def check_ambiguous_poolings(self):
with self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(self.v, 3)
with testing.assert_warns(DeprecationWarning), \
self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(
self.v, 3, pooling_class=functions.MaxPooling2D, pooling='max')
def check_invalid_poolings(self):
with self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(self.v, 3, pooling='avg')
with testing.assert_warns(DeprecationWarning), \
self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(
self.v, 3, pooling_class=functions.AveragePooling2D)
def check_ambiguous_poolings(self):
with self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(self.v, 3)
with testing.assert_warns(DeprecationWarning), \
self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(
self.v, 3, pooling_class=functions.MaxPooling2D, pooling='max')
def check_invalid_poolings(self):
with self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(self.v, 3, pooling='avg')
with testing.assert_warns(DeprecationWarning), \
self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(
self.v, 3, pooling_class=functions.AveragePooling2D)
def func(self, x):
if hasattr(self, 'pooling'):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, pooling=self.pooling)
elif hasattr(self, 'pooling_class'):
with testing.assert_warns(DeprecationWarning):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class)
else:
assert False
return y
def func(self, x):
if hasattr(self, 'pooling'):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, pooling=self.pooling)
elif hasattr(self, 'pooling_class'):
with testing.assert_warns(DeprecationWarning):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class)
else:
assert False
return y
def calc_loss(self, x, t, layer,train=True): self.clear() if(layer ==0) : h = F.max_pooling_2d(F.relu(model.conv1(x)),4) h = self.norm2(h,test= not train) h = F.relu(model.l1(h)) h = F.relu(model.l2(h)) elif layer ==1 : h = F.spatial_pyramid_pooling_2d(F.relu(model.conv1(x)),4,F.MaxPooling2D) h = self.norm5(h,test= not train) h = F.relu(model.l4(h)) h = F.relu(model.l2(h)) elif layer ==2 : h = F.average_pooling_2d(F.relu(model.conv1(x)),4) h = self.norm2(h,test= not train) h = F.relu(model.l1(h)) h = F.relu(model.l2(h)) elif layer ==3 : h = F.max_pooling_2d(F.relu(model.conv1(x)),2) h = self.norm2(h,test= not train) h = F.max_pooling_2d(F.relu(model.conv2(h)),2) h = self.norm1(h,test= not train) h = F.relu(model.l3(h)) h = F.relu(model.l2(h)) elif layer ==4 : h = F.max_pooling_2d(F.relu(model.conv4(x)),2) h = self.norm3(h,test= not train) h = F.spatial_pyramid_pooling_2d(F.relu(model.conv3(h)),2,F.MaxPooling2D) h = self.norm6(h,test= not train) h = F.relu(model.l5(h)) h = F.relu(model.l2(h)) elif layer ==5 : h = F.average_pooling_2d(F.relu(model.conv1(x)),2) h = self.norm2(h,test= not train) h = F.average_pooling_2d(F.relu(model.conv2(h)),2) h = self.norm1(h,test= not train) h = F.relu(model.l3(h)) h = F.relu(model.l2(h)) ''' print (h.data.shape) #t=t.T.data print (t.data) print (h.data.shape) #print (h.data.T) print (t.data.shape) ''' loss = F.mean_squared_error(h, t) return loss
def __call__(self, x):
h = x
h = self.__dict__["P1_1"](F.leaky_relu(h))
h = self.__dict__["BN1_1"](h)
h = self.__dict__["P1_2"](F.leaky_relu(h))
h = self.__dict__["BN1_2"](h)
h = F.max_pooling_2d(F.leaky_relu(h), ksize=3, stride=2, cover_all=False)
h = self.__dict__["P2_1"](h)
h = self.__dict__["BN2_1"](h)
h = self.__dict__["P2_2"](F.leaky_relu(h))
h = self.__dict__["BN2_2"](h)
h = self.__dict__["P2_2"](F.leaky_relu(h))
h = self.__dict__["BN2_3"](h)
h = F.max_pooling_2d(F.leaky_relu(h), ksize=3, stride=2, cover_all=False)
h = self.__dict__["P3_1"](h)
h = self.__dict__["BN3_1"](h)
