def fasterrcnn_ resnet50_ FPN () instance test

Training phase

import torch
import torchvision
from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)

# for train
images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4]
labels = torch.randint(1, 91, (4, 11))

images = list(image for image in images)
targets = []
for i in range(len(images)):  # i = 0, 1, 2, 3
    d = {
    }
    d['boxes'] = boxes[i]
    d['labels'] = labels[i]
    targets.append(d)

output = model(images, targets)
print(output)
#
# # for inference
# model.eval()
#
# x = [torch.rand(3, 300, 400), torch.rand(3, 300, 400)]
# predictions = model(x)

result ：

{
    'loss_classifier': tensor(0.3633, grad_fn=<NllLossBackward0>), 'loss_box_reg': tensor(0.0262, grad_fn=<DivBackward0>), 'loss_objectness': tensor(1.9085, grad_fn=<BinaryCrossEntropyWithLogitsBackward0>), 'loss_rpn_box_reg': tensor(1.0729, grad_fn=<DivBackward0>)}

The reasoning stage

import torch
import torchvision
from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)

# # for train
# images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
# boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4]
# labels = torch.randint(1, 91, (4, 11))
#
# images = list(image for image in images)
# targets = []
# for i in range(len(images)): # i = 0, 1, 2, 3
# d = {}
# d['boxes'] = boxes[i]
# d['labels'] = labels[i]
# targets.append(d)
#
# output = model(images, targets)
# print(output)
#
# for inference
model.eval()

x = [torch.rand(3, 300, 400), torch.rand(3, 300, 400)]
predictions = model(x)
print(predictions)

result ：
It is speculated that the reason why there is no prediction box is that the data is randomly generated

[{
    'boxes': tensor([], size=(0, 4), grad_fn=<StackBackward0>), 'labels': tensor([], dtype=torch.int64), 'scores': tensor([], grad_fn=<IndexBackward0>)}, 
{
    'boxes': tensor([], size=(0, 4), grad_fn=<StackBackward0>), 'labels': tensor([], dtype=torch.int64), 'scores': tensor([], grad_fn=<IndexBackward0>)}]