def __init__(self, data_or_path, mode=None, caption=None, grouping=None):
"""
Accepts numpy array of image data, or a PIL image. The class attempts to infer
the data format and converts it.
If grouping is set to a number the interface combines N images.
"""
self._grouping = grouping
self._caption = caption
self._width = None
self._height = None
self._image = None
if isinstance(data_or_path, six.string_types):
super(Image, self).__init__(data_or_path, is_tmp=False)
else:
data = data_or_path
PILImage = util.get_module(
"PIL.Image",
required=
'wandb.Image needs the PIL package. To get it, run "pip install pillow".'
)
if util.is_matplotlib_typename(util.get_full_typename(data)):
buf = six.BytesIO()
util.ensure_matplotlib_figure(data).savefig(buf)
self._image = PILImage.open(buf)
elif isinstance(data, PILImage.Image):
self._image = data
elif util.is_pytorch_tensor_typename(util.get_full_typename(data)):
vis_util = util.get_module(
"torchvision.utils",
"torchvision is required to render images")
if hasattr(data, "requires_grad") and data.requires_grad:
data = data.detach()
data = vis_util.make_grid(data, normalize=True)
self._image = PILImage.fromarray(
data.mul(255).clamp(0,
255).byte().permute(1, 2,
0).cpu().numpy())
else:
if hasattr(data, "numpy"): # TF data eager tensors
data = data.numpy()
if data.ndim > 2:
data = data.squeeze(
) # get rid of trivial dimensions as a convenience
self._image = PILImage.fromarray(self.to_uint8(data),
mode=mode
or self.guess_mode(data))
self._width, self._height = self._image.size
tmp_path = os.path.join(MEDIA_TMP.name,
util.generate_id() + '.png')
self._image.save(tmp_path, transparency=None)
super(Image, self).__init__(tmp_path, is_tmp=True)
def plot_to_json(obj):
"""Converts a matplotlib or plotly object to json so that we can pass
it the the wandb server and display it nicely there"""
if util.is_matplotlib_typename(util.get_full_typename(obj)):
tools = util.get_module(
"plotly.tools", required="plotly is required to log interactive plots, install with: pip install plotly or convert the plot to an image with `wandb.Image(plt)`")
obj = tools.mpl_to_plotly(obj)
if util.is_plotly_typename(util.get_full_typename(obj)):
return {"_type": "plotly", "plot": numpy_arrays_to_lists(obj.to_plotly_json())}
else:
return obj
def plot_to_json(obj):
if util.is_matplotlib_typename(util.get_full_typename(obj)):
tools = util.get_module(
"plotly.tools",
required=
"plotly is required to log interactive plots, install with: pip install plotly or convert the plot to an image with `wandb.Image(plt)`"
)
obj = tools.mpl_to_plotly(obj)
if util.is_plotly_typename(util.get_full_typename(obj)):
return {"_type": "plotly", "plot": obj.to_plotly_json()}
else:
return obj
def __init__(self, data, mode=None, caption=None, grouping=None):
"""
Accepts numpy array of image data, or a PIL image. The class attempts to infer
the data format and converts it.
If grouping is set to a number the interface combines N images.
"""
PILImage = util.get_module(
"PIL.Image",
required=
"wandb.Image requires the PIL package, to get it run: pip install pillow"
)
if util.is_matplotlib_typename(util.get_full_typename(data)):
buf = six.BytesIO()
util.ensure_matplotlib_figure(data).savefig(buf)
self.image = PILImage.open(buf)
elif isinstance(data, PILImage.Image):
self.image = data
elif util.is_pytorch_tensor_typename(util.get_full_typename(data)):
vis_util = util.get_module(
"torchvision.utils",
"torchvision is required to render images")
if hasattr(data, "requires_grad") and data.requires_grad:
data = data.detach()
data = vis_util.make_grid(data, normalize=True)
self.image = PILImage.fromarray(
data.mul(255).clamp(0, 255).byte().permute(1, 2,
0).cpu().numpy())
else:
# Handle TF eager tensors
if hasattr(data, "numpy"):
data = data.numpy()
data = data.squeeze(
) # get rid of trivial dimensions as a convenience
self.image = PILImage.fromarray(self.to_uint8(data),
mode=mode or self.guess_mode(data))
self.grouping = grouping
self.caption = caption
def val_to_json(key, val, mode="summary", step=None):
"""Converts a wandb datatype to its JSON representation"""
converted = val
typename = util.get_full_typename(val)
if util.is_matplotlib_typename(typename):
