def __call__(self, x):
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv2(h))), 2, stride=2)
h = F.relu(self.conv3(h))
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv4(h))), 2, stride=2)
h = F.relu(self.conv5(h))
h = F.relu(self.conv6(h))
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv7(h))), 2, stride=2)
h = F.relu(self.conv8(h))
h = F.relu(self.conv9(h))
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv10(h))), 2, stride=2)
h = F.relu(self.conv11(h))
h = F.relu(self.conv12(h))
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv13(h))), 2, stride=2)
h = F.dropout(F.relu(self.fc14(h)))
h = F.dropout(F.relu(self.fc15(h)))
h = self.fc16(h)
return h
def __call__(self, x, t=None):
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv1(x))),
ksize=2,
stride=2)
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv2(h))),
ksize=2,
stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), ksize=2, stride=2)
h = F.dropout(F.relu(self.fc6(h)), ratio=0.9)
h = F.dropout(F.relu(self.fc7(h)), ratio=0.9)
y = self.fc8(h)
if self.train:
self.loss = F.softmax_cross_entropy(y, t)
self.accuracy = F.accuracy(y, t)
chainer.report({
'loss': self.loss,
'accuracy': self.accuracy
}, self)
return self.loss
else:
return h
def __call__(self, x, t=None):
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))),
3,
stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))),
3,
stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
self.score = h
if t is None:
assert not self.train
return
self.loss = F.softmax_cross_entropy(self.score, t)
self.accuracy = F.accuracy(self.score, t)
chainer.report({'loss': self.loss, 'accuracy': self.accuracy})
return self.loss
def extract_features(model, data1, data2):
feature_list = np.empty((0, 1024), dtype=float)
# count = 0
for i in range(len(data1)):
# Forward処理を記述
root1_h1 = F.relu(model.conv1(data1[i]))
root1_h2 = F.max_pooling_2d(root1_h1, stride=2, ksize=2)
root1_h3 = F.local_response_normalization(root1_h2, k=1, n=5, alpha=0.00002, beta=0.75)
root1_h4 = F.relu(model.conv2(root1_h3))
root1_h5 = F.max_pooling_2d(root1_h4, stride=2, ksize=3)
root1_h6 = F.local_response_normalization(root1_h5, k=1, n=5, alpha=0.00002, beta=0.75)
root2_h1 = F.relu(model.conv1(data2[i]))
root2_h2 = F.max_pooling_2d(root2_h1, stride=2, ksize=2)
root2_h3 = F.local_response_normalization(root2_h2, k=1, n=5, alpha=0.00002, beta=0.75)
root2_h4 = F.relu(model.conv2(root2_h3))
root2_h5 = F.max_pooling_2d(root2_h4, stride=2, ksize=3)
root2_h6 = F.local_response_normalization(root2_h5, k=1, n=5, alpha=0.00002, beta=0.75)
h6 = F.concat((root1_h6, root2_h6), axis=1)
feature = F.relu(model.fc3(h6))
# h8 = model.fc4(h7)
# print("fc3層の出力は：" + str(feature.data[0]))
feature_list = np.append(feature_list, np.array([feature.data[0]]), axis=0)
# feature_list = feature_list.flatten
return feature_list
def __call__(self, x, y, t):
self.clear()
hR = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.convR1(x))), 3, stride=2)
hR = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.convR2(hR))), 3, stride=2)
hR = F.relu(self.convR3(hR))
hR = F.relu(self.convR4(hR))
hR = F.max_pooling_2d(F.relu(self.convR5(hR)), 3, stride=2)
hR = F.dropout(F.relu(self.fcR6(hR)), train=self.train)
hR = F.dropout(F.relu(self.fcR7(hR)), train=self.train)
hD = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.convD1(y))), 3, stride=2)
hD = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.convD2(hD))), 3, stride=2)
hD = F.relu(self.convD3(hD))
hD = F.relu(self.convD4(hD))
hD = F.max_pooling_2d(F.relu(self.convD5(hD)), 3, stride=2)
hD = F.dropout(F.relu(self.fcD6(hD)), train=self.train)
hD = F.dropout(F.relu(self.fcD7(hD)), train=self.train)
h = F.dropout(F.relu(self.fc8(hR, hD)), train=self.train)
h = self.fc9(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
return self.loss
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 forward(x_data, y_data, L=L, batchsize=batchsize):
L = Variable(cuda.to_gpu(L))
x, t = Variable(cuda.to_gpu(x_data)), Variable(cuda.to_gpu(y_data))
# x, t = Variable(x_data), Variable(y_data)
h = F.max_pooling_2d(F.relu(F.local_response_normalization(
model.conv1(x))),
3,
stride=2)
h = F.max_pooling_2d(F.relu(F.local_response_normalization(
model.conv2(h))),
3,
stride=2)
h = F.relu(model.conv3(h))
h = F.relu(model.conv4(h))
h = F.max_pooling_2d(F.relu(model.conv5(h)), 3, stride=2)
h = F.relu(model.fc6(h))
y = model.fc7(h)
y_f = F.sigmoid(y)
y_ft = F.expand_dims(y_f, 2)
# print F.batch_matmul(y_f,L,transa=True).data.shape
# print F.batch_matmul(F.batch_matmul(y_f,L,transa=True),y_ft).data.shape
term = (F.sum(F.batch_matmul(F.batch_matmul(y_f, L, transa=True),
y_ft))) / batchsize
sce = F.sigmoid_cross_entropy(y, t)
#E=sce+(rate*term)
E = sce
return E, sce, term, y_f
def __call__(self, x):
h = self.conv1(x)
h = self.conv2(h)
h = F.relu(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = F.local_response_normalization(h)
h = self.conv3(h)
