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show_feature_map.py
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show_feature_map.py
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import torch
import torchvision.transforms as transforms
import skimage.data
import skimage.io
import skimage.transform
import numpy as np
import matplotlib.pyplot as plt
from completion_segmentation_model import DepthCompletionFrontNet
# from completion_segmentation_model_v3_eca_attention import DepthCompletionFrontNet
import math
# https://blog.csdn.net/missyougoon/article/details/85645195
# https://blog.csdn.net/grayondream/article/details/99090247
# 定义是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 定义数据预处理方式(将输入的类似numpy中arrary形式的数据转化为pytorch中的张量(tensor))
transform = transforms.ToTensor()
def get_picture(picture_dir, transform):
'''
该算法实现了读取图片,并将其类型转化为Tensor
'''
img = skimage.io.imread(picture_dir)
img256 = skimage.transform.resize(img, (128, 256))
img256 = np.asarray(img256)
img256 = img256.astype(np.float32)
return transform(img256)
def get_picture_rgb(picture_dir):
'''
该函数实现了显示图片的RGB三通道颜色
'''
img = skimage.io.imread(picture_dir)
img256 = skimage.transform.resize(img, (256, 256))
skimage.io.imsave('4.jpg', img256)
# 取单一通道值显示
# for i in range(3):
# img = img256[:,:,i]
# ax = plt.subplot(1, 3, i + 1)
# ax.set_title('Feature {}'.format(i))
# ax.axis('off')
# plt.imshow(img)
# r = img256.copy()
# r[:,:,0:2]=0
# ax = plt.subplot(1, 4, 1)
# ax.set_title('B Channel')
# # ax.axis('off')
# plt.imshow(r)
# g = img256.copy()
# g[:,:,0]=0
# g[:,:,2]=0
# ax = plt.subplot(1, 4, 2)
# ax.set_title('G Channel')
# # ax.axis('off')
# plt.imshow(g)
# b = img256.copy()
# b[:,:,1:3]=0
# ax = plt.subplot(1, 4, 3)
# ax.set_title('R Channel')
# # ax.axis('off')
# plt.imshow(b)
# img = img256.copy()
# ax = plt.subplot(1, 4, 4)
# ax.set_title('image')
# # ax.axis('off')
# plt.imshow(img)
img = img256.copy()
ax = plt.subplot()
ax.set_title('image')
# ax.axis('off')
plt.imshow(img)
plt.show()
def visualize_feature_map_sum(item, name):
'''
将每张子图进行相加
:param feature_batch:
:return:
'''
feature_map = item.squeeze(0)
c = item.shape[1]
print(feature_map.shape)
feature_map_combination = []
for i in range(0, c):
feature_map_split = feature_map.data.cpu().numpy()[i, :, :]
feature_map_combination.append(feature_map_split)
feature_map_sum = sum(one for one in feature_map_combination)
# feature_map = np.squeeze(feature_batch,axis=0)
plt.figure()
plt.title("combine figure")
plt.imshow(feature_map_sum)
plt.savefig('E:/Dataset/qhms/feature_map/feature_map_sum_' + name + '.png') # 保存图像到本地
plt.show()
def get_feature():
# 输入数据
root_path = 'E:/Dataset/qhms/data/small_data/'
pic_dir = 'test_umm_000067.png'
pc_path = root_path + 'knn_pc_crop_0.6/' + pic_dir
rgb_path = root_path + 'train_image_2_lane_crop_0.6/' + pic_dir
img_rgb = get_picture(rgb_path, transform)
# 插入维度
img_rgb = img_rgb.unsqueeze(0)
img_rgb = img_rgb.to(device)
img_pc = get_picture(pc_path, transform)
# 插入维度
img_pc = img_pc.unsqueeze(0)
img_pc = img_pc.to(device)
# 加载模型
checkpoint = torch.load('E:/Dataset/qhms/all_result/v3/crop_0.6_old/hah/checkpoint-195.pth.tar')
args = checkpoint['args']
print(args)
model = DepthCompletionFrontNet(args)
print(model.keys())
model.load_state_dict(checkpoint['model'])
model.to(device)
exact_list = ["conv1", "conv2", "conv3", "conv4", "convt4", "convt3", "convt2_", "convt1_", "lane"]
# myexactor = FeatureExtractor(model, exact_list)
img1 = {
'pc': img_pc, 'rgb': img_rgb
}
# print(img1['pc'])
# x = myexactor(img1)
result, all_dict = model(img1)
outputs = []
# 挑选exact_list的层
for item in exact_list:
x = all_dict[item]
outputs.append(x)
# 特征输出可视化
x = outputs
k = 0
print(x[0].shape[1])
for item in x:
c = item.shape[1]
plt.figure()
name = exact_list[k]
plt.suptitle(name)
for i in range(c):
wid = math.ceil(math.sqrt(c))
ax = plt.subplot(wid, wid, i + 1)
ax.set_title('Feature {}'.format(i))
ax.axis('off')
figure_map = item.data.cpu().numpy()[0, i, :, :]
plt.imshow(figure_map, cmap='jet')
plt.savefig('E:/Dataset/qhms/feature_map/feature_map_' + name + '.png') # 保存图像到本地
visualize_feature_map_sum(item, name)
k = k + 1
plt.show()
# 训练
if __name__ == "__main__":
# get_picture_rgb(pic_dir)
get_feature()