
|
||||
# in case converting from namespace, todo: rather test if it is namespace
|
||||
params = vars(params)
|
||||
except TypeError:
|
||||
pass
|
||||
if params is not None:
|
||||
# `add_hparams` requires both - hparams and metric
|
||||
self.experiment.add_hparams(hparam_dict=dict(params), metric_dict={})
|
||||
|
||||
@rank_zero_only
|
||||
def log_metrics(self, metrics, step_idx=None):
|
||||
def log_metrics(self, metrics, step=None):
|
||||
for k, v in metrics.items():
|
||||
if isinstance(v, torch.Tensor):
|
||||
v = v.item()
|
||||
self.experiment.add_scalar(k, v, step_idx)
|
||||
self.experiment.add_scalar(k, v, step)
|
||||
|
||||
@rank_zero_only
|
||||
def save(self):
|
||||
self.experiment.flush()
|
||||
try:
|
||||
self.experiment.flush()
|
||||
except AttributeError:
|
||||
# you are using PT version (<v1.2) which does not have implemented flush
|
||||
self.experiment._get_file_writer().flush()
|
||||
|
||||
@rank_zero_only
|
||||
def finalize(self, status):
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `test_tube_logger` module has been renamed to `test_tube` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `test_tube_logger` module has been renamed to `test_tube` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`test_tube_logger` module has been renamed to `test_tube` since v0.5.3"
|
||||
warnings.warn("`test_tube_logger` module has been renamed to `test_tube` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.logging.test_tube import TestTubeLogger # noqa: E402
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
"""
|
||||
.. warning:: `override_data_parallel` module has been renamed to `data_parallel` since v0.5.3
|
||||
.. warning:: `override_data_parallel` module has been renamed to `data_parallel` since v0.6.0
|
||||
and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`override_data_parallel` module has been renamed to `data_parallel` since v0.5.3"
|
||||
warnings.warn("`override_data_parallel` module has been renamed to `data_parallel` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.overrides.data_parallel import ( # noqa: E402
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `pt_overrides` package has been renamed to `overrides` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `pt_overrides` package has been renamed to `overrides` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`pt_overrides` package has been renamed to `overrides` since v0.5.3"
|
||||
warnings.warn("`pt_overrides` package has been renamed to `overrides` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.overrides import override_data_parallel # noqa: E402
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
"""
|
||||
.. warning:: `root_module` package has been renamed to `core` since v0.5.3 and will be removed in v0.8.0
|
||||
.. warning:: `root_module` package has been renamed to `core` since v0.6.0 and will be removed in v0.8.0
|
||||
"""
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.warn("`root_module` package has been renamed to `core` since v0.5.3"
|
||||
warnings.warn("`root_module` package has been renamed to `core` since v0.6.0"
|
||||
" and will be removed in v0.8.0", DeprecationWarning)
|
||||
|
||||
from pytorch_lightning.core import ( # noqa: E402
|
||||
|
||||
@@ -36,6 +36,15 @@ class TrainerDataLoadingMixin(ABC):
|
||||
self.shown_warnings = None
|
||||
self.val_check_interval = None
|
||||
|
||||
def _percent_range_check(self, name):
|
||||
value = getattr(self, name)
|
||||
msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}."
|
||||
if name == "val_check_interval":
|
||||
msg += " If you want to disable validation set `val_percent_check` to 0.0 instead."