h = F.relu(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = F.local_response_normalization(h)
h = self.conv4(h)
h = F.relu(h)
self.cam = h
h = F.max_pooling_2d(h, 3, stride=2)
h = F.local_response_normalization(h)
h = F.dropout(h, ratio=0.5)
h = self.fc1(h)
h = F.relu(h)
h = F.dropout(h, ratio=0.5)
h = self.fc2(h)
return h
def __call__(self, data, face, eyes_grid):
# the network that uses data as input
pool1 = F.max_pooling_2d(F.relu(self.conv1(data)), ksize=3, stride=2)
norm1 = F.local_response_normalization(pool1,
n=5,
alpha=0.0001,
beta=0.75)
pool2 = F.max_pooling_2d(F.relu(self.conv2(norm1)), ksize=3, stride=2)
norm2 = norm1 = F.local_response_normalization(pool2,
n=5,
alpha=0.0001,
beta=0.75)
conv3 = F.relu(self.conv3(norm2))
conv4 = F.relu(self.conv4(conv3))
conv5 = F.relu(self.conv5(conv4))
conv5_red = F.relu(self.conv5_red(conv5))
# the network that uses face as input
pool1_face = F.max_pooling_2d(F.relu(self.conv1_face(face)),
ksize=3,
stride=2)
norm1_face = F.local_response_normalization(pool1_face,
n=5,
alpha=0.0001,
beta=0.75)
pool2_face = F.max_pooling_2d(F.relu(self.conv2_face(norm1_face)),
ksize=3,
stride=2)
norm2_face = F.local_response_normalization(pool2_face,
n=5,
alpha=0.0001,
beta=0.75)
conv3_face = F.relu(self.conv3_face(norm2_face))
conv4_face = F.relu(self.conv4_face(conv3_face))
pool5_face = F.max_pooling_2d(F.relu(self.conv5_face(conv4_face)),
ksize=3,
stride=2)
fc6_face = F.relu(self.fc6_face(pool5_face))
# now the eyes
eyes_grid_flat = F.flatten(eyes_grid)
eyes_grid_mult = 24 * eyes_grid_flat
eyes_grid_reshaped = F.reshape(
eyes_grid_mult,
(1, eyes_grid_mult.size)) # give it same ndim as fc6
# now bring everything together
face_input = F.concat((fc6_face, eyes_grid_reshaped), axis=1)
fc7_face = F.relu(self.fc7_face(face_input))
fc8_face = F.relu(self.fc8_face(fc7_face))
importance_map_reshape = F.reshape(
F.sigmoid(self.importance_no_sigmoid(fc8_face)), (1, 1, 13, 13))
fc_7 = conv5_red * self.importance_map(importance_map_reshape)
fc_0_0 = self.fc_0_0(fc_7)
fc_1_0 = self.fc_1_0(fc_7)
fc_0_1 = self.fc_0_1(fc_7)
fc_m1_0 = self.fc_m1_0(fc_7)
fc_0_m1 = self.fc_0_m1(fc_7)
return fc_0_0, fc_1_0, fc_0_1, fc_0_m1, fc_m1_0
def __call__(self, x):
# for chainer v1.x.x
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv1(x))),
3,
stride=2)
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv2(h))),
3,
stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.my_fc6(h)), train=self.train)
h = F.dropout(F.relu(self.my_fc7(h)), train=self.train)
h = self.my_fc8(h)
# for chainer v2.x.x
# You don't need to use DelGradient hook.
# with chainer.no_backprop_mode():
# h = F.max_pooling_2d(F.local_response_normalization(
# F.relu(self.conv1(x))), 3, stride=2)
# h = F.max_pooling_2d(F.local_response_normalization(
# F.relu(self.conv2(h))), 3, stride=2)
# h = F.relu(self.conv3(h))
# h = F.relu(self.conv4(h))
# h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
# with chainer.force_backprop_mode():
# h = F.dropout(F.relu(self.my_fc6(h)), train=self.train)
# h = F.dropout(F.relu(self.my_fc7(h)), train=self.train)
# h = self.my_fc8(h)
return h
def __call__(self, x):
h_P1 = self.P1(x)
if self.P1N_Normalize: h_P1 = F.local_response_normalization(h_P1)
h_P1 = F.max_pooling_2d(F.relu(h_P1),
ksize=self.P1P_ksize,
cover_all=True)
h_P2 = self.P2(h_P1)
if self.P2N_Normalize: h_P2 = F.local_response_normalization(h_P2)
h_P2 = F.max_pooling_2d(F.relu(h_P2),
ksize=self.P2P_ksize,
cover_all=True)
h_P3 = self.P3(h_P2)
if self.P3N_Normalize: h_P3 = F.local_response_normalization(h_P3)
h_P3 = F.max_pooling_2d(F.relu(h_P3),
ksize=self.P3P_ksize,
cover_all=True)
h_L1 = F.dropout(F.relu(self.L1(h_P3)),
ratio=self.L1_dropout,
train=self.IsTrain)
h_L2 = F.dropout(F.relu(self.L2(h_L1)),
ratio=self.L2_dropout,
train=self.IsTrain)
y = h_L2
return y
def __call__(self, img_list):
self.clear()
xs = np.zeros((len(img_list), 3, self.ref_len, self.ref_len)).astype(np.float32)
for i, img in enumerate(img_list):
xs[i] = self.preprocess(img)
# conv1->relu1->pool1->norm1
h = F.local_response_normalization(
F.max_pooling_2d(
F.relu(self.conv1(xs)),
ksize=3,
stride=2
# pad=0
)
# n(local_size)=5
# alpha=0.0001
# beta=0.75
)
# conv2->relu2->pool2->norm2
h = F.local_response_normalization(F.max_pooling_2d(F.relu(self.conv2(h)), ksize=3, stride=2))
# conv3->relu3
h = F.relu(self.conv3(h))
# conv4->relu4
h = F.relu(self.conv4(h))
# conv5->relu5->pooling5
h = F.max_pooling_2d(F.relu(self.conv5(h)), ksize=3, stride=2)
return self.fc6(h).data
def predictor(self, x):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 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.loss3_fc(F.dropout(h, 0.4, train=self.train))
return h
def forward(self, x_img, x_doc, y_data, train=True):
x_img = cuda.cupy.asarray(x_img)
x_doc = cuda.cupy.asarray(x_doc)
y_data = cuda.cupy.asarray(y_data)
img, doc, t = Variable(x_img), Variable(x_doc), Variable(y_data)
h = F.max_pooling_2d(F.relu(self.conv1(img)), ksize=3, stride=2, pad=0)
