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python3-imagej-tiff
Commit 0452a446 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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1. packing type 2 

2. epochs, batches changes
parent 45a473f0
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pack_tile.py
View file @ 0452a446
......@@ -6,7 +6,8 @@ __email__ = "oleg@elphel.com"
import numpy as np
# pack from 9x9x4 to 25x1
# pack from 9x9x4 to 100x1
# type 1, all the same
def pack_tile_type1(tile):
out = np.empty(100)
......@@ -125,13 +126,143 @@ def pack_tile_type1(tile):
return out
# pack from 9x9x4 to 104x1
# type 1, all the same
def pack_tile_type2(tile):
out = np.empty(104)
# pack diagm-pair (not tested)
l = np.ravel(tile[:,:,0])
out[ 0] = 1.0*l[ 0]+1.0*l[ 1]+1.0*l[ 2]+1.0*l[ 9]+1.0*l[10]+1.0*l[18]
out[ 1] = 1.0*l[ 3]+1.0*l[11]+1.0*l[19]+1.0*l[27]
out[ 2] = 1.0*l[ 4]+1.0*l[12]+1.0*l[20]+1.0*l[28]+1.0*l[36]
out[ 3] = 1.0*l[ 5]+1.0*l[ 6]+1.0*l[14]
out[ 4] = 1.0*l[ 7]+1.0*l[15]+1.0*l[23]
out[ 5] = 1.0*l[ 8]+1.0*l[16]+1.0*l[24]
out[ 6] = 1.0*l[13]+1.0*l[21]+1.0*l[29]+1.0*l[37]
out[ 7] = 1.0*l[17]+1.0*l[25]+1.0*l[33]
out[ 8] = 1.0*l[22]+1.0*l[30]+1.0*l[38]
out[ 9] = 1.0*l[26]+1.0*l[34]+1.0*l[35]
out[10] = 1.0*l[31]
out[11] = 1.0*l[32]
out[12] = 1.0*l[39]
out[13] = 1.0*l[40]
out[14] = 1.0*l[41]
out[15] = 1.0*l[42]+1.0*l[50]+1.0*l[58]
out[16] = 1.0*l[43]+1.0*l[51]+1.0*l[59]+1.0*l[67]
out[17] = 1.0*l[44]+1.0*l[52]+1.0*l[60]+1.0*l[68]+1.0*l[76]
out[18] = 1.0*l[45]+1.0*l[46]+1.0*l[54]
out[19] = 1.0*l[47]+1.0*l[55]+1.0*l[63]
out[20] = 1.0*l[48]
out[21] = 1.0*l[49]
out[22] = 1.0*l[53]+1.0*l[61]+1.0*l[69]+1.0*l[77]
out[23] = 1.0*l[56]+1.0*l[64]+1.0*l[72]
out[24] = 1.0*l[57]+1.0*l[65]+1.0*l[73]
out[25] = 1.0*l[62]+1.0*l[70]+1.0*l[71]+1.0*l[78]+1.0*l[79]+1.0*l[80]
out[26] = 1.0*l[66]+1.0*l[74]+1.0*l[75]
# pack diago-pair (not tested)
l = np.ravel(tile[:,:,1])
out[27] = 1.0*l[ 0]+1.0*l[10]+1.0*l[20]
out[28] = 1.0*l[ 1]+1.0*l[11]+1.0*l[21]
out[29] = 1.0*l[ 2]+1.0*l[ 3]+1.0*l[12]
out[30] = 1.0*l[ 4]+1.0*l[14]+1.0*l[24]+1.0*l[34]+1.0*l[44]
out[31] = 1.0*l[ 5]+1.0*l[15]+1.0*l[25]+1.0*l[35]
out[32] = 1.0*l[ 6]+1.0*l[ 7]+1.0*l[ 8]+1.0*l[16]+1.0*l[17]+1.0*l[26]
out[33] = 1.0*l[ 9]+1.0*l[19]+1.0*l[29]
out[34] = 1.0*l[13]+1.0*l[23]+1.0*l[43]
out[35] = 1.0*l[18]+1.0*l[27]+1.0*l[28]
out[36] = 1.0*l[22]+1.0*l[32]+1.0*l[42]
out[37] = 1.0*l[30]
out[38] = 1.0*l[31]
out[39] = 1.0*l[36]+1.0*l[46]+1.0*l[56]+1.0*l[66]+1.0*l[76]
