學習筆記|Pytorch使用教程31
本學習筆記主要摘自“深度之眼”，做一個總結，方便查閱。
使用Pytorch版本爲1.2
- 模型是如何將圖像分類的?
- resnet18模型inference代碼
- resnet18結構分析
一.模型是如何將圖像分類的?
3-d張量>字符串
1.類別名與標籤的轉換
- label_name = {“ants”: 0, “bees”: 1}
2.取輸出向量最大值的標號
- _,predicted = torch.max(outputs.data, 1)
3.複雜運算
- outptus = resnet18(img_tensor)
二.resnet18模型inference代碼
圖像分類的Inference(推理)
步驟:
- 1.獲取數據與標籤
- 2.選擇模型，損失函數，優化器
- 3.寫訓練代碼
- 4.寫inference代碼
Inference代碼基本步驟:
- 1.獲取數據與模型
- 2.數據變換，如RGB > 4D-Tensor
- 3.前向傳播
- 4.輸出保存預測結果
查看resnet reference代碼
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):
"""
將數據轉換爲模型讀取的形式
:param img_rgb: PIL Image
:param transform: torchvision.transform
:return: tensor
"""
if transform is None:
raise ValueError("找不到transform！必須有transform對img進行處理")
img_t = transform(img_rgb)
return img_t
def get_img_name(img_dir, format="jpg"):
"""
獲取文件夾下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{}格式數據".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()))
輸出：
----------------------------------------------------------------
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階段注意事項:
- 1.確保mode|處於eval狀態而非training
- 2.設置torch.no_ grad() ，減少內存消耗
- 3.數據預處理需保持一致，RGB or BGR ?
三.resnet18結構分析
下面圖片不清晰，可以看上述代碼輸出。
