def test_model(config):
input_folder = config[PredictionParams.input_folder]
output_folder = config[PredictionParams.output_folder]
output_fields = config[ProjTrainingParams.output_fields]
model_weights_file = config[PredictionParams.model_weights_file]
output_imgs_folder = config[PredictionParams.output_imgs_folder]
field_names_model = config[ProjTrainingParams.fields_names]
field_names_obs = config[ProjTrainingParams.fields_names_obs]
rows = config[ProjTrainingParams.rows]
cols = config[ProjTrainingParams.cols]
run_name = config[TrainingParams.config_name]
norm_type = config[ProjTrainingParams.norm_type]
output_imgs_folder = join(output_imgs_folder, run_name)
create_folder(output_imgs_folder)
# *********** Chooses the proper model ***********
print('Reading model ....')
net_type = config[ProjTrainingParams.network_type]
if net_type == NetworkTypes.UNET or net_type == NetworkTypes.UNET_MultiStream:
model = select_2d_model(config, last_activation=None)
if net_type == NetworkTypes.SimpleCNN_2:
model = simpleCNN(config, nn_type="2d", hid_lay=2, out_lay=2)
if net_type == NetworkTypes.SimpleCNN_4:
model = simpleCNN(config, nn_type="2d", hid_lay=4, out_lay=2)
if net_type == NetworkTypes.SimpleCNN_8:
model = simpleCNN(config, nn_type="2d", hid_lay=8, out_lay=2)
if net_type == NetworkTypes.SimpleCNN_16:
model = simpleCNN(config, nn_type="2d", hid_lay=16, out_lay=2)
plot_model(model,
to_file=join(output_folder, F'running.png'),
show_shapes=True)
# *********** Reads the weights***********
print('Reading weights ....')
model.load_weights(model_weights_file)
# *********** Read files to predict***********
all_files = os.listdir(input_folder)
all_files.sort()
model_files = np.array([x for x in all_files if x.startswith('model')])
z_layers = [0]
var_file = join(input_folder, "cov_mat", "tops_ias_std.nc")
field_names_std = config[ProjTrainingParams.fields_names_var]
if len(field_names_std) > 0:
input_fields_std = read_netcdf(var_file, field_names_std, z_layers)
else:
input_fields_std = []
cmap_out = chooseCMAP(output_fields)
cmap_model = chooseCMAP(field_names_model)
cmap_obs = chooseCMAP(field_names_obs)
cmap_std = chooseCMAP(field_names_std)
tot_rows = 891
tot_cols = 1401
all_whole_mean_times = []
all_whole_sum_times = []
all_whole_rmse = []
# np.random.shuffle(model_files) # TODO this is only for testing
for id_file, c_file in enumerate(model_files):
# Find current and next date
year = int(c_file.split('_')[1])
day_of_year = int(c_file.split('_')[2].split('.')[0])
if day_of_year != 5:
continue
model_file = join(input_folder, F'model_{year}_{day_of_year:03d}.nc')
inc_file = join(input_folder, F'increment_{year}_{day_of_year:03d}.nc')
obs_file = join(input_folder, F'obs_{year}_{day_of_year:03d}.nc')
# *********************** Reading files **************************
input_fields_model = read_netcdf(model_file, field_names_model,
z_layers)
input_fields_obs = read_netcdf(obs_file, field_names_obs, z_layers)
output_field_increment = read_netcdf(inc_file, output_fields, z_layers)
# ******************* Normalizing and Cropping Data *******************
whole_cnn = np.zeros((891, 1401))
whole_y = np.zeros((891, 1401))
this_file_times = []
