tnt.joint(model=ppnet_multi, log=log)
joint_lr_scheduler.step()
_ = tnt.train(model=ppnet_multi,
dataloader=train_loader,
optimizer=joint_optimizer,
class_specific=class_specific,
coefs=coefs,
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) + '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=
prune_threshold=prune_threshold,
preprocess_input_function=preprocess_input_function, # normalize
original_model_dir=original_model_dir,
epoch_number=epoch,
#model_name=None,
log=log,
copy_prototype_imgs=True)
accu = tnt.test(model=ppnet_multi,
dataloader=test_loader,
dataloader1=inst_loader,
class_specific=class_specific,
log=log)
save.save_model_w_condition(model=ppnet,
model_dir=model_dir,
model_name=original_model_name.split('push')[0] +
'prune',
accu=accu,
target_accu=0.70,
log=log)
# last layer optimization
if optimize_last_layer:
last_layer_optimizer_specs = [{
'params': ppnet.last_layer.parameters(),
'lr': 1e-4
}]
last_layer_optimizer = torch.optim.Adam(last_layer_optimizer_specs)
coefs = {
'crs_ent': 1,
'clst': 0.8,
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)
save.save_model_w_condition(model=vgg,
model_dir=model_dir,
model_name='best_model_protos_opt',
accu=acc,
target_accu=best_acc,
log=log)
is_best = acc > best_acc
best_acc = max(acc, best_acc)
if is_best:
best_epoch = epoch
tnt.last_layers(model=vgg_multi, log=log)
_ = tnt.train(model=vgg_multi,
dataloader=train_loader,
label2name=label2name,
optimizer=last_layers_optimizer,
args=args,
class_specific=class_specific,
prune_threshold=prune_threshold,
preprocess_input_function=preprocess_input_function, # normalize
original_model_dir=original_model_dir,
epoch_number=epoch,
# model_name=None,
log=log,
copy_prototype_imgs=True,
)
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=original_model_name.split("push")[0] + "prune",
accu=accu,
target_accu=0.70,
log=log,
)
# last layer optimization
if optimize_last_layer:
last_layer_optimizer_specs = [{
"params": ppnet.last_layer.parameters(),
"lr": 1e-4
}]
last_layer_optimizer = torch.optim.Adam(last_layer_optimizer_specs)
coefs = {
"crs_ent": 1,
"clst": 0.8,