def skip(inp, isdown, chl):
if isdown == -1:
return inp
global idx
l1 = inp
if isdown != 0:
l1 = Pooling2D("pooling1_{}".format(idx),
inp,
window=1,
stride=2,
mode="AVERAGE")
l1 = relu_conv_bn(l1, 1, 1, 0, chl // 2, isrelu=False, isbn=False)
l2 = inp
if isdown != 0:
l2 = Pooling2D("pooling2_{}".format(idx),
inp[:, :, 1:, 1:],
window=1,
stride=2,
mode="AVERAGE")
l2 = relu_conv_bn(l2, 1, 1, 0, chl // 2, isrelu=False, isbn=False)
lay = O.Concat([l1, l2], axis=1)
lay = BN("bn_down_{}".format(isdown), lay, eps=1e-9)
lay = ElementwiseAffine("bnaff_down_{}".format(isdown),
lay,
shared_in_channels=False,
k=C(1),
b=C(0))
return lay
def transition(inp, i):
l1 = bn_relu_conv(inp, 1, 1, 0, inp.partial_shape[1], True, True, i != 2)
global idx
idx += 1
if i != 2:
l2 = Pooling2D("Pooling{}".format(idx), l1, window=2, mode="AVERAGE")
else:
l2 = Pooling2D("Pooling{}".format(idx),
l1,
window=8,
stride=8,
mode="AVERAGE")
return l2
def make_network(minibatch_size=128):
patch_size = 32
inp = DataProvider("data",
shape=(minibatch_size, 15, patch_size, patch_size))
label = DataProvider("label", shape=(minibatch_size, ))
#lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False)
lay, conv = conv_bn(inp, 3, 1, 1, 16, True)
out = [conv]
for chl in [32, 64, 128]:
for i in range(10):
lay, conv = conv_bn(lay, 3, 1, 1, chl, True)
out.append(conv)
if chl != 128:
lay = b_resize("pooling{}".format(chl), lay)
lay = Pooling2D("pooling{}".format(chl), lay, window=2, mode="MAX")
#global average pooling
print(lay.partial_shape)
feature = lay.mean(axis=2).mean(axis=2)
#feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE")
pred = Softmax(
"pred",
FullyConnected("fc0",
feature,
output_dim=10,
W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5),
b=C(0),
nonlinearity=Identity()))
network = Network(outputs=[pred] + out)
network.loss_var = CrossEntropyLoss(pred, label)
return network
def make_network(minibatch_size = 128, debug = False):
patch_size = 32
inp = DataProvider("data", shape = (minibatch_size, 3, patch_size, patch_size), dtype = np.float32)
label = DataProvider("label", shape = (minibatch_size, ), dtype = np.int32)
lay = conv_bn(inp, 3, 1, 1, 16, True)
n = 18
lis = [16, 32, 64]
for i in lis:
lay = res_block(lay, i, n)
#global average pooling
#feature = lay.mean(axis = 2).mean(axis = 2)
feature = Pooling2D("pooling", lay, window = 8, stride = 8, padding = 0, mode = "AVERAGE")
pred = Softmax("pred", FullyConnected(
"fc0", feature, output_dim = 10,
nonlinearity = Identity()
))
network = Network(outputs = [pred])
network.loss_var = CrossEntropyLoss(pred, label)
if debug:
visitor = NetworkVisitor(network.loss_var)
for i in visitor.all_oprs:
print(i)
print(i.partial_shape)
print("input = ", i.inputs)
print("output = ", i.outputs)
print()
return network
def make_network(minibatch_size = 128):
patch_size = 32
inp = DataProvider("data", shape = (minibatch_size, 3, patch_size, patch_size))
label = DataProvider("label", shape = (minibatch_size, ))
#lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False)
lay, conv = conv_bn(inp, 3, 1, 1, 16, True)
out = [conv]
for chl in [32 * 3, 64 * 3, 128 * 3]:
for i in range(10):
lay, conv1, conv2 = xcep_layer(lay, chl)
out.append(conv1)
out.append(conv2)
if chl != 128 * 3:
lay = Pooling2D("pooling{}".format(chl), lay, window = 2, mode = "MAX")
#global average pooling
print(lay.partial_shape)
feature = lay.mean(axis = 2).mean(axis = 2)
#feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE")
W = ortho_group.rvs(feature.partial_shape[1])
W = W[:, :10]
W = ConstProvider(W)
b = ConstProvider(np.zeros((10, )))
pred = Softmax("pred", FullyConnected(
"fc0", feature, output_dim = 10,
W = W,
b = b,
nonlinearity = Identity()
))
network = Network(outputs = [pred] + out)
network.loss_var = CrossEntropyLoss(pred, label)
return network
def make_network(minibatch_size=128, debug=False):
patch_size = 32
inp = DataProvider("data",
shape=(minibatch_size, 3, patch_size, patch_size),
dtype=np.float32)
label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32)
lay, w = conv_bn(inp, 3, 1, 1, 16, True)
lis_w = [w]
n = 3
lis = [16, 32, 64]
for i in lis:
lay, lis_new = res_block(lay, i, n)
lis_w += lis_new
#global average pooling
#feature = lay.mean(axis = 2).mean(axis = 2)
feature = Pooling2D("pooling",
lay,
window=8,
stride=8,
padding=0,
mode="AVERAGE")
pred = Softmax(
"pred",
FullyConnected(
"fc0",
feature,
output_dim=10,
#W = G(mean = 0, std = (1 / 64)**0.5),
#b = C(0),
nonlinearity=Identity()))
network = Network(outputs=[pred])
network.loss_var = CrossEntropyLoss(pred, label)
lmd = 1
for w in lis_w:
w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0)
w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0)
A = O.MatMul(w.dimshuffle(1, 0), w)
network.loss_var += lmd * (
(A - np.identity(A.partial_shape[0]))**2).mean()
if debug:
visitor = NetworkVisitor(network.loss_var)
for i in visitor.all_oprs:
print(i)
print(i.partial_shape)
print("input = ", i.inputs)
print("output = ", i.outputs)
print()
return network
def get(args):
img_size = 224
num_inputs = 3
data = DataProvider('data', shape=(args.batch_size, num_inputs,
img_size, img_size))
inp = data
f = create_bn_relu("conv1", inp, ksize=7, stride=2, pad=3, num_outputs=64,
has_relu=True, conv_name_fun=None,
args=args)
f = Pooling2D("pool1", f, window=3, stride=2, padding=1, mode="MAX",
format=args.format)
pre = [2, 3, 4, 5]
stages = [3, 4, 6, 3]
mid_outputs = [64, 128, 256, 512]
enable_stride = [False, True, True, True]
for p, s, o, es in zip(pre, stages, mid_outputs, enable_stride):
for i in range(s):
has_proj = False if i > 0 else True
stride = 1 if not es or i > 0 else 2
prefix = "{}{}".format(p, chr(ord("a") + i))
f = create_bottleneck(prefix, f, stride, o, o * 4, args, has_proj)
print("{}\t{}".format(prefix, f.partial_shape))
f = Pooling2D("pool5", f, window=7, stride=7, padding=0, mode="AVERAGE",
format=args.format)
f = FullyConnected("fc1000", f, output_dim=1000,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f = Softmax("cls_softmax", f)
f.init_weights()
net = RawNetworkBuilder(inputs=[data], outputs=[f])
return net
def make_network(minibatch_size=128, debug=False):
patch_size = 32
inp = DataProvider("data",
shape=(minibatch_size, 3, patch_size, patch_size),
dtype=np.float32)
label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32)
lay = conv_bn(inp, 3, 1, 1, 16 * 4 * 2, True)
n = 4 * 3
group = 8
lis = [16 * 4, 32 * 4, 64 * 4]
for i in range(len(lis)):
lay = res_block(lay, lis[i], i, n, group)
#global average pooling
#feature = lay.mean(axis = 2).mean(axis = 2)
feature = Pooling2D("pooling",
lay,
window=8,
stride=8,
padding=0,
mode="AVERAGE")
pred = Softmax(
"pred",
FullyConnected(
"fc0",
feature,
output_dim=10,
#W = G(mean = 0, std = (1 / 64)**0.5),
#b = C(0),
nonlinearity=Identity()))
network = Network(outputs=[pred])
network.loss_var = CrossEntropyLoss(pred, label)
