diff --git a/modules/deeplearning/unet.py b/modules/deeplearning/unet.py
index bab6c4d57ca1a18561f7ee57b83aebb59f4d93c7..5c66a3cd64cba08afdbad09cc93bac434e113243 100644
--- a/modules/deeplearning/unet.py
+++ b/modules/deeplearning/unet.py
@@ -2,7 +2,6 @@ import glob
import tensorflow as tf
from util.setup import logdir, modeldir, cachepath, now, ancillary_path, home_dir
from util.util import EarlyStop, normalize, make_for_full_domain_predict
-import matplotlib.pyplot as plt
import os, datetime
import numpy as np
@@ -187,102 +186,6 @@ class UNET:
tf.debugging.set_log_device_placement(LOG_DEVICE_PLACEMENT)
- # Doesn't seem to play well with SLURM
- # gpus = tf.config.experimental.list_physical_devices('GPU')
- # if gpus:
- # try:
- # # Currently, memory growth needs to be the same across GPUs
- # for gpu in gpus:
- # tf.config.experimental.set_memory_growth(gpu, True)
- # logical_gpus = tf.config.experimental.list_logical_devices('GPU')
- # print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
- # except RuntimeError as e:
- # # Memory growth must be set before GPUs have been initialized
- # print(e)
-
- # def get_in_mem_data_batch(self, idxs, is_training):
- #
- # # sort these to use as numpy indexing arrays
- # nd_idxs = np.array(idxs)
- # nd_idxs = np.sort(nd_idxs)
- #
- # data = []
- # for param in self.train_params:
- # nda = self.get_parameter_data(param, nd_idxs, is_training)
- # nda = normalize(nda, param, mean_std_dct)
- # if DO_ZERO_OUT and is_training:
- # try:
- # zero_out_params.index(param)
- # nda[:,] = 0.0
- # except ValueError:
- # pass
- # data.append(nda)
- # data = np.stack(data)
- # data = data.astype(np.float32)
- # data = np.transpose(data, axes=(1, 2, 3, 0))
- #
- # data_alt = self.get_scalar_data(nd_idxs, is_training)
- #
- # label = self.get_label_data(nd_idxs, is_training)
- # label = np.where(label == -1, 0, label)
- #
- # # binary, two class
- # if NumClasses == 2:
- # label = np.where(label != 0, 1, label)
- # label = label.reshape((label.shape[0], 1))
- # elif NumClasses == 3:
- # label = np.where(np.logical_or(label == 1, label == 2), 1, label)
- # label = np.where(np.invert(np.logical_or(label == 0, label == 1)), 2, label)
- # label = label.reshape((label.shape[0], 1))
- #
- # if is_training and DO_AUGMENT:
- # data_ud = np.flip(data, axis=1)
- # data_alt_ud = np.copy(data_alt)
- # label_ud = np.copy(label)
- #
- # data_lr = np.flip(data, axis=2)
- # data_alt_lr = np.copy(data_alt)
- # label_lr = np.copy(label)
- #
- # data = np.concatenate([data, data_ud, data_lr])
- # data_alt = np.concatenate([data_alt, data_alt_ud, data_alt_lr])
- # label = np.concatenate([label, label_ud, label_lr])
- #
- # return data, data_alt, label
-
- def get_in_mem_data_batch_salt(self, idxs, is_training):
- data = []
- for k in idxs:
- if is_training:
- nda = plt.imread(self.train_data_files[k])
- else:
- nda = plt.imread(self.test_data_files[k])
- data.append(nda[0:64, 0:64])
- data = np.stack(data)
- data = data.astype(np.float32)
-
- label = []
- for k in idxs:
- if is_training:
- nda = plt.imread(self.train_label_files[k])
- else:
- nda = plt.imread(self.test_label_files[k])
- label.append(nda[0:64, 0:64])