h = F.local_response_normalization(h)
h = F.max_pooling_2d(F.relu(self.conv2(h)), ksize=3, stride=2, pad=0)
h = F.local_response_normalization(h)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), ksize=3, stride=2, pad=0)
h = F.dropout(F.relu(self.fc6(h)), train=train, ratio=0.5)
h = F.dropout(F.relu(self.fc7(h)), train=train, ratio=0.5)
h2 = F.relu(self.doc_fc1(doc))
h2 = F.relu(self.doc_fc2(h2))
b = F.relu(self.bi1(h, h2))
y = self.fc8(b)
if train:
return F.softmax_cross_entropy(y, t)
else:
return F.accuracy(y, t)
def forward(self, x_img, x_doc, y_data, train=True):
x_img = cuda.cupy.asarray(x_img)
x_doc = cuda.cupy.asarray(x_doc)
y_data = cuda.cupy.asarray(y_data)
img, doc, t = Variable(x_img), Variable(x_doc), Variable(y_data)
h = F.max_pooling_2d(F.relu(self.conv1(img)), ksize=3, stride=2, pad=0)
h = F.local_response_normalization(h)
h = F.max_pooling_2d(F.relu(self.conv2(h)), ksize=3, stride=2, pad=0)
h = F.local_response_normalization(h)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), ksize=3, stride=2, pad=0)
h = F.dropout(F.relu(self.fc6(h)), train=train, ratio=0.5)
h = F.dropout(F.relu(self.fc7(h)), train=train, ratio=0.5)
h2 = F.relu(self.doc_fc1(doc))
h2 = F.relu(self.doc_fc2(h2))
b = F.relu(self.bi1(h, h2))
y = self.fc8(b)
if train:
return F.softmax_cross_entropy(y, t)
else:
return F.accuracy(y, t)
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 predict(self, x):
with chainer.function.no_backprop_mode(), chainer.using_config('train', False):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 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.loss3_fc(F.dropout(h, 0.4))
return F.softmax(h)
def forward(self, x):
y1 = self.model['conv1/7x7_s2'](x)
h = F.relu(y1)
h = F.local_response_normalization(self.pool_func(h, 3, stride=2), n=5)
h = F.relu(self.model['conv2/3x3_reduce'](h))
y2 = self.model['conv2/3x3'](h)
h = F.relu(y2)
h = self.pool_func(F.local_response_normalization(h, n=5), 3, stride=2)
out1 = self.model['inception_3a/1x1'](h)
out3 = self.model['inception_3a/3x3'](F.relu(
self.model['inception_3a/3x3_reduce'](h)))
out5 = self.model['inception_3a/5x5'](F.relu(
self.model['inception_3a/5x5_reduce'](h)))
pool = self.model['inception_3a/pool_proj'](self.pool_func(h,
3,
stride=1,
pad=1))
y3 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y3)
out1 = self.model['inception_3b/1x1'](h)
out3 = self.model['inception_3b/3x3'](F.relu(
self.model['inception_3b/3x3_reduce'](h)))
out5 = self.model['inception_3b/5x5'](F.relu(
self.model['inception_3b/5x5_reduce'](h)))
pool = self.model['inception_3b/pool_proj'](self.pool_func(h,
3,
stride=1,
pad=1))
y4 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y4)
h = self.pool_func(h, 3, stride=2)
out1 = self.model['inception_4a/1x1'](h)
out3 = self.model['inception_4a/3x3'](F.relu(
self.model['inception_4a/3x3_reduce'](h)))
out5 = self.model['inception_4a/5x5'](F.relu(
self.model['inception_4a/5x5_reduce'](h)))
pool = self.model['inception_4a/pool_proj'](self.pool_func(h,
3,
stride=1,
pad=1))
y5 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y5)
out1 = self.model['inception_4b/1x1'](h)
out3 = self.model['inception_4b/3x3'](F.relu(
self.model['inception_4b/3x3_reduce'](h)))
out5 = self.model['inception_4b/5x5'](F.relu(
self.model['inception_4b/5x5_reduce'](h)))
pool = self.model['inception_4b/pool_proj'](self.pool_func(h,
3,
stride=1,
pad=1))
y6 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y6)
return [y1, y2, y3, y4, y5, y6]
def __call__(self, x, t, train):
#if train:
# self.train=True
#else:
# self.train=False
x = chainer.Variable(x, volatile=not train)
t = chainer.Variable(t, volatile=not train)
h = F.relu(self.conv1(x))
h = F.local_response_normalization(F.max_pooling_2d(h, 3, stride=2),
n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(F.local_response_normalization(h, n=5),
3,
stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
self.loss1 = F.softmax_cross_entropy(l, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
self.loss2 = F.softmax_cross_entropy(l, t)
h = self.inc4e(h)
#print h.data
h = F.max_pooling_2d(h, 3, stride=2)
#print h.data
h = self.inc5a(h)
#print h.data
h = self.inc5b(h)
#print h.data
h = F.average_pooling_2d(h, 7, stride=1)
#print h.data
#h = self.loss3_fc(F.dropout(h, 0.4, train=self.train))
h = self.loss3_fc(F.dropout(h, 0.4, train=train))
#print h.data
self.loss3 = F.softmax_cross_entropy(h, t)
print h.data
print t.data
self.loss = 0.3 * (self.loss1 + self.loss2) + self.loss3
self.accuracy = F.accuracy(h, t)
return self.loss, self.accuracy
def forward_fe(self, x):
h1 = F.local_response_normalization(F.relu(self.conv1(x)))
h1 = F.max_pooling_2d(h1, 3, stride=2)
h2 = F.local_response_normalization(F.relu(self.conv2(h1)))
h2 = F.max_pooling_2d(h2, 3, stride=2)
h3 = F.relu(self.conv3(h2))
h4 = F.relu(self.conv4(h3))
h5 = F.max_pooling_2d(F.relu(self.conv5(h4)), 3, stride=2)
return h5
