def res_layer(inp, chl, stride = 1, proj = False):
pre = inp
inp = conv_bn(inp, 1, stride, 0, chl // 4, True)
chl //= 4
name = inp.name
#Global Average Pooling
SE = inp.mean(axis = 3).mean(axis = 2)
sum_lay = 0
out_lay = 0
width = 4
lay = FullyConnected(
"fc0({})".format(name), SE, output_dim = chl,
nonlinearity = ReLU()
)
#fc1
lay = FullyConnected(
"fc1({})".format(name), lay, output_dim = chl * width,
nonlinearity = Identity()
)
lay = lay.reshape(inp.shape[0], chl, width)
lay = Softmax("softmax({})".format(name), lay, axis = 2)
for i in range(width):
if i == 0:
inp_lay = inp
else:
inp_lay = O.Concat([inp[:, width:, :, :], inp[:, :width, :, :]], axis = 1)
inp_lay = inp_lay * lay[:, :, i].dimshuffle(0, 1, 'x', 'x')
inp = inp_lay
chl *= 4
inp = conv_bn(inp, 3, 1, 1, chl // 4, True)
inp = conv_bn(inp, 1, 1, 0, chl, False)
if proj:
pre = conv_bn(pre, 1, stride, 0, chl, False)
name = inp.name
#Global Average Pooling
SE = inp.mean(axis = 3).mean(axis = 2)
sum_lay = 0
out_lay = 0
width = 4
lay = FullyConnected(
"fc0({})".format(name), SE, output_dim = chl,
nonlinearity = ReLU()
)
#fc1
lay = FullyConnected(
"fc1({})".format(name), lay, output_dim = chl * width,
nonlinearity = Identity()
)
lay = lay.reshape(inp.shape[0], chl, width)
lay = Softmax("softmax({})".format(name), lay, axis = 2)
for i in range(width):
if i == 0:
inp_lay = inp
else:
inp_lay = O.Concat([inp[:, width:, :, :], inp[:, :width, :, :]], axis = 1)
inp_lay = inp_lay * lay[:, :, i].dimshuffle(0, 1, 'x', 'x')
inp = inp_lay
inp = arith.ReLU(inp + pre)
return inp
def dense_block(inp, k, l):
lay = inp
for i in range(l):
cur_lay = bn_relu_conv(lay, 3, 1, 1, k, True, True)
name = cur_lay.name
group = k // 4
#G.P.
SE = cur_lay.mean(axis=3).mean(axis=2)
SE = FullyConnected("fc0({})".format(name),
SE,
output_dim=(k // group)**2 * group,
nonlinearity=ReLU())
SE = FullyConnected("fc1({})".format(name),
SE,
output_dim=(k // group)**2 * group,
nonlinearity=Sigmoid())
print(SE.name)
SE = SE.reshape(cur_lay.shape[0] * group, k // group, k // group, 1, 1)
preshape = cur_lay.shape
cur_lay = cur_lay.reshape(1, cur_lay.shape[0] * cur_lay.shape[1],
cur_lay.shape[2], cur_lay.shape[3])
cur_lay = Conv2D("conv({})".format(name),
cur_lay,
kernel_shape=1,
stride=1,
padding=0,
W=SE,
nonlinearity=Identity())
cur_lay = cur_lay.reshape(preshape)
#cur_lay = cur_lay * SE.dimshuffle(0, 1, 'x', 'x')
lay = Concat([lay, cur_lay], axis=1)
return lay
def res_layer(inp, chl):
pre = inp
inp = conv_bn(inp, 3, 1, 1, chl, True)
inp = conv_bn(inp, 3, 1, 1, chl, False)
name = inp.name
#Global Average Pooling
SE = inp.mean(axis=3).mean(axis=2)
group = 1
#fc0
SE = FullyConnected("fc0({})".format(name),
SE,
output_dim=chl,
nonlinearity=ReLU())
#fc1
SE = FullyConnected("fc1({})".format(name),
SE,
output_dim=(chl // group)**2 * group,
nonlinearity=Sigmoid())
SE = SE.reshape(inp.shape[0] * group, chl // group, chl // group, 1, 1)
w = SE
SE /= SE.sum(axis=4).sum(axis=3).sum(axis=2).dimshuffle(
0, 1, "x", "x", "x")
#inp = inp * SE.dimshuffle(0, 1, 'x', 'x')
inp = inp.reshape(1, inp.shape[0] * inp.shape[1], inp.shape[2],
inp.shape[3])
inp = Conv2D(
"conv({})".format(name),
inp,
kernel_shape=1,
stride=1,
padding=0,
#output_nr_channel = chl,
W=SE,
nonlinearity=Identity(),
#group = group
)
inp = inp.reshape(pre.shape)
inp = arith.ReLU(inp + pre)
return inp, w
def dfpooling(name, inp, window = 2, padding = 0, dx = [0, 1], dy = [0, 1]):
#inp = ConstProvider([[[[1, 2], [3, 4]]]], dtype = np.float32)
"""
Add a new conv&bn to insure that the scale of the feature map is variance 1.
