def rbf_kernel(X):
XY = T.dot(X, X.T)
x2 = T.sum(X**2, axis=1).dimshuffle(0, 'x')
X2e = T.repeat(x2, X.shape[0], axis=1)
H = X2e + X2e.T - 2. * XY
V = H.flatten()
# median distance
h = T.switch(T.eq((V.shape[0] % 2), 0),
# if even vector
T.mean(T.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]),
# if odd vector
T.sort(V)[V.shape[0] // 2])
h = .5 * h / T.log(T.cast(H.shape[0] + 1., theano.config.floatX))
# compute the rbf kernel
kxy = T.exp(-H / h / 2.0)
dxkxy = -T.dot(kxy, X)
sumkxy = T.sum(kxy, axis=1).dimshuffle(0, 'x')
dxkxy = T.add(dxkxy, T.mul(X, sumkxy)) / h
return kxy, dxkxy
def vgd_kernel_tensor(X0):
XY = T.batched_dot(X0, X0.transpose(0,2,1))
x2 = T.reshape(T.sum(T.square(X0),axis=2), (X0.shape[0], X0.shape[1], 1))
X2e = T.repeat(x2, X0.shape[1], axis=2)
H = T.sub(T.add(X2e, X2e.transpose(0,2,1)), 2 * XY)
V = H.flatten(2)
# median distance
h = T.switch(T.eq((V.shape[1] % 2), 0),
# if even vector
T.mean(T.sort(V)[:, ((V.shape[1] // 2) - 1): ((V.shape[1] // 2) + 1)], axis=1),
# if odd vector
T.sort(V)[:, V.shape[1] // 2])
h = T.sqrt(0.5 * h / T.log(X0.shape[1].astype('float32') + 1.0))
# h = T.maximum(h, T.zeros_like(h) + 1e-4)
# h = h / 2
Kxy = T.exp(-H / T.tile(h.dimshuffle(0, 'x', 'x'), (1, X0.shape[1], X0.shape[1])) ** 2 / 2.0)
dxkxy = - T.batched_dot(Kxy, X0)
sumkxy = T.sum(Kxy, axis=2).dimshuffle(0, 1, 'x')
dxkxy = T.add(dxkxy, T.mul(X0, sumkxy)) / (T.tile(h.dimshuffle(0, 'x', 'x'), (1, X0.shape[1], X0.shape[2])) ** 2)
return (Kxy, dxkxy, h)
def vgd_kernel(X0):
XY = T.dot(X0, X0.transpose())
x2 = T.reshape(T.sum(T.square(X0), axis=1), (X0.shape[0], 1))
X2e = T.repeat(x2, X0.shape[0], axis=1)
H = T.sub(T.add(X2e, X2e.transpose()), 2 * XY)
V = H.flatten()
# median distance
h = T.switch(
T.eq((V.shape[0] % 2), 0),
# if even vector
T.mean(T.sort(V)[((V.shape[0] // 2) - 1):((V.shape[0] // 2) + 1)]),
# if odd vector
T.sort(V)[V.shape[0] // 2])
h = T.sqrt(0.5 * h / T.log(X0.shape[0].astype('float32') + 1.0)) / 2.
Kxy = T.exp(-H / h**2 / 2.0)
dxkxy = -T.dot(Kxy, X0)
sumkxy = T.sum(Kxy, axis=1).dimshuffle(0, 'x')
dxkxy = T.add(dxkxy, T.mul(X0, sumkxy)) / (h**2)
return (Kxy, dxkxy, h)
def median(x):
return T.switch(
T.eq((x.shape[0] % 2), 0),
T.mean(
T.sort(x)[((x.shape[0] // 2) - 1):((x.shape[0] // 2) +
1)]),
T.sort(x)[x.shape[0] // 2])
def _inferred_labels(self, s, M, threshold):
inference = T.tensordot(s,
T.switch(T.eq(M, 0), 0, M / T.sum(M, axis=0)),
axes=[1, 1])
BvSB = T.sort(inference, axis=1)[:, -1] - T.sort(inference, axis=1)[:,
-2]
L_inf = T.switch(T.gt(BvSB, threshold),
T.cast(T.argmax(inference, axis=1), 'int32'), -1)
return L_inf
def median(tensor):
"""
MAD tensor from https://groups.google.com/forum/#!topic/theano-users/I4eHjbAetEQ
:param tensor: Input tensor
:return: Median expression
"""
tensor = tensor.flatten(1)
return T.switch(T.eq((tensor.shape[0] % 2), 0),
# if even vector
T.mean(T.sort(tensor)[((tensor.shape[0] / 2) - 1): ((tensor.shape[0] / 2) + 1)]),
# if odd vector
T.sort(tensor)[tensor.shape[0] // 2])
def _inferred_labels(self, s, M, threshold):
inference = T.tensordot(
s,
T.switch(T.eq(M,0), 0, M/T.sum(M, axis=0)),
axes=[1,1])
BvSB = T.sort(inference,axis=1)[:,-1]-T.sort(inference,axis=1)[:,-2]
L_inf = T.switch(
T.gt(BvSB,threshold),
T.cast(T.argmax(inference,axis=1),'int32'),
-1
)
return L_inf
def get_output_for(self, input, deterministic=False, **kwargs):
if self.maxpool:
input = T.signal.pool.pool_2d(input,
ds=(self.pool_size, ) * 2,
ignore_border=True,
st=(1, 1),
mode='max')
if deterministic:
input = T.signal.pool.pool_2d(input,
ds=(self.pool_size, ) * 2,
ignore_border=True,
st=(self.pool_size, ) * 2,
padding=(self.pool_size / 2, ) * 2,
mode='average_exc_pad')
return input
# return input[:, :, ::self.pool_size, ::self.pool_size]
else:
w, h = self.input_shape[2:]
n_w, n_h = w / self.grid_size, h / self.grid_size
n_sample_per_grid = self.grid_size / self.pool_size
idx_w = []
idx_h = []
for i in range(n_w):
offset = self.grid_size * i
if i < n_w - 1:
this_n = self.grid_size
else:
this_n = input.shape[2] - offset
this_idx = T.sort(
self.rng.permutation(size=(1, ),
n=this_n)[0, :n_sample_per_grid])
idx_w.append(offset + this_idx)
for i in range(n_h):
offset = self.grid_size * i
if i < n_h - 1:
this_n = self.grid_size
else:
this_n = input.shape[3] - offset
this_idx = T.sort(
self.rng.permutation(size=(1, ),
n=this_n)[0, :n_sample_per_grid])
idx_h.append(offset + this_idx)
idx_w = T.concatenate(idx_w, axis=0)
idx_h = T.concatenate(idx_h, axis=0)
output = input[:, :, idx_w][:, :, :, idx_h]
return output
def median_distance(H, e=1e-6):
if H.ndim != 2:
raise NotImplementedError
V = H.flatten()
# median distance
h = T.switch(T.eq((V.shape[0] % 2), 0),
# if even vector
T.mean(T.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]),
# if odd vector
T.sort(V)[V.shape[0] // 2])
#h = h / T.log(H.shape[0] + 1).astype(theano.config.floatX)
return h
def setup(self, bottom, top):
if len(bottom) != 2:
raise Exception("The layer needs two inputs!")