start_row = 0
donerow = False
while not (donerow):
donecol = False
start_col = 0
while not (donecol):
# print(F"{start_row}-{start_row+rows} {start_col}-{start_col+cols}")
# Generate the proper inputs for the NN
try:
perc_ocean = .05
input_data, y_data = generateXandY(input_fields_model,
input_fields_obs,
input_fields_std,
output_field_increment,
field_names_model,
field_names_obs,
field_names_std,
output_fields,
start_row,
start_col,
rows,
cols,
norm_type=norm_type,
perc_ocean=perc_ocean)
except Exception as e:
print(F"Land for {c_file} row:{start_row} col:{start_col}")
start_col, donecol = verifyBoundaries(
start_col, cols, tot_cols)
continue
# ******************* Replacing nan values *********
# We set a value of 0.5 on the land. Trying a new loss function that do not takes into account land
input_data_nans = np.isnan(input_data)
input_data = np.nan_to_num(input_data, nan=0)
y_data = np.nan_to_num(y_data, nan=-0.5)
X = np.expand_dims(input_data, axis=0)
Y = np.expand_dims(y_data, axis=0)
# Make the prediction of the network
start = time.time()
output_nn_original = model.predict(X, verbose=1)
toc = time.time() - start
this_file_times.append(toc)
# print(F"Time to get prediction {toc:0.3f} seconds")
# PLOT RAW DATA
# import matplotlib.pyplot as plt
# plt.imshow(np.flip(output_nn_original[0,:,:,0], axis=0))
# plt.imshow(np.flip(Y[0,:,:,0], axis=0))
# plt.show()
# Original MSE
# print(F"MSE: {mean_squared_error(Y[0,:,:,0], output_nn_original[0,:,:,0])}")
# Make nan all values inside the land
land_indexes = Y == -0.5
output_nn_original[land_indexes] = np.nan
# ====================== PLOTS RAW DATA NOT NECESSARY =============================
# viz_obj = EOAImageVisualizer(output_folder=output_imgs_folder, disp_images=False)
# viz_obj.plot_2d_data_np_raw(np.concatenate((input_data.swapaxes(0,2), Y[0,:,:,:].swapaxes(0,2), output_nn_original[0,:,:,:].swapaxes(0,2))),
# var_names=[F"in_model_{x}" for x in field_names_model] +
# [F"in_obs_{x}" for x in field_names_obs] +
# [F"in_var_{x}" for x in field_names_std] +
# [F"out_inc_{x}" for x in output_fields] +
# [F"cnn_{x}" for x in output_fields],
# file_name=F"RAW_Input_and_CNN_{c_file}_{start_row:03d}_{start_col:03d}",
# rot_90=True,
# cols_per_row=len(field_names_model),
# title=F"Input data: {field_names_model} and obs {field_names_obs}, increment {output_fields}, cnn {output_fields}")
# Denormalize the data to the proper units in each field
denorm_cnn_output = np.zeros(output_nn_original.shape)
denorm_y = np.zeros(Y.shape)
# ==== Denormalizingallinput and outputs
denorm_cnn_output = denormalizeData(output_nn_original,
output_fields,
PreprocParams.type_inc,
norm_type)
denorm_y = denormalizeData(Y, output_fields,
PreprocParams.type_inc, norm_type)
input_types = [
PreprocParams.type_model for i in input_fields_model
] + [PreprocParams.type_obs for i in input_fields_obs
] + [PreprocParams.type_std for i in input_fields_std]
denorm_input = denormalizeData(
input_data,
field_names_model + field_names_obs + field_names_std,
input_types, norm_type)
# Recover the original land areas, they are lost after denormalization
denorm_input[input_data_nans] = np.nan
