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python3-imagej-tiff
Commit befe9e85 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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1. grouping for inter layer 0 

2. fixed borders
3. trying to add color to summary.image
parent 03a86da7
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Showing with 115 additions and 34 deletions
nn_ds_neibs1_tmp.py
View file @ befe9e85
......@@ -48,8 +48,8 @@ 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 = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True
NET_ARCH1 = 6 #0 # 4 # 3 # overwrite with argv?
NET_ARCH2 = 6 # 0 # 3 # overwrite with argv?
NET_ARCH1 = 0 #0 # 4 # 3 # overwrite with argv?
NET_ARCH2 = 0 # 0 # 3 # overwrite with argv?
ONLY_TILE = None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
ZIP_LHVAR = True # combine _lvar and _hvar as odd/even elements
......@@ -249,6 +249,11 @@ files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/
"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train006_R1_GT_1.5.tfrecords",
"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",
]
files_train_lvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
]
files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_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/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"
......@@ -438,6 +443,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
# lowest index
l1 = layout.index(next(filter(lambda x: x!=0, layout)))
global test_op
with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
# histograms
......@@ -446,6 +452,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b)
# weights 2D pics
tmpvar = tf.get_variable('tmp_tile',shape=(TILE_SIDE,TILE_SIDE))
if network_scope=='sub':
# draw for the 1st layer
......@@ -455,43 +462,67 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
# red - the values will be automapped to 0-255 range
# grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
# yellow - the values will be automapped to 0-255 range
grid = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
wt = tf.transpose(w,[1,0])
wt = wt[:,:-1]
tmp1 = []
for i in range(layout[index]):
tmp2 = []
# reset when even
if i%2==0:
tmp2 = []
for j in range(TILE_LAYERS):
si = (j+0)*TILE_SIZE
ei = (j+1)*TILE_SIZE
tile = tf.reshape(wt[i,si:ei],shape=(TILE_SIDE,TILE_SIDE))
# color here?
#tile = tf.cond()
# stack to RGB
tiles = tf.stack([tile]*3,axis=2)
tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+0)*[grid],0)],axis=0)
tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+1)*[grid],1)],axis=1)
zers = tf.zeros(shape=(TILE_SIDE,TILE_SIDE))
test_op = tmpvar.assign(tile)
#tile = tmpvar
tiles = tf.stack([tile]*3,axis=2)
# vertical border
if (j==TILE_LAYERS-1):
tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+0)*[grid_r],1)],axis=1)
else:
tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+0)*[grid_y],1)],axis=1)
# horizontal border
tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+1)*[grid_r],0)],axis=0)
tmp2.append(tiles)
ts = tf.concat(tmp2,axis=1)
tmp1.append(ts)
# concat when odd
if i%2==1:
ts = tf.concat(tmp2,axis=1)
tmp1.append(ts)
imsum1 = tf.concat(tmp1,axis=0)
tf.summary.image("sub_w8s",tf.reshape(imsum1,[1,layout[index]*(TILE_SIDE+1),TILE_LAYERS*(TILE_SIDE+1),3]))
imsum1_1 = tf.reshape(imsum1,[1,layout[index]*(TILE_SIDE+1)//2,2*TILE_LAYERS*(TILE_SIDE+1),3])
tf.summary.image("sub_w8s",imsum1_1)
# tests
#tf.summary.image("s_weights_test",tf.reshape(w,[1,w.shape[0],w.shape[1],1]))
#tf.summary.image("s_weights_test_transposed",tf.reshape(wt,[1,wt.shape[0],wt.shape[1],1]))
if network_scope=='inter':
blocks_number = int(math.pow(2*CLUSTER_RADIUS+1,2))
cluster_side = 2*CLUSTER_RADIUS+1
blocks_number = int(math.pow(cluster_side,2))
if index==l1:
# red - the values will be automapped to 0-255 range
# grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
# yellow - the values will be automapped to 0-255 range
grid = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
wt = tf.transpose(w,[1,0])
......@@ -505,28 +536,61 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
tmp1 = []
for i in range(layout[index]):
tmp2 = []
for j in range(blocks_number):
si = (j+0)*block_size
ei = (j+1)*block_size
# reset when even
if i%4==0:
tmp2 = []
tmp4 = []
# need to group these
for j1 in range(cluster_side):
# wtm is expanded... only tested for 0
if missing_in_block!=0:
wtm = tf.concat(wt[i,si:ei],missing_in_block*[tf.reduce_min(w)])
else:
tmp3 = []
for j2 in range(cluster_side):
si = (cluster_side*j1+j2+0)*block_size
ei = (cluster_side*j1+j2+1)*block_size
wtm = wt[i,si:ei]
tile = tf.reshape(wtm,shape=(block_side,block_side))
# stack to RGB
tiles = tf.stack([tile]*3,axis=2)
tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid],0)],axis=0)
tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid],1)],axis=1)
tmp2.append(tiles)
ts = tf.concat(tmp2,axis=1)
tmp1.append(ts)
tile = tf.reshape(wtm,shape=(block_side,block_side))
# stack to RGB
tiles = tf.stack([tile]*3,axis=2)
# yellow first
if j2==cluster_side-1:
if j1==cluster_side-1:
tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid_r],0)],axis=0)
else:
tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid_y],0)],axis=0)
tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid_r],1)],axis=1)
else:
tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid_y],1)],axis=1)
if j1==cluster_side-1:
tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid_r],0)],axis=0)
else:
tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid_y],0)],axis=0)
tmp3.append(tiles)
# hor
tmp4.append(tf.concat(tmp3,axis=1))
tmp2.append(tf.concat(tmp4,axis=0))
if i%4==3:
ts = tf.concat(tmp2,axis=1)
tmp1.append(ts)
imsum2 = tf.concat(tmp1,axis=0)
tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index]*(block_side+1),blocks_number*(block_side+1),3]))
print("imsum2 shape: ")
print(imsum2.shape)
tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index]*cluster_side*(block_side+1)//4,4*cluster_side*(block_side+1),3]))
......@@ -792,6 +856,17 @@ with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all()
l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[325,32])
wd = w[...,tf.newaxis]
wds = tf.stack([wd]*3,axis=0)
#print(wd.shape)
#some_image = tf.summary.image("tfsi_test",wds.eval(),max_outputs=1)
some_image = tf.summary.image("tfsi_test",wds,max_outputs=1)
train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph)
......@@ -823,8 +898,8 @@ with tf.Session() as sess:
for i in range(dataset_train_size):
try:
# 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(
train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[ merged,
_, train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[ test_op, merged,
G_opt,
G_loss,
out,
......@@ -846,6 +921,7 @@ with tf.Session() as sess:
loss_train_hist[i] = G_loss_trained
loss2_train_hist[i] = out_cost1
gtvar_train_hist[i] = gt_variance
except tf.errors.OutOfRangeError:
print("train done at step %d"%(i))
break
......@@ -879,6 +955,9 @@ with tf.Session() as sess:
loss_test_hist[i] = G_loss_tested
loss2_test_hist[i] = out_cost1
gtvar_test_hist[i] = gt_variance
# #print(str(wed.shape)+" "+str(wed[0,0]))
except tf.errors.OutOfRangeError:
print("test done at step %d"%(i))
break
......@@ -888,6 +967,8 @@ with tf.Session() as sess:
gtvar_test_avg = np.average(gtvar_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})
train_writer.add_summary(some_image.eval(), epoch)
train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summaries[0], epoch)
......
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