基于ResNet模型微调的自定义图像数据分类
发布时间：2023年12月20日
# Import necessary packages.
import torch
import torch.nn as nn
from torchvision import datasets, models, transforms
from torchsummary import summary
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import time
import os
# Device configuration.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
device(type='cuda')
# Hyper-parameters setting.
batch_size = 256
num_epochs = 30
# Image preprocessing modules.
image_transforms = {
'train': transforms.Compose([transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),
transforms.RandomRotation(degrees=15),
transforms.RandomHorizontalFlip(),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])]),
'valid':transforms.Compose([transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])]),
'test':transforms.Compose([transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
}
# Load the Subset-datasets of Caltech 256.
dataset = './Datasets/'
# Set the dataset directory.
train_directory = os.path.join(dataset, 'train')
valid_directory = os.path.join(dataset, 'valid')
test_directory = os.path.join(dataset, 'test')
# Number of classes.
# num_classes = len(os.listdir(valid_directory))
# print(num_classes) # num_classes is 10.
# Load Data from folders.
data = {
'train':datasets.ImageFolder(root=train_directory,
transform=image_transforms['train']),
'valid':datasets.ImageFolder(root=valid_directory,
transform=image_transforms['valid']),
'test': datasets.ImageFolder(root=test_directory,
transform=image_transforms['test'])
}
# Define the DataLoader.
train_data_loader = torch.utils.data.DataLoader(data['train'],
batch_size=batch_size,
shuffle=True)
valid_data_loader = torch.utils.data.DataLoader(data['valid'],
batch_size=batch_size,
shuffle=True)
test_data_loader = torch.utils.data.DataLoader(data['test'],
batch_size=batch_size,
shuffle=False)
# Load pretrained ResNet50 Model.
resnet50 = models.resnet50(pretrained=True).to(device)
print(resnet50)
/home/wsl_ubuntu/anaconda3/envs/xy_trans/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/home/wsl_ubuntu/anaconda3/envs/xy_trans/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
# Freeze model parameters, make a preparation for fine-tuning.
for param in resnet50.parameters():
param.requires_grad = False
# Change the final layer of ResNet50 for Transfer Learning.
fc_inputs = resnet50.fc.in_features
resnet50.fc = nn.Sequential(nn.Linear(fc_inputs, 256),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(256, len(os.listdir(valid_directory))), # Related to the 10 outputs classes.
nn.LogSoftmax(dim=1)) # For using NLLLoss()
# Convert model to be used on GPU.
resnet50 = resnet50.to(device)
# Loss and optimizer.
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(resnet50.parameters())
# Define Train & Validate Utils Function.
def train_and_validate(model, loss_criterion, optimizer, epochs=25):
"""
Function used to train and validate.
Parameters:
:param model: Model to train and validate.
:param loss_criterion: Loss Criterion to minimize.
:param optimizer: Optimizer for computing gradients.
:param epochs: Number of epochs (default=25)
"""
history = []
best_loss = 100000.0
best_epoch = None
for epoch in range(epochs):
epoch_start = time.time()
print("Epoch: {}/{}".format(epoch+1, epochs))
# Set to training mode.
model.train()
# Loss and Accuracy within the epoch.
train_loss = 0.0
train_acc = 0.0
valid_loss = 0.0
valid_acc = 0.0
for _, (inputs, labels) in enumerate(train_data_loader):
# Move the data pair to device.
inputs = inputs.to(device)
labels = labels.to(device)
# Forward pass and calculate loss.
outputs = model(inputs)
loss = loss_criterion(outputs, labels)
# Backward and optimize.
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Compute the total loss for the batch and add it to train_loss.
train_loss += loss.item() * inputs.size(0)
