pytorch 检测图片中是否有人

照搬pytorch官方代码，只是将数据集换成了INRIAPerson数据集中的train和test文件夹。

贴下代码和效果，代码是官方的，就不详细解释了。

# License: BSD
# Author: Sasank Chilamkurthy

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

plt.ion()   # interactive mode

# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'person'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated

# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title('predicted: {}'.format(class_names[preds[j]]))
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)

model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=25)

Epoch 0/24
----------
train Loss: 0.4124 Acc: 0.8477
val Loss: 0.0737 Acc: 0.9744

Epoch 1/24
----------
train Loss: 0.2891 Acc: 0.9023
val Loss: 0.0836 Acc: 0.9703

Epoch 2/24
----------
train Loss: 0.3094 Acc: 0.9050
val Loss: 0.0614 Acc: 0.9771

Epoch 3/24
----------
train Loss: 0.2308 Acc: 0.9279
val Loss: 0.0429 Acc: 0.9865

Epoch 4/24
----------
train Loss: 0.1748 Acc: 0.9498
val Loss: 0.0331 Acc: 0.9906

Epoch 5/24
----------
train Loss: 0.2252 Acc: 0.9301
val Loss: 0.0702 Acc: 0.9906

Epoch 6/24
----------
train Loss: 0.1726 Acc: 0.9531
val Loss: 0.0442 Acc: 0.9852

Epoch 7/24
----------
train Loss: 0.1595 Acc: 0.9536
val Loss: 0.0359 Acc: 0.9906

Epoch 8/24
----------
train Loss: 0.1310 Acc: 0.9651
val Loss: 0.0355 Acc: 0.9892

Epoch 9/24
----------
train Loss: 0.1172 Acc: 0.9689
val Loss: 0.0325 Acc: 0.9906

Epoch 10/24
----------
train Loss: 0.1070 Acc: 0.9733
val Loss: 0.0515 Acc: 0.9838

Epoch 11/24
----------
train Loss: 0.1304 Acc: 0.9683
val Loss: 0.0452 Acc: 0.9892

Epoch 12/24
----------
train Loss: 0.1164 Acc: 0.9656
val Loss: 0.0424 Acc: 0.9892

Epoch 13/24
----------
train Loss: 0.0751 Acc: 0.9809
val Loss: 0.0396 Acc: 0.9906

Epoch 14/24
----------
train Loss: 0.1091 Acc: 0.9749
val Loss: 0.0279 Acc: 0.9946

Epoch 15/24
----------
train Loss: 0.0751 Acc: 0.9842
val Loss: 0.0352 Acc: 0.9906

Epoch 16/24
----------
train Loss: 0.1353 Acc: 0.9705
val Loss: 0.0413 Acc: 0.9879

Epoch 17/24
----------
train Loss: 0.0957 Acc: 0.9787
val Loss: 0.0332 Acc: 0.9906

Epoch 18/24
----------
train Loss: 0.1091 Acc: 0.9689
val Loss: 0.0317 Acc: 0.9906

Epoch 19/24
----------
train Loss: 0.1101 Acc: 0.9700
val Loss: 0.0402 Acc: 0.9879

Epoch 20/24
----------
train Loss: 0.1133 Acc: 0.9754
val Loss: 0.0392 Acc: 0.9892

Epoch 21/24
----------
train Loss: 0.0970 Acc: 0.9776
val Loss: 0.0424 Acc: 0.9865

Epoch 22/24
----------
train Loss: 0.0865 Acc: 0.9814
val Loss: 0.0348 Acc: 0.9919

Epoch 23/24
----------
train Loss: 0.1319 Acc: 0.9656
val Loss: 0.0341 Acc: 0.9892

Epoch 24/24
----------
train Loss: 0.0997 Acc: 0.9771
val Loss: 0.0328 Acc: 0.9906

Training complete in 9m 32s
Best val Acc: 0.994602
In [30]:

visualize_model(model_ft)

visualize_model(model_ft)

model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False

# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)

model_conv = model_conv.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opoosed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)

model_conv = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, num_epochs=25)

Epoch 0/24
----------
train Loss: 0.3994 Acc: 0.8466
val Loss: 0.2137 Acc: 0.9109

Epoch 1/24
----------
train Loss: 0.2783 Acc: 0.8963
val Loss: 0.0649 Acc: 0.9744

Epoch 2/24
----------
train Loss: 0.2976 Acc: 0.8870
val Loss: 0.0577 Acc: 0.9811

Epoch 3/24
----------
train Loss: 0.2873 Acc: 0.9039
val Loss: 0.0477 Acc: 0.9825

Epoch 4/24
----------
train Loss: 0.3214 Acc: 0.8843
val Loss: 0.0499 Acc: 0.9798

Epoch 5/24
----------
train Loss: 0.3244 Acc: 0.8772
val Loss: 0.0483 Acc: 0.9798

Epoch 6/24
----------
train Loss: 0.2855 Acc: 0.8985
val Loss: 0.0446 Acc: 0.9825

Epoch 7/24
----------
train Loss: 0.2425 Acc: 0.9121
val Loss: 0.0460 Acc: 0.9798

Epoch 8/24
----------
train Loss: 0.2070 Acc: 0.9219
val Loss: 0.0390 Acc: 0.9879

Epoch 9/24
----------
train Loss: 0.2189 Acc: 0.9127
val Loss: 0.0408 Acc: 0.9825

Epoch 10/24
----------
train Loss: 0.2243 Acc: 0.9148
val Loss: 0.0577 Acc: 0.9825

Epoch 11/24
----------
train Loss: 0.2042 Acc: 0.9236
val Loss: 0.0519 Acc: 0.9852

Epoch 12/24
----------
train Loss: 0.2425 Acc: 0.9083
val Loss: 0.0440 Acc: 0.9838

Epoch 13/24
----------
train Loss: 0.2127 Acc: 0.9198
val Loss: 0.0454 Acc: 0.9865

Epoch 14/24
----------
train Loss: 0.2479 Acc: 0.9045
val Loss: 0.0551 Acc: 0.9771

Epoch 15/24
----------
train Loss: 0.2562 Acc: 0.8990
val Loss: 0.0491 Acc: 0.9852

Epoch 16/24
----------
train Loss: 0.2104 Acc: 0.9143
val Loss: 0.0448 Acc: 0.9852

Epoch 17/24
----------
train Loss: 0.2606 Acc: 0.8974
val Loss: 0.0480 Acc: 0.9798

Epoch 18/24
----------
train Loss: 0.2474 Acc: 0.9067
val Loss: 0.0639 Acc: 0.9798

Epoch 19/24
----------
train Loss: 0.2159 Acc: 0.9176
val Loss: 0.0495 Acc: 0.9852

Epoch 20/24
----------
train Loss: 0.2107 Acc: 0.9170
val Loss: 0.0482 Acc: 0.9838

Epoch 21/24
----------
train Loss: 0.2128 Acc: 0.9121
val Loss: 0.0522 Acc: 0.9838

Epoch 22/24
----------
train Loss: 0.2263 Acc: 0.9176
val Loss: 0.0459 Acc: 0.9852

Epoch 23/24
----------
train Loss: 0.1907 Acc: 0.9329
val Loss: 0.0460 Acc: 0.9906

Epoch 24/24
----------
train Loss: 0.2302 Acc: 0.9181
val Loss: 0.0425 Acc: 0.9879

Training complete in 4m 31s
Best val Acc: 0.990553
In [33]:

visualize_model(model_conv)

visualize_model(model_conv)

plt.ioff()
plt.show()

微调和特征提取两种方法的效果都很棒