def forward(self, x, add_noise=False):
h = x
if self.blur_k is None:
k = np.asarray([1, 2, 1]).astype('f')
k = k[:, None] * k[None, :]
k = k / np.sum(k)
self.blur_k = self.xp.asarray(k)[None, None, :]
if self.enable_blur:
h = blur(upscale2x(h), self.blur_k)
else:
h = upscale2x(h)
h = self.c0(h)
# h should be (batch, ch, size, size)
if add_noise:
h = self.n0(h)
h = F.leaky_relu(self.b0(h))
h = F.normalize(h)
h = self.c1(h)
if add_noise:
h = self.n1(h)
h = F.leaky_relu(self.b1(h))
h = F.normalize(h)
return h
def __call__(self, x, t):
h = self.base(x, layers=['res5'])['res5']
self.cam = h
h = _global_average_pooling_2d(h)
################################################################################
# ResNet50の後ろにArcFace実装
################################################################################
# --------------------------- cos(theta) & phi(theta) ---------------------------
cosine = F.linear(F.normalize(h), F.normalize(self.weight)) # fc8
sine = F.sqrt(F.clip((1.0 - F.square(cosine)),0, 1))
phi = cosine * cos_m - sine * sin_m
if easy_margin:
phi = F.where(cosine.data > 0, phi, cosine)
else:
phi = F.where(cosine.data > th, phi, cosine - mm)
# --------------------------- convert label to one-hot ---------------------------
one_hot = cp.eye(10)[t].astype(cp.float32)
one_hot = Variable(one_hot)
# -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= s
################################################################################
#h = self.fc(h)
return output
def mp_matching_func(v1, v2, w):
"""
Implementation of m = f_m(v_1, v_2, W).
m_k = cosine(W_k \odot v_1, W_k \odot v_2)
Similar to multi-head attention mechanism
:param v1: (mb, N_1, hidden_dim)
:param v2: (mb, N_1, hidden_dim) or (mb, hidden_size)
:param w: (head, hidden_dim)
:return: m: (mb, N_1, head)
"""
mb, N_1, _ = v1.shape
# w: (hidden_dim, head)
w = F.transpose(w, axes=(1, 0))
# w: (1, 1, hidden_dim, head)
w = F.expand_dims(F.expand_dims(w, axis=0), axis=0)
# v1: (mb, N_1, hidden_dim, head)
v1 = F.tile(w, reps=(mb, N_1, 1, 1)) * F.stack([v1] * self.head, axis=3)
if len(v2.shape) == 3:
v2 = F.tile(w, reps=(mb, N_1, 1, 1)) * F.stack([v2] * self.head, axis=3)
else:
# v2: (mb, hidden_dim) -> (mb, N_1, hidden_dim) -> (mb, N_1, hidden_dim, head)
v2 = F.tile(w, reps=(mb, N_1, 1, 1)) * F.stack([F.stack([v2] * N_1, axis=1)] * self.head, axis=3)
# v1/v2: (mb, N_1, hidden_dim, head)
v1_normed = F.normalize(v1, axis=2)
v2_normed = F.normalize(v2, axis=2)
# (mb, N_1, head, head)
sim = F.matmul(F.transpose(v1_normed, axes=(0, 1, 3, 2)), v2_normed)
# sim: (mb, N_1, head, head) -> (mb, N_1, head)
sim = sim[:, :, :, 0]
return sim
def __call__(self, *args, **kwargs):
with chainer.no_backprop_mode():
ndisp = int(get_arg(args[2])) if len(args) >= 2 else 64
g0 = self._to_gray(args[0])
g1 = self._to_gray(args[1])
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print("layer_1 ...\n")
x0 = self._extract_feature(g0)
x1 = self._extract_feature(g1)
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
# fast mode
if len(self._layers2) == 0:
print("fast mode...")
x0 = F.normalize(x0)
x1 = F.normalize(x1)
#v = -su.reduce_to_vol(x0, x1, ndisp)
v = -su.reduce_to_prodvol(x0, x1, ndisp)
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
return v
# accurate mode
print("accurate mode...")