# Compute the accuracy.
_, predictions = torch.max(outputs.data, 1)
correct_counts = predictions.eq(labels.data.view_as(predictions))
# Convert correct_counts to float and then compute the mean.
acc = torch.mean(correct_counts.type(torch.FloatTensor))
# Compute total accuracy in the whole batch and add to train_acc.
train_acc += acc.item() * inputs.size(0)
#print("Batch number: {:03d}, Training: Loss: {:.4f}, Accuracy: {:.4f}".format(i, loss.item(), acc.item()))
# Validation - No gradient tracking needed.
with torch.no_grad():
# Set to evaluation mode.
model.eval()
# Validation loop.
for _, (inputs, labels) in enumerate(valid_data_loader):
inputs = inputs.to(device)
labels = labels.to(device)
# Forward pass - compute outputs on input data using the model
outputs = model(inputs)
# Compute loss
loss = loss_criterion(outputs, labels)
# Compute the total loss for the batch and add it to valid_loss
valid_loss += loss.item() * inputs.size(0)
# Calculate validation accuracy
_, predictions = torch.max(outputs.data, 1)
correct_counts = predictions.eq(labels.data.view_as(predictions))
# Convert correct_counts to float and then compute the mean
acc = torch.mean(correct_counts.type(torch.FloatTensor))
# Compute total accuracy in the whole batch and add to valid_acc
valid_acc += acc.item() * inputs.size(0)
#print("Validation Batch number: {:03d}, Validation: Loss: {:.4f}, Accuracy: {:.4f}".format(j, loss.item(), acc.item()))
if valid_loss < best_loss:
best_loss = valid_loss
best_epoch = epoch
# Find average training loss and train accuracy.
avg_train_loss = train_loss/len(data['train'])
avg_train_acc = train_acc/len(data['train'])
# Find average training loss and training accuracy.
avg_valid_loss = valid_loss/len(data['valid'])
avg_valid_acc = valid_acc/len(data['valid'])
history.append([avg_train_loss, avg_valid_loss, avg_train_acc, avg_valid_acc])
epoch_end = time.time()
print("Epoch : {:03d}, Training: Loss - {:.4f}, Accuracy - {:.4f}%, \n\t\tValidation : Loss - {:.4f}, Accuracy - {:.4f}%, Time: {:.4f}s".format(epoch, avg_train_loss, avg_train_acc*100, avg_valid_loss, avg_valid_acc*100, epoch_end-epoch_start))
# Save if the model has best accuracy till now
torch.save(model, dataset+'_model_'+str(epoch)+'.pt')
return model, history, best_epoch
# Print the model to be trained
summary(resnet50, input_size=(3, 224, 224), batch_size=batch_size, device='cuda')
# Train the model for 25 epochs
# num_epochs = 30
# trained_model, history, best_epoch = train_and_validate(resnet50, loss_func, optimizer, num_epochs)
# torch.save(history, dataset+'_history.pt')
=================================================================
Layer (type:depth-idx) Param #
=================================================================
├─Conv2d: 1-1 (9,408)
├─BatchNorm2d: 1-2 (128)
├─ReLU: 1-3 --
├─MaxPool2d: 1-4 --
├─Sequential: 1-5 --
| └─Bottleneck: 2-1 --
| | └─Conv2d: 3-1 (4,096)
| | └─BatchNorm2d: 3-2 (128)
| | └─Conv2d: 3-3 (36,864)
| | └─BatchNorm2d: 3-4 (128)
| | └─Conv2d: 3-5 (16,384)