probs_tmp = T.ftensor4()
stat_inp = T.ftensor4()
stat = stat_inp[:, :, :, 1:]
probs_bg = probs_tmp[:, 0, :, :]
probs = probs_tmp[:, 1:, :, :]
probs_max = T.max(probs, axis=3).max(axis=2)
q_fg = 0.996
probs_sort = T.sort(probs.reshape((-1, 20, 41 * 41)), axis=2)
weights = np.array([q_fg**i
for i in range(41 * 41 - 1, -1, -1)])[None,
None, :]
Z_fg = np.sum(weights)
weights = T.addbroadcast(theano.shared(weights), 0, 1)
probs_mean = T.sum((probs_sort * weights) / Z_fg, axis=2)
q_bg = 0.999
probs_bg_sort = T.sort(probs_bg.reshape((-1, 41 * 41)), axis=1)
weights_bg = np.array([q_bg**i
for i in range(41 * 41 - 1, -1, -1)])[None, :]
Z_bg = np.sum(weights_bg)
weights_bg = T.addbroadcast(theano.shared(weights_bg), 0)
probs_bg_mean = T.sum((probs_bg_sort * weights_bg) / Z_bg, axis=1)
stat_2d = stat[:, 0, 0, :] > 0.5
loss_1 = -T.mean(
T.sum((stat_2d * T.log(probs_mean) /
T.sum(stat_2d, axis=1, keepdims=True)),
axis=1))
loss_2 = -T.mean(
T.sum(((1 - stat_2d) * T.log(1 - probs_max) /
T.sum(1 - stat_2d, axis=1, keepdims=True)),
axis=1))
loss_3 = -T.mean(T.log(probs_bg_mean))
loss = loss_1 + loss_2 + loss_3
self.forward_theano = theano.function([probs_tmp, stat_inp], loss)
self.backward_theano = theano.function([probs_tmp, stat_inp],
T.grad(loss, probs_tmp))
def median_distance(H, e=1e-6, log_ratio_bandwidth=False):
if H.ndim != 2:
raise NotImplementedError
V = H.flatten()
# median distance
h = T.switch(T.eq((V.shape[0] % 2), 0),
# if even vector
T.mean(T.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]),
# if odd vector
T.sort(V)[V.shape[0] // 2])
if log_ratio_bandwidth:
h /= T.log(H.shape[0].astype('float32') + 1.0)
return h
def kmaxpooling(self, input, k):
"""
Записывет k максимальных значений по оси 3, отвечающей результату одного фильтра
:param input: символичный тензор размера 4: (batch size, nfilters, output row, output col)
(output row = высота - итоговое количество окон предложения для одного фильтра)
:param k: int, количество максимальных элементов
"""
# axis=2 так как нам нужна сортировка внутри каждого фильтра
pooling_args_sorted = T.argsort(input, axis=2)
args_of_k_max = pooling_args_sorted[:, :, -k:, :]
# не хочу терять порядок слов, поэтому ещё раз сортирую номера максимумов:
args_of_k_max_sorted = T.sort(args_of_k_max, axis=2)
# use nonsymbolic shape if possible
input_shape = self.input_shape
if any(s is None for s in input_shape):
input_shape = input.shape
dim0 = T.arange(input_shape[0]).repeat(input_shape[1] * k *
input_shape[3])
dim1 = T.arange(input_shape[1]).repeat(k * input_shape[3]).reshape((1, -1))\
.repeat(input_shape[0], axis=0).flatten()
dim2 = args_of_k_max_sorted.flatten()
dim3 = T.arange(input_shape[3]).reshape((1, -1))\
.repeat(input_shape[0] * input_shape[1] * k, axis=0).flatten()
return input[dim0, dim1, dim2, dim3].reshape(
(input_shape[0], input_shape[1], k, input_shape[3]))
def apply(self, x, y, prefix):
W, = self.parameters
mask = tensor.alloc(0, y.shape[0] * self.n_classes)
ind = tensor.arange(y.shape[0]) * self.n_classes + y
mask = tensor.set_subtensor(mask[ind], 1.0)
mask = tensor.reshape(mask, (y.shape[0], self.n_classes))
#Compute distance matrix
D = ((W**2).sum(axis=1, keepdims=True).T +
(x**2).sum(axis=1, keepdims=True) - 2 * tensor.dot(x, W.T))
self.add_auxiliary_variable(D, name=prefix + '_D')
d_correct = tensor.reshape(D[mask.nonzero()], (y.shape[0], 1))
d_incorrect = tensor.reshape(D[(1.0 - mask).nonzero()],
(y.shape[0], self.n_classes - 1))
c = (d_correct - d_incorrect) / (d_correct + d_incorrect)
c_sorted = tensor.sort(c, axis=1)[:, ::-1]
c = (self.weighting * c_sorted).sum(axis=1, keepdims=True)
self.add_auxiliary_variable(c, name=prefix + '_cost')
if self.nonlin:
c = tensor.exp(self.gamma * c)
cost = c.mean()
misclass = (tensor.switch(c_sorted[:, 0] < 0, 0.0, 1.0)).mean()
return cost, misclass
def dynamic_kmaxPooling(self, curConv_out, k):
neighborsForPooling = TSN.images2neibs(ten4=curConv_out, neib_shape=(1,curConv_out.shape[3]), mode='ignore_borders')
self.neighbors = neighborsForPooling
neighborsArgSorted = T.argsort(neighborsForPooling, axis=1)
kNeighborsArg = neighborsArgSorted[:,-k:]
#self.bestK = kNeighborsArg
kNeighborsArgSorted = T.sort(kNeighborsArg, axis=1)
ii = T.repeat(T.arange(neighborsForPooling.shape[0]), k)
jj = kNeighborsArgSorted.flatten()
pooledkmaxTmp = neighborsForPooling[ii, jj]
new_shape = T.cast(T.join(0,
T.as_tensor([neighborsForPooling.shape[0]]),
T.as_tensor([k])),
'int64')
pooledkmax_matrix = T.reshape(pooledkmaxTmp, new_shape, ndim=2)
rightWidth=self.unifiedWidth-k
right_padding = T.zeros((neighborsForPooling.shape[0], rightWidth), dtype=theano.config.floatX)
matrix_padded = T.concatenate([pooledkmax_matrix, right_padding], axis=1)
#recover tensor form
new_shape = T.cast(T.join(0, curConv_out.shape[:-2],
T.as_tensor([curConv_out.shape[2]]),
T.as_tensor([self.unifiedWidth])),
'int64')
curPooled_out = T.reshape(matrix_padded, new_shape, ndim=4)
