前言：本篇主要偏向图像分类实战部分，使用MMclassification工具进行代码应用，最后对水果分类进行实战演示，本次环境和代码配置部分省略，具体内容建议参考前一篇文章：计算机视觉框架OpenMMLab开源学习（二）：图像分类
计算机视觉框架OpenMMLab开源学习（三）：图像分类实战
一、安装OpenMMLab v2.0
Step 1. Install MMCV
mim install "mmcv>=2.0.0rc0"
Step 2. Install MMClassification and MMDetection
mim install "mmcls>=1.0.0rc0" "mmdet>=3.0.0rc0"
代码模版讲解：
-
model =
dict(
-
type=
'ImageClassifier',
# 分类器类型
-
backbone=
dict(
-
type=
'ResNet',
# 主干网络类型
-
depth=
50,
# 主干网网络深度， ResNet 一般有18, 34, 50, 101, 152 可以选择
-
num_stages=
4,
# 主干网络状态(stages)的数目，这些状态产生的特征图作为后续的 head 的输入。
-
out_indices=(
3, ),
# 输出的特征图输出索引。越远离输入图像，索引越大
-
frozen_stages=-
1,
# 网络微调时，冻结网络的stage（训练时不执行反相传播算法），若num_stages=4，backbone包含stem 与 4 个 stages。frozen_stages为-1时，不冻结网络； 为0时，冻结 stem； 为1时，冻结 stem 和 stage1； 为4时，冻结整个backbone
-
style=
'pytorch'),
# 主干网络的风格，'pytorch' 意思是步长为2的层为 3x3 卷积， 'caffe' 意思是步长为2的层为 1x1 卷积。
-
neck=
dict(
type=
'GlobalAveragePooling'),
# 颈网络类型
-
head=
dict(
-
type=
'LinearClsHead',
# 线性分类头，
-
num_classes=
1000,
# 输出类别数，这与数据集的类别数一致
-
in_channels=
2048,
# 输入通道数，这与 neck 的输出通道一致
-
loss=
dict(
type=
'CrossEntropyLoss', loss_weight=
1.0),
# 损失函数配置信息
-
topk=(
1,
5),
# 评估指标，Top-k 准确率， 这里为 top1 与 top5 准确率
-
))
二、Pytorch图像分类任务
本次任务训练数据为FashionMNIST，完整代码如下：
-
# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
-
-
import torch
-
from torch
import nn
-
from torch.utils.data
import DataLoader
-
from torchvision
import datasets
-
from torchvision.transforms
import ToTensor
-
-
# Training
-
-
## Construct Dataset and Dataloader
-
training_data = datasets.FashionMNIST(
-
root=
"data",
-
train=
True,
-
download=
True,
-
transform=ToTensor(),
-
)
-
test_data = datasets.FashionMNIST(
-
root=
"data",
-
train=
False,
-
download=
True,
-
transform=ToTensor(),
-
)
-
-
batch_size =
64
-
-
train_dataloader = DataLoader(training_data, batch_size=batch_size)
-
test_dataloader = DataLoader(test_data, batch_size=batch_size)
-
-
## Define model
-
class
NeuralNetwork(nn.Module):
-
def
__init__(
self):
-
super(NeuralNetwork, self).__init__()
-
self.flatten = nn.Flatten()
-
self.linear_relu_stack = nn.Sequential(
-
nn.Linear(
28*
28,
512),
-
nn.ReLU(),
-
nn.Linear(
512,
512),
-
nn.ReLU(),
-
nn.Linear(
512,
10)
-
)
-
-
def
forward(
self, x):
-
x = self.flatten(x)
-
logits = self.linear_relu_stack(x)
-
return logits
-
-
device =
"cuda"
if torch.cuda.is_available()
else
"cpu"
-
model = NeuralNetwork().to(device)
-
-
## Define loss function and Optimizer
-
loss_fn = nn.CrossEntropyLoss()
-
optimizer = torch.optim.SGD(model.parameters(), lr=
1e-3)
-
-
## Inner loop for training
-
def
train(
dataloader, model, loss_fn, optimizer):
-
size =
len(dataloader.dataset)
-
model.train()
-
for batch, (X, y)
in
enumerate(dataloader):
-
X, y = X.to(device), y.to(device)
-
-
# Compute prediction error
-
pred = model(X)
-
loss = loss_fn(pred, y)
-
-
# Backpropagation
-
optimizer.zero_grad()
-
loss.backward()
-
optimizer.step()
-
-
# Output Logs
-
if batch %
100 ==
0:
-
loss, current = loss.item(), batch *
len(X)
-
print(
f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
-
-
## Inner loop for test
-
def
test(
dataloader, model, loss_fn):
-
size =
len(dataloader.dataset)
-
num_batches =
len(dataloader)
-
model.
