def pre_block(input):
# 32 x 32 x 3
conv1a = conv_bn_relu(input, (3, 3), 16, (1, 1))
# 32 x 32 x 32
conv1b = conv_bn_relu(conv1a, (3, 3), 16, (1, 1))
# 32 x 32 x 32
conv1c = conv_bn_relu(conv1b, (3, 3), 16, (1, 1))
c1 = MaxPooling((3, 3), strides=(1, 1), pad=True)(conv1c)
c2 = conv_bn_relu(conv1c, (3, 3), 16, (1, 1))
d = splice(c1, c2, axis=0)
# 32 x 32 x 32
e1 = conv_bn_relu(d, (1, 1), 32, (1, 1))
e2 = conv_bn_relu(e1, (3, 3), 32, (1, 1))
f1 = conv_bn_relu(d, (1, 1), 32, (1, 1))
f2 = conv_bn_relu(f1, (3, 1), 32, (1, 1))
f3 = conv_bn_relu(f2, (1, 3), 32, (1, 1))
f4 = conv_bn_relu(f3, (3, 3), 32, (1, 1))
g = splice(e2, f4, axis=0)
# 32 x 32 x 64
h1 = conv_bn_relu(g, (3, 3), 64, (1, 1))
i1 = MaxPooling((3, 3), strides=(1, 1), pad=True)(g)
out = splice(h1, i1, axis=0)
# 32 x 32 x 128
return out
def pre_block(input, bnTimeConst):
# 32 x 32 x 3
conv1a = conv_bn_relu_layer(input, 32, (3, 3), (1, 1), True, bnTimeConst)
# 32 x 32 x 32
conv1b = conv_bn_relu_layer(conv1a, 32, (3, 3), (1, 1), True, bnTimeConst)
# 32 x 32 x 32
conv1c = conv_bn_relu_layer(conv1b, 64, (3, 3), (1, 1), True, bnTimeConst)
c1 = MaxPooling((3, 3), strides=(1, 1), pad=True)(conv1c)
c2 = conv_bn_relu_layer(conv1c, 96, (3, 3), (1, 1), True, bnTimeConst)
d = splice(c1, c2, axis=0)
e1 = conv_bn_relu_layer(d, 64, (1, 1), (1, 1), True, bnTimeConst)
e2 = conv_bn_relu_layer(e1, 96, (3, 3), (1, 1), True, bnTimeConst)
f1 = conv_bn_relu_layer(d, 64, (1, 1), (1, 1), True, bnTimeConst)
f2 = conv_bn_relu_layer(f1, 64, (3, 1), (1, 1), True, bnTimeConst)
f3 = conv_bn_relu_layer(f2, 64, (1, 3), (1, 1), True, bnTimeConst)
f4 = conv_bn_relu_layer(f3, 96, (3, 3), (1, 1), True, bnTimeConst)
g = splice(e2, f4, axis=0)
h1 = conv_bn_relu_layer(g, 128, (3, 3), (1, 1), True, bnTimeConst)
i1 = MaxPooling((3, 3), strides=(1, 1), pad=True)(g)
out = splice(h1, i1, axis=0)
return out
def inception_block_5(input, num1x1, num3x3, num3x3_3x3, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1,1), (1,1), True, bnTimeConst)
# 3x3 Convolution
branch3x3_1 = conv_bn_relu_layer(input, num3x3[0], (1,1), (1,1), True, bnTimeConst)
branch3x3_2 = conv_bn_relu_layer(branch3x3_1, num3x3[1], (1,3), (1,1), True, bnTimeConst)
branch3x3_3 = conv_bn_relu_layer(branch3x3_1, num3x3[2], (3,1), (1,1), True, bnTimeConst)
branch3x3 = splice(branch3x3_2, branch3x3_3, axis=0)
# 3x3 3x3 Convolution
branch3x3_3x3_1 = conv_bn_relu_layer(input, num3x3_3x3[0], (1,1), (1,1), True, bnTimeConst)
branch3x3_3x3_2 = conv_bn_relu_layer(branch3x3_3x3_1, num3x3_3x3[1], (3,3), (1,1), True, bnTimeConst)
branch3x3_3x3_3 = conv_bn_relu_layer(branch3x3_3x3_2, num3x3_3x3[1], (1,3), (1,1), True, bnTimeConst)
branch3x3_3x3_4 = conv_bn_relu_layer(branch3x3_3x3_2, num3x3_3x3[3], (3,1), (1,1), True, bnTimeConst)
branch3x3_3x3 = splice(branch3x3_3x3_3, branch3x3_3x3_4, axis=0)
# Average Pooling
branchPool_avgpool = AveragePooling((3,3), strides=(1,1), pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1,1), (1,1), True, bnTimeConst)
out = splice(branch1x1, branch3x3, branch3x3_3x3, branchPool, axis=0)
return out
def inception_block_5(input, num1x1, num3x3, num3x3_3x3, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1,1), (1,1), True, bnTimeConst)
# 3x3 Convolution
branch3x3_1 = conv_bn_relu_layer(input, num3x3[0], (1,1), (1,1), True, bnTimeConst)
branch3x3_2 = conv_bn_relu_layer(branch3x3_1, num3x3[1], (1,3), (1,1), True, bnTimeConst)
branch3x3_3 = conv_bn_relu_layer(branch3x3_1, num3x3[2], (3,1), (1,1), True, bnTimeConst)