return curPooled_out
def __call__(self, X):
XY = X.dot(X.T)
x2 = tt.sum(X ** 2, axis=1).dimshuffle(0, 'x')
X2e = tt.repeat(x2, X.shape[0], axis=1)
H = X2e + X2e.T - 2. * XY
V = tt.sort(H.flatten())
length = V.shape[0]
# median distance
m = tt.switch(tt.eq((length % 2), 0),
# if even vector
tt.mean(V[((length // 2) - 1):((length // 2) + 1)]),
# if odd vector
V[length // 2])
h = .5 * m / tt.log(floatX(H.shape[0]) + floatX(1))
# RBF
Kxy = tt.exp(-H / h / 2.0)
# Derivative
dxkxy = -tt.dot(Kxy, X)
sumkxy = tt.sum(Kxy, axis=1).dimshuffle(0, 'x')
dxkxy = tt.add(dxkxy, tt.mul(X, sumkxy)) / h
return Kxy, dxkxy
def kmaxpooling_output(input):
'''
实现 k-max pooling
1. 先排序
2. 再分别取出前k个值
:param k: k top higiest value
:type k: int
:return:
'''
input = T.transpose(input, axes=(0, 1, 3, 2))
sorted_values = T.argsort(input, axis=3)
topmax_indexes = sorted_values[:, :, :, -k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
# given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0, input.shape[0]).repeat(input.shape[1] *
input.shape[2] * k)
dim1 = T.arange(0,
input.shape[1]).repeat(k * input.shape[2]).reshape(
(1, -1)).repeat(input.shape[0],
axis=0).flatten()
dim2 = T.arange(0, input.shape[2]).repeat(k).reshape(
(1, -1)).repeat(input.shape[0] * input.shape[1],
axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
return T.transpose(input[dim0, dim1, dim2, dim3].reshape(
(input.shape[0], input.shape[1], input.shape[2], k)),
axes=(0, 1, 3, 2))
def k_max_pool(self, x, k):
"""
perform k-max pool on the input along the rows
input: theano.tensor.tensor4
k: theano.tensor.iscalar
the k parameter
Returns:
4D tensor
"""
x = T.reshape(x, (x.shape[0], x.shape[1], 1, x.shape[2] * x.shape[3]))
ind = T.argsort(x, axis=3)
sorted_ind = T.sort(ind[:, :, :, -k:], axis=3)
dim0, dim1, dim2, dim3 = sorted_ind.shape
indices_dim0 = T.arange(dim0).repeat(dim1 * dim2 * dim3)
indices_dim1 = (
T.arange(dim1).repeat(dim2 * dim3).reshape((dim1 * dim2 * dim3, 1)).repeat(dim0, axis=1).T.flatten()
)
indices_dim2 = T.arange(dim2).repeat(dim3).reshape((dim2 * dim3, 1)).repeat(dim0 * dim1, axis=1).T.flatten()
result = x[indices_dim0, indices_dim1, indices_dim2, sorted_ind.flatten()].reshape(sorted_ind.shape)
shape = (result.shape[0], result.shape[1], result.shape[2] * result.shape[3], 1)
result = T.reshape(result, shape)
return result
def softmax(self, D, I):
D = D * T.constant(self.attrs['sharpening'], 'float32')
if self.attrs['norm'] == 'exp':
E = T.exp(-D) * I
E = E / T.maximum(T.sum(E,axis=0,keepdims=True),T.constant(1e-20,'float32'))
elif self.attrs['norm'] == 'sigmoid':
E = (numpy.float32(1) - T.tanh(D)**2) * I
elif self.attrs['norm'] == 'lstm':
n_out = self.attrs['template']
def lstm(z, i_t, s_p, h_p):
z += T.dot(h_p, self.N_re)
i = T.outer(i_t, T.alloc(numpy.cast['int8'](1), n_out))
ingate = T.nnet.sigmoid(z[:,n_out: 2 * n_out])
forgetgate = T.nnet.sigmoid(z[:,2 * n_out:3 * n_out])
outgate = T.nnet.sigmoid(z[:,3 * n_out:])
input = T.tanh(z[:,:n_out])
s_t = input * ingate + s_p * forgetgate
h_t = T.tanh(s_t) * outgate
return theano.gradient.grad_clip(s_t * i, -50, 50), h_t * i
E, _ = theano.scan(lstm, sequences=[D,I], outputs_info=[T.zeros((n_out,), 'float32'), T.zeros((n_out,), 'int32')])
E = T.nnet.sigmoid(T.dot(E,self.N_out))
else:
raise NotImplementedError()
if self.attrs['nbest'] > 1:
opt = T.minimum(self.attrs['nbest'], E.shape[0])
score = (T.sort(E, axis=0)[-opt]).dimshuffle('x',0).repeat(E.shape[0],axis=0)
E = T.switch(T.lt(E,score), T.zeros_like(E), E)
return E
def _pooling_function(self, inputs, pool_size, strides, border_mode, dim_ordering):
if pool_size[0]<-1:
# k-max pooling
input_layer = T.transpose(inputs, axes=(0, 1, 3, 2))
sorted_values = T.argsort(input_layer, axis=3)
topmax_indexes = sorted_values[:, :, :, -self.k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
# given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0, input_layer.shape[0]).repeat(input_layer.shape[1] * input_layer.shape[2] * self.k)
dim1 = T.arange(0, input_layer.shape[1]).repeat(self.k * input_layer.shape[2]).reshape((1, -1)).repeat(
input_layer.shape[0],
axis=0).flatten()
dim2 = T.arange(0, input_layer.shape[2]).repeat(self.k).reshape((1, -1)).repeat(
input_layer.shape[0] * input_layer.shape[1],
axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
x = T.transpose(
input_layer[dim0, dim1, dim2, dim3].reshape(
(input_layer.shape[0], input_layer.shape[1], input_layer.shape[2], self.k)),
axes=(0, 1, 3, 2))
return x
else:
return super(MaxPooling2DWrapper, self)._pooling_function(inputs, pool_size, strides, border_mode, dim_ordering)
def kmaxpooling_output(input):
'''
实现 k-max pooling
1. 先排序
2. 再分别取出前k个值
:param k: k top higiest value
:type k: int
:return:
'''
input = T.transpose(input, axes=(0, 1, 3, 2))
sorted_values = T.argsort(input, axis=3)
topmax_indexes = sorted_values[:, :, :, -k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
