removed deprecated wandb code, fixed styles and tests (be741b65) · Commits · 钟慕尧 / deepchem

deepchem/models/callbacks.py

+4 −0

Original line number	Diff line number	Diff line
		@@ -6,10 +6,12 @@ import sys

		class ValidationCallback(object):
		"""Performs validation while training a KerasModel.

		This is a callback that can be passed to fit(). It periodically computes a
		set of metrics over a validation set and writes them to a file. In addition,
		it can save the best model parameters found so far to a directory on disk,
		updating them every time it finds a new best validation score.

		If Tensorboard logging is enabled on the KerasModel, the metrics are also
		logged to Tensorboard. This only happens when validation coincides with a
		step on which the model writes to the log. You should therefore make sure
		@@ -26,6 +28,7 @@ class ValidationCallback(object):
		save_metric=0,
		save_on_minimum=True):
		"""Create a ValidationCallback.

		Parameters
		----------
		dataset: dc.data.Dataset
		@@ -58,6 +61,7 @@ class ValidationCallback(object):

		def __call__(self, model, step):
		"""This is invoked by the KerasModel after every step of fitting.

		Parameters
		----------
		model: KerasModel

+3 −6

Original line number	Diff line number	Diff line
		@@ -198,6 +198,9 @@ class KerasModel(Model):
		self.wandb = wandb and _has_wandb

		self.wandb_logger = wandb_logger
		# If `wandb=True` and no logger is provided, initialize default logger
		if self.wandb and (self.wandb_logger is None):
		self.wandb_logger = WandbLogger()

		# Setup and initialize W&B logging
		if (self.wandb_logger is not None) and (not self.wandb_logger.initialized):
		@@ -461,16 +464,10 @@ class KerasModel(Model):
		c(self, current_step)
		if self.tensorboard and should_log:
		self._log_scalar_to_tensorboard('loss', batch_loss, current_step)
		# Wandb flag support (DEPRECATED)
		if self.wandb and should_log:
		wandb.log({'loss': batch_loss}, step=current_step)
		if (self.wandb_logger is not None) and should_log:
		all_data = dict({'train/loss': batch_loss})
		self.wandb_logger.log_data(all_data, step=current_step)

		if self.wandb:
		wandb.finish()

		# Close WandbLogger
		if self.wandb_logger is not None:
		self.wandb_logger.finish()

+8 −1

Original line number	Diff line number	Diff line
		@@ -11,6 +11,12 @@ try:
		except:
		has_pytorch = False

		try:
		import wandb
		has_wandb = True
		except:
		has_wandb = False


		@unittest.skipIf(not has_pytorch, 'PyTorch is not installed')
		def test_overfit_subclass_model():
		@@ -335,7 +341,8 @@ def test_tensorboard():
		assert file_size > 0


		@unittest.skipIf(not has_pytorch, 'PyTorch is not installed')
		@unittest.skipIf((not has_pytorch) or (not has_wandb),
		'PyTorch and/or Wandb is not installed')
		def test_wandblogger():
		"""Test logging to Weights & Biases."""
		# Load dataset and Models

+3 −5

Original line number	Diff line number	Diff line
		@@ -190,6 +190,9 @@ class TorchModel(Model):
		self.wandb = wandb and _has_wandb

		self.wandb_logger = wandb_logger
		# If `wandb=True` and no logger is provided, initialize default logger
		if self.wandb and (self.wandb_logger is None):
		self.wandb_logger = WandbLogger()

		# Setup and initialize W&B logging
		if (self.wandb_logger is not None) and (not self.wandb_logger.initialized):
		@@ -427,15 +430,10 @@ class TorchModel(Model):
		c(self, current_step)
		if self.tensorboard and should_log:
		self._log_scalar_to_tensorboard('loss', batch_loss, current_step)
		if self.wandb and should_log:
		wandb.log({'loss': batch_loss}, step=current_step)
		if (self.wandb_logger is not None) and should_log:
		all_data = dict({'train/loss': batch_loss})
		self.wandb_logger.log_data(all_data, step=current_step)

		if self.wandb:
		wandb.finish()

		# Close WandbLogger
		if self.wandb_logger is not None:
		self.wandb_logger.finish()

+3 −1

Original line number	Diff line number	Diff line
		@@ -33,7 +33,9 @@ class WandbLogger(object):
		anonymous: Optional[str] = "never",
		save_run_history: Optional[bool] = False,
		**kwargs):
		"""Parameters
		"""Creates a WandbLogger.

		Parameters
		----------
		name: str
		a display name for the run in the W&B dashboard