h = self.__dict__["P3_2"](F.leaky_relu(h))
h = self.__dict__["BN3_2"](h)
h = self.__dict__["P3_3"](F.leaky_relu(h))
h = self.__dict__["BN3_3"](h)
#h = F.average_pooling_2d(F.leaky_relu(h), ksize=6)
h = F.spatial_pyramid_pooling_2d(F.leaky_relu(h), 3, F.MaxPooling2D)
h = self.__dict__["BNL0"](h)
h = self.__dict__["L1"](F.leaky_relu(h))
h = self.__dict__["BNL1"](h)
h = self.__dict__["L2"](F.leaky_relu(h))
y = h
#h = F.spatial_pyramid_pooling_2d(F.leaky_relu(h), 3)
#y = F.reshape(h,(len(h.data),self.F_unit))
return y
def check_forward(self, x_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class, use_cudnn=use_cudnn)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
def check_forward_ones(self, x_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class, use_cudnn=use_cudnn)
y_data = cuda.to_cpu(y.data)
self.assertEqual((self.n, self.output_dim, 1, 1), y_data.shape)
gradient_check.assert_allclose(y_data, numpy.ones_like(y_data))
def __call__(self, x):
h = F.max_pooling_2d(F.relu(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.relu(self.conv2(h)), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.relu(self.fc4(h))
h = self.fc5(h)
return h
def feat(self, h):
h = F.convolution_2d(h, self.W1) * F.convolution_2d(h, self.W2)
h = F.spatial_pyramid_pooling_2d(h, self.p, F.MaxPooling2D)
if self.l2normalize:
h = F.reshape(h, (h.data.shape[0], -1))
h = ssq(h)
h = F.normalize(h)
return h
def check_forward_ones(self, x_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class, use_cudnn=use_cudnn)
y_data = cuda.to_cpu(y.data)
self.assertEqual((self.n, self.output_dim, 1, 1), y_data.shape)
gradient_check.assert_allclose(y_data, numpy.ones_like(y_data))
def __call__(self, x):
h = F.max_pooling_2d(F.relu(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.relu(self.conv2(h)), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.dropout(F.relu(self.fc4(h)), ratio=0.5)
h = self.fc5(h)
return h
def check_forward(self, x_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class,
use_cudnn=use_cudnn)
self.assertEqual(y.data.dtype, numpy.float32)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
def __call__(self, x, turn, train=True):
h = F.max_pooling_2d(self.act(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(self.act(self.conv2(h)), 3, stride=2)
h = self.act(self.conv3(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = self.act(self.fc4(h))
h = self.fc5(h)
h = self.bn_list[turn](h, test=not train)
return h
def check_forward(self, x_data, use_cudnn='always'):
x = chainer.Variable(x_data)
with chainer.using_config('use_cudnn', use_cudnn):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
def check_forward_ones(self, x_data, use_cudnn='always'):
x = chainer.Variable(x_data)
with chainer.using_config('use_cudnn', use_cudnn):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class)
y_data = cuda.to_cpu(y.data)
self.assertEqual(y_data.shape, (self.n, self.output_dim, 1, 1))
self.assertEqual(y_data.dtype, self.dtype)
testing.assert_allclose(y_data, numpy.ones_like(y_data))
def check_backward(self, x_data, y_grad, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class, use_cudnn=use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(cuda.to_cpu(gx), cuda.to_cpu(x.grad), atol=1e-04)
def __call__(self, x):
h = x.reshape(tuple((1, )) + x.shape)
h = self.conv0(h)
h = F.leaky_relu(h)
for i in range(1, self.n):
h = self[f"conv{i}"](h)
h = F.spatial_pyramid_pooling_2d(h, 5)
h = self.l0(h)
h = F.leaky_relu(h)
h = self.l1(h)
return h
def check_backward(self, x_data, y_grad, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class, use_cudnn=use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(cuda.to_cpu(gx), cuda.to_cpu(x.grad))
def __call__(self, x):