branch3x3 = splice(branch3x3_2, branch3x3_3, axis=0)
# 3x3 3x3 Convolution
branch3x3_3x3_1 = conv_bn_relu_layer(input, num3x3_3x3[0], (1,1), (1,1), True, bnTimeConst)
branch3x3_3x3_2 = conv_bn_relu_layer(branch3x3_3x3_1, num3x3_3x3[1], (3,3), (1,1), True, bnTimeConst)
branch3x3_3x3_3 = conv_bn_relu_layer(branch3x3_3x3_2, num3x3_3x3[1], (1,3), (1,1), True, bnTimeConst)
branch3x3_3x3_4 = conv_bn_relu_layer(branch3x3_3x3_2, num3x3_3x3[3], (3,1), (1,1), True, bnTimeConst)
branch3x3_3x3 = splice(branch3x3_3x3_3, branch3x3_3x3_4, axis=0)
# Average Pooling
branchPool_avgpool = AveragePooling((3,3), strides=(1,1), pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1,1), (1,1), True, bnTimeConst)
out = splice(branch1x1, branch3x3, branch3x3_3x3, branchPool, axis=0)
return out
def inception_block_with_avgpool(input, num1x1, num3x3r, num3x3, num3x3dblr,
num3x3dbl, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1, 1), (1, 1), True,
bnTimeConst)
# 3x3 Convolution
branch3x3_reduce = conv_bn_relu_layer(input, num3x3r, (1, 1), (1, 1), True,
bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_reduce, num3x3, (3, 3), (1, 1),
True, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_reduce = conv_bn_relu_layer(input, num3x3dblr, (1, 1), (1, 1),
True, bnTimeConst)
branch3x3dbl_conv = conv_bn_relu_layer(branch3x3dbl_reduce, num3x3dbl,
(3, 3), (1, 1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_conv, num3x3dbl, (3, 3),
(1, 1), True, bnTimeConst)
# Average Pooling
branchPool_avgpool = AveragePooling((3, 3), strides=(1, 1),
pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1, 1),
(1, 1), True, bnTimeConst)
out = splice(branch1x1, branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def inception_block_1(input, num1x1, num5x5, num3x3dbl, numPool, bnTimeConst):
# 1x1
branch1x1 = conv_bn_relu_layer(input, num1x1, (1, 1), (1, 1), True,
bnTimeConst)
# 1x1 -> 5x5
branch5x5_1 = conv_bn_relu_layer(input, num5x5[0], (1, 1), (1, 1), True,
bnTimeConst)
branch5x5 = conv_bn_relu_layer(branch5x5_1, num5x5[1], (5, 5), (1, 1),
True, bnTimeConst)
# 1x1 -> 3x3 -> 3x3
branch3x3dbl_1 = conv_bn_relu_layer(input, num3x3dbl[0], (1, 1), (1, 1),
True, bnTimeConst)
branch3x3dbl_2 = conv_bn_relu_layer(branch3x3dbl_1, num3x3dbl[1], (3, 3),
(1, 1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_2, num3x3dbl[2], (3, 3),
(1, 1), True, bnTimeConst)
# Average Pooling
branchPool_avgpool = AveragePooling((3, 3), strides=(1, 1),
pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1, 1),
(1, 1), True, bnTimeConst)
out = splice(branch1x1, branch5x5, branch3x3dbl, branchPool, axis=0)
return out
def inception_block_3(input, num1x1, num7x7, num7x7dbl, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1, 1), (1, 1), True,
bnTimeConst)
# 1x1 -> 1x3 -> 3x1
branch7x7_1 = conv_bn_relu_layer(input, num7x7[0], (1, 1), (1, 1), True,
bnTimeConst)
branch7x7_2 = conv_bn_relu_layer(branch7x7_1, num7x7[1], (1, 3), (1, 1),
True, bnTimeConst)
branch7x7 = conv_bn_relu_layer(branch7x7_2, num7x7[2], (3, 1), (1, 1),
True, bnTimeConst)
# 1x1 -> 1x3 -> 3x1 -> 1x3 -> 3x1
branch7x7dbl_1 = conv_bn_relu_layer(input, num7x7dbl[0], (1, 1), (1, 1),
True, bnTimeConst)
branch7x7dbl_2 = conv_bn_relu_layer(branch7x7dbl_1, num7x7dbl[1], (3, 1),
(1, 1), True, bnTimeConst)
branch7x7dbl_3 = conv_bn_relu_layer(branch7x7dbl_2, num7x7dbl[2], (1, 3),
(1, 1), True, bnTimeConst)
branch7x7dbl_4 = conv_bn_relu_layer(branch7x7dbl_3, num7x7dbl[3], (3, 1),