# given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0, input.shape[0]).repeat(input.shape[1] * input.shape[2] * k)
dim1 = T.arange(0, input.shape[1]).repeat(k * input.shape[2]).reshape((1, -1)).repeat(input.shape[0],
axis=0).flatten()
dim2 = T.arange(0, input.shape[2]).repeat(k).reshape((1, -1)).repeat(input.shape[0] * input.shape[1],
axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
return T.transpose(
input[dim0, dim1, dim2, dim3].reshape((input.shape[0], input.shape[1], input.shape[2], k)),
axes=(0, 1, 3, 2))
def output(self, x, index_selection_func=None):
if self.n_out > 1:
iWin = self.k
if self.n_in == 1:
iWin = 1
rnd_proj = T.dot(
x.reshape((x.shape[0], x.shape[1]*x.shape[2])),
self.rand_proj_mat
)
if index_selection_func is not None:
self.out_idxs = index_selection_func(rnd_proj)
else:
self.out_idxs = T.argsort(rnd_proj)
self.out_idxs = T.sort(self.out_idxs[:, -self.k:])
# self.out_idxs.set_value(
# np.random.randint(0, self.n_out, (self.batch_size, self.k))
# )
sparse = sparse_block_dot_SS(
self.W,
x,
self.in_idxs,
self.b,
self.out_idxs
)
return (sparse if self.activation is None
else self.activation(sparse))
def link(self, input):
self.input = input
# select the lines where we apply k-max pooling
neighbors_for_pooling = TSN.images2neibs(
ten4=self.input,
neib_shape=(self.input.shape[2], 1), # we look the max on every dimension
mode='valid' # 'ignore_borders'
)
neighbors_arg_sorted = T.argsort(neighbors_for_pooling, axis=1)
k_neighbors_arg = neighbors_arg_sorted[:, -self.k_max:]
k_neighbors_arg_sorted = T.sort(k_neighbors_arg, axis=1)
ii = T.repeat(T.arange(neighbors_for_pooling.shape[0]), self.k_max)
jj = k_neighbors_arg_sorted.flatten()
flattened_pooled_out = neighbors_for_pooling[ii, jj]
pooled_out_pre_shape = T.join(
0,
self.input.shape[:-2],
[self.input.shape[3]],
[self.k_max]
)
self.output = flattened_pooled_out.reshape(
pooled_out_pre_shape,
ndim=self.input.ndim
).dimshuffle(0, 1, 3, 2)
return self.output
def _step(x, k, max_seq_len):
tmp = x[
T.arange(x.shape[0])[:, np.newaxis, np.newaxis],
T.sort(T.argsort(x, axis=1)[:, -k:, :], axis=1),
T.arange(x.shape[2])[np.newaxis, np.newaxis,:],
]
return T.concatenate([tmp, T.zeros([x.shape[0], max_seq_len-k, x.shape[2]])], axis=1)
def dynamic_kmaxPooling(self, curConv_out, k):
neighborsForPooling = TSN.images2neibs(
ten4=curConv_out,
neib_shape=(1, curConv_out.shape[3]),
mode='ignore_borders')
self.neighbors = neighborsForPooling
neighborsArgSorted = T.argsort(neighborsForPooling, axis=1)
kNeighborsArg = neighborsArgSorted[:, -k:]
#self.bestK = kNeighborsArg
kNeighborsArgSorted = T.sort(kNeighborsArg, axis=1)
ii = T.repeat(T.arange(neighborsForPooling.shape[0]), k)
jj = kNeighborsArgSorted.flatten()
pooledkmaxTmp = neighborsForPooling[ii, jj]
new_shape = T.cast(
T.join(0, T.as_tensor([neighborsForPooling.shape[0]]),
T.as_tensor([k])), 'int64')
pooledkmax_matrix = T.reshape(pooledkmaxTmp, new_shape, ndim=2)
rightWidth = self.unifiedWidth - k
right_padding = T.zeros((neighborsForPooling.shape[0], rightWidth),
dtype=theano.config.floatX)
matrix_padded = T.concatenate([pooledkmax_matrix, right_padding],
axis=1)
#recover tensor form
new_shape = T.cast(
T.join(0, curConv_out.shape[:-2],
T.as_tensor([curConv_out.shape[2]]),
T.as_tensor([self.unifiedWidth])), 'int64')
curPooled_out = T.reshape(matrix_padded, new_shape, ndim=4)
return curPooled_out
def softmax(self, D, I):
D = D * T.constant(self.attrs['sharpening'], 'float32')
if self.attrs['norm'] == 'exp':
E = T.exp(-D) * I
E = E / T.maximum(T.sum(E,axis=0,keepdims=True),T.constant(1e-20,'float32'))
elif self.attrs['norm'] == 'sigmoid':
E = (numpy.float32(1) - T.tanh(D)**2) * I
elif self.attrs['norm'] == 'lstm':
n_out = self.attrs['template']
def lstm(z, i_t, s_p, h_p):
z += T.dot(h_p, self.N_re)
i = T.outer(i_t, T.alloc(numpy.cast['int8'](1), n_out))
ingate = T.nnet.sigmoid(z[:,n_out: 2 * n_out])
forgetgate = T.nnet.sigmoid(z[:,2 * n_out:3 * n_out])
outgate = T.nnet.sigmoid(z[:,3 * n_out:])
input = T.tanh(z[:,:n_out])
s_t = input * ingate + s_p * forgetgate
h_t = T.tanh(s_t) * outgate
return theano.gradient.grad_clip(s_t * i, -50, 50), h_t * i
E, _ = theano.scan(lstm, sequences=[D,I], outputs_info=[T.zeros((n_out,), 'float32'), T.zeros((n_out,), 'int32')])
E = T.nnet.sigmoid(T.dot(E,self.N_out))
else:
raise NotImplementedError()
if self.attrs['nbest'] > 1:
opt = T.minimum(self.attrs['nbest'], E.shape[0])
score = (T.sort(E, axis=0)[-opt]).dimshuffle('x',0).repeat(E.shape[0],axis=0)
E = T.switch(T.lt(E,score), T.zeros_like(E), E)
return E
def beam_search_forward(self, col, score, embedding, pre_hid_info,
s_embedding):
n = col.shape[0]
input_info = T.concatenate([embedding, col, pre_hid_info], axis=-1)
u1 = get_output(self.gru_update_3, input_info)
r1 = get_output(self.gru_reset_3, input_info)
reset_h1 = pre_hid_info * r1
c_in = T.concatenate([embedding, col, reset_h1], axis=1)
