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python3-imagej-tiff
Commit 6f404273 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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testing weights displaying for WLOSS_LAMBDA

parent 364684d5
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Showing with 126 additions and 80 deletions
nn_ds_neibs1_tmp.py
View file @ 6f404273
......@@ -50,10 +50,12 @@ BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles
SHUFFLE_EPOCH = True
NET_ARCH1 = 0 #0 # 4 # 3 # overwrite with argv?
NET_ARCH2 = 0 # 0 # 3 # overwrite with argv?
SYM8_SUB = False # True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
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
#DEBUG_PACK_TILES = True
WLOSS_LAMBDA = 0.001 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY])
# CLUSTER_RADIUS should match input data
CLUSTER_RADIUS = 1 # 1 - 3x3, 2 - 5x5 tiles
......@@ -445,6 +447,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
global test_op
# the scope is known
with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
# histograms
w = tf.get_variable('weights',shape=[in_shape,out_shape])
......@@ -462,8 +465,13 @@ 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_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)])
#grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
# black
grid_y = tf.stack([tf.reduce_min(w),tf.reduce_min(w),tf.reduce_min(w)])
#grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
# white
grid_r = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)])
wt = tf.transpose(w,[1,0])
wt = wt[:,:-1]
......@@ -521,8 +529,12 @@ 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_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)])
# black
grid_y = tf.stack([tf.reduce_min(w),tf.reduce_min(w),tf.reduce_min(w)])
#grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
# white
grid_r = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)])
wt = tf.transpose(w,[1,0])
......@@ -592,22 +604,48 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
def network_sub(input, layout, reuse):
def network_sub(input, layout, reuse, sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
else:
inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
if sym8:
inp8 = sym_inputs8(inp)
num_non_sum = num_outs % len(inp8) # if number of first layer outputs is not multiple of 8
num_sym8 = num_outs // len(inp8) # number of symmetrical groups
fc_sym = []
for j in range (len(inp8)): # ==8
reuse_this = reuse | (j > 0)
scp = 'g_fc_sub'+str(i)
fc_sym.append(slim.fully_connected(inp8[j], num_sym8, activation_fn=lrelu, scope= scp, reuse = reuse_this))
if not reuse_this:
with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub')
if num_non_sum > 0:
reuse_this = reuse
scp = 'g_fc_sub'+str(i)+"r"
fc_sym.append(slim.fully_connected(inp, num_non_sum, activation_fn=lrelu, scope=scp, reuse = reuse_this))
if not reuse_this:
with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub')
fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))
else:
scp = 'g_fc_sub'+str(i)
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope= scp, reuse = reuse))
if not reuse:
with tf.variable_scope(scp, reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub')
if not reuse:
network_summary_w_b('g_fc_sub'+str(i), inp.shape[1], num_outs, layout, i, 'sub')
return fc[-1]
return fc[-1], inp_weights
def network_inter(input, layout):
last_indx = None;
......@@ -624,28 +662,37 @@ def network_inter(input, layout):
if USE_CONFIDENCE:
fc_out = slim.fully_connected(fc[-1], 2, activation_fn=lrelu,scope='g_fc_inter_out')
network_summary_w_b('g_fc_inter_out',fc[-1].shape[1], 2, layout, -1, 'inter')
else:
else:
fc_out = slim.fully_connected(fc[-1], 1, activation_fn=None,scope='g_fc_inter_out')
network_summary_w_b('g_fc_inter_out',fc[-1].shape[1], 1, layout, -1, 'inter')
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc_out
def network_siam(input, # now [?:9,325]
layout1,
def network_siam(input, # now [?,9,325]-> [?,25,325]
layout1,
layout2,
only_tile=None): # just for debugging - feed only data from the center sub-network
sym8 = False,
only_tile = None): # just for debugging - feed only data from the center sub-network
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 9
inter_list = []
reuse = False
for i in range (num_legs):
if (only_tile is None) or (i == only_tile):
inter_list.append(network_sub(input[:,i,:],
# inter_list.append(network_sub(input[:,i,:],
# layout= layout1,
# reuse= reuse,
# sym8 = sym8))
ns, ns_weights = network_sub(input[:,i,:],
layout= layout1,
reuse= reuse))
reuse= reuse,
sym8 = sym8)
inter_list.append(ns)
inp_weights += ns_weights
reuse = True
inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
return network_inter (inter_tensor, layout2)
return network_inter (inter_tensor, layout2), inp_weights
#corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[None,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [None,cluster_size, 1])],2)
def debug_gt_variance(
......@@ -774,6 +821,36 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
else:
return cost1b, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
def weightsLoss(inp_weights): # [batch_size,(1..2)] tf_result
# weights_lambdas): # single lambda or same length as inp_weights.shape[1]
"""
Enforcing 'smooth' weights for the input 2d correlation tiles
@return mean squared difference for each weight and average of 8 neighbors divided by mean squared weights
