diff --git a/test/test_visualize_postprocess.py b/test/test_visualize_postprocess.py
index aebcda3754dd3599c1da1c7c5b0cec1df8364b94..458430f632cf2d2ccf374ef1ed5e76dc3a7b8b50 100644
--- a/test/test_visualize_postprocess.py
+++ b/test/test_visualize_postprocess.py
@@ -17,7 +17,8 @@ batch_size = 2
num_samples = 16
num_stochastic_samples = 2
gpu_mem_frac = 0.5
-seed=12345
+seed = 12345
+eval_metrics=["mse", "psnr"]
class MyClass:
@@ -31,28 +32,25 @@ args = MyClass(results_dir)
@pytest.fixture(scope="module")
def vis_case1():
return Postprocess(results_dir=results_dir,checkpoint=checkpoint,
- mode=mode,batch_size=batch_size,num_samples=num_samples,num_stochastic_samples=num_stochastic_samples,
- gpu_mem_frac=gpu_mem_frac,seed=seed,args=args)
+ mode=mode,batch_size=batch_size, num_samples=num_samples,
+ num_stochastic_samples=num_stochastic_samples,
+ seed=seed,args=args,eval_metrics=eval_metrics)
######instance2
num_samples2 = 200000
@pytest.fixture(scope="module")
def vis_case2():
- return Postprocess(results_dir=results_dir,checkpoint=checkpoint,
- mode=mode,batch_size=batch_size,num_samples=num_samples2,num_stochastic_samples=num_stochastic_samples,
- gpu_mem_frac=gpu_mem_frac,seed=seed,args=args)
+ return Postprocess(results_dir=results_dir, checkpoint=checkpoint,
+ mode=mode, batch_size=batch_size, num_samples=num_samples2,
+ num_stochastic_samples=num_stochastic_samples,
+ seed=seed, args=args,eval_metrics=eval_metrics)
def test_load_jsons(vis_case1):
- vis_case1.set_seed()
- vis_case1.save_args_to_option_json()
- vis_case1.copy_data_model_json()
- vis_case1.load_jsons()
assert vis_case1.dataset == "era5"
assert vis_case1.model == "savp"
assert vis_case1.input_dir_tfr == "/p/project/deepacf/deeprain/video_prediction_shared_folder/preprocessedData/era5-Y2007-2019M01to12-92x56-3840N0000E-2t_tcc_t_850/tfrecords_seq_len_24"
assert vis_case1.run_mode == "deterministic"
def test_get_metadata(vis_case1):
- vis_case1.get_metadata()
assert vis_case1.height == 56
assert vis_case1.width == 92
assert vis_case1.vars_in[0] == "2t"
@@ -60,20 +58,11 @@ def test_get_metadata(vis_case1):
def test_setup_test_dataset(vis_case1):
- vis_case1.setup_test_dataset()
vis_case1.test_dataset.mode == mode
-
-#def test_copy_data_model_json(vis_case1):
-# vis_case1.copy_data_model_json()
-# isfile_copy = os.path.isfile(os.path.join(checkpoint,"options.json"))
-# assert isfile_copy == True
-# isfile_copy_model_hpamas = os.path.isfile(os.path.join(checkpoint,"model_hparams.json"))
-# assert isfile_copy_model_hpamas == True
-
def test_setup_num_samples_per_epoch(vis_case1):
vis_case1.setup_test_dataset()
- vis_case1.setup_num_samples_per_epoch()
+ vis_case1.setup_num_samples_per_epoch()
assert vis_case1.num_samples_per_epoch == num_samples
def test_get_data_params(vis_case1):
@@ -86,14 +75,13 @@ def test_run_deterministic(vis_case1):
vis_case1.init_session()
vis_case1.restore(vis_case1.sess,vis_case1.checkpoint)
vis_case1.sample_ind = 0
- vis_case1.init_eval_metrics_list()
vis_case1.input_results,vis_case1.input_images_denorm_all, vis_case1.t_starts = vis_case1.run_and_plot_inputs_per_batch()
assert len(vis_case1.t_starts_results) == batch_size
ts_1 = vis_case1.t_starts[0][0]
year = str(ts_1)[:4]
month = str(ts_1)[4:6]
filename = "ecmwf_era5_" + str(ts_1)[2:] + ".nc"
- fl = os.path.join("/p/project/deepacf/deeprain/video_prediction_shared_folder/extractedData",year,month,filename)
+ fl = os.path.join("/p/project/deepacf/deeprain/video_prediction_shared_folder/extractedData",year, month, filename)
print("netCDF file name:",fl)
with Dataset(fl,"r") as data_file:
t2_var = data_file.variables["2t"][0,:,:]
@@ -105,10 +93,11 @@ def test_run_deterministic(vis_case1):
input_img_min = np.min(input_image)
print("input_image",input_image[0,:10])
assert t2_max == input_img_max
- assert t2_min == input_img_min
+ assert t2_min == input_img_min
feed_dict = {input_ph: vis_case1.input_results[name] for name, input_ph in vis_case1.inputs.items()}
gen_images = vis_case1.sess.run(vis_case1.video_model.outputs['gen_images'], feed_dict=feed_dict)
+
############Test persistenct value#############
vis_case1.ts = Postprocess.generate_seq_timestamps(vis_case1.t_starts[0], len_seq=vis_case1.sequence_length)
vis_case1.get_and_plot_persistent_per_sample(sample_id=0)
@@ -124,6 +113,14 @@ def test_run_deterministic(vis_case1):
t2_per_max = np.max(t2_per_var)
per_image_max = np.max(vis_case1.persistence_images[0])
assert t2_per_max == per_image_max
+
+
+