(1, 1), True, bnTimeConst)
branch7x7dbl = conv_bn_relu_layer(branch7x7dbl_4, num7x7dbl[4], (1, 3),
(1, 1), True, bnTimeConst)
# Average Pooling
branchPool_avgpool = AveragePooling((3, 3), strides=(1, 1),
pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1, 1),
(1, 1), True, bnTimeConst)
out = splice(branch1x1, branch7x7, branch7x7dbl, branchPool, axis=0)
return out
def inception_block_with_maxpool(input, num1x1, num3x3r, num3x3, num3x3dblr,
num3x3dbl, numPool, bnTimeConst):
# 1x1
branch1x1 = conv_bn_relu_layer(input, num1x1, (1, 1), (1, 1), True,
bnTimeConst)
# 1x1 -> 3x3
branch3x3_reduce = conv_bn_relu_layer(input, num3x3r, (1, 1), (1, 1), True,
bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_reduce, num3x3, (3, 3), (1, 1),
True, bnTimeConst)
# 1x1 -> 3x3 -> 3x3
branch3x3dbl_reduce = conv_bn_relu_layer(input, num3x3dblr, (1, 1), (1, 1),
True, bnTimeConst)
branch3x3dbl_conv = conv_bn_relu_layer(branch3x3dbl_reduce, num3x3dbl,
(3, 3), (1, 1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_conv, num3x3dbl, (3, 3),
(1, 1), True, bnTimeConst)
# max pooling -> 1x1
branchPool_maxpool = MaxPooling((3, 3), strides=(1, 1), pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_maxpool, numPool, (1, 1),
(1, 1), True, bnTimeConst)
out = splice(branch1x1, branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def attention_model(context_memory,
query_memory,
init_status,
hidden_dim,
att_dim,
max_steps=5,
init=glorot_uniform()):
"""
Create the attention model for reasonet
Args:
context_memory: Context memory
query_memory: Query memory
init_status: Intialize status
hidden_dim: The dimention of hidden state
att_dim: The dimention of attention
max_step: Maxuim number of step to revisit the context memory
"""
gru = gru_cell((hidden_dim * 2, ), name='control_status')
status = init_status
output = [None] * max_steps * 2
sum_prob = None
context_cos_sim = project_cosine_sim(att_dim, name='context_attention')
query_cos_sim = project_cosine_sim(att_dim, name='query_attention')
ans_cos_sim = project_cosine_sim(att_dim, name='candidate_attention')
stop_gate = termination_gate(name='terminate_prob')
prev_stop = 0
for step in range(max_steps):
context_attention_weight = context_cos_sim(status, context_memory)
query_attention_weight = query_cos_sim(status, query_memory)
context_attention = sequence.reduce_sum(times(context_attention_weight,
context_memory),
name='C-Att')
query_attention = sequence.reduce_sum(times(query_attention_weight,
query_memory),
name='Q-Att')
attention = ops.splice(query_attention,
context_attention,
name='att-sp')
status = gru(attention, status).output
termination_prob = stop_gate(status)
ans_attention = ans_cos_sim(status, context_memory)
output[step * 2] = ans_attention
if step < max_steps - 1:
stop_prob = prev_stop + ops.log(termination_prob, name='log_stop')
else:
stop_prob = prev_stop
output[step * 2 + 1] = sequence.broadcast_as(
ops.exp(stop_prob, name='exp_log_stop'),
output[step * 2],
name='Stop_{0}'.format(step))
prev_stop += ops.log(1 - termination_prob, name='log_non_stop')
final_ans = None
for step in range(max_steps):
if final_ans is None:
final_ans = output[step * 2] * output[step * 2 + 1]
else:
final_ans += output[step * 2] * output[step * 2 + 1]
combine_func = combine(output + [final_ans], name='Attention_func')
return combine_func
def reduction_A(input, b1, c1, c2, c3):
A1 = MaxPooling((3, 3), strides=(2, 2), pad=True)(input)
B1 = conv_bn_relu(input, (3, 3), b1, (2, 2))
