学习笔记|Pytorch使用教程28
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
分享一个关于冻结/解冻的方法：知乎
- Transfer Learning & Model Finetune
- PyTorch中的Finetune
一.Transfer Learning & Model Finetune
二.PyTorch中的Finetune
模型微调步骤:
- 1.获取预训练模型参数
- 2.加载模型(load. state dict )
- 3.修改输出层
模型微调训练方法:
- 1.固定预训练的参数(requires. grad =False ; lr=0)
- 2.Features Extractor较小学习率( params group )
首先构建AntsDataset：
class AntsDataset(Dataset):
def __init__(self, data_dir, transform=None):
self.label_name = {"ants": 0, "bees": 1}
self.data_info = self.get_img_info(data_dir) # data_info存储所有图片路径和标签，在DataLoader中通过index读取样本
self.transform = transform
def __getitem__(self, index):
path_img, label = self.data_info[index]
img = Image.open(path_img).convert('RGB') # 0~255
if self.transform is not None:
img = self.transform(img) # 在这里做transform，转为tensor等等
return img, label
def __len__(self):
return len(self.data_info)
def get_img_info(self,data_dir):
data_info = list()
for root, dirs, _ in os.walk(data_dir):
# 遍历类别
for sub_dir in dirs:
img_names = os.listdir(os.path.join(root, sub_dir))
img_names = list(filter(lambda x: x.endswith('.jpg'), img_names))
# 遍历图片
for i in range(len(img_names)):
img_name = img_names[i]
path_img = os.path.join(root, sub_dir, img_name)
label = self.label_name[sub_dir]
data_info.append((path_img, int(label)))
if len(data_info) == 0:
raise Exception("\ndata_dir:{} is a empty dir! Please checkout your path to images!".format(data_dir))
return data_info
完整测试代码：
import os
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import torch.optim as optim
from matplotlib import pyplot as plt
from tools.my_dataset import AntsDataset
from tools.common_tools import set_seed
import torchvision.models as models
import torchvision
BASEDIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("use device :{}".format(device))
set_seed(1) # 设置随机种子
label_name = {"ants": 0, "bees": 1}
# 参数设置
MAX_EPOCH = 25
BATCH_SIZE = 16
LR = 0.001
log_interval = 10
val_interval = 1
classes = 2
start_epoch = -1
lr_decay_step = 7
# ============================ step 1/5 数据 ============================
data_dir = os.path.join(BASEDIR, "..", "..", "data/hymenoptera_data")
train_dir = os.path.join(data_dir, "train")
valid_dir = os.path.join(data_dir, "val")
norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]
train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
valid_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# 构建MyDataset实例
train_data = AntsDataset(data_dir=train_dir, transform=train_transform)
valid_data = AntsDataset(data_dir=valid_dir, transform=valid_transform)
# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)
# ============================ step 2/5 模型 ============================
# 1/3 构建模型
resnet18_ft = models.resnet18()
# 2/3 加载参数
flag = 0
# flag = 1
if flag:
path_pretrained_model = os.path.join(BASEDIR, "..", "..", "data/resnet18-5c106cde.pth")
state_dict_load = torch.load(path_pretrained_model)
resnet18_ft.load_state_dict(state_dict_load)
# 法1 : 冻结卷积层
flag_m1 = 0
# flag_m1 = 1
if flag_m1:
for param in resnet18_ft.parameters():
param.requires_grad = False
print("conv1.weights[0, 0, ...]:\n {}".format(resnet18_ft.conv1.weight[0, 0, ...]))