|
||||
|
||||
if not 0. <= value <= 1.:
|
||||
raise ValueError(msg)
|
||||
|
||||
def init_train_dataloader(self, model):
|
||||
"""
|
||||
Dataloaders are provided by the model
|
||||
@@ -48,6 +57,8 @@ class TrainerDataLoadingMixin(ABC):
|
||||
if EXIST_ITER_DATASET and isinstance(self.get_train_dataloader().dataset, IterableDataset):
|
||||
self.num_training_batches = float('inf')
|
||||
else:
|
||||
self._percent_range_check('train_percent_check')
|
||||
|
||||
self.num_training_batches = len(self.get_train_dataloader())
|
||||
self.num_training_batches = int(self.num_training_batches * self.train_percent_check)
|
||||
|
||||
@@ -56,7 +67,14 @@ class TrainerDataLoadingMixin(ABC):
|
||||
# otherwise, it checks in [0, 1.0] % range of a training epoch
|
||||
if isinstance(self.val_check_interval, int):
|
||||
self.val_check_batch = self.val_check_interval
|
||||
if self.val_check_batch > self.num_training_batches:
|
||||
raise ValueError(
|
||||
f"`val_check_interval` ({self.val_check_interval}) must be less than or equal "
|
||||
f"to the number of the training batches ({self.num_training_batches}). "
|
||||
f"If you want to disable validation set `val_percent_check` to 0.0 instead.")
|
||||
else:
|
||||
self._percent_range_check('val_check_interval')
|
||||
|
||||
self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
|
||||
self.val_check_batch = max(1, self.val_check_batch)
|
||||
|
||||
@@ -89,13 +107,15 @@ class TrainerDataLoadingMixin(ABC):
|
||||
:return:
|
||||
"""
|
||||
self.get_val_dataloaders = model.val_dataloader
|
||||
self.num_val_batches = 0
|
||||
|
||||
# determine number of validation batches
|
||||
# val datasets could be none, 1 or 2+
|
||||
if self.get_val_dataloaders() is not None:
|
||||
self._percent_range_check('val_percent_check')
|
||||
|
||||
self.num_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())
|
||||
self.num_val_batches = int(self.num_val_batches * self.val_percent_check)
|
||||
self.num_val_batches = max(1, self.num_val_batches)
|
||||
|
||||
on_ddp = self.use_ddp or self.use_ddp2
|
||||
if on_ddp and self.get_val_dataloaders() is not None:
|
||||
@@ -134,10 +154,11 @@ class TrainerDataLoadingMixin(ABC):
|
||||
|
||||
# determine number of test batches
|
||||
if self.get_test_dataloaders() is not None:
|
||||
self._percent_range_check('test_percent_check')
|
||||
|
||||
len_sum = sum(len(dataloader) for dataloader in self.get_test_dataloaders())
|
||||
self.num_test_batches = len_sum
|
||||
self.num_test_batches = int(self.num_test_batches * self.test_percent_check)
|
||||
self.num_test_batches = max(1, self.num_test_batches)
|
||||
|
||||
on_ddp = self.use_ddp or self.use_ddp2
|
||||
if on_ddp and self.get_test_dataloaders() is not None:
|
||||
@@ -208,6 +229,10 @@ class TrainerDataLoadingMixin(ABC):
|
||||
self.val_percent_check = val_percent_check
|
||||
self.test_percent_check = test_percent_check
|
||||
if overfit_pct > 0:
|
||||
if overfit_pct > 1:
|
||||
raise ValueError(f"`overfit_pct` must be not greater than 1.0, but got "
|
||||
f"{overfit_pct:.3f}.")
|
||||
|
||||
self.train_percent_check = overfit_pct
|
||||
self.val_percent_check = overfit_pct
|
||||
self.test_percent_check = overfit_pct
|
||||
|
||||
@@ -145,8 +145,8 @@ class TrainerEvaluationLoopMixin(ABC):
|
||||
self.single_gpu = None
|
||||
self.data_parallel_device_ids = None
|
||||
self.model = None
|
||||
self.nb_test_batches = None
|
||||
self.nb_val_batches = None
|
||||
self.num_test_batches = None
|
||||
self.num_val_batches = None
|
||||
self.fast_dev_run = None
|
||||
self.process_position = None
|
||||
self.show_progress_bar = None
|
||||
|
||||
@@ -21,7 +21,7 @@ class TrainerLoggingMixin(ABC):
|
||||
self.use_ddp2 = None
|
||||
self.num_gpus = None
|
||||
|
||||
def log_metrics(self, metrics, grad_norm_dic):
|
||||
def log_metrics(self, metrics, grad_norm_dic, step=None):
|
||||
"""Logs the metric dict passed in.
