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added latest code, runners, and notebooks
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cssprad1 committed Jun 10, 2024
1 parent b84c587 commit 4f1f2d2
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512 changes: 512 additions & 0 deletions notebooks/satvision-toa-reconstruction-comparison-plotting-scaled.ipynb
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1,500 changes: 1,500 additions & 0 deletions notebooks/satvision-toa-reconstruction-generation-scaled.ipynb
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158 changes: 123 additions & 35 deletions pytorch_caney/data/transforms.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
from .utils import RandomResizedCropNP
from .utils import TransformBrightnessAndReflectance
from .utils import SimmimMaskGenerator

import torchvision.transforms as T
import numpy as np


class SimmimTransform:
Expand All @@ -15,6 +15,7 @@ def __init__(self, config):

self.transform_img = \
T.Compose([
MinMaxEmissiveScaleReflectance(), # New transform for MinMax
RandomResizedCropNP(scale=(0.67, 1.),
ratio=(3. / 4., 4. / 3.)),
T.ToTensor(),
Expand Down Expand Up @@ -46,7 +47,8 @@ def __call__(self, img):

return img, mask

class SimmimTransformScale:

class TensorResizeTransform:
"""
torchvision transform which transforms the input imagery into
addition to generating a MiM mask
Expand All @@ -56,55 +58,141 @@ def __init__(self, config):

self.transform_img = \
T.Compose([
TransformBrightnessAndReflectance(),
RandomResizedCropNP(scale=(0.67, 1.),
ratio=(3. / 4., 4. / 3.)),
T.ToTensor(),
#lambda x: x / 500.0,
#T.ConvertImageDtype(dtype=torch.float32),
#torchvision.ops.Permute(dims=[1, 2, 0]),
T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)),
])

if config.MODEL.TYPE in ['swin', 'swinv2']:

model_patch_size = config.MODEL.SWINV2.PATCH_SIZE

else:

raise NotImplementedError

self.mask_generator = SimmimMaskGenerator(
input_size=config.DATA.IMG_SIZE,
mask_patch_size=config.DATA.MASK_PATCH_SIZE,
model_patch_size=model_patch_size,
mask_ratio=config.DATA.MASK_RATIO,
)

def __call__(self, img):

img = self.transform_img(img)
mask = self.mask_generator()

return img, mask
return img


class TensorResizeTransform:
class MinMaxEmissiveScaleReflectance(object):
"""
torchvision transform which transforms the input imagery into
addition to generating a MiM mask
Performs scaling of MODIS TOA data
- Scales reflectance percentages to reflectance units (% -> (0,1))
- Performs per-channel minmax scaling for emissive bands (k -> (0,1))
"""

def __init__(self, config):
def __init__(self):

self.reflectance_indices = [0, 1, 2, 3, 4, 6]
self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]

self.transform_img = \
T.Compose([
T.ToTensor(),
T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)),
])
self.emissive_mins = np.array(
[223.1222, 178.9174, 204.3739, 204.7677,
194.8686, 202.1759, 201.3823, 203.3537],
dtype=np.float32)

self.emissive_maxs = np.array(
[352.7182, 261.2920, 282.5529, 319.0373,
295.0209, 324.0677, 321.5254, 285.9848],
dtype=np.float32)

def __call__(self, img):

# Reflectance % to reflectance units
img[:, :, self.reflectance_indices] = \
img[:, :, self.reflectance_indices] * 0.01

# Brightness temp scaled to (0,1) range
img[:, :, self.emissive_indices] = \
(img[:, :, self.emissive_indices] - self.emissive_mins) / \
(self.emissive_maxs - self.emissive_mins)

return img


img = self.transform_img(img)
class TransformBrightnessAndReflectance(object):
"""
Performs conversion of calibrated MODIS TOA data to radiance units
- Converts TOA brightness temperature to TOA radiance units
- Converts TOA reflectance percentage to TOA radiance units
"""

# Planck constant (Joule second)
h__ = np.float32(6.6260755e-34)

# Speed of light in vacuum (meters per second)
c__ = np.float32(2.9979246e+8)

# Boltzmann constant (Joules per Kelvin)
k__ = np.float32(1.380658e-23)

def __init__(self):

self.reflectance_indices = [0, 1, 2, 3, 4, 6]
self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]

self.emi_radiance_offsets = np.array([
2730.583496, 2730.583252, 2317.488281, 2730.583496,
1560.333252, 1577.339722, 1658.221313, 2501.297607],
dtype=np.float32)[np.newaxis, np.newaxis, :]

self.emi_radiance_scales = np.array([
0.003149510128, 0.0001175572979, 0.0001924497337,
0.0005324869417, 0.0004063234373, 0.0008400219958,
0.0007296975818, 0.0002622638713],
dtype=np.float32)[np.newaxis, np.newaxis, :]