h = x
for iL in range(self.NPLayers):
h = self.__dict__["P%d"%iL](h)
if iL==0: h = F.local_response_normalization(h)
h = F.max_pooling_2d(F.elu(h), ksize=self.NKsize[iL+1], cover_all=True)
h = F.spatial_pyramid_pooling_2d(F.elu(h), 3, F.MaxPooling2D)
h = F.dropout(F.elu(self.L1(h)),ratio=self.L1_dropout,train=self.IsTrain)
h = F.elu(self.L2(h))
y = h
return y
def forward(self, x,layer, train=True): self.clear() if(layer ==0) : h = F.max_pooling_2d(F.relu(model.conv1(x)),4) h = self.norm2(h,test= not train) h = F.relu(model.l1(h)) h = F.relu(model.l2(h)) elif layer ==1 : h = F.spatial_pyramid_pooling_2d(F.relu(model.conv1(x)),4,F.MaxPooling2D) h = self.norm5(h,test= not train) h = F.relu(model.l4(h)) h = F.relu(model.l2(h)) elif layer ==2 : h = F.average_pooling_2d(F.relu(model.conv1(x)),4) h = self.norm2(h,test= not train) h = F.relu(model.l1(h)) h = F.relu(model.l2(h)) elif layer ==3 : h = F.max_pooling_2d(F.relu(model.conv1(x)),2) h = self.norm2(h,test= not train) h = F.max_pooling_2d(F.relu(model.conv2(h)),2) h = self.norm1(h,test= not train) h = F.relu(model.l3(h)) h = F.relu(model.l2(h)) elif layer ==4 : h = F.max_pooling_2d(F.relu(model.conv4(x)),2) h = self.norm3(h,test= not train) h = F.spatial_pyramid_pooling_2d(F.relu(model.conv3(h)),2,F.MaxPooling2D) h = self.norm6(h,test= not train) h = F.relu(model.l5(h)) h = F.relu(model.l2(h)) elif layer ==5 : h = F.average_pooling_2d(F.relu(model.conv1(x)),2) h = self.norm2(h,test= not train) h = F.average_pooling_2d(F.relu(model.conv2(h)),2) h = self.norm1(h,test= not train) h = F.relu(model.l3(h)) h = F.relu(model.l2(h)) return h
def forward(self, x_data, y_data, train = True,gpu=-1):
if gpu >= 0:
x_data = cuda.to_gpu(x_data)
y_data = cuda.to_gpu(y_data)
x = Variable(x_data)
t = Variable(y_data)
h = F.max_pooling_2d(F.sigmoid(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.relu(self.conv2(h)), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.dropout(F.relu(self.fc4(h)), ratio=0.4, train=train)
y = self.fc5(h)
return F.softmax_cross_entropy(y, t), F.accuracy(y, t)
def __call__(self, x, t):
h = F.max_pooling_2d(F.relu(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.relu(self.conv2(h)), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.dropout(F.relu(self.fc4(h)), ratio=0.5, train=self.train)
y = self.fc5(h)
if self.train:
self.loss = F.softmax_cross_entropy(y, t)
self.acc = F.accuracy(y, t)
return self.loss
else:
self.pred = F.softmax(y)
return self.pred
def __call__(self, x):
h = F.elu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = self.res2(h, self.train)
h = self.res3(h, self.train)
h = self.res4(h, self.train)
h = self.res5(h, self.train)
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.elu(self.conv2(h))
h = F.dropout(h, ratio=0.5)
h = self.conv3(h)
h = F.reshape(h, (-1, self.num_class))
return h
def __call__(self, x, t):
h = F.max_pooling_2d(F.relu(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.relu(self.conv2(h)), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.dropout(F.relu(self.fc4(h)), ratio=0.5, train=self.train)
h = self.fc5(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
if self.train:
return self.loss
else:
self.pred = F.softmax(h)
return self.pred
def __call__(self, x):
h = x
for iL in range(self.NPLayers):
h = self.__dict__["P%d" % iL](h)
if iL == 0: h = F.local_response_normalization(h)
h = F.max_pooling_2d(F.elu(h),
ksize=self.NKsize[iL + 1],
cover_all=True)
h = F.spatial_pyramid_pooling_2d(F.elu(h), 3, F.MaxPooling2D)
h = F.dropout(F.elu(self.L1(h)),
ratio=self.L1_dropout,
train=self.IsTrain)
h = F.elu(self.L2(h))
y = h
return y
def __call__(self, x, t=None):
self.clear()
x.volatile = not self.train
t.volatile = 'AUTO'
