Merge pull request #1286 from peastman/reshape (53aa9724) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/tensor_graph.py

+30 −38

Original line number	Diff line number	Diff line
		@@ -1062,36 +1062,22 @@ class TensorGraph(Model):
		training: bool
		True during training, False during prediction
		"""
		if tfe.in_eager_mode():
		train_value = 1.0 if training else 0.0
		if self.queue_installed:
		while True:
		yield {self._training_placeholder: train_value}
		else:
		for d in generator:
		feed_dict = {}
		for key, value in d.items():
		if isinstance(key, Input):
		# Add or remove dimensions of size 1 to match the shape of the layer.
		try:
		value_dims = len(value.shape)
		layer_dims = len(key.shape)
		if value_dims < layer_dims:
		if all(i == 1 for i in key.shape[value_dims:]):
		value = tf.reshape(value,
		list(value.shape) + [1] *
		(layer_dims - value_dims))
		if value_dims > layer_dims:
		if all(i == 1 for i in value.shape[layer_dims:]):
		value = tf.reshape(value, value.shape[:layer_dims])
		except:
		pass
		feed_dict[key] = tf.cast(value, key.dtype)
		else:
		value = _ensure_value_shape(value, key)
		if tfe.in_eager_mode():
		value = tf.cast(value, key.dtype)
		feed_dict[key] = value
		yield feed_dict
		else:
		train_value = 1.0 if training else 0.0
		if self.queue_installed:
		while True:
		yield {self._training_placeholder: train_value}
		for d in generator:
		feed_dict = dict(d)
		feed_dict[key] = value
		if not tfe.in_eager_mode():
		feed_dict[self._training_placeholder] = train_value
		yield feed_dict

		@@ -1272,6 +1258,24 @@ class TensorGraph(Model):
		return tensors


		def _ensure_value_shape(value, layer):
		"""Ensure that a value has the right shape for an input layer."""
		# Add or remove dimensions of size 1 to match the shape of the layer.
		try:
		value_dims = len(value.shape)
		layer_dims = len(layer.shape)
		if value_dims < layer_dims:
		if all(i == 1 for i in layer.shape[value_dims:]):
		value = value.reshape(
		list(value.shape) + [1] * (layer_dims - value_dims))
		if value_dims > layer_dims:
		if all(i == 1 for i in value.shape[layer_dims:]):
		value = value.reshape(value.shape[:layer_dims])
		except:
		pass
		return value


		def _enqueue_batch(tg, generator, graph, sess, n_enqueued, final_sample):
		"""
		Function to load data into
		@@ -1294,19 +1298,7 @@ def _enqueue_batch(tg, generator, graph, sess, n_enqueued, final_sample):
		for layer in tg.features + tg.labels + tg.task_weights:
		if layer in feed_dict:
		value = feed_dict[layer]
		# Add or remove dimensions of size 1 to match the shape of the layer.
		try:
		value_dims = len(value.shape)
		layer_dims = len(layer.shape)
		if value_dims < layer_dims:
		if all(i == 1 for i in layer.shape[value_dims:]):
		value = value.reshape(
		list(value.shape) + [1] * (layer_dims - value_dims))
		if value_dims > layer_dims:
		if all(i == 1 for i in value.shape[layer_dims:]):
		value = value.reshape(value.shape[:layer_dims])
		except:
		pass
		value = _ensure_value_shape(value, layer)
		else:
		value = np.zeros(
		[0] + list(layer.shape[1:]), dtype=layer.dtype.as_numpy_dtype)

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