C1 = conv_bn_relu(input, (1, 1), c1, (1, 1))
C2 = conv_bn_relu(C1, (3, 3), c2, (1, 1))
C3 = conv_bn_relu(C2, (3, 3), c3, (2, 2))
out = splice(A1, B1, C3, axis=0)
return out
def _convolution(x):
if group == 1:
apply_x = _conv_ops(w, x)
else:
groups_data = [ops.slice(x, axis=0, begin_index=i * sub_input_channels,
end_index=(i + 1) * sub_input_channels) for i in range(0, group)]
apply_sub = [_conv_ops(group_kernel, group_data)
for group_kernel, group_data in zip(groups_kernel, groups_data)]
apply_x = ops.splice(*apply_sub, axis=0)
if bias_init is not None:
apply_x += b
return apply_x
def reduction_A(input, b1, c1, c2, c3, bnTimeConst):
A1 = MaxPooling((3, 3), strides=(2, 2), pad=True)(input)
B1 = conv_bn_relu_layer(input, b1, (3, 3), (2, 2), True, bnTimeConst)
C1 = conv_bn_relu_layer(input, c1, (1, 1), (1, 1), True, bnTimeConst)
C2 = conv_bn_relu_layer(C1, c2, (3, 3), (1, 1), True, bnTimeConst)
C3 = conv_bn_relu_layer(C2, c3, (3, 3), (2, 2), True, bnTimeConst)
out = splice(A1, B1, C3, axis=0)
return out
def Res_C(input, n, m):
A1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
B1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
B2 = conv_bn(B1, (1,3),n, bn_init_scale = 1)
B3 = conv_bn(B2, (3,1), n, bn_init_scale = 1)
C = splice(A1, B3, axis = 0)
D = conv_bn(C, (1,1), m, bn_init_scale = 1)
p = D + input
return relu(p)
def mobilenet_basic(input, num_filters):
num3x3 = input.shape[0]
l_in = input[0]
c = conv_bn_relu(l_in, (3, 3), 1)
for i in range(1, num3x3):
l = input[i]
l_out = conv_bn_relu(l, (3, 3), 1)
c = splice(c, l_out, axis=0)
c1 = conv_bn_relu(c, (3, 3), 1)
c2 = conv_bn_relu(c1, (1, 1), num_filters)
return c2
def Res_C(input, a1, b1, b2, b3, c1):
A1 = conv_bn(input, (1, 1), a1, bn_init_scale=1)
B1 = conv_bn(input, (1, 1), b1, bn_init_scale=1)
B2 = conv_bn(B1, (1, 3), b2, bn_init_scale=1)
B3 = conv_bn(B2, (3, 1), b3, bn_init_scale=1)
C = splice(A1, B3, axis=0)
D = conv_bn(C, (1, 1), c1, bn_init_scale=1)
p = D + input
return relu(p)
def splice(cntk_layer, inputs):
'''
Setup splice op with given parameters
Args:
cntk_layer (:class:`~cntk.contrib.crosstalkcaffe.unimodel.cntkmodel.CntkLayersDefinition`):
the layer definition of splice op
inputs (list): a list contains all :class:`~cntk.ops.functions.Function` or
:class:`~cntk.input`
Return:
:func:`~cntk.ops.functions.Function`: instaced cntk splice op
'''
return ops.splice(*inputs, axis=0, name=cntk_layer.op_name)
def BNBiRecurrence(fwd, bwd, test_dual=True): # special version that calls one shared BN instance at two places, for testing BN param tying
F = Recurrence(fwd)
G = Recurrence(fwd, go_backwards=True)
BN = BatchNormalization(normalization_time_constant=-1)
x = Placeholder()
# The following code applies the same BN function object twice.
# When running whole-corpus estimation of means/vars, this must lead to the same estimate
# although it is estimated on twice the amount of data (each sample is used twice).
# Hence, this is the test that proves that the parameter sharing works.
x1 = BN(x)
x2 = BN(x) if test_dual else x1
# In double precision with corpus aggregation, these lead to the same result.
apply_x = splice (F(x1), G(x2))
return apply_x
def BNBiRecurrence(fwd, bwd, test_dual=True): # special version that calls one shared BN instance at two places, for testing BN param tying
F = Recurrence(fwd)
G = Recurrence(fwd, go_backwards=True)
BN = BatchNormalization(normalization_time_constant=-1)
x = placeholder()