| | └─BatchNorm2d: 3-6 (512)
| | └─ReLU: 3-7 --
| | └─Sequential: 3-8 (16,896)
| └─Bottleneck: 2-2 --
| | └─Conv2d: 3-9 (16,384)
| | └─BatchNorm2d: 3-10 (128)
| | └─Conv2d: 3-11 (36,864)
| | └─BatchNorm2d: 3-12 (128)
| | └─Conv2d: 3-13 (16,384)
| | └─BatchNorm2d: 3-14 (512)
| | └─ReLU: 3-15 --
| └─Bottleneck: 2-3 --
| | └─Conv2d: 3-16 (16,384)
| | └─BatchNorm2d: 3-17 (128)
| | └─Conv2d: 3-18 (36,864)
| | └─BatchNorm2d: 3-19 (128)
| | └─Conv2d: 3-20 (16,384)
| | └─BatchNorm2d: 3-21 (512)
| | └─ReLU: 3-22 --
├─Sequential: 1-6 --
| └─Bottleneck: 2-4 --
| | └─Conv2d: 3-23 (32,768)
| | └─BatchNorm2d: 3-24 (256)
| | └─Conv2d: 3-25 (147,456)
| | └─BatchNorm2d: 3-26 (256)
| | └─Conv2d: 3-27 (65,536)
| | └─BatchNorm2d: 3-28 (1,024)
| | └─ReLU: 3-29 --
| | └─Sequential: 3-30 (132,096)
| └─Bottleneck: 2-5 --
| | └─Conv2d: 3-31 (65,536)
| | └─BatchNorm2d: 3-32 (256)
| | └─Conv2d: 3-33 (147,456)
| | └─BatchNorm2d: 3-34 (256)
| | └─Conv2d: 3-35 (65,536)
| | └─BatchNorm2d: 3-36 (1,024)
| | └─ReLU: 3-37 --
| └─Bottleneck: 2-6 --
| | └─Conv2d: 3-38 (65,536)
| | └─BatchNorm2d: 3-39 (256)
| | └─Conv2d: 3-40 (147,456)
| | └─BatchNorm2d: 3-41 (256)
| | └─Conv2d: 3-42 (65,536)
| | └─BatchNorm2d: 3-43 (1,024)
| | └─ReLU: 3-44 --
| └─Bottleneck: 2-7 --
| | └─Conv2d: 3-45 (65,536)
| | └─BatchNorm2d: 3-46 (256)
| | └─Conv2d: 3-47 (147,456)
| | └─BatchNorm2d: 3-48 (256)
| | └─Conv2d: 3-49 (65,536)
| | └─BatchNorm2d: 3-50 (1,024)
| | └─ReLU: 3-51 --
├─Sequential: 1-7 --
| └─Bottleneck: 2-8 --
| | └─Conv2d: 3-52 (131,072)
| | └─BatchNorm2d: 3-53 (512)
| | └─Conv2d: 3-54 (589,824)
| | └─BatchNorm2d: 3-55 (512)
| | └─Conv2d: 3-56 (262,144)
| | └─BatchNorm2d: 3-57 (2,048)
| | └─ReLU: 3-58 --
| | └─Sequential: 3-59 (526,336)
| └─Bottleneck: 2-9 --
| | └─Conv2d: 3-60 (262,144)
| | └─BatchNorm2d: 3-61 (512)
| | └─Conv2d: 3-62 (589,824)
| | └─BatchNorm2d: 3-63 (512)
| | └─Conv2d: 3-64 (262,144)
| | └─BatchNorm2d: 3-65 (2,048)
| | └─ReLU: 3-66 --
| └─Bottleneck: 2-10 --
| | └─Conv2d: 3-67 (262,144)
| | └─BatchNorm2d: 3-68 (512)
| | └─Conv2d: 3-69 (589,824)
| | └─BatchNorm2d: 3-70 (512)
| | └─Conv2d: 3-71 (262,144)
| | └─BatchNorm2d: 3-72 (2,048)
| | └─ReLU: 3-73 --
| └─Bottleneck: 2-11 --
| | └─Conv2d: 3-74 (262,144)
| | └─BatchNorm2d: 3-75 (512)
| | └─Conv2d: 3-76 (589,824)
| | └─BatchNorm2d: 3-77 (512)
| | └─Conv2d: 3-78 (262,144)
| | └─BatchNorm2d: 3-79 (2,048)
| | └─ReLU: 3-80 --
| └─Bottleneck: 2-12 --
| | └─Conv2d: 3-81 (262,144)
| | └─BatchNorm2d: 3-82 (512)
| | └─Conv2d: 3-83 (589,824)
| | └─BatchNorm2d: 3-84 (512)
| | └─Conv2d: 3-85 (262,144)
| | └─BatchNorm2d: 3-86 (2,048)
| | └─ReLU: 3-87 --
| └─Bottleneck: 2-13 --
| | └─Conv2d: 3-88 (262,144)
| | └─BatchNorm2d: 3-89 (512)
| | └─Conv2d: 3-90 (589,824)
| | └─BatchNorm2d: 3-91 (512)
| | └─Conv2d: 3-92 (262,144)
| | └─BatchNorm2d: 3-93 (2,048)
| | └─ReLU: 3-94 --
├─Sequential: 1-8 --
| └─Bottleneck: 2-14 --
| | └─Conv2d: 3-95 (524,288)
| | └─BatchNorm2d: 3-96 (1,024)
| | └─Conv2d: 3-97 (2,359,296)
| | └─BatchNorm2d: 3-98 (1,024)