def __forward(self, x):
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
return self.fc6(h)
def __call__(self, x, train=True):
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.dropout(F.relu(self.fc4(h)), train=train)
h = self.fc5(h)
return h
def __call__(self, x, t):
h = F.relu(self['conv1/7x7_s2'](x))
h = F.local_response_normalization(F.max_pooling_2d(h, 3, stride=2),
n=5,
alpha=(1e-4) / 5,
k=1)
h = F.relu(self['conv2/3x3_reduce'](h))
h = F.relu(self['conv2/3x3'](h))
h = F.max_pooling_2d(F.local_response_normalization(h,
n=5,
alpha=(1e-4) / 5,
k=1),
3,
stride=2)
h = self.call_inception(h, 'inception_3a')
h = self.call_inception(h, 'inception_3b')
h = F.max_pooling_2d(h, 3, stride=2)
h = self.call_inception(h, 'inception_4a')
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self['loss1/conv'](l))
l = F.dropout(F.relu(self['loss1/fc'](l)), 0.7, train=self.train)
l = self['loss1/classifier'](l)
loss1 = F.softmax_cross_entropy(l, t)
h = self.call_inception(h, 'inception_4b')
h = self.call_inception(h, 'inception_4c')
h = self.call_inception(h, 'inception_4d')
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self['loss2/conv'](l))
l = F.dropout(F.relu(self['loss2/fc'](l)), 0.7, train=self.train)
l = self['loss2/classifier'](l)
loss2 = F.softmax_cross_entropy(l, t)
h = self.call_inception(h, 'inception_4e')
h = F.max_pooling_2d(h, 3, stride=2)
h = self.call_inception(h, 'inception_5a')
h = self.call_inception(h, 'inception_5b')
h = F.average_pooling_2d(h, 7, stride=1)
h = self['fc8-20'](F.dropout(h, 0.4, train=self.train))
loss3 = F.softmax_cross_entropy(h, t)
loss = 0.3 * (loss1 + loss2) + loss3
accuracy = F.accuracy(h, t)
chainer.report(
{
'loss': loss,
'loss1': loss1,
'loss2': loss2,
'loss3': loss3,
'accuracy': accuracy
}, self)
return loss
def forward(self, x_img, x_doc, y_data, train=True):
x_img = cuda.cupy.asarray(x_img)
x_doc = cuda.cupy.asarray(x_doc)
y_data = cuda.cupy.asarray(y_data)
img, doc, t = Variable(x_img), Variable(x_doc), Variable(y_data)
h = F.relu(self.conv1(img))
h = F.local_response_normalization(F.max_pooling_2d(h, 3, stride=2),
n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(F.local_response_normalization(h, n=5),
3,
stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
self.loss1 = F.softmax_cross_entropy(l, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
self.loss2 = F.softmax_cross_entropy(l, t)
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.loss3_fc1(F.dropout(h, 0.4, train=train))
h2 = F.relu(self.doc_fc1(F.dropout(doc, train=train)))
h2 = F.relu(self.doc_fc2(h2))
b = F.relu(self.bi1(h, h2))
h = self.loss3_fc2(b)
self.loss3 = F.softmax_cross_entropy(h, t)
if train:
return 0.3 * (self.loss1 + self.loss2) + self.loss3
else:
return F.accuracy(h, t)
def __call__(self, x, t):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
#l = F.average_pooling_2d(h, 5, stride=3)
l = F.average_pooling_2d(h, 5*(GoogLeNet.scale),
stride=3*(GoogLeNet.scale))
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
loss1 = F.softmax_cross_entropy(l, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
#l = F.average_pooling_2d(h, 5, stride=3)
l = F.average_pooling_2d(h, 5*(GoogLeNet.scale),
stride=3*(GoogLeNet.scale))
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
loss2 = F.softmax_cross_entropy(l, t)
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 = F.average_pooling_2d(h, 7*(GoogLeNet.scale),
stride=1*(GoogLeNet.scale))
h = self.loss3_fc(F.dropout(h, 0.4))
loss3 = F.softmax_cross_entropy(h, t)
loss = 0.3 * (loss1 + loss2) + loss3
accuracy = F.accuracy(h, t)
chainer.report({
'loss': loss,
'loss1': loss1,
'loss2': loss2,
'loss3': loss3,
'accuracy': accuracy
}, self)
return loss
def __call__(self, x, train=True):
h = F.max_pooling_2d(F.relu(F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
return h
def __call__(self, x):
self.clear()
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 2, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 2, stride=2)
h = F.relu(self.fc6(h))
h = F.relu(self.fc7(h))
h = self.fc8(h)
return h
def __call__(self, x_img, t_detection, **others):
# Alexnet
h = F.relu(self.conv1(x_img)) # conv1
h = F.max_pooling_2d(h, 3, stride=2, pad=0) # max1
h = F.local_response_normalization(h) # norm1
h = F.relu(self.conv2(h)) # conv2
h = F.max_pooling_2d(h, 3, stride=2, pad=0) # max2
h = F.local_response_normalization(h) # norm2
h = F.relu(self.conv3(h)) # conv3
h = F.relu(self.conv4(h)) # conv4
h = F.relu(self.conv5(h)) # conv5
h = F.max_pooling_2d(h, 3, stride=2, pad=0) # pool5
with chainer.using_config('train', True):
h = F.dropout(F.relu(self.fc6(h)), ratio=0.0) # fc6
with chainer.using_config('train', True):
h = F.dropout(F.relu(self.fc7(h)), ratio=0.0) # fc7
h_detection = self.fc8(h) # fc8
# Loss
loss = F.softmax_cross_entropy(h_detection, t_detection)
chainer.report({'loss': loss}, self)
# Prediction
h_detection = F.argmax(h_detection, axis=1)