denorm_y[land_indexes] = np.nan
# Remove the 'extra dimension'
denorm_cnn_output = np.squeeze(denorm_cnn_output)
denorm_y = np.squeeze(denorm_y)
whole_cnn[
start_row:start_row + rows, start_col:start_col +
cols] = denorm_cnn_output # Add the the 'whole prediction'
whole_y[start_row:start_row + rows, start_col:start_col +
cols] = denorm_y # Add the the 'whole prediction'
# if np.random.random() > .99: # Plot 1% of the times
if True: # Plot 1% of the times
if len(
denorm_cnn_output.shape
) == 2: # In this case we only had one output and we need to make it 'array' to plot
denorm_cnn_output = np.expand_dims(denorm_cnn_output,
axis=2)
denorm_y = np.expand_dims(denorm_y, axis=2)
# Compute RMSE
rmse_cnn = np.zeros(len(output_fields))
for i in range(len(output_fields)):
ocean_indexes = np.logical_not(
np.isnan(denorm_y[:, :, i]))
rmse_cnn[i] = np.sqrt(
mean_squared_error(
denorm_cnn_output[:, :, i][ocean_indexes],
denorm_y[:, :, i][ocean_indexes]))
# viz_obj = EOAImageVisualizer(output_folder=output_imgs_folder, disp_images=False, mincbar=mincbar, maxcbar=maxcbar)
viz_obj = EOAImageVisualizer(
output_folder=output_imgs_folder, disp_images=False)
# ================== DISPLAYS ALL INPUTS AND OUTPUTS DENORMALIZED ===================
# viz_obj.plot_2d_data_np_raw(np.concatenate((input_data.swapaxes(0,2), Y[0,:,:,:].swapaxes(0,2), output_nn_original[0,:,:,:].swapaxes(0,2))),
viz_obj.plot_2d_data_np_raw(
np.concatenate(
(denorm_input.swapaxes(0,
2), denorm_y.swapaxes(0, 2),
denorm_cnn_output.swapaxes(0, 2))),
var_names=[F"in_model_{x}"
for x in field_names_model] +
[F"in_obs_{x}" for x in field_names_obs] +
[F"in_var_{x}" for x in field_names_std] +
[F"out_inc_{x}" for x in output_fields] +
[F"cnn_{x}" for x in output_fields],
file_name=
F"Input_and_CNN_{c_file}_{start_row:03d}_{start_col:03d}",
cmap=cmap_model + cmap_obs + cmap_std + cmap_out +
cmap_out,
rot_90=True,
cols_per_row=len(field_names_model),
title=
F"Input data: {field_names_model} and obs {field_names_obs}, increment {output_fields}, cnn {output_fields}"
)
# =========== Making the same color bar for desired output and the NN =====================
mincbar = [
np.nanmin(denorm_y[:, :, x])
for x in range(denorm_cnn_output.shape[-1])
]
maxcbar = [
np.nanmax(denorm_y[:, :, x])
for x in range(denorm_cnn_output.shape[-1])
]
error = (denorm_y - denorm_cnn_output).swapaxes(0, 2)
mincbarerror = [
np.nanmin(error[i, :, :])
for i in range(len(output_fields))
]
maxcbarerror = [
np.nanmax(error[i, :, :])
for i in range(len(output_fields))
]
viz_obj = EOAImageVisualizer(
output_folder=output_imgs_folder,
disp_images=False,
mincbar=mincbar + mincbar + mincbarerror,
maxcbar=maxcbar + maxcbar + maxcbarerror)
# ================== Displays CNN and TSIS with RMSE ================
viz_obj.output_folder = join(output_imgs_folder,
'JoinedErrrorCNN')
cmap = chooseCMAP(output_fields)
error_cmap = cmocean.cm.diff
viz_obj.plot_2d_data_np_raw(
np.concatenate((denorm_cnn_output.swapaxes(
0, 2), denorm_y.swapaxes(0, 2), error),
axis=0),
var_names=[F"CNN INC {x}" for x in output_fields] +
[F"TSIS INC {x}" for x in output_fields] +
[F'RMSE {c_rmse_cnn:0.4f}' for c_rmse_cnn in rmse_cnn],
file_name=
