学习笔记|Pytorch使用教程31
本学习笔记主要摘自“深度之眼”，做一个总结，方便查阅。
使用Pytorch版本为1.2
- 模型是如何将图像分类的?
- resnet18模型inference代码
- resnet18结构分析
一.模型是如何将图像分类的?
3-d张量>字符串
1.类别名与标签的转换
- label_name = {“ants”: 0, “bees”: 1}
2.取输出向量最大值的标号
- _,predicted = torch.max(outputs.data, 1)
3.复杂运算
- outptus = resnet18(img_tensor)
二.resnet18模型inference代码
图像分类的Inference(推理)
步骤:
- 1.获取数据与标签
- 2.选择模型，损失函数，优化器
- 3.写训练代码
- 4.写inference代码
Inference代码基本步骤:
- 1.获取数据与模型
- 2.数据变换，如RGB > 4D-Tensor
- 3.前向传播
- 4.输出保存预测结果
查看resnet reference代码
import os
import time
import torch.nn as nn
import torch
import torchvision.transforms as transforms
from PIL import Image
from matplotlib import pyplot as plt
import torchvision.models as models
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu")
# config
vis = True
# vis = False
vis_row = 4
norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]
inference_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
classes = ["ants", "bees"]
def img_transform(img_rgb, transform=None):
"""
将数据转换为模型读取的形式
:param img_rgb: PIL Image
:param transform: torchvision.transform
:return: tensor
"""
if transform is None:
raise ValueError("找不到transform！必须有transform对img进行处理")
img_t = transform(img_rgb)
return img_t
def get_img_name(img_dir, format="jpg"):
"""
获取文件夹下format格式的文件名
:param img_dir: str
:param format: str
:return: list
"""
file_names = os.listdir(img_dir)
img_names = list(filter(lambda x: x.endswith(format), file_names))
if len(img_names) < 1:
raise ValueError("{}下找不到{}格式数据".format(img_dir, format))
return img_names
def get_model(m_path, vis_model=False):
resnet18 = models.resnet18()
num_ftrs = resnet18.fc.in_features
resnet18.fc = nn.Linear(num_ftrs, 2)
checkpoint = torch.load(m_path)
resnet18.load_state_dict(checkpoint['model_state_dict'])
if vis_model:
from torchsummary import summary
summary(resnet18, input_size=(3, 224, 224), device="cpu")
return resnet18
if __name__ == "__main__":
img_dir = os.path.join("..", "..", "data/hymenoptera_data/val/bees")
model_path = "./checkpoint_14_epoch.pkl"
time_total = 0
img_list, img_pred = list(), list()
# 1. data
img_names = get_img_name(img_dir)
num_img = len(img_names)
# 2. model
resnet18 = get_model(model_path, True)
resnet18.to(device)
resnet18.eval()
with torch.no_grad():
for idx, img_name in enumerate(img_names):
path_img = os.path.join(img_dir, img_name)
# step 1/4 : path --> img
img_rgb = Image.open(path_img).convert('RGB')
# step 2/4 : img --> tensor
img_tensor = img_transform(img_rgb, inference_transform)
img_tensor.unsqueeze_(0)
img_tensor = img_tensor.to(device)
# step 3/4 : tensor --> vector
time_tic = time.time()
outputs = resnet18(img_tensor)
time_toc = time.time()
# step 4/4 : visualization
_, pred_int = torch.max(outputs.data, 1)
pred_str = classes[int(pred_int)]
if vis:
img_list.append(img_rgb)
img_pred.append(pred_str)
if (idx+1) % (vis_row*vis_row) == 0 or num_img == idx+1:
for i in range(len(img_list)):
plt.subplot(vis_row, vis_row, i+1).imshow(img_list[i])
plt.title("predict:{}".format(img_pred[i]))
plt.show()
plt.close()
img_list, img_pred = list(), list()
time_s = time_toc-time_tic
time_total += time_s
print('{:d}/{:d}: {} {:.3f}s '.format(idx + 1, num_img, img_name, time_s))
print("\ndevice:{} total time:{:.1f}s mean:{:.3f}s".
