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# pylint: skip-file
from __future__ import print_function
import numpy as np
import mxnet as mx
import random
import itertools
from numpy . testing import assert_allclose , assert_array_equal
from mxnet . test_utils import *
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from common import assert_raises_cudnn_not_satisfied , xfail_when_nonstandard_decimal_separator
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import unittest
def test_box_nms_op ( ) :
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def test_box_nms_forward ( data , expected , thresh = 0.5 , valid = 0 , topk = - 1 , coord = 2 , score = 1 , cid = 0 , bid = - 1 ,
force = False , in_format = ' corner ' , out_format = ' corner ' ) :
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for dtype in [ ' float16 ' , ' float32 ' , ' float64 ' ] :
data = mx . nd . array ( data , dtype = dtype )
out = mx . contrib . nd . box_nms ( data , overlap_thresh = thresh , valid_thresh = valid , topk = topk ,
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coord_start = coord , score_index = score , id_index = cid , background_id = bid ,
force_suppress = force , in_format = in_format , out_format = out_format )
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assert_almost_equal ( out . asnumpy ( ) , expected . astype ( dtype ) , rtol = 1e-3 , atol = 1e-3 )
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def test_box_nms_backward ( data , grad , expected , thresh = 0.5 , valid = 0 , topk = - 1 , coord = 2 , score = 1 ,
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cid = 0 , bid = - 1 , force = False , in_format = ' corner ' , out_format = ' corner ' ) :
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in_var = mx . sym . Variable ( ' data ' )
arr_data = mx . nd . array ( data )
arr_grad = mx . nd . empty ( arr_data . shape )
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op = mx . contrib . sym . box_nms ( in_var , overlap_thresh = thresh , valid_thresh = valid , topk = topk ,
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coord_start = coord , score_index = score , id_index = cid , background_id = bid ,
force_suppress = force , in_format = in_format , out_format = out_format )
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exe = op . _bind ( ctx = default_device ( ) , args = [ arr_data ] , args_grad = [ arr_grad ] )
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exe . forward ( is_train = True )
exe . backward ( mx . nd . array ( grad ) )
assert_almost_equal ( arr_grad . asnumpy ( ) , expected )
def corner_to_center ( data ) :
out = np . reshape ( data , ( - 1 , 6 ) ) . copy ( )
out [ : , 2 ] = ( data [ : , 2 ] + data [ : , 4 ] ) / 2.0
out [ : , 3 ] = ( data [ : , 3 ] + data [ : , 5 ] ) / 2.0
out [ : , 4 ] = data [ : , 4 ] - data [ : , 2 ]
out [ : , 5 ] = data [ : , 5 ] - data [ : , 3 ]
invalid = np . where ( data [ : , 0 ] < 0 ) [ 0 ]
out [ invalid , : ] = - 1
return out
def center_to_corner ( data ) :
data = np . reshape ( data , ( - 1 , 6 ) ) . copy ( )
