def across_down(self, stage_num, level_num, dim_reduced, init, norm):
feature = self.feature_grids[stage_num][level_num]
feature = mx.sym.UpSampling(
feature,
scale=2,
sample_type='nearest',
name='P%s_%s_acrossdown_upsample' % (level_num, stage_num),
num_args=1,
)
feature = X.reluconvbn(data=feature,filters=dim_reduced, kernel=3, pad=1, stride=1, init=init, norm=norm,\
name='P%s_acrossdown_conv'%level_num, prefix='S%s_'%stage_num)
p0_feature = self.feature_grids[0][level_num - 1]
feature = mx.sym.slice_like(feature,
p0_feature,
name='P%s_%s_slicelike' %
(stage_num, level_num))
return feature
def get_fused_P_feature(p_features, stage, dim_reduced, init, norm):
prefix = "S{}_".format(stage)
with mx.name.Prefix(prefix):
P3_0 = p_features['S{}_P3'.format(stage - 1)] # s8
P4_0 = p_features['S{}_P4'.format(stage - 1)] # s16
P5_0 = p_features['S{}_P5'.format(stage - 1)] # s32
P6_0 = p_features['S{}_P6'.format(stage - 1)] # s64
P7_0 = p_features['S{}_P7'.format(stage - 1)] # s128
# P4_1 = gp(P6_0, P4_0)
P6_0_to_P4 = mx.sym.UpSampling(P6_0,
scale=4,
sample_type='nearest',
name="P6_0_to_P4",
num_args=1)
P6_0_to_P4 = mx.sym.slice_like(P6_0_to_P4, P4_0)
P4_1 = merge_gp(P6_0_to_P4, P4_0, name="gp_P6_0_P4_0")
P4_1 = X.reluconvbn(P4_1,
dim_reduced,
init,
norm,
name="P4_1",
prefix=prefix)
# P4_2 = sum(P4_0, P4_1)
P4_2 = X.merge_sum([P4_0, P4_1], name="sum_P4_0_P4_1")
P4_2 = X.reluconvbn(P4_2,
dim_reduced,
init,
norm,
name="P4_2",
prefix=prefix)
# P3_3 = sum(P4_2, P3_0) end node
P4_2_to_P3 = mx.sym.UpSampling(P4_2,
scale=2,
sample_type='nearest',
name="P4_2_to_P3",
num_args=1)
P4_2_to_P3 = mx.sym.slice_like(P4_2_to_P3, P3_0)
P3_3 = X.merge_sum([P4_2_to_P3, P3_0], name="sum_P4_2_P3_0")
P3_3 = X.reluconvbn(P3_3,
dim_reduced,
init,
norm,
name="P3_3",
prefix=prefix)
P3 = P3_3
# P4_4 = sum(P4_2, P3_3) end node
P3_3_to_P4 = X.pool(P3_3,
name="P3_3_to_P4",
kernel=2,
stride=2,
pad=0)
P3_3_to_P4 = mx.sym.slice_like(P3_3_to_P4, P4_2)
P4_4 = X.merge_sum([P4_2, P3_3_to_P4], name="sum_P4_4_P3_3")
P4_4 = X.reluconvbn(P4_4,
dim_reduced,
init,
norm,
name="P4_4",
prefix=prefix)
P4 = P4_4
# P5_5 = sum(gp(P4_4, P3_3), P5_0) end node
P4_4_to_P5 = X.pool(P4_4,
kernel=2,
stride=2,
name="P4_4_to_P5",
pad=0)
P4_4_to_P5 = mx.sym.slice_like(P4_4_to_P5, P5_0)
P3_3_to_P5 = X.pool(P3_3,
kernel=4,
stride=4,
name="P3_3_to_P5",
pad=0)
P3_3_to_P5 = mx.sym.slice_like(P3_3_to_P5, P5_0)
gp_P4_4_P3_3 = merge_gp(P4_4_to_P5,
