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python3-imagej-tiff
Commit 07c7d46a authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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display weights

parent a8911582
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Showing with 160 additions and 79 deletions
nn_ds_inmem4_tmp.py
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#!/usr/bin/env python3
from numpy import float64
from _stat import S_IEXEC
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
......@@ -33,17 +34,17 @@ MAX_EPOCH = 500
#LR = 1e-4 # learning rate
LR = 1e-3 # learning rate
USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False # True # False
ABSOLUTE_DISPARITY = True # True # False
DEBUG_PLT_LOSS = True
FEATURES_PER_TILE = 324
EPOCHS_TO_RUN = 10000 #0
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches
BATCH_SIZE = 1000 # Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True
NET_ARCH = 3 # overwrite with argv?
NET_ARCH = 0 # overwrite with argv?
#DEBUG_PACK_TILES = True
SUFFIX=str(NET_ARCH)+ (["R","A"][ABSOLUTE_DISPARITY])
MAX_TRAIN_FILES_TFR = 4
MAX_TRAIN_FILES_TFR = 6
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors:
HEADER = '\033[95m'
......@@ -211,6 +212,9 @@ def lrelu(x):
# return tf.nn.relu(x)
def network_fc_simple(input, arch = 0):
global image_summary_op1
layouts = {0:[0, 0, 0, 32, 20, 16],
1:[0, 0, 0, 256, 128, 64],
2:[0, 128, 32, 32, 32, 16],
......@@ -226,9 +230,86 @@ def network_fc_simple(input, arch = 0):
inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu,scope='g_fc'+str(i)))
with tf.variable_scope('g_fc'+str(i)+'/fully_connected',reuse=tf.AUTO_REUSE):
#with tf.variable_scope('g_fc'+str(i)+'/fully_connected',reuse=tf.AUTO_REUSE):
with tf.variable_scope('g_fc'+str(i),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[inp.shape[1],num_outs])
b = tf.get_variable('weights',shape=[inp.shape[1],num_outs])
#image = tf.get_variable('w_images',shape=[1, inp.shape[1],num_outs,1])
if (i==3):
# red border
grid = tf.constant([255,100,100],dtype=tf.float32,name="GRID")
# (325,32)
wimg_1 = w
# (32,325)
wimg_2 = tf.transpose(wimg_1,[1,0])
# (32,324)
wimg_3 = wimg_2[:,:-1]
# res?
#wimg_res = tf.get_variable('wimg_res',shape=[32*(9+1),(9+1)*4, 3])
# long list
tmp1 = []
for mi in range(32):
tmp2 = []
for mj in range(4):
s_i = mj*81
e_i = (mj+1)*81
tile = tf.reshape(wimg_3[mi,s_i:e_i],shape=(9,9))
tiles = tf.stack([tile]*3,axis=2)
#gtiles1 = tf.concat([tiles, tf.reshape(9*[grid],shape=(1,9,3))],axis=0)
gtiles1 = tf.concat([tiles, tf.expand_dims(9*[grid],0)],axis=0)
gtiles2 = tf.concat([gtiles1,tf.expand_dims(10*[grid],1)],axis=1)
tmp2.append(gtiles2)
ts = tf.concat(tmp2,axis=2)
tmp1.append(ts)
image_summary_op2 = tf.concat(tmp1,axis=0)
#image_summary_op1 = tf.assign(wimg_res,tf.zeros(shape=[32*(9+1),(9+1)*4, 3],dtype=tf.float32))
#wimgo1 = tf.zeros(shape=[32*(9+1),(9+1)*4, 3],dtype=tf.float32)
#tf.summary.image("wimg_res1",tf.reshape(wimg_res,[1,32*(9+1),(9+1)*4, 3]))
#tf.summary.image("wimgo1",tf.reshape(wimgo1,[1,32*(9+1),(9+1)*4, 3]))
#tf.summary.image("wimgo2",tf.reshape(wimgo2,[1,32*(9+1),(9+1)*4, 3]))
tf.summary.image("SWEIGTS",tf.reshape(gtiles2,[1,10,10,3]))
tf.summary.image("WEIGTS",tf.reshape(image_summary_op2,[1,320,40,3]))
# borders
#for mi in range(0,wimg_res.shape[0],10):
# for mj in range(wimg_res.shape[1]):
# wimg_res[mi,mj].assign([255,255,255])
#wimg_res[9::(9+1),:].assign([255,0,0])
#wimg_res[:,9::(9+1)].assign([255,0,0])
#for mi in range(0,wimg_res.shape[0],10):
# print(mi)
#wimg_res = tf.stack([wing_res,])
#wimg_1 = tf.reshape(w,[1,inp.shape[1],num_outs,1])
#wimg_1t = tf.transpose(wimg_1,[0,2,1,3])
# w = w[a,b]
# wt = w[b,a]
# for i in range(b):
# tmp =
#tf.summary.image("wimg_1",wimg_1)
#tf.summary.image("wimg_1t",wimg_1t)
#tf.summary.image("wimg_res1",tf.reshape(wimg_res,[1,32*(9+1),(9+1)*4, 3]))
b = tf.get_variable('biases',shape=[num_outs])
tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b)
"""
......@@ -247,7 +328,8 @@ def network_fc_simple(input, arch = 0):
with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[fc[-1].shape[1],2])
b = tf.get_variable('biases',shape=[fc[-1].shape[1],2])
tf.summary.image("wimage",tf.reshape(w,[1,fc[-1].shape[1],2,1]))
b = tf.get_variable('biases',shape=[2])
tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b)
......@@ -256,6 +338,7 @@ def network_fc_simple(input, arch = 0):
with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[fc[-1].shape[1],1])
tf.summary.image("wimage",tf.reshape(w,[1,fc[-1].shape[1],1,1]))
b = tf.get_variable('biases',shape=[1])
tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b)
......@@ -504,6 +587,8 @@ with tf.Session() as sess:
# if SHUFFLE_EPOCH:
# dataset_train = dataset_train.shuffle(buffer_size=10000)
# RUN TRAIN SESSION
sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[train_file_index],
target_disparity_train_placeholder: target_disparity_trains[train_file_index],
gt_ds_train_placeholder: gt_ds_trains[train_file_index]})
......@@ -530,6 +615,7 @@ with tf.Session() as sess:
#train_writer.add_summary(train_summary, i)
loss_train_hist[i] = G_loss_trained
loss2_train_hist[i] = out_cost1
except tf.errors.OutOfRangeError:
print("train done at step %d"%(i))
break
......@@ -537,10 +623,7 @@ with tf.Session() as sess:
train_avg = np.average(loss_train_hist).astype(np.float32)
train2_avg = np.average(loss2_train_hist).astype(np.float32)
#_,_=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_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
# RUN TEST SESSION
sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_test,
target_disparity_train_placeholder: target_disparity_test,
......@@ -567,12 +650,10 @@ with tf.Session() as sess:
print("test done at step %d"%(i))
break
# print_time("%d:%d -> %f"%(epoch,i,G_current))
test_avg = np.average(loss_test_hist).astype(np.float32)
test2_avg = np.average(loss2_test_hist).astype(np.float32)
# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})
# they include image summaries as well
train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summary, epoch)
......
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