# The following code applies the same BN function object twice.
# When running whole-corpus estimation of means/vars, this must lead to the same estimate
# although it is estimated on twice the amount of data (each sample is used twice).
# Hence, this is the test that proves that the parameter sharing works.
x1 = BN(x)
x2 = BN(x) if test_dual else x1
# In double precision with corpus aggregation, these lead to the same result.
apply_x = splice (F(x1), G(x2))
return apply_x
def splice(cntk_layer, inputs):
'''
Setup splice op with given parameters
Args:
cntk_layer (:class:`~cntk.contrib.crosstalkcaffe.unimodel.cntkmodel.CntkLayersDefinition`):
the layer definition of splice op
inputs (list): a list contains all :class:`~cntk.ops.functions.Function` or
:class:`~cntk.input`
Return:
:func:`~cntk.ops.functions.Function`: instaced cntk splice op
'''
return ops.splice(*inputs, axis=0, name=cntk_layer.op_name)
def Res_A(input, a1, b1, c1, c2, c3, d1):
A1 = conv_bn_relu(input, (1, 1), a1)
B1 = conv_bn(input, (1, 1), b1, bn_init_scale=1)
B2 = conv_bn(B1, (3, 3), b1, bn_init_scale=1)
C1 = conv_bn(input, (1, 1), c1, bn_init_scale=1)
C2 = conv_bn(C1, (3, 3), c2, bn_init_scale=1)
C3 = conv_bn(C2, (3, 3), c3, bn_init_scale=1)
out = splice(A1, B2, C3, axis=0)
out2 = conv_bn(out, (1, 1), d1, bn_init_scale=1)
p = out2 + input
return relu(p)
def Inception_A(input, a1, b1, c1, c2, d1, d2, bnTimeConst):
A1 = AveragePooling((3, 3), strides=(1, 1), pad=True)(input)
A2 = conv_bn_relu_layer(A1, a1, (3, 3), (1, 1), True, bnTimeConst)
B1 = conv_bn_relu_layer(input, b1, (1, 1), (1, 1), True, bnTimeConst)
C1 = conv_bn_relu_layer(input, c1, (1, 1), (1, 1), True, bnTimeConst)
C2 = conv_bn_relu_layer(C1, c2, (3, 3), (1, 1), True, bnTimeConst)
D1 = conv_bn_relu_layer(input, d1, (1, 1), (1, 1), True, bnTimeConst)
D2 = conv_bn_relu_layer(D1, d2, (3, 3), (1, 1), True, bnTimeConst)
D3 = conv_bn_relu_layer(D2, d2, (3, 3), (1, 1), True, bnTimeConst)
out = splice(A2, B1, C2, D3, axis=0)
return out
def inception_block_2(input, num3x3, num3x3dbl, bnTimeConst):
# 3x3 Convolution
branch3x3 = conv_bn_relu_layer(input, num3x3, (3,3), (2,2), True, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_1 = conv_bn_relu_layer(input, num3x3dbl[0], (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_2 = conv_bn_relu_layer(branch3x3dbl_1, num3x3dbl[1], (3,3), (1,1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_2, num3x3dbl[2], (3,3), (2,2), True, bnTimeConst)
# Max Pooling
branchPool = MaxPooling((3,3), strides=(2,2), pad=True)(input)
out = splice(branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def Res_A(input, n, m):
a1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
b1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
b2 = conv_bn(b1, (3,3), n, bn_init_scale = 1)
c1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
c2 = conv_bn(c1, (3,3), n, bn_init_scale = 1)
c3 = conv_bn(c2, (3,3), n, bn_init_scale = 1)
out = splice(a1, b2, c3, axis = 0)
out2 = conv_bn(out, (1,1), m, bn_init_scale = 1)
p = out2 + input
return relu(p)
def reduction_B(input, b1, b2, c1, c2, d1, d2, d3):
A1 = MaxPooling(filter_shape=(3, 3), strides=(2, 2), pad=True)(input)
B1 = conv_bn_relu(input, (1, 1), b1, (1, 1))
B2 = conv_bn_relu(B1, (3, 3), b2, (2, 2))
C1 = conv_bn_relu(input, (1, 1), c1, (1, 1))
C2 = conv_bn_relu(C1, (3, 3), c2, (2, 2))
D1 = conv_bn_relu(input, (1, 1), d1, (1, 1))
D2 = conv_bn_relu(D1, (3, 3), d2, (1, 1))
D3 = conv_bn_relu(D2, (3, 3), d3, (2, 2))
out = splice(A1, B2, C2, D3, axis=0)
return out
def inception_block_2(input, num3x3, num3x3dbl, bnTimeConst):
# 3x3 Convolution
branch3x3 = conv_bn_relu_layer(input, num3x3, (3,3), (2,2), False, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_1 = conv_bn_relu_layer(input, num3x3dbl[0], (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_2 = conv_bn_relu_layer(branch3x3dbl_1, num3x3dbl[1], (3,3), (1,1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_2, num3x3dbl[2], (3,3), (2,2), False, bnTimeConst)
# Max Pooling
branchPool = MaxPooling((3,3), strides=(2,2), pad=False)(input)
out = splice(branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def inception_block_pass_through(input, num1x1, num3x3r, num3x3, num3x3dblr, num3x3dbl, numPool, bnTimeConst):