v = su.reduce_to_vol(
x0, x1, ndisp,
lambda y0, y1: self._looping(F.concat(
(y0, y1), 1), self._layers2, F.relu, F.sigmoid))
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
return v
def __call__(self, x):
bs, channel, width, height = x.shape
assert channel == self.D
N = width * height
# assignment
a = self.wb(x)
a = F.softmax(a)
a = F.reshape(a, (bs, self.K, N))
a = F.stack([a] * self.D, axis=1)
# attention
w = F.relu(self.attn(x))
w = F.reshape(w, (bs, 1, N))
w = F.stack([w] * self.D * self.K)
w = F.reshape(w, (bs, self.D, self.K, N))
x = F.reshape(x, (bs, self.D, N))
x = F.stack([x] * self.K, axis=2)
_c = F.broadcast_to(
F.stack([self.c] * N, axis=2), (bs, self.D, self.K, N))
v = F.sum(w * a * (x - _c), axis=3)
v = F.normalize(v, axis=2)
v = F.reshape(v, (bs, self.D * self.K))
v = F.normalize(v, axis=1)
return v
def __call__(self, x):
# x.shape == (batchsize, 3, 128, 64)
batchsize = x.shape[0]
h = F.elu(self.bn1(self.conv1_1(x)))
h = F.elu(self.bn2(self.conv1_2(h)))
h = F.max_pooling_2d(h, 3, 2, cover_all=False)
h = self.conv2_1(h)
h = self.conv2_3(h)
h = self.conv3_1(h)
h = self.conv3_3(h)
h = self.conv4_1(h)
h = self.conv4_3(h)
h = h.reshape(batchsize, -1)
h = F.dropout(h, ratio=0.6)
h = F.elu(self.fc1_bn(self.fc1(h)))
# Features in rows, normalize axis 1.
weights = self.mean_vectors
features = self.ball(h)
features = F.normalize(features, eps=1e-8)
scale = F.softplus(self.scale)
normalized_weight = F.normalize(weights, axis=0, eps=1e-8)
logits = F.tile(scale[None, ], (batchsize, 1)) * \
F.matmul(features, normalized_weight)
return logits
def __call__(self, x):
# x.shape == (batchsize, 3, 128, 64)
batchsize = x.shape[0]
h = F.elu(self.bn1(self.conv1_1(x)))
h = F.elu(self.bn2(self.conv1_2(h)))
h = F.max_pooling_2d(h, 3, 2, cover_all=False)
h = self.conv2_1(h)
h = self.conv2_3(h)
h = self.conv3_1(h)
h = self.conv3_3(h)
h = self.conv4_1(h)
h = self.conv4_3(h)
h = h.reshape(batchsize, -1)
h = F.dropout(h, ratio=0.6)
h = F.elu(self.fc1_bn(self.fc1(h)))
# Features in rows, normalize axis 1.
weights = self.mean_vectors
features = self.ball(h)
features = F.normalize(features, eps=1e-8)
scale = F.softplus(self.scale)
normalized_weight = F.normalize(weights, axis=0, eps=1e-8)
logits = F.tile(scale[None, ], (batchsize, 1)) * \
F.matmul(features, normalized_weight)
return logits
def Normal(self, xp):
tmplist = [1]
tmp1 = self.embedE(xp.array(tmplist, 'i'))
print(tmp1.data)
allE = list()
for i in range(self.e_size):
allE.append(i)
allEembed = self.embedE(xp.array(allE, 'i'))
#print allEembed.data
allEembed = F.normalize(allEembed)
tmp2 = self.embedE(xp.array(tmplist, 'i'))
print(tmp2.data)
allR = list()
for i in range(self.r_size):
allR.append(i)
allRembed = self.embedR(xp.array(allR, 'i'))
#print allRembed.data
allRembed = F.normalize(allRembed)
#print allRembed.shape
return 0
def mp_matching_func_pairwise(v1, v2, w):
"""
Implementation of m = f_m(v_1, v_2, W).
