Datasets:
jblitzar
/
github-python-corpus

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writer.add_scalar("{}".format(l), v, self.step)
for j in range(min(4, self.opt.batch_size)): # write a maxmimum of four images
for s in self.opt.scales:
for frame_id in self.opt.frame_ids:
writer.add_image(
"color_{}_{}/{}".format(frame_id, s, j),
inputs[("color", frame_id, s)][j].data, self.step)
if s == 0 and frame_id != 0:
writer.add_image(
"color_pred_{}_{}/{}".format(frame_id, s, j),
outputs[("color", frame_id, s)][j].data, self.step)
writer.add_image(
"disp_{}/{}".format(s, j),
normalize_image(outputs[("disp", s)][j]), self.step)
if self.opt.predictive_mask:
for f_idx, frame_id in enumerate(self.opt.frame_ids[1:]):
writer.add_image(
"predictive_mask_{}_{}/{}".format(frame_id, s, j),
outputs["predictive_mask"][("disp", s)][j, f_idx],
self.step)
elif not self.opt.disable_automasking:
writer.add_image(
"automask_{}/{}".format(s, j),
outputs["identity_selection/{}".format(s)][j], self.step)
# depth hint logging
if self.opt.use_depth_hints:
if s == 0:
disp = 1 / (inputs['depth_hint'] + 1e-7) * inputs['depth_hint_mask']
writer.add_image(
"depth_hint/{}".format(j),
normalize_image(disp[j]), self.step)
writer.add_image(
"depth_hint_pixels_{}/{}".format(s, j),
outputs["depth_hint_pixels/{}".format(s)][j][None, ...], self.step)
def save_opts(self):
"""Save options to disk so we know what we ran this experiment with
"""
models_dir = os.path.join(self.log_path, "models")
if not os.path.exists(models_dir):
os.makedirs(models_dir)
to_save = self.opt.__dict__.copy()
with open(os.path.join(models_dir, 'opt.json'), 'w') as f:
json.dump(to_save, f, indent=2)
def save_model(self):
"""Save model weights to disk
"""
save_folder = os.path.join(self.log_path, "models", "weights_{}".format(self.epoch))
if not os.path.exists(save_folder):
os.makedirs(save_folder)
for model_name, model in self.models.items():
save_path = os.path.join(save_folder, "{}.pth".format(model_name))
to_save = model.state_dict()
if model_name == 'encoder':
# save the sizes - these are needed at prediction time
to_save['height'] = self.opt.height
to_save['width'] = self.opt.width
to_save['use_stereo'] = self.opt.use_stereo
torch.save(to_save, save_path)
save_path = os.path.join(save_folder, "{}.pth".format("adam"))
torch.save(self.model_optimizer.state_dict(), save_path)
def load_model(self):
"""Load model(s) from disk"""
self.opt.load_weights_folder = os.path.expanduser(self.opt.load_weights_folder)
assert os.path.isdir(self.opt.load_weights_folder), f"Cannot find folder {self.opt.load_weights_folder}"
print("loading model from folder {}".format(self.opt.load_weights_folder))
for n in self.opt.models_to_load:
print("Loading {} weights...".format(n))
path = os.path.join(self.opt.load_weights_folder, "{}.pth".format(n))
model_dict = self.models[n].state_dict()
pretrained_dict = torch.load(path)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
self.models[n].load_state_dict(model_dict)
print(f"Loaded {len(pretrained_dict)} terms from weights to {n} ({len(model_dict)}).")
# loading adam state
optimizer_load_path = os.path.join(self.opt.load_weights_folder, "adam.pth")
if os.path.isfile(optimizer_load_path):
print("Loading Adam weights")
optimizer_dict = torch.load(optimizer_load_path)
self.model_optimizer.load_state_dict(optimizer_dict)
else:
print("Cannot find Adam weights so Adam is randomly initialized")
# <FILESEP>
"""An example of how to setup and start an Accessory.