c1 = get_output(self.gru_candidate_3, c_in)
h1 = (1.0 - u1) * pre_hid_info + u1 * c1
s = get_output(self.score, h1)
sample_score = T.dot(s, s_embedding.T)
k = sample_score.shape[-1]
sample_score = sample_score.reshape((n, 1, k))
sample_score += score
sample_score = sample_score.reshape((n, 10 * k))
sort_index = T.argsort(-sample_score, axis=-1)
sample_score = T.sort(-sample_score, axis=-1)
tops = sort_index[:, :10]
sample_score = -sample_score[:, :10]
tops = T.cast(T.divmod(tops, self.target_vocab_size), "int8")
embedding = get_output(self.target_input_embedding, tops)
d = embedding.shape[-1]
embedding = embedding.reshape((n * 10, d))
return sample_score, embedding, h1, tops
def k_max_pool(self, x, k):
"""
perform k-max pool on the input along the rows
input: theano.tensor.tensor4
k: theano.tensor.iscalar
the k parameter
Returns:
4D tensor
"""
ind = T.argsort(x, axis=3)
sorted_ind = T.sort(ind[:, :, :, -k:], axis=3)
dim0, dim1, dim2, dim3 = sorted_ind.shape
indices_dim0 = T.arange(dim0).repeat(dim1 * dim2 * dim3)
indices_dim1 = T.arange(dim1).repeat(dim2 * dim3).reshape(
(dim1 * dim2 * dim3, 1)).repeat(dim0, axis=1).T.flatten()
indices_dim2 = T.arange(dim2).repeat(dim3).reshape(
(dim2 * dim3, 1)).repeat(dim0 * dim1, axis=1).T.flatten()
return x[indices_dim0, indices_dim1, indices_dim2,
sorted_ind.flatten()].reshape(sorted_ind.shape)
def update_active_paths(self, l, scores, active_paths_current, N,
beam_size):
best_scores_l_all = T.max(scores, axis=1) # N * D
best_scores_l = T.sort(best_scores_l_all,
axis=-1)[:, -beam_size:] # N * B
active_words_l = T.argsort(best_scores_l_all,
axis=1)[:, -beam_size:] # N * B
best_paths_l_all = T.argmax(scores, axis=1) # N * D
best_paths_l_inds = best_paths_l_all[T.repeat(T.arange(N), beam_size),
active_words_l.flatten()]
best_paths_l_inds = best_paths_l_inds.reshape((N, beam_size)) # N * B
best_paths_l = active_paths_current[
T.repeat(T.arange(N), beam_size),
best_paths_l_inds.flatten()].reshape(
(N, beam_size, self.max_length)) # N * B * L
active_paths_new = T.set_subtensor(best_paths_l[:, :, l],
active_words_l)
return best_scores_l, active_paths_new
def _pooling_function(self, inputs, pool_size, strides, border_mode,
dim_ordering):
if pool_size[0] < -1:
# k-max pooling
input_layer = T.transpose(inputs, axes=(0, 1, 3, 2))
sorted_values = T.argsort(input_layer, axis=3)
topmax_indexes = sorted_values[:, :, :, -self.k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
# given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0, input_layer.shape[0]).repeat(
input_layer.shape[1] * input_layer.shape[2] * self.k)
dim1 = T.arange(0, input_layer.shape[1]).repeat(
self.k * input_layer.shape[2]).reshape(
(1, -1)).repeat(input_layer.shape[0], axis=0).flatten()
dim2 = T.arange(0, input_layer.shape[2]).repeat(self.k).reshape(
(1, -1)).repeat(input_layer.shape[0] * input_layer.shape[1],
axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
x = T.transpose(input_layer[dim0, dim1, dim2, dim3].reshape(
(input_layer.shape[0], input_layer.shape[1],
input_layer.shape[2], self.k)),
axes=(0, 1, 3, 2))
return x
else:
return super(MaxPooling2DWrapper,
self)._pooling_function(inputs, pool_size, strides,
border_mode, dim_ordering)
def apply(self, x, y, prefix):
W, = self.parameters
mask = tensor.alloc(0, y.shape[0]*self.n_classes)
ind = tensor.arange(y.shape[0])*self.n_classes + y
mask = tensor.set_subtensor(mask[ind], 1.0)
mask = tensor.reshape(mask, (y.shape[0], self.n_classes))
#Compute distance matrix
D = ((W**2).sum(axis=1, keepdims=True).T + (x**2).sum(axis=1, keepdims=True) - 2*tensor.dot(x, W.T))
self.add_auxiliary_variable(D, name=prefix+'_D')
d_correct = tensor.reshape(D[mask.nonzero()], (y.shape[0], 1))
d_incorrect = tensor.reshape(D[(1.0-mask).nonzero()], (y.shape[0], self.n_classes-1))
c = (d_correct - d_incorrect)/(d_correct + d_incorrect)
c_sorted = tensor.sort(c, axis=1)[:, ::-1]
c = (self.weighting*c_sorted).sum(axis=1, keepdims=True)
self.add_auxiliary_variable(c, name=prefix+'_cost')
if self.nonlin:
c = tensor.exp(self.gamma*c)
cost = c.mean()
misclass = (tensor.switch(c_sorted[:, 0] < 0, 0.0, 1.0)).mean()
return cost, misclass
def get_stencil(self, t, r=None, texp=None):
if r is None or texp is None:
return tt.shape_padright(t)
z = tt.zeros_like(self.a)
r = tt.as_tensor_variable(r)
R = self.r_star + z
hp = 0.5 * self.period
if self.ecc is None:
# Equation 14 from Winn (2010)
k = r / self.r_star
arg1 = tt.square(1 + k) - tt.square(self.b)
arg2 = tt.square(1 - k) - tt.square(self.b)
factor = R / (self.a * self.sin_incl)
hdur1 = hp * tt.arcsin(factor * tt.sqrt(arg1)) / np.pi
hdur2 = hp * tt.arcsin(factor * tt.sqrt(arg2)) / np.pi
ts = [-hdur1, -hdur2, hdur2, hdur1]
flag = z
else:
M_contact1 = self.contact_points_op(self.a, self.ecc,
self.cos_omega, self.sin_omega,
self.cos_incl + z,
self.sin_incl + z, R + r)
M_contact2 = self.contact_points_op(self.a, self.ecc,
self.cos_omega, self.sin_omega,