self.rsb_reflectance_offsets = np.array([
0.0, 0.0, 0.0, 0.0, 0.0, 316.9721985],
dtype=np.float32)[np.newaxis, np.newaxis, :]

self.rsb_reflectance_scales = np.array([
5.665329445e-05, 3.402091534e-05, 6.13320808e-05,
3.468021168e-05, 3.117151937e-05, 2.858474545e-05],
dtype=np.float32)[np.newaxis, np.newaxis, :]

self.rsb_radiance_offsets = np.array([
0.0, 0.0, 0.0, 0.0, 0.0, 316.9721985],
dtype=np.float32)[np.newaxis, np.newaxis, :]

self.rsb_radiance_scales = np.array([
0.02995670214, 0.01111282408, 0.04215827957,
0.002742749639, 0.0009269224829, 0.003434347222],
dtype=np.float32)[np.newaxis, np.newaxis, :]

# Derived constants
self.c_1 = 2 * self.h__ * self.c__ * self.c__
self.c_2 = (self.h__ * self.c__) / self.k__

self.cwn = np.array([
2.505277E+3, 1.477967E+3, 1.362737E+3, 1.173190E+3,
1.027715E+3, 9.080884E+2, 8.315399E+2, 7.483394E+2],
dtype=np.float32)[np.newaxis, np.newaxis, :]
self.cwn = 1. / (self.cwn * 100)

self.tcs = np.array([
9.998646E-1, 9.994877E-1, 9.994918E-1, 9.995495E-1,
9.997398E-1, 9.995608E-1, 9.997256E-1, 9.999160E-1],
dtype=np.float32)[np.newaxis, np.newaxis, :]

self.tci = np.array([
9.262664E-2, 2.204921E-1, 2.046087E-1, 1.599191E-1,
8.253401E-2, 1.302699E-1, 7.181833E-2, 1.972608E-2],
dtype=np.float32)[np.newaxis, np.newaxis, :]

def __call__(self, img):

# Reflectance to radiance units
reflectance_bands = img[:, :, self.reflectance_indices]
img[:, :, self.reflectance_indices] = \
self.rsb_radiance_scales * (
(((reflectance_bands * 0.01) / self.rsb_reflectance_scales) + \
self.rsb_reflectance_offsets) - self.rsb_radiance_offsets)

img[:, :, self.reflectance_indices] = \
img[:, :, self.reflectance_indices] * 0.01

# Brightness temp to radiance units:
emissive_bands = img[:, :, self.emissive_indices]
intermediate = emissive_bands * self.tcs + self.tci
exponent = self.c_2 / (intermediate * self.cwn)
img[:, :, self.emissive_indices] = self.c_1 / \
(1000000 * self.cwn ** 5 * ((np.e ** exponent) - 1))

return img

137 changes: 36 additions & 101 deletions pytorch_caney/data/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,113 +115,42 @@ def make_simmim_mask(token_count, mask_count, rand_size, scale):
return mask


class ModisScalesAndOffsets(object):

reflectance_indices = [0, 1, 2, 3, 4, 6]
emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]

@staticmethod
def rsb_radiance_scales() -> torch.tensor:

radiance_scales_array = np.array([0.02995670214, 0.01111282408, 0.04215827957,
0.002742749639, 0.0009269224829, 0.003434347222], dtype=np.float32)

return torch.tensor(radiance_scales_array).view(6, 1, 1)

@staticmethod
def rsb_radiance_offsets() -> torch.tensor:

radiance_offsets_array = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 316.9721985], dtype=np.float32)

return torch.tensor(radiance_offsets_array).view(6, 1, 1)

@staticmethod
def rsb_reflectance_scales() -> torch.tensor:

reflectance_scales_array = np.array([5.665329445e-05, 3.402091534e-05, 6.13320808e-05,
3.468021168e-05, 3.117151937e-05, 2.858474545e-05], dtype=np.float32)

return torch.tensor(reflectance_scales_array).view(6, 1, 1)

@staticmethod
def rsb_reflectance_offsets() -> torch.tensor:

reflectance_offsets_array = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 316.9721985], dtype=np.float32)

return torch.tensor(reflectance_offsets_array).view(6, 1, 1)
class MinMaxEmissiveScaleReflectance(object):
"""
Performs scaling of MODIS TOA data
- Scales reflectance percentages to reflectance units (% -> (0,1))
- Performs per-channel minmax scaling for emissive bands (k -> (0,1))
"""