m_k = cosine(W_k \odot v_1, W_k \odot v_2)
:param v1: (mb, N_1, hidden_dim)
:param v2: (mb, N_2, hidden_dim)
:param w: (head, hidden_dim)
:return: sim: (mb, N_1, N_2, head)
"""
mb, N_1, _ = v1.shape
N_2 = v2.shape[1]
# w: (head, hidden_dim) -> (1, head, hidden_dim) -> (1, head, 1, hidden_dim)
w = F.expand_dims(F.expand_dims(w, axis=0), axis=2)
# v1: (mb, head, N_1, hidden_dim)
v1 = F.tile(w, reps=(mb, 1, N_1, 1)) * F.stack([v1] * self.head, axis=1)
# v2: (mb, head, N_2, hidden_dim)
v2 = F.tile(w, reps=(mb, 1, N_2, 1)) * F.stack([v2] * self.head, axis=1)
# v1: (mb, head, N_1, hidden_dim), normalized on hidden_dim
v1_normed = F.normalize(v1, axis=3)
# v2: (mb, head, N_2, hidden_dim), normalized on hidden_dim
v2_normed = F.normalize(v2, axis=3)
# sim: (mb, head, N_1, N_2)
sim = F.matmul(v1_normed, F.transpose(v2_normed, axes=(0, 1, 3, 2)))
# sim: (mb, N_1, N_2, head)
sim = F.transpose(sim, axes=(0, 2, 3, 1))
return sim
def cos_sim(x, y):
"""
Variableを2つ受け取ってcosine類似度を返す関数
Chainerにはない
"""
norm_x = F.normalize(F.squeeze(x, axis=(1, 2)))
norm_y = F.normalize(F.squeeze(y, axis=(1, 2)))
return F.batch_matmul(norm_x, norm_y, transa=True)
def spectral_normalize(weight, init_u):
W = weight.reshape(weight.shape[0], -1) #C x N
v = F.normalize(F.matmul(W, init_u, transa=True), eps=1e-12,
axis=0) #N x C * C x 1 -> N x 1
u = F.normalize(F.matmul(W, v), eps=1e-12, axis=0) #C x N * N x 1 -> C x 1
sigma = F.matmul(F.matmul(u, W, transa=True),
v) #1 x C * C x N * N x -> 1 x 1 (spectral norm)
return weight / sigma
def cos_sim(x, y):
if len(x.shape) > 2:
norm_x = F.normalize(F.squeeze(F.squeeze(x,axis=(2,)),axis=(2,)))
norm_y = F.normalize(F.squeeze(F.squeeze(y,axis=(2,)),axis=(2,)))
else:
norm_x = F.normalize(x)
norm_y = F.normalize(y)
return F.batch_matmul(norm_x, norm_y, transa=True)
def attention_layer(self, features, features_proj, Xp):
h = F.expand_dims(self.w_att(Xp), 1)
features_proj = F.normalize(features_proj, axis=-1)
h = F.normalize(h, axis=-1)
h_att = F.relu(features_proj + F.broadcast_to(h, features_proj.shape)) # (N, self.D, self.C) + (N, 1, self.C)
out_att = self.w(F.reshape(h_att, (-1, self.C))) # (Nxself.D, self.C) -> (Nxself.D, 1)
out_att = F.reshape(out_att, (-1, self.D)) # (N, self.D)
alpha = F.softmax(out_att) # (N, self.D)
context = F.sum(features * F.broadcast_to(F.expand_dims(alpha, 1), features.shape), axis=2) # (N, self.C, self.D) * (N, 1, self.D)
return context, alpha
def __call__(self, x1, x2, eps=1e-5):
with chainer.using_config('enable_backprop', False):
hs1 = self.predict_single(x1)
hs2 = self.predict_single(x2)
xp = chainer.cuda.get_array_module(x1)
loss = chainer.Variable(xp.array(0, 'float32'))
for h1, h2 in zip(hs1, hs2):
h1 = cf.normalize(h1, axis=1)
h2 = cf.normalize(h2, axis=1)
loss += cf.sum(
cf.square(h1 - h2)) / (h1.shape[0] * h1.shape[2] * h1.shape[3])
return loss
def content_based_addressing(memory, keys, strengths):
""" (M,n_locations,width) -> (M,N,width) -> (M,N) -> (M,N,n_locations) """
M, n_locations, width = memory.shape
N = keys.shape[1]
m, k, s = memory, keys, strengths
m = F.reshape(F.normalize(F.reshape(m, (-1, width))),
(M, n_locations, width))
k = F.reshape(F.normalize(F.reshape(k, (-1, width))), (M, N, width))
t = F.scale(F.batch_matmul(k, m, transb=True), s, axis=0)
r = F.reshape(F.softmax(F.reshape(t, (-1, n_locations))),
(M, N, n_locations))
return r
def forward(self,
z,
stage,
camera_matrices,
z2=None,
z3=None,
z4=None,
theta=None):
# z1 and z2 are for foreground, z3 and z4 are for background
proj_mappings = list()
for i in range(len(camera_matrices)):
proj_mappings.append(
self.projection.compute_proj_idcs(camera_matrices[i]))
proj_frustrum_idcs, proj_grid_coords = list(zip(*proj_mappings))
if not isinstance(z, Variable):
z = Variable(z)
if not isinstance(z2, Variable):
z2 = Variable(z2)
w = self.mapping(z)
voxel = self.voxel_gen(w)
img_feature = self.deepvoxel(
proj_frustrum_idcs,
proj_grid_coords,
voxel,
return_foreground_weight=self.use_background_generator)
if self.use_background_generator:
novel_feats, depth, foreground_weight = img_feature