self.cos_incl + z,
self.sin_incl + z, R - r)
flag = M_contact1[2] + M_contact2[2]
ts = [
tt.mod(
(M_contact1[0] - self.M0) / self.n + hp, self.period) - hp,
tt.mod(
(M_contact2[0] - self.M0) / self.n + hp, self.period) - hp,
tt.mod(
(M_contact2[1] - self.M0) / self.n + hp, self.period) - hp,
tt.mod(
(M_contact1[1] - self.M0) / self.n + hp, self.period) - hp
]
start = self.period * tt.floor((tt.min(t) - self.t0) / self.period)
end = self.period * (tt.ceil((tt.max(t) - self.t0) / self.period) + 1)
start += self.t0
end += self.t0
tout = []
for i in range(4):
if z.ndim < 1:
tout.append(ts[i] + tt.arange(start, end, self.period))
else:
tout.append(
theano.scan(
fn=lambda t0, s0, e0, p0: t0 + tt.arange(s0, e0, p0),
sequences=[ts[i], start, end, self.period],
)[0].flatten())
ts = tt.sort(tt.concatenate(tout))
return ts, flag
def dog_output(self, input_image):
_, self.channels, self.height, self.width = input_image.shape
conv_output = T.nnet.conv2d(input_image,
dog_W,
filter_flip=False,
border_mode='half',
subsample=(1, 1))
conv_output2 = conv_output[:, ::-1, :, :]
dog_maps = conv_output - conv_output2
dog_maps = T.ge(dog_maps, 0) * dog_maps
dog_maps = T.switch(T.ge(dog_maps, T.mean(dog_maps)), dog_maps, 0.0)
#dog_maps=T.set_subtensor(dog_maps[:,1:2,:,:],np.float32(0.0))
sorted_dog = T.sort(T.reshape(dog_maps, (-1, )))
#sorted_dog=T.shape(sorted_dog)-T.sum(T.neq(sorted_dog,0.0))
num_spikes = T.neq(sorted_dog, 0.0)
num_spikes_per_bin = T.sum(num_spikes) // self.time_steps
i = T.shape(sorted_dog)[0] - num_spikes_per_bin
bin_limits = T.zeros(self.time_steps + 1)
bin_limits = T.set_subtensor(bin_limits[0], sorted_dog[-1])
for j in range(0, self.time_steps):
bin_limits = T.set_subtensor(bin_limits[j + 1], sorted_dog[i])
i = i - num_spikes_per_bin
#return dog_maps,sorted_dog,bin_limits
#return self.temporal_encoding(dog_maps,bin_limits)
return T.reshape(
self.temporal_encoding(dog_maps, bin_limits) * np.float32(1.0), [
self.time_steps, self.batch_size, self.channels * 2,
self.height, self.width
])
def __call__(self, X):
XY = X.dot(X.T)
x2 = tt.sum(X**2, axis=1).dimshuffle(0, 'x')
X2e = tt.repeat(x2, X.shape[0], axis=1)
H = X2e + X2e.T - 2. * XY
V = tt.sort(H.flatten())
length = V.shape[0]
# median distance
m = tt.switch(
tt.eq((length % 2), 0),
# if even vector
tt.mean(V[((length // 2) - 1):((length // 2) + 1)]),
# if odd vector
V[length // 2])
h = .5 * m / tt.log(floatX(H.shape[0]) + floatX(1))
# RBF
Kxy = tt.exp(-H / h / 2.0)
# Derivative
dxkxy = -tt.dot(Kxy, X)
sumkxy = tt.sum(Kxy, axis=-1, keepdims=True)
dxkxy = tt.add(dxkxy, tt.mul(X, sumkxy)) / h
return Kxy, dxkxy
def link(self, input):
self.input = input.dimshuffle(0, 1, 3, 2)
# get the indexes that give the max on every line and sort them
ind = T.argsort(self.input, axis=3)
sorted_ind = T.sort(ind[:, :, :, -self.k_max:], axis=3)
dim0, dim1, dim2, dim3 = sorted_ind.shape
# prepare indices for selection
indices_dim0 = T.arange(dim0)\
.repeat(dim1 * dim2 * dim3)
indices_dim1 = T.arange(dim1)\
.repeat(dim2 * dim3)\
.reshape((dim1 * dim2 * dim3, 1))\
.repeat(dim0, axis=1)\
.T\
.flatten()
indices_dim2 = T.arange(dim2)\
.repeat(dim3)\
.reshape((dim2 * dim3, 1))\
.repeat(dim0 * dim1, axis=1)\
.T\
.flatten()
# output
self.output = self.input[
indices_dim0,
indices_dim1,
indices_dim2,
sorted_ind.flatten()
].reshape(sorted_ind.shape).dimshuffle(0, 1, 3, 2)
return self.output
def kite_area(r, b):
abc = tt.sort(tt.stack((r, b, tt.ones_like(r)), axis=0), axis=0)
A = abc[0]
B = abc[1]
C = abc[2]
return 0.5 * tt.sqrt(
tt.abs_((A + (B + C)) * (C - (A - B)) * (C + (A - B)) * (A + (B - C))))
def fix_k_max(self, k, masked_data):
# @ref: https://github.com/fchollet/keras/issues/373
result = masked_data[
T.arange(masked_data.shape[0]).dimshuffle(0, "x", "x"),
T.sort(T.argsort(masked_data, axis=1)[:, -k:, :], axis=1),
T.arange(masked_data.shape[2]).dimshuffle("x", "x", 0)
]
return result
def hinge_cost(self, y, delta=0.1, outputs=T.arange(26)):
c = self.p_y_given_x[T.arange(y.shape[0]), y]
p_y_given_x_temp = T.sort(self.p_y_given_x)
a = p_y_given_x_temp[:, -1]
b = p_y_given_x_temp[:, -2]
cost = T.mean(T.maximum(0, T.sub(2 * c, T.add(a, b)) + delta))
return cost
def __call__(self,X):
ind = T.argsort(X, axis = 3)
sorted_ind = T.sort(ind[:,:,:, -self.poolsize:], axis = 3)
dim0, dim1, dim2, dim3 = sorted_ind.shape
indices_dim0 = T.arange(dim0).repeat(dim1 * dim2 * dim3)
indices_dim1 = T.arange(dim1).repeat(dim2 * dim3).reshape((dim1*dim2*dim3, 1)).repeat(dim0, axis=1).T.flatten()
indices_dim2 = T.arange(dim2).repeat(dim3).reshape((dim2*dim3, 1)).repeat(dim0 * dim1, axis = 1).T.flatten()
return X[indices_dim0, indices_dim1, indices_dim2, sorted_ind.flatten()].reshape(sorted_ind.shape)
def median(grid_split_image, grid_counts):
shp = grid_split_image.shape
reshape = grid_split_image.reshape((shp[0], shp[1], shp[2], -1), ndim=4)
img_sort = T.sort(reshape)
pixels = img_sort.shape[-1]