out [ : , 2 ] = data [ : , 2 ] - data [ : , 4 ] / 2.0
out [ : , 3 ] = data [ : , 3 ] - data [ : , 5 ] / 2.0
out [ : , 4 ] = data [ : , 2 ] + data [ : , 4 ] / 2.0
out [ : , 5 ] = data [ : , 3 ] + data [ : , 5 ] / 2.0
invalid = np . where ( data [ : , 0 ] < 0 ) [ 0 ]
out [ invalid , : ] = - 1
return out
def swap_position ( data , expected , coord = 2 , score = 1 , cid = 0 , new_col = 0 ) :
data = np . reshape ( data , ( - 1 , 6 ) )
expected = np . reshape ( expected , ( - 1 , 6 ) )
new_coord = random . randint ( 0 , 6 + new_col - 4 )
others = list ( range ( new_coord ) ) + list ( range ( new_coord + 4 , 6 + new_col ) )
random . shuffle ( others )
new_score = others [ 0 ]
new_cid = others [ 1 ]
new_data = np . full ( ( data . shape [ 0 ] , data . shape [ 1 ] + new_col ) , - 1.0 )
new_expected = np . full ( ( expected . shape [ 0 ] , expected . shape [ 1 ] + new_col ) , - 1.0 )
new_data [ : , new_coord : new_coord + 4 ] = data [ : , coord : coord + 4 ]
new_data [ : , new_score ] = data [ : , score ]
new_data [ : , new_cid ] = data [ : , cid ]
new_expected [ : , new_coord : new_coord + 4 ] = expected [ : , coord : coord + 4 ]
new_expected [ : , new_score ] = expected [ : , score ]
new_expected [ : , new_cid ] = expected [ : , cid ]
return new_data , new_expected , new_coord , new_score , new_cid
# manually set up test cases
boxes = [ [ 0 , 0.5 , 0.1 , 0.1 , 0.2 , 0.2 ] , [ 1 , 0.4 , 0.1 , 0.1 , 0.2 , 0.2 ] ,
[ 0 , 0.3 , 0.1 , 0.1 , 0.14 , 0.14 ] , [ 2 , 0.6 , 0.5 , 0.5 , 0.7 , 0.8 ] ]
# case1
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force = True
thresh = 0.5
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expected = [ [ 2 , 0.6 , 0.5 , 0.5 , 0.7 , 0.8 ] , [ 0 , 0.5 , 0.1 , 0.1 , 0.2 , 0.2 ] ,
[ 0 , 0.3 , 0.1 , 0.1 , 0.14 , 0.14 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ]
grad = np . random . rand ( 4 , 6 )
expected_in_grad = grad [ ( 1 , 3 , 2 , 0 ) , : ]
expected_in_grad [ 1 , : ] = 0
test_box_nms_forward ( np . array ( boxes ) , np . array ( expected ) , force = force , thresh = thresh )
test_box_nms_backward ( np . array ( boxes ) , grad , expected_in_grad , force = force , thresh = thresh )
# case2: multi batch
boxes2 = [ boxes ] * 3
expected2 = [ expected ] * 3
grad2 = np . array ( [ grad . tolist ( ) ] * 3 )
expected_in_grad2 = np . array ( [ expected_in_grad . tolist ( ) ] * 3 )
test_box_nms_forward ( np . array ( boxes2 ) , np . array ( expected2 ) , force = force , thresh = thresh )
test_box_nms_backward ( np . array ( boxes2 ) , grad2 , expected_in_grad2 , force = force , thresh = thresh )
# another new dim
boxes2 = [ boxes2 ] * 2
expected2 = [ expected2 ] * 2
grad2 = np . array ( [ grad2 . tolist ( ) ] * 2 )
expected_in_grad2 = np . array ( [ expected_in_grad2 . tolist ( ) ] * 2 )
test_box_nms_forward ( np . array ( boxes2 ) , np . array ( expected2 ) , force = force , thresh = thresh )
test_box_nms_backward ( np . array ( boxes2 ) , grad2 , expected_in_grad2 , force = force , thresh = thresh )
# case3: thresh
thresh = 0.1
boxes3 = boxes
