add DINCAE_benchmark.jl

test/perf/DINCAE_benchmark.jl

@@ -0,0 +1,132 @@
+# # Benchmark DINCAE
+#
+# Load the necessary modules
+
+using CUDA
+using DINCAE
+using Dates
+using NCDatasets
+using AMDGPU
+
+# ## Data download
+#
+# First we define the domain and time interval.
+#
+# The example is tested here for a short time frame but for realistic
+# applications one should use a much longer time-range (like 10, 20 years or more)
+
+# local directory
+localdir = expanduser("~/Data/SST-AlboranSea-example")
+# create directory
+mkpath(localdir)
+# filename of the subset
+fname_subset = joinpath(localdir,"modis_subset.nc")
+# filename of the clean data
+fname = joinpath(localdir,"modis_cleanup.nc")
+# filename of the data with added clouds for cross-validation
+fname_cv = joinpath(localdir,"modis_cleanup_add_clouds.nc")
+varname = "sst"
+
+# Results of DINCAE will be placed in a sub-directory under `localdir`
+
+outdir = joinpath(localdir,"Results")
+mkpath(outdir)
+
+
+# # Reconstruct missing data
+#
+#
+# F is the floating point number type for the neural network. Here we use
+# single precision.
+
+const F = Float32
+
+# Test if CUDA is functional to use the GPU, otherwise the CPU is used.
+
+if CUDA.functional()
+    Atype = CuArray{F}
+elseif AMDGPU.functional()
+    Atype = ROMArray{F}
+else
+    @warn "No supported GPU found. We will use the CPU which is very slow. Please check https://developer.nvidia.com/cuda-gpus"
+    Atype = Array{F}
+end
+
+# Setting the parameters of neural network.
+# See the documentation of `DINCAE.reconstruct` for more information.
+
+epochs = 100
+batch_size = 32
+enc_nfilter_internal = round.(Int,32 * 2 .^ (0:4))
+clip_grad = 5.0
+regularization_L2_beta = 0
+ntime_win = 3
+upsampling_method = :nearest
+loss_weights_refine = (0.3,0.7)
+save_epochs = [epochs]
+
+
+data = [
+   (filename = fname_cv,
+    varname = varname,
+    obs_err_std = 1,
+    jitter_std = 0.05,
+    isoutput = true,
+   )
+]
+data_test = data;
+fnames_rec = [joinpath(outdir,"data-avg-benchmark.nc")]
+data_all = [data,data_test]
+
+# Use these parameters for a quick test:
+
+# epochs = 10
+# save_epochs = epochs:epochs
+
+# Start the training and reconstruction of the neural network
+
+loss = DINCAE.reconstruct(
+    Atype,data_all,fnames_rec;
+    epochs = epochs,
+    batch_size = batch_size,
+    enc_nfilter_internal = enc_nfilter_internal,
+    clip_grad = clip_grad,
+    save_epochs = save_epochs,
+    upsampling_method = upsampling_method,
+    loss_weights_refine = loss_weights_refine,
+    ntime_win = ntime_win,
+)
+
+@show loss[1]
+
+# # Post process results
+#
+# Compute the RMS (Root Mean Squared error) with the independent validation data
+
+#=
+case = (
+    fname_orig = fname,
+    fname_cv = fname_cv,
+    varname = varname,
+)
+fnameavg = joinpath(outdir,"data-avg.nc")
+cvrms = DINCAE_utils.cvrms(case,fnameavg)
+@info "Cross-validation RMS error is: $cvrms"
+
+# Next we plot all time instances. The figures with be placed in the
+# directory `figdir`
+
+figdir = joinpath(outdir,"Fig")
+DINCAE_utils.plotres(case,fnameavg, clim = nothing, figdir = figdir,
+                     clim_quantile = (0.01,0.99),
+                     which_plot = :cv)
+@info "Figures are in $(figdir)"
+
+
+# Example reconstruction for 2001-09-12
+# ![reconstruction for the 2001-09-12](Fig/data-avg_2001-09-12.png)
+# Panel (a) is the original data where we have added clouds (panel (b)). The
+# reconstuction based on the data in panel (b) is shown in panel (c) together
+# with its expected standard deviation error (panel (d)).
+
+=#