"""
weight_ortho = 1.0
weight_diag = 0.7
sw = 4.0 * (weight_ortho + weight_diag)
weight_ortho /= sw
weight_diag /= sw
# w_neib = tf.const([[weight_diag, weight_ortho, weight_diag],
# [weight_ortho, -1.0, weight_ortho],
# [weight_diag, weight_ortho, weight_diag]])
with tf.name_scope("WeightsLoss"):
# Adding 1 tile border
tf_inp = tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
tf_inp_ext_h = tf.concat([tf_inp [:, :, :1, :], tf_inp, tf_inp [:, :, -1:, :]], axis = 2, name ="tf_inp_ext_h")
tf_inp_ext = tf.concat([tf_inp_ext_h [:, :1, :, :], tf_inp_ext_h, tf_inp_ext_h[:, -1:, :, :]], axis = 1, name ="tf_inp_ext")
s_ortho = tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:,:] + tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:, :]
s_corn = tf_inp_ext[:, :-2,:-2,:] + tf_inp_ext[:, :-2, 2:,:] + tf_inp_ext[:,2:, :-2,:] + tf_inp_ext[:,2: , 2:, :]
w_diff = tf.subtract(tf_inp, s_ortho * weight_ortho + s_corn * weight_diag, name="w_diff")
w_diff2 = tf.multiply(w_diff, w_diff, name="w_diff2")
w_var = tf.reduce_mean(w_diff2, name="w_var")
w2_mean = tf.reduce_mean(inp_weights * inp_weights, name="w2_mean")
w_rel = tf.divide(w_var, w2_mean, name= "w_rel")
return w_rel # scalar, cost for weights non-smoothness in 2d
#In GPU - reformat inputs
##corr2d325 = tf.concat([next_element_tt['corr2d'], next_element_tt['target_disparity']],1)
......@@ -781,6 +858,9 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
#Should have shape (?,9,325)
corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1])],2)
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")], axis=2, name = "corr2d_Nx325")
#corr2d9x324 = tf.reshape( next_element_tt['corr2d'], [-1, cluster_size, FEATURES_PER_TILE], name = 'corr2d9x324')
#td9x1 = tf.reshape(next_element_tt['target_disparity'], [-1, cluster_size, 1], name = 'td9x1')
#corr2d9x325 = tf.concat([corr2d9x324 , td9x1],2, name = 'corr2d9x325')
......@@ -789,10 +869,11 @@ corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,F
# in_features = tf.concat([corr2d,target_disparity],0)
#out = network_fc_simple(input=corr2d325, arch = NET_ARCH1)
out = network_siam(input=corr2d9x325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
only_tile = ONLY_TILE) #Remove/put None for normal operation
out, inp_weights = network_siam(input=corr2d_Nx325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE) #Remove/put None for normal operation
# w_slice = tf.reshape(gt_ds_batch[:,1],[-1], name = "w_gt_slice")
# Extract target disparity and GT corresponding to the center tile (reshape - just to name)
......@@ -803,17 +884,24 @@ G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _
target_disparity_batch= next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)], # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
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 = 2.0,
error2_offset = 0.0025, # (0.05^2)
error2_offset = 0, # 0.0025, # (0.05^2)
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False) # use calculated disparity for disparity weight boosting (False - use target disparity)
if WLOSS_LAMBDA > 0.0:
W_loss = weightsLoss(inp_weights[0]) # inp_weights - list of tensors, currently - just [0]
GW_loss = tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
else:
GW_loss = G_loss
W_loss = tf.constant(0.0)
#debug
GT_variance = debug_gt_variance(indx = 0, # This tile index (0..8)
center_indx = 4, # center tile index
......@@ -834,7 +922,9 @@ with tf.name_scope('epoch_average'):
t_vars=tf.trainable_variables()
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)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
saver=tf.train.Saver()
......@@ -856,7 +946,7 @@ with tf.Session() as sess:
merged = tf.summary.merge_all()
# display weights, part 1 begin
import numpy_image_test as nit
import numpy_visualize_weights as npw
l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
......@@ -899,8 +989,8 @@ 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(
[ test_op, 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(
[ merged,
G_opt,
G_loss,
out,
......@@ -978,7 +1068,7 @@ with tf.Session() as sess:
with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
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 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True)
img1 = img1[np.newaxis,...]
train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)
......@@ -986,7 +1076,7 @@ with tf.Session() as sess:
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 = npw.tiles(npw.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(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)
......
numpy_image_test.py → numpy_visualize_weights.py
View file @ 6f404273
......@@ -4,10 +4,6 @@ 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):
......@@ -59,7 +55,7 @@ def tiles(img,shape,tiles_per_line=1,borders=True):
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]
#inner_color = [img_max,img_max,img_min]
group_h = shape[0]
group_w = shape[1]
......@@ -141,52 +137,12 @@ def tiles(img,shape,tiles_per_line=1,borders=True):
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)
#image = np.zeros((32,144))
image = np.random.rand(32,144)
rgb_img_0 = tiles(coldmap(image),(3,3,4,4),tiles_per_line=8,borders=True)
fig = plt.figure()
fig.suptitle("HaWi")
fig.suptitle("Test")
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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