- label = np.stack(label)
- label = label.astype(np.int32)
-
- if is_training and DO_AUGMENT:
- data_ud = np.flip(data, axis=1)
- label_ud = np.flip(label, axis=1)
-
- data_lr = np.flip(data, axis=2)
- label_lr = np.flip(label, axis=2)
-
- data = np.concatenate([data, data_ud, data_lr])
- label = np.concatenate([label, label_ud, label_lr])
-
- return data, data, label
-
def get_in_mem_data_batch(self, idxs, is_training):
if is_training:
train_data = []
@@ -298,6 +201,7 @@ class UNET:
data = np.concatenate(train_data)
data = np.expand_dims(data, axis=3)
+
label = np.concatenate(train_label)
label = np.expand_dims(label, axis=3)
else:
@@ -336,38 +240,6 @@ class UNET:
return data, data, label
- # def get_parameter_data(self, param, nd_idxs, is_training):
- # if is_training:
- # if param in self.train_params_l1b:
- # h5f = self.h5f_l1b_trn
- # else:
- # h5f = self.h5f_l2_trn
- # else:
- # if param in self.train_params_l1b:
- # h5f = self.h5f_l1b_tst
- # else:
- # h5f = self.h5f_l2_tst
- #
- # nda = h5f[param][nd_idxs,]
- # return nda
- #
- # def get_label_data(self, nd_idxs, is_training):
- # # Note: labels will be same for nd_idxs across both L1B and L2
- # if is_training:
- # if self.h5f_l1b_trn is not None:
- # h5f = self.h5f_l1b_trn
- # else:
- # h5f = self.h5f_l2_trn
- # else:
- # if self.h5f_l1b_tst is not None:
- # h5f = self.h5f_l1b_tst
- # else:
- # h5f = self.h5f_l2_tst
- #
- # label = h5f['icing_intensity'][nd_idxs]
- # label = label.astype(np.int32)
- # return label
-
def get_in_mem_data_batch_train(self, idxs):
return self.get_in_mem_data_batch(idxs, True)
@@ -437,55 +309,6 @@ class UNET:
dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
self.eval_dataset = dataset
- # def setup_pipeline(self, filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst, trn_idxs=None, tst_idxs=None, seed=None):
- # if filename_l1b_trn is not None:
- # self.h5f_l1b_trn = h5py.File(filename_l1b_trn, 'r')
- # if filename_l1b_tst is not None:
- # self.h5f_l1b_tst = h5py.File(filename_l1b_tst, 'r')
- # if filename_l2_trn is not None:
- # self.h5f_l2_trn = h5py.File(filename_l2_trn, 'r')
- # if filename_l2_tst is not None:
- # self.h5f_l2_tst = h5py.File(filename_l2_tst, 'r')
- #
- # if trn_idxs is None:
- # # Note: time is same across both L1B and L2 for idxs
- # if self.h5f_l1b_trn is not None:
- # h5f = self.h5f_l1b_trn
- # else:
- # h5f = self.h5f_l2_trn
- # time = h5f['time']
- # trn_idxs = np.arange(time.shape[0])
- # if seed is not None:
- # np.random.seed(seed)
- # np.random.shuffle(trn_idxs)
- #
- # if self.h5f_l1b_tst is not None:
- # h5f = self.h5f_l1b_tst
- # else:
- # h5f = self.h5f_l2_tst
- # time = h5f['time']
- # tst_idxs = np.arange(time.shape[0])
- # if seed is not None:
- # np.random.seed(seed)
- # np.random.shuffle(tst_idxs)
- #
- # self.num_data_samples = trn_idxs.shape[0]
- #
- # self.get_train_dataset(trn_idxs)
- # self.get_test_dataset(tst_idxs)
- #
- # print('datetime: ', now)
- # print('training and test data: ')
- # print(filename_l1b_trn)
- # print(filename_l1b_tst)
- # print(filename_l2_trn)
- # print(filename_l2_tst)
- # print('---------------------------')
- # print('num train samples: ', self.num_data_samples)
