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python3-imagej-tiff
Commit ded0d987 authored by Andrey Filippov's avatar Andrey Filippov
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added npy w/r to improve read speed

parent 6363bf5b
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Showing with 87 additions and 65 deletions
nn_ds_inmem5.py
View file @ ded0d987
...@@ -113,6 +113,7 @@ def read_and_decode(filename_queue): ...@@ -113,6 +113,7 @@ def read_and_decode(filename_queue):
#http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/ #http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/
#Main code #Main code
"""
try: try:
train_filenameTFR = sys.argv[1] train_filenameTFR = sys.argv[1]
except IndexError: except IndexError:
...@@ -121,17 +122,30 @@ try: ...@@ -121,17 +122,30 @@ try:
test_filenameTFR = sys.argv[2] test_filenameTFR = sys.argv[2]
except IndexError: except IndexError:
test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords" test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"
#FILES_PER_SCENE
train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords" train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"
"""
#FILES_PER_SCENE
files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords", files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords"] "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_GT_1.5.tfrecords",
files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords"] "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-003_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-004_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-005_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-006_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-007_R1_GT_1.5.tfrecords",
]
#files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords",
# "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords"]
files_train_hvar = []
#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
#file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/test-TEST_R1_GT_1.5.tfrecords"
file_test_hvar= None
file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
weight_hvar = 0.13 weight_hvar = 0.13
weight_lvar = 1.0 - weight_hvar weight_lvar = 1.0 - weight_hvar
...@@ -170,15 +184,6 @@ if (file_test_hvar): ...@@ -170,15 +184,6 @@ if (file_test_hvar):
"target_disparity":target_disparity, "target_disparity":target_disparity,
"gt_ds":gt_ds} "gt_ds":gt_ds}
print_time(" Done") print_time(" Done")
"""
corr2d_trains = [corr2d_train, corr2d_train1]
target_disparity_trains = [target_disparity_train, target_disparity_train1]
gt_ds_trains = [gt_ds_train, gt_ds_train1]
corr2d_train_placeholder = tf.placeholder(corr2d_train.dtype, (None,324)) # corr2d_train.shape)
target_disparity_train_placeholder = tf.placeholder(target_disparity_train.dtype, (None,1)) #target_disparity_train.shape)
gt_ds_train_placeholder = tf.placeholder(gt_ds_train.dtype, (None,2)) #gt_ds_train.shape)
"""
corr2d_train_placeholder = tf.placeholder(datasets_train_lvar[0]['corr2d'].dtype, (None,324)) # corr2d_train.shape) corr2d_train_placeholder = tf.placeholder(datasets_train_lvar[0]['corr2d'].dtype, (None,324)) # corr2d_train.shape)
target_disparity_train_placeholder = tf.placeholder(datasets_train_lvar[0]['target_disparity'].dtype, (None,1)) #target_disparity_train.shape) target_disparity_train_placeholder = tf.placeholder(datasets_train_lvar[0]['target_disparity'].dtype, (None,1)) #target_disparity_train.shape)
gt_ds_train_placeholder = tf.placeholder(datasets_train_lvar[0]['gt_ds'].dtype, (None,2)) #gt_ds_train.shape) gt_ds_train_placeholder = tf.placeholder(datasets_train_lvar[0]['gt_ds'].dtype, (None,2)) #gt_ds_train.shape)
...@@ -198,6 +203,8 @@ dataset_test_size //= BATCH_SIZE ...@@ -198,6 +203,8 @@ dataset_test_size //= BATCH_SIZE
#print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size)) #print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
dataset_tt = dataset_tt.batch(BATCH_SIZE) dataset_tt = dataset_tt.batch(BATCH_SIZE)
dataset_tt = dataset_tt.prefetch(BATCH_SIZE)
iterator_tt = dataset_tt.make_initializable_iterator() iterator_tt = dataset_tt.make_initializable_iterator()
next_element_tt = iterator_tt.get_next() next_element_tt = iterator_tt.get_next()
#print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size)) #print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size))
...@@ -441,11 +448,6 @@ with tf.Session() as sess: ...@@ -441,11 +448,6 @@ with tf.Session() as sess:
file_index = epoch % num_train_variants file_index = epoch % num_train_variants
# if SHUFFLE_EPOCH: # if SHUFFLE_EPOCH:
# dataset_tt = dataset_tt.shuffle(buffer_size=10000) # dataset_tt = dataset_tt.shuffle(buffer_size=10000)
"""
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[file_index],
target_disparity_train_placeholder: target_disparity_trains[file_index],
gt_ds_train_placeholder: gt_ds_trains[file_index]})