|
||||
|
||||
:param metrics:
|
||||
@@ -41,9 +41,10 @@ class TrainerLoggingMixin(ABC):
|
||||
# turn all tensors to scalars
|
||||
scalar_metrics = self.metrics_to_scalars(metrics)
|
||||
|
||||
step = step if step is not None else self.global_step
|
||||
# log actual metrics
|
||||
if self.proc_rank == 0 and self.logger is not None:
|
||||
self.logger.log_metrics(scalar_metrics, step=self.global_step)
|
||||
self.logger.log_metrics(scalar_metrics, step=step)
|
||||
self.logger.save()
|
||||
|
||||
def add_tqdm_metrics(self, metrics):
|
||||
@@ -176,7 +177,7 @@ class TrainerLoggingMixin(ABC):
|
||||
elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
|
||||
pass
|
||||
|
||||
# reduce only metrics that have the same nb of gpus
|
||||
# reduce only metrics that have the same number of gpus
|
||||
elif output[k].size(0) == num_gpus:
|
||||
reduced = torch.mean(output[k])
|
||||
output[k] = reduced
|
||||
|
||||
@@ -359,7 +359,7 @@ class Trainer(TrainerIOMixin,
|
||||
tqdm_dict['split_idx'] = self.split_idx
|
||||
|
||||
if self.logger is not None and self.logger.version is not None:
|
||||
tqdm_dict['v_nb'] = self.logger.version
|
||||
tqdm_dict['v_num'] = self.logger.version
|
||||
|
||||
tqdm_dict.update(self.tqdm_metrics)
|
||||
|
||||
|
||||
@@ -50,6 +50,9 @@ To modify this behavior, pass in your own EarlyStopping callback.
|
||||
# pass in your own to override the default callback
|
||||
trainer = Trainer(early_stop_callback=early_stop_callback)
|
||||
|
||||
# pass in min_epochs to enable the callback after min_epochs have run
|
||||
trainer = Trainer(early_stop_callback=early_stop_callback, min_epochs=5)
|
||||
|
||||
# pass in None to disable it
|
||||
trainer = Trainer(early_stop_callback=None)
|
||||
|
||||
@@ -151,6 +154,7 @@ When this flag is enabled each batch is split into sequences of size truncated_b
|
||||
|
||||
import inspect
|
||||
from abc import ABC, abstractmethod
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
|
||||
@@ -169,22 +173,22 @@ class TrainerTrainLoopMixin(ABC):
|
||||
def __init__(self):
|
||||
# this is just a summary on variables used in this abstract class,
|
||||
# the proper values/initialisation should be done in child class
|
||||
self.max_nb_epochs = None
|
||||
self.max_epochs = None
|
||||
self.min_epochs = None
|
||||
self.use_ddp = None
|
||||
self.use_dp = None
|
||||
self.use_ddp2 = None
|
||||
self.single_gpu = None
|
||||
self.data_parallel_device_ids = None
|
||||
self.check_val_every_n_epoch = None
|
||||
self.nb_training_batches = None
|
||||
self.num_training_batches = None
|
||||
self.val_check_batch = None
|
||||
self.nb_val_batches = None
|
||||
self.num_val_batches = None
|
||||
self.fast_dev_run = None
|
||||
self.is_iterable_train_dataloader = None
|
||||
self.main_progress_bar = None
|
||||
self.accumulation_scheduler = None
|
||||
self.lr_schedulers = None
|
||||
self.min_nb_epochs = None
|
||||
self.enable_early_stop = None
|
||||
self.early_stop_callback = None
|
||||
self.callback_metrics = None
|
||||
@@ -194,7 +198,7 @@ class TrainerTrainLoopMixin(ABC):
|
||||
self.log_save_interval = None
|
||||
self.proc_rank = None
|
||||
self.row_log_interval = None
|
||||
self.total_batch_nb = None
|
||||
self.total_batches = None
|
||||
self.truncated_bptt_steps = None
|
||||
self.optimizers = None
|
||||
self.accumulate_grad_batches = None
|
||||
@@ -207,6 +211,24 @@ class TrainerTrainLoopMixin(ABC):
|
||||
self.get_train_dataloader = None
|
||||
self.reduce_lr_on_plateau_scheduler = None
|
||||
|
||||
@property
|
||||
def max_nb_epochs(self):
|
||||
"""
|
||||
.. warning:: `max_nb_epochs` is deprecated and will be removed in v0.8.0, use `max_epochs` instead.
