def draw_weights_of_net(net,
img_name_pre="",
blank_size=2,
img_save_dir="Saved_Virtualizations"):
"""
绘制网络所有的特征层的内核参数情况
:param net: 网络
:param img_name_pre: 存储的图片的前缀
:param blank_size: 样图之间的间距（pixel）
:param img_save_dir: 存储的目录
:return: None
"""
layer_number = 0
stat_dict = net.state_dict()
for dic in stat_dict.keys():
dic_splited = dic.split(".")
if dic_splited[0] == 'features' and dic_splited[2] == 'weight':
img_save_name = img_name_pre + "_weight_layer_" + str(layer_number)
weights = stat_dict[dic]
# print(dic, weights)
out_channel_num = len(weights)
in_channel_num = len(weights[0])
kernel_height = len(weights[0][0])
kernel_width = len(weights[0][0][0])
img_arr = np.array([[
0.5 for _ in range(kernel_width * in_channel_num +
(in_channel_num - 1) * blank_size)
] for _ in range(kernel_height * out_channel_num +
(out_channel_num - 1) * blank_size)])
# print(img_arr.shape)
for out_channel_number in range(out_channel_num):
for in_channel_number in range(in_channel_num):
start_row_index, start_col_index = (out_channel_number) * (
blank_size + kernel_height), (in_channel_number) * (
blank_size + kernel_width)
kernel = weights[out_channel_number][in_channel_number]
# print(kernel.shape)
for row_pixel_index in range(kernel.shape[0]):
for col_pixel_index in range(kernel.shape[1]):
img_arr[start_row_index + row_pixel_index][
start_col_index + col_pixel_index] = kernel[
row_pixel_index][col_pixel_index]
img = Image.fromarray(img_arr)
if not os.path.exists(img_save_dir):
os.mkdir(img_save_dir)
utils.draw_img(img,
os.path.join(img_save_dir, img_save_name),
plt_show=False)
layer_number += 1
def build_and_draw_img_of_features(images,
feature_index,
type="",
img_name_pre="",
img_save_dir="Saved_Virtualizations"):
"""
构建并绘制特征层的图像
:param images: 钩子函数获取的特征层，[tuple, tuple, ..., tuple]
:param feature_index: 特征序号
:param type: 特征类型('in'表示特征层的输入，'out'表示特征层的输出，'end'表示网络最终特征输出)
:param img_name_pre: 存储的特征层 img 名称前缀
:param img_save_dir: 存储的特征层 img 路径
:return: 保存的img路径
"""
# print(images.shape)
img_save_name = img_name_pre + "_feature_layer_" + str(
feature_index) + "_" + type
for i in range(len(images)):
# print("-----------img %s------------" % str(i))
image = images[i]
start_build_time = time.time()
img = build_img_of_features(image)
end_build_time = time.time()
build_duration = round((end_build_time - start_build_time) * 1000, 2)
print("build_duration:", build_duration)
if not os.path.exists(img_save_dir):
os.mkdir(img_save_dir)
img_save_path = os.path.join(img_save_dir,
img_save_name + "_of_img_" + str(i))
start_draw_time = time.time()
utils.draw_img(img, img_save_path, plt_show=False)
end_draw_time = time.time()
draw_duration = round((end_draw_time - start_draw_time) * 1000, 2)
print("draw_duration:", draw_duration)
break
return img_save_path
读取label分类信息
:return: 无
'''
with open(self.cla_data_file_path) as file:
lines = file.readlines()
# 将分类信息处理成map
for line in lines:
file_name, classification = line.strip().split(" ")
file_name = file_name.split(".")[0]
# print(file_name, classification)
self.cla_map[file_name] = int(classification) - 1
self.cla_num_list[int(classification) - 1] += 1
print("分类信息读取完毕！")
print("数据集已分类的图片个数：", len(self.cla_map))
print("已分类各类图片个数：", self.cla_num_list)
if __name__ == "__main__":
f1 = RAF_no_fl(is_train=True,
img_dir_pre_path="../data/RAF_no_fl",
RAF_img_dir_pre_path="../data/RAF")
f2 = RAF_no_fl(is_train=False,
img_dir_pre_path="../data/RAF_no_fl",
RAF_img_dir_pre_path="../data/RAF")
print(f1.__len__(), f2.__len__())
from utils.utils import draw_img
draw_img(f1.__getitem__(0)[0])
draw_img(f2.__getitem__(0)[0])
img, cla = self.test_data[index], self.test_classes[index]
# 由于存在 random_crop 等的随机处理，应该是读取的时候进行，这样每个epoch都能够获取不同的random处理
if self.transform is not None:
img = self.transform(img)
return img, cla
def __len__(self):
"""
Returns:
int: data num.
"""
if self.is_train:
return self.train_data_num
else:
return self.test_data_num
def set_transform(self, transform):
self.transform = transform
if __name__ == "__main__":
c1 = CKPlus(is_train=True, img_dir_pre_path="../data/CK+")
c2 = CKPlus(is_train=False, img_dir_pre_path="../data/CK+")
print(c1.__len__(), c2.__len__())
from utils.utils import draw_img
draw_img(c1.__getitem__(0)[0])
draw_img(c2.__getitem__(0)[0])
# 由于存在 random_crop 等的随机处理，应该是读取的时候进行，这样每个epoch都能够获取不同的random处理
if self.transform is not None:
img = self.transform(img)
return img, cla
def __len__(self):
"""
Returns:
int: data num.
"""
if self.is_train:
return self.train_data_num
else:
return self.test_data_num
def set_transform(self, transform):
self.transform = transform
if __name__ == "__main__":
j1 = JAFFE(is_train=True, img_dir_pre_path="../data/jaffe")
j2 = JAFFE(is_train=False, img_dir_pre_path="../data/jaffe")
print(j1.__len__(), j2.__len__())
from utils.utils import draw_img
draw_img(j1.__getitem__(0)[0])
draw_img(j2.__getitem__(0)[0])