info = CInfo()
info.get_complexity(network.outputs).as_table().show()
"""
if debug:
visitor = NetworkVisitor(network.loss_var)
for i in visitor.all_oprs:
print(i)
print(i.partial_shape)
print("input = ", i.inputs)
print("output = ", i.outputs)
print()
"""
return network
def make_network(minibatch_size=128):
patch_size = 32
inp = DataProvider("data",
shape=(minibatch_size, 3, patch_size, patch_size))
label = DataProvider("label", shape=(minibatch_size, ))
#lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False)
lay, conv = conv_bn(inp, 3, 1, 1, 16, True)
out = [conv]
for chl in [32, 64, 128]:
for i in range(10):
lay, conv = conv_bn(lay, 3, 1, 1, chl, True)
out.append(conv)
if chl != 128:
lay = Pooling2D("pooling{}".format(chl), lay, window=2, mode="MAX")
#global average pooling
print(lay.partial_shape)
feature = lay.mean(axis=2).mean(axis=2)
#feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE")
pred = Softmax(
"pred",
FullyConnected("fc0",
feature,
output_dim=10,
W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5),
b=C(0),
nonlinearity=Identity()))
network = Network(outputs=[pred] + out)
network.loss_var = CrossEntropyLoss(pred, label)
#conv1 = out[0]
#print(conv1.inputs[1].partial_shape)
lmd = 0.01
for conv_lay in out:
w = conv_lay
#w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0)
w = w.dimshuffle(1, 0, 2, 3)
w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0)
w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0)
A = MatMul(w.dimshuffle(1, 0), w)
#print(A.partial_shape)
network.loss_var += lmd * (
(A - np.identity(A.partial_shape[0]))**2).sum()
return network
minibatch_size = 20
img_size = 28
input_mat = DataProvider(name = "input_mat",
shape = (minibatch_size, 1, img_size, img_size))
conv1 = Conv2D("conv1", input_mat, kernel_shape = 3, output_nr_channel = 5,
W = G(mean = 0.0001, std = (1 / (3 * 3))**0.5),
b = C(0),
padding = (1, 1),
nonlinearity = ReLU())
conv2 = Conv2D("conv2", conv1, kernel_shape = 3, output_nr_channel = 5,
W = G(mean = 0.0001, std = (1 / (5 * 3 * 3))**0.5),
b = C(0),
padding = (1, 1),
nonlinearity = ReLU())
pooling1 = Pooling2D("pooling1", conv2, window = (2, 2), mode = "max")
conv3 = Conv2D("conv3", pooling1, kernel_shape = 3, output_nr_channel = 10,
W = G(mean = 0.0001, std = (1 / (5 * 3 * 3))**0.5),
b = C(0),
padding = (1, 1),
nonlinearity = ReLU())
conv4 = Conv2D("conv4", conv3, kernel_shape = 3, output_nr_channel = 10,
W = G(mean = 0.0001, std = (1 / (10 * 3 * 3))**0.5),
b = C(0),
padding = (1, 1),
nonlinearity = ReLU())
pooling2 = Pooling2D("pooling2", conv4, window = (2, 2), mode = "max")
feature = pooling2.reshape((-1, 7 * 7 * 10))
fc1 = FC("fc1", feature, output_dim = 100,
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 = 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]))**0.5),
nonlinearity = Identity()
)
offsety = 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]))**0.5),
nonlinearity = Identity()
)
offset = Concat([offsetx, offsety], axis = 1)
ndim = ker_shape**2 * offsetx.partial_shape[2] * offsetx.partial_shape[3] * 2
offset = FullyConnected(
name + "offset", offsetx, output_dim = ndim,
W = G(mean = 0, std = (1 / ndim)**2),
#W = C(0),
b = C(0),
nonlinearity = Identity()
)
offsetx = offset[:, :ndim // 2].reshape(offsetx.shape)
offsety = offset[:, ndim // 2:].reshape(offsety.shape)
"""
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")
def make_network():
batch_size = config.minibatch_size
