Shortcuts

PSNR#

class ignite.metrics.PSNR(data_range, output_transform=<function PSNR.<lambda>>, device=device(type='cpu'))[source]#

Computes average Peak signal-to-noise ratio (PSNR).

PSNR(I,J)=10log10(MAXI2 MSE )\text{PSNR}(I, J) = 10 * \log_{10}\left(\frac{ MAX_{I}^2 }{ \text{ MSE } }\right)

where MSE\text{MSE} is mean squared error.

  • y_pred and y must have (batch_size, …) shape.

  • y_pred and y must have same dtype and same shape.

Parameters
  • data_range (Union[int, float]) – The data range of the target image (distance between minimum and maximum possible values). For other data types, please set the data range, otherwise an exception will be raised.

  • output_transform (Callable) – A callable that is used to transform the Engine’s process_function’s output into the form expected by the metric.

  • device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

Examples

To use with Engine and process_function, simply attach the metric instance to the engine. The output of the engine’s process_function needs to be in format of (y_pred, y) or {'y_pred': y_pred, 'y': y, ...}.

psnr = PSNR(data_range=1.0)
psnr.attach(default_evaluator, 'psnr')
preds = torch.rand([4, 3, 16, 16])
target = preds * 0.75
state = default_evaluator.run([[preds, target]])
print(state.metrics['psnr'])
16.8671405...

This metric by default accepts Grayscale or RGB images. But if you have YCbCr or YUV images, only Y channel is needed for computing PSNR. And, this can be done with output_transform. For instance,

def get_y_channel(output):
    y_pred, y = output
    # y_pred and y are (B, 3, H, W) and YCbCr or YUV images
    # let's select y channel
    return y_pred[:, 0, ...], y[:, 0, ...]

psnr = PSNR(data_range=219, output_transform=get_y_channel)
psnr.attach(default_evaluator, 'psnr')
preds = 219 * torch.rand([4, 3, 16, 16])
target = preds * 0.75
state = default_evaluator.run([[preds, target]])
print(state.metrics['psnr'])
16.7027966...

New in version 0.4.3.

Methods

compute

Computes the metric based on it's accumulated state.

reset

Resets the metric to it's initial state.

update

Updates the metric's state using the passed batch output.

compute()[source]#

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None

update(output)[source]#

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters

output (Sequence[torch.Tensor]) – the is the output from the engine’s process function.

Return type

None