F"AllError_{c_file}_{start_row:03d}_{start_col:03d}",
rot_90=True,
cmap=cmap + cmap + [error_cmap],
cols_per_row=len(output_fields),
title=F"{output_fields} RMSE: {np.mean(rmse_cnn):0.5f}"
)
start_col, donecol = verifyBoundaries(start_col, cols,
tot_cols)
# Column for
start_row, donerow = verifyBoundaries(start_row, rows, tot_rows)
# Row for
# ======= Plots whole output with RMSE
mincbar = np.nanmin(whole_y) / 2
maxcbar = np.nanmax(whole_y) / 2
error = whole_y - whole_cnn
mincbarerror = np.nanmin(error) / 2
maxcbarerror = np.nanmax(error) / 2
no_zero_ids = np.count_nonzero(whole_cnn)
rmse_cnn = np.sqrt(np.nansum((whole_y - whole_cnn)**2) / no_zero_ids)
all_whole_rmse.append(rmse_cnn)
all_whole_mean_times.append(np.mean(np.array(this_file_times)))
all_whole_sum_times.append(np.sum(np.array(this_file_times)))
if np.random.random(
) > .9 or day_of_year == 353: # Plot 10% of the times
viz_obj = EOAImageVisualizer(
output_folder=output_imgs_folder,
disp_images=False,
mincbar=mincbar + mincbar + mincbarerror,
maxcbar=maxcbar + maxcbar + maxcbarerror)
# mincbar=[-5, -5, -1],
# maxcbar=[10, 10, 1])
# ================== Displays CNN and TSIS with RMSE ================
viz_obj.output_folder = join(output_imgs_folder,
'WholeOutput_CNN_TSIS')
viz_obj.plot_2d_data_np_raw(
[
np.flip(whole_cnn, axis=0),
np.flip(whole_y, axis=0),
np.flip(error, axis=0)
],
var_names=[F"CNN INC {x}" for x in output_fields] +
[F"TSIS INC {x}"
for x in output_fields] + [F'RMSE {rmse_cnn:0.4f}'],
file_name=F"WholeOutput_CNN_TSIS_{c_file}",
rot_90=False,
cols_per_row=3,
cmap=cmocean.cm.algae,
title=F"{output_fields} RMSE: {np.mean(rmse_cnn):0.5f}")
def test_model(config):
input_folder = config[PredictionParams.input_folder]
output_folder = config[PredictionParams.output_folder]
output_fields = config[ProjTrainingParams.output_fields]
model_weights_file = config[PredictionParams.model_weights_file]
output_imgs_folder = config[PredictionParams.output_imgs_folder]
field_names_model = config[ProjTrainingParams.fields_names]
field_names_obs = config[ProjTrainingParams.fields_names_obs]
rows = config[ProjTrainingParams.rows]
cols = config[ProjTrainingParams.cols]
run_name = config[TrainingParams.config_name]
norm_type = config[ProjTrainingParams.norm_type]
output_imgs_folder = join(output_imgs_folder, run_name)
create_folder(output_imgs_folder)
# *********** Chooses the proper model ***********
print('Reading model ....')
net_type = config[ProjTrainingParams.network_type]
if net_type == NetworkTypes.UNET or net_type == NetworkTypes.UNET_MultiStream:
model = select_2d_model(config, last_activation=None)
if net_type == NetworkTypes.SimpleCNN_2:
model = simpleCNN(config, nn_type="2d", hid_lay=2, out_lay=2)
if net_type == NetworkTypes.SimpleCNN_4:
model = simpleCNN(config, nn_type="2d", hid_lay=4, out_lay=2)
if net_type == NetworkTypes.SimpleCNN_8:
model = simpleCNN(config, nn_type="2d", hid_lay=8, out_lay=2)
if net_type == NetworkTypes.SimpleCNN_16:
model = simpleCNN(config, nn_type="2d", hid_lay=16, out_lay=2)
plot_model(model,
to_file=join(output_folder, F'running.png'),
show_shapes=True)
# *********** Reads the weights***********
print('Reading weights ....')
