Utilities

IO

class ezflow.utils.io.InputPadder(dims, divisor=8, mode='sintel')[source]

Class to pad / unpad the input to a network with a given padding

Parameters

  • dims (tuple) – Dimensions of the input

  • divisor (int) – Divisor to make the input evenly divisible by

  • mode (str) – Padding mode

pad(*inputs)[source]

Pad the input

Parameters

inputs (list) – List of inputs to pad

Returns

Padded inputs

Return type

list

unpad(x)[source]

Unpad the input

Parameters

x (torch.Tensor) – Input to unpad

Returns

Unpadded input

Return type

torch.Tensor

ezflow.utils.io.read_flow(file_name)[source]

Read ground truth flow from a variety of file formats

Parameters

file_name (str) – Path to flow file

Returns

  • flow (np.ndarray) – Optical flow map

  • valid (None if .flo and .pfm files else np.ndarray) – Valid flow map

ezflow.utils.io.read_flow_middlebury(fn)[source]

Read .flo file in Middlebury format

Parameters

fn (str) – Absolute path to flow file

Returns

flow – Optical flow map

Return type

np.ndarray

ezflow.utils.io.read_flow_pfm(file)[source]

Read optical flow from a .pfm file

Parameters

file (str) – Path to flow file

Returns

flow – Optical flow map

Return type

np.ndarray

ezflow.utils.io.read_flow_png(filename)[source]

Read optical flow from a png file.

Parameters

filename (str) – Path to flow file

Returns

  • flow (np.ndarray) – Optical flow map

  • valid (np.ndarray) – Valid flow map

ezflow.utils.io.read_image(file_name)[source]

Read images from a variety of file formats

Parameters

file_name (str) – Path to flow file

Returns

flow – Optical flow map

Return type

np.ndarray

ezflow.utils.io.write_flow(filename, uv, v=None)[source]

Write optical flow to file.

If v is None, uv is assumed to contain both u and v channels, stacked in depth.

Parameters

  • filename (str) – Path to file

  • uv (np.ndarray) – Optical flow

  • v (np.ndarray, optional) – Optional second channel

Metrics

ezflow.utils.metrics.endpointerror(pred, target, multi_magnitude=False)[source]

Endpoint error

Parameters

  • pred (torch.Tensor) – Predicted flow

  • target (torch.Tensor) – Target flow

Returns

Endpoint error

Return type

torch.Tensor

Other Utilities

class ezflow.utils.other_utils.AverageMeter[source]

Computes and stores the average and current value

reset()[source]

Resets the meter

update(val, n=1)[source]

Updates the meter

Parameters

  • val (float) – Value to update the meter with

  • n (int) – Number of samples to update the meter with

ezflow.utils.other_utils.coords_grid(batch_size, h, w)[source]

Returns a grid of coordinates in the shape of (batch_size, h, w, 2)

Parameters

  • batch_size (int) – Batch size

  • h (int) – Height of the image

  • w (int) – Width of the image

Returns

Grid of coordinates

Return type

torch.Tensor

ezflow.utils.other_utils.find_free_port()[source]

Find an available free ports in the host machine.

Returns

port number

Return type

str

ezflow.utils.other_utils.is_port_available(port)[source]

Check if the provided port is free in the host machine.

portint

Port number of the host.

Returns

Return True is the port is free otherwise False.

Return type

boolean

Registry

Adapted from Detectron2 (https://github.com/facebookresearch/detectron2)

class ezflow.utils.registry.Registry(name)[source]

Class to register objects and then retrieve them by name.

Parameters

name (str) – Name of the registry

get(name)[source]

Method to retrieve an object from the registry

Parameters

name (str) – Name of the object to retrieve

Returns

Object registered under the given name

Return type

object

get_list()[source]

Method to retrieve all objects from the registry

Returns

List of all objects registered in the registry

Return type

list

register(obj=None, name=None)[source]

Method to register an object in the registry

Parameters

  • obj (object, optional) – Object to register, defaults to None (which will return the decorator)

  • name (str, optional) – Name of the object to register, defaults to None (which will use the name of the object)

Resampling

ezflow.utils.resampling.bilinear_sampler(img, coords, mask=False)[source]

Biliear sampler for images

Parameters

  • img (torch.Tensor) – Image to be sampled

  • coords (torch.Tensor) – Coordinates to be sampled

Returns

Sampled image

Return type

torch.Tensor

ezflow.utils.resampling.convex_upsample_flow(flow, mask_logits, out_stride)[source]

Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination

Parameters

  • flow (torch.Tensor) – Flow field to be upsampled

  • mask_logits (torch.Tensor) – Mask logits

  • out_stride (int) – Output stride

Returns

Upsampled flow field

Return type

torch.Tensor

ezflow.utils.resampling.forward_interpolate(flow)[source]

Forward interpolation of flow field

Parameters

flow (torch.Tensor) – Flow field to be interpolated

Returns

Forward interpolated flow field

Return type

torch.Tensor

ezflow.utils.resampling.upflow(flow, scale=8, mode='bilinear')[source]

Interpolate flow field

Parameters

  • flow (torch.Tensor) – Flow field to be interpolated

  • scale (int) – Scale of the interpolated flow field

  • mode (str) – Interpolation mode

Returns

Interpolated flow field

Return type

torch.Tensor

Visualization

Warping

ezflow.utils.warp.warp(x, flow)[source]

Warps an image x according to the optical flow field specified

Parameters

  • x (torch.Tensor) – Image to be warped

  • flow (torch.Tensor) – Optical flow field

Returns

Warped image

Return type

torch.Tensor