def load_trained_model(
experiment_key: str = "055b697548e048b78202cfebb78d6d8c", # or simply use "latest"
model_weights_path: str = "model/weights/srgan_generator_model_weights.npz",
):
"""
Returns a trained Generator DeepBedMap neural network model.
The model's weights and hyperparameters settings are retrieved from
https://comet.ml/weiji14/deepbedmap using an `experiment_key` setting
which can be set to 'latest' or some 32-character alphanumeric string.
"""
srgan_train = _load_ipynb_modules("srgan_train.ipynb")
# Download either 'latest' model weights from Comet.ML or one using experiment_key
# Will also get the hyperparameters "num_residual_blocks" and "residual_scaling"
num_residual_blocks, residual_scaling = _download_model_weights_from_comet(
experiment_key=experiment_key, download_path=model_weights_path
)
# Architect the model with appropriate "num_residual_blocks" and "residual_scaling"
model = srgan_train.GeneratorModel(
num_residual_blocks=num_residual_blocks, residual_scaling=residual_scaling
)
# Load trained neural network weights into model
chainer.serializers.load_npz(file=model_weights_path, obj=model)
return model
def load_trained_model(
experiment_key:
str = "77126218f3504a06adbc7dfe3851bb28", # or simply use "latest"
model_weights_path: str = "model/weights/srgan_generator_model_weights.npz",
):
"""
Returns a trained Generator DeepBedMap neural network model,
and the hyperparameters that were used to train it.
The model's weights and hyperparameters settings are retrieved from
https://comet.ml/weiji14/deepbedmap using an `experiment_key` setting
which can be set to 'latest' or some 32-character alphanumeric string.
"""
srgan_train = _load_ipynb_modules("srgan_train.ipynb")
# Download either 'latest' model weights from Comet.ML or one using experiment_key
# Will also get the hyperparameters "num_residual_blocks" and "residual_scaling"
hyperparameters = _download_model_weights_from_comet(
experiment_key=experiment_key, download_path=model_weights_path)
# Architect the model with appropriate "num_residual_blocks" and "residual_scaling"
model = srgan_train.GeneratorModel(
num_residual_blocks=int(hyperparameters["num_residual_blocks"]),
residual_scaling=float(hyperparameters["residual_scaling"]),
)
# Load trained neural network weights into model
chainer.serializers.load_npz(file=model_weights_path, obj=model)
return model, hyperparameters
def _unit_test_ipynb(path: str):
"""
Unit tests on loaded modules from a .ipynb file.
Uses doctest.
"""
assert path.endswith(".ipynb")
module = _load_ipynb_modules(ipynb_path=path)
num_failures, num_attempted = doctest.testmod(m=module, verbose=True)
if num_failures > 0:
sys.exit(num_failures)
def get_deepbedmap_model_inputs(
window_bound: rasterio.coords.BoundingBox,
padding: int = 1000,
use_whole_rema: bool = False,
) -> (np.ndarray, np.ndarray, np.ndarray, np.ndarray):
"""
Outputs one large tile for each of:
BEDMAP2, REMA, MEASURES Ice Flow Velocity and Antarctic Snow Accumulation
according to a given window_bound in the form of (xmin, ymin, xmax, ymax).
"""
data_prep = _load_ipynb_modules("data_prep.ipynb")
if window_bound == rasterio.coords.BoundingBox(
left=-1_594_000.0, bottom=-166_500.0, right=-1_575_000.0, top=-95_500.0
):
# Quickly pull from cached quilt storage if using (hardcoded) test region
quilt.install(package="weiji14/deepbedmap/model/test", force=True)
pkg = quilt.load(pkginfo="weiji14/deepbedmap/model/test")
X_tile = pkg.X_tile()
W1_tile = pkg.W1_tile()
W2_tile = pkg.W2_tile()
W3_tile = pkg.W3_tile()
# to_skip,
to_end,
to_generate,
)
from paper.figures.PlotNeuralNet.pycore.tikzeng3 import (
to_scalefont,
to_flatimage,
to_curvedskip,
to_InOut,
to_RRDB,
to_ConvRelu,
to_Upsample,
)
from features.environment import _load_ipynb_modules
data_prep = _load_ipynb_modules("data_prep.ipynb")
deepbedmap = _load_ipynb_modules("deepbedmap.ipynb")
# %% [markdown]
# # **Methodology**
#
# Uses personal [fork](https://github.com/weiji14/PlotNeuralNet)
# of [PlotNeuralNet](https://github.com/HarisIqbal88/PlotNeuralNet)
# for drawing convolutional neural network architecture diagram.
# %% [markdown]
# ### **Figure 1: DeepBedMap Model Architecture**
# %% [markdown]
# ### Thumbnail figures
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
import pandas as pd
import pygmt as gmt
import quilt
import rasterio
import skimage
import comet_ml
import chainer
import cupy
from features.environment import _load_ipynb_modules, _download_model_weights_from_comet
data_prep = _load_ipynb_modules("data_prep.ipynb")
# %% [markdown]
# # 1. Gather datasets
# %% [markdown]
# ## 1.1 Get bounding box of our area of interest
#
# Basically predict on an place where we have groundtruth data to validate against.
# %%
def get_image_with_bounds(filepaths: list, indexers: dict = None) -> xr.DataArray:
"""
Retrieve raster image in xarray.DataArray format patched
with projected coordinate bounds as (xmin, ymin, xmax, ymax)