@staticmethod
def emi_radiance_scales() -> torch.tensor:
def __init__(self):

self.reflectance_indices = [0, 1, 2, 3, 4, 6]
self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]

radiance_scales_array = np.array([
0.003149510128, 0.0001175572979, 0.0001924497337,
0.0005324869417, 0.0004063234373, 0.0008400219958,
0.0007296975818, 0.0002622638713],
self.emissive_mins = np.array(
[223.1222, 178.9174, 204.3739, 204.7677,
194.8686, 202.1759, 201.3823, 203.3537],
dtype=np.float32)

return torch.tensor(radiance_scales_array).view(8, 1, 1)

@staticmethod
def emi_radiance_offsets() -> torch.tensor:

# Radiance offsets
radiance_offsets_array = np.array([
2730.583496, 2730.583252, 2317.488281, 2730.583496,
1560.333252, 1577.339722, 1658.221313, 2501.297607],
self.emissive_maxs = np.array(
[352.7182, 261.2920, 282.5529, 319.0373,
295.0209, 324.0677, 321.5254, 285.9848],
dtype=np.float32)

return torch.tensor(radiance_offsets_array).view(8, 1, 1)

@staticmethod
def derived_constants() -> torch.tensor:
def __call__(self, img):

# Planck constant (Joule second)
h__ = np.float32(6.6260755e-34)

# Speed of light in vacuum (meters per second)
c__ = np.float32(2.9979246e+8)

# Boltzmann constant (Joules per Kelvin)
k__ = np.float32(1.380658e-23)

# Derived constants
c_1 = 2 * h__ * c__ * c__
c_2 = (h__ * c__) / k__
# Reflectance % to reflectance units
img[:, :, self.reflectance_indices] = \
img[:, :, self.reflectance_indices] * 0.01

return torch.tensor(c_1), torch.tensor(c_2)

# ev1km_band_names = [20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36]
# ev1km_band_names = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15]

@staticmethod
def cwn() -> torch.tensor:
cwn_array = np.array([
2.505277E+3, 1.477967E+3, 1.362737E+3, 1.173190E+3,
1.027715E+3, 9.080884E+2, 8.315399E+2, 7.483394E+2],
dtype=np.float32)
cwn_array = 1. / (cwn_array * 100)
return torch.tensor(cwn_array).view(8, 1, 1)

@staticmethod
def tcs() -> torch.tensor:
# Temperature correction slope (no units)
tcs_array = np.array([
9.998646E-1, 9.994877E-1, 9.994918E-1, 9.995495E-1,
9.997398E-1, 9.995608E-1, 9.997256E-1, 9.999160E-1],
dtype=np.float32)
return torch.tensor(tcs_array).view(8, 1, 1)

@staticmethod
def tci() -> torch.tensor:
# Temperature correction intercept (Kelvin)
tci_array = np.array([
9.262664E-2, 2.204921E-1, 2.046087E-1, 1.599191E-1,
8.253401E-2, 1.302699E-1, 7.181833E-2, 1.972608E-2],
dtype=np.float32)
return torch.tensor(tci_array).view(8, 1, 1)


# Brightness temp scaled to (0,1) range
img[:, :, self.emissive_indices] = \
(img[:, :, self.emissive_indices] - self.emissive_mins) / \
(self.emissive_maxs - self.emissive_mins)

return img


class TransformBrightnessAndReflectance(object):

# Planck constant (Joule second)
Expand Down Expand Up @@ -291,13 +220,19 @@ def __call__(self, img):

# Reflectance to radiance units
reflectance_bands = img[:, :, self.reflectance_indices]
img[:, :, self.reflectance_indices] = self.rsb_radiance_scales * ((((reflectance_bands * 0.01) / self.rsb_reflectance_scales) + self.rsb_reflectance_offsets) - self.rsb_radiance_offsets)
img[:, :, self.reflectance_indices] = img[:, :, self.reflectance_indices] * 0.01
img[:, :, self.reflectance_indices] = \
self.rsb_radiance_scales * (
(((reflectance_bands * 0.01) / self.rsb_reflectance_scales) + \
self.rsb_reflectance_offsets) - self.rsb_radiance_offsets)

img[:, :, self.reflectance_indices] = \
img[:, :, self.reflectance_indices] * 0.01

# Brightness temp to radiance units:
emissive_bands = img[:, :, self.emissive_indices]
intermediate = emissive_bands * self.tcs + self.tci
exponent = self.c_2 / (intermediate * self.cwn)
img[:, :, self.emissive_indices] = self.c_1 / (1000000 * self.cwn ** 5 * ((np.e ** exponent) - 1))
img[:, :, self.emissive_indices] = self.c_1 / \
(1000000 * self.cwn ** 5 * ((np.e ** exponent) - 1))

return img
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