# 3/3 替换fc层
num_ftrs = resnet18_ft.fc.in_features
resnet18_ft.fc = nn.Linear(num_ftrs, classes)
resnet18_ft.to(device)
# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss() # 选择损失函数
# ============================ step 4/5 优化器 ============================
# 法2 : conv 小学习率
flag = 0
# flag = 1
if flag:
fc_params_id = list(map(id, resnet18_ft.fc.parameters())) # 返回的是parameters的 内存地址
base_params = filter(lambda p: id(p) not in fc_params_id, resnet18_ft.parameters())
optimizer = optim.SGD([
{'params': base_params, 'lr': LR*0}, # 0
{'params': resnet18_ft.fc.parameters(), 'lr': LR}], momentum=0.9)
else:
optimizer = optim.SGD(resnet18_ft.parameters(), lr=LR, momentum=0.9) # 选择优化器
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_step, gamma=0.1) # 设置学习率下降策略
# ============================ step 5/5 训练 ============================
train_curve = list()
valid_curve = list()
for epoch in range(start_epoch + 1, MAX_EPOCH):
loss_mean = 0.
correct = 0.
total = 0.
resnet18_ft.train()
for i, data in enumerate(train_loader):
# forward
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = resnet18_ft(inputs)
# backward
optimizer.zero_grad()
loss = criterion(outputs, labels)
loss.backward()
# update weights
optimizer.step()
# 统计分类情况
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).squeeze().cpu().sum().numpy()
# 打印训练信息
loss_mean += loss.item()
train_curve.append(loss.item())
if (i+1) % log_interval == 0:
loss_mean = loss_mean / log_interval
print("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(
epoch, MAX_EPOCH, i+1, len(train_loader), loss_mean, correct / total))
loss_mean = 0.
# if flag_m1:
print("epoch:{} conv1.weights[0, 0, ...] :\n {}".format(epoch, resnet18_ft.conv1.weight[0, 0, ...]))
scheduler.step() # 更新学习率
# validate the model
if (epoch+1) % val_interval == 0:
correct_val = 0.
total_val = 0.
loss_val = 0.
resnet18_ft.eval()
with torch.no_grad():
for j, data in enumerate(valid_loader):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = resnet18_ft(inputs)
loss = criterion(outputs, labels)
_, predicted = torch.max(outputs.data, 1)
total_val += labels.size(0)
correct_val += (predicted == labels).squeeze().cpu().sum().numpy()
loss_val += loss.item()
loss_val_mean = loss_val/len(valid_loader)
valid_curve.append(loss_val_mean)
print("Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(
epoch, MAX_EPOCH, j+1, len(valid_loader), loss_val_mean, correct_val / total_val))
resnet18_ft.train()
train_x = range(len(train_curve))
train_y = train_curve
train_iters = len(train_loader)
valid_x = np.arange(1, len(valid_curve)+1) * train_iters*val_interval # 由于valid中记录的是epochloss，需要对记录点进行转换到iterations
valid_y = valid_curve
plt.plot(train_x, train_y, label='Train')
plt.plot(valid_x, valid_y, label='Valid')
plt.legend(loc='upper right')
plt.ylabel('loss value')
plt.xlabel('Iteration')
plt.show()
输出：
use device :cuda
Training:Epoch[000/025] Iteration[010/016] Loss: 0.7283 Acc:49.38%
epoch:0 conv1.weights[0, 0, ...] :
tensor([[ 0.0395, -0.0116, 0.0342, -0.0307, 0.0298, 0.0031, 0.0097],
[-0.0049, 0.0319, 0.0666, 0.0388, 0.0096, -0.0193, -0.0174],
[ 0.0075, -0.0277, -0.0006, -0.0376, 0.0052, -0.0493, -0.0200],
[ 0.0002, 0.0206, -0.0210, -0.0059, 0.0125, -0.0291, -0.0089],
[-0.0003, 0.0149, -0.0235, 0.0127, 0.0009, -0.0142, -0.0263],
[ 0.0133, 0.0055, 0.0211, 0.0162, 0.0200, -0.0261, 0.0222],
[ 0.0015, 0.0067, -0.0032, -0.0021, 0.0130, -0.0060, 0.0133]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[000/025] Iteration[010/010] Loss: 0.6738 Acc:54.90%
Training:Epoch[001/025] Iteration[010/016] Loss: 0.6975 Acc:55.00%
......