# Report results
predict_data = {'img': x_img, 'detection': h_detection}
teacher_data = {'img': x_img, 'detection': t_detection}
chainer.report({'predict': predict_data}, self)
chainer.report({'teacher': teacher_data}, self)
# Report layer weights
chainer.report(
{
'conv1_w': {
'weights': self.conv1.W
},
'conv2_w': {
'weights': self.conv2.W
},
'conv3_w': {
'weights': self.conv3.W
},
'conv4_w': {
'weights': self.conv4.W
},
'conv5_w': {
'weights': self.conv5.W
}
}, self)
return loss
def forward(self, x_img, x_doc, y_data, train=True):
x_img = cuda.cupy.asarray(x_img)
x_doc = cuda.cupy.asarray(x_doc)
y_data = cuda.cupy.asarray(y_data)
img, doc, t = Variable(x_img), Variable(x_doc), Variable(y_data)
h = F.relu(self.conv1(img))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
self.loss1 = F.softmax_cross_entropy(l, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
self.loss2 = F.softmax_cross_entropy(l, t)
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.loss3_fc1(F.dropout(h, 0.4, train=train))
h2 = F.relu(self.doc_fc1(F.dropout(doc, train=train)))
h2 = F.relu(self.doc_fc2(h2))
b = F.relu(self.bi1(h, h2))
h = self.loss3_fc2(b)
self.loss3 = F.softmax_cross_entropy(h, t)
if train:
return 0.3 * (self.loss1 + self.loss2) + self.loss3
else:
return F.accuracy(h, t)
def reduct(self, x):
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))),
ksize=3,
stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))),
ksize=4,
stride=2)
h = F.relu(self.conv3(h))
return h
def __call__(self, x):
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
return self.fc8(h)
def __call__(self, x, layers):
ret = {}
en = layers[-1]
h = self.conv1(x)
if 'conv1' in layers:
ret.update({'conv1': h})
if en == 'conv1':
return ret
h = F.max_pooling_2d(F.local_response_normalization(F.relu(h)),
3,
stride=2)
h = F.dropout(self.conv2(h), ratio=self.dropout_rate)
if 'conv2' in layers:
ret.update({'conv2': h})
if en == 'conv2':
return ret
h = F.max_pooling_2d(F.local_response_normalization(F.relu(h)),
3,
stride=2)
h = F.dropout(self.conv3(h), ratio=self.dropout_rate)
if 'conv3' in layers:
ret.update({'conv3': h})
if en == 'conv3':
return ret
h = F.relu(h)
h = F.dropout(self.conv4(h), ratio=self.dropout_rate)
if 'conv4' in layers:
ret.update({'conv4': h})
if en == 'conv4':
return ret
h = F.relu(h)
h = F.dropout(self.conv5(h), ratio=self.dropout_rate)
if 'conv5' in layers:
ret.update({'conv5': h})
if en == 'conv5':
return ret
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = F.dropout(self.fc6(h), ratio=self.dropout_rate)
if 'fc6' in layers:
ret.update({'fc6': h})
if en == 'fc6':
return ret
h = F.relu(h)
h = F.dropout(self.fc7(h), ratio=self.dropout_rate)
if 'fc7' in layers:
ret.update({'fc7': h})
if en == 'fc7':
return ret
h = F.relu(h)
h = self.fc8(h)
if 'fc8' in layers:
ret.update({'fc8': h})
return ret
def forward(self, x):
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)))
h = F.dropout(F.relu(self.fc7(h)))
h = self.fc8(h)
return h
def forward(self, x, t=None):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(F.max_pooling_2d(h, 3, stride=2),
n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(F.local_response_normalization(h, n=5),
3,
stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
if t is not None:
loss1 = F.softmax_cross_entropy(l, t)
self.loss1 = loss1
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
if t is not None:
loss2 = F.softmax_cross_entropy(l, t)
self.loss2 = loss2
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.loss3_fc(F.dropout(h, 0.4))
if t is not None:
loss3 = F.softmax_cross_entropy(h, t)
self.loss3 = loss3
if t is not None:
loss = 0.3 * (loss1 + loss2) + loss3
accuracy = F.accuracy(h, t)
self.loss = loss
self.accuracy = accuracy
else:
self.out = h
def __call__(self, x, return_activations=False):
# Activations for feature matching are returned before applying
# any non-linearities.
activations = []
h = self.conv1(x)
if return_activations:
activations.append(h) # [0]
h = F.max_pooling_2d(F.local_response_normalization(F.relu(h)),
3,
stride=2)
h = self.conv2(h)
if return_activations:
activations.append(h) # [1]
h = F.max_pooling_2d(F.local_response_normalization(F.relu(h)),
3,
stride=2)
h = self.conv3(h)
if return_activations:
activations.append(h) # [2]
h = F.relu(h)
h = self.conv4(h)
if return_activations:
activations.append(h) # [3]
h = F.relu(h)
h = self.conv5(h)
if return_activations:
activations.append(h) # [4]
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = self.fc6(h)
if return_activations:
activations.append(h) # [5]
h = F.dropout(F.relu(h))
h = self.fc7(h)
if return_activations:
activations.append(h) # [6]
h = F.dropout(F.relu(h))
h = self.fc8(h)
if return_activations:
activations.append(h) # [7]
if return_activations:
return h, activations # return non-softmax model output and activations
return h # only return model output (non-softmax)
def fwd(self, x):
h = F.max_pooling_nd(F.local_response_normalization(
F.relu(self.conv1(x))),
3,
