Merge pull request #1450 from peastman/bugs (9219d4ed) · Commits · 钟慕尧 / deepchem

deepchem/data/data_loader.py

+3 −0

Original line number	Diff line number	Diff line
		@@ -369,6 +369,7 @@ class ImageLoader(DataLoader):
		remainder = []
		for input_file in input_files:
		filename, extension = os.path.splitext(input_file)
		extension = extension.lower()
		# TODO(rbharath): Add support for more extensions
		if os.path.isdir(input_file):
		dirfiles = [
		@@ -386,6 +387,7 @@ class ImageLoader(DataLoader):
		]
		for zip_file in zip_files:
		_, extension = os.path.splitext(zip_file)
		extension = extension.lower()
		if extension in [".png", ".tif"]:
		image_files.append(zip_file)
		elif extension in [".png", ".tif"]:
		@@ -405,6 +407,7 @@ class ImageLoader(DataLoader):
		images = []
		for image_file in image_files:
		_, extension = os.path.splitext(image_file)
		extension = extension.lower()
		if extension == ".png":
		image = misc.imread(image_file)
		images.append(image)

deepchem/models/tensorgraph/layers.py

+37 −6

Original line number	Diff line number	Diff line
		@@ -446,6 +446,16 @@ class SharedVariableScope(Layer):
		return self._shared_with._get_scope_name()


		def _conv_size(width, size, stride, padding):
		"""Compute the output size of a convolutional layer."""
		if padding.lower() == 'valid':
		return 1 + (width - size) // stride
		elif padding.lower() == 'same':
		return 1 + (width - 1) // stride
		else:
		raise ValueError('Unknown padding type: %s' % padding)


		class Conv1D(Layer):
		"""A 1D convolution on the input.

		@@ -485,7 +495,6 @@ class Conv1D(Layer):
		`(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
		or `(None, 128)` for variable-length sequences of 128-dimensional vectors.

		TODO(LESWING): Calculate output shape at construction time
		Arguments:
		filters: Integer, the dimensionality of the output space
		(i.e. the number output of filters in the convolution).
		@@ -541,6 +550,17 @@ class Conv1D(Layer):
		self.kernel_constraint = kernel_constraint
		self.bias_constraint = bias_constraint
		super(Conv1D, self).__init__(in_layers, **kwargs)
		try:
		parent_shape = self.in_layers[0].shape
		if isinstance(strides, int):
		strides = (strides,)
		if isinstance(kernel_size, int):
		kernel_size = (kernel_size,)
		self._shape = (parent_shape[0],
		_conv_size(parent_shape[1], kernel_size[0], strides[0],
		padding), filters)
		except:
		pass

		def _build_layer(self):
		return tf.keras.layers.Conv1D(
		@@ -1983,8 +2003,13 @@ class Conv2D(SharedVariableScope):
		strides = stride
		if isinstance(stride, int):
		strides = (stride, stride)
		self._shape = (parent_shape[0], parent_shape[1] // strides[0],
		parent_shape[2] // strides[1], num_outputs)
		if isinstance(kernel_size, int):
		kernel_size = (kernel_size, kernel_size)
		self._shape = (parent_shape[0],
		_conv_size(parent_shape[1], kernel_size[0], strides[0],
		padding),
		_conv_size(parent_shape[2], kernel_size[1], strides[1],
		padding), num_outputs)
		except:
		pass

		@@ -2100,9 +2125,15 @@ class Conv3D(SharedVariableScope):
		strides = stride
		if isinstance(stride, int):
		strides = (stride, stride, stride)
		self._shape = (parent_shape[0], parent_shape[1] // strides[0],
		parent_shape[2] // strides[1],
		parent_shape[3] // strides[2], num_outputs)
		if isinstance(kernel_size, int):
		kernel_size = (kernel_size, kernel_size, kernel_size)
		self._shape = (parent_shape[0],
		_conv_size(parent_shape[1], kernel_size[0], strides[0],
		padding),
		_conv_size(parent_shape[2], kernel_size[1], strides[1],
		padding),
		_conv_size(parent_shape[3], kernel_size[2], strides[2],
		padding), num_outputs)
		except:
		pass

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