| | └─Conv2d: 3-99 (1,048,576)
| | └─BatchNorm2d: 3-100 (4,096)
| | └─ReLU: 3-101 --
| | └─Sequential: 3-102 (2,101,248)
| └─Bottleneck: 2-15 --
| | └─Conv2d: 3-103 (1,048,576)
| | └─BatchNorm2d: 3-104 (1,024)
| | └─Conv2d: 3-105 (2,359,296)
| | └─BatchNorm2d: 3-106 (1,024)
| | └─Conv2d: 3-107 (1,048,576)
| | └─BatchNorm2d: 3-108 (4,096)
| | └─ReLU: 3-109 --
| └─Bottleneck: 2-16 --
| | └─Conv2d: 3-110 (1,048,576)
| | └─BatchNorm2d: 3-111 (1,024)
| | └─Conv2d: 3-112 (2,359,296)
| | └─BatchNorm2d: 3-113 (1,024)
| | └─Conv2d: 3-114 (1,048,576)
| | └─BatchNorm2d: 3-115 (4,096)
| | └─ReLU: 3-116 --
├─AdaptiveAvgPool2d: 1-9 --
├─Sequential: 1-10 --
| └─Linear: 2-17 524,544
| └─ReLU: 2-18 --
| └─Dropout: 2-19 --
| └─Linear: 2-20 2,570
| └─LogSoftmax: 2-21 --
=================================================================
Total params: 24,035,146
Trainable params: 527,114
Non-trainable params: 23,508,032
=================================================================
# Train the model for 25 epochs
trained_model, history, best_epoch = train_and_validate(resnet50, criterion, optimizer, num_epochs)
torch.save(history, dataset+'_history.pt')
Epoch: 1/30
Epoch : 000, Training: Loss - 2.1844, Accuracy - 21.0000%,
Validation : Loss - 1.4458, Accuracy - 87.0000%, Time: 23.0077s
Epoch: 2/30
Epoch : 001, Training: Loss - 1.4131, Accuracy - 71.6667%,
Validation : Loss - 0.8001, Accuracy - 85.0000%, Time: 11.8903s
Epoch: 3/30
Epoch : 002, Training: Loss - 0.8666, Accuracy - 87.5000%,
Validation : Loss - 0.4689, Accuracy - 97.0000%, Time: 12.4398s
Epoch: 4/30
Epoch : 003, Training: Loss - 0.5742, Accuracy - 88.1667%,
Validation : Loss - 0.3205, Accuracy - 96.0000%, Time: 11.9200s
Epoch: 5/30
Epoch : 004, Training: Loss - 0.3776, Accuracy - 93.1667%,
Validation : Loss - 0.2615, Accuracy - 97.0000%, Time: 11.8079s
Epoch: 6/30
Epoch : 005, Training: Loss - 0.2893, Accuracy - 94.1667%,
Validation : Loss - 0.2209, Accuracy - 95.0000%, Time: 11.7730s
Epoch: 7/30
Epoch : 006, Training: Loss - 0.2247, Accuracy - 95.0000%,
Validation : Loss - 0.2052, Accuracy - 96.0000%, Time: 11.8106s
Epoch: 8/30
Epoch : 007, Training: Loss - 0.1790, Accuracy - 96.3333%,
Validation : Loss - 0.1859, Accuracy - 95.0000%, Time: 11.8970s
Epoch: 9/30
Epoch : 008, Training: Loss - 0.1688, Accuracy - 96.6667%,
Validation : Loss - 0.1819, Accuracy - 96.0000%, Time: 11.7606s
Epoch: 10/30
Epoch : 009, Training: Loss - 0.1302, Accuracy - 97.3333%,
Validation : Loss - 0.1788, Accuracy - 94.0000%, Time: 11.7755s
Epoch: 11/30
Epoch : 010, Training: Loss - 0.1205, Accuracy - 97.0000%,
Validation : Loss - 0.1410, Accuracy - 98.0000%, Time: 11.6803s
Epoch: 12/30
Epoch : 011, Training: Loss - 0.1158, Accuracy - 97.3333%,
Validation : Loss - 0.1378, Accuracy - 98.0000%, Time: 11.6497s
Epoch: 13/30
Epoch : 012, Training: Loss - 0.0967, Accuracy - 98.0000%,
Validation : Loss - 0.1727, Accuracy - 96.0000%, Time: 11.7946s
Epoch: 14/30
Epoch : 013, Training: Loss - 0.0874, Accuracy - 98.1667%,