expected3 = [ [ 2 , 0.6 , 0.5 , 0.5 , 0.7 , 0.8 ] , [ 0 , 0.5 , 0.1 , 0.1 , 0.2 , 0.2 ] ,
[ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ]
grad3 = np . random . rand ( 4 , 6 )
expected_in_grad3 = grad3 [ ( 1 , 3 , 2 , 0 ) , : ]
expected_in_grad3 [ ( 1 , 2 ) , : ] = 0
test_box_nms_forward ( np . array ( boxes3 ) , np . array ( expected3 ) , force = force , thresh = thresh )
test_box_nms_backward ( np . array ( boxes3 ) , grad3 , expected_in_grad3 , force = force , thresh = thresh )
# case4: non-force
boxes4 = boxes
force = False
expected4 = [ [ 2 , 0.6 , 0.5 , 0.5 , 0.7 , 0.8 ] , [ 0 , 0.5 , 0.1 , 0.1 , 0.2 , 0.2 ] ,
[ 1 , 0.4 , 0.1 , 0.1 , 0.2 , 0.2 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ]
grad4 = np . random . rand ( 4 , 6 )
expected_in_grad4 = grad4 [ ( 1 , 2 , 3 , 0 ) , : ]
expected_in_grad4 [ 2 , : ] = 0
test_box_nms_forward ( np . array ( boxes4 ) , np . array ( expected4 ) , force = force , thresh = thresh )
test_box_nms_backward ( np . array ( boxes4 ) , grad4 , expected_in_grad4 , force = force , thresh = thresh )
# case5: different coding
boxes5 = corner_to_center ( np . array ( boxes4 ) )
test_box_nms_forward ( np . array ( boxes5 ) , np . array ( expected4 ) , force = force , thresh = thresh ,
in_format = ' center ' )
expected5 = corner_to_center ( np . array ( expected4 ) )
test_box_nms_forward ( np . array ( boxes4 ) , np . array ( expected5 ) , force = force , thresh = thresh ,
out_format = ' center ' )
test_box_nms_forward ( np . array ( boxes5 ) , np . array ( expected5 ) , force = force , thresh = thresh ,
in_format = ' center ' , out_format = ' center ' )
# case6: different position
boxes6 , expected6 , new_coord , new_score , new_id = swap_position ( np . array ( boxes4 ) ,
np . array ( expected4 ) , new_col = 2 )
test_box_nms_forward ( np . array ( boxes6 ) , np . array ( expected6 ) , force = force , thresh = thresh ,
coord = new_coord , score = new_score , cid = new_id )
# case7: no id, should be same with force=True
force = False
thresh = 0.5
test_box_nms_forward ( np . array ( boxes ) , np . array ( expected ) , force = force , thresh = thresh , cid = - 1 )
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# case8: multi-batch thresh + topk
boxes8 = [ [ [ 1 , 1 , 0 , 0 , 10 , 10 ] , [ 1 , 0.4 , 0 , 0 , 10 , 10 ] , [ 1 , 0.3 , 0 , 0 , 10 , 10 ] ] ,
[ [ 2 , 1 , 0 , 0 , 10 , 10 ] , [ 2 , 0.4 , 0 , 0 , 10 , 10 ] , [ 2 , 0.3 , 0 , 0 , 10 , 10 ] ] ,
[ [ 3 , 1 , 0 , 0 , 10 , 10 ] , [ 3 , 0.4 , 0 , 0 , 10 , 10 ] , [ 3 , 0.3 , 0 , 0 , 10 , 10 ] ] ]
expected8 = [ [ [ 1 , 1 , 0 , 0 , 10 , 10 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ] ,
[ [ 2 , 1 , 0 , 0 , 10 , 10 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ] ,
[ [ 3 , 1 , 0 , 0 , 10 , 10 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ] ]
grad8 = np . random . rand ( 3 , 3 , 6 )
expected_in_grad8 = np . zeros ( ( 3 , 3 , 6 ) )
expected_in_grad8 [ ( 0 , 1 , 2 ) , ( 0 , 0 , 0 ) , : ] = grad8 [ ( 0 , 1 , 2 ) , ( 0 , 0 , 0 ) , : ]
force = False
thresh = 0.5
valid = 0.5
topk = 2