P3_3_to_P5,
name="gp_P4_4_P3_3")
P5_5 = X.merge_sum([gp_P4_4_P3_3, P5_0],
name="sum_[gp_P4_4_P3_3]_P5_0")
P5_5 = X.reluconvbn(P5_5,
dim_reduced,
init,
norm,
name="P5_5",
prefix=prefix)
P5 = P5_5
# P7_6 = sum(gp(P5_5, P4_2), P7_0) end node
P4_2_to_P7 = X.pool(P4_2,
name="P4_2_to_P7",
kernel=8,
stride=8,
pad=0)
P4_2_to_P7 = mx.sym.slice_like(P4_2_to_P7, P7_0)
P5_5_to_P7 = X.pool(P5_5,
name="P5_5_to_P7",
kernel=4,
stride=4,
pad=0)
P5_5_to_P7 = mx.sym.slice_like(P5_5_to_P7, P7_0)
gp_P5_5_P4_2 = merge_gp(P5_5_to_P7,
P4_2_to_P7,
name="gp_P5_5_P4_2")
P7_6 = X.merge_sum([gp_P5_5_P4_2, P7_0],
name="sum_[gp_P5_5_P4_2]_P7_0")
P7_6 = X.reluconvbn(P7_6,
dim_reduced,
init,
norm,
name="P7_6",
prefix=prefix)
P7 = P7_6
# P6_7 = gp(P7_6, P5_5) end node
P7_6_to_P6 = mx.sym.UpSampling(P7_6,
scale=2,
sample_type='nearest',
name="P7_6_to_P6",
num_args=1)
P7_6_to_P6 = mx.sym.slice_like(P7_6_to_P6, P6_0)
P5_5_to_P6 = X.pool(P5_5,
name="p5_5_to_P6",
kernel=2,
stride=2,
pad=0)
P5_5_to_P6 = mx.sym.slice_like(P5_5_to_P6, P6_0)
P6_7 = merge_gp(P7_6_to_P6,
P5_5_to_P6,
name="gp_P7_6_to_P6_P5_5_to_P6")
P6_7 = X.reluconvbn(P6_7,
dim_reduced,
init,
norm,
name="P6_7",
prefix=prefix)
P6 = P6_7
return {
'S{}_P3'.format(stage): P3,
'S{}_P4'.format(stage): P4,
'S{}_P5'.format(stage): P5,
'S{}_P6'.format(stage): P6,
'S{}_P7'.format(stage): P7
}
def get_fused_P_feature(p_features, stage, dim_reduced, init, norm):
prefix = "S{}_".format(stage)
with mx.name.Prefix(prefix):
P3_0 = p_features['S{}_P3'.format(stage - 1)] # s8
P4_0 = p_features['S{}_P4'.format(stage - 1)] # s16
P5_0 = p_features['S{}_P5'.format(stage - 1)] # s32
P6_0 = p_features['S{}_P6'.format(stage - 1)] # s64
P7_0 = p_features['S{}_P7'.format(stage - 1)] # s128
# P7_1
P7_1 = P7_0
# P6_1 = sum(P6_0, P7_1)
P7_1_to_P6 = mx.sym.UpSampling(P7_1,
scale=2,
sample_type='nearest',
name="P7_1_to_P6",
num_args=1)
P6_1 = X.merge_sum([P6_0, P7_1_to_P6], name="sum_P6_0_P7_1")
P6_1 = X.reluconvbn(P6_1,
dim_reduced,
init,
norm,
name="P6_1",
prefix=prefix)
# P5_1 = sum(P5_0, P6_1)
P6_1_to_P5 = mx.sym.UpSampling(P6_1,
scale=2,
sample_type='nearest',
name="P6_1_to_P5",
num_args=1)
P5_1 = X.merge_sum([P5_0, P6_1_to_P5], name="sum_P5_0_P6_1")
P5_1 = X.reluconvbn(P5_1,
dim_reduced,
init,
norm,
name="P5_1",
prefix=prefix)
# P4_1 = sum(P4_0, P5_1)
P5_1_to_P4 = mx.sym.UpSampling(P5_1,
scale=2,
sample_type='nearest',
name="P5_1_to_P4",
num_args=1)