+def test_run_determinstic_quantile_plot(vis_case1):
+ vis_case1.init_metric_ds()
+
+
+
#def test_make_test_dataset_iterator(vis_case1):
# vis_case1.make_test_dataset_iterator()
# pass
@@ -159,7 +156,4 @@ def test_run_deterministic(vis_case1):
-############Test case 2##################
-
-
diff --git a/video_prediction_tools/main_scripts/main_visualize_postprocess.py b/video_prediction_tools/main_scripts/main_visualize_postprocess.py
index cc580b3e59e5aeed8e12aba2f4cccce2c870e92e..58cb731f1b83d73e47598684118dee5556e65590 100644
--- a/video_prediction_tools/main_scripts/main_visualize_postprocess.py
+++ b/video_prediction_tools/main_scripts/main_visualize_postprocess.py
@@ -31,7 +31,7 @@ from postprocess_plotting import plot_avg_eval_metrics, plot_cond_quantile, crea
class Postprocess(TrainModel):
def __init__(self, results_dir=None, checkpoint=None, mode="test", batch_size=None, num_stochastic_samples=1,
- stochastic_plot_id=0, gpu_mem_frac=None, seed=None, channel=0, args=None, run_mode="deterministic",
+ stochastic_plot_id=0, seed=None, channel=0, args=None, run_mode="deterministic",
eval_metrics=None):
"""
Initialization of the class instance for postprocessing (generation of forecasts from trained model +
@@ -43,7 +43,6 @@ class Postprocess(TrainModel):
:param num_stochastic_samples: number of ensemble members for variational models (SAVP, VAE), default: 1
not supported yet!!!
:param stochastic_plot_id: not supported yet!
- :param gpu_mem_frac: fraction of GPU memory to be pre-allocated
:param seed: Integer controlling randomization
:param channel: Channel of interest for statistical evaluation
:param args: namespace of parsed arguments
@@ -54,7 +53,6 @@ class Postprocess(TrainModel):
self.results_dir = self.output_dir = os.path.normpath(results_dir)
_ = check_dir(self.results_dir, lcreate=True)
self.batch_size = batch_size
- self.gpu_mem_frac = gpu_mem_frac
self.seed = seed
self.set_seed()
self.num_stochastic_samples = num_stochastic_samples
@@ -72,7 +70,7 @@ class Postprocess(TrainModel):
self.nboots_block = 1000
self.block_length = 7 * 24 # this corresponds to a block length of 7 days in case of hourly forecasts
- # initialize evrything to get an executable Postprocess instance
+ # initialize everything to get an executable Postprocess instance
self.save_args_to_option_json() # create options.json-in results directory
self.copy_data_model_json() # copy over JSON-files from model directory
# get some parameters related to model and dataset
@@ -779,8 +777,8 @@ class Postprocess(TrainModel):
Postprocess.clean_obj_attribute(self, "eval_metrics_ds")
# the variables for conditional quantile plot
- var_fcst = "{0}_{1}_fcst".format(self.vars_in[self.channel], self.model)
- var_ref = "{0}_ref".format(self.vars_in[self.channel])
+ var_fcst = self.cond_quantile_vars[0]
+ var_ref = self.cond_quantile_vars[1]
data_fcst = get_era5_varatts(self.cond_quantiple_ds[var_fcst], self.cond_quantiple_ds[var_fcst].name)
data_ref = get_era5_varatts(self.cond_quantiple_ds[var_ref], self.cond_quantiple_ds[var_ref].name)
@@ -1091,7 +1089,7 @@ class Postprocess(TrainModel):
if not set(varnames).issubset(ds_in.data_vars):
raise ValueError("%{0}: Could not find all variables ({1}) in input dataset ds_in.".format(method,
varnames_str))
-
+ #Bing : why using dtype as an aurument since it seems you only want ton configure dtype as np.double
if dtype is None:
dtype = np.double
else:
diff --git a/video_prediction_tools/utils/general_utils.py b/video_prediction_tools/utils/general_utils.py
index 8e003e4533bfb62a943d6b6b9f8d13513558bb88..5d0fb397ef4be4d9c1e8bd3aad6d80bdc1a9925b 100644
--- a/video_prediction_tools/utils/general_utils.py
+++ b/video_prediction_tools/utils/general_utils.py
@@ -12,7 +12,6 @@ Provides: * get_unique_vars
# import modules
import os
-import sys
import numpy as np
import xarray as xr
@@ -138,7 +137,7 @@ def check_dir(path2dir: str, lcreate=False):
if (path2dir is None) or not isinstance(path2dir, str):
raise ValueError("%{0}: path2dir must be a string defining a pat to a directory.".format(method))
- if os.path.isdir(path2dir):
+ elif os.path.isdir(path2dir):
return True
else:
if lcreate:
@@ -177,7 +176,7 @@ def provide_default(dict_in, keyname, default=None, required=False):
return dict_in[keyname]
-def get_era5_varatts(data_arr: xr.DataArray, name):
+def get_era5_varatts(data_arr: xr.DataArray, name: str):
"""
Writes longname and unit to data arrays given their name is known
:param data_arr: the data array