test_box_nms_forward ( np . array ( boxes8 ) , np . array ( expected8 ) , force = force , thresh = thresh , valid = valid , topk = topk )
test_box_nms_backward ( np . array ( boxes8 ) , grad8 , expected_in_grad8 , force = force , thresh = thresh , valid = valid , topk = topk )
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# case9: background id filter out
# default background id -1
boxes9 = [ [ 0 , 0.5 , 0.1 , 0.1 , 0.2 , 0.2 ] , [ 0 , 0.4 , 0.1 , 0.1 , 0.2 , 0.2 ] ,
[ 1 , 0.3 , 0.1 , 0.1 , 0.14 , 0.14 ] , [ - 1 , 0.6 , 0.5 , 0.5 , 0.7 , 0.8 ] ]
expected9 = [ [ 0 , 0.5 , 0.1 , 0.1 , 0.2 , 0.2 ] , [ 1 , 0.3 , 0.1 , 0.1 , 0.14 , 0.14 ] ,
[ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ]
force = True
thresh = 0.5
grad9 = np . random . rand ( 4 , 6 )
expected_in_grad9 = grad9 [ ( 0 , 2 , 1 , 3 ) , : ]
expected_in_grad9 [ ( 1 , 3 ) , : ] = 0
test_box_nms_forward ( np . array ( boxes9 ) , np . array ( expected9 ) , force = force , thresh = thresh )
test_box_nms_backward ( np . array ( boxes9 ) , grad9 , expected_in_grad9 , force = force , thresh = thresh )
# set background id
background_id = 0
expected9 = [ [ - 1 , 0.6 , 0.5 , 0.5 , 0.7 , 0.8 ] , [ 1 , 0.3 , 0.1 , 0.1 , 0.14 , 0.14 ] ,
[ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] , [ - 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ]
grad9 = np . random . rand ( 4 , 6 )
expected_in_grad9 = grad9 [ ( 2 , 3 , 1 , 0 ) , : ]
expected_in_grad9 [ ( 0 , 1 ) , : ] = 0
test_box_nms_forward ( np . array ( boxes9 ) , np . array ( expected9 ) , force = force , thresh = thresh , bid = background_id )
test_box_nms_backward ( np . array ( boxes9 ) , grad9 , expected_in_grad9 , force = force , thresh = thresh , bid = background_id )
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def test_box_iou_op ( ) :
def numpy_box_iou ( a , b , fmt = ' corner ' ) :
def area ( left , top , right , bottom ) :
return np . maximum ( 0 , right - left ) * np . maximum ( 0 , bottom - top )
assert a . shape [ - 1 ] == 4
assert b . shape [ - 1 ] == 4
oshape = a . shape [ : - 1 ] + b . shape [ : - 1 ]
a = a . reshape ( ( - 1 , 4 ) )
ashape = a . shape
b = b . reshape ( ( - 1 , 4 ) )
a = np . tile ( a , reps = [ 1 , b . shape [ 0 ] ] ) . reshape ( ( - 1 , 4 ) )
b = np . tile ( b , reps = [ ashape [ 0 ] , 1 ] ) . reshape ( ( - 1 , 4 ) )
if fmt == ' corner ' :
al , at , ar , ab = np . split ( a , 4 , axis = - 1 )
bl , bt , br , bb = np . split ( b , 4 , axis = - 1 )
elif fmt == ' center ' :
ax , ay , aw , ah = np . split ( a , 4 , axis = - 1 )
bx , by , bw , bh = np . split ( b , 4 , axis = - 1 )
al , at , ar , ab = ax - aw / 2 , ay - ah / 2 , ax + aw / 2 , ay + ah / 2
bl , bt , br , bb = bx - bw / 2 , by - bh / 2 , bx + bw / 2 , by + bh / 2
else :
raise NotImplementedError ( " Fmt {} not supported " . format ( fmt ) )
width = np . maximum ( 0 , np . minimum ( ar , br ) - np . maximum ( al , bl ) )
height = np . maximum ( 0 , np . minimum ( ab , bb ) - np . maximum ( at , bt ) )
intersect = width * height
union = area ( al , at , ar , ab ) + area ( bl , bt , br , bb ) - intersect
