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_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_compare_pruning(dataset_params, retrain, total, split_ratio=1.0):
debug_params = DebugHelper(only_test_end=True)
pruning_param_no_prune = PruningParams(max_percent_per_iteration=0.0,
prune_ratio=0.0)
exec_param = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=retrain,
n_epoch_total=total,
batch_size=64,
pruner=CompleteTaylorExpansionFilterPruner)
exec_param_large = ExecParams(n_pretrain_epoch=0,
n_epoch_retrain=retrain,
n_epoch_total=total,
batch_size=32,
pruner=CompleteTaylorExpansionFilterPruner)
all_scores = {}
exec_name = "AlexNet-degrad"
score = []
exec_param.best_result_save_path = "../saved/AlexNet-base/Pruned.pth".format(
exec_name)
exec_param.retrain_if_weight_loaded = True
for i in range(0, 11):
desired_pruning = (5.0 * i) / 100.0
if desired_pruning != 0.0:
pruning_param_no_prune.max_percent_per_iteration = desired_pruning * split_ratio
pruning_param_no_prune.prune_ratio = desired_pruning
else:
pruning_param_no_prune.max_percent_per_iteration = None
pruning_param_no_prune.prune_ratio = None
h, s = exec_alexnet(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param,
dataset_params=dataset_params,
debug_params=debug_params)
score.append(s)
all_scores["AlexNet"] = score
display_graphs(score, all_scores, "AlexNet")
exec_name = "ResNet18-degrad"
score = []
exec_param.best_result_save_path = "../saved/ResNet18-base/Pruned.pth".format(
exec_name)
exec_param.retrain_if_weight_loaded = True
for i in range(0, 11):
desired_pruning = (5.0 * i) / 100.0
if desired_pruning != 0.0:
pruning_param_no_prune.max_percent_per_iteration = desired_pruning * split_ratio
pruning_param_no_prune.prune_ratio = desired_pruning
else:
pruning_param_no_prune.max_percent_per_iteration = None
pruning_param_no_prune.prune_ratio = None
h, s = exec_resnet18(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param,
dataset_params=dataset_params,
debug_params=debug_params)
score.append(s)
all_scores["ResNet18"] = score
display_graphs(score, all_scores, "ResNet18")
# exec_name = "ResNet34-degrad"
# score = []
# exec_param.best_result_save_path = "../saved/ResNet34-base/Pruned.pth".format(exec_name)
# exec_param.retrain_if_weight_loaded = True
# for i in range(0, 11):
# desired_pruning = (5.0 * i)/100.0
# if desired_pruning != 0.0:
# pruning_param_no_prune.max_percent_per_iteration = desired_pruning * split_ratio
# pruning_param_no_prune.prune_ratio = desired_pruning
# else:
# pruning_param_no_prune.max_percent_per_iteration = None
# pruning_param_no_prune.prune_ratio = None
# h, s = exec_resnet34(exec_name, pruning_params=pruning_param_no_prune, exec_params=exec_param,
# dataset_params=dataset_params, debug_params=debug_params)
# score.append(s)
#
# all_scores["ResNet34"] = score
# display_graphs(score, all_scores, "ResNet34")
#
# exec_name = "ResNet50-degrad"
# score = []
# exec_param.best_result_save_path = "../saved/ResNet50-base/Pruned.pth".format(exec_name)
# exec_param.retrain_if_weight_loaded = True
# for i in range(0, 11):
# desired_pruning = (5.0 * i)/100.0
# if desired_pruning != 0.0:
# pruning_param_no_prune.max_percent_per_iteration = desired_pruning * split_ratio
# pruning_param_no_prune.prune_ratio = desired_pruning
# else:
# pruning_param_no_prune.max_percent_per_iteration = None
# pruning_param_no_prune.prune_ratio = None
# h, s = exec_resnet50(exec_name, pruning_params=pruning_param_no_prune, exec_params=exec_param,
# dataset_params=dataset_params, debug_params=debug_params)
# score.append(s)
#
# all_scores["ResNet50"] = score
# display_graphs(score, all_scores, "ResNet50")
exec_name = "VGG16-degrad"
score = []
exec_param.best_result_save_path = "../saved/VGG16-base/Pruned.pth".format(
exec_name)
exec_param.retrain_if_weight_loaded = True
for i in range(0, 11):
desired_pruning = (5.0 * i) / 100.0
if desired_pruning != 0.0:
pruning_param_no_prune.max_percent_per_iteration = desired_pruning * split_ratio
pruning_param_no_prune.prune_ratio = desired_pruning
else:
pruning_param_no_prune.max_percent_per_iteration = None
pruning_param_no_prune.prune_ratio = None
h, s = exec_dense_net(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_large,
dataset_params=dataset_params,
debug_params=debug_params)
score.append(s)
all_scores["VGG16"] = score
display_graphs(score, all_scores, "VGG16")
exec_name = "DenseNet121-degrad"
score = []
exec_param.best_result_save_path = "../saved/DenseNet121-base/Pruned.pth".format(
exec_name)
exec_param.retrain_if_weight_loaded = True
for i in range(0, 11):
desired_pruning = (5.0 * i) / 100.0
if desired_pruning != 0.0:
pruning_param_no_prune.max_percent_per_iteration = desired_pruning * split_ratio
pruning_param_no_prune.prune_ratio = desired_pruning
else:
pruning_param_no_prune.max_percent_per_iteration = None
pruning_param_no_prune.prune_ratio = None
h, s = exec_dense_net(exec_name,
pruning_params=pruning_param_no_prune,
exec_params=exec_param_large,
dataset_params=dataset_params,
debug_params=debug_params)
score.append(s)
all_scores["DenseNet121"] = score
display_graphs(score, all_scores, "DenseNet121")
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 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")