eval()
-
test_loss, correct =
0,
0
-
with torch.no_grad():
-
for X, y
in dataloader:
-
X, y = X.to(device), y.to(device)
-
pred = model(X)
-
test_loss += loss_fn(pred, y).item()
-
correct += (pred.argmax(
1) == y).
type(torch.
float).
sum().item()
-
test_loss /= num_batches
-
correct /= size
-
print(
f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
-
-
-
## Launch training / test loops#
-
epochs =
5
-
for t
in
range(epochs):
-
print(
f"Epoch {t+1}\n-------------------------------")
-
train(train_dataloader, model, loss_fn, optimizer)
-
test(test_dataloader, model, loss_fn)
-
print(
"Done!")
-
-
-
## Saving Models
-
-
torch.save(model.state_dict(),
"model.pth")
-
print(
"Saved PyTorch Model State to model.pth")
-
-
# Deployment
-
-
## Loading Models
-
model = NeuralNetwork()
-
model.load_state_dict(torch.load(
"model.pth"))
-
-
# Predict new images
-
-
classes = [
-
"T-shirt/top",
-
"Trouser",
-
"Pullover",
-
"Dress",
-
"Coat",
-
"Sandal",
-
"Shirt",
-
"Sneaker",
-
"Bag",
-
"Ankle boot",
-
]
-
-
model.
eval()
-
x, y = test_data[
0][
0], test_data[
0][
1]
-
with torch.no_grad():
-
pred = model(x)
-
predicted, actual = classes[pred[
0].argmax(
0)], classes[y]
-
print(
f'Predicted: "{predicted}", Actual: "{actual}"')
三、利用MMClassification提供的预训练模型推理：
安装环境：
-
pip install openmim, mmengine
-
mim install mmcv-full mmcls
Inference using high-level API
-
from mmcls.apis
import init_model, inference_model
-
-
model = init_model(
'mobilenet-v2_8xb32_in1k.py',
-
'mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth',
-
device=
'cuda:0')
-
load checkpoint
from local path: mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
-
result = inference_model(model,
'banana.png')
-
result
-
{
'pred_label':
954,
'pred_score':
0.9999284744262695,
'pred_class':
'banana'}
-
from mmcls.apis
import show_result_pyplot
-
-
show_result_pyplot(model,
'banana.png', result)
PyTorch codes under the hood
Let write some raw PyTorch codes to do the same thing.
These are actual codes wrapped in high-level APIs.
construct an ImageClassifier
Note: current implementation only allow configs of backbone, neck and classification head instead of Python objects.
from mmcls.models import ImageClassifier
classifier = ImageClassifier(
backbone=dict(type='MobileNetV2', widen_factor=1.0),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1280)
)
Load trained parameters
import torch
ckpt = torch.load('mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth')
classifier.load_state_dict(ckpt['state_dict'])
Construct data preprocessing pipeline
Important: A models work only if image preprocessing pipelines is correct.