union [ np . where ( intersect < = 0 ) ] = 1e-12
iou = intersect / union
return iou . reshape ( oshape )
def generate_boxes ( dims ) :
s1 , off1 , s2 , off2 = np . random . rand ( 4 ) * 100
xy = np . random . rand ( * ( dims + [ 2 ] ) ) * s1 + off1
wh = np . random . rand ( * ( dims + [ 2 ] ) ) * s2 + off2
xywh = np . concatenate ( [ xy , wh ] , axis = - 1 )
ltrb = np . concatenate ( [ xy - wh / 2 , xy + wh / 2 ] , axis = - 1 )
return xywh , ltrb
for ndima in range ( 1 , 6 ) :
for ndimb in range ( 1 , 6 ) :
dims_a = np . random . randint ( low = 1 , high = 3 , size = ndima ) . tolist ( )
dims_b = np . random . randint ( low = 1 , high = 3 , size = ndimb ) . tolist ( )
# generate left, top, right, bottom
xywh_a , ltrb_a = generate_boxes ( dims_a )
xywh_b , ltrb_b = generate_boxes ( dims_b )
iou_np = numpy_box_iou ( ltrb_a , ltrb_b , fmt = ' corner ' )
iou_np2 = numpy_box_iou ( xywh_a , xywh_b , fmt = ' center ' )
iou_mx = mx . nd . contrib . box_iou ( mx . nd . array ( ltrb_a ) , mx . nd . array ( ltrb_b ) , format = ' corner ' )
iou_mx2 = mx . nd . contrib . box_iou ( mx . nd . array ( xywh_a ) , mx . nd . array ( xywh_b ) , format = ' center ' )
assert_allclose ( iou_np , iou_np2 , rtol = 1e-5 , atol = 1e-5 )
assert_allclose ( iou_np , iou_mx . asnumpy ( ) , rtol = 1e-5 , atol = 1e-5 )
assert_allclose ( iou_np , iou_mx2 . asnumpy ( ) , rtol = 1e-5 , atol = 1e-5 )
def test_bipartite_matching_op ( ) :
def assert_match ( inputs , x , y , threshold , is_ascend = False ) :
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for dtype in [ ' float16 ' , ' float32 ' , ' float64 ' ] :
inputs = mx . nd . array ( inputs , dtype = dtype )
x = np . array ( x , dtype = dtype )
y = np . array ( y , dtype = dtype )
a , b = mx . nd . contrib . bipartite_matching ( inputs , threshold = threshold , is_ascend = is_ascend )
assert_array_equal ( a . asnumpy ( ) . astype ( ' int64 ' ) , x . astype ( ' int64 ' ) )
assert_array_equal ( b . asnumpy ( ) . astype ( ' int64 ' ) , y . astype ( ' int64 ' ) )
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assert_match ( [ [ 0.5 , 0.6 ] , [ 0.1 , 0.2 ] , [ 0.3 , 0.4 ] ] , [ 1 , - 1 , 0 ] , [ 2 , 0 ] , 1e-12 , False )
assert_match ( [ [ 0.5 , 0.6 ] , [ 0.1 , 0.2 ] , [ 0.3 , 0.4 ] ] , [ - 1 , 0 , 1 ] , [ 1 , 2 ] , 100 , True )
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def test_multibox_target_op ( ) :
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anchors = mx . nd . array ( [ [ 0.1 , 0.2 , 0.3 , 0.4 ] , [ 0.5 , 0.6 , 0.7 , 0.8 ] ] , ctx = default_device ( ) ) . reshape ( ( 1 , - 1 , 4 ) )
cls_pred = mx . nd . array ( list ( range ( 10 ) ) , ctx = default_device ( ) ) . reshape ( ( 1 , - 1 , 2 ) )
label = mx . nd . array ( [ 1 , 0.1 , 0.1 , 0.5 , 0.6 ] , ctx = default_device ( ) ) . reshape ( ( 1 , - 1 , 5 ) )
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loc_target , loc_mask , cls_target = \
mx . nd . contrib . MultiBoxTarget ( anchors , label , cls_pred ,
overlap_threshold = 0.5 ,
negative_mining_ratio = 3 ,
negative_mining_thresh = 0.4 )