model.load_weights(model_weights_file)
# *********** Read files to predict***********
all_files = os.listdir(input_folder)
all_files.sort()
model_files = np.array([x for x in all_files if x.startswith('model')])
z_layers = [0]
var_file = join(input_folder, "cov_mat", "tops_ias_std.nc")
field_names_std = config[ProjTrainingParams.fields_names_var]
if len(field_names_std) > 0:
input_fields_std = read_netcdf(var_file, field_names_std, z_layers)
else:
input_fields_std = []
cmap_out = chooseCMAP(output_fields)
cmap_model = chooseCMAP(field_names_model)
cmap_obs = chooseCMAP(field_names_obs)
cmap_std = chooseCMAP(field_names_std)
tot_rows = 891
tot_cols = 1401
all_whole_mean_times = []
all_whole_sum_times = []
all_whole_rmse = []
# np.random.shuffle(model_files) # TODO this is only for testing
for id_file, c_file in enumerate(model_files):
# Find current and next date
year = int(c_file.split('_')[1])
day_of_year = int(c_file.split('_')[2].split('.')[0])
model_file = join(input_folder, F'model_{year}_{day_of_year:03d}.nc')
inc_file = join(input_folder, F'increment_{year}_{day_of_year:03d}.nc')
obs_file = join(input_folder, F'obs_{year}_{day_of_year:03d}.nc')
# *********************** Reading files **************************
input_fields_model = read_netcdf(model_file, field_names_model,
z_layers)
input_fields_obs = read_netcdf(obs_file, field_names_obs, z_layers)
output_field_increment = read_netcdf(inc_file, output_fields, z_layers)
# ******************* Normalizing and Cropping Data *******************
this_file_times = []
try:
perc_ocean = .01
input_data, y_data = generateXandY(input_fields_model,
input_fields_obs,
input_fields_std,
output_field_increment,
field_names_model,
field_names_obs,
field_names_std,
output_fields,
0,
0,
grows,
gcols,
norm_type=norm_type,
perc_ocean=perc_ocean)
except Exception as e:
print(F"Exception {e}")
# ******************* Replacing nan values *********
# We set a value of 0.5 on the land. Trying a new loss function that do not takes into account land
input_data_nans = np.isnan(input_data)
input_data = np.nan_to_num(input_data, nan=0)
y_data = np.nan_to_num(y_data, nan=-0.5)
X = np.expand_dims(input_data, axis=0)
Y = np.expand_dims(y_data, axis=0)
# Make the prediction of the network
start = time.time()
output_nn_original = model.predict(X, verbose=1)
toc = time.time() - start
this_file_times.append(toc)
# Make nan all values inside the land
land_indexes = Y == -0.5
output_nn_original[land_indexes] = np.nan
# ==== Denormalizingallinput and outputs
denorm_cnn_output = denormalizeData(output_nn_original, output_fields,
PreprocParams.type_inc, norm_type)
denorm_y = denormalizeData(Y, output_fields, PreprocParams.type_inc,
norm_type)
input_types = [PreprocParams.type_model
for i in input_fields_model] + [
PreprocParams.type_obs for i in input_fields_obs
] + [PreprocParams.type_std for i in input_fields_std]
denorm_input = denormalizeData(
input_data, field_names_model + field_names_obs + field_names_std,
input_types, norm_type)
# Recover the original land areas, they are lost after denormalization
denorm_y[land_indexes] = np.nan
# Remove the 'extra dimension'
denorm_cnn_output = np.squeeze(denorm_cnn_output)
denorm_y = np.squeeze(denorm_y)
whole_cnn = denorm_cnn_output # Add the the 'whole prediction'
whole_y = denorm_y # Add the the 'whole prediction'
if len(
denorm_cnn_output.shape
) == 2: # In this case we only had one output and we need to make it 'array' to plot
denorm_cnn_output = np.expand_dims(denorm_cnn_output, axis=2)
denorm_y = np.expand_dims(denorm_y, axis=2)