|
||||
"""
|
||||
warnings.warn("`max_nb_epochs` is deprecated and will be removed in "
|
||||
"v0.8.0, use `max_epochs` instead.", DeprecationWarning)
|
||||
return self.max_epochs
|
||||
|
||||
@property
|
||||
def min_nb_epochs(self):
|
||||
"""
|
||||
.. warning:: `min_nb_epochs` is deprecated and will be removed in v0.8.0, use `min_epochs` instead.
|
||||
"""
|
||||
warnings.warn("`min_nb_epochs` is deprecated and will be removed in "
|
||||
"v0.8.0, use `min_epochs` instead.", DeprecationWarning)
|
||||
return self.min_epochs
|
||||
|
||||
@abstractmethod
|
||||
def get_model(self):
|
||||
# this is just empty shell for code from other class
|
||||
@@ -320,7 +342,7 @@ class TrainerTrainLoopMixin(ABC):
|
||||
self.reduce_lr_on_plateau_scheduler.step(val_loss, epoch=self.current_epoch)
|
||||
|
||||
# early stopping
|
||||
met_min_epochs = epoch > self.min_epochs
|
||||
met_min_epochs = epoch >= self.min_epochs - 1
|
||||
if self.enable_early_stop and (met_min_epochs or self.fast_dev_run):
|
||||
should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch,
|
||||
logs=self.callback_metrics)
|
||||
@@ -345,6 +367,10 @@ class TrainerTrainLoopMixin(ABC):
|
||||
|
||||
# run epoch
|
||||
for batch_idx, batch in enumerate(self.get_train_dataloader()):
|
||||
# stop epoch if we limited the number of training batches
|
||||
if batch_idx >= self.num_training_batches:
|
||||
break
|
||||
|
||||
self.batch_idx = batch_idx
|
||||
|
||||
model = self.get_model()
|
||||
@@ -391,11 +417,6 @@ class TrainerTrainLoopMixin(ABC):
|
||||
if early_stop_epoch or self.fast_dev_run:
|
||||
break
|
||||
|
||||
# stop epoch if we limited nb batches
|
||||
met_batch_limit = batch_idx >= self.num_training_batches
|
||||
if met_batch_limit:
|
||||
break
|
||||
|
||||
# epoch end hook
|
||||
if self.is_function_implemented('on_epoch_end'):
|
||||
model = self.get_model()
|
||||
@@ -433,6 +454,13 @@ class TrainerTrainLoopMixin(ABC):
|
||||
|
||||
# call training_step once per optimizer
|
||||
for opt_idx, optimizer in enumerate(self.optimizers):