out[40] = 1.0*l[37]+1.0*l[47]+1.0*l[57]+1.0*l[67]
out[41] = 1.0*l[38]+1.0*l[48]+1.0*l[58]
out[42] = 1.0*l[39]
out[43] = 1.0*l[40]
out[44] = 1.0*l[41]
out[45] = 1.0*l[45]+1.0*l[55]+1.0*l[65]+1.0*l[75]
out[46] = 1.0*l[49]
out[47] = 1.0*l[50]
out[48] = 1.0*l[51]+1.0*l[61]+1.0*l[71]
out[49] = 1.0*l[52]+1.0*l[53]+1.0*l[62]
out[50] = 1.0*l[54]+1.0*l[63]+1.0*l[64]+1.0*l[72]+1.0*l[73]+1.0*l[74]
out[51] = 1.0*l[59]+1.0*l[69]+1.0*l[79]
out[52] = 1.0*l[60]+1.0*l[70]+1.0*l[80]
out[53] = 1.0*l[68]+1.0*l[77]+1.0*l[78]
# pack hor-pairs
l = np.ravel(tile[:,:,2])
out[54] = 1.0*l[ 0]+1.0*l[ 1]+1.0*l[ 9]+1.0*l[10]+1.0*l[18]+1.0*l[27]+1.0*l[36]+1.0*l[45]+1.0*l[54]+1.0*l[63]+1.0*l[64]+1.0*l[72]+1.0*l[73]
out[55] = 1.0*l[ 2]+1.0*l[11]+1.0*l[20]
out[56] = 1.0*l[ 3]+1.0*l[12]+1.0*l[21]
out[57] = 1.0*l[ 4]+1.0*l[13]+1.0*l[22]
out[58] = 1.0*l[ 5]+1.0*l[14]+1.0*l[23]
out[59] = 1.0*l[ 6]+1.0*l[15]+1.0*l[24]
out[60] = 1.0*l[ 7]+1.0*l[ 8]+1.0*l[16]+1.0*l[17]+1.0*l[26]+1.0*l[35]+1.0*l[44]+1.0*l[53]+1.0*l[62]+1.0*l[70]+1.0*l[71]+1.0*l[79]+1.0*l[80]
out[61] = 1.0*l[19]+1.0*l[28]+1.0*l[37]+1.0*l[46]+1.0*l[55]
out[62] = 1.0*l[25]+1.0*l[34]+1.0*l[43]+1.0*l[52]+1.0*l[61]
out[63] = 1.0*l[29]+1.0*l[38]+1.0*l[47]
out[64] = 1.0*l[30]
out[65] = 1.0*l[31]
out[66] = 1.0*l[32]
out[67] = 1.0*l[33]+1.0*l[42]+1.0*l[51]
out[68] = 1.0*l[39]
out[69] = 1.0*l[40]
out[70] = 1.0*l[41]
out[71] = 1.0*l[48]
out[72] = 1.0*l[49]
out[73] = 1.0*l[50]
out[74] = 1.0*l[56]+1.0*l[65]+1.0*l[74]
out[75] = 1.0*l[57]+1.0*l[66]+1.0*l[75]
out[76] = 1.0*l[58]+1.0*l[67]+1.0*l[76]
out[77] = 1.0*l[59]+1.0*l[68]+1.0*l[77]
out[78] = 1.0*l[60]+1.0*l[69]+1.0*l[78]
# pack vert-pairs
l = np.ravel(tile[:,:,3])
out[79] = 1.0*l[ 0]+1.0*l[ 1]+1.0*l[ 2]+1.0*l[ 3]+1.0*l[ 4]+1.0*l[ 5]+1.0*l[ 6]+1.0*l[ 7]+1.0*l[ 8]+1.0*l[ 9]+1.0*l[10]+1.0*l[11]+1.0*l[16]+1.0*l[17]
out[80] = 1.0*l[11]+1.0*l[12]+1.0*l[13]+1.0*l[14]+1.0*l[15]
out[81] = 1.0*l[18]+1.0*l[19]+1.0*l[20]
out[82] = 1.0*l[21]+1.0*l[22]+1.0*l[23]
out[83] = 1.0*l[24]+1.0*l[25]+1.0*l[26]
out[84] = 1.0*l[27]+1.0*l[28]+1.0*l[29]
out[85] = 1.0*l[30]
out[86] = 1.0*l[31]
out[87] = 1.0*l[32]
out[88] = 1.0*l[33]+1.0*l[34]+1.0*l[35]
out[89] = 1.0*l[36]+1.0*l[37]+1.0*l[38]
out[90] = 1.0*l[39]
out[91] = 1.0*l[40]
out[92] = 1.0*l[41]
out[93] = 1.0*l[42]+1.0*l[43]+1.0*l[44]
out[94] = 1.0*l[45]+1.0*l[46]+1.0*l[47]
out[95] = 1.0*l[48]
out[96] = 1.0*l[49]
out[97] = 1.0*l[50]
out[98] = 1.0*l[51]+1.0*l[52]+1.0*l[53]