expected_loc_target = np . array ( [ [ 5.0 , 2.5000005 , 3.4657357 , 4.581454 , 0. , 0. , 0. , 0. ] ] )
expected_loc_mask = np . array ( [ [ 1 , 1 , 1 , 1 , 0 , 0 , 0 , 0 ] ] )
expected_cls_target = np . array ( [ [ 2 , 0 ] ] )
assert_allclose ( loc_target . asnumpy ( ) , expected_loc_target , rtol = 1e-5 , atol = 1e-5 )
assert_array_equal ( loc_mask . asnumpy ( ) , expected_loc_mask )
assert_array_equal ( cls_target . asnumpy ( ) , expected_cls_target )
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@xfail_when_nonstandard_decimal_separator
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def test_gradient_multiplier_op ( ) :
# We use the quadratic function in combination with gradient multiplier
def f ( x , a , b , c ) :
return a * x * * 2 + b * x + c
a = np . random . random_sample ( )
b = np . random . random_sample ( )
c = np . random . random_sample ( )
m = np . random . random_sample ( ) - 0.5
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data = mx . symbol . Variable ( ' data ' )
quad_sym = mx . sym . contrib . quadratic ( data = data , a = a , b = b , c = c )
gr_q_sym = mx . sym . contrib . gradientmultiplier ( quad_sym , scalar = m )
for dtype in [ np . float16 , np . float32 , np . float64 ] :
for ndim in range ( 1 , 6 ) :
shape = rand_shape_nd ( ndim , 5 )
data_np = np . random . randn ( * shape ) . astype ( dtype )
expected = f ( data_np , a , b , c )
backward_expected = ( 2 * a * data_np + b ) * m
# check imperative forward
output = mx . nd . contrib . quadratic ( mx . nd . array ( data_np ) , a = a , b = b , c = c )
output = mx . nd . contrib . gradientmultiplier ( output , scalar = m )
assert_almost_equal ( output . asnumpy ( ) , expected ,
rtol = 1e-2 if dtype is np . float16 else 1e-5 ,
atol = 1e-2 if dtype is np . float16 else 1e-5 )
# check forward
check_symbolic_forward ( gr_q_sym , [ data_np ] , [ expected ] ,
rtol = 1e-2 if dtype is np . float16 else 1e-5 ,
atol = 1e-2 if dtype is np . float16 else 1e-5 )
# check backward
check_symbolic_backward ( gr_q_sym , [ data_np ] , [ np . ones ( expected . shape ) ] ,
[ backward_expected ] ,
rtol = 1e-2 if dtype is np . float16 else 1e-5 ,
atol = 1e-2 if dtype is np . float16 else 1e-5 )
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def test_multibox_prior_op ( ) :
h = 561
w = 728
X = mx . nd . random . uniform ( shape = ( 1 , 3 , h , w ) )
Y = mx . contrib . nd . MultiBoxPrior ( X , sizes = [ 0.75 , 0.5 , 0.25 ] , ratios = [ 1 , 2 , 0.5 ] )
assert_array_equal ( Y . shape , np . array ( ( 1 , 2042040 , 4 ) ) )
boxes = Y . reshape ( ( h , w , 5 , 4 ) )
assert_allclose ( boxes . asnumpy ( ) [ 250 , 250 , 0 , : ] , np . array ( [ 0.055117 , 0.071524 , 0.63307 , 0.821524 ] ) , atol = 1e-5 , rtol = 1e-5 )
# relax first ratio if user insists
Y = mx . contrib . nd . MultiBoxPrior ( X , sizes = [ 0.75 , 0.5 , 0.25 ] , ratios = [ 20 , 2 , 0.5 ] )
boxes = Y . reshape ( ( h , w , 5 , 4 ) )
assert_allclose ( boxes . asnumpy ( ) [ 250 , 250 , 0 , : ] , np . array ( [ - 0.948249 , 0.362671 , 1.636436 , 0.530377 ] ) , atol = 1e-5 , rtol = 1e-5 )