# 3x3 Convolution
branch3x3_reduce = conv_bn_relu_layer(input, num3x3r, (1,1), (1,1), True, bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_reduce, num3x3, (3,3), (2,2), True, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_reduce = conv_bn_relu_layer(input, num3x3dblr, (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_conv = conv_bn_relu_layer(branch3x3dbl_reduce, num3x3dbl, (3,3), (1,1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_conv, num3x3dbl, (3,3), (2,2), True, bnTimeConst)
# Max Pooling
branchPool = MaxPooling((3,3), strides=(2,2), pad=True)(input)
out = splice(branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def inception_block_pass_through(input, num1x1, num3x3r, num3x3, num3x3dblr, num3x3dbl, numPool, bnTimeConst):
# 3x3 Convolution
branch3x3_reduce = conv_bn_relu_layer(input, num3x3r, (1,1), (1,1), True, bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_reduce, num3x3, (3,3), (2,2), True, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_reduce = conv_bn_relu_layer(input, num3x3dblr, (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_conv = conv_bn_relu_layer(branch3x3dbl_reduce, num3x3dbl, (3,3), (1,1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_conv, num3x3dbl, (3,3), (2,2), True, bnTimeConst)
# Max Pooling
branchPool = MaxPooling((3,3), strides=(2,2), pad=True)(input)
out = splice(branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def inception_block_4(input, num3x3, num7x7_3x3, bnTimeConst):
# 3x3 Convolution
branch3x3_1 = conv_bn_relu_layer(input, num3x3[0], (1,1), (1,1), True, bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_1, num3x3[1], (3,3), (2,2), False, bnTimeConst)
# 7x7 3x3 Convolution
branch7x7_3x3_1 = conv_bn_relu_layer(input, num7x7_3x3[0], (1,1), (1,1), True, bnTimeConst)
branch7x7_3x3_2 = conv_bn_relu_layer(branch7x7_3x3_1, num7x7_3x3[1], (1,7), (1,1), True, bnTimeConst)
branch7x7_3x3_3 = conv_bn_relu_layer(branch7x7_3x3_2, num7x7_3x3[2], (7,1), (1,1), True, bnTimeConst)
branch7x7_3x3 = conv_bn_relu_layer(branch7x7_3x3_3, num7x7_3x3[3], (3,3), (2,2), False, bnTimeConst)
# Max Pooling
branchPool = MaxPooling((3,3), strides=(2,2), pad=False)(input)
out = splice(branch3x3, branch7x7_3x3, branchPool, axis=0)
return out
def inception_block_4(input, num3x3, num7x7_3x3, bnTimeConst):
# 3x3 Convolution
branch3x3_1 = conv_bn_relu_layer(input, num3x3[0], (1,1), (1,1), True, bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_1, num3x3[1], (3,3), (2,2), False, bnTimeConst)
# 7x7 3x3 Convolution
branch7x7_3x3_1 = conv_bn_relu_layer(input, num7x7_3x3[0], (1,1), (1,1), True, bnTimeConst)
branch7x7_3x3_2 = conv_bn_relu_layer(branch7x7_3x3_1, num7x7_3x3[1], (1,7), (1,1), True, bnTimeConst)
branch7x7_3x3_3 = conv_bn_relu_layer(branch7x7_3x3_2, num7x7_3x3[2], (7,1), (1,1), True, bnTimeConst)
branch7x7_3x3 = conv_bn_relu_layer(branch7x7_3x3_3, num7x7_3x3[3], (3,3), (2,2), False, bnTimeConst)
# Max Pooling
branchPool = MaxPooling((3,3), strides=(2,2), pad=False)(input)
out = splice(branch3x3, branch7x7_3x3, branchPool, axis=0)
return out
def _convolution(x):
if group == 1:
apply_x = _conv_ops(w, x)
else:
groups_data = [
ops.slice(x,
axis=0,
begin_index=i * sub_input_channels,
end_index=(i + 1) * sub_input_channels)
for i in range(0, group)
]
apply_sub = [
_conv_ops(group_kernel, group_data)
for group_kernel, group_data in zip(
groups_kernel, groups_data)
]
apply_x = ops.splice(*apply_sub, axis=0)
if bias_init is not None:
apply_x += b
return apply_x
def inception_block_with_avgpool(input, num1x1, num3x3r, num3x3, num3x3dblr, num3x3dbl, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1,1), (1,1), True, bnTimeConst)
# 3x3 Convolution
branch3x3_reduce = conv_bn_relu_layer(input, num3x3r, (1,1), (1,1), True, bnTimeConst)
branch3x3 = conv_bn_relu_layer(branch3x3_reduce, num3x3, (3,3), (1,1), True, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_reduce = conv_bn_relu_layer(input, num3x3dblr, (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_conv = conv_bn_relu_layer(branch3x3dbl_reduce, num3x3dbl, (3,3), (1,1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_conv, num3x3dbl, (3,3), (1,1), True, bnTimeConst)
