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python3-imagej-tiff
Commit 09000d5a authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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testing train&test

parent be6b2244
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Showing with 11 additions and 179 deletions
nn_ds_inmem_tmp.py
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...@@ -297,7 +297,6 @@ t_vars=tf.trainable_variables() ...@@ -297,7 +297,6 @@ t_vars=tf.trainable_variables()
lr=tf.placeholder(tf.float32) lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss) G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)
saver=tf.train.Saver() saver=tf.train.Saver()
ROOT_PATH = './attic/nn_ds_inmem_graph1/' ROOT_PATH = './attic/nn_ds_inmem_graph1/'
...@@ -327,15 +326,15 @@ with tf.Session() as sess: ...@@ -327,15 +326,15 @@ with tf.Session() as sess:
while True: while True:
# overall are 307, start 'testing' testing from START_TEST # overall are 307, start 'testing' testing from START_TEST
START_TEST = 300 START_TEST = 200
# Train run # Train run
if i<START_TEST: if i<START_TEST:
try: try:
# _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run( # _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
_, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run( train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(
[ [ merged,
G_opt, G_opt,
G_loss, G_loss,
out, out,
...@@ -354,6 +353,7 @@ with tf.Session() as sess: ...@@ -354,6 +353,7 @@ with tf.Session() as sess:
# save all for now as a test # save all for now as a test
#train_writer.add_summary(summary, i) #train_writer.add_summary(summary, i)
#train_writer.add_summary(train_summary, i)
except tf.errors.OutOfRangeError: except tf.errors.OutOfRangeError:
break break
...@@ -362,7 +362,7 @@ with tf.Session() as sess: ...@@ -362,7 +362,7 @@ with tf.Session() as sess:
else: else:
try: try:
summary, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run( test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(
[merged, [merged,
G_loss, G_loss,
out, out,
...@@ -377,13 +377,18 @@ with tf.Session() as sess: ...@@ -377,13 +377,18 @@ with tf.Session() as sess:
], ],
feed_dict={lr:LR}) feed_dict={lr:LR})
#test_writer.add_summary(test_summary, i)
except tf.errors.OutOfRangeError: except tf.errors.OutOfRangeError:
break break
i+=1 i+=1
# print_time("%d:%d -> %f"%(epoch,i,G_current)) # print_time("%d:%d -> %f"%(epoch,i,G_current))
print_time("%d:%d -> %f"%(epoch,i,G_current)) train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summary, epoch)
print_time("%d:%d -> %f"%(epoch,i,G_loss_trained))
# Close writers # Close writers
train_writer.close() train_writer.close()
...@@ -392,176 +397,3 @@ with tf.Session() as sess: ...@@ -392,176 +397,3 @@ with tf.Session() as sess:
print("All done") print("All done")
exit (0) exit (0)
filename_queue = tf.train.string_input_producer(
[train_filenameTFR], num_epochs = EPOCHS_TO_RUN) #0)
# Even when reading in multiple threads, share the filename
# queue.
corr2d325, target_disparity, gt_ds = read_and_decode(filename_queue)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
#sess = tf.Session()
out = network(corr2d325)
#Try standard loss functions first
G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch = out, # [batch_size,(1..2)] tf_result
target_disparity_batch= target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 1.0)
t_vars=tf.trainable_variables()
lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)
saver=tf.train.Saver()
# ?!!!!!
#merged = tf.summary.merge_all()
#train_writer = tf.summary.FileWriter(result_dir + '/train', sess.graph)
#test_writer = tf.summary.FileWriter(result_dir + '/test')
#http://rtfcode.com/xref/tensorflow-1.4.1/tensorflow/docs_src/api_guides/python/reading_data.md
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# sess.run(init_op) # Was reporting beta1 not initialized in Adam
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
writer = tf.summary.FileWriter('./attic/nn_ds_inmem_graph1', sess.graph)
writer.close()
# for i in range(1000):
loss_hist = np.zeros(RUN_TOT_AVG, dtype=np.float32)
i = 0
try:
while not coord.should_stop():
print_time("%d: Run "%(i), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
[G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1, corr2d325, target_disparity, gt_ds],
feed_dict={lr: LR})
# print_time("loss=%f, running average=%f"%(G_current,mean_loss))
loss_hist[i % RUN_TOT_AVG] = G_current
if (i < RUN_TOT_AVG):
loss_avg = np.average(loss_hist[:i])
else:
loss_avg = np.average(loss_hist)
print_time("loss=%f, running average=%f"%(G_current,loss_avg))
# print ("%d: corr2d_out.shape="%(i),corr2d325_out.shape)
## print ("target_disparity_out.shape=",target_disparity_out.shape)
## print ("gt_ds_out.shape=",gt_ds_out.shape)
i += 1
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
coord.join(threads)
#sess.close() ('whith' does that)
'''
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
allfolders = glob.glob('./result/*0')
lastepoch = 0
for folder in allfolders:
lastepoch = np.maximum(lastepoch, int(folder[-4:]))
recorded_loss = []
recorded_mean_loss = []
recorded_gt_d = []
recorded_gt_c = []
recorded_pr_d = []
recorded_pr_c = []
LR = 1e-3
print(bcolors.HEADER+"Last Epoch = "+str(lastepoch)+bcolors.ENDC)
if DEBUG_PLT_LOSS:
plt.ion() # something about plotting
plt.figure(1, figsize=(4,12))
pass
training_tiles = np.array([])
training_values = np.array([])
graph_saved = False
for epoch in range(20): #MAX_EPOCH):
print_time("epoch="+str(epoch))
train_seed_list = np.arange(len(ex_data.files_train))
np.random.shuffle(train_seed_list)
g_loss = np.zeros(len(train_seed_list))
for nscene, seed_index in enumerate(train_seed_list):
corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(seed_index)
num_tiles = corr2d_batch.shape[0] # 1000
num_tile_slices = corr2d_batch.shape[1] # 4
num_cell_in_slice = corr2d_batch.shape[2] # 81
in_data = np.empty((num_tiles, num_tile_slices*num_cell_in_slice + 1), dtype = np.float32)
in_data[...,0:num_tile_slices*num_cell_in_slice] = corr2d_batch.reshape((corr2d_batch.shape[0],corr2d_batch.shape[1]*corr2d_batch.shape[2]))
in_data[...,num_tile_slices*num_cell_in_slice] = target_disparity_batch
st=time.time()
#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#run_metadata = tf.RunMetadata()
#_,G_current,output = sess.run([G_opt,G_loss,out],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR},options=run_options,run_metadata=run_metadata)
print_time("%d:%d Run "%(epoch, nscene), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm = sess.run([G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm],
feed_dict={in_tile: in_data,
gt: gt_ds_batch,
target_d: target_disparity_batch,
lr: LR})
if not graph_saved:
writer = tf.summary.FileWriter('./attic/nn_ds_single_graph1', sess.graph)
writer.close()
graph_saved = True
# exit(0)
g_loss[nscene]=G_current
mean_loss = np.mean(g_loss[np.where(g_loss)])
print_time("loss=%f, running average=%f"%(G_current,mean_loss))
pass
'''
#if wait_and_show: # wait and show images
# plt.show()
print_time("All done, exiting...")
\ No newline at end of file
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