"""
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train_lvar[file_index]['corr2d'], sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train_lvar[file_index]['corr2d'],
target_disparity_train_placeholder: datasets_train_lvar[file_index]['target_disparity'], target_disparity_train_placeholder: datasets_train_lvar[file_index]['target_disparity'],
gt_ds_train_placeholder: datasets_train_lvar[file_index]['gt_ds']}) gt_ds_train_placeholder: datasets_train_lvar[file_index]['gt_ds']})
...@@ -480,11 +482,6 @@ with tf.Session() as sess: ...@@ -480,11 +482,6 @@ with tf.Session() as sess:
# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg}) # _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg})
#tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg') #tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg')
#tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg') #tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
"""
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: corr2d_test,
target_disparity_train_placeholder: target_disparity_test,
gt_ds_train_placeholder: gt_ds_test})
"""
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_test_lvar['corr2d'], sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_test_lvar['corr2d'],
target_disparity_train_placeholder: datasets_test_lvar['target_disparity'], target_disparity_train_placeholder: datasets_test_lvar['target_disparity'],
gt_ds_train_placeholder: datasets_test_lvar['gt_ds']}) gt_ds_train_placeholder: datasets_test_lvar['gt_ds']})
......
nn_ds_neibs.py
View file @ ded0d987
...@@ -33,7 +33,7 @@ MAX_EPOCH = 500 ...@@ -33,7 +33,7 @@ MAX_EPOCH = 500
LR = 1e-4 # learning rate LR = 1e-4 # learning rate
LR100 = 1e-4 LR100 = 1e-4
USE_CONFIDENCE = False USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = True # False # True # False ABSOLUTE_DISPARITY = False # True # False # True # False
DEBUG_PLT_LOSS = True DEBUG_PLT_LOSS = True
TILE_LAYERS = 4 TILE_LAYERS = 4
FILE_TILE_SIDE = 9 FILE_TILE_SIDE = 9
...@@ -46,9 +46,9 @@ RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batche ...@@ -46,9 +46,9 @@ RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batche
#BATCH_SIZE = 1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches #BATCH_SIZE = 1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True SHUFFLE_EPOCH = True
NET_ARCH1 = 4# 3 # overwrite with argv? NET_ARCH1 = 0 # 4 # 3 # overwrite with argv?
NET_ARCH2 = 0# 3 # overwrite with argv? NET_ARCH2 = 0 # 3 # overwrite with argv?
ONLY_TILE = None # 0 # 4# None # (remove all but center tile data), put None here for normal operation) ONLY_TILE = None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
#DEBUG_PACK_TILES = True #DEBUG_PACK_TILES = True
...@@ -87,24 +87,44 @@ def print_time(txt="",end="\n"): ...@@ -87,24 +87,44 @@ def print_time(txt="",end="\n"):
#reading to memory (testing) #reading to memory (testing)
def readTFRewcordsEpoch(train_filename): def readTFRewcordsEpoch(train_filename):
# filenames = [train_filename] # filenames = [train_filename]
# dataset = tf.data.TFRecordDataset(filenames) # dataset = tf.data.TFRecorDataset(filenames)
if not '.tfrecords' in train_filename: if not '.tfrecords' in train_filename:
train_filename += '.tfrecords' train_filename += '.tfrecords'
record_iterator = tf.python_io.tf_record_iterator(path=train_filename) npy_dir_name = "npy"
corr2d_list=[] dirname = os.path.dirname(train_filename)
target_disparity_list=[] npy_dir = os.path.join(dirname, npy_dir_name)
gt_ds_list = [] filebasename, file_extension = os.path.splitext(train_filename)
for string_record in record_iterator: filebasename = os.path.basename(filebasename)
example = tf.train.Example() file_corr2d = os.path.join(npy_dir,filebasename + '_corr2d.npy')
example.ParseFromString(string_record) file_target_disparity = os.path.join(npy_dir,filebasename + '_target_disparity.npy')
corr2d_list.append (np.array(example.features.feature['corr2d'].float_list.value, dtype=np.float32)) file_gt_ds = os.path.join(npy_dir,filebasename + '_gt_ds.npy')
# target_disparity_list.append(np.array(example.features.feature['target_disparity'].float_list.value[0], dtype=np.float32)) if (os.path.exists(file_corr2d) and
target_disparity_list.append (np.array(example.features.feature['target_disparity'].float_list.value, dtype=np.float32)) os.path.exists(file_target_disparity) and
gt_ds_list.append (np.array(example.features.feature['gt_ds'].float_list.value, dtype= np.float32)) os.path.exists(file_gt_ds)):
corr2d= np.load (file_corr2d)
target_disparity = np.load(file_target_disparity)
gt_ds = np.load(file_gt_ds)
pass pass
corr2d= np.array(corr2d_list) else:
target_disparity = np.array(target_disparity_list) record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
gt_ds = np.array(gt_ds_list) corr2d_list=[]
target_disparity_list=[]
gt_ds_list = []
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