P4_1 = X.merge_sum([P4_0, P5_1_to_P4], name="sum_P4_0_P5_1")
P4_1 = X.reluconvbn(P4_1,
dim_reduced,
init,
norm,
name="P4_1",
prefix=prefix)
# P3_1 = sum(P3_0, P4_1)
P4_1_to_P3 = mx.sym.UpSampling(P4_1,
scale=2,
sample_type='nearest',
name="P4_1_to_P3",
num_args=1)
P3_1 = X.merge_sum([P3_0, P4_1_to_P3], name="sum_P3_0_P4_1")
P3_1 = X.reluconvbn(P3_1,
dim_reduced,
init,
norm,
name="P3_1",
prefix=prefix)
P3_2 = P3_1
P3 = P3_2
# P4_2 = sum(P3_2, P4_1)
P3_2_to_P4 = X.pool(P3_2,
name="P3_2_to_P4",
kernel=2,
stride=2,
pad=0)
P4_2 = X.merge_sum([P4_1, P3_2_to_P4], name="sum_P4_1_P3_2")
P4_2 = X.reluconvbn(P4_2,
dim_reduced,
init,
norm,
name="P4_2",
prefix=prefix)
P4 = P4_2
# P5_2 = sum(P4_2, P5_1)
P4_2_to_P5 = X.pool(P4_2,
name="P4_2_to_P5",
kernel=2,
stride=2,
pad=0)
P5_2 = X.merge_sum([P5_1, P4_2_to_P5], name="sum_P5_1_P4_2")
P5_2 = X.reluconvbn(P5_2,
dim_reduced,
init,
norm,
name="P5_2",
prefix=prefix)
P5 = P5_2
# P6_2 = sum(P5_2, P6_1)
P5_2_to_P6 = X.pool(P5_2,
name="P5_2_to_P6",
kernel=2,
stride=2,
pad=0)
P6_2 = X.merge_sum([P6_1, P5_2_to_P6], name="sum_P6_1_P5_2")
P6_2 = X.reluconvbn(P6_2,
dim_reduced,
init,
norm,
name="P6_2",
prefix=prefix)
P6 = P6_2
# P7_2 = sum(P6_2, P7_1)
P6_2_to_P7 = X.pool(P6_2,
name="P6_2_to_P7",
kernel=2,
stride=2,
pad=0)
P7_2 = X.merge_sum([P7_1, P6_2_to_P7], name="sum_P7_1_P6_2")
P7_2 = X.reluconvbn(P7_2,
dim_reduced,
init,
norm,
name="P7_2",
prefix=prefix)
P7 = P7_2
return {
'S{}_P3'.format(stage): P3,
'S{}_P4'.format(stage): P4,
'S{}_P5'.format(stage): P5,
'S{}_P6'.format(stage): P6,
'S{}_P7'.format(stage): P7
}
def get_fused_P_feature(p_features, stage, dim_reduced, init, norm):
prefix = "S{}_".format(stage)
with mx.name.Prefix(prefix):
P2_0 = p_features['S{}_P2'.format(stage - 1)] # s4
P3_0 = p_features['S{}_P3'.format(stage - 1)] # s8
P4_0 = p_features['S{}_P4'.format(stage - 1)] # s16
P5_0 = p_features['S{}_P5'.format(stage - 1)] # s32
P6_0 = p_features['S{}_P6'.format(stage - 1)] # s64
P6_1 = P6_0
P6_1_to_P5 = mx.sym.UpSampling(P6_1,
scale=2,
sample_type='nearest',
name="P6_1_to_P5",
num_args=1)
P6_1_to_P5 = mx.sym.slice_like(P6_1_to_P5, P5_0)
P5_1 = X.merge_sum([P5_0, P6_1_to_P5], name="sum_P5_0_P6_1")
P5_1 = X.reluconvbn(P5_1,
dim_reduced,
init,
norm,
name="P5_1",
prefix=prefix)
# P4_1 = sum(P4_0, P5_1)
P5_1_to_P4 = mx.sym.UpSampling(P5_1,
scale=2,
sample_type='nearest',
name="P5_1_to_P4",
num_args=1)
P5_1_to_P4 = mx.sym.slice_like(P5_1_to_P4, P4_0)