Valid: Epoch[021/025] Iteration[010/010] Loss: 0.5759 Acc:73.86%
Training:Epoch[022/025] Iteration[010/016] Loss: 0.5115 Acc:73.12%
epoch:22 conv1.weights[0, 0, ...] :
tensor([[ 0.0382, -0.0111, 0.0356, -0.0281, 0.0345, 0.0083, 0.0154],
[-0.0058, 0.0325, 0.0695, 0.0423, 0.0134, -0.0157, -0.0130],
[ 0.0081, -0.0260, 0.0013, -0.0336, 0.0101, -0.0449, -0.0151],
[ 0.0021, 0.0228, -0.0197, -0.0022, 0.0177, -0.0238, -0.0045],
[ 0.0009, 0.0161, -0.0210, 0.0162, 0.0054, -0.0090, -0.0223],
[ 0.0120, 0.0054, 0.0236, 0.0201, 0.0244, -0.0215, 0.0266],
[-0.0014, 0.0048, -0.0023, -0.0002, 0.0148, -0.0022, 0.0179]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[022/025] Iteration[010/010] Loss: 0.6039 Acc:71.24%
Training:Epoch[023/025] Iteration[010/016] Loss: 0.5715 Acc:67.50%
epoch:23 conv1.weights[0, 0, ...] :
tensor([[ 0.0382, -0.0111, 0.0356, -0.0281, 0.0345, 0.0083, 0.0154],
[-0.0058, 0.0325, 0.0695, 0.0423, 0.0134, -0.0157, -0.0130],
[ 0.0081, -0.0260, 0.0013, -0.0336, 0.0101, -0.0449, -0.0151],
[ 0.0021, 0.0228, -0.0197, -0.0022, 0.0177, -0.0238, -0.0045],
[ 0.0009, 0.0161, -0.0210, 0.0162, 0.0054, -0.0090, -0.0223],
[ 0.0120, 0.0054, 0.0236, 0.0201, 0.0244, -0.0215, 0.0266],
[-0.0014, 0.0048, -0.0023, -0.0002, 0.0148, -0.0022, 0.0179]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[023/025] Iteration[010/010] Loss: 0.5763 Acc:71.24%
Training:Epoch[024/025] Iteration[010/016] Loss: 0.5469 Acc:73.75%
epoch:24 conv1.weights[0, 0, ...] :
tensor([[ 0.0382, -0.0111, 0.0356, -0.0281, 0.0345, 0.0083, 0.0154],
[-0.0058, 0.0325, 0.0695, 0.0423, 0.0134, -0.0157, -0.0130],
[ 0.0081, -0.0260, 0.0013, -0.0336, 0.0101, -0.0449, -0.0151],
[ 0.0021, 0.0228, -0.0197, -0.0022, 0.0177, -0.0238, -0.0045],
[ 0.0009, 0.0161, -0.0210, 0.0162, 0.0054, -0.0090, -0.0223],
[ 0.0120, 0.0054, 0.0236, 0.0201, 0.0244, -0.0215, 0.0266],
[-0.0014, 0.0048, -0.0023, -0.0002, 0.0148, -0.0022, 0.0179]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[024/025] Iteration[010/010] Loss: 0.5581 Acc:71.24%
效果不是特别好，因此加载预训练模型。
# 2/3 加载参数
# flag = 0
flag = 1
if flag:
path_pretrained_model = os.path.join(BASEDIR, "..", "..", "data/resnet18-5c106cde.pth")
state_dict_load = torch.load(path_pretrained_model)
resnet18_ft.load_state_dict(state_dict_load)
输出：
use device :cuda
Training:Epoch[000/025] Iteration[010/016] Loss: 0.7385 Acc:50.00%
epoch:0 conv1.weights[0, 0, ...] :
tensor([[-0.0106, -0.0064, -0.0021, 0.0746, 0.0564, 0.0168, -0.0130],
[ 0.0109, 0.0092, -0.1102, -0.2807, -0.2714, -0.1293, 0.0036],
