def run_test_using_image_net():
exec_param_w_prune = ExecParams(n_pretrain_epoch=10,
n_epoch_retrain=3,
n_epoch_total=20,
pruner=TaylorExpansionFilterPruner)
pruning_param_w_prune = PruningParams(max_percent_per_iteration=0.2,
prune_ratio=0.2)
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
train_dataset = torchvision.datasets.ImageFolder("TrainPATH",
transform=transform)
test_dataset = torchvision.datasets.ImageFolder("TestPATH",
transform=transform)
dataset_params = DatasetParams(transform, train_dataset, test_dataset)
multi_history = MultiHistory()
h, s = exec_resnet18(pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune,
dataset_params=dataset_params)
multi_history.append_history("Resnet18 20%", h)
multi_history.display_single_key(History.VAL_ACC_KEY, title="Image_net")
def run_fast_validation(dataset_params):
multi_history = MultiHistory()
exec_param = ExecParams(n_pretrain_epoch=1,
n_epoch_retrain=1,
n_epoch_total=3,
pruner=ActivationMeanFilterPruner)
exec_name = "Alexnet test"
h, s = exec_alexnet(exec_name,
PruningParams(max_percent_per_iteration=0.2,
prune_ratio=0.2),
exec_params=exec_param,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY, title="TEST_RUN")
def run_alex_prune_compare_soft(dataset_params):
multi_history = MultiHistory()
exec_param = ExecParams(n_pretrain_epoch=3,
n_epoch_retrain=1,
n_epoch_total=15,
pruner=TaylorExpansionFilterPruner)
exec_param_no_prune = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=0,
n_epoch_total=15,
pruner=TaylorExpansionFilterPruner)
exec_name = "Alexnet 0%"
h, s = exec_alexnet(exec_name,
PruningParams(max_percent_per_iteration=0.0,
prune_ratio=None),
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
exec_name = "Alexnet 10%"
h, s = exec_alexnet(exec_name,
PruningParams(max_percent_per_iteration=0.01,
prune_ratio=0.1),
exec_params=exec_param,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
exec_name = "Alexnet 30%"
h, s = exec_alexnet(exec_name,
PruningParams(max_percent_per_iteration=0.03,
prune_ratio=0.3),
exec_params=exec_param,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
exec_name = "Alexnet 50%"
h, s = exec_alexnet(exec_name,
PruningParams(max_percent_per_iteration=0.05,
prune_ratio=0.5),
exec_params=exec_param,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
exec_name = "Alexnet 75%"
h, s = exec_alexnet(exec_name,
PruningParams(max_percent_per_iteration=0.075,
prune_ratio=0.75),
exec_params=exec_param,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
save_obj(multi_history, "history_alex_soft")
multi_history.display_single_key(
History.VAL_ACC_KEY, title="Comparing AlexNet by Level of Pruning")
def run_strategy_prune_compare(dataset_params):
exec_param_no_prune_large = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=0,
n_epoch_total=15,
batch_size=16,
pruner=TaylorExpansionFilterPruner)
exec_param_no_prune_medium = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=0,
n_epoch_total=15,
batch_size=32,
pruner=TaylorExpansionFilterPruner)
exec_param_w_prune_large = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=16,
pruner=TaylorExpansionFilterPruner)
exec_param_w_prune_medium = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=32,
pruner=TaylorExpansionFilterPruner)
exec_param_no_prune = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=0,
n_epoch_total=15,
batch_size=64,
pruner=TaylorExpansionFilterPruner)
exec_param_w_prune = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=TaylorExpansionFilterPruner)
exec_param_w_prune_squeeze = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=TaylorExpansionFilterPruner,
force_forward_view=True)
pruning_param_no_prune = PruningParams(max_percent_per_iteration=0.0,
prune_ratio=None)
pruning_param_w_prune = PruningParams(max_percent_per_iteration=0.05,
prune_ratio=0.3)
multi_history = MultiHistory()
exec_name = "SqueezeNet-0"
h, s = exec_squeeze_net(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
exec_name = "SqueezeNet-30"
h, s = exec_squeeze_net(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune_squeeze,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
exec_name = "densenet 121-0"
h, s = exec_dense_net(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune_large,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
exec_name = "densenet 121-30"
h, s = exec_dense_net(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune_large,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
exec_name = "Resnet 50-0"
h, s = exec_resnet50(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune_medium,
dataset_params=dataset_params,
out_count=10)
multi_history.append_history(exec_name, h)
exec_name = "Resnet 50-30"
h, s = exec_resnet50(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune_medium,
dataset_params=dataset_params,
out_count=10)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
#create a second history since I am not sure it will look nice in one graph
multi_history2 = MultiHistory()
exec_name = "vgg16 0"
h, s = exec_vgg16(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune_medium,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history2.append_history(exec_name, h)
exec_name = "vgg16 30"
h, s = exec_vgg16(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune_medium,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history2.append_history(exec_name, h)
exec_name = "Resnet 18-0"
h, s = exec_resnet18(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params,
out_count=10)
multi_history.append_history(exec_name, h)
multi_history2.append_history(exec_name, h)
exec_name = "Resnet 18-30"
h, s = exec_resnet18(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune,
dataset_params=dataset_params,
out_count=10)