P4_1 = X.merge_sum([P4_0, P5_1_to_P4], name="sum_P4_0_P5_1")
P4_1 = X.reluconvbn(P4_1,
dim_reduced,
init,
norm,
name="P4_1",
prefix=prefix)
P4_1_to_P3 = mx.sym.UpSampling(P4_1,
scale=2,
sample_type='nearest',
name="P4_1_to_P3",
num_args=1)
P4_1_to_P3 = mx.sym.slice_like(P4_1_to_P3, P3_0)
P3_1 = X.merge_sum([P3_0, P4_1_to_P3], name="sum_P3_0_P4_1")
P3_1 = X.reluconvbn(P3_1,
dim_reduced,
init,
norm,
name="P3_1",
prefix=prefix)
P3_1_to_P2 = mx.sym.UpSampling(P3_1,
scale=2,
sample_type='nearest',
name="P3_1_to_P2",
num_args=1)
P3_1_to_P2 = mx.sym.slice_like(P3_1_to_P2, P2_0)
P2_1 = X.merge_sum([P2_0, P3_1_to_P2], name="sum_P2_0_P3_1")
P2_1 = X.reluconvbn(P2_1,
dim_reduced,
init,
norm,
name="P2_1",
prefix=prefix)
P2_2 = P2_1
P2 = P2_2
P2_2_to_P3 = X.pool(P2_2,
name="P2_2_to_P3",
kernel=3,
stride=2,
pad=1)
P3_2 = X.merge_sum([P3_1, P2_2_to_P3], name="sum_P3_1_P2_2")
P3_2 = X.reluconvbn(P3_2,
dim_reduced,
init,
norm,
name="P3_2",
prefix=prefix)
P3 = P3_2
P3_2_to_P4 = X.pool(P3_2,
name="P3_2_to_P4",
kernel=3,
stride=2,
pad=1)
P4_2 = X.merge_sum([P4_1, P3_2_to_P4], name="sum_P4_1_P3_2")
P4_2 = X.reluconvbn(P4_2,
dim_reduced,
init,
norm,
name="P4_2",
prefix=prefix)
P4 = P4_2
P4_2_to_P5 = X.pool(P4_2,
name="P4_2_to_P5",
kernel=3,
stride=2,
pad=1)
P5_2 = X.merge_sum([P5_1, P4_2_to_P5], name="sum_P5_1_P4_2")
P5_2 = X.reluconvbn(P5_2,
dim_reduced,
init,
norm,
name="P5_2",
prefix=prefix)
P5 = P5_2
P5_2_to_P6 = X.pool(P5_2,
name="P5_2_to_P6",
kernel=3,
stride=2,
pad=1)
P6_2 = X.merge_sum([P6_1, P5_2_to_P6], name="sum_P6_1_P5_2")
P6_2 = X.reluconvbn(P6_2,
dim_reduced,
init,
norm,
name="P6_2",
prefix=prefix)
P6 = P6_2
return {
'S{}_P2'.format(stage): P2,
'S{}_P3'.format(stage): P3,
'S{}_P4'.format(stage): P4,
'S{}_P5'.format(stage): P5,
'S{}_P6'.format(stage): P6,
}
def across_skip(self, stage_num, level_num, curr_stage_num, dim_reduced,
init, norm):
feature = self.feature_grids[stage_num][level_num]
feature = X.reluconvbn(data=feature,filters=dim_reduced, kernel=1, pad=0, stride=1, init=init, norm=norm,\
name='P%s_acrossskip_conv'%level_num, prefix='S%s_2_%s'%(stage_num, curr_stage_num))
return feature
def same_up(self, stage_num, level_num, dim_reduced, init, norm):
feature = self.feature_grids[stage_num][level_num]
feature = X.reluconvbn(data=feature,filters=dim_reduced, kernel=3, pad=1, stride=2, init=init, norm=norm,\
name='P%s_sameup_conv'%level_num, prefix='S%s_'%stage_num)
return feature