utils.py

import torch


def bilinear_interpolate(loc_ip, data):
    """bilinear interpolation for raster data"""
    # Taken from: https://github.com/elijahcole/sinr/blob/main/utils.py

    # loc is N x 2 vector, where each row is [lon,lat] entry
    #   each entry spans range [-1,1]
    # data is H x W x C, height x width x channel data matrix
    # op will be N x C matrix of interpolated features

    assert data is not None

    # map to [0,1], then scale to data size
    loc = (loc_ip.clone() + 1) / 2.0
    loc[:, 1] = (
        1 - loc[:, 1]
    )  # this is because latitude goes from +90 on top to bottom while
    # longitude goes from -90 to 90 left to right

    assert not torch.any(torch.isnan(loc))

    # cast locations into pixel space
    loc[:, 0] *= data.shape[1] - 1
    loc[:, 1] *= data.shape[0] - 1

    loc_int = torch.floor(loc).long()  # integer pixel coordinates
    xx = loc_int[:, 0]
    yy = loc_int[:, 1]
    xx_plus = xx + 1
    xx_plus[xx_plus > (data.shape[1] - 1)] = data.shape[1] - 1
    yy_plus = yy + 1
    yy_plus[yy_plus > (data.shape[0] - 1)] = data.shape[0] - 1

    loc_delta = loc - torch.floor(loc)  # delta values
    dx = loc_delta[:, 0].unsqueeze(1)
    dy = loc_delta[:, 1].unsqueeze(1)

    interp_val = (
        data[yy, xx, :] * (1 - dx) * (1 - dy)
        + data[yy, xx_plus, :] * dx * (1 - dy)
        + data[yy_plus, xx, :] * (1 - dx) * dy
        + data[yy_plus, xx_plus, :] * dx * dy
    )

    return interp_val


class DummyParams:
    """Dummy class needed for DefaultParams"""

    def __init__(self):
        pass


class DefaultParams:
    """DefaultParams for convenience when creating experiments without the hydra wrapper."""

    def __init__(self, sinr):
        self.dataset = DummyParams()
        self.local = DummyParams()

        self.pos_weight = 2048
        self.lr = 5e-4
        self.l2_dec = 0
        self.epochs = 7
        self.model = "sat_sinr_mf"
        self.dataset.predictors = "loc_env_sent2"
        self.sinr_layers = 8
        self.sinr_hidden = 512
        self.dropout = 0.3
        self.tag = "DefaultParams"
        self.embedder = "cnn_default"
        self.validate = False
        self.checkpoint = "None"

        self.dataset.batchsize = 2048
        self.dataset.use_ds_samples = True
        self.dataset.num_workers = 16
        self.local.sent_data_path = ""
        self.local.bioclim_path = ""
        self.local.dataset_file_path = ""
        self.local.cp_dir_path = ""
        self.local.logs_dir_path = ""
        self.local.test_data_path = ""
        self.local.gpu = True

        if sinr:
            self.dataset.predictors = "loc_env"  # "loc"
            self.epochs = 15
            self.pos_weight = 2048
            self.model = "sinr"