h = F.elu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire2(h)
h = self.fire3(h)
h = self.fire4(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire5(h)
h = self.fire6(h)
h = self.fire7(h)
h = self.fire8(h)
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.elu(self.conv9(h))
memory_h = chainer.Variable(h.data, volatile='AUTO')
with chainer.no_backprop_mode():
weight, self.memory = \
self.apply_memory(memory_h, t, self.update_weight, self.train)
if self.train:
self.apply_memory.memory.data = self.memory.data
h = F.dropout(h, ratio=0.5, train=self.train)
h = self.conv_infer(h)
h = F.reshape(h, (-1, self.n_class))
h = h*weight
self.h = h
self.prob = F.softmax(h)
if self.active_learn:
t = mask_gt_for_active_learning(self.prob, t, self.xp, self.n_class)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
chainer.report({'loss': self.loss, 'accuracy': self.accuracy}, self)
return self.loss
def __call__(self, x, t):
x.volatile = True
h = F.max_pooling_2d(F.elu(self.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.elu(self.conv2(h)), 3, stride=2)
h = F.elu(self.conv3(h))
h.volatile = False
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.dropout(F.elu(self.fc4(h)), ratio=0.5, train=self.train)
h = self.fc5(h)
self.prob = F.softmax(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
chainer.report({'loss': self.loss, 'accuracy': self.accuracy}, self)
return self.loss
def __call__(self, x, t):
h_lists = []
self.loss = 0
for i in range(self.iter_time):
h_list_p2 = h_lists[i-2] if i>0 and i%2==0 else None
h_list = self.rec(x, h=h_list_p2)
h_lists.append(h_list)
h = F.spatial_pyramid_pooling_2d(h_list[-1], 3, F.MaxPooling2D)
h = F.elu(h)
h = self.conv_infer(h)
h = F.reshape(h, (-1, self.n_class))
self.loss += F.softmax_cross_entropy(h, t)
# x = F.elu(h_list[-1])
self.h = h
return h_list[-1]
def __call__(self, x):
h = self.conv1(x)
F.max_pooling_2d(h, 2)
h = self.block1_1(h, 0.2)
h = self.block1_2(h, 0.2)
h = self.block1_3(h, 0.2)
h = self.block2_1(h, 0.3)
h = self.block2_2(h, 0.3)
h = self.block2_3(h, 0.3)
h = self.block3_1(h, 0.4)
h = self.block3_2(h, 0.4)
h = self.block3_3(h, 0.4)
# h = F.spatial_pyramid_pooling_2d(h, 3, F.average_pooling_2d)
h = F.spatial_pyramid_pooling_2d(h, 4, F.MaxPooling2D)
h = self.l1(h)
return F.tanh(self.l2(h))
def __call__(self, x):
h = F.elu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire2(h)
h = self.fire3(h)
h = self.fire4(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire5(h)
h = self.fire6(h)
h = self.fire7(h)
self.sorce_hidden_state = self.fire8(h) # H in the paper.
h = F.spatial_pyramid_pooling_2d( \
self.sorce_hidden_state, 3, F.MaxPooling2D)
h = F.elu(self.conv9(h))
h = F.dropout(h, ratio=0.5, train=self.train)
self.context_vec = self.conv10(h)
def __call__(self, x):
h = F.elu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire2(h)
h = self.fire3(h)
h = self.fire4(h)
# h = F.max_pooling_2d(h, 3, stride=2)
#
# h = self.fire5(h)
# h = self.fire6(h)
# h = self.fire7(h)
# h = self.fire8(h)
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.elu(self.conv9(h))
query = F.reshape(h, h.data.shape[:2])
return query
def __call__(self, x):
h = x
h = self.__dict__["P1_1"](F.leaky_relu(h))
h = self.__dict__["BN1_1"](h)
h = self.__dict__["P1_2"](F.leaky_relu(h))
h = self.__dict__["BN1_2"](h)
h = F.max_pooling_2d(F.leaky_relu(h),
ksize=3,
stride=2,
cover_all=False)
h = self.__dict__["P2_1"](h)
h = self.__dict__["BN2_1"](h)
h = self.__dict__["P2_2"](F.leaky_relu(h))
h = self.__dict__["BN2_2"](h)
h = self.__dict__["P2_2"](F.leaky_relu(h))
h = self.__dict__["BN2_3"](h)
h = F.max_pooling_2d(F.leaky_relu(h),
ksize=3,
stride=2,
cover_all=False)
h = self.__dict__["P3_1"](h)
h = self.__dict__["BN3_1"](h)