if z3 is None:
z3 = Variable(self.make_hidden(z.shape[0]))
z4 = Variable(self.make_hidden(z.shape[0]))
w3 = self.mapping(z3)
w4 = self.mapping(z4)
background, background_depth = self.background_generator(
w3, w4, theta)
novel_feats = F.normalize(novel_feats, axis=1) + \
F.normalize(background, axis=1) * (1 - foreground_weight)
depth = depth + background_depth * (1 - foreground_weight)
print(foreground_weight.array.mean(),
foreground_weight.array.std(), depth.array.mean(),
background_depth.mean(), novel_feats.array.std(),
background.array.std())
else:
novel_feats, depth = img_feature
if z2 is None:
z2 = self.make_hidden(z.shape[0])
w2 = self.mapping(z2)
novel_img = self.style_generator(novel_feats, w2, stage)
x_fake = F.concat([novel_img, depth], axis=1)
return x_fake
def max_singular_value_fully_differentiable(W, Ip=1):
"""
Apply power iteration for the weight parameter (fully differentiable version)
"""
xp = cuda.get_array_module(W.data)
u = xp.random.normal(size=(1, W.shape[0])).astype(dtype="f")
for _ in range(Ip):
_v = F.normalize(F.matmul(u, W), eps=1e-12)
_u = F.normalize(F.matmul(_v, F.transpose(W)), eps=1e-12)
sigma = F.sum(F.linear(_u, F.transpose(W)) * _v)
return sigma
def attention(v1, v2):
"""
Implementation of cosine-similarity-based attention mechanism
:param v1: (mb, N_1, hidden_dim)
:param v2: (mb, N_2, hidden_dim)
:return: att: (mb, N_1, N_2)
"""
# (mb, N_1, hidden_dim) -> (mb, N_1, hidden_dim)
v1_normed = F.normalize(v1, axis=2)
# (mb, N_2, hidden_dim) -> (mb, N_2, hidden_dim)
v2_normed = F.normalize(v2, axis=2)
# (mb, N_1, N_2)
att = F.matmul(v1_normed, F.transpose(v2_normed, axes=(0, 2, 1)))
return att
def ocr_mapping_net(self, h_ocr_act, h_act, h_ocr_rsn, h_rsn):
h_act = F.relu(self.act_l(h_act))
h_act = F.normalize(h_act)
h_rsn = F.relu(self.rsn_l(h_rsn))
h_rsn = F.normalize(h_rsn)
h_ocr_act = F.relu(h_ocr_act)
h_ocr_act = F.normalize(h_ocr_act)
h_ocr_rsn = F.relu(h_ocr_rsn)
h_ocr_rsn = F.normalize(h_ocr_rsn)
return h_ocr_act, h_act, h_ocr_rsn, h_rsn
def __call__(self, x, t):
# Deep layers
h1 = F.max_pooling_2d(F.relu(
self.googlenetbn.norm1(self.googlenetbn.conv1(x))),
3,
stride=2,
pad=1)
h1 = F.max_pooling_2d(F.relu(
self.googlenetbn.norm2(self.googlenetbn.conv2(h1))),
3,
stride=2,
pad=1)
h1 = self.googlenetbn.inc3a(h1)
h1 = self.googlenetbn.inc3b(h1)
h1 = self.googlenetbn.inc3c(h1)
h1 = self.googlenetbn.inc4a(h1)
h1 = self.googlenetbn.inc4b(h1)
h1 = self.googlenetbn.inc4c(h1)
h1 = self.googlenetbn.inc4d(h1)
h1 = self.googlenetbn.inc4e(h1)
h1 = self.googlenetbn.inc5a(h1)
h1 = F.average_pooling_2d(self.googlenetbn.inc5b(h1), 7)
h1 = self.googlenetbn.loss3_fc(h1)
h1 = F.normalize(h1)
# Shallow layers
h2 = F.average_pooling_2d(x, 4, stride=4, pad=2)
h2 = F.max_pooling_2d(F.relu(self.norm_s1(self.conv_s1(h2))),
5,
stride=4,
pad=1)
h3 = F.average_pooling_2d(x, 8, stride=8, pad=4)
h3 = F.max_pooling_2d(F.relu(self.norm_s2(self.conv_s2(h3))),
4,
stride=2,
pad=1)
h23 = F.concat((h2, h3), axis=1)
h23 = F.normalize(F.reshape(h23, (x.data.shape[0], 3072)))
h = F.concat((h1, h23), axis=1)
h = F.normalize(F.relu(self.fc4_1(h)))
h = self.fc4_2(h)
return h
def silhouette_loss(target, prediction, num_levels=5):
batch_size = target.shape[0]
loss_list = []
t2 = target[:, None, :, :]
p2 = prediction[:, None, :, :]
for i in range(num_levels):
if i != 0:
t2 = cf.average_pooling_2d(t2, 2, 2)
p2 = cf.average_pooling_2d(p2, 2, 2)
t3 = cf.normalize(cf.reshape(t2, (batch_size, -1)))
p3 = cf.normalize(cf.reshape(p2, (batch_size, -1)))
loss_list.append(cf.sum(cf.square(t3 - p3)) / batch_size)
loss = sum(loss_list)
return loss
def test(self, filename, noscore=True):
testres = 0
self.prepare_data(filename=filename, train=False, noscore=noscore)
if noscore:
pred = pd.DataFrame()
for j in range(self.test_T):
x_j = Variable(self.test_X[j])
pred_j = self.predict(x_j)
pred_j = F.reshape(pred_j, (pred_j.data.shape[0], ))
pred = pd.concat(
[pred, pd.DataFrame(np.sort(pred_j.data)[::-1]).T])
pred.to_csv("new_results.csv", index=False)
print("save new_results.csv !")