stride=2)
h = F.max_pooling_nd(F.local_response_normalization(
F.relu(self.conv2(h))),
3,
stride=2)
h = F.dropout(F.relu(self.fc3(h)), train=self.train)
h = self.fc4(h)
return h
def forward(self, x_data, y_data, train=True):
x = Variable(x_data, volatile=not train)
t = Variable(y_data, volatile=not train)
h = F.relu(self.conv1(x))
h = F.local_response_normalization(F.max_pooling_2d(h, 3, stride=2),
n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(F.local_response_normalization(h, n=5),
3,
stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
if train:
loss1 = F.average_pooling_2d(h, 5, stride=3)
loss1 = F.relu(self.loss1_conv(loss1))
loss1 = F.relu(self.loss1_fc1(loss1))
loss1 = self.loss1_fc2(loss1)
loss1 = F.softmax_cross_entropy(loss1, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
if train:
loss2 = F.average_pooling_2d(h, 5, stride=3)
loss2 = F.relu(self.loss2_conv(loss2))
loss2 = F.relu(self.loss2_fc1(loss2))
loss2 = self.loss2_fc2(loss2)
loss2 = F.softmax_cross_entropy(loss2, t)
h = self.inc4e(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc5a(h)
h = self.inc5b(h)
h = F.dropout(F.average_pooling_2d(h, 7, stride=1), 0.4, train=train)
h = self.loss3_fc(h)
loss3 = F.softmax_cross_entropy(h, t)
if train:
loss = 0.3 * (loss1 + loss2) + loss3
else:
loss = loss3
accuracy = F.accuracy(h, t)
return loss, accuracy
def __call__(self, x, t):
h = F.max_pooling_2d(F.relu(F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
return self.loss
def predict(self, x_data):
x = chainer.Variable(x_data, volatile=True)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
return F.softmax(h)
def forward(self, x):
y1 = self.model['conv1/7x7_s2'](x)
h = F.relu(y1)
h = F.local_response_normalization(self.pool_func(h, 3, stride=2), n=5)
h = F.relu(self.model['conv2/3x3_reduce'](h))
y2 = self.model['conv2/3x3'](h)
h = F.relu(y2)
h = self.pool_func(F.local_response_normalization(h, n=5), 3, stride=2)
out1 = self.model['inception_3a/1x1'](h)
out3 = self.model[
'inception_3a/3x3'](F.relu(self.model['inception_3a/3x3_reduce'](h)))
out5 = self.model[
'inception_3a/5x5'](F.relu(self.model['inception_3a/5x5_reduce'](h)))
pool = self.model[
'inception_3a/pool_proj'](self.pool_func(h, 3, stride=1, pad=1))
y3 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y3)
out1 = self.model['inception_3b/1x1'](h)
out3 = self.model[
'inception_3b/3x3'](F.relu(self.model['inception_3b/3x3_reduce'](h)))
out5 = self.model[
'inception_3b/5x5'](F.relu(self.model['inception_3b/5x5_reduce'](h)))
pool = self.model[
'inception_3b/pool_proj'](self.pool_func(h, 3, stride=1, pad=1))
y4 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y4)
h = self.pool_func(h, 3, stride=2)
out1 = self.model['inception_4a/1x1'](h)
out3 = self.model[
'inception_4a/3x3'](F.relu(self.model['inception_4a/3x3_reduce'](h)))
out5 = self.model[
'inception_4a/5x5'](F.relu(self.model['inception_4a/5x5_reduce'](h)))
pool = self.model[
'inception_4a/pool_proj'](self.pool_func(h, 3, stride=1, pad=1))
y5 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y5)
out1 = self.model['inception_4b/1x1'](h)
out3 = self.model[
'inception_4b/3x3'](F.relu(self.model['inception_4b/3x3_reduce'](h)))
out5 = self.model[
'inception_4b/5x5'](F.relu(self.model['inception_4b/5x5_reduce'](h)))
pool = self.model[
'inception_4b/pool_proj'](self.pool_func(h, 3, stride=1, pad=1))
y6 = F.concat((out1, out3, out5, pool), axis=1)
h = F.relu(y6)
return [y1, y2, y3, y4, y5, y6]
def forward(self, x_data, y_data, train=True):
x = chainer.Variable(x_data, volatile=not train)
t = chainer.Variable(y_data, volatile=not train)
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
if train:
loss1 = F.average_pooling_2d(h, 5, stride=3)
loss1 = F.relu(self.loss1_conv(loss1))
loss1 = F.relu(self.loss1_fc1(loss1))
loss1 = self.loss1_fc2(loss1)
loss1 = F.softmax_cross_entropy(loss1, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
if train:
loss2 = F.average_pooling_2d(h, 5, stride=3)
loss2 = F.relu(self.loss2_conv(loss2))
loss2 = F.relu(self.loss2_fc1(loss2))
loss2 = self.loss2_fc2(loss2)
loss2 = F.softmax_cross_entropy(loss2, t)
h = self.inc4e(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc5a(h)
h = self.inc5b(h)
h = F.dropout(F.average_pooling_2d(h, 7, stride=1), 0.4, train=train)
h = self.loss3_fc(h)
loss3 = F.softmax_cross_entropy(h, t)
if train:
loss = 0.3 * (loss1 + loss2) + loss3
else:
loss = loss3
accuracy = F.accuracy(h, t)
return loss, accuracy
def __call__(self, x, t):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
loss1 = F.softmax_cross_entropy(l, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
loss2 = F.softmax_cross_entropy(l, t)
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.loss3_fc(F.dropout(h, 0.4))
loss3 = F.softmax_cross_entropy(h, t)
loss = 0.3 * (loss1 + loss2) + loss3
accuracy = F.accuracy(h, t)
chainer.report({
'loss': loss,
'loss1': loss1,
'loss2': loss2,
'loss3': loss3,
'accuracy': accuracy
}, self)
