mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-12 12:40:20 +08:00
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aeef648199 |
@@ -7,6 +7,7 @@ test_tube_data/
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datasets/
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model_weights/
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app/models/
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pip-wheel-metadata/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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@@ -86,7 +86,7 @@ class Trainer(TrainerIO):
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self.test_percent_check = overfit_pct
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def __is_function_implemented(self, f_name):
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f_op = getattr(self, f_name, None)
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f_op = getattr(self.model, f_name, None)
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return callable(f_op)
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@property
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@@ -195,6 +195,7 @@ class Trainer(TrainerIO):
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# -----------------------------
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def fit(self, model):
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self.model = model
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model.trainer = self
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# transfer data loaders from model
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self.__get_dataloaders(model)
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@@ -266,10 +267,6 @@ class Trainer(TrainerIO):
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if met_batch_limit:
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break
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# give model a chance to end epoch early
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if self.model.should_stop_epoch(data_batch):
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break
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# ---------------
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# RUN TRAIN STEP
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# ---------------
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@@ -331,7 +328,9 @@ class Trainer(TrainerIO):
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# hook
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if self.__is_function_implemented('on_batch_start'):
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self.model.on_batch_start()
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response = self.model.on_batch_start(data_batch)
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if response == -1:
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return
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if self.enable_tqdm:
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self.prog_bar.update(1)
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@@ -1,7 +1,7 @@
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import torch
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class ModelHooks(torch.nn.Module):
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def on_batch_start(self):
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def on_batch_start(self, data_batch):
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pass
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def on_batch_end(self):
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@@ -19,5 +19,3 @@ class ModelHooks(torch.nn.Module):
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def on_post_performance_check(self):
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pass
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def should_stop_epoch(self, data_batch):
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return False
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+1
@@ -24,6 +24,7 @@ class RootModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
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self.overfit = hparams.overfit
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self.gradient_clip = hparams.gradient_clip
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self.num = 2
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self.trainer = None
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# track if gpu was requested for checkpointing
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self.on_gpu = False
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@@ -9,7 +9,6 @@ from pytorch_lightning.utils.arg_parse import add_default_args
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from time import sleep
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from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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@@ -1,16 +1,13 @@
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#!/usr/bin/env python
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from setuptools import setup, find_packages, os
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from setuptools import setup, find_packages
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# https://packaging.python.org/guides/single-sourcing-package-version/
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version = {}
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with open(os.path.join("src", "pytorch-lightning", "__init__.py")) as fp:
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exec(fp.read(), version)
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# http://blog.ionelmc.ro/2014/05/25/python-packaging/
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setup(
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name="pytorch-lightning",
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version=version["__version__"],
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version='0.1.dev14',
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description="The Keras for ML researchers using PyTorch",
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author="William Falcon",
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author_email="waf2107@columbia.edu",
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@@ -24,34 +21,7 @@ setup(
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"tqdm",
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"test-tube",
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],
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extras_require={
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"dev": [
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"black ; python_version>='3.6'",
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"coverage",
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"isort",
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"pytest",
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"pytest-cov<2.6.0",
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"pycodestyle",
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"sphinx",
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"nbsphinx",
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"ipython>=5.0",
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"jupyter-client",
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]
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},
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packages=find_packages("src"),
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package_dir={"": "src"},
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classifiers=[
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"Development Status :: 4 - Beta",
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"Intended Audience :: Education",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: MIT License",
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"Operating System :: OS Independent",
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"Programming Language :: Python",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.5",
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"Programming Language :: Python :: 3.6",
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"Programming Language :: Python :: 3.7",
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],
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packages=find_packages(),
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long_description=open("README.md", encoding="utf-8").read(),
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include_package_data=True,
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zip_safe=False,
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@@ -1,10 +0,0 @@
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"""
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=================
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pytorch-lightning
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=================
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The Keras for ML researchers using PyTorch. More control. Less boilerplate.
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"""
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__version__ = "0.1.dev01"
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