h = self.__dict__["P3_2"](F.leaky_relu(h))
h = self.__dict__["BN3_2"](h)
h = self.__dict__["P3_3"](F.leaky_relu(h))
h = self.__dict__["BN3_3"](h)
#h = F.average_pooling_2d(F.leaky_relu(h), ksize=6)
h = F.spatial_pyramid_pooling_2d(F.leaky_relu(h), 3, F.MaxPooling2D)
h = self.__dict__["BNL0"](h)
h = self.__dict__["L1"](F.leaky_relu(h))
h = self.__dict__["BNL1"](h)
h = self.__dict__["L2"](F.leaky_relu(h))
y = h
#h = F.spatial_pyramid_pooling_2d(F.leaky_relu(h), 3)
#y = F.reshape(h,(len(h.data),self.F_unit))
return y
def f(x):
y = functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, self.pooling_class)
return y * y
def forward(self):
x = chainer.Variable(self.x)
return functions.spatial_pyramid_pooling_2d(
x, 3, functions.MaxPooling2D)
def check_invalid_dtype(self):
functions.spatial_pyramid_pooling_2d(
self.v, 3, functions.MaxPooling2D)
def check_invalid_dtype(self):
functions.spatial_pyramid_pooling_2d(
self.v, 3, pooling='max')
def check_invalid_dtype(self):
functions.spatial_pyramid_pooling_2d(
self.v, 3, functions.MaxPooling2D)
def __call__(self, x):
h1 = F.spatial_pyramid_pooling_2d(F.relu(self.conv1(x)), 2,
F.MaxPooling2D)
h2 = F.dropout(F.relu(self.l1(h1)))
y = self.l2(h2)
return y
def check_ambiguous_poolings(self):
with self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(self.v, 3)
def check_invalid_poolings(self):
with self.assertRaises(ValueError):
functions.spatial_pyramid_pooling_2d(self.v, 3, pooling='avg')
def func(self, x):
return functions.spatial_pyramid_pooling_2d(
x, self.pyramid_height, pooling=self.pooling)
def check_invalid_dtype(self):
functions.spatial_pyramid_pooling_2d(self.v, 3, pooling='max')
def __call__(self, x):
""" Forward propagation
Args:
x (a Variable of feature array): a feature array
Returns:
h (a Variable of hidden state array): a hidden state array
"""
dropout_ratio = 0.2
if self.model_arch == 'MP-C-SPP-FC-LSTM': # 69.565
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.tanh(self.input_conv(z)) # 512, ksize=3, stride=1. pad=1
#z = F.spatial_pyramid_pooling_2d(z, 3, pooling_class=F.MaxPooling2D)
z = F.spatial_pyramid_pooling_2d(z, 3, pooling="max")
z = F.tanh(self.input(z))
elif self.model_arch == 'MP-C-SPP-FC-DO-LSTM': # 68.944
z = F.max_pooling_2d(x, 2)
z = F.tanh(self.input_conv(z))
#z = F.spatial_pyramid_pooling_2d(z, 3, pooling_class=F.MaxPooling2D)
z = F.spatial_pyramid_pooling_2d(z, 3, pooling="max")
z = F.tanh(self.input(z))
z = F.dropout(z, ratio=dropout_ratio)
# 12182019
elif self.model_arch == 'MP-C-SPP4-FC-LSTM': # revert stride=2 as previously doesn't work - 12192019
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.tanh(self.input_conv(z)) # 512, ksize=3, stride=1. pad=1
z = F.spatial_pyramid_pooling_2d(z, 4, pooling="max")
z = F.tanh(self.input(z))
elif self.model_arch == 'MP-C-SPP4-FC-DO-LSTM': # 68.944
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.tanh(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.tanh(self.input(z))
z = F.dropout(z, ratio=dropout_ratio)
elif self.model_arch == 'MP-C-SPP4-RL-LSTM':
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.tanh(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.relu(self.input(z))
elif self.model_arch == 'MP-C-SPP4-RL-DO-LSTM':
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.tanh(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.relu(self.input(z))
z = F.dropout(z, ratio=dropout_ratio)
elif self.model_arch == 'MP-C-SPP4-RL2-LSTM':
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.relu(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.relu(self.input(z))
elif self.model_arch == 'MP-C-SPP4-RL2-DO-LSTM':
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.relu(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.relu(self.input(z))