from mmcls.datasets.pipelines import Compose
test_pipeline = Compose([
dict(type='LoadImageFromFile'),
dict(type='Resize', size=(256, -1), backend='pillow'),
dict(type='CenterCrop', crop_size=224),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
data = dict(img_info=dict(filename='banana.png'), img_prefix=None) data = test_pipeline(data) data
{'img_metas': DataContainer({'filename': 'banana.png', 'ori_filename': 'banana.png', 'ori_shape': (403, 393, 3), 'img_shape': (224, 224, 3), 'img_norm_cfg': {'mean': array([123.675, 116.28 , 103.53 ], dtype=float32), 'std': array([58.395, 57.12 , 57.375], dtype=float32), 'to_rgb': True}}),
'img': tensor([[[ 0.3309, 0.2967, 0.3138, ..., 2.0263, 2.0092, 1.9920],
[ 0.3481, 0.3309, 0.2282, ..., 2.0263, 2.0092, 1.9920],
[ 0.2796, 0.2967, 0.2967, ..., 1.9920, 2.0263, 1.9749],
...,
[ 0.1939, 0.1768, 0.2282, ..., 0.3994, 0.3309, 0.3823],
[ 0.1426, 0.1254, 0.2111, ..., 0.5878, 0.5364, 0.5536],
[-0.0116, -0.0801, 0.1597, ..., 0.5707, 0.5536, 0.5364]],
[[ 0.3803, 0.3803, 0.3803, ..., 2.1660, 2.1485, 2.1134],
[ 0.4153, 0.4153, 0.3102, ..., 2.1835, 2.1310, 2.1134],
[ 0.3452, 0.3803, 0.3803, ..., 2.1134, 2.1485, 2.1134],
...,
[ 0.2752, 0.2577, 0.3102, ..., 0.5028, 0.4328, 0.4328],
[ 0.2227, 0.1877, 0.3102, ..., 0.6604, 0.6254, 0.5728],
[ 0.0301, -0.0049, 0.2402, ..., 0.6604, 0.6254, 0.5728]],
[[ 0.5485, 0.5485, 0.5485, ..., 2.3437, 2.3263, 2.2914],
[ 0.5834, 0.5834, 0.4788, ..., 2.3611, 2.3088, 2.2914],
[ 0.5136, 0.5485, 0.5485, ..., 2.3088, 2.3437, 2.3088],
...,
[ 0.4091, 0.3916, 0.4439, ..., 0.5834, 0.5136, 0.5311],
[ 0.3568, 0.3045, 0.4265, ..., 0.7576, 0.7228, 0.7054],
[ 0.1651, 0.1128, 0.3742, ..., 0.7576, 0.7402, 0.7054]]])}
equivalent in torchvision
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, Normalize, ToTensor
tv_transform = Compose([Resize(256),
CenterCrop(224),
ToTensor(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = Image.open('banana.png').convert('RGB')
tv_data = tv_transform(image)
Forward through the model
## IMPORTANT: set the classifier to eval mode
classifier.eval()
imgs = data['img'].unsqueeze(0)
imgs = tv_data.unsqueeze(0)
with torch.no_grad():
# class probabilities
prob = classifier.forward_test(imgs)[0]
# features
feat = classifier.extract_feat(imgs, stage='neck')[0]
print(len(prob))
print(prob.argmax().item())
print(feat.shape)
1000 954 torch.Size([1, 1280])
3.使用MMClassificaiton完整进行水果分类实战：
数据集下载：
GitHub - TommyZihao/MMClassification_Tutorials: Jupyter notebook tutorials for MMClassificationJupyter notebook tutorials for MMClassification. Contribute to TommyZihao/MMClassification_Tutorials development by creating an account on GitHub.