return loss
def __call__(self, x, t):
h = F.relu(self['conv1/7x7_s2'](x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5, alpha=(1e-4)/5, k=1)
h = F.relu(self['conv2/3x3_reduce'](h))
h = F.relu(self['conv2/3x3'](h))
h = F.max_pooling_2d(F.local_response_normalization(
h, n=5, alpha=(1e-4)/5, k=1), 3, stride=2)
h = self.call_inception(h, 'inception_3a')
h = self.call_inception(h, 'inception_3b')
h = F.max_pooling_2d(h, 3, stride=2)
h = self.call_inception(h, 'inception_4a')
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self['loss1/conv'](l))
l = F.dropout(F.relu(self['loss1/fc'](l)), 0.7, train=self.train)
l = self['loss1/classifier'](l)
loss1 = F.softmax_cross_entropy(l, t)
h = self.call_inception(h, 'inception_4b')
h = self.call_inception(h, 'inception_4c')
h = self.call_inception(h, 'inception_4d')
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self['loss2/conv'](l))
l = F.dropout(F.relu(self['loss2/fc'](l)), 0.7, train=self.train)
l = self['loss2/classifier'](l)
loss2 = F.softmax_cross_entropy(l, t)
h = self.call_inception(h, 'inception_4e')
h = F.max_pooling_2d(h, 3, stride=2)
h = self.call_inception(h, 'inception_5a')
h = self.call_inception(h, 'inception_5b')
h = F.average_pooling_2d(h, 7, stride=1)
h = self['loss3/classifier'](F.dropout(h, 0.4, train=self.train))
loss3 = F.softmax_cross_entropy(h, t)
loss = 0.3 * (loss1 + loss2) + loss3
accuracy = F.accuracy(h, t)
chainer.report({
'loss': loss,
'loss1': loss1,
'loss2': loss2,
'loss3': loss3,
'accuracy': accuracy
}, self)
return loss
def forward (x_data, y_data, train = True):
x, t = chainer.Variable(x_data), chainer.Variable(y_data)
h = F.max_pooling_2d(F.relu(F.local_response_normalization(model.conv1(x))), 2, stride=2)
h = F.max_pooling_2d(F.relu(F.local_response_normalization(model.conv2(h))), 2, stride=2)
h = F.relu(model.conv3(h))
h = F.relu(model.conv4(h))
h = F.max_pooling_2d(F.relu(model.conv5(h)), 2, stride=2)
h = F.dropout(F.relu(model.fc6(h)), train=train)
h = F.dropout(F.relu(model.fc7(h)), train=train)
y = model.fc8(h)
if train:
return F.softmax_cross_entropy(y, t)
else:
return F.accuracy(y, t)
def forward(self, x_data, y_data, train=True):
x = chainer.Variable(x_data, volatile=not train)
t = chainer.Variable(y_data, volatile=not train)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)))
h = F.dropout(F.relu(self.fc7(h)))
h = self.fc8(h)
return F.softmax_cross_entropy(h, t), F.accuracy(h, t)
def __call__(self, x, t):
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)))
h = F.dropout(F.relu(self.fc7(h)))
h = self.fc8(h)
loss = F.softmax_cross_entropy(h, t)
chainer.report({'loss': loss, 'accuracy': F.accuracy(h, t)}, self)
return loss
def __call__(self, x, rois, t=None, train=False):
h = self.conv1(x)
h = F.relu(h)
h = F.local_response_normalization(h, n=5, k=2, alpha=5e-4, beta=.75)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv2(h)
h = F.relu(h)
h = F.local_response_normalization(h, n=5, k=2, alpha=5e-4, beta=.75)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv3(h)
h = F.relu(h)
h = self.conv4(h)
h = F.relu(h)
h = self.conv5(h)
h = F.relu(h)
h = roi_pooling_2d(h, rois, 6, 6, spatial_scale=0.0625)
h = self.fc6(h)
h = F.relu(h)
h = F.dropout(h, train=train, ratio=.5)
h = self.fc7(h)
h = F.relu(h)
h = F.dropout(h, train=train, ratio=.5)
h_cls_score = self.cls_score(h)
cls_score = F.softmax(h_cls_score)
bbox_pred = self.bbox_pred(h)
if t is None:
assert train is False
return cls_score, bbox_pred
assert train
t_cls, t_bbox = t
self.cls_loss = F.softmax_cross_entropy(h_cls_score, t_cls)
self.bbox_loss = F.smooth_l1_loss(bbox_pred, t_bbox)
xp = cuda.get_array_module(x.data)
lambda_ = (0.5 * (t_cls.data != self.bg_label)).astype(xp.float32)
lambda_ = Variable(lambda_, volatile=not train)
L = self.cls_loss + F.sum(lambda_ * self.bbox_loss)
return L
def forward(self, x, train=False):
self.data = x
self.conv1 = F.relu(self.caffe.conv1(self.data))
self.pool1 = F.max_pooling_2d(self.conv1, ksize=3, stride=2)
self.norm1 = F.local_response_normalization(self.pool1, k=5, n=5, alpha=0.0001, beta=0.75) * np.power(5, 0.75)
self.conv2 = F.relu(self.caffe.conv2(self.norm1))
self.pool2 = F.max_pooling_2d(self.conv2, ksize=3, stride=2)
self.norm2 = F.local_response_normalization(self.pool2, k=5, n=5, alpha=0.0001, beta=0.75) * np.power(5, 0.75)
self.conv3 = F.relu(self.caffe.conv3(self.norm2))
self.conv4 = F.relu(self.caffe.conv4(self.conv3))
self.conv5 = F.relu(self.caffe.conv5(self.conv4))
self.pool5 = F.max_pooling_2d(self.conv5, ksize=3, stride=2)
self.fc6 = F.dropout(F.relu(self.caffe.fc6(self.pool5)), train=train)
self.fc7 = F.dropout(F.relu(self.caffe.fc7(self.fc6)), train=train)
self.fc8 = self.fine.fc8ft(self.fc7)
return self.fc8
def __call__(self, x):
"""Compute an image-wise score from a batch of images
Args:
x (chainer.Variable): A variable with 4D image array.
Returns:
chainer.Variable:
An image-wise score. Its channel size is :obj:`self.n_class`.