else:
for j in range(self.test_T):
sorted_idxes = np.argsort(self.test_Y[j])[::-1]
nthres = min(self.n_thres_cand, sorted_idxes.shape[0])
x_j = Variable(self.test_X[j][sorted_idxes[:nthres]])
y_j = Variable(self.test_Y[j][sorted_idxes[:nthres]])
y_j = F.reshape(y_j, (1, y_j.shape[0]))
# normalize output score to avoid divergence
y_j = F.normalize(y_j)
pred_j = self.predict(x_j)
pred_j = F.reshape(pred_j, (pred_j.data.shape[0], ))
testres += ndcg(y_j.data, pred_j.data, self.n_thres_cand)
print("test_ndcg:{}".format(testres / self.test_T))
def __call__(self, x, subtract_mean=True):
if subtract_mean:
x = x - self._image_mean
# h = super(ModifiedGoogLeNet, self).__call__(
# x, layers=['pool5'], train=train)['pool5']
# h = self.bn_fc(h, test=not train)
# y = self.fc(h)
# return y
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4 / 5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4 / 5)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
h = self.inc4e(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc5a(h)
h = self.inc5b(h)
h = F.average_pooling_2d(h, 7, stride=1)
h = self.bn_fc(h)
y = self.fc(h)
if self.normalize_output:
y = F.normalize(y)
return y
def render_normal(self, vertices, faces):
# fill back
if self.fill_back:
faces = cf.concat((faces, faces[:, :, ::-1]), axis=1).data
# normal
faces_normal = nr.vertices_to_faces(vertices, faces)
(bs, nf) = faces_normal.shape[:2]
faces_normal = faces_normal.reshape((bs * nf, 3, 3))
v10 = faces_normal[:, 0] - faces_normal[:, 1]
v12 = faces_normal[:, 2] - faces_normal[:, 1]
normals = cf.normalize(nr.cross(v10, v12))
normals = normals.reshape((bs, nf, 3))
textures = normals[:, :, None, None, None, :]
textures = cf.tile(textures, (1, 1, 2, 2, 2, 1))
# viewpoint transformation
if self.camera_mode == 'look_at':
vertices = nr.look_at(vertices, self.eye)
elif self.camera_mode == 'look':
vertices = nr.look(vertices, self.eye, self.camera_direction, self.up)
# perspective transformation
if self.perspective:
vertices = nr.perspective(vertices, angle=self.viewing_angle)
# rasterization
faces = nr.vertices_to_faces(vertices, faces)
images = nr.rasterize(
faces, textures, self.image_size, self.anti_aliasing, self.near, self.far, self.rasterizer_eps,
self.background_color)
return images
def check_forward(self, x_data, axis):
eps = self.eps
x = chainer.Variable(x_data)
y = functions.normalize(x, eps=eps, axis=axis)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
y_expect = numpy.empty_like(self.x)
shape = self.x.shape
indices = []
axis_tuple = axis if isinstance(axis, tuple) else (axis,)
for i in six.moves.range(len(shape)):
if i not in axis_tuple:
indices.append(six.moves.range(shape[i]))
else:
indices.append([slice(None)])
indices_tuple = list(itertools.product(*indices))
for index in indices_tuple:
# Note: Casting back the result of `numpy.linalg.norm` to `x.dtype`
# because old NumPy casts it to float32 when a float16 value is
# given.
numerator = numpy.linalg.norm(self.x[index]).astype(x.dtype) + eps
y_expect[index] = self.x[index] / numerator
testing.assert_allclose(y_expect, y_data, **self.check_forward_options)
def compact_bilinear_pooling(x, randweight):
h = F.convolution_2d(x, randweight['W1']) * F.convolution_2d(
x, randweight['W2'])
h = global_average_pooling_2d(h)
h = power_normalize(h)
h = F.normalize(h)
return h
def check_forward(self, x_data, proxy_data, labels_data):
x = chainer.Variable(x_data)
proxy = chainer.Variable(proxy_data)
x = F.normalize(x)
loss = proxy_nca_loss(x, proxy, labels_data)
self.assertEqual(loss.dtype, np.float32)
def check_forward(self, x_data, axis):
eps = self.eps
x = chainer.Variable(x_data)
y = functions.normalize(x, eps=eps, axis=axis)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
y_expect = numpy.empty_like(self.x)
shape = self.x.shape
indices = []
axis_tuple = axis if isinstance(axis, tuple) else (axis, )
for i in six.moves.range(len(shape)):
if i not in axis_tuple:
indices.append(six.moves.range(shape[i]))
else:
indices.append([slice(None)])
indices_tuple = list(itertools.product(*indices))
for index in indices_tuple:
# Note: Casting back the result of `numpy.linalg.norm` to `x.dtype`
# because old NumPy casts it to float32 when a float16 value is
# given.
numerator = numpy.linalg.norm(self.x[index]).astype(x.dtype) + eps
y_expect[index] = self.x[index] / numerator
testing.assert_allclose(y_expect, y_data, **self.check_forward_options)
def proxy_nca_loss(x, proxy, labels):
"""Proxy-NCA loss function.
Args:
x (:class:`~chainer.Variable`):
L2 normalized anchor points whose shape is (B, D), where B is the
batch size and D is the number of dimensions of feature vector.
proxy (:class:`~chainer.Variable` or :class:`~chainer.Parameter`):
Proxies whose shape is (K, D), where K is the number of classes
in the dataset.
labels (:class:`numpy.ndarray`):
Class labels associated to x. The shape is (B,) and dtype is int.
Note that the class IDs must be 0, 1, ..., K-1.
Returns:
:class:`~chainer.Variable`: Loss value.
See: `No Fuss Distance Metric Learning using Proxies \
<http://openaccess.thecvf.com/content_ICCV_2017/papers/\
Movshovitz-Attias_No_Fuss_Distance_ICCV_2017_paper.pdf>`_
"""
proxy = F.normalize(proxy)
distance = squared_distance_matrix(x, proxy)
d_posi = distance[np.arange(len(x)), labels]
# For each row, remove one element corresponding to the positive distance
B, K = distance.shape # batch size and the number of classes
mask = np.tile(np.arange(K), (B, 1)) != labels[:, None]
d_nega = distance[mask].reshape(B, K - 1)
log_denominator = F.logsumexp(-d_nega, axis=1)
loss = d_posi + log_denominator
return F.average(loss)
def __call__(self, x, subtract_mean=True):
if subtract_mean:
x = x - self._image_mean
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4 / 5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4 / 5)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
h = self.inc4e(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc5a(h)
h = self.inc5b(h)
h = F.average_pooling_2d(h, 7, stride=1)
h = self.bn_fc(h)
y = self.fc(h)
if self.normalize_output:
y = F.normalize(y, axis=1)
return y
def update(gen, dis, optimizer_gen, optimizer_dis, x_batch, margin):
xp = gen.xp
batch_size = len(x_batch)
# from generated image
z = xp.random.normal(0, 1, (batch_size, latent_size)).astype(np.float32)
z = z / (xp.linalg.norm(z, axis=1, keepdims=True) + 1e-12)
x_gen = gen(z)
total_size = np.prod(x_gen.shape)
y_gen, h_gen = dis(x_gen)
h_gen = F.normalize(F.reshape(h_gen, (batch_size, -1)))
similarity = F.sum(F.matmul(h_gen, h_gen,
transb=True)) / (batch_size * batch_size)
loss_gen = F.mean_squared_error(x_gen, y_gen) + 0.1 * similarity
loss_dis = F.sum(
F.relu(margin * margin -
F.batch_l2_norm_squared(x_gen - y_gen))) / total_size
# from real image
x = xp.asarray(x_batch)
y, h = dis(x)
