dl

Deep learning based operators

---

source

n2f

 n2f (intf:numpy.ndarray, chunks:tuple=None, depths:tuple=(0, 0),
      model:str=None)

	Type	Default	Details
intf	ndarray		interferogram, 2d np.complex64 or cp.complex64
chunks	tuple	None	chunksize, intf.shape by default
depths	tuple	(0, 0)	width of the boundary
model	str	None	path to the model in onnx format, use the model comes with this package by default

Usage:

import zarr
import numpy as np
import holoviews as hv
hv.extension('bokeh')
hv.output(widget_location='bottom')
from bokeh.models import WheelZoomTool
from holoviews import opts

rslc_zarr = zarr.open('../CLI/raw/rslc.zarr/','r')
intf = rslc_zarr[:,:,7]*rslc_zarr[:,:,13].conj()

filtered_intf = n2f(intf,depths=(10,10))

CPU times: user 4min 1s, sys: 41 s, total: 4min 42s
Wall time: 4.4 s

raw_intf_plot = mr.ras_plot(np.angle(intf))
filtered_intf_plot = mr.ras_plot(np.angle(filtered_intf))
(raw_intf_plot+filtered_intf_plot).opts(
    opts.Image(
        cmap='colorwheel',width=600, height=600, colorbar=True,invert_yaxis=True,
        default_tools=['pan',WheelZoomTool(zoom_on_axis=False),'save','reset','hover'],
        active_tools=['wheel_zoom'])
)

if is_cuda_available():
    intf_cp = cp.asarray(intf)

if is_cuda_available():
    filtered_intf_cp = n2f(intf_cp)

CPU times: user 4.52 s, sys: 1.59 s, total: 6.12 s
Wall time: 6.02 s

if is_cuda_available():
    filtered_intf_ = _n2f_np_in_gpu(intf)
    raw_intf_plot = mr.ras_plot(np.angle(intf))
    filtered_intf_plot = mr.ras_plot(np.angle(filtered_intf_))
(raw_intf_plot+filtered_intf_plot).opts(
        opts.Image(
            cmap='colorwheel',width=600, height=600, colorbar=True,invert_yaxis=True,
            default_tools=['pan',WheelZoomTool(zoom_on_axis=False),'save','reset','hover'],
            active_tools=['wheel_zoom'])
)