def _document_fe_graph(self) -> None:
"""Add FE execution graphs into the traceability document.
"""
with self.doc.create(Section("FastEstimator Architecture")):
for mode in self.system.pipeline.data.keys():
scheduled_items = self.system.pipeline.get_scheduled_items(
mode) + self.system.network.get_scheduled_items(
mode) + self.system.traces
signature_epochs = get_signature_epochs(
scheduled_items,
total_epochs=self.system.epoch_idx,
mode=mode)
epochs_with_data = self.system.pipeline.get_epochs_with_data(
total_epochs=self.system.epoch_idx, mode=mode)
if set(signature_epochs) & epochs_with_data:
self.doc.append(NoEscape(r'\FloatBarrier'))
with self.doc.create(Subsection(mode.capitalize())):
for epoch in signature_epochs:
if epoch not in epochs_with_data:
continue
self.doc.append(NoEscape(r'\FloatBarrier'))
with self.doc.create(
Subsubsection(
f"Epoch {epoch}",
label=Label(
Marker(name=f"{mode}{epoch}",
prefix="ssubsec")))):
ds_ids = self.system.pipeline.get_ds_ids(
epoch=epoch, mode=mode)
for ds_id in ds_ids:
with NonContext(
) if ds_id == '' else self.doc.create(
Paragraph(
f"Dataset {ds_id}",
label=Label(
Marker(
name=
f"{mode}{epoch}{ds_id}",
prefix="para")))):
diagram = self._draw_diagram(
mode, epoch, ds_id)
ltx = d2t.dot2tex(diagram.to_string(),
figonly=True)
args = Arguments(
**{
'max width':
r'\textwidth, max height=0.9\textheight'
})
args.escape = False
with self.doc.create(Center()):
with self.doc.create(
AdjustBox(arguments=args)
) as box:
box.append(NoEscape(ltx))
def render_table(self,
doc: Document,
name_override: Optional[LatexObject] = None,
toc_ref: Optional[str] = None,
extra_rows: Optional[List[Tuple[str, Any]]] = None) -> None:
"""Write this table into a LaTeX document.
Args:
doc: The LaTeX document to be appended to.
name_override: An optional replacement for this table's name field.
toc_ref: A reference to be added to the table of contents.
extra_rows: Any extra rows to be added to the table before the kwargs.
"""
with doc.create(Table(position='htp!')) as table:
table.append(NoEscape(r'\refstepcounter{table}'))
table.append(Label(Marker(name=str(self.fe_id), prefix="tbl")))
if toc_ref:
table.append(NoEscape(r'\addcontentsline{toc}{subsection}{' + escape_latex(toc_ref) + '}'))
with doc.create(Tabularx('|lX|', booktabs=True)) as tabular:
package = Package('xcolor', options='table')
if package not in tabular.packages:
# Need to invoke a table color before invoking TextColor (bug?)
tabular.packages.append(package)
package = Package('seqsplit')
if package not in tabular.packages:
tabular.packages.append(package)
tabular.add_row((name_override if name_override else bold(self.name),
MultiColumn(size=1, align='r|', data=TextColor('blue', self.fe_id))))
tabular.add_hline()
type_str = f"{self.type}"
match = re.fullmatch(r'^<.* \'(?P<typ>.*)\'>$', type_str)
type_str = match.group("typ") if match else type_str
tabular.add_row(("Type: ", escape_latex(type_str)))
if self.path:
if isinstance(self.path, LatexObject):
tabular.add_row(("", self.path))
else:
tabular.add_row(("", escape_latex(self.path)))
for k, v in self.fields.items():
tabular.add_hline()
tabular.add_row((f"{k.capitalize()}: ", v))
if self.args:
tabular.add_hline()
tabular.add_row(("Args: ", self.args))
if extra_rows:
for (key, val) in extra_rows:
tabular.add_hline()
tabular.add_row(key, val)
if self.kwargs:
tabular.add_hline()
for idx, (kwarg, val) in enumerate(self.kwargs.items()):
tabular.add_row((italic(kwarg), val), color='white' if idx % 2 else 'black!5')
def _document_models(self) -> None:
"""Add model summaries to the traceability document.
"""
with self.doc.create(Section("Models")):
for model in humansorted(self.system.network.models,
key=lambda m: m.model_name):
if not isinstance(model, (tf.keras.Model, torch.nn.Module)):
continue
self.doc.append(NoEscape(r'\FloatBarrier'))
with self.doc.create(Subsection(f"{model.model_name}")):
if isinstance(model, tf.keras.Model):
# Text Summary
summary = []
model.summary(line_length=92,
print_fn=lambda x: summary.append(x))
summary = "\n".join(summary)
self.doc.append(Verbatim(summary))
with self.doc.create(Center()):
self.doc.append(
HrefFEID(FEID(id(model)), model.model_name))
# Visual Summary
# noinspection PyBroadException
try:
file_path = os.path.join(
self.resource_dir,
"{}_{}.pdf".format(self.report_name,
model.model_name))
dot = tf.keras.utils.model_to_dot(
model, show_shapes=True, expand_nested=True)
