Delete temp directories (331239d4) · Commits · 钟慕尧 / deepchem

deepchem/models/models.py

+7 −0

Original line number	Diff line number	Diff line
		@@ -14,6 +14,7 @@ import numpy as np
		import pandas as pd
		import joblib
		import os
		import shutil
		import tempfile
		import sklearn
		from sklearn.base import BaseEstimator
		@@ -44,17 +45,23 @@ class Model(BaseEstimator):
		model_dir: str
		Path to directory where model will be stored.
		"""
		self.model_dir_is_temp = False
		if model_dir is not None:
		if not os.path.exists(model_dir):
		os.makedirs(model_dir)
		else:
		model_dir = tempfile.mkdtemp()
		self.model_dir_is_temp = True
		self.model_dir = model_dir
		self.model_instance = model_instance
		self.model_class = model_instance.__class__

		self.verbose = verbose

		def __del__(self):
		if 'model_dir_is_temp' in dir(self) and self.model_dir_is_temp:
		shutil.rmtree(self.model_dir)

		def fit_on_batch(self, X, y, w):
		"""
		Updates existing model with new information.

deepchem/models/multitask.py

+1 −7

Original line number	Diff line number	Diff line
		@@ -22,16 +22,10 @@ class SingletaskToMultitask(Model):
		Warning: This current implementation is only functional for sklearn models.
		"""
		def __init__(self, tasks, model_builder, model_dir=None, verbose=True):
		super().__init__(self, model_dir=model_dir, verbose=verbose)
		self.tasks = tasks
		if model_dir is not None:
		if not os.path.exists(model_dir):
		os.makedirs(model_dir)
		else:
		model_dir = tempfile.mkdtemp()
		self.model_dir = model_dir
		self.task_model_dirs = {}
		self.model_builder = model_builder
		self.verbose = True
		log("About to initialize singletask to multitask model", self.verbose)
		for task in self.tasks:
		task_model_dir = os.path.join(self.model_dir, str(task))

deepchem/models/sequential.py

+2 −7

Original line number	Diff line number	Diff line
		@@ -52,6 +52,7 @@ class Sequential(Model):
		"""

		def __init__(self, name=None, logdir=None):
		super().__init__(self, model_dir=logdir)
		self.layers = [] # stack of layers
		self.outputs = None # tensors (length 1)

		@@ -64,13 +65,7 @@ class Sequential(Model):
		config = tf.ConfigProto(allow_soft_placement=True)
		self.session = tf.Session(graph=self.graph, config=config)
		# Path to save checkpoint files
		if logdir is not None:
		if not os.path.exists(logdir):
		os.makedirs(logdir)
		else:
		logdir = tempfile.mkdtemp()
		self.logdir = logdir
		self._save_path = os.path.join(self.logdir, 'model.ckpt')
		self._save_path = os.path.join(self.model_dir, 'model.ckpt')

		def add(self, layer):
		"""Adds a layer instance on top of the layer stack.

deepchem/models/tensorflow_models/init.py

+6 −12

Original line number	Diff line number	Diff line
		@@ -190,22 +190,16 @@ class TensorflowGraphModel(Model):
		self.batch_size = batch_size
		self.n_classes = n_classes
		self.pad_batches = pad_batches
		self.verbose = verbose
		self.seed = seed

		if logdir is not None:
		if not os.path.exists(logdir):
		os.makedirs(logdir)
		else:
		logdir = tempfile.mkdtemp()
		self.logdir = logdir
		super().__init__(self, model_dir=logdir, verbose=verbose)

		# Guard variable to make sure we don't Restore() this model
		# from a disk checkpoint more than once.
		self._restored_model = False
		# Path to save checkpoint files, which matches the
		# replicated supervisor's default path.
		self._save_path = os.path.join(logdir, 'model.ckpt')
		self._save_path = os.path.join(self.model_dir, 'model.ckpt')

		self.train_graph = self.construct_graph(training=True, seed=self.seed)
		self.eval_graph = self.construct_graph(training=False, seed=self.seed)
		@@ -593,9 +587,9 @@ class TensorflowGraphModel(Model):
		def _find_last_checkpoint(self):
		"""Finds last saved checkpoint."""
		highest_num, last_checkpoint = -np.inf, None
		for filename in os.listdir(self.logdir):
		# checkpoints look like logdir/model.ckpt-N
		# self._save_path is "logdir/model.ckpt"
		for filename in os.listdir(self.model_dir):
		# checkpoints look like model_dir/model.ckpt-N
		# self._save_path is "model_dir/model.ckpt"
		if os.path.basename(self._save_path) in filename:
		try:
		N = int(filename.split("-")[1].split(".")[0])
		@@ -604,7 +598,7 @@ class TensorflowGraphModel(Model):
		last_checkpoint = "model.ckpt-" + str(N)
		except ValueError:
		pass
		return os.path.join(self.logdir, last_checkpoint)
		return os.path.join(self.model_dir, last_checkpoint)


		class TensorflowClassifier(TensorflowGraphModel):

deepchem/models/tf_new_models/multitask_classifier.py

+2 −8

Original line number	Diff line number	Diff line
		@@ -84,17 +84,11 @@ class MultitaskGraphClassifier(Model):
		pad_batches=True,
		verbose=True):

		self.verbose = verbose
		super().__init__(self, model_dir=logdir, verbose=verbose)
		self.n_tasks = n_tasks
		self.final_loss = final_loss
		self.model = model
		self.sess = tf.Session(graph=self.model.graph)
		if logdir is not None:
		if not os.path.exists(logdir):
		os.makedirs(logdir)
		else:
		logdir = tempfile.mkdtemp()
		self.logdir = logdir

		with self.model.graph.as_default():
		# Extract model info
		@@ -129,7 +123,7 @@ class MultitaskGraphClassifier(Model):

		# Path to save checkpoint files, which matches the
		# replicated supervisor's default path.
		self._save_path = os.path.join(logdir, 'model.ckpt')
		self._save_path = os.path.join(self.model_dir, 'model.ckpt')

		def build(self):
		# Create target inputs

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