def push_1epoch(self):
print('==> Epoch:[{0}/{1}][pushing stage]'.format(
self.epoch, self.args.num_epochs))
push.push_prototypes(
self.device,
self.
push_loader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel=self.
model, # pytorch network with prototype_vectors
class_specific=True,
preprocess_input_function=None, # normalize if needed
prototype_layer_stride=1,
epoch_number=self.
epoch, # if not provided, prototypes saved previously will be overwritten
root_dir_for_saving_prototypes=os.path.join(
self.log_dir, 'prototypes'),
prototype_img_filename_prefix='prototype_',
prototype_self_act_filename_prefix='prototype_act_',
proto_bound_boxes_filename_prefix='prototype_bb_',
save_prototype_class_identity=True)
step=step,
weighting_attention=args.weighting_attention)
push_model_state_epoch = None
if epoch >= config.push_start and epoch in config.push_epochs:
mode = TrainMode.PUSH
else:
epoch += 1
elif mode == TrainMode.PUSH:
write_mode(TrainMode.PUSH, log_writer, step)
push_prototypes(
train_push_loader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel=ppnet,
class_specific=config.class_specific,
# preprocess_input_function=preprocess_input_function, # normalize if needed
prototype_layer_stride=1,
root_dir_for_saving_prototypes=img_dir,
epoch_number=epoch,
prototype_img_filename_prefix=prototype_img_filename_prefix,
prototype_self_act_filename_prefix=
prototype_self_act_filename_prefix,
proto_bound_boxes_filename_prefix=proto_bound_boxes_filename_prefix,
save_prototype_class_identity=True)
accu = valid(model=ppnet,
dataloader=valid_loader,
config=config,
log_writer=log_writer,
step=step,
weighting_attention=args.weighting_attention)
push_model_state_epoch = epoch
current_push_best_accu = 0.
if config.mil_pooling == 'gated_attention' and not ppnet.mil_pooling == 'gated_attention':
model_dir=model_dir,
model_name=str(epoch) + 'nopush',
accu=accu,
target_accu=0.70,
log=log)
if epoch >= push_start and epoch in push_epochs:
push.push_prototypes(
train_push_loader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel=
ppnet_multi, # pytorch network with prototype_vectors
class_specific=class_specific,
preprocess_input_function=
preprocess_input_function, # normalize if needed
prototype_layer_stride=1,
root_dir_for_saving_prototypes=
img_dir, # if not None, prototypes will be saved here
epoch_number=
epoch, # if not provided, prototypes saved previously will be overwritten
prototype_img_filename_prefix=prototype_img_filename_prefix,
prototype_self_act_filename_prefix=
prototype_self_act_filename_prefix,
proto_bound_boxes_filename_prefix=proto_bound_boxes_filename_prefix,
save_prototype_class_identity=True,
log=log)
accu = tnt.test(model=ppnet_multi,
dataloader=test_loader,
class_specific=class_specific,
log=log)
save.save_model_w_condition(model=ppnet,
model_dir=model_dir,
model_name=str(epoch) + 'push',
label2name=label2name,
optimizer=through_protos_optimizer,
args=args,
class_specific=class_specific,
log=log)
if epoch > 0 and epoch % args.push_every == 0 or epoch == through_proto_opt - 1:
push.push_prototypes(
train_push_loader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel=
vgg_multi, # pytorch network with prototype_vectors
label2name=label2name,
class_specific=class_specific,
preprocess_input_function=
preprocess_input_function, # normalize if needed
prototype_layer_stride=1,
root_dir_for_saving_prototypes=
model_dir, # if not None, prototypes will be saved here
prototype_img_filename_prefix=prototype_img_filename_prefix,
prototype_original_img_filename_prefix=
prototype_original_img_filename_prefix,
proto_bound_boxes_filename_prefix=
proto_bound_boxes_filename_prefix,
save_prototype_class_identity=True,
log=log)
acc, _ = tnt.valid(model=vgg_multi,
dataloader=valid_loader,
label2name=label2name,
args=args,
class_specific=class_specific,
log=log)
