gistlib - implement fastercnn in python

To implement Faster R-CNN in Python, we will need to follow these steps:

Install PyTorch and torchvision
Prepare the dataset
Define the Faster R-CNN Model
Train and Fine-tune the network
Test the model

Here is an overview of how to implement Faster R-CNN in Python using PyTorch and torchvision.

1. Install PyTorch and torchvision

To install PyTorch and torchvision, run the following command in your terminal.


main.py
pip install torch torchvision
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2. Prepare the dataset

Prepare your dataset in the required format. The dataset should be in COCO format, with annotations and images in separate folders.

3. Define the Faster R-CNN Model

Define the Faster R-CNN Model in PyTorch using torchvision models. Here's an example:


main.py
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor

# load a pre-trained model for classification and return only the features
backbone = torchvision.models.mobilenet_v2(pretrained=True).features
# FasterRCNN needs to know the number of
# output channels in a backbone. For mobilenet_v2, it's 1280
# so we need to add it here
backbone.out_channels = 1280

# let's make the RPN generate 5 x 3 anchors per spatial location, with
# 5 different sizes and 3 different aspect ratios. We have a Tuple[Tuple[int]]
# because each feature map could potentially have different sizes and
# aspect ratios
anchor_generator = torchvision.models.detection.rpn.AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
                                                                    aspect_ratios=((0.5, 1.0, 2.0),))

# let's define what are the feature maps that we will use to perform the region of
# interest cropping, as well as the size of the crop after rescaling.
# if your backbone returns a Tensor, featmap_names is expected to
# be [0]. More generally, the backbone should return an OrderedDict[Tensor],
# and in featmap_names you can choose which feature maps to use.
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
                                                output_size=7,
                                                sampling_ratio=2)

# put the pieces together inside a FasterRCNN model
model = torchvision.models.detection.FasterRCNN(backbone,
                                                 num_classes=2,
                                                 rpn_anchor_generator=anchor_generator,
                                                 box_roi_pool=roi_pooler,
                                                 box_predictor=FastRCNNPredictor(256, num_classes=2))
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4. Train and Fine-tune the network

Train and Fine-tune the network on your dataset using the defined model. Use a data loader to load data and use the optimizer to minimize the loss. Here's an example:


main.py
import torchvision.transforms as T

def get_transform(train):
    transforms = []
    if train:
        transforms.append(T.RandomHorizontalFlip(0.5))
    transforms.append(T.ToTensor())
    return T.Compose(transforms)

# use our dataset and defined transformations
dataset = CocoDataset(root, get_transform(train=True))
dataset_test = CocoDataset(root, get_transform(train=False))

# define training and validation data loaders
data_loader = torch.utils.data.DataLoader(
    dataset, batch_size=2, shuffle=True, num_workers=0,
    collate_fn=utils.collate_fn)

data_loader_test = torch.utils.data.DataLoader(
    dataset_test, batch_size=1, shuffle=False, num_workers=0,
    collate_fn=utils.collate_fn)

# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005,
                            momentum=0.9, weight_decay=0.0005)

# and a learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
                                               step_size=3,
                                               gamma=0.1)
# let's train it for 10 epochs
num_epochs = 10

for epoch in range(num_epochs):
    # train for one epoch, printing every 10 iterations
    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
    # update the learning rate
    lr_scheduler.step()
    # evaluate on the test dataset
    evaluate(model, data_loader_test, device=device)
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5. Test the model

Finally, Test the trained model by running inference on the test dataset. You can use the following code to test the model:


main.py
# pick one image from the test set
img, _ = dataset_test[0]
# put the model in evaluation mode
model.eval()
with torch.no_grad():
    prediction = model([img.to(device)])
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The prediction variable now contains the predicted bounding boxes and classes for the image.

implement fastercnn in python

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