[-0.0071, 0.0587, 0.2951, 0.5870, 0.5195, 0.2561, 0.0634],
[ 0.0303, -0.0674, -0.2988, -0.4390, -0.2711, -0.0008, 0.0574],
[-0.0278, 0.0156, 0.0721, -0.0545, -0.3332, -0.4208, -0.2580],
[ 0.0303, 0.0405, 0.0623, 0.2386, 0.4135, 0.3935, 0.1660],
[-0.0140, -0.0041, -0.0245, -0.0662, -0.1509, -0.0822, -0.0057]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[000/025] Iteration[010/010] Loss: 0.3317 Acc:88.24%
Training:Epoch[001/025] Iteration[010/016] Loss: 0.3401 Acc:85.62%
epoch:1 conv1.weights[0, 0, ...] :
tensor([[-0.0114, -0.0074, -0.0031, 0.0738, 0.0556, 0.0162, -0.0136],
[ 0.0101, 0.0084, -0.1110, -0.2813, -0.2719, -0.1297, 0.0033],
[-0.0078, 0.0580, 0.2944, 0.5865, 0.5192, 0.2558, 0.0631],
[ 0.0297, -0.0681, -0.2995, -0.4394, -0.2714, -0.0010, 0.0573],
[-0.0283, 0.0151, 0.0715, -0.0547, -0.3334, -0.4209, -0.2579],
[ 0.0299, 0.0400, 0.0619, 0.2385, 0.4136, 0.3936, 0.1662],
[-0.0144, -0.0046, -0.0248, -0.0661, -0.1506, -0.0819, -0.0054]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[001/025] Iteration[010/010] Loss: 0.2388 Acc:90.85%
Training:Epoch[002/025] Iteration[010/016] Loss: 0.3021 Acc:85.00%
epoch:2 conv1.weights[0, 0, ...] :
tensor([[-0.0121, -0.0080, -0.0038, 0.0733, 0.0550, 0.0156, -0.0141],
[ 0.0094, 0.0076, -0.1117, -0.2819, -0.2725, -0.1303, 0.0027],
[-0.0086, 0.0573, 0.2938, 0.5859, 0.5186, 0.2553, 0.0626],
[ 0.0289, -0.0689, -0.3002, -0.4399, -0.2719, -0.0014, 0.0567],
[-0.0290, 0.0142, 0.0707, -0.0553, -0.3339, -0.4213, -0.2583],
[ 0.0293, 0.0392, 0.0611, 0.2380, 0.4132, 0.3931, 0.1658],
[-0.0152, -0.0054, -0.0255, -0.0667, -0.1511, -0.0824, -0.0058]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[002/025] Iteration[010/010] Loss: 0.2047 Acc:94.12%
Training:Epoch[003/025] Iteration[010/016] Loss: 0.2093 Acc:91.25%
epoch:3 conv1.weights[0, 0, ...] :
tensor([[-0.0122, -0.0081, -0.0040, 0.0729, 0.0544, 0.0151, -0.0145],
[ 0.0093, 0.0074, -0.1120, -0.2823, -0.2730, -0.1309, 0.0021],
[-0.0088, 0.0571, 0.2934, 0.5855, 0.5181, 0.2547, 0.0619],
[ 0.0287, -0.0691, -0.3006, -0.4403, -0.2723, -0.0019, 0.0562],
[-0.0291, 0.0140, 0.0704, -0.0557, -0.3343, -0.4216, -0.2587],
[ 0.0292, 0.0389, 0.0608, 0.2376, 0.4129, 0.3928, 0.1655],
[-0.0154, -0.0058, -0.0258, -0.0670, -0.1515, -0.0827, -0.0061]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[003/025] Iteration[010/010] Loss: 0.1952 Acc:95.42%
Training:Epoch[004/025] Iteration[010/016] Loss: 0.3119 Acc:88.75%
epoch:4 conv1.weights[0, 0, ...] :