img_size = config.img_size
data = DataProvider("data", shape=(batch_size, 3, img_size, img_size))
label = DataProvider("label", shape=(batch_size, 8))
f = create_bn_relu("conv1", data, ksize=3, stride=2, pad=1, num_outputs=24)
f = Pooling2D("pool1", f, window=3, stride=2, padding=1, mode="MAX")
pre = [2, 3, 4]
stages = [4, 8, 4]
mid_outputs = [32, 64, 128]
enable_stride = [True, True, True]
for p, s, o, es in zip(pre, stages, mid_outputs, enable_stride):
for i in range(s):
prefix = "{}{}".format(p, chr(ord("a") + i))
stride = 1 if not es or i > 0 else 2
has_proj = False if i > 0 else True
f = create_xception(prefix, f, stride, o, o * 4, has_proj)
print("{}\t{}".format(prefix, f.partial_shape))
f1 = Pooling2D("pool5_1", f, window=7, stride=7, padding=0, mode="AVERAGE")
f1 = FullyConnected("fc3_1",
f1,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f1 = Softmax("cls_softmax_1", f1)
f2 = Pooling2D("pool5_2", f, window=7, stride=7, padding=0, mode="AVERAGE")
f2 = FullyConnected("fc3_2",
f2,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f2 = Softmax("cls_softmax_2", f2)
f3 = Pooling2D("pool5_3", f, window=7, stride=7, padding=0, mode="AVERAGE")
f3 = FullyConnected("fc3_3",
f3,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f3 = Softmax("cls_softmax_3", f3)
f4 = Pooling2D("pool5_4", f, window=7, stride=7, padding=0, mode="AVERAGE")
f4 = FullyConnected("fc3_4",
f4,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f4 = Softmax("cls_softmax_4", f4)
f5 = Pooling2D("pool5_5", f, window=7, stride=7, padding=0, mode="AVERAGE")
f5 = FullyConnected("fc3_5",
f5,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f5 = Softmax("cls_softmax_5", f5)
f6 = Pooling2D("pool5_6", f, window=7, stride=7, padding=0, mode="AVERAGE")
f6 = FullyConnected("fc3_6",
f6,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f6 = Softmax("cls_softmax_6", f6)
f7 = Pooling2D("pool5_7", f, window=7, stride=7, padding=0, mode="AVERAGE")
f7 = FullyConnected("fc3_7",
f7,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f7 = Softmax("cls_softmax_7", f7)
f8 = Pooling2D("pool5_8", f, window=7, stride=7, padding=0, mode="AVERAGE")
f8 = FullyConnected("fc3_8",
f8,
output_dim=2,
nonlinearity=mgsk.opr.helper.elemwise_trans.Identity())
f8 = Softmax("cls_softmax_8", f8)
losses = {}
# cross-entropy loss
# from IPython import embed
# embed()
label_1 = label[:, 0]
label_2 = label[:, 1]
label_3 = label[:, 2]
label_4 = label[:, 3]
label_5 = label[:, 4]
label_6 = label[:, 5]
label_7 = label[:, 6]
label_8 = label[:, 7]
loss_xent_0 = O.cross_entropy(f1, label_1, name='loss_pose')
try:
loss_xent_1 = O.cross_entropy_with_mask(f2, label_2, label_1)
loss_xent_2 = O.cross_entropy_with_mask(f3, label_3, label_1)
loss_xent_3 = O.cross_entropy_with_mask(f4, label_4, label_1)
loss_xent_4 = O.cross_entropy_with_mask(f5, label_5, label_1)
loss_xent_5 = O.cross_entropy_with_mask(f6, label_6, label_1)
loss_xent_6 = O.cross_entropy_with_mask(f7, label_7, label_1)
loss_xent_7 = O.cross_entropy_with_mask(f8, label_8, label_1)
except Exception as err:
print(err)
loss_xent = loss_xent_0 + loss_xent_1 + loss_xent_2 + loss_xent_3 + loss_xent_4 + loss_xent_5 + loss_xent_6 + loss_xent_7
losses['loss_xent'] = loss_xent
# weight decay regularization loss
loss_weight_decay = 0
if config.weight_decay:
weight_decay = config.weight_decay
with GroupNode('weight_decay').context_reg():
for opr in iter_dep_opr(loss_xent):