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def test_box_encode_op ( ) :
anchors = mx . nd . array ( [ [ 0.1 , 0.2 , 0.3 , 0.4 ] , [ 0.5 , 0.6 , 0.7 , 0.8 ] ] ) . reshape ( ( 1 , - 1 , 4 ) )
refs = mx . nd . array ( [ [ 0.1 , 0.2 , 0.3 , 0.4 ] , [ 0.5 , 0.6 , 0.7 , 0.8 ] ] ) . reshape ( ( 1 , - 1 , 4 ) )
samples = mx . nd . array ( [ [ 0 , 1 ] ] )
matches = mx . nd . array ( [ [ 0 , 1 ] ] )
means = mx . nd . array ( [ 0.0 , 0.0 , 0.0 , 0.0 ] )
stds = mx . nd . array ( [ 0.1 , 0.1 , 0.2 , 0.2 ] )
Y , mask = mx . nd . contrib . box_encode ( samples , matches , anchors , refs , means , stds )
assert_allclose ( Y . asnumpy ( ) , np . zeros ( ( 1 , 2 , 4 ) ) , atol = 1e-5 , rtol = 1e-5 )
assert_allclose ( mask . asnumpy ( ) , np . array ( [ [ [ 0. , 0. , 0. , 0. ] , [ 1. , 1. , 1. , 1. ] ] ] ) , atol = 1e-5 , rtol = 1e-5 )
def test_box_decode_op ( ) :
data = mx . nd . array ( [ [ 0.1 , 0.2 , 0.3 , 0.4 ] , [ 0.5 , 0.6 , 0.7 , 0.8 ] ] ) . reshape ( ( 1 , - 1 , 4 ) )
anchors = mx . nd . array ( [ [ 0.1 , 0.2 , 0.3 , 0.4 ] , [ 0.5 , 0.6 , 0.7 , 0.8 ] ] ) . reshape ( ( 1 , - 1 , 4 ) )
Y = mx . nd . contrib . box_decode ( data , anchors , .1 , .1 , .2 , .2 )
assert_allclose ( Y . asnumpy ( ) , np . array ( [ [ [ - 0.0562755 , - 0.00865743 , 0.26227552 , 0.42465743 ] , \
[ 0.13240421 , 0.17859563 , 0.93759584 , 1.1174043 ] ] ] ) , atol = 1e-5 , rtol = 1e-5 )
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def test_op_mrcnn_mask_target ( ) :
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if default_device ( ) . device_type != ' gpu ' :
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return
num_rois = 2
num_classes = 4
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mask_size = ( 3 , 3 )
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ctx = mx . gpu ( 0 )
# (B, N, 4)
rois = mx . nd . array ( [ [ [ 2.3 , 4.3 , 2.2 , 3.3 ] ,
[ 3.5 , 5.5 , 0.9 , 2.4 ] ] ] , ctx = ctx )
gt_masks = mx . nd . arange ( 0 , 4 * 32 * 32 , ctx = ctx ) . reshape ( 1 , 4 , 32 , 32 )
# (B, N)
matches = mx . nd . array ( [ [ 2 , 0 ] ] , ctx = ctx )
# (B, N)
cls_targets = mx . nd . array ( [ [ 2 , 1 ] ] , ctx = ctx )
mask_targets , mask_cls = mx . nd . contrib . mrcnn_mask_target ( rois , gt_masks , matches , cls_targets ,
num_rois = num_rois ,
num_classes = num_classes ,
mask_size = mask_size )
# Ground truth outputs were generated with GluonCV's target generator
# gluoncv.model_zoo.mask_rcnn.MaskTargetGenerator(1, num_rois, num_classes, mask_size)
gt_mask_targets = mx . nd . array ( [ [ [ [ [ 2193.4 , 2193.7332 , 2194.0667 ] ,
[ 2204.0667 , 2204.4 , 2204.7334 ] ,
[ 2214.7334 , 2215.0667 , 2215.4 ] ] ,
[ [ 2193.4 , 2193.7332 , 2194.0667 ] ,
[ 2204.0667 , 2204.4 , 2204.7334 ] ,
[ 2214.7334 , 2215.0667 , 2215.4 ] ] ,
[ [ 2193.4 , 2193.7332 , 2194.0667 ] ,
[ 2204.0667 , 2204.4 , 2204.7334 ] ,
[ 2214.7334 , 2215.0667 , 2215.4 ] ] ,
[ [ 2193.4 , 2193.7332 , 2194.0667 ] ,
[ 2204.0667 , 2204.4 , 2204.7334 ] ,
[ 2214.7334 , 2215.0667 , 2215.4 ] ] ] ,
[ [ [ 185. , 185.33334 , 185.66667 ] ,