tensor([[-0.0120, -0.0086, -0.0047, 0.0716, 0.0532, 0.0135, -0.0160],
[ 0.0103, 0.0076, -0.1120, -0.2829, -0.2737, -0.1318, 0.0010],
[-0.0080, 0.0570, 0.2932, 0.5849, 0.5174, 0.2536, 0.0607],
[ 0.0294, -0.0692, -0.3008, -0.4410, -0.2730, -0.0029, 0.0551],
[-0.0286, 0.0141, 0.0706, -0.0563, -0.3347, -0.4223, -0.2595],
[ 0.0297, 0.0390, 0.0611, 0.2372, 0.4126, 0.3923, 0.1647],
[-0.0144, -0.0055, -0.0256, -0.0675, -0.1518, -0.0831, -0.0066]],
device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[004/025] Iteration[010/010] Loss: 0.2563 Acc:90.85%
Training:Epoch[005/025] Iteration[010/016] Loss: 0.1568 Acc:94.38%
epoch:5 conv1.weights[0, 0, ...] :
tensor([[-1.1914e-02, -9.2948e-03, -5.4747e-03, 7.0540e-02, 5.2237e-02,
1.2424e-02, -1.7096e-02],
[ 1.0687e-02, 7.2866e-03, -1.1245e-01, -2.8357e-01, -2.7444e-01,
-1.3269e-01, 9.2068e-05],
[-7.6915e-03, 5.6600e-02, 2.9263e-01, 5.8414e-01, 5.1659e-01,
2.5261e-01, 5.9574e-02],
[ 2.9540e-02, -6.9700e-02, -3.0130e-01, -4.4178e-01, -2.7389e-01,
-3.9087e-03, 5.3965e-02],
[-2.8397e-02, 1.3780e-02, 7.0120e-02, -5.7001e-02, -3.3545e-01,
-4.2310e-01, -2.6042e-01],
[ 2.9838e-02, 3.8765e-02, 6.0738e-02, 2.3653e-01, 4.1202e-01,
3.9173e-01, 1.6396e-01],
[-1.4109e-02, -5.6574e-03, -2.5943e-02, -6.8227e-02, -1.5250e-01,
-8.3795e-02, -7.1628e-03]], device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[005/025] Iteration[010/010] Loss: 0.2009 Acc:94.77%
Training:Epoch[006/025] Iteration[010/016] Loss: 0.1340 Acc:95.62%
......
epoch:24 conv1.weights[0, 0, ...] :
tensor([[-1.1233e-02, -9.2924e-03, -5.8125e-03, 6.9800e-02, 5.1569e-02,
1.2115e-02, -1.7353e-02],
[ 1.1191e-02, 7.1573e-03, -1.1309e-01, -2.8462e-01, -2.7534e-01,
-1.3321e-01, -2.6183e-04],
[-7.2236e-03, 5.6297e-02, 2.9193e-01, 5.8307e-01, 5.1566e-01,
2.5216e-01, 5.9334e-02],
[ 3.0158e-02, -6.9869e-02, -3.0195e-01, -4.4273e-01, -2.7472e-01,
-4.2718e-03, 5.3910e-02],
[-2.7460e-02, 1.4040e-02, 6.9704e-02, -5.7888e-02, -3.3597e-01,
-4.2325e-01, -2.6032e-01],
[ 3.0869e-02, 3.9258e-02, 6.0584e-02, 2.3591e-01, 4.1185e-01,
3.9188e-01, 1.6421e-01],
[-1.3088e-02, -5.1690e-03, -2.6042e-02, -6.8676e-02, -1.5253e-01,
-8.3346e-02, -6.6183e-03]], device='cuda:0', grad_fn=<SelectBackward>)
Valid: Epoch[024/025] Iteration[010/010] Loss: 0.1847 Acc:94.12%
在第五个epoch的生活，就已经达到95%的准确率。
下面测试进行卷积核冻结。
# 法1 : 冻结卷积层
# flag_m1 = 0
flag_m1 = 1
if flag_m1:
for param in resnet18_ft.parameters():
param.requires_grad = False
print("conv1.weights[0, 0, ...]:\n {}".format(resnet18_ft.conv1.weight[0, 0, ...]))