multi_history.append_history(exec_name, h)
multi_history2.append_history(exec_name, h)
exec_name = "Alexnet 0"
h, s = exec_alexnet(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history2.append_history(exec_name, h)
exec_name = "Alexnet 30"
h, s = exec_alexnet(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history2.append_history(exec_name, h)
save_obj(multi_history, "history_compare")
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
multi_history2.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
def run_strategy_prune_compare_taylor(dataset_params):
exec_param_no_prune = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=0,
n_epoch_total=15,
batch_size=64,
pruner=ActivationMeanFilterPrunerV2)
exec_param_w_prune_2 = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=TaylorExpansionFilterPrunerV2)
exec_param_w_prune_3 = ExecParams(
n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=CompleteTaylorExpansionFilterPruner)
exec_param_w_prune_4 = ExecParams(
n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=CompleteTaylorExpansionFilterPruner)
exec_param_w_prune_t = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=TaylorExpansionFilterPruner)
exec_param_w_prune_o = ExecParams(n_pretrain_epoch=5,
n_epoch_retrain=1,
n_epoch_total=15,
batch_size=64,
pruner=ActivationMeanFilterPruner)
pruning_param_no_prune = PruningParams(max_percent_per_iteration=0.0,
prune_ratio=None)
pruning_param_w_prune = PruningParams(max_percent_per_iteration=0.075,
prune_ratio=0.30)
pruning_param_w_prune2 = PruningParams(max_percent_per_iteration=0.05,
prune_ratio=0.15)
multi_history = MultiHistory()
exec_name = "Squeeze - 30 full"
h, s = exec_squeeze_net(exec_name,
pruning_params=pruning_param_w_prune2,
exec_params=exec_param_w_prune_4,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
exec_name = "Squeeze - 30 Simple"
h, s = exec_squeeze_net(exec_name,
pruning_params=pruning_param_w_prune,
exec_params=exec_param_w_prune_o,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-0"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_no_prune, exec_params=exec_param_no_prune,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# exec_name = "Resnet 18-30-Simple_prune"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune, exec_params=exec_param_w_prune_o,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# multi_history.display_single_key(History.VAL_ACC_KEY, title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-30-v2"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune, exec_params=exec_param_w_prune_2,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# multi_history.display_single_key(History.VAL_ACC_KEY, title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-30-v3"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune, exec_params=exec_param_w_prune_3,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# multi_history.display_single_key(History.VAL_ACC_KEY, title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-30-v3-2"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune2, exec_params=exec_param_w_prune_3,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# multi_history.display_single_key(History.VAL_ACC_KEY, title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-30-v4-p1"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune, exec_params=exec_param_w_prune_4,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# multi_history.display_single_key(History.VAL_ACC_KEY, title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-30-v4-p2"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune2, exec_params=exec_param_w_prune_4,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# multi_history.display_single_key(History.VAL_ACC_KEY, title="Comparing Models at 30% Pruning")
# exec_name = "Resnet 18-30-Taylor"
# h, s = exec_resnet18(exec_name, pruning_params=pruning_param_w_prune, exec_params=exec_param_w_prune_t,
# dataset_params=dataset_params, out_count=10)
# multi_history.append_history(exec_name, h)
# exec_name = "Alexnet 0"
# h, s = exec_alexnet(exec_name, pruning_params=pruning_param_no_prune, exec_params=exec_param_no_prune,
# dataset_params=dataset_params)
# multi_history.append_history(exec_name, h)
# exec_name = "Alexnet 30"
# h, s = exec_alexnet(exec_name, pruning_params=pruning_param_w_prune, exec_params=exec_param_w_prune,
# dataset_params=dataset_params)
# multi_history.append_history(exec_name, h)
save_obj(multi_history, "history_compare")
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Comparing Models at 30% Pruning")
def train_models(dataset_params):
pruning_param_no_prune = PruningParams(max_percent_per_iteration=0.0,
prune_ratio=None)
exec_param_no_prune = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=0,
n_epoch_total=15,
batch_size=32,
pruner=None)
multi_history = MultiHistory()
exec_name = "AlexNet-base"
h, s = exec_alexnet(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")
exec_name = "ResNet18-base"
h, s = exec_resnet18(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")
exec_name = "ResNet34-base"
h, s = exec_resnet34(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")
exec_name = "ResNet50-base"
h, s = exec_resnet50(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")
exec_name = "Squeeze-base"
h, s = exec_squeeze_net(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")
exec_name = "DenseNet121-base"
h, s = exec_dense_net(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")
exec_name = "VGG16-base"
h, s = exec_vgg16(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_no_prune,
dataset_params=dataset_params)
multi_history.append_history(exec_name, h)
multi_history.display_single_key(History.VAL_ACC_KEY,
title="Models training without pruning")