mattiagatti

import segmentation_models_pytorch as smp

model = smp.Unet(
    encoder_name="resnet50",        # choose encoder
    encoder_weights="imagenet",     # choose pretrained (not required)
    in_channels=3,                  # model input channels
    classes=10,                     # model output channels
    activation="None"               # None|"sigmoid"|"softmax"; default is None
)

mattiagatti

from torchmetrics import MetricCollection, Accuracy, Precision, Recall

class MyModule(LightningModule):
    def __init__(self):
        ...
        self.metric_collection = MetricCollection({
            'acc': Accuracy(),
            'prec': Precision(num_classes=10, average='macro'),
            'rec': Recall(num_classes=10, average='macro')
        })

mattiagatti

import torch
import torchmetrics

class MyAccuracy(Metric):
    def __init__(self):
        super().__init__()
        # to count the correct predictions
        self.add_state('corrects', default=torch.tensor(0))
        # to count the total predictions
        self.add_state('total', default=torch.tensor(0))

mattiagatti

import torch
from torchmetrics import MetricCollection, Accuracy, Precision, Recall

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = YourModel().to(device)
# collection of all validation metrics
metric_collection = MetricCollection({
    'acc': Accuracy(),
    'prec': Precision(num_classes=10, average='macro'),
    'rec': Recall(num_classes=10, average='macro')

mattiagatti

import torch
import torchmetrics

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = YourModel().to(device)
metric = torchmetrics.Accuracy()

for batch_idx, (data, target) in enumerate(val_dataloader):
    data, target = data.to(device), target.to(device)
    preds = model(data)

mattiagatti

import numpy
import open3d as o3d
from PIL import Image

# data
image = Image.open('image.jpg')
model = Model()
output = model(image)  # depth, 512 x 512 matrix

H, W = output.shape[0], output.shape[1]