loss_dis += F.mean_squared_error(x, y)
gen.cleargrads()
loss_gen.backward()
optimizer_gen.update()
dis.cleargrads()
loss_dis.backward()
optimizer_dis.update()
return float(loss_gen.data), float(loss_dis.data)
def forward(self, x):
"""
h1 : (1, 64, 112, 112)
h2 : (1, 128, 56, 56)
h3 : (1, 256, 28, 28)
h4 : (1, 512, 14, 14)
h5 : (1, 512, 7, 7)
:param x:
:return:
"""
h = x
h = F.relu((self.conv1_1(h)))
h = F.relu((self.conv1_2(h)))
pool1 = F.max_pooling_2d(h, 2, stride=2)
h = F.relu((self.conv2_1(pool1)))
h = F.relu((self.conv2_2(h)))
pool2 = F.max_pooling_2d(h, 2, stride=2)
h = F.relu((self.conv3_1(pool2)))
h = F.relu((self.conv3_2(h)))
h = F.relu((self.conv3_3(h)))
pool3 = F.max_pooling_2d(h, 2, stride=2)
h = F.relu((self.conv4_1(pool3)))
h = F.relu((self.conv4_2(h)))
h = F.relu((self.conv4_3(h)))
pool4 = F.max_pooling_2d(h, 2, stride=2)
if self.texture:
h = {
'pool1': pool1,
'pool2': pool2,
'pool3': pool3,
'pool4': pool4
}[self.texture_layer]
if self.cbp:
h = F.convolution_2d(h, self.W1) * F.convolution_2d(h, self.W2)
h = global_average_pooling_2d(h)
if self.normalize:
h = power_normalize(h)
h = F.normalize(h)
h = self.fc8(F.dropout(h, 0.2))
return h
else:
b, ch, height, width = h.data.shape
h = F.reshape(h, (b, ch, width * height))
h = F.batch_matmul(h, h, transb=True) / self.xp.float32(
width * height)
h = self.fc8(F.dropout(h, 0.4))
return h
else:
h = F.relu((self.conv5_1(pool4)))
h = F.relu((self.conv5_2(h)))
h = F.relu((self.conv5_3(h)))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.dropout(F.relu(self.fc6(h)), ratio=0.5)
h = F.dropout(F.relu(self.fc7(h)), ratio=0.5)
h = self.fc8(h)
return h
def __call__(self, x):
if self.normalizedW is None:
if self.norm_to_one:
self.normalizedW = F.normalize(self.vocab_freq * self.W)
else:
self.normalizedW = self.norm_by_freq(self.vocab_freq)
return embed_id.embed_id(x, self.normalizedW, ignore_label=self.ignore_label)
def check_eps(self, x_data):
x = chainer.Variable(x_data)
y = functions.normalize(x, axis=self.axis)
self.assertEqual(y.data.dtype, numpy.float32)
y_data = cuda.to_cpu(y.data)
y_expect = numpy.zeros_like(self.x)
testing.assert_allclose(y_expect, y_data)
def grad_norm_hook(optimizer):
for p in optimizer.target.params():
grad_data = p.grad
shape = grad_data.shape
reshape = (1, np.prod(shape), )
grad = Variable(grad_data)
grad_reshape = F.reshape(grad, reshape)
grad_norm = F.normalize(grad_reshape)
grad_norm_reshape = F.reshape(grad_norm, shape)
p.grad = grad_norm_reshape.data
def check_forward(self, x_data):
x = chainer.Variable(x_data)
y = functions.normalize(x)
self.assertEqual(y.data.dtype, numpy.float32)
y_data = cuda.to_cpu(y.data)
y_expect = numpy.empty_like(self.x)
for n in six.moves.range(len(self.x)):
y_expect[n] = self.x[n] / numpy.linalg.norm(self.x[n])
testing.assert_allclose(y_expect, y_data)
def look_at(vertices, eye, at=None, up=None):
"""
"Look at" transformation of vertices.
"""
assert (vertices.ndim == 3)
xp = chainer.cuda.get_array_module(vertices)
batch_size = vertices.shape[0]
if at is None:
at = xp.array([0, 0, 0], 'float32')
if up is None:
up = xp.array([0, 1, 0], 'float32')
if isinstance(eye, list) or isinstance(eye, tuple):
eye = xp.array(eye, 'float32')
if eye.ndim == 1:
eye = cf.tile(eye[None, :], (batch_size, 1))
if at.ndim == 1:
at = cf.tile(at[None, :], (batch_size, 1))
if up.ndim == 1:
up = cf.tile(up[None, :], (batch_size, 1))
# create new axes
z_axis = cf.normalize(at - eye)
x_axis = cf.normalize(neural_renderer.cross(up, z_axis))
y_axis = cf.normalize(neural_renderer.cross(z_axis, x_axis))
# create rotation matrix: [bs, 3, 3]
r = cf.concat((x_axis[:, None, :], y_axis[:, None, :], z_axis[:, None, :]), axis=1)
if r.shape[0] != vertices.shape[0]:
r = cf.broadcast_to(r, (vertices.shape[0], 3, 3))
# apply
# [bs, nv, 3] -> [bs, nv, 3] -> [bs, nv, 3]
if vertices.shape != eye.shape:
eye = cf.broadcast_to(eye[:, None, :], vertices.shape)
vertices = vertices - eye
vertices = cf.matmul(vertices, r, transb=True)
return vertices
def __call__(self, x):
"""Normalize input and scale it.