z = F.dropout(z, ratio=dropout_ratio)
# end 12182019
# 12242019
elif self.model_arch == 'MP-C-RL-SPP4-LSTM':
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.relu(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.tanh(self.input(z))
elif self.model_arch == 'MP-C-RL-SPP4-DO-LSTM':
z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.relu(self.input_conv(z))
z = F.spatial_pyramid_pooling_2d(z, 4,
pooling="max") #pyramid_height=4
z = F.tanh(self.input(z))
z = F.dropout(z, ratio=dropout_ratio)
# end 12242019
elif self.model_arch == 'DO-MP-C-SPP-FC-LSTM':
z = F.dropout(x, ratio=dropout_ratio)
z = F.max_pooling_2d(z, 2)
z = F.tanh(self.input_conv(z))
#z = F.spatial_pyramid_pooling_2d(z, 3, pooling_class=F.MaxPooling2D)
z = F.spatial_pyramid_pooling_2d(z, 3, pooling="max")
z = F.tanh(self.input(z))
elif self.model_arch == 'MP-C-SPP-FC-DO-LSTM2' or self.model_arch == 'MP-C-SPP-FC-DO-LSTM3': # 10252019, 10282019
#z = F.max_pooling_2d(x, 2) #11012019
z = F.tanh(self.input_conv(x))
z = F.dropout(z, ratio=dropout_ratio) #11012019
#z = F.max_pooling_2d(z, 2) #11012019
z = F.tanh(self.input_middle_conv(z))
#z = F.max_pooling_2d(z, 2) # 10272019, 11012019
z = F.dropout(z, ratio=dropout_ratio) #11012019
z = F.tanh(self.input_sec_middle_conv(z)) # 10272019
#z = F.max_pooling_2d(z, 2) # 10292019, 11012019
z = F.dropout(z, ratio=dropout_ratio) #11012019
z = F.tanh(self.input_third_middle_conv(z)) # 10292019
#z = F.spatial_pyramid_pooling_2d(z, 3, pooling_class=F.MaxPooling2D)
z = F.spatial_pyramid_pooling_2d(z, 3, pooling="max")
z = F.tanh(self.input(z))
z = F.dropout(z, ratio=dropout_ratio)
elif self.model_arch == 'MP-C-SPP-FC-LSTM2' or self.model_arch == 'MP-C-SPP-FC-LSTM3': # 10252019, 10282019
#z = F.max_pooling_2d(x, 2) # ksize=2, stride=2
z = F.tanh(self.input_conv(x)) # 512, ksize=3, stride=1. pad=1
z = F.dropout(z, ratio=dropout_ratio) #11012019
#z = F.max_pooling_2d(x, 2) #11012019
z = F.tanh(self.input_middle_conv(z))
#z = F.max_pooling_2d(z, 2) # 10272019
z = F.dropout(z, ratio=dropout_ratio) #11012019
z = F.tanh(self.input_sec_middle_conv(z)) # 10272019
#z = F..max_pooling_2d(z, 2) # 10292019
z = F.dropout(z, ratio=dropout_ratio) #11012019
z = F.tanh(self.input_third_middle_conv(z)) # 10292019
#z = F.spatial_pyramid_pooling_2d(z, 3, pooling_class=F.MaxPooling2D)
z = F.spatial_pyramid_pooling_2d(z, 3, pooling="max")
z = F.tanh(self.input(z))
if self.model_arch == 'MP-C-SPP-FC-DO-LSTM2' or self.model_arch == 'MP-C-SPP-FC-LSTM2': #10252019
return self.lstm2(self.lstm(z))
elif self.model_arch == 'MP-C-SPP-FC-DO-LSTM3' or self.model_arch == 'MP-C-SPP-FC-LSTM3': #10282019
return self.lstm3(self.lstm2(self.lstm(z)))
else:
return self.lstm(z)
def __call__(self, x):
return F.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class, self.use_cudnn)
def forward(self, x, t, train=True):
h1 = F.spatial_pyramid_pooling_2d(F.relu(self.conv1(x)), 2,
F.MaxPooling2D)
h2 = F.dropout(F.relu(self.l1(h1)), train=train)
y = self.l2(h2)
return F.softmax_cross_entropy(y, t), F.accuracy(y, t)
def __call__(self, x): return F.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class, self.use_cudnn)
def f(x_data):
return functions.spatial_pyramid_pooling_2d(
x_data, self.pyramid_height, self.pooling_class)
def forward(self):
x = chainer.Variable(self.x)
return functions.spatial_pyramid_pooling_2d(
x, 3, functions.MaxPooling2D,
use_cudnn=self.use_cudnn)
def forward(self):
x = chainer.Variable(self.x)
return functions.spatial_pyramid_pooling_2d(
x, 3, pooling='max')
def __call__(self, x): return functions.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class)
def __call__(self, x): return functions.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class)