indicies = (pixels + grid_counts) // 2
indicies = T.cast(indicies, 'int32')
medians = img_sort.take(indicies)
return medians
def _global_rank_pooling(input, **kwargs):
[n_songs, n_fmaps, n_time, n_freq] = input.shape
input2d = input.reshape((n_songs*n_fmaps, n_time*n_freq))
weight1d = kwargs['weight1d']
out2d = T.sort(input2d, axis=-1)[:, ::-1] * weight1d / T.sum(weight1d)
out4d = out2d.reshape((n_songs, n_fmaps, n_time, n_freq))
return T.sum(out4d, axis=(2,3))
def median(grid_split_image, grid_counts):
shp = grid_split_image.shape
reshape = grid_split_image.reshape((shp[0], shp[1], shp[2], -1), ndim=4)
img_sort = T.sort(reshape)
pixels = img_sort.shape[-1]
indicies = (pixels + grid_counts) // 2
indicies = T.cast(indicies, 'int32')
medians = img_sort.take(indicies)
return medians
def _k_max_pooling(input, kmax): pool = input.dimshuffle(0, 2, 1, 3).flatten(ndim=3).dimshuffle(1,0,2).flatten(ndim=2).dimshuffle(1,0) neighborsArgSorted = T.argsort(pool, axis=1) yy = T.sort(neighborsArgSorted[:, -kmax:], axis=1).flatten() xx = T.repeat(T.arange(neighborsArgSorted.shape[0]), kmax) pool_kmax = pool[xx, yy] pool_kmax_shape = T.join(0, T.as_tensor([input.shape[0], input.shape[1], input.shape[3], kmax])) pooled_out = pool_kmax.reshape(pool_kmax_shape, ndim=4).dimshuffle(0, 1, 3, 2) return pooled_out
def sort():
# cands = T.itensor3('cands')
cands = T.imatrix('cands')
# h_in = np.asarray([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype='int32')
# h_in = np.asarray([[1, 2], [3, 4]], dtype='int32')
h_in = np.asarray([[4, 2], [3, 1]], dtype='int32')
y = T.sort(cands, axis=1)
f = theano.function(inputs=[cands], outputs=[y], on_unused_input='ignore')
print f(h_in)
def get_stencil(self, t, r=None, texp=None):
if r is None or texp is None:
return tt.shape_padright(t)
z = tt.zeros_like(self.a)
r = tt.as_tensor_variable(r)
R = self.r_star + z
hp = 0.5 * self.period
if self.ecc is None:
# Equation 14 from Winn (2010)
k = r / self.r_star
arg1 = tt.square(1 + k) - tt.square(self.b)
arg2 = tt.square(1 - k) - tt.square(self.b)
factor = R / (self.a * self.sin_incl)
hdur1 = hp * tt.arcsin(factor * tt.sqrt(arg1)) / np.pi
hdur2 = hp * tt.arcsin(factor * tt.sqrt(arg2)) / np.pi
ts = [-hdur1, -hdur2, hdur2, hdur1]
flag = z
else:
M_contact1 = self.contact_points_op(
self.a, self.ecc, self.cos_omega, self.sin_omega,
self.cos_incl + z, self.sin_incl + z, R + r)
M_contact2 = self.contact_points_op(
self.a, self.ecc, self.cos_omega, self.sin_omega,
self.cos_incl + z, self.sin_incl + z, R - r)
flag = M_contact1[2] + M_contact2[2]
ts = [
tt.mod((M_contact1[0]-self.M0)/self.n+hp, self.period)-hp,
tt.mod((M_contact2[0]-self.M0)/self.n+hp, self.period)-hp,
tt.mod((M_contact2[1]-self.M0)/self.n+hp, self.period)-hp,
tt.mod((M_contact1[1]-self.M0)/self.n+hp, self.period)-hp
]
start = self.period * tt.floor((tt.min(t) - self.t0) / self.period)
end = self.period * (tt.ceil((tt.max(t) - self.t0) / self.period) + 1)
start += self.t0
end += self.t0
tout = []
for i in range(4):
if z.ndim < 1:
tout.append(ts[i] + tt.arange(start, end, self.period))
else:
tout.append(theano.scan(
fn=lambda t0, s0, e0, p0: t0 + tt.arange(s0, e0, p0),
sequences=[ts[i], start, end, self.period],
)[0].flatten())
ts = tt.sort(tt.concatenate(tout))
return ts, flag
def kmaxPool(self, conv_out, pool_shape, k):
'''
Perform k-max Pooling.
'''
n0, n1, d, size = pool_shape
imgs = images2neibs(conv_out, T.as_tensor_variable((1, size)))
indices = T.argsort(T.mul(imgs, -1))
k_max_indices = T.sort(indices[:, :k])
S = T.arange(d*n1*n0).reshape((d*n1*n0, 1))
return imgs[S, k_max_indices].reshape((n0, n1, d, k))
def __init__(self, conv_out, k=1):
"""
Allocate a LeNetConvPoolLayer with shared variable internal parameters.
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height,filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows,#cols)
"""
#images2neibs produces a 2D matrix
neighborsForPooling = TSN.images2neibs(ten4=conv_out, neib_shape=(conv_out.shape[2], 1), mode='ignore_borders')
#k = poolsize[1]
neighborsArgSorted = T.argsort(neighborsForPooling, axis=1)
kNeighborsArg = neighborsArgSorted[:,-k:]
kNeighborsArgSorted = T.sort(kNeighborsArg, axis=1)
ii = T.repeat(T.arange(neighborsForPooling.shape[0]), k)
jj = kNeighborsArgSorted.flatten()
pooledkmaxTmp = neighborsForPooling[ii, jj]
# reshape pooledkmaxTmp
new_shape = T.cast(T.join(0, conv_out.shape[:-2],
T.as_tensor([conv_out.shape[3]]),
T.as_tensor([k])),
'int32')
pooled_out = T.reshape(pooledkmaxTmp, new_shape, ndim=4)
# downsample each feature map individually, using maxpooling
'''
pooled_out = downsample.max_pool_2d(input=conv_out,
ds=poolsize, ignore_border=True)
'''
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1,n_filters,1,1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
self.output = T.tanh(pooled_out)
def setup(self, bottom, top):
if len(bottom) != 2:
raise Exception("The layer needs two inputs!")
