def add_image(self, tag, img, step):
summary = Summary()
bio = BytesIO()
if type(img) == str:
img = PIL.Image.open(img)
elif type(img) == PIL.Image.Image:
pass
else:
img = PIL.Image.fromarray(img)
img.save(bio, format="png")
image_summary = Summary.Image(encoded_image_string=bio.getvalue())
summary.value.add(tag=tag, image=image_summary)
self.summary_writer.add_summary(summary, global_step=step)
def image_summary(self, tag, value, step):
for idx, img in enumerate(value):
summary = Summary()
bio = BytesIO()
if type(img) == str:
img = PIL.Image.open(img)
elif type(img) == PIL.Image.Image:
pass
else:
img = scipy.misc.toimage(img)
img.save(bio, format="png")
image_summary = Summary.Image(encoded_image_string=bio.getvalue())
summary.value.add(tag=f"{tag}/{idx}", image=image_summary)
self.summary_writer.add_summary(summary, global_step=step)
def add_image(self, tag, img, step):
summary = Summary()
bio = BytesIO()
if type(img) == str:
img = Image.open(img)
elif type(img) == Image.Image:
pass
else:
img = Image.fromarray(img) #scipy.misc.toimage(img)
if img.mode == "F":
img = img.convert('RGB')
img.save(bio, format="png")
image_summary = Summary.Image(encoded_image_string=bio.getvalue())
summary.value.add(tag=tag, image=image_summary)
self.summary_writer.add_summary(summary, global_step=step)
def image_summary(self, tag, value, step):
for idx, img in enumerate(value):
summary = Summary() ##for creating image summary for model
bio = BytesIO() ##for converting image to encoded form (binary)
if type(img) == str: ##url to image
img = PIL.Image.open(img) ##open the image
elif type(
img
) == PIL.Image.Image: ##Image class of PIL(directly an image)
pass
else:
img = scipy.misc.toimage(
img) ##Takes a numpy array and returns a PIL image.
img.save(bio, format="png")
image_summary = Summary.Image(encoded_image_string=bio.getvalue()
) ##binary encoded string for images
summary.value.add(tag=f"{tag}/{idx}", image=image_summary)
self.summary_writer.add_summary(
summary, global_step=step) ##image summary with tag for model
def add_mpl_plot(self, tag, fig, global_step):
if self.__show_mpl:
import matplotlib.pyplot as plt
plt.show()
else:
from PIL import Image
import io
fig.canvas.draw()
pil_image = Image.frombytes('RGB', fig.canvas.get_width_height(),
fig.canvas.tostring_rgb())
output = io.BytesIO()
pil_image.save(output, format='PNG')
pil_image_string = output.getvalue()
output.close()
img_summary = Summary.Image(height=pil_image.height,
width=pil_image.width,
colorspace=3,
encoded_image_string=pil_image_string)
summary = Summary(
value=[Summary.Value(tag=_clean_tag(tag), image=img_summary)])
self.file_writer.add_summary(summary, global_step=global_step)
def add_scalar(self, tag, value, step):
summary = Summary(value=[Summary.Value(tag=tag, simple_value=value)])
self.summary_writer.add_summary(summary, global_step=step)
def scalar_summary(
self, tag, value, step
): ##to save various summary(tag: action, train-loss,valreward,actor,critic obtained in trainer_rl_typeloss file
summary = Summary(value=[Summary.Value(tag=tag, simple_value=value)])
self.summary_writer.add_summary(summary, global_step=step)
def histogram(name, values, bins, collections=None):
name = _clean_tag(name)
values = make_np(values)
hist = make_histogram(values.astype(float), bins)
return Summary(value=[Summary.Value(tag=name, histo=hist)])
def counter(name, counter_dict):
name = _clean_tag(name)
hist = make_histogram_from_counter(counter_dict)
return Summary(value=[Summary.Value(tag=name, histo=hist)])
def _to_image_summary_safe(tag, tensor):
tag = _clean_tag(tag)
img = make_image_summary(to_image(tensor))
return Summary(value=[Summary.Value(tag=tag, image=img)])
