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Commit d6807531 authored by tomrink's avatar tomrink
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modules/deeplearning/icing_cnn.py
+ 117
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import tensorflow as tf
import tensorflow_addons as tfa
from util.setup import logdir, modeldir, cachepath
from util.setup import logdir, modeldir, cachepath, now
from util.util import homedir, EarlyStop
from util.geos_nav import GEOSNavigation
from util.plot import make_icing_image
import os, datetime
import numpy as np
import pickle
import h5py
from icing.pirep_goes import normalize
from icing.pirep_goes import normalize, make_for_full_domain_predict
LOG_DEVICE_PLACEMENT = False
......@@ -103,6 +105,7 @@ class IcingIntensityNN:
self.train_dataset = None
self.inner_train_dataset = None
self.test_dataset = None
self.eval_dataset = None
self.X_img = None
self.X_prof = None
self.X_u = None
......@@ -172,6 +175,8 @@ class IcingIntensityNN:
self.num_data_samples = None
self.initial_learning_rate = None
self.data_dct = None
n_chans = len(train_params)
if TRIPLET:
n_chans *= 3
......@@ -253,6 +258,22 @@ class IcingIntensityNN:
def get_in_mem_data_batch_test(self, idxs):
return self.get_in_mem_data_batch(idxs, False)
def get_in_mem_data_batch_eval(self, idxs):
# sort these to use as numpy indexing arrays
nd_idxs = np.array(idxs)
nd_idxs = np.sort(nd_idxs)
data = []
for param in train_params:
nda = self.data_dct[param][nd_idxs, ]
nda = normalize(nda, param, mean_std_dct)
data.append(nda)
data = np.stack(data)
data = data.astype(np.float32)
data = np.transpose(data, axes=(1, 2, 3, 0))
return data
@tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
def data_function(self, indexes):
out = tf.numpy_function(self.get_in_mem_data_batch_train, [indexes], [tf.float32, tf.int32])
......@@ -263,6 +284,11 @@ class IcingIntensityNN:
out = tf.numpy_function(self.get_in_mem_data_batch_test, [indexes], [tf.float32, tf.int32])
return out
@tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
def data_function_evaluate(self, indexes):
out = tf.numpy_function(self.get_in_mem_data_batch_eval, [indexes], tf.float32)
return out
def get_train_dataset(self, indexes):
indexes = list(indexes)
......@@ -283,6 +309,15 @@ class IcingIntensityNN:
dataset = dataset.cache()
self.test_dataset = dataset
def get_evaluate_dataset(self, indexes):
indexes = list(indexes)
dataset = tf.data.Dataset.from_tensor_slices(indexes)
dataset = dataset.batch(PROC_BATCH_SIZE)
dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
dataset = dataset.cache()
self.eval_dataset = dataset
def setup_pipeline(self, filename_trn, filename_tst, trn_idxs=None, tst_idxs=None, seed=None):
self.filename_trn = filename_trn
self.h5f_trn = h5py.File(filename_trn, 'r')
......@@ -330,6 +365,13 @@ class IcingIntensityNN:
print('num test samples: ', tst_idxs.shape[0])
print('setup_test_pipeline: Done')
def setup_eval_pipeline(self, data_dct, num_tiles):
self.data_dct = data_dct
idxs = np.arange(num_tiles)
self.num_data_samples = idxs.shape[0]
self.get_evaluate_dataset(idxs)
def build_1d_cnn(self):
print('build_1d_cnn')
# padding = 'VALID'
......@@ -600,7 +642,14 @@ class IcingIntensityNN:
step = 0
total_time = 0
last_test_loss = np.finfo(dtype=np.float).max
best_test_loss = np.finfo(dtype=np.float).max
best_test_acc = 0
best_test_recall = 0
best_test_precision = 0
best_test_auc = 0
best_test_f1 = 0
best_test_mcc = 0
if EARLY_STOP:
es = EarlyStop()
......@@ -682,8 +731,15 @@ class IcingIntensityNN:
self.optimizer.assign_average_vars(self.model.trainable_variables)
tst_loss = self.test_loss.result().numpy()
if tst_loss < last_test_loss:
last_test_loss = tst_loss
if tst_loss < best_test_loss:
best_test_loss = tst_loss
best_test_acc = self.test_accuracy.result().numpy()
best_test_recall = self.test_recall.result().numpy()
best_test_precision = self.test_precision.result().numpy()
best_test_auc = self.test_auc.result().numpy()
best_test_f1 = f1.numpy()
best_test_mcc = mcc.numpy()
ckpt_manager.save()
if self.DISK_CACHE and epoch == 0:
......@@ -701,6 +757,10 @@ class IcingIntensityNN:
self.h5f_trn.close()
self.h5f_tst.close()
f = open('/home/rink/best_stats_'+now+'.pkl', 'wb')
pickle.dump((best_test_loss, best_test_acc, best_test_recall, best_test_precision, best_test_auc, best_test_f1, best_test_mcc), f)
f.close()
def build_model(self):
flat = self.build_cnn()
# flat_1d = self.build_1d_cnn()
......@@ -742,6 +802,49 @@ class IcingIntensityNN:
self.h5f_tst.close()
def do_evaluate(self, ckpt_dir, ll, cc):
ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
ckpt.restore(ckpt_manager.latest_checkpoint)
pred_s = []
for data in self.eval_dataset:
ds = tf.data.Dataset.from_tensor_slices(data)
ds = ds.batch(BATCH_SIZE)
for mini_batch in ds:
pred = self.model([mini_batch], training=False)
pred_s.append(pred)
preds = np.concatenate(pred_s)
if NumClasses == 2:
preds = np.where(preds > 0.6, 1, 0)
else:
preds = np.argmax(preds, axis=1)
print(preds.shape[0], np.sum(preds == 1))
preds = preds[:,0]
cc = np.array(cc)
ll = np.array(ll)
ice_mask = preds == 1
print(cc.shape, ll.shape, ice_mask.shape)
ice_cc = cc[ice_mask]
ice_ll = ll[ice_mask]
nav = GEOSNavigation(sub_lon=-75.0, CFAC=5.6E-05, COFF=-0.101332, LFAC=-5.6E-05, LOFF=0.128212, num_elems=2500,
num_lines=1500)
ice_lons = []
ice_lats = []
for k in range(ice_cc.shape[0]):
lon, lat = nav.lc_to_earth(ice_cc[k], ice_ll[k])
ice_lons.append(lon)
ice_lats.append(lat)
return ice_lons, ice_lats
def run(self, filename_trn, filename_tst):
with tf.device('/device:GPU:'+str(self.gpu_device)):
self.setup_pipeline(filename_trn, filename_tst)
......@@ -757,6 +860,15 @@ class IcingIntensityNN:
self.build_evaluation()
self.restore(ckpt_dir)
def run_evaluate(self, filename, ckpt_dir):
data_dct, ll, cc = make_for_full_domain_predict(filename)
self.setup_eval_pipeline(data_dct, len(ll))
self.build_model()
self.build_training()
self.build_evaluation()
ice_lons, ice_lats = self.do_evaluate(ckpt_dir, ll, cc)
return filename, ice_lons, ice_lats
if __name__ == "__main__":
nn = IcingIntensityNN()
......
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