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test_loss_metrics.py
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test_loss_metrics.py
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# -*- coding: utf-8 -*-
"""Test the basic training of super resolution GAN"""
import numpy as np
import tensorflow as tf
from sup3r.utilities.loss_metrics import (MmdMseLoss, CoarseMseLoss,
TemporalExtremesLoss, LowResLoss)
from sup3r.utilities.utilities import spatial_coarsening, temporal_coarsening
def test_mmd_loss():
"""Test content loss using mse + mmd for content loss."""
x = np.zeros((6, 10, 10, 8, 3))
y = np.zeros((6, 10, 10, 8, 3))
x[:, 7:9, 7:9, :, :] = 1
y[:, 2:5, 2:5, :, :] = 1
# distributions differing by only a small peak should give small mse and
# larger mmd
mse_fun = tf.keras.losses.MeanSquaredError()
mmd_mse_fun = MmdMseLoss()
mse = mse_fun(x, y)
mmd_plus_mse = mmd_mse_fun(x, y)
assert mmd_plus_mse > mse
x = np.random.rand(6, 10, 10, 8, 3)
x /= np.max(x)
y = np.random.rand(6, 10, 10, 8, 3)
y /= np.max(y)
# scaling the same distribution should give high mse and smaller mmd
mse = mse_fun(5 * x, x)
mmd_plus_mse = mmd_mse_fun(5 * x, x)
assert mmd_plus_mse < mse
def test_coarse_mse_loss():
"""Test the coarse MSE loss on spatial average data"""
x = np.random.uniform(0, 1, (6, 10, 10, 8, 3))
y = np.random.uniform(0, 1, (6, 10, 10, 8, 3))
mse_fun = tf.keras.losses.MeanSquaredError()
cmse_fun = CoarseMseLoss()
mse = mse_fun(x, y)
coarse_mse = cmse_fun(x, y)
assert isinstance(mse, tf.Tensor)
assert isinstance(coarse_mse, tf.Tensor)
assert mse.numpy().size == 1
assert coarse_mse.numpy().size == 1
assert mse.numpy() > 10 * coarse_mse.numpy()
def test_tex_loss():
"""Test custom TemporalExtremesLoss function that looks at min/max values
in the timeseries."""
loss_obj = TemporalExtremesLoss()
x = np.zeros((1, 1, 1, 72, 1))
y = np.zeros((1, 1, 1, 72, 1))
# loss should be dominated by special min/max values
x[..., 24, 0] = 20
y[..., 25, 0] = 25
loss = loss_obj(x, y)
assert loss.numpy() > 1.5
# loss should be dominated by special min/max values
x[..., 24, 0] = -20
y[..., 25, 0] = -25
loss = loss_obj(x, y)
assert loss.numpy() > 1.5
def test_lr_loss():
"""Test custom LowResLoss that re-coarsens synthetic and true high-res
fields and calculates pointwise loss on the low-res fields"""
# test w/o enhance
t_meth = 'average'
loss_obj = LowResLoss(s_enhance=1, t_enhance=1, t_method=t_meth,
tf_loss='MeanSquaredError')
xarr = np.random.uniform(-1, 1, (3, 10, 10, 48, 2))
yarr = np.random.uniform(-1, 1, (3, 10, 10, 48, 2))
xtensor = tf.convert_to_tensor(xarr)
ytensor = tf.convert_to_tensor(yarr)
loss = loss_obj(xtensor, ytensor)
assert np.allclose(loss, loss_obj._tf_loss(xtensor, ytensor))
# test 5D with s_enhance
s_enhance = 5
loss_obj = LowResLoss(s_enhance=s_enhance, t_enhance=1, t_method=t_meth,
tf_loss='MeanSquaredError')
xarr_lr = spatial_coarsening(xarr, s_enhance=s_enhance, obs_axis=True)
yarr_lr = spatial_coarsening(yarr, s_enhance=s_enhance, obs_axis=True)
loss = loss_obj(xtensor, ytensor)
assert np.allclose(loss, loss_obj._tf_loss(xarr_lr, yarr_lr))
# test 5D with s/t enhance
s_enhance = 5
t_enhance = 12
loss_obj = LowResLoss(s_enhance=s_enhance, t_enhance=t_enhance,
t_method=t_meth, tf_loss='MeanSquaredError')
xarr_lr = spatial_coarsening(xarr, s_enhance=s_enhance, obs_axis=True)
yarr_lr = spatial_coarsening(yarr, s_enhance=s_enhance, obs_axis=True)
xarr_lr = temporal_coarsening(xarr_lr, t_enhance=t_enhance, method=t_meth)
yarr_lr = temporal_coarsening(yarr_lr, t_enhance=t_enhance, method=t_meth)
loss = loss_obj(xtensor, ytensor)
assert np.allclose(loss, loss_obj._tf_loss(xarr_lr, yarr_lr))
# test 5D with subsample
t_meth = 'subsample'
loss_obj = LowResLoss(s_enhance=s_enhance, t_enhance=t_enhance,
t_method=t_meth, tf_loss='MeanSquaredError')
xarr_lr = spatial_coarsening(xarr, s_enhance=s_enhance, obs_axis=True)
yarr_lr = spatial_coarsening(yarr, s_enhance=s_enhance, obs_axis=True)
xarr_lr = temporal_coarsening(xarr_lr, t_enhance=t_enhance, method=t_meth)
yarr_lr = temporal_coarsening(yarr_lr, t_enhance=t_enhance, method=t_meth)
loss = loss_obj(xtensor, ytensor)
assert np.allclose(loss, loss_obj._tf_loss(xarr_lr, yarr_lr))
# test 4D spatial only
xarr = np.random.uniform(-1, 1, (3, 10, 10, 2))
yarr = np.random.uniform(-1, 1, (3, 10, 10, 2))
xtensor = tf.convert_to_tensor(xarr)
ytensor = tf.convert_to_tensor(yarr)
s_enhance = 5
loss_obj = LowResLoss(s_enhance=s_enhance, t_enhance=1, t_method=t_meth,
tf_loss='MeanSquaredError')
xarr_lr = spatial_coarsening(xarr, s_enhance=s_enhance, obs_axis=True)
yarr_lr = spatial_coarsening(yarr, s_enhance=s_enhance, obs_axis=True)
loss = loss_obj(xtensor, ytensor)
assert np.allclose(loss, loss_obj._tf_loss(xarr_lr, yarr_lr))