https://github.com/TommyZihao/MMClassification_Tutorials
代码框架：
-
def
main():
-
model = build_classifier(cfg.model)
-
model.init_weights()
-
-
datasets = [build_dataset(cfg.data.train)]
-
-
train_model(
-
model,
-
datasets,
-
cfg,
-
distributed=distributed,
-
validate=(
not args.no_validate),
-
timestamp=timestamp,
-
device=cfg.device,
-
meta=meta)
-
mmcls/apis/train_model.py
-
-
def
train_model(
model,
-
dataset,
-
cfg):
-
-
data_loaders = [build_dataloader(ds, **train_loader_cfg)
for ds
in dataset]
-
-
optimizer = build_optimizer(model, cfg.optimizer)
-
-
runner = build_runner(
-
cfg.runner,
-
default_args=
dict(
-
model=model,
-
optimizer=optimizer))
-
-
runner.register_training_hooks(
-
cfg.lr_config,
-
optimizer_config,
-
cfg.checkpoint_config,
-
cfg.log_config,
-
cfg.get(
'momentum_config',
None),
-
custom_hooks_config=cfg.get(
'custom_hooks',
None))
-
-
runner.run(data_loaders, cfg.workflow)
-
mmcv/runner/epoch_based_runner.py
-
-
class
EpochBasedRunner(
BaseRunner):
-
-
def
run_iter(
self, data_batch: Any, train_mode: bool, **kwargs) ->
None:
-
if train_mode:
-
outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
-
else:
-
outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
-
-
self.outputs = outputs
-
-
def
train(
self, data_loader, **kwargs):
-
self.model.train()
-
self.data_loader = data_loader
-
for i, data_batch
in
enumerate(self.data_loader):
-
self.run_iter(data_batch, train_mode=
True, **kwargs)
-
self.call_hook(
'after_train_iter')
-
mmcls/models/classifiers/base.py
-
-
class
BaseClassifier(BaseModule, metaclass=ABCMeta):
-
-
def
forward(
self, img, return_loss=True, **kwargs):
-
"""Calls either forward_train or forward_test depending on whether
-
return_loss=True.
-
-
Note this setting will change the expected inputs. When
-
`return_loss=True`, img and img_meta are single-nested (i.e. Tensor and
-
List[dict]), and when `resturn_loss=False`, img and img_meta should be
-
double nested (i.e. List[Tensor], List[List[dict]]), with the outer
-
list indicating test time augmentations.
-
"""
-
if return_loss:
-
return self.forward_train(img, **kwargs)
-
else:
-
return self.forward_test(img, **kwargs)
-
-
def
train_step(
self, data, optimizer=None, **kwargs):
-
losses = self(**data)
-
loss, log_vars = self._parse_losses(losses)
-
-
outputs =
dict(
-
loss=loss, log_vars=log_vars, num_samples=
len(data[
'img'].data))
-
-
return outputs
-
mmcls/models/classifiers/image.py
-
-
class
ImageClassifier(
BaseClassifier):
-
-
def
__init__(
self,
-
backbone,
-
neck=None,
-
head=None,
-
pretrained=None,
-
train_cfg=None,
-
init_cfg=None):
-
super(ImageClassifier, self).__init__(init_cfg)
-
-
if pretrained
is
not
None:
-
self.init_cfg =
dict(
type=
'Pretrained', checkpoint=pretrained)
-
self.backbone = build_backbone(backbone)
-
-
if neck
is
not
None:
-
self.neck = build_neck(neck)
-
-
if head
is
not
None:
-
self.head = build_head(head)
-
-
def
extract_feat(
self, img):
-
x = self.backbone(img)
-
-
if self.with_neck:
-
x = self.neck(x)
-
-
return x
-
-
def
forward_train(
self, img, gt_label, **kwargs):
-
x = self.extract_feat(img)
-
-
losses =
dict()
-
loss = self.head.forward_train(x, gt_label)
-
-
losses.update(loss)
-
-
return losses
-
mmcv/runner/hooks/optimizer.py
-
-
class
OptimizerHook(
Hook):
-
def
after_train_iter(
self, runner):
-
runner.optimizer.zero_grad()
-
runner.outputs[
'loss'].backward()
-
runner.optimizer.step()
总结：本篇主要偏向图像分类实战部分，使用MMclassification工具进行代码应用，熟悉其框架应用，为后续处理不同场景下分类问题提供帮助。
本文参考：GitHub - wangruohui/sjtu-openmmlab-tutorial
转载：https://blog.csdn.net/qq_36816848/article/details/128884766
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