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PruningVGG1316.py
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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from VGG13 import vgg13
from NEUCLSDataLoad import NEUCLASSDATA
import numpy as np
import os
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
BATCH_SIZE = 1
LEARNING_RATE = 0.001
original_list_name = []
weights_l1 = []
list_acc = []
remove_index = {}
base_min_acc = 98.00
original_layer = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']
model = vgg13(original_layer).to(device)
model.eval()
train_data, test_data = NEUCLASSDATA()._get_data_from_index()
train_data_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)
test_data_loader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=True)
def _get_Name():
for name in model.state_dict():
original_list_name.append(name)
def _load_model():
path = "./SaveInfo/VGG16/Para/resnet18/"
for name in original_list_name:
temp_load_numpy = np.load(path + "%s.ndim.npy" % (name))
tensor_load = torch.tensor(temp_load_numpy)
model.state_dict()[name].copy_(tensor_load)
def _computer_L1_value():
for module in model.modules():
if isinstance(module, nn.Conv2d):
weights_copy = module.weight.data.abs().clone()
weights_copy = weights_copy.cpu().numpy()
weights_copy_l1 = np.sum(weights_copy, axis=(1, 2, 3))
weights_l1.append(weights_copy_l1.tolist())
#寻找层中最N个最小值的坐标
def _get_minValue_index(value, nums):
value_sort = np.argsort(value)
index = value_sort[:nums]
return index
# #获取当前索引层的名称
def _get_layer_name(index):
if index < 13:
start = index * 6
end = (index + 1) * 6
else:
start = index * 6
end = len(original_list_name)
feature_name = original_list_name[start:end]
return feature_name
def _pruning_iter():
pruning_layer = [65, 63, 'M', 128, 128, 'M', 254, 253, 255, 'M', 507, 512, 511, 'M', 512, 512, 512, 'M']
layer_sign = [True]*len(pruning_layer)
for i in range(len(pruning_layer)):
if pruning_layer[i] == "M": layer_sign[i] = False
while True:
for i in range(len(pruning_layer)):
if layer_sign[i] is False: continue
pruning_layer[i] = pruning_layer[i] - 1
pruning_model = vgg13(pruning_layer).to(device)
print("-------------------------------------------------------------------------------")
print("Current Layer Infor:")
print(pruning_layer)
print("-------------------------------------------------------------------------------")
for j in range(len(weights_l1)):
feature_name = _get_layer_name(j)
for name in feature_name:
original_weight_shape = model.state_dict()[feature_name[0]].shape
pruning_weight_shape = pruning_model.state_dict()[feature_name[0]].shape
axis_0_dis = original_weight_shape[0] - pruning_weight_shape[0]
axis_1_dis = original_weight_shape[1] - pruning_weight_shape[1]
index_0_pruning = _get_minValue_index(weights_l1[j], axis_0_dis)
remove_index[j] = index_0_pruning
temp_feature = model.state_dict()[name].cpu()
if axis_1_dis > 0 and len(temp_feature.shape) > 1:
temp_feature = _resizeFeatureMap(temp_feature, remove_index[j-1], 1)
if axis_0_dis > 0 and len(temp_feature.shape) > 0:
temp_feature = _resizeFeatureMap(temp_feature, remove_index[j], 0)
pruning_model.state_dict()[name].copy_(temp_feature)
class_name = _get_layer_name(13)
for name in class_name:
if len(model.state_dict()[name].shape) > 1:
for t in range(model.state_dict()[name].shape[0]):
pruning_model.state_dict()[name][t].copy_(model.state_dict()[name][t][:pruning_model.state_dict()[name].shape[1]])
else:
pruning_model.state_dict()[name].copy_(model.state_dict()[name])
for _ in range(10):
list_acc.append(_test_model(pruning_model))
print("---------------------本轮剪枝精度-------------------------")
print(list_acc)
pruning_min_acc = min(list_acc)
# list_acc.clear()
# save_sign = True
# print("本轮 min 精度：" + str(pruning_min_acc) + "%")
# if pruning_min_acc < base_min_acc:
# layer_sign[i] = False
# pruning_layer[i] = pruning_layer[i] + 1
# save_sign = False
# if save_sign:
# _save_model(pruning_model)
_save_model(pruning_model)
break
# print(layer_sign)
# if True not in layer_sign:
# print("Goal Layer Numbers: " + str(pruning_layer))
# _saveLayer(pruning_layer, 'pruning_layer_info_iter1')
# break
break
def _resizeFeatureMap(featureMap, index, fm_type):
featureMap = featureMap.numpy()
if fm_type == 0:
featureMap = np.delete(featureMap, obj=index, axis=0)
return torch.tensor(featureMap)
else:
featureMap = np.delete(featureMap, obj=index, axis=1)
return torch.tensor(featureMap)
def _test_model(pruning_model):
with torch.no_grad():
total = 0
correct = 0
for data in test_data_loader:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = pruning_model(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
return round((correct / total) * 100, 2)
def _save_model(pruning_model):
path = "./SaveInfo/VGG16/Para/PruningModel/"
if not os.path.exists(path):
os.makedirs(path)
for name in original_list_name:
temp_np = pruning_model.state_dict()[name].cpu().numpy()
np.save(path + "%s.ndim" % (name), temp_np)
def _saveLayer(layer_info, layer_name):
with open("./SaveResult/PruningResult/"+str(layer_name)+".txt", 'w') as fw:
for layer in layer_info:
fw.write(str(layer)+"-")
fw.close()
if __name__ == "__main__":
_get_Name()
_load_model()
_computer_L1_value()
_pruning_iter()