Args:
x (chainer.Variable): A variable holding 4-dimensional array.
Its :obj:`dtype` is :obj:`numpy.float32`.
Returns:
chainer.Variable:
The shape and :obj:`dtype` are same as those of input.
"""
x = F.normalize(x, eps=self.eps, axis=1)
scale = F.broadcast_to(self.scale[:, np.newaxis, np.newaxis], x.shape)
return x * scale
def lighting(
faces, textures, intensity_ambient=0.5, intensity_directional=0.5, color_ambient=(1, 1, 1),
color_directional=(1, 1, 1), direction=(0, 1, 0)):
xp = chainer.cuda.get_array_module(faces)
bs, nf = faces.shape[:2]
# arguments
if isinstance(color_ambient, tuple) or isinstance(color_ambient, list):
color_ambient = xp.array(color_ambient, 'float32')
if isinstance(color_directional, tuple) or isinstance(color_directional, list):
color_directional = xp.array(color_directional, 'float32')
if isinstance(direction, tuple) or isinstance(direction, list):
direction = xp.array(direction, 'float32')
if color_ambient.ndim == 1:
color_ambient = cf.broadcast_to(color_ambient[None, :], (bs, 3))
if color_directional.ndim == 1:
color_directional = cf.broadcast_to(color_directional[None, :], (bs, 3))
if direction.ndim == 1:
direction = cf.broadcast_to(direction[None, :], (bs, 3))
# create light
light = xp.zeros((bs, nf, 3), 'float32')
# ambient light
if intensity_ambient != 0:
light = light + intensity_ambient * cf.broadcast_to(color_ambient[:, None, :], light.shape)
# directional light
if intensity_directional != 0:
faces = faces.reshape((bs * nf, 3, 3))
v10 = faces[:, 0] - faces[:, 1]
v12 = faces[:, 2] - faces[:, 1]
normals = cf.normalize(neural_renderer.cross(v10, v12))
normals = normals.reshape((bs, nf, 3))
if direction.ndim == 2:
direction = cf.broadcast_to(direction[:, None, :], normals.shape)
cos = cf.relu(cf.sum(normals * direction, axis=2))
light = (
light + intensity_directional * cfmath.mul(*cf.broadcast(color_directional[:, None, :], cos[:, :, None])))
# apply
light = cf.broadcast_to(light[:, :, None, None, None, :], textures.shape)
textures = textures * light
return textures
def _compute_loss_with_noise(self, x_l, y_l, x_u):
x_l_0 = x_l
x_l_grad = Variable(x_l.grad)
shape = x_l_grad.shape
bs = shape[0]
d = np.prod(shape[1:])
noise = F.reshape(F.normalize(F.reshape(x_l_grad, (bs, d))), shape)
x_l_noise = x_l + noise * 0.1
# label confidence loss
h = self.ae.encoder(x_l)
y = self.ae.mlp(h,)
h = self.ae.encoder(x_l_0)
y_0 = self.ae.mlp(h,)
l_lc_l = 0
l_lc_l += F.mean_squared_error(y_0, y)
loss = l_lc_l * self.lambda_
return loss
def check_forward(self, x_data, axis):
eps = self.eps
x = chainer.Variable(x_data)
y = functions.normalize(x, eps=eps, axis=axis)
self.assertEqual(y.data.dtype, numpy.float32)
y_data = cuda.to_cpu(y.data)
y_expect = numpy.empty_like(self.x)
shape = self.x.shape
indices = []
for i in six.moves.range(len(shape)):
if i != axis:
indices.append(six.moves.range(shape[i]))
else:
indices.append([slice(None)])
indices_tuple = list(itertools.product(*indices))
for index in indices_tuple:
numerator = numpy.linalg.norm(self.x[index]) + eps
y_expect[index] = self.x[index] / numerator
testing.assert_allclose(y_expect, y_data)
def f(x):
return functions.normalize(x, eps=self.eps, axis=axis)