- # print('BATCH SIZE: ', BATCH_SIZE)
- # print('num test samples: ', tst_idxs.shape[0])
- # print('setup_pipeline: Done')
-
def setup_pipeline(self, data_nda, label_nda, perc=0.20):
num_samples = data_nda.shape[0]
@@ -572,47 +395,6 @@ class UNET:
self.get_evaluate_dataset(idxs)
- def setup_salt_pipeline(self, data_path, label_path, perc=0.15):
- data_files = np.array(glob.glob(data_path+'*.png'))
- label_files = np.array(glob.glob(label_path+'*.png'))
-
- num_data_samples = len(data_files)
- idxs = np.arange(num_data_samples)
- np.random.shuffle(idxs)
- num_train = int(num_data_samples*(1-perc))
- self.num_data_samples = num_train
- trn_idxs = idxs[0:num_train]
- np.sort(trn_idxs)
- tst_idxs = idxs[num_train:]
- np.sort(tst_idxs)
-
- self.train_data_files = data_files[trn_idxs]
- self.train_label_files = label_files[trn_idxs]
-
- self.test_data_files = data_files[tst_idxs]
- self.test_label_files = label_files[tst_idxs]
-
- trn_idxs = np.arange(len(trn_idxs))
- tst_idxs = np.arange(len(tst_idxs))
-
- np.random.shuffle(trn_idxs)
- self.get_train_dataset(trn_idxs)
- self.get_test_dataset(tst_idxs)
-
- f = open(home_dir+'/salt_test_files.pkl', 'wb')
- pickle.dump((self.test_data_files, self.test_label_files), f)
- f.close()
-
- def setup_salt_restore(self, test_files='/Users/tomrink/salt_test_files.pkl'):
- tup = pickle.load(open(test_files, 'rb'))
-
- self.test_data_files = tup[0]
- self.test_label_files = tup[1]
- self.num_data_samples = len(self.test_data_files)
-
- tst_idxs = np.arange(self.num_data_samples)
- self.get_test_dataset(tst_idxs)
-
def build_unet(self):
print('build_cnn')
# padding = "VALID"
@@ -623,7 +405,6 @@ class UNET:
activation = tf.nn.leaky_relu
momentum = 0.99
- # num_filters = len(self.train_params) * 4
num_filters = self.n_chans * 4
input_2d = self.inputs[0]
@@ -1075,21 +856,7 @@ class UNET:
preds = np.argmax(preds, axis=1)
self.test_preds = preds
- def run(self, filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst):
- self.setup_pipeline(filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst)
- self.build_model()
- self.build_training()
- self.build_evaluation()
- self.do_training()
-
- def run_salt(self, data_path='/Users/tomrink/data/salt/train/images/', label_path='/Users/tomrink/data/salt/train/masks/'):
- self.setup_salt_pipeline(data_path, label_path)
- self.build_model()
- self.build_training()
- self.build_evaluation()
- self.do_training()
-
- def run_test(self, directory):
+ def run(self, directory):
data_files = glob.glob(directory+'mod_res*.npy')
label_files = [f.replace('mod', 'img') for f in data_files]
self.setup_pipeline_files(data_files, label_files)
@@ -1098,14 +865,6 @@ class UNET:
self.build_evaluation()
self.do_training()
- def run_restore_test(self, data_path='/Users/tomrink/data/salt/train/images/', label_path='/Users/tomrink/data/salt/train/masks/', ckpt_dir=None):
- #self.setup_salt_pipeline(data_path, label_path)
- self.setup_salt_restore()
- self.build_model()
- self.build_training()
- self.build_evaluation()
- self.restore(ckpt_dir)
-
def run_restore(self, filename_l1b, filename_l2, ckpt_dir):
self.setup_test_pipeline(filename_l1b, filename_l2)
self.build_model()