|
||||
# make sure only the gradients of the current optimizer's paramaters are calculated
|
||||
# in the training step to prevent dangling gradients in multiple-optimizer setup.
|
||||
for param in self.get_model().parameters():
|
||||
param.requires_grad = False
|
||||
for group in optimizer.param_groups:
|
||||
for param in group['params']:
|
||||
param.requires_grad = True
|
||||
|
||||
# wrap the forward step in a closure so second order methods work
|
||||
def optimizer_closure():
|
||||
|
||||
+2
-1
@@ -4,4 +4,5 @@ numpy>=1.16.4
|
||||
torch>=1.1
|
||||
torchvision>=0.4.0
|
||||
pandas>=0.24 # lower version do not support py3.7
|
||||
future>=0.17.1 # required for buildins in setup.py
|
||||
test-tube>=0.7.5
|
||||
future>=0.17.1 # required for builtins in setup.py
|
||||
@@ -0,0 +1,22 @@
|
||||
import pytest
|
||||
|
||||
import torch.multiprocessing as mp
|
||||
|
||||
|
||||
def pytest_configure(config):
|
||||
config.addinivalue_line("markers", "spawn: spawn test in a separate process using torch.multiprocessing.spawn")
|
||||
|
||||
|
||||
def wrap(i, fn, args):
|
||||
return fn(*args)
|
||||
|
||||
|
||||
@pytest.mark.tryfirst
|
||||
def pytest_pyfunc_call(pyfuncitem):
|
||||
if pyfuncitem.get_closest_marker("spawn"):
|
||||
testfunction = pyfuncitem.obj
|
||||
funcargs = pyfuncitem.funcargs
|
||||
testargs = tuple([funcargs[arg] for arg in pyfuncitem._fixtureinfo.argnames])
|
||||
|
||||
mp.spawn(wrap, (testfunction, testargs))
|
||||
return True
|
||||
@@ -8,4 +8,5 @@ check-manifest
|
||||
# test_tube # already installed in main req.
|
||||
mlflow
|
||||
comet_ml
|
||||
twine==1.13.0
|
||||
twine==1.13.0
|
||||
pillow<7.0.0
|
||||
+12
-24
@@ -32,6 +32,7 @@ def test_amp_single_gpu(tmpdir):
|
||||
tutils.run_model_test(trainer_options, model)
|
||||
|
||||
|
||||
@pytest.mark.spawn
|
||||
def test_no_amp_single_gpu(tmpdir):
|
||||
"""Make sure DDP + AMP work."""
|
||||
tutils.reset_seed()
|
||||
@@ -51,8 +52,10 @@ def test_no_amp_single_gpu(tmpdir):
|
||||
use_amp=True
|
||||
)
|
||||
|
||||
with pytest.raises((MisconfigurationException, ModuleNotFoundError)):
|
||||
tutils.run_model_test(trainer_options, model)
|
||||
trainer = Trainer(**trainer_options)
|
||||
result = trainer.fit(model)
|
||||
|
||||
assert result == 1
|
||||
|
||||
|
||||
def test_amp_gpu_ddp(tmpdir):
|
||||
@@ -78,6 +81,7 @@ def test_amp_gpu_ddp(tmpdir):
|
||||
tutils.run_model_test(trainer_options, model)
|
||||
|
||||
|
||||
@pytest.mark.spawn
|
||||
def test_amp_gpu_ddp_slurm_managed(tmpdir):
|
||||
"""Make sure DDP + AMP work."""
|
||||
if not tutils.can_run_gpu_test():
|
||||
@@ -124,26 +128,6 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
|
||||
assert trainer.resolve_root_node_address('abc[23-24]') == 'abc23'
|
||||
assert trainer.resolve_root_node_address('abc[23-24, 45-40, 40]') == 'abc23'
|
||||
|
||||
# test model loading with a map_location
|
||||
pretrained_model = tutils.load_model(logger.experiment, trainer.checkpoint_callback.filepath)
|
||||
|
||||
# test model preds
|
||||
for dataloader in trainer.get_test_dataloaders():
|
||||
tutils.run_prediction(dataloader, pretrained_model)
|
||||
|
||||
if trainer.use_ddp:
|
||||
# on hpc this would work fine... but need to hack it for the purpose of the test
|
||||
trainer.model = pretrained_model
|
||||
trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
|
||||
|
||||
# test HPC loading / saving
|
||||
trainer.hpc_save(tmpdir, logger)
|
||||
trainer.hpc_load(tmpdir, on_gpu=True)
|
||||
|
||||
# test freeze on gpu
|
||||
model.freeze()
|
||||
model.unfreeze()
|
||||
|
||||
|
||||
def test_cpu_model_with_amp(tmpdir):
|
||||
"""Make sure model trains on CPU."""