if not isinstance(opr, ParamProvider) or opr.freezed:
continue
param = opr
name = param.name
if not (name.endswith('W')):
continue
# logger.info('L2 regularization on `{}`'.format(name))
loss_weight_decay += 0.5 * weight_decay * (param**2).sum()
losses['loss_weight_decay'] = loss_weight_decay
# total loss
with GroupNode('loss').context_reg():
loss = sum(losses.values())
losses['loss'] = loss
# for multi-GPU task, tell the GPUs to summarize the final loss
O.utils.hint_loss_subgraph([loss_xent, loss_weight_decay], loss)
# --------3.23-----------
net = RawNetworkBuilder(inputs=[data, label],
outputs=[f1, f2, f3, f4, f5, f6, f7, f8],
loss=loss)
# net = RawNetworkBuilder(inputs=[data, label], outputs=f1, loss=loss)
metrics1 = get_metrics(f1, label_1)
# metrics2 = get_metrics(f2, label_2)
# metrics3 = get_metrics(f3, label_3)
# metrics4 = get_metrics(f4, label_4)
# metrics5 = get_metrics(f5, label_5)
net.extra['extra_outputs'] = {
'pred_0': f1,
'pred_1': f1,
'pred_2': f2,
'pred_3': f3,
'pred_4': f4,
'pred_5': f5,
'pred_6': f6,
'pred_7': f7,
'label': label
}
# net.extra['extra_outputs'] = {'pred': f1, 'label': label}
net.extra['extra_outputs'].update(metrics1)
# net.extra['extra_outputs'].update(metrics2)
# net.extra['extra_outputs'].update(metrics3)
# net.extra['extra_outputs'].update(metrics4)
# net.extra['extra_outputs'].update(metrics5)
net.extra['extra_outputs'].update(losses)
net.extra['extra_config'] = {
'monitor_vars': list(losses.keys()) + list(metrics1.keys())
}
return net
def dfpooling(name, inp, window=2, padding=0, dx=[0, 1], dy=[0, 1]):
#inp = ConstProvider([[[[1, 2], [3, 4]]]], dtype = np.float32)
ker_shape = window
stride = window
gamma = 0.1
offsetx = gamma * inp.partial_shape[2] * Conv2D(name + "offsetx",
inp,
kernel_shape=ker_shape,
stride=stride,
padding=padding,
output_nr_channel=ker_shape
**2,
W=C(0),
nonlinearity=Identity())
offsety = gamma * inp.partial_shape[3] * Conv2D(name + "offsety",
inp,
kernel_shape=ker_shape,
stride=stride,
padding=padding,
output_nr_channel=ker_shape
**2,
W=C(0),
nonlinearity=Identity())
outputs = []
for sx in range(2):
for sy in range(2):
if sx == 0:
ofx = Floor(offsetx)
bilx = offsetx - ofx + Equal(Floor(offsetx), Ceil(offsetx))
else:
ofx = Ceil(offsetx)
bilx = ofx - offsetx
if sy == 0:
ofy = Floor(offsety)
bily = offsety - ofy + Equal(Floor(offsety), Ceil(offsety))
else:
ofy = Ceil(offsety)
bily = 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.partial_shape[2], ofx.partial_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.partial_shape[0], ker_shape, ker_shape,
inp.partial_shape[2], inp.partial_shape[3])
inp = inp.dimshuffle(0, 3, 1, 4, 2)
inp = inp.reshape(inp.partial_shape[0],
inp.partial_shape[1] * inp.partial_shape[2],
inp.partial_shape[3] * inp.partial_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.partial_shape[0],
arg_fea.partial_shape[1], -1)
of = of.reshape(ofx.partial_shape[0], -1)
of = of.dimshuffle(0, 'x', 1)
#of = Concat([of] * arg_fea.partial_shape[1], axis = 1)
of = of.broadcast((of.partial_shape[0], arg_fea.partial_shape[1],
of.partial_shape[2]))
arx = Linspace(0,
arg_fea.partial_shape[0],
arg_fea.partial_shape[0],
endpoint=False)
arx = arx.add_axis(1).add_axis(2).broadcast(of.shape)
ary = Linspace(0,
arg_fea.partial_shape[1],
arg_fea.partial_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.partial_shape[0],
inp.partial_shape[1],
bilx.partial_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")