# LaTeX \maxdim is around 575cm (226 inches), so the image must have max dimension less than
# 226 inches. However, the 'size' parameter doesn't account for the whole node height, so
# set the limit lower (100 inches) to leave some wiggle room.
dot.set('size', '100')
dot.write(file_path, format='pdf')
except Exception:
file_path = None
print(
f"FastEstimator-Warn: Model {model.model_name} could not be visualized by Traceability"
)
elif isinstance(model, torch.nn.Module):
if hasattr(model, 'fe_input_spec'):
# Text Summary
# noinspection PyUnresolvedReferences
inputs = model.fe_input_spec.get_dummy_input()
self.doc.append(
Verbatim(
pms.summary(
model.module if
self.system.num_devices > 1 else model,
inputs,
print_summary=False)))
with self.doc.create(Center()):
self.doc.append(
HrefFEID(FEID(id(model)),
model.model_name))
# Visual Summary
# Import has to be done while matplotlib is using the Agg backend
old_backend = matplotlib.get_backend() or 'Agg'
matplotlib.use('Agg')
# noinspection PyBroadException
try:
# Fake the IPython import when user isn't running from Jupyter
sys.modules.setdefault('IPython', MagicMock())
sys.modules.setdefault('IPython.display',
MagicMock())
import hiddenlayer as hl
with Suppressor():
graph = hl.build_graph(
model.module if
self.system.num_devices > 1 else model,
inputs)
graph = graph.build_dot()
graph.attr(
rankdir='TB'
) # Switch it to Top-to-Bottom instead of Left-to-Right
# LaTeX \maxdim is around 575cm (226 inches), so the image must have max dimension less
# than 226 inches. However, the 'size' parameter doesn't account for the whole node
# height, so set the limit lower (100 inches) to leave some wiggle room.
graph.attr(size="100,100")
graph.attr(margin='0')
file_path = graph.render(
filename="{}_{}".format(
self.report_name, model.model_name),
directory=self.resource_dir,
format='pdf',
cleanup=True)
except Exception:
file_path = None
print(
"FastEstimator-Warn: Model {} could not be visualized by Traceability"
.format(model.model_name))
finally:
matplotlib.use(old_backend)
else:
file_path = None
self.doc.append(
"This model was not used by the Network during training."
)
if file_path:
with self.doc.create(Figure(position='ht!')) as fig:
fig.append(
Label(
Marker(name=str(FEID(id(model))),
prefix="model")))
fig.add_image(
os.path.relpath(file_path,
start=self.save_dir),
width=NoEscape(
r'1.0\textwidth,height=0.95\textheight,keepaspectratio'
))
fig.add_caption(
NoEscape(
HrefFEID(FEID(id(model)),
model.model_name).dumps()))
def _document_models(self) -> None:
"""Add model summaries to the traceability document.
"""
with self.doc.create(Section("Models")):
for model in humansorted(self.system.network.models,
key=lambda m: m.model_name):
if not isinstance(model, (tf.keras.Model, torch.nn.Module)):
continue
self.doc.append(NoEscape(r'\FloatBarrier'))
with self.doc.create(Subsection(f"{model.model_name}")):
if isinstance(model, tf.keras.Model):
# Text Summary
summary = []
model.summary(line_length=92,
print_fn=lambda x: summary.append(x))
summary = "\n".join(summary)
self.doc.append(Verbatim(summary))
with self.doc.create(Center()):
self.doc.append(
HrefFEID(FEID(id(model)), model.model_name))
# Visual Summary
# noinspection PyBroadException
try:
file_path = os.path.join(
self.figure_dir,
f"FE_Model_{model.model_name}.pdf")
tf.keras.utils.plot_model(model,
to_file=file_path,
show_shapes=True,
expand_nested=True)
# TODO - cap output image size like in the pytorch implementation in case of huge network
# TODO - save raw .dot file in case system lacks graphviz
except Exception:
file_path = None
print(
f"FastEstimator-Warn: Model {model.model_name} could not be visualized by Traceability"
)
elif isinstance(model, torch.nn.Module):
if hasattr(model, 'fe_input_spec'):
# Text Summary
# noinspection PyUnresolvedReferences
inputs = model.fe_input_spec.get_dummy_input()
self.doc.append(
Verbatim(pms.summary(model, inputs)))
with self.doc.create(Center()):
self.doc.append(
HrefFEID(FEID(id(model)),
model.model_name))
# Visual Summary
# Import has to be done while matplotlib is using the Agg backend
old_backend = matplotlib.get_backend() or 'Agg'
matplotlib.use('Agg')
# noinspection PyBroadException
try:
# Fake the IPython import when user isn't running from Jupyter
sys.modules.setdefault('IPython', MagicMock())
sys.modules.setdefault('IPython.display',
MagicMock())
import hiddenlayer as hl
with Suppressor():
graph = hl.build_graph(model, inputs)
graph = graph.build_dot()
graph.attr(
rankdir='TB'
) # Switch it to Top-to-Bottom instead of Left-to-Right
graph.attr(
size="200,200"
) # LaTeX \maxdim is around 575cm (226 inches)
graph.attr(margin='0')
# TODO - save raw .dot file in case system lacks graphviz
file_path = graph.render(
filename=f"FE_Model_{model.model_name}",
directory=self.figure_dir,
format='pdf',
cleanup=True)
except Exception:
file_path = None
print(
"FastEstimator-Warn: Model {} could not be visualized by Traceability"
.format(model.model_name))
finally:
matplotlib.use(old_backend)
else:
self.doc.append(
"This model was not used by the Network during training."
)
if file_path:
with self.doc.create(Figure(position='ht!')) as fig:
fig.append(
Label(
Marker(name=str(FEID(id(model))),
prefix="model")))
fig.add_image(
os.path.relpath(file_path,
start=self.save_dir),
width=NoEscape(
r'1.0\textwidth,height=0.95\textheight,keepaspectratio'
))
fig.add_caption(
NoEscape(
HrefFEID(FEID(id(model)),
model.model_name).dumps()))