|
||||
@@ -165,6 +149,7 @@ def test_cpu_model_with_amp(tmpdir):
|
||||
tutils.run_model_test(trainer_options, model, on_gpu=False)
|
||||
|
||||
|
||||
@pytest.mark.spawn
|
||||
def test_amp_gpu_dp(tmpdir):
|
||||
"""Make sure DP + AMP work."""
|
||||
tutils.reset_seed()
|
||||
@@ -180,8 +165,11 @@ def test_amp_gpu_dp(tmpdir):
|
||||
distributed_backend='dp',
|
||||
use_amp=True
|
||||
)
|
||||
with pytest.raises(MisconfigurationException):
|
||||
tutils.run_model_test(trainer_options, model, hparams)
|
||||
|
||||
trainer = Trainer(**trainer_options)
|
||||
result = trainer.fit(model)
|
||||
|
||||
assert result == 1
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -97,7 +97,7 @@ def test_running_test_after_fitting(tmpdir):
|
||||
trainer_options = dict(
|
||||
default_save_path=tmpdir,
|
||||
show_progress_bar=False,
|
||||
max_epochs=1,
|
||||
max_epochs=4,
|
||||
train_percent_check=0.4,
|
||||
val_percent_check=0.2,
|
||||
test_percent_check=0.2,
|
||||
|
||||
+14
-16
@@ -9,7 +9,7 @@ from pytorch_lightning import Trainer
|
||||
from pytorch_lightning.logging import (
|
||||
LightningLoggerBase,
|
||||
rank_zero_only,
|
||||
TensorboardLogger,
|
||||
TensorBoardLogger,
|
||||
)
|
||||
from pytorch_lightning.testing import LightningTestModel
|
||||
|
||||
@@ -169,8 +169,8 @@ def test_comet_pickle(tmpdir, monkeypatch):
|
||||
except ModuleNotFoundError:
|
||||
return
|
||||
|
||||
hparams = tutils.get_hparams()
|
||||
model = LightningTestModel(hparams)
|
||||
# hparams = tutils.get_hparams()
|
||||
# model = LightningTestModel(hparams)
|
||||
|
||||
comet_dir = os.path.join(tmpdir, "cometruns")
|
||||
|
||||
@@ -199,9 +199,9 @@ def test_tensorboard_logger(tmpdir):
|
||||
hparams = tutils.get_hparams()
|
||||
model = LightningTestModel(hparams)
|
||||
|
||||
logger = TensorboardLogger(save_dir=tmpdir, name="tensorboard_logger_test")
|
||||
logger = TensorBoardLogger(save_dir=tmpdir, name="tensorboard_logger_test")
|
||||
|
||||
trainer_options = dict(max_num_epochs=1, train_percent_check=0.01, logger=logger)
|
||||
trainer_options = dict(max_epochs=1, train_percent_check=0.01, logger=logger)
|
||||
|
||||
trainer = Trainer(**trainer_options)
|
||||
result = trainer.fit(model)
|
||||
@@ -213,14 +213,12 @@ def test_tensorboard_logger(tmpdir):
|
||||
def test_tensorboard_pickle(tmpdir):
|
||||
"""Verify that pickling trainer with Tensorboard logger works."""
