Commit 39c85178 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev

colored displaying of weights

parent bb3cc9de
......@@ -250,10 +250,10 @@ 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/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_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"
......@@ -855,19 +855,19 @@ with tf.Session() as sess:
merged = tf.summary.merge_all()
vis_placeholder = tf.placeholder(tf.float32, [1,32,325,3])
some_image2 = tf.summary.image('custom_test', vis_placeholder)
# display weights, part 1 begin
import numpy_image_test as nit
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)
wimg1_placeholder = tf.placeholder(tf.float32, [1,160,80,3])
wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder)
wimg2_placeholder = tf.placeholder(tf.float32, [1,120,60,3])
wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder)
# display weights, part 1 end
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)
......@@ -970,23 +970,27 @@ with tf.Session() as sess:
# _,_=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)
# display weights, part 2 begin
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=-1)
timg_min = tf.reduce_min(w).eval()
timg_max = tf.reduce_max(w).eval()
w = tf.get_variable('weights',shape=[325,NN_LAYOUT1[l1]])
w = tf.transpose(w,(1,0))
img1 = nit.tiles(nit.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True)
img1 = img1[np.newaxis,...]
timg = wds.eval()
timg[:,:,:,0] = timg_min
timg[:,:,:,1] = timg_min
timg = np.transpose(timg,(0,2,1,3))
train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)
with tf.variable_scope('g_fc_inter'+str(l2),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[144,NN_LAYOUT1[l2]])
w = tf.transpose(w,(1,0))
img2 = nit.tiles(nit.coldmap(w.eval(),zero_span=0.0002),(3,3,4,4),tiles_per_line=4,borders=True)
img2 = img2[np.newaxis,...]
train_writer.add_summary(some_image2.eval(feed_dict={vis_placeholder: timg}), epoch)
train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)
# display weights, part 2 end
train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summaries[0], epoch)
......
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import math
def hamming_window(x,N):
y = 0.2 - 0.46*math.cos(2*math.pi*x/(N-1))
return y
# input: np.array(a,b) - 1 channel
# output: np.array(a,b,3) - 3 color channels
def coldmap(img,zero_span=0.2):
out = np.dstack(3*[img])
img_min = np.nanmin(img)
img_max = np.nanmax(img)
#print("min: "+str(img_min)+", max: "+str(img_max))
ch_r = out[...,0]
ch_g = out[...,1]
ch_b = out[...,2]
# blue for <0
ch_r[img<0] = 0
ch_g[img<0] = 0
ch_b[img<0] = -ch_b[img<0]
# red for >0
ch_r[img>0] = ch_b[img>0]
ch_g[img>0] = 0
ch_b[img>0] = 0
# green for 0
ch_r[img==0] = 0
ch_g[img==0] = img_max
ch_b[img==0] = 0
# green for zero vicinity
ch_r[abs(img)<zero_span/2] = 0
ch_g[abs(img)<zero_span/2] = img_max/2
ch_b[abs(img)<zero_span/2] = 0
return out
# has to be pre transposed
# it just suppose to match
def tiles(img,shape,tiles_per_line=1,borders=True):
# shape is (n0,n1,n2,n3)
# n0*n1*n2*n3 = img.shape[1]
img_min = np.nanmin(img)
img_max = np.nanmax(img)
outer_color = [img_max,img_max,img_min]
outer_color = [img_max,img_max,img_max]
inner_color = [img_max/4,img_max/4,img_min]
inner_color = [img_min,img_min,img_min]
inner_color = [img_max,img_max,img_min]
group_h = shape[0]
group_w = shape[1]
group_size = group_h*group_w
tile_h = shape[2]
tile_w = shape[3]
tile_size = tile_h*tile_w
tpl = tiles_per_line
# main
tmp1 = []
for i in range(img.shape[0]):
if i%tpl==0:
tmp2 = []
tmp3 = []
for igh in range(group_h):
tmp4 = []
for igw in range(group_w):
si = (group_w*igh + igw + 0)*tile_size
ei = (group_w*igh + igw + 1)*tile_size
tile = img[i,si:ei]
tile = np.reshape(tile,(tile_h,tile_w,tile.shape[1]))
if borders:
if igw==group_w-1:
b_h_inner = [[inner_color]*(tile_w+0)]*( 1)
b_h_outer = [[outer_color]*(tile_w+0)]*( 1)
b_v_outer = [[outer_color]*( 1)]*(tile_h+1)
# outer hor
if igh==group_h-1:
tile = np.concatenate([tile,b_h_outer],axis=0)
# inner hor
else:
tile = np.concatenate([tile,b_h_inner],axis=0)
# outer vert
tile = np.concatenate([tile,b_v_outer],axis=1)
else:
b_v_inner = [[inner_color]*( 1)]*(tile_h+0)
b_h_inner = [[inner_color]*(tile_w+1)]*( 1)
b_h_outer = [[outer_color]*(tile_w+1)]*( 1)
# inner vert
tile = np.concatenate([tile,b_v_inner],axis=1)
# outer hor
if igh==group_h-1:
tile = np.concatenate([tile,b_h_outer],axis=0)
# inner hor
else:
tile = np.concatenate([tile,b_h_inner],axis=0)
tmp4.append(tile)
tmp3.append(np.concatenate(tmp4,axis=1))
tmp2.append(np.concatenate(tmp3,axis=0))
if i%tpl==(tpl-1):
tmp1.append(np.concatenate(tmp2,axis=1))
out = np.concatenate(tmp1,axis=0)
#out = img
return out
if __name__=="__main__":
#
hw = hamming_window
#
image = np.array([[1*hw(i,512)*hw(j,512) for i in range(512)] for j in range(512)],np.float32)
zeros = np.zeros((512,512))
# 32x324
#image2 = np.zeros((32,324))
#rgb_img_0 = tiles(image2,(1,4,9,9),tiles_per_line=2,borders=True)
#image2 = np.zeros((32,144))
image2 = np.array([[1*hw(i,144)*hw(j,32) for i in range(144)] for j in range(32)],np.float32)
#image3 = coldmap(image2)
rgb_img_0 = tiles(coldmap(image2),(3,3,4,4),tiles_per_line=8,borders=True)
fig = plt.figure()
fig.suptitle("HaWi")
plt.imshow(rgb_img_0)
rgb_img = coldmap(image)
#print(rgb_img)
'''
for i in range(512):
for j in range(512):
if image[i,j]<0:
rgb_img[i,j,0] = 0
rgb_img[i,j,1] = 0
#rgb_img[i,j,2] = 255
if image[i,j]>0:
#rgb_img[i,j,0] = 255
rgb_img[i,j,1] = 0
rgb_img[i,j,2] = 0
if image[i,j]==0:
rgb_img[i,j,0] = 0
rgb_img[i,j,1] = 255
rgb_img[i,j,2] = 0
'''
print(rgb_img.shape)
fig = plt.figure()
fig.suptitle("HamWindow")
plt.imshow(rgb_img)
#plt.colorbar()
plt.show()
\ No newline at end of file
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