probs_tmp = T.ftensor4()
stat_inp = T.ftensor4()
stat = stat_inp[:, :, :, 1:]
probs_bg = probs_tmp[:, 0, :, :]
probs = probs_tmp[:, 1:, :, :]
probs_max = T.max(probs, axis=3).max(axis=2)
q_fg = 0.996
probs_sort = T.sort(probs.reshape((-1, 20, 41 * 41)), axis=2)
weights = np.array([q_fg ** i for i in range(41 * 41 - 1, -1, -1)])[None, None, :]
Z_fg = np.sum(weights)
weights = T.addbroadcast(theano.shared(weights), 0, 1)
probs_mean = T.sum((probs_sort * weights) / Z_fg, axis=2)
q_bg = 0.999
probs_bg_sort = T.sort(probs_bg.reshape((-1, 41 * 41)), axis=1)
weights_bg = np.array([q_bg ** i for i in range(41 * 41 - 1, -1, -1)])[None, :]
Z_bg = np.sum(weights_bg)
weights_bg = T.addbroadcast(theano.shared(weights_bg), 0)
probs_bg_mean = T.sum((probs_bg_sort * weights_bg) / Z_bg, axis=1)
stat_2d = stat[:, 0, 0, :] > 0.5
loss_1 = -T.mean(T.sum((stat_2d * T.log(probs_mean) / T.sum(stat_2d, axis=1, keepdims=True)), axis=1))
loss_2 = -T.mean(T.sum(((1 - stat_2d) * T.log(1 - probs_max) / T.sum(1 - stat_2d, axis=1, keepdims=True)), axis=1))
loss_3 = -T.mean(T.log(probs_bg_mean))
loss = loss_1 + loss_2 + loss_3
self.forward_theano = theano.function([probs_tmp, stat_inp], loss)
self.backward_theano = theano.function([probs_tmp, stat_inp], T.grad(loss, probs_tmp))
def kmaxpooling(self,input,k):
sorted_values = T.argsort(input,axis=3)
topmax_indexes = sorted_values[:,:,:,-k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
#given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0,self.input_shape[0]).repeat(self.input_shape[1]*self.input_shape[2]*k)
dim1 = T.arange(0,self.input_shape[1]).repeat(k*self.input_shape[2]).reshape((1,-1)).repeat(self.input_shape[0],axis=0).flatten()
dim2 = T.arange(0,self.input_shape[2]).repeat(k).reshape((1,-1)).repeat(self.input_shape[0]*self.input_shape[1],axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
return input[dim0,dim1,dim2,dim3].reshape((self.input_shape[0], self.input_shape[1], self.input_shape[2], k))
def softmax(self, D, I):
D = D * T.constant(self.attrs['sharpening'], 'float32')
if self.attrs['norm'] == 'exp':
E = T.exp(-D)
elif self.attrs['norm'] == 'sigmoid':
E = T.nnet.sigmoid(D)
else:
raise NotImplementedError()
E = E * I
if self.attrs['nbest'] > 0:
opt = T.minimum(self.attrs['nbest'], E.shape[0])
score = (T.sort(E, axis=0)[-opt]).dimshuffle('x',0).repeat(E.shape[0],axis=0)
E = T.switch(T.lt(E,score), T.zeros_like(E), E)
return E / T.maximum(T.sum(E,axis=0,keepdims=True),T.constant(1e-20,'float32'))
def calculate_cost(self):
output1 = self.model1.output
output2 = self.model2.output
#Median loss with cross-entropy
distances = ((output1 - output2) ** 2).sum(axis=1)
sorted_distances = T.sort(distances)
median = (sorted_distances[BATCH_SIZE/2] + sorted_distances[BATCH_SIZE/2 - 1]) / 2.0
p = distances[0:BATCH_SIZE:2] #pairs
q = distances[1:BATCH_SIZE:2] #non-pairs
loss = (T.log(1.0 + T.exp(-60.0*(q - median)))).mean() + \
(T.log(1.0 + T.exp(-60.0*(median - p)))).mean() #cross-entropy
return loss
def k_max_pool(conv, k):
c_shape = conv.shape
# c_shape = tPrint('conv_shape')(c_shape)
pool_size = (1, conv.shape[-1])
neighbors_to_pool = TSN.images2neibs(ten4=conv,
neib_shape=pool_size,
mode='ignore_borders')
arg_sorted = T.argsort(neighbors_to_pool, axis=1)
top_k = arg_sorted[:, -k:]
top_k_sorted = T.sort(top_k, axis=1)
ii = T.repeat(T.arange(neighbors_to_pool.shape[0], dtype='int32'), k)
jj = top_k_sorted.flatten()
values = neighbors_to_pool[ii, jj]
pooled_out = T.reshape(values, (c_shape[0], c_shape[1], c_shape[2], k), ndim=4)
return pooled_out
def k_max_pooling(input, kmax):
nbatches, nchannels, nwords, ndim = input.shape[0], input.shape[1], input.shape[2], input.shape[3]
x = input.dimshuffle(0,1,3,2)
neighborsArgSorted = T.argsort(x, axis=3)
ax0 = T.repeat(T.arange(nbatches), nchannels*ndim*kmax)
ax1 = T.repeat(T.arange(nchannels), ndim * kmax).dimshuffle('x', 0)
ax1 = T.repeat(ax1, nbatches, axis=0).flatten()
ax2 = T.repeat(T.arange(ndim), kmax, axis=0).dimshuffle('x', 'x', 0)
ax2 = T.repeat(ax2, nchannels, axis=1)
ax2 = T.repeat(ax2, nbatches, axis=0).flatten()
ax3 = T.sort(neighborsArgSorted[:,:,:,-kmax:], axis=3).flatten()
pooled_out = x[ax0, ax1, ax2, ax3]
pooled_out = pooled_out.reshape((nbatches, nchannels, ndim, kmax)).dimshuffle(0,1,3,2)
return pooled_out
def extract_ind_app(lookup):
assert isinstance(lookup, LookupTable)
param = lookup.W
branches = VariableFilter(bricks=[lookup], name='output_0')(cg)
assert all([
isinstance(branch.owner.inputs[0].owner.inputs[0].owner.op,
tensor.subtensor.AdvancedSubtensor1)
for branch in branches])
outputs = [branch.owner.inputs[0].owner.inputs[0] for branch in branches]
indices = [branch.owner.inputs[0].owner.inputs[0].owner.inputs[1] for branch in branches]
canonized_indices = tensor.concatenate(indices, axis=0)
canonized_indices = canonized_indices % lookup.length # Replace -1 with lookup.length - 1
canonized_indices = tensor.extra_ops.Unique()(tensor.sort(canonized_indices))
subparam = param[canonized_indices]
return {param: (subparam, canonized_indices, outputs, indices)}
def hard_bootstrapping_binary_crossentropy(pred,
target,
num_taken,
num_skipped=0,
weight=1):
"""
from
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
http://arxiv.org/abs/1604.04339
"""
pixel_loss = treeano.utils.weighted_binary_crossentropy(pred,
target,
weight=weight)
flat_loss = pixel_loss.flatten(2)
sorted_flat_loss = T.sort(flat_loss)
chosen_loss = sorted_flat_loss[:, -(num_taken + num_skipped):-num_skipped]
return chosen_loss
def fprop(self, inputs):
# format inputs
inputs = self.input_space.format_as(inputs, self.mlp.input_space)
rval = self.mlp.fprop(inputs)
if self.pooling_mode == 0:
rval = tensor.max(rval, axis=0)
elif self.pooling_mode == 1:
rval = block_gradient(tensor.sort(rval, axis=0))[-3:][::-1]
rval = tensor.sum(rval * self.probs, axis=0) / 3
#rval = tensor.mean(rval, axis=0)
else:
raise Exception("Others are not implemented yet!")
rval = rval.dimshuffle('x', 0)
rval = self.final_layer.fprop(rval)
return rval