|
||||
|
||||
hparams = tutils.get_hparams()
|
||||
model = LightningTestModel(hparams)
|
||||
# hparams = tutils.get_hparams()
|
||||
# model = LightningTestModel(hparams)
|
||||
|
||||
comet_dir = os.path.join(tmpdir, "cometruns")
|
||||
logger = TensorBoardLogger(save_dir=tmpdir, name="tensorboard_pickle_test")
|
||||
|
||||
logger = TensorboardLogger(save_dir=tmpdir, name="tensorboard_pickle_test")
|
||||
|
||||
trainer_options = dict(max_num_epochs=1, logger=logger)
|
||||
trainer_options = dict(max_epochs=1, logger=logger)
|
||||
|
||||
trainer = Trainer(**trainer_options)
|
||||
pkl_bytes = pickle.dumps(trainer)
|
||||
@@ -235,7 +233,7 @@ def test_tensorboard_automatic_versioning(tmpdir):
|
||||
root_dir.mkdir("0")
|
||||
root_dir.mkdir("1")
|
||||
|
||||
logger = TensorboardLogger(save_dir=tmpdir, name="tb_versioning")
|
||||
logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning")
|
||||
|
||||
assert logger.version == 2
|
||||
|
||||
@@ -248,14 +246,14 @@ def test_tensorboard_manual_versioning(tmpdir):
|
||||
root_dir.mkdir("1")
|
||||
root_dir.mkdir("2")
|
||||
|
||||
logger = TensorboardLogger(save_dir=tmpdir, name="tb_versioning", version=1)
|
||||
logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=1)
|
||||
|
||||
assert logger.version == 1
|
||||
|
||||
|
||||
@pytest.mark.parametrize("step_idx", [10, None])
|
||||
def test_tensorboard_log_metrics(tmpdir, step_idx):
|
||||
logger = TensorboardLogger(tmpdir)
|
||||
logger = TensorBoardLogger(tmpdir)
|
||||
metrics = {
|
||||
"float": 0.3,
|
||||
"int": 1,
|
||||
@@ -266,7 +264,7 @@ def test_tensorboard_log_metrics(tmpdir, step_idx):
|
||||
|
||||
|
||||
def test_tensorboard_log_hyperparams(tmpdir):
|
||||
logger = TensorboardLogger(tmpdir)
|
||||
logger = TensorBoardLogger(tmpdir)
|
||||
hparams = {
|
||||
"float": 0.3,
|
||||
"int": 1,
|
||||
@@ -311,7 +309,7 @@ def test_custom_logger(tmpdir):
|
||||
|
||||
trainer_options = dict(
|
||||
max_epochs=1,
|
||||
train_percent_check=0.01,
|
||||
train_percent_check=0.05,
|
||||
logger=logger,
|
||||
default_save_path=tmpdir
|
||||
)
|
||||
|
||||
@@ -153,7 +153,7 @@ def test_running_test_pretrained_model_dp(tmpdir):
|
||||
|
||||
trainer_options = dict(
|
||||
show_progress_bar=True,
|
||||
max_epochs=1,
|
||||
max_epochs=4,
|
||||
train_percent_check=0.4,
|
||||
val_percent_check=0.2,
|
||||
checkpoint_callback=checkpoint,
|
||||
@@ -269,12 +269,12 @@ def test_cpu_restore_training(tmpdir):
|
||||
logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
|
||||
|
||||
trainer_options = dict(
|
||||
max_epochs=2,
|
||||
max_epochs=8,
|
||||
val_check_interval=0.50,
|
||||
val_percent_check=0.2,
|
||||
train_percent_check=0.2,
|
||||
logger=logger,
|
||||
checkpoint_callback=ModelCheckpoint(tmpdir)
|
||||
checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1)
|
||||
)
|
||||
|
||||
# fit model
|
||||
|
||||
@@ -393,7 +393,7 @@ def test_multiple_test_dataloader(tmpdir):
|
||||
default_save_path=tmpdir,
|
||||
max_epochs=1,
|
||||
val_percent_check=0.1,
|
||||
train_percent_check=0.1,
|
||||
train_percent_check=0.2,
|
||||
)
|
||||
|
||||
# fit model
|
||||
|
||||
Reference in New Issue
Block a user