[ 195.66667 , 196.00002 , 196.33334 ] ,
[ 206.33333 , 206.66666 , 207. ] ] ,
[ [ 185. , 185.33334 , 185.66667 ] ,
[ 195.66667 , 196.00002 , 196.33334 ] ,
[ 206.33333 , 206.66666 , 207. ] ] ,
[ [ 185. , 185.33334 , 185.66667 ] ,
[ 195.66667 , 196.00002 , 196.33334 ] ,
[ 206.33333 , 206.66666 , 207. ] ] ,
[ [ 185. , 185.33334 , 185.66667 ] ,
[ 195.66667 , 196.00002 , 196.33334 ] ,
[ 206.33333 , 206.66666 , 207. ] ] ] ] ] )
gt_mask_cls = mx . nd . array ( [ [ 0 , 0 , 1 , 0 ] , [ 0 , 1 , 0 , 0 ] ] )
gt_mask_cls = gt_mask_cls . reshape ( 1 , 2 , 4 , 1 , 1 ) . broadcast_axes ( axis = ( 3 , 4 ) , size = ( 3 , 3 ) )
assert_almost_equal ( mask_targets . asnumpy ( ) , gt_mask_targets . asnumpy ( ) )
assert_almost_equal ( mask_cls . asnumpy ( ) , gt_mask_cls . asnumpy ( ) )
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def test_dynamic_reshape ( ) :
def dynamic_reshape_testcases ( src_shape , shape_arg , dst_shape ) :
data = mx . sym . Variable ( ' data ' )
shape = mx . sym . Variable ( ' shape ' )
net = mx . sym . contrib . dynamic_reshape ( data , shape )
js = net . tojson ( )
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net = mx . sym . fromjson ( js )
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dat_npy = np . random . rand ( * src_shape )
grad_npy = np . random . rand ( * dst_shape )
args = {
' data ' : mx . nd . array ( dat_npy ) ,
' shape ' : mx . nd . array ( shape_arg )
}
args_grad = {
' data ' : mx . nd . empty ( src_shape )
}
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exe = net . _bind ( default_device ( ) , args , args_grad )
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exe . forward ( is_train = True )
assert np . square ( exe . outputs [ 0 ] . asnumpy ( ) - dat_npy . reshape ( dst_shape ) ) . mean ( ) < 1E-7
exe . backward ( out_grads = mx . nd . array ( grad_npy ) )
assert np . square ( exe . grad_dict [ ' data ' ] . asnumpy ( ) - grad_npy . reshape ( src_shape ) ) . mean ( ) < 1E-7
# test ndarray
X = mx . nd . random . uniform ( shape = src_shape )
Y = mx . contrib . nd . dynamic_reshape ( X , mx . nd . array ( shape_arg ) )
assert_array_equal ( Y . shape , dst_shape )
test_cases = [
[ ( 2 , 3 , 5 , 5 ) , ( 0 , - 1 ) , ( 2 , 75 ) ] ,
[ ( 2 , 3 , 5 , 5 ) , ( 0 , 0 , - 1 ) , ( 2 , 3 , 25 ) ] ,
[ ( 5 , 3 , 4 , 5 ) , ( 0 , - 1 , 0 ) , ( 5 , 15 , 4 ) ] ,
[ ( 2 , 3 , 5 , 4 ) , ( - 1 , 0 , 0 ) , ( 8 , 3 , 5 ) ] ,
[ ( 2 , 3 , 5 , 5 ) , ( 0 , 0 , 0 , 0 ) , ( 2 , 3 , 5 , 5 ) ] ,
[ ( 2 , 4 , 5 , 3 ) , ( - 1 , 2 , 2 , 1 ) , ( 30 , 2 , 2 , 1 ) ] ,
[ ( 2 , 3 , 5 , 6 ) , ( - 2 , ) , ( 2 , 3 , 5 , 6 ) ] ,
[ ( 2 , 3 , 5 , 6 ) , ( 6 , 1 , - 2 ) , ( 6 , 1 , 5 , 6 ) ] ,
[ ( 2 , 3 , 5 , 6 ) , ( - 3 , - 3 ) , ( 6 , 30 ) ] ,
[ ( 2 , 3 , 5 , 6 ) , ( - 3 , - 1 ) , ( 6 , 30 ) ] ,
[ ( 64 , ) , ( - 4 , 16 , 4 ) , ( 16 , 4 ) ] ,
[ ( 64 , ) , ( - 4 , 16 , - 1 ) , ( 16 , 4 ) ] ,
[ ( 64 , 1 , 2 , 3 ) , ( - 4 , 16 , - 1 , - 2 ) , ( 16 , 4 , 1 , 2 , 3 ) ] ]
for test_case in test_cases :
dynamic_reshape_testcases ( * test_case )
if __name__ == ' __main__ ' :
import nose
nose . runmodule ( )