输出：
use device :cuda
conv1.weights[0, 0, ...]:
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]])
Training:Epoch[000/025] Iteration[010/016] Loss: 0.7759 Acc:45.62%
epoch:0 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[000/025] Iteration[010/010] Loss: 0.4140 Acc:84.31%
Training:Epoch[001/025] Iteration[010/016] Loss: 0.4420 Acc:80.00%
epoch:1 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[001/025] Iteration[010/010] Loss: 0.2643 Acc:90.85%
Training:Epoch[002/025] Iteration[010/016] Loss: 0.3811 Acc:80.62%
epoch:2 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[002/025] Iteration[010/010] Loss: 0.2335 Acc:91.50%
Training:Epoch[003/025] Iteration[010/016] Loss: 0.2952 Acc:86.25%
epoch:3 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[003/025] Iteration[010/010] Loss: 0.2113 Acc:92.16%
Training:Epoch[004/025] Iteration[010/016] Loss: 0.4194 Acc:83.75%
......
发现卷积核参数没有改变。
设置conv 小学习率，这里设置学习率为0。
# ============================ step 4/5 优化器 ============================
# 法2 : conv 小学习率
# flag = 0
flag = 1
if flag:
fc_params_id = list(map(id, resnet18_ft.fc.parameters())) # 返回的是parameters的 内存地址
base_params = filter(lambda p: id(p) not in fc_params_id, resnet18_ft.parameters())
optimizer = optim.SGD([
{'params': base_params, 'lr': LR*0}, # 0
{'params': resnet18_ft.fc.parameters(), 'lr': LR}], momentum=0.9)
else:
optimizer = optim.SGD(resnet18_ft.parameters(), lr=LR, momentum=0.9) # 选择优化器
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_step, gamma=0.1) # 设置学习率下降策略
输出：
use device :cuda
conv1.weights[0, 0, ...]:
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]])
Training:Epoch[000/025] Iteration[010/016] Loss: 0.7759 Acc:45.62%
epoch:0 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[000/025] Iteration[010/010] Loss: 0.4140 Acc:84.31%
Training:Epoch[001/025] Iteration[010/016] Loss: 0.4420 Acc:80.00%
epoch:1 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[001/025] Iteration[010/010] Loss: 0.2643 Acc:90.85%
Training:Epoch[002/025] Iteration[010/016] Loss: 0.3811 Acc:80.62%
epoch:2 conv1.weights[0, 0, ...] :
tensor([[-0.0104, -0.0061, -0.0018, 0.0748, 0.0566, 0.0171, -0.0127],
[ 0.0111, 0.0095, -0.1099, -0.2805, -0.2712, -0.1291, 0.0037],
[-0.0069, 0.0591, 0.2955, 0.5872, 0.5197, 0.2563, 0.0636],
[ 0.0305, -0.0670, -0.2984, -0.4387, -0.2709, -0.0006, 0.0576],
[-0.0275, 0.0160, 0.0726, -0.0541, -0.3328, -0.4206, -0.2578],
[ 0.0306, 0.0410, 0.0628, 0.2390, 0.4138, 0.3936, 0.1661],
[-0.0137, -0.0037, -0.0241, -0.0659, -0.1507, -0.0822, -0.0058]],
device='cuda:0')
Valid: Epoch[002/025] Iteration[010/010] Loss: 0.2335 Acc:91.50%
Training:Epoch[003/025] Iteration[010/016] Loss: 0.2952 Acc:86.25%
参数没有更新，但任然很快的使准确率达到了90%以上。