# Compute RMSE
# rmse_cnn = np.zeros(len(output_fields))
# for i in range(len(output_fields)):
# ocean_indexes = np.logical_not(np.isnan(denorm_y[:,:,i]))
# rmse_cnn[i] = np.sqrt(mean_squared_error(denorm_cnn_output[:,:,i][ocean_indexes], denorm_y[:,:,i][ocean_indexes]))
# ================== DISPLAYS ALL INPUTS AND OUTPUTS DENORMALIZED ===================
# Adding back mask to all the input variables
denorm_input[input_data_nans] = np.nan
# ======= Plots whole output with RMSE
mincbar = np.nanmin(whole_y)
maxcbar = np.nanmax(whole_y)
error = whole_y - whole_cnn
mincbarerror = np.nanmin(error)
maxcbarerror = np.nanmax(error)
no_zero_ids = np.count_nonzero(whole_cnn)
if output_fields[
0] == 'srfhgt': # This should only be for SSH to adjust the units
whole_cnn /= 9.81
whole_y = np.array(whole_y) / 9.81
rmse_cnn = np.sqrt(np.nansum((whole_y - whole_cnn)**2) / no_zero_ids)
all_whole_rmse.append(rmse_cnn)
all_whole_mean_times.append(np.mean(np.array(this_file_times)))
all_whole_sum_times.append(np.sum(np.array(this_file_times)))
# if day_of_year == 353: # Plot 10% of the times
if True: # Plot 10% of the times
# viz_obj = EOAImageVisualizer(output_folder=output_imgs_folder, disp_images=False, mincbar=mincbar, maxcbar=maxcbar)
viz_obj = EOAImageVisualizer(output_folder=output_imgs_folder,
disp_images=False)
# viz_obj.plot_2d_data_np_raw(np.concatenate((input_data.swapaxes(0,2), Y[0,:,:,:].swapaxes(0,2), output_nn_original[0,:,:,:].swapaxes(0,2))),
viz_obj.plot_2d_data_np_raw(
np.concatenate(
(denorm_input.swapaxes(0, 2), denorm_y.swapaxes(0, 2),
denorm_cnn_output.swapaxes(0, 2))),
var_names=[F"in_model_{x}" for x in field_names_model] +
[F"in_obs_{x}" for x in field_names_obs] +
[F"in_var_{x}" for x in field_names_std] +
[F"out_inc_{x}"
for x in output_fields] + [F"cnn_{x}" for x in output_fields],
file_name=F"Global_Input_and_CNN_{c_file}",
rot_90=True,
cmap=cmap_model + cmap_obs + cmap_std + cmap_out + cmap_out,
cols_per_row=len(field_names_model),
title=
F"Input data: {field_names_model} and obs {field_names_obs}, increment {output_fields}, cnn {output_fields}"
)
minmax = getMinMaxPlot(output_fields)[0]
viz_obj = EOAImageVisualizer(
output_folder=output_imgs_folder,
disp_images=False,
# mincbar=mincbar + mincbar + mincbarerror,
# maxcbar=maxcbar + maxcbar + maxcbarerror)
# mincbar=[minmax[0], minmax[0], max(minmax[0],-1)],
# maxcbar=[minmax[1], minmax[1], min(minmax[1],1)])
mincbar=[minmax[0], minmax[0], -1],
maxcbar=[minmax[1], minmax[1], 1])
# ================== Displays CNN and TSIS with RMSE ================
error_cmap = cmocean.cm.diff
viz_obj.output_folder = join(output_imgs_folder,
'WholeOutput_CNN_TSIS')
viz_obj.plot_2d_data_np_raw(
[
np.flip(whole_cnn, axis=0),
np.flip(whole_y, axis=0),
np.flip(error, axis=0)
],
# var_names=[F"CNN INC {x}" for x in output_fields] + [F"TSIS INC {x}" for x in output_fields] + [F'TSIS - CNN (Mean RMSE {rmse_cnn:0.4f} m)'],
var_names=[F"CNN increment SSH" for x in output_fields] +
[F"TSIS increment SSH" for x in output_fields] +
[F'TSIS - CNN \n (Mean RMSE {rmse_cnn:0.4f} m)'],
file_name=F"Global_WholeOutput_CNN_TSIS_{c_file}",
rot_90=False,
cmap=cmap_out + cmap_out + [error_cmap],
cols_per_row=3,
# title=F"{output_fields[0]} RMSE: {np.mean(rmse_cnn):0.5f} m.")
title=F"SSH RMSE: {np.mean(rmse_cnn):0.5f} m.")
print("DONE ALL FILES!!!!!!!!!!!!!")
dic_summary = {
"File": model_files,
"rmse": all_whole_rmse,
"times mean": all_whole_mean_times,
"times sum": all_whole_sum_times,
}
df = pd.DataFrame.from_dict(dic_summary)
df.to_csv(join(output_imgs_folder, "Global_RMSE_and_times.csv"))