"""
p1 = F.MaxPooling2D(2, 2)
p2 = F.MaxPooling2D(2, 2)
p3 = F.MaxPooling2D(2, 2)
p4 = F.MaxPooling2D(2, 2)
h = F.local_response_normalization(x, 5, 1, 1e-4 / 5., 0.75)
h = _pool_without_cudnn(p1, F.relu(self.conv1_bn(self.conv1(h))))
h = _pool_without_cudnn(p2, F.relu(self.conv2_bn(self.conv2(h))))
h = _pool_without_cudnn(p3, F.relu(self.conv3_bn(self.conv3(h))))
h = _pool_without_cudnn(p4, F.relu(self.conv4_bn(self.conv4(h))))
h = self._upsampling_2d(h, p4)
h = self.conv_decode4_bn(self.conv_decode4(h))
h = self._upsampling_2d(h, p3)
h = self.conv_decode3_bn(self.conv_decode3(h))
h = self._upsampling_2d(h, p2)
h = self.conv_decode2_bn(self.conv_decode2(h))
h = self._upsampling_2d(h, p1)
h = self.conv_decode1_bn(self.conv_decode1(h))
score = self.conv_classifier(h)
return score
def forward(self, x):
pool1 = lambda x: F.max_pooling_2d(F.relu(F.local_response_normalization(x)), 3, stride=2) # (55 - 3)/2 + 1 = 27
pool2 = lambda x: F.max_pooling_2d(F.relu(F.local_response_normalization(x)), 3, stride=2) # (27 - 3)/2 + 1 = 13
pool5 = lambda x: F.max_pooling_2d(F.relu(x), 3, stride=2) # (13 - 3)
h = pool1(self.conv1(x))
h = pool2(self.conv2(h))
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = pool5(self.conv5(h))
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = F.dropout(F.sigmoid(self.fc8(h)), train=self.train)
y = self.fc9(h)
return y
def __call__(self, x):
h_P1 = self.P1(x)
if self.P1N_Normalize: h_P1 = F.local_response_normalization(h_P1)
h_P1 = F.max_pooling_2d(F.relu(h_P1), ksize=self.P1P_ksize, cover_all=True)
h_P2 = self.P2(h_P1)
if self.P2N_Normalize: h_P2 = F.local_response_normalization(h_P2)
h_P2 = F.max_pooling_2d(F.relu(h_P2), ksize=self.P2P_ksize, cover_all=True)
h_P3 = self.P3(h_P2)
if self.P3N_Normalize: h_P3 = F.local_response_normalization(h_P3)
h_P3 = F.max_pooling_2d(F.relu(h_P3), ksize=self.P3P_ksize, cover_all=True)
h_L1 = F.dropout(F.relu(self.L1(h_P3)),ratio=self.L1_dropout,train=self.IsTrain)
h_L2 = F.dropout(F.relu(self.L2(h_L1)),ratio=self.L2_dropout,train=self.IsTrain)
y = h_L2
return y
def _setup_lrn(self, layer):
param = layer.lrn_param
if param.norm_region != param.ACROSS_CHANNELS:
raise RuntimeError("Within-channel LRN is not supported")
fwd = lambda x: functions.local_response_normalization(
x, n=param.local_size, k=param.k, alpha=param.alpha / param.local_size, beta=param.beta
)
self.forwards[layer.name] = fwd
self._add_layer(layer)
def __call__(self, x, t):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
self.loss1 = F.softmax_cross_entropy(l, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
self.loss2 = F.softmax_cross_entropy(l, t)
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.loss3_fc(F.dropout(h, 0.4, train=self.train))
self.loss3 = F.softmax_cross_entropy(h, t)
self.loss = 0.3 * (self.loss1 + self.loss2) + self.loss3
self.accuracy = F.accuracy(h, t)
return self.loss
def check_forward(self, inputs, backend_config):
y_expect, = self.forward_cpu(inputs)
if backend_config.use_cuda:
inputs = cuda.to_gpu(inputs)
with backend_config:
y = functions.local_response_normalization(*inputs)
assert y.data.dtype == self.dtype
testing.assert_allclose(y_expect, y.data, **self.check_forward_options)
def __call__(self, x, t=None):
h = F.local_response_normalization(self.conv1(x))
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = F.local_response_normalization(self.conv2(h))
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)))
h = F.dropout(F.relu(self.fc7(h)))
h = self.fc8(h)
self.pred = F.softmax(h)
if t is None:
assert not chainer.config.train
return
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
return self.loss
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = local_response_normalization(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,), eps=1)
assert_allclose(gx, x.grad, atol=1e-3)
def __call__(self, x):
h = x
for iL in range(self.NPLayers):
h = self.__dict__["P%d"%iL](h)
h = F.local_response_normalization(h)
h = F.max_pooling_2d(F.relu(h), ksize=self.NKsize[iL+1], cover_all=True)
h = F.dropout(F.relu(self.L1(h)),ratio=self.L1_dropout,train=self.IsTrain)
h = F.dropout(F.relu(self.L2(h)),ratio=self.L2_dropout,train=self.IsTrain)
y = h
return y
def predict(self, x_test, gpu=-1):
if gpu >= 0:
x_test = cuda.to_gpu(x_test)
x = Variable(x_test)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)))
h = F.dropout(F.relu(self.fc7(h)))
y = self.fc8(h)
predictions = np.array([], np.float32)
for o in y.data:
predictions = np.append(predictions, np.array([np.argmax(o)], np.float32))
return predictions
def __call__(self, x):
c1 = F.relu(self.conv1(x))
m1 = F.max_pooling_2d(c1, 3, stride=2, pad=0)
m1_n = F.local_response_normalization(m1)
c1a = F.relu(self.conv1a(m1_n))
c2 = F.relu(self.conv2(m1_n))
m2 = F.max_pooling_2d(c2, 3, stride=2, pad=0)
m2_n = F.local_response_normalization(m2)
c3 = F.relu(self.conv3(m2_n))
c3a = F.relu(self.conv3a(c3))
c4 = F.relu(self.conv4(c3))
c5 = F.relu(self.conv5(c4))
m5 = F.max_pooling_2d(c5, 3, stride=2, pad=0)
c = F.concat((c1a, c3a, m5))
c_all = F.relu(self.conv_all(c))
fc = F.relu(self.fc_full(c_all))
detection = F.relu(self.fc_detection1(fc))
detection = self.fc_detection2(detection)
detection = F.softmax(detection)
landmark = F.relu(self.fc_landmarks1(fc))
landmark = self.fc_landmarks2(landmark)
visibility = F.relu(self.fc_visibility1(fc))
visibility = self.fc_visibility2(visibility)
pose = F.relu(self.fc_pose1(fc))
pose = self.fc_pose2(pose)
gender = F.relu(self.fc_gender1(fc))
gender = self.fc_gender2(gender)
gender = F.softmax(gender)
detection = F.softmax(detection)[:, 1]
gender = F.softmax(gender)[:, 1]
return {'detection': detection,
'landmark': landmark,
'visibility': visibility,
'gender': gender,
'pose': pose}