"""
ker_shape = window
stride = window
offsetlay = Conv2D(
name + "conv", inp, kernel_shape = 3, stride = 1, padding = 1,
output_nr_channel = ker_shape**2,
W = G(mean = 0, std = ((1) / (3**2 * inp.partial_shape[1]))**0.5),
nonlinearity = Identity()
)
offsetlay = BN(name + "BN", offsetlay, eps = 1e-9)
offsetx = inp.partial_shape[2] * Conv2D(
name + "conv1x", offsetlay, kernel_shape = ker_shape, stride = stride,
padding = padding,
output_nr_channel = ker_shape**2,
W = G(mean = 0, std = 1 / (ker_shape**2 * inp.partial_shape[2])),
nonlinearity = Identity()
)
offsety = inp.partial_shape[3] * Conv2D(
name + "conv1y", offsetlay, kernel_shape = ker_shape, stride = stride,
padding = padding,
output_nr_channel = ker_shape**2,
W = G(mean = 0, std = 1 / (ker_shape**2 * inp.partial_shape[3])),
nonlinearity = Identity()
)
gamma = 0.0001
ndim = ker_shape**2 * offsetx.partial_shape[2] * offsetx.partial_shape[3]
offsetx = FullyConnected(
name + "offsetx", offsetx, output_dim = ndim,
W = G(mean = 0, std = gamma / ndim),
b = C(0),
nonlinearity = Identity()
)
offsetx = offsetx.reshape(offsety.shape)
offsety = FullyConnected(
name + "offsety", offsety, output_dim = ndim,
W = G(mean = 0, std = gamma / ndim),
b = C(0),
nonlinearity = Identity()
)
offsety = offsety.reshape(offsetx.shape)
print(offsety.partial_shape)
#offsetx = ZeroGrad(offsetx)
#offsety = ZeroGrad(offsety)
outputs = []
for sx in range(2):
for sy in range(2):
if sx == 0:
ofx = Floor(offsetx)
bilx = 1 - (offsetx - ofx)
else:
ofx = Ceil(offsetx)
bilx = 1 - (ofx - offsetx)
if sy == 0:
ofy = Floor(offsety)
bily = 1 - (offsety - ofy)
else:
ofy = Ceil(offsety)
bily = 1 - (ofy - offsety)
"""
No padding
padding1 = ConstProvider(np.zeros((inp.partial_shape[0], inp.partial_shape[1], 1, inp.partial_shape[3])))
padding2 = ConstProvider(np.zeros((inp.partial_shape[0], inp.partial_shape[1], inp.partial_shape[2] + 2, 1)))
arg_fea = Concat([padding1, inp, padding1], axis = 2)
arg_fea = Concat([padding2, arg_fea, padding2], axis = 3)
"""
arg_fea = inp
#one_mat = ConstProvider(np.ones((inp.partial_shape[2], inp.partial_shape[3])), dtype = np.int32)
one_mat = ConstProvider(1, dtype = np.int32).add_axis(0).broadcast((ofx.shape[2], ofx.shape[3]))
affx = (Cumsum(one_mat, axis = 0) - 1) * stride
affy = (Cumsum(one_mat, axis = 1) - 1) * stride
