require('image') imgPath = "image.jpg" img = torch.Tensor(1, 3, imgDim, imgDim) img[1] = image.load(imgPath, 3, byte) image is stored as float, conversion is (intensity/255.). stored top->bottom, line by line. format RGB. display require 'gnuplot' gnuplot.figure(1) gnuplot.imagesc(img[1]) Torch<->numpy dictionary https://github.com/torch/torch7/wiki/Torch-for-Numpy-users
How to visualize gradients with torch-lightning and tensorboard in your model class define a optimizer_step. class Model(pl.LightningModule): # ... def optimizer_step( self, epoch: int, batch_idx: int, optimizer, optimizer_idx: int, second_order_closure = None, ) -> None: if self.trainer.use_tpu and XLA_AVAILABLE: xm.optimizer_step(optimizer) elif isinstance(optimizer, torch.optim.LBFGS): optimizer.step(second_order_closure) else: optimizer.step() #### Gradient reporting start ### if batch_idx % 500 == 0: for tag, param in self.model.named_parameters(): self.logger.experiment.add_histogram('{}_grad'.format(tag), param.grad.cpu().detach()) #### Gradient reporting end ### # clear gradients optimizer....