Validation : Loss - 0.1780, Accuracy - 95.0000%, Time: 11.9912s
Epoch: 15/30
Epoch : 014, Training: Loss - 0.0901, Accuracy - 97.8333%,
Validation : Loss - 0.1570, Accuracy - 96.0000%, Time: 12.9568s
Epoch: 16/30
Epoch : 015, Training: Loss - 0.0746, Accuracy - 98.1667%,
Validation : Loss - 0.1415, Accuracy - 96.0000%, Time: 12.2158s
Epoch: 17/30
Epoch : 016, Training: Loss - 0.0719, Accuracy - 98.3333%,
Validation : Loss - 0.1419, Accuracy - 95.0000%, Time: 12.5166s
Epoch: 18/30
Epoch : 017, Training: Loss - 0.0780, Accuracy - 98.0000%,
Validation : Loss - 0.1261, Accuracy - 98.0000%, Time: 12.3080s
Epoch: 19/30
Epoch : 018, Training: Loss - 0.0710, Accuracy - 98.1667%,
Validation : Loss - 0.1370, Accuracy - 97.0000%, Time: 11.9036s
Epoch: 20/30
Epoch : 019, Training: Loss - 0.0686, Accuracy - 98.3333%,
Validation : Loss - 0.1563, Accuracy - 97.0000%, Time: 11.8692s
Epoch: 21/30
Epoch : 020, Training: Loss - 0.0649, Accuracy - 98.8333%,
Validation : Loss - 0.1606, Accuracy - 96.0000%, Time: 12.0231s
Epoch: 22/30
Epoch : 021, Training: Loss - 0.0528, Accuracy - 99.0000%,
Validation : Loss - 0.1435, Accuracy - 96.0000%, Time: 11.9158s
Epoch: 23/30
Epoch : 022, Training: Loss - 0.0606, Accuracy - 98.1667%,
Validation : Loss - 0.1376, Accuracy - 97.0000%, Time: 11.8283s
Epoch: 24/30
Epoch : 023, Training: Loss - 0.0606, Accuracy - 98.8333%,
Validation : Loss - 0.1445, Accuracy - 96.0000%, Time: 11.7824s
Epoch: 25/30
Epoch : 024, Training: Loss - 0.0530, Accuracy - 99.1667%,
Validation : Loss - 0.1682, Accuracy - 94.0000%, Time: 11.7249s
Epoch: 26/30
Epoch : 025, Training: Loss - 0.0528, Accuracy - 98.8333%,
Validation : Loss - 0.1614, Accuracy - 93.0000%, Time: 12.2925s
Epoch: 27/30
Epoch : 026, Training: Loss - 0.0583, Accuracy - 98.6667%,
Validation : Loss - 0.1518, Accuracy - 94.0000%, Time: 12.1458s
Epoch: 28/30
Epoch : 027, Training: Loss - 0.0407, Accuracy - 98.8333%,
Validation : Loss - 0.1408, Accuracy - 96.0000%, Time: 11.9303s
Epoch: 29/30
Epoch : 028, Training: Loss - 0.0671, Accuracy - 98.5000%,
Validation : Loss - 0.1508, Accuracy - 96.0000%, Time: 11.9089s
Epoch: 30/30
Epoch : 029, Training: Loss - 0.0443, Accuracy - 98.8333%,
Validation : Loss - 0.1763, Accuracy - 95.0000%, Time: 12.5318s
# Plot the learning curve.
history = np.array(history)
plt.plot(history[:, 0], color='red', marker='o')
plt.plot(history[:, 1], color='blue', marker='x')
plt.legend(['Tr Loss', 'Val Loss'])
plt.xlabel('Epoch Number')
plt.ylabel('Loss')
plt.ylim(0,1)
plt.savefig(dataset+'_loss_curve.png')
plt.show()
# Plot the learning curve.
history = np.array(history)
plt.plot(history[:, 2], color='red', marker='o')
plt.plot(history[:, -1], color='blue', marker='x')
plt.legend(['Tr Loss', 'Val Loss'])
plt.xlabel('Epoch Number')
plt.ylabel('Loss')
plt.ylim(0,1)
plt.savefig(dataset+'_loss_curve.png')
plt.show()
def computeTestSetAccuracy(model, loss_criterion):
'''
Function to compute the accuracy on the test set
Parameters
:param model: Model to test
:param loss_criterion: Loss Criterion to minimize
'''