ofx = ofx + affx.dimshuffle('x', 'x', 0, 1)
ofy = ofy + affy.dimshuffle('x', 'x', 0, 1)
one_mat = ConstProvider(np.ones((ker_shape, ofx.partial_shape[2], ofx.partial_shape[3])))
#ofx[:, :ker_shape, :, :] -= 1
#ofx[:, ker_shape*2:, :, :] += 1
ofx += Concat([one_mat * i for i in dx], axis = 0).dimshuffle('x', 0, 1, 2)
#ofy[:, ::3, :, :] -= 1
#ofy[:, 2::3, :, :] += 1
one_mat = ones((1, ofx.partial_shape[2], ofx.partial_shape[3]))
one_mat = Concat([one_mat * i for i in dy], axis = 0)
one_mat = Concat([one_mat] * ker_shape, axis = 0)
ofy += one_mat.dimshuffle('x', 0, 1, 2)
ofx = Max(Min(ofx, arg_fea.partial_shape[2] - 1), 0)
ofy = Max(Min(ofy, arg_fea.partial_shape[3] - 1), 0)
def DeformReshape(inp, ker_shape):
inp = inp.reshape(inp.shape[0], ker_shape, ker_shape, inp.shape[2], inp.partial_shape[3])
inp = inp.dimshuffle(0, 3, 1, 4, 2)
inp = inp.reshape(inp.shape[0], inp.shape[1] * inp.shape[2], inp.shape[3] * inp.shape[4])
return inp
ofx = DeformReshape(ofx, ker_shape)
ofy = DeformReshape(ofy, ker_shape)
bilx = DeformReshape(bilx, ker_shape)
bily = DeformReshape(bily, ker_shape)
of = ofx * arg_fea.partial_shape[2] + ofy
arg_fea = arg_fea.reshape(arg_fea.shape[0], arg_fea.shape[1], -1)
of = of.reshape(ofx.shape[0], -1)
of = of.dimshuffle(0, 'x', 1)
#of = Concat([of] * arg_fea.partial_shape[1], axis = 1)
of = of.broadcast((of.shape[0], arg_fea.shape[1], of.shape[2]))
arx = Linspace(0, arg_fea.shape[0], arg_fea.shape[0], endpoint = False)
arx = arx.add_axis(1).add_axis(2).broadcast(of.shape)
ary = Linspace(0, arg_fea.shape[1], arg_fea.shape[1], endpoint = False)
ary = ary.add_axis(0).add_axis(2).broadcast(of.shape)
of = of.add_axis(3)
arx = arx.add_axis(3)
ary = ary.add_axis(3)
idxmap = Astype(Concat([arx, ary, of], axis = 3), np.int32)
"""
sample = []
for i in range(arg_fea.partial_shape[0]):
for j in range(arg_fea.partial_shape[1]):
sample.append(arg_fea[i][j].ai[of[i][j]].dimshuffle('x', 0))
sample = Concat(sample, axis = 0)
"""
sample = IndexingRemap(arg_fea, idxmap).reshape(inp.shape[0], inp.shape[1], bilx.shape[1], -1)
bilx = bilx.dimshuffle(0, 'x', 1, 2).broadcast(sample.shape)
bily = bily.dimshuffle(0, 'x', 1, 2).broadcast(sample.shape)
sample *= bilx * bily
outputs.append(sample)
output = outputs[0]
for i in outputs[1:]:
output += i
return Pooling2D(name, output, window = 2, mode = "AVERAGE")