# Average Pooling
branchPool_avgpool = AveragePooling((3,3), strides=(1,1), pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1,1), (1,1), True, bnTimeConst)
out = splice(branch1x1, branch3x3, branch3x3dbl, branchPool, axis=0)
return out
def inception_block_1(input, num1x1, num5x5, num3x3dbl, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1,1), (1,1), True, bnTimeConst)
# 5x5 Convolution
branch5x5_1 = conv_bn_relu_layer(input, num5x5[0], (1,1), (1,1), True, bnTimeConst)
branch5x5 = conv_bn_relu_layer(branch5x5_1, num5x5[1], (5,5), (1,1), True, bnTimeConst)
# Double 3x3 Convolution
branch3x3dbl_1 = conv_bn_relu_layer(input, num3x3dbl[0], (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_2 = conv_bn_relu_layer(branch3x3dbl_1, num3x3dbl[1], (3,3), (1,1), True, bnTimeConst)
branch3x3dbl = conv_bn_relu_layer(branch3x3dbl_2, num3x3dbl[2], (3,3), (1,1), True, bnTimeConst)
# Average Pooling
branchPool_avgpool = AveragePooling((3,3), strides=(1,1), pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1,1), (1,1), True, bnTimeConst)
out = splice(branch1x1, branch5x5, branch3x3dbl, branchPool, axis=0)
return out
def Res_B(input, n, m):
a1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
b1 = conv_bn(input, (1,1), n, bn_init_scale = 1)
b2 = conv_bn(b1, (1,3), n, bn_init_scale = 1)
b3 = conv_bn(b2, (3,1), n, bn_init_scale = 1)
c = splice(a1, b3, axis = 0)
d = conv_bn(c, (1,1), m, bn_init_scale = 1)
p = d + input
return relu(p)
def inception_block2(input, num1x1, num3x3r, num3x3, num3x3dblr, num3x3dbl, numPool, bnTimeConst):
# 1x1
branch1x1 = conv_bn_relu_layer(input, num1x1, (1,1), (1,1), True, bnTimeConst)
# 1x1 -> 1x3 -> 3x1
branch3x3_reduce = conv_bn_relu_layer(input, num3x3r, (1,1), (1,1), True, bnTimeConst)
branch3x3_conv1 = conv_bn_relu_layer(branch3x3_reduce, num3x3, (1,3), (1,1), True, bnTimeConst)
branch3x3_conv2 = conv_bn_relu_layer(branch3x3_conv1, num3x3, (3,1), (1,1), True, bnTimeConst)
# 1x1 -> 1x3 -> 3x1 -> 1x3 -> 3x1
branch3x3dbl_reduce = conv_bn_relu_layer(input, num3x3dblr, (1,1), (1,1), True, bnTimeConst)
branch3x3dbl_conv1 = conv_bn_relu_layer(branch3x3dbl_reduce, num3x3dbl, (1,3), (1,1), True, bnTimeConst)
branch3x3dbl_conv2 = conv_bn_relu_layer(branch3x3dbl_conv1, num3x3dbl, (3,1), (1,1), True, bnTimeConst)
branch3x3dbl_conv3 = conv_bn_relu_layer(branch3x3dbl_conv2, num3x3dbl, (1,3), (1,1), True, bnTimeConst)
branch3x3dbl_conv4 = conv_bn_relu_layer(branch3x3dbl_conv3, num3x3dbl, (3,1), (1,1), True, bnTimeConst)
# avg pooling -> 1x1
branchPool_avgpool = AveragePooling((3,3), strides = (1,1), pad = True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1,1), (1,1), True, bnTimeConst)
out = splice(branch1x1, branch3x3_conv2, branch3x3dbl_conv4, branchPool, axis = 0)
return out
def inception_block_3(input, num1x1, num7x7, num7x7dbl, numPool, bnTimeConst):
# 1x1 Convolution
branch1x1 = conv_bn_relu_layer(input, num1x1, (1,1), (1,1), True, bnTimeConst)
# 7x7 Convolution
branch7x7_1 = conv_bn_relu_layer(input, num7x7[0], (1,1), (1,1), True, bnTimeConst)
branch7x7_2 = conv_bn_relu_layer(branch7x7_1, num7x7[1], (1,7), (1,1), True, bnTimeConst)
branch7x7 = conv_bn_relu_layer(branch7x7_2, num7x7[2], (7,1), (1,1), True, bnTimeConst)
# Double 7x7 Convolution
branch7x7dbl_1 = conv_bn_relu_layer(input, num7x7dbl[0], (1,1), (1,1), True, bnTimeConst)
branch7x7dbl_2 = conv_bn_relu_layer(branch7x7dbl_1, num7x7dbl[1], (7,1), (1,1), True, bnTimeConst)
branch7x7dbl_3 = conv_bn_relu_layer(branch7x7dbl_2, num7x7dbl[2], (1,7), (1,1), True, bnTimeConst)
branch7x7dbl_4 = conv_bn_relu_layer(branch7x7dbl_3, num7x7dbl[3], (7,1), (1,1), True, bnTimeConst)
branch7x7dbl = conv_bn_relu_layer(branch7x7dbl_4, num7x7dbl[4], (1,7), (1,1), True, bnTimeConst)
# Average Pooling
branchPool_avgpool = AveragePooling((3,3), strides=(1,1), pad=True)(input)
branchPool = conv_bn_relu_layer(branchPool_avgpool, numPool, (1,1), (1,1), True, bnTimeConst)
out = splice(branch1x1, branch7x7, branch7x7dbl, branchPool, axis=0)
return out
def create_model(params: model_params):
"""
Create ReasoNet model
Args:
params (class:`model_params`): The parameters used to create the model
"""
logger.log(
"Create model: dropout_rate: {0}, init:{1}, embedding_init: {2}".