test_acc = 0.0
test_loss = 0.0
# Validation - No gradient tracking needed.
with torch.no_grad():
# Set to evaluation mode.
model.eval()
# Validation loop
for j, (inputs, labels) in enumerate(test_data_loader):
inputs = inputs.to(device)
labels = labels.to(device)
# Forward pass - compute outputs on input data using the model.
outputs = model(inputs)
# Compute loss
loss = loss_criterion(outputs, labels)
# Compute the total loss for the batch and add it to valid_loss.
test_loss += loss.item() * inputs.size(0)
# Calculate validation accuracy.
ret, predictions = torch.max(outputs.data, 1)
correct_counts = predictions.eq(labels.data.view_as(predictions))
# Convert correct_counts to float and then compute the mean.
acc = torch.mean(correct_counts.type(torch.FloatTensor))
# Compute total accuracy in the whole batch and add to valid_acc.
test_acc += acc.item() * inputs.size(0)
print("Test Batch number: {:03d}, Test: Loss: {:.4f}, Accuracy: {:.4f}".format(j, loss.item(), acc.item()))
# Find average test loss and test accuracy
avg_test_loss = test_loss/len(data['test'])
avg_test_acc = test_acc/len(data['test'])
print("Test accuracy : " + str(avg_test_acc))
# Get a mapping of the indices to the class names, in order to see the output classes of the test images.
idx_to_class = {v: k for k, v in data['train'].class_to_idx.items()}
print(idx_to_class)
{0: 'bear', 1: 'chimp', 2: 'giraffe', 3: 'gorilla', 4: 'llama', 5: 'ostrich', 6: 'porcupine', 7: 'skunk', 8: 'triceratops', 9: 'zebra'}
def predict(model, test_image_name):
'''
Function to predict the class of a single test image
Parameters
:param model: Model to test
:param test_image_name: Test image
'''
transform = image_transforms['test']
test_image = Image.open(test_image_name)
plt.imshow(test_image)
test_image_tensor = transform(test_image)
if torch.cuda.is_available():
test_image_tensor = test_image_tensor.view(1, 3, 224, 224).cuda()
else:
test_image_tensor = test_image_tensor.view(1, 3, 224, 224)
with torch.no_grad():
model.eval()
# Model outputs log probabilities.
out = model(test_image_tensor)
ps = torch.exp(out)
topk, topclass = ps.topk(3, dim=1)
cls = idx_to_class[topclass.cpu().numpy()[0][0]]
score = topk.cpu().numpy()[0][0]
for i in range(3):
print("Predcition", i+1, ":", idx_to_class[topclass.cpu().numpy()[0][i]], ", Score: ", topk.cpu().numpy()[0][i])
# Test a particular model on a test image
# ! wget https://cdn.pixabay.com/photo/2018/10/01/12/28/skunk-3716043_1280.jpg -O skunk.jpg
model = torch.load("./Datasets/_model_{}.pt".format(best_epoch))
predict(model, './Datasets/skunk-3716043_1280.jpg')
# Load Data from folders
#computeTestSetAccuracy(model, loss_func)
Predcition 1 : skunk , Score: 0.99803644
Predcition 2 : porcupine , Score: 0.001755606
Predcition 3 : bear , Score: 8.1849976e-05
文章来源:https://blog.csdn.net/AIHUBEI/article/details/135097100
本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。 如若内容造成侵权/违法违规/事实不符，请联系我的编程经验分享网邮箱：chenni525@qq.com进行投诉反馈，一经查实，立即删除！
本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。 如若内容造成侵权/违法违规/事实不符，请联系我的编程经验分享网邮箱：chenni525@qq.com进行投诉反馈，一经查实，立即删除！
最新文章
- Python教程
- 深入理解 MySQL 中的 HAVING 关键字和聚合函数
- Qt之QChar编码（1）
- MyBatis入门基础篇
- 用Python脚本实现FFmpeg批量转换
- web基础
- 垃圾收集器与内存分配策略
- STM32Cube高效开发教程＜基础篇＞(十六)----CAN总线通信
- C++/CLI——2类和对象生存期
- leetcode每日一题41
- CentOS7配置python和django项目开发环境(MySQL8.0+python3.9)
- LeetCode-加一（66）
- ps5计时收费系统怎么计时收费？电玩体验店控制系统操作教程
- 新手能掌握 PyTorch 的填充技术：深入理解反射、复制、零值和常数填充
- COMSOL 各版本安装指南