# This handles plots with images in it because plotly doesn't support it
# TODO: should we handle a list of plots?
val = util.ensure_matplotlib_figure(val)
if any(len(ax.images) > 0 for ax in val.axes):
PILImage = util.get_module(
"PIL.Image",
required=
"Logging plots with images requires pil: pip install pillow")
buf = six.BytesIO()
val.savefig(buf)
val = Image(PILImage.open(buf))
else:
converted = util.convert_plots(val)
elif util.is_plotly_typename(typename):
converted = util.convert_plots(val)
if isinstance(val, IterableMedia):
val = [val]
if isinstance(val, collections.Sequence) and len(val) > 0:
is_media = [isinstance(v, IterableMedia) for v in val]
if all(is_media):
cwd = wandb.run.dir if wandb.run else "."
if step is None:
step = "summary"
if isinstance(val[0], Image):
converted = Image.transform(val, cwd,
"{}_{}.jpg".format(key, step))
elif isinstance(val[0], Audio):
converted = Audio.transform(val, cwd, key, step)
elif isinstance(val[0], Html):
converted = Html.transform(val, cwd, key, step)
elif isinstance(val[0], Object3D):
converted = Object3D.transform(val, cwd, key, step)
elif any(is_media):
raise ValueError(
"Mixed media types in the same list aren't supported")
elif isinstance(val, Histogram):
converted = Histogram.transform(val)
elif isinstance(val, Graph):
if mode == "history":
raise ValueError("Graphs are only supported in summary")
converted = Graph.transform(val)
elif isinstance(val, Table):
converted = Table.transform(val)
return converted
def val_to_json(run, key, val, step='summary'):
"""Converts a wandb datatype to its JSON representation.
"""
converted = val
typename = util.get_full_typename(val)
if util.is_pandas_data_frame(val):
assert step == 'summary', "We don't yet support DataFrames in History."
return data_frame_to_json(val, run, key, step)
elif util.is_matplotlib_typename(typename):
# This handles plots with images in it because plotly doesn't support it
# TODO: should we handle a list of plots?
val = util.ensure_matplotlib_figure(val)
if any(len(ax.images) > 0 for ax in val.axes):
PILImage = util.get_module(
"PIL.Image",
required=
"Logging plots with images requires pil: pip install pillow")
buf = six.BytesIO()
val.savefig(buf)
val = Image(PILImage.open(buf))
else:
converted = plot_to_json(val)
elif util.is_plotly_typename(typename):
converted = plot_to_json(val)
elif isinstance(val, collections.Sequence) and all(
isinstance(v, WBValue) for v in val):
# This check will break down if Image/Audio/... have child classes.
if len(val) and isinstance(val[0], BatchableMedia) and all(
isinstance(v, type(val[0])) for v in val):
return val[0].seq_to_json(val, run, key, step)
else:
# TODO(adrian): Good idea to pass on the same key here? Maybe include
# the array index?
# There is a bug here: if this array contains two arrays of the same type of
# anonymous media objects, their eventual names will collide.
# This used to happen. The frontend doesn't handle heterogenous arrays
#raise ValueError(
# "Mixed media types in the same list aren't supported")
return [val_to_json(run, key, v, step=step) for v in val]
if isinstance(val, WBValue):
if isinstance(val, Media) and not val.is_bound():
val.bind_to_run(run, key, step)
return val.to_json(run)
return converted