out[99] = 1.0*l[54]+1.0*l[55]+1.0*l[56]
out[100] = 1.0*l[57]+1.0*l[58]+1.0*l[59]
out[101] = 1.0*l[60]+1.0*l[61]+1.0*l[62]
out[102] = 1.0*l[63]+1.0*l[64]+1.0*l[70]+1.0*l[71]+1.0*l[72]+1.0*l[73]+1.0*l[74]+1.0*l[75]+1.0*l[76]+1.0*l[77]+1.0*l[78]+1.0*l[79]+1.0*l[80]
out[103] = 1.0*l[65]+1.0*l[66]+1.0*l[67]+1.0*l[68]+1.0*l[69]
return out
# pack single
def pack_tile(tile):
return pack_tile_type1(tile)
# pack all tiles
def pack(tiles):
output = np.array([[pack_tile(tiles[i,j]) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])
def pack(tiles,ptype=1):
if ptype==1:
pack_func = pack_tile_type1
elif ptype==2:
pack_func = pack_tile_type2
output = np.array([[pack_func(tiles[i,j]) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])
return output
......
test_nn_feed.py
View file @ 0452a446
This diff is collapsed.
test_nn_infer.py
View file @ 0452a446
......@@ -44,6 +44,7 @@ def print_time():
VALUES_LAYER_NAME = 'other'
LAYERS_OF_INTEREST = ['diagm-pair', 'diago-pair', 'hor-pairs', 'vert-pairs']
RADIUS = 1
TILE_PACKING_TYPE = 1
try:
src = sys.argv[1]
......@@ -83,7 +84,11 @@ def network(input):
sess = tf.Session()
in_tile = tf.placeholder(tf.float32,[None,101])
if TILE_PACKING_TYPE==1:
in_tile = tf.placeholder(tf.float32,[None,101])
elif TILE_PACKING_TYPE==2:
in_tile = tf.placeholder(tf.float32,[None,105])
gt = tf.placeholder(tf.float32,[None,2])
out = network(in_tile)
......@@ -143,7 +148,11 @@ for item in tlist:
# tiles and values
# might not need it because going to loop through anyway
packed_tiles = pile.pack(tiles)
if TILE_PACKING_TYPE==1:
packed_tiles = pile.pack(tiles)
elif TILE_PACKING_TYPE==2:
packed_tiles = pile.pack(tiles,TILE_PACKING_TYPE)
packed_tiles = np.dstack((packed_tiles,values[:,:,0]))
print(packed_tiles.shape)
......@@ -168,11 +177,12 @@ for item in tlist:
packed_tiles_flat = packed_tiles[i]
values_flat = values[i]
# whole row at once
output = sess.run(out,feed_dict={in_tile:packed_tiles_flat})
output_image[i] = output
# so, let's print
for j in range(output.shape[0]):
for j in range(packed_tiles.shape[0]):
p = output[j,0]
pc = output[j,1]
fv = values_flat[j,0]
......@@ -204,6 +214,15 @@ for item in tlist:
tif = np.dstack((im1,im2,im3))
im3 = np.ravel(im3)
print(im3.shape)
im4 = im3[~np.isnan(im3)]
rms = np.sqrt(np.mean(np.square(im4)))
print("RMS = "+str(rms))
imagej_tiffwriter.save('prediction_results.tiff',tif)
#sys.exit(0)
......
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