corr2d_list.append (np.array(example.features.feature['corr2d'].float_list.value, dtype=np.float32))
# target_disparity_list.append(np.array(example.features.feature['target_disparity'].float_list.value[0], dtype=np.float32))
target_disparity_list.append (np.array(example.features.feature['target_disparity'].float_list.value, dtype=np.float32))
gt_ds_list.append (np.array(example.features.feature['gt_ds'].float_list.value, dtype= np.float32))
pass
corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list)
gt_ds = np.array(gt_ds_list)
np.save(file_corr2d, corr2d)
np.save(file_target_disparity, target_disparity)
np.save(file_gt_ds, gt_ds)
return corr2d, target_disparity, gt_ds return corr2d, target_disparity, gt_ds
#from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/ #from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
...@@ -157,6 +177,7 @@ def reduce_tile_size(datasets_data, num_tile_layers, reduced_tile_side): ...@@ -157,6 +177,7 @@ def reduce_tile_size(datasets_data, num_tile_layers, reduced_tile_side):
""" """
Start of the main code Start of the main code
""" """
"""
try: try:
train_filenameTFR = sys.argv[1] train_filenameTFR = sys.argv[1]
except IndexError: except IndexError:
...@@ -167,29 +188,32 @@ except IndexError: ...@@ -167,29 +188,32 @@ except IndexError:
test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords" test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"
#FILES_PER_SCENE #FILES_PER_SCENE
train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords" train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"
"""
files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train000_R1_LE_1.5.tfrecords", files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train001_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train001_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train002_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train002_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train003_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train003_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train004_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train004_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train005_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train005_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train006_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train006_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train007_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train007_R1_LE_1.5.tfrecords",
] ]
files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train000_R1_GT_1.5.tfrecords", files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train001_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train001_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train002_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train002_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train003_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train003_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train004_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train004_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train005_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train005_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train006_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train006_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train007_R1_GT_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",
] ]
#files_train_hvar = []
#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords" #file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/testTEST_R1_LE_1.5.tfrecords" file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"
file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/testTEST_R1_GT_1.5.tfrecords" # None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords" file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_rand2/testTEST_R1_GT_1.5.tfrecords" # None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
#file_test_hvar= None
weight_hvar = 0.13 weight_hvar = 0.13
weight_lvar = 1.0 - weight_hvar weight_lvar = 1.0 - weight_hvar
...@@ -298,6 +322,7 @@ dataset_test_size //= BATCH_SIZE ...@@ -298,6 +322,7 @@ dataset_test_size //= BATCH_SIZE
#print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size)) #print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
dataset_tt = dataset_tt.batch(BATCH_SIZE) dataset_tt = dataset_tt.batch(BATCH_SIZE)
dataset_tt = dataset_tt.prefetch(BATCH_SIZE)
iterator_tt = dataset_tt.make_initializable_iterator() iterator_tt = dataset_tt.make_initializable_iterator()
next_element_tt = iterator_tt.get_next() next_element_tt = iterator_tt.get_next()
#print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size)) #print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size))
......
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