format(device, time_total, time_total/num_img))
if torch.cuda.is_available():
print("GPU name:{}".format(torch.cuda.get_device_name()))
输出：
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 112, 112] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
Conv2d-5 [-1, 64, 56, 56] 36,864
BatchNorm2d-6 [-1, 64, 56, 56] 128
ReLU-7 [-1, 64, 56, 56] 0
Conv2d-8 [-1, 64, 56, 56] 36,864
BatchNorm2d-9 [-1, 64, 56, 56] 128
ReLU-10 [-1, 64, 56, 56] 0
BasicBlock-11 [-1, 64, 56, 56] 0
Conv2d-12 [-1, 64, 56, 56] 36,864
BatchNorm2d-13 [-1, 64, 56, 56] 128
ReLU-14 [-1, 64, 56, 56] 0
Conv2d-15 [-1, 64, 56, 56] 36,864
BatchNorm2d-16 [-1, 64, 56, 56] 128
ReLU-17 [-1, 64, 56, 56] 0
BasicBlock-18 [-1, 64, 56, 56] 0
Conv2d-19 [-1, 128, 28, 28] 73,728
BatchNorm2d-20 [-1, 128, 28, 28] 256
ReLU-21 [-1, 128, 28, 28] 0
Conv2d-22 [-1, 128, 28, 28] 147,456
BatchNorm2d-23 [-1, 128, 28, 28] 256
Conv2d-24 [-1, 128, 28, 28] 8,192
BatchNorm2d-25 [-1, 128, 28, 28] 256
ReLU-26 [-1, 128, 28, 28] 0
BasicBlock-27 [-1, 128, 28, 28] 0
Conv2d-28 [-1, 128, 28, 28] 147,456
BatchNorm2d-29 [-1, 128, 28, 28] 256
ReLU-30 [-1, 128, 28, 28] 0
Conv2d-31 [-1, 128, 28, 28] 147,456
BatchNorm2d-32 [-1, 128, 28, 28] 256
ReLU-33 [-1, 128, 28, 28] 0
BasicBlock-34 [-1, 128, 28, 28] 0
Conv2d-35 [-1, 256, 14, 14] 294,912
BatchNorm2d-36 [-1, 256, 14, 14] 512
ReLU-37 [-1, 256, 14, 14] 0
Conv2d-38 [-1, 256, 14, 14] 589,824
BatchNorm2d-39 [-1, 256, 14, 14] 512
Conv2d-40 [-1, 256, 14, 14] 32,768
BatchNorm2d-41 [-1, 256, 14, 14] 512
ReLU-42 [-1, 256, 14, 14] 0
BasicBlock-43 [-1, 256, 14, 14] 0
Conv2d-44 [-1, 256, 14, 14] 589,824
BatchNorm2d-45 [-1, 256, 14, 14] 512
ReLU-46 [-1, 256, 14, 14] 0
Conv2d-47 [-1, 256, 14, 14] 589,824
BatchNorm2d-48 [-1, 256, 14, 14] 512
ReLU-49 [-1, 256, 14, 14] 0
BasicBlock-50 [-1, 256, 14, 14] 0
Conv2d-51 [-1, 512, 7, 7] 1,179,648
BatchNorm2d-52 [-1, 512, 7, 7] 1,024
ReLU-53 [-1, 512, 7, 7] 0
Conv2d-54 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-55 [-1, 512, 7, 7] 1,024
Conv2d-56 [-1, 512, 7, 7] 131,072
BatchNorm2d-57 [-1, 512, 7, 7] 1,024
ReLU-58 [-1, 512, 7, 7] 0
BasicBlock-59 [-1, 512, 7, 7] 0
Conv2d-60 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-61 [-1, 512, 7, 7] 1,024
ReLU-62 [-1, 512, 7, 7] 0
Conv2d-63 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-64 [-1, 512, 7, 7] 1,024
ReLU-65 [-1, 512, 7, 7] 0
BasicBlock-66 [-1, 512, 7, 7] 0
AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0
Linear-68 [-1, 2] 1,026
================================================================
Total params: 11,177,538
Trainable params: 11,177,538
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 62.79
Params size (MB): 42.64
Estimated Total Size (MB): 106.00
----------------------------------------------------------------
1/83: 1032546534_06907fe3b3.jpg 0.380s
2/83: 10870992_eebeeb3a12.jpg 0.008s
3/83: 1181173278_23c36fac71.jpg 0.008s
4/83: 1297972485_33266a18d9.jpg 0.008s
......
83/83: abeja.jpg 0.007s
device:cuda total time:1.0s mean:0.013s
GPU name:GeForce RTX 2070
Inference阶段注意事项:
- 1.确保mode|处于eval状态而非training
- 2.设置torch.no_ grad() ，减少内存消耗
- 3.数据预处理需保持一致，RGB or BGR ?
三.resnet18结构分析
下面图片不清晰，可以看上述代码输出。