format(params.dropout_rate, params.init, params.embedding_init))
# Query and Doc/Context/Paragraph inputs to the model
query_seq_axis = Axis('sourceAxis')
context_seq_axis = Axis('contextAxis')
query_sequence = sequence.input(shape=(params.vocab_dim),
is_sparse=True,
sequence_axis=query_seq_axis,
name='query')
context_sequence = sequence.input(shape=(params.vocab_dim),
is_sparse=True,
sequence_axis=context_seq_axis,
name='context')
entity_ids_mask = sequence.input(shape=(1, ),
is_sparse=False,
sequence_axis=context_seq_axis,
name='entity_ids_mask')
# embedding
if params.embedding_init is None:
embedding_init = create_random_matrix(params.vocab_dim,
params.embedding_dim)
else:
embedding_init = params.embedding_init
embedding = parameter(shape=(params.vocab_dim, params.embedding_dim),
init=None)
embedding.value = embedding_init
embedding_matrix = constant(embedding_init,
shape=(params.vocab_dim, params.embedding_dim))
if params.dropout_rate is not None:
query_embedding = ops.dropout(times(query_sequence, embedding),
params.dropout_rate,
name='query_embedding')
context_embedding = ops.dropout(times(context_sequence, embedding),
params.dropout_rate,
name='context_embedding')
else:
query_embedding = times(query_sequence,
embedding,
name='query_embedding')
context_embedding = times(context_sequence,
embedding,
name='context_embedding')
contextGruW = Parameter(_INFERRED + _as_tuple(params.hidden_dim),
init=glorot_uniform(),
name='gru_params')
queryGruW = Parameter(_INFERRED + _as_tuple(params.hidden_dim),
init=glorot_uniform(),
name='gru_params')
entity_embedding = ops.times(context_sequence,
embedding_matrix,
name='constant_entity_embedding')
# Unlike other words in the context, we keep the entity vectors fixed as a random vector so that each vector just means an identifier of different entities in the context and it has no semantic meaning
full_context_embedding = ops.element_select(entity_ids_mask,
entity_embedding,
context_embedding)
context_memory = ops.optimized_rnnstack(full_context_embedding,
contextGruW,
params.hidden_dim,
1,
True,
recurrent_op='gru',
name='context_mem')
query_memory = ops.optimized_rnnstack(query_embedding,
queryGruW,
params.hidden_dim,
1,
True,
recurrent_op='gru',
name='query_mem')
qfwd = ops.slice(sequence.last(query_memory),
-1,
0,
params.hidden_dim,
name='fwd')
qbwd = ops.slice(sequence.first(query_memory),
-1,
params.hidden_dim,
params.hidden_dim * 2,
name='bwd')
init_status = ops.splice(
qfwd, qbwd,
name='Init_Status') # get last fwd status and first bwd status
return attention_model(context_memory,
query_memory,
init_status,
params.hidden_dim,
params.attention_dim,
max_steps=params.max_rl_steps)
def BiRecurrence(fwd, bwd):
F = Recurrence(fwd)
G = Recurrence(fwd, go_backwards=True)
x = placeholder()
apply_x = splice (F(x), G(x))
return apply_x
def with_lookahead():
x = Placeholder()
future_x = future_value(x)
apply_x = splice (x, future_x)
return apply_x
def BiRecurrence(fwd, bwd):
F = Recurrence(fwd)
G = Recurrence(fwd, go_backwards=True)
x = Placeholder()
apply_x = splice (F(x), G(x))
return apply_x
def with_lookahead():
x = placeholder()
future_x = sequence.future_value(x)
apply_x = splice (x, future_x)
return apply_x
