def forward(self, x):
"""Performs forward propagation.
This function can be called using ``__call__`` method.
See following example of method usage.
Args:
x (ndarray): Input data as ndarray.
Returns:
(Node): predicted values for input ndarray.
Example:
>>> import renom as rm
>>> import numpy as np
>>> from renom_rg.api.regression.gcnn import GCNet
>>> n, c, variables, neighbors = (2, 10, 20, 5)
>>> x = rm.Variable(np.random.rand(n, c, variables, neighbors))
>>> feature_graph = np.random.rand(0, variables-1, (variables, neighbors))
>>> model = GCNet(feature_graph)
>>> t = model.forward(x)
>>> t.shape
(2, 1)
"""
h = rm.relu(self.gc1(x))
h = self.dropout(h)
h = rm.relu(self.gc2(h))
h = self.dropout(h)
h = rm.relu(self.gc3(h))
h = self.dropout(h)
h = rm.flatten(h.reshape(h.shape[0], -1, h.shape[1]))
h = self.dropout(rm.relu(self.fc1(h)))
h = self.dropout(rm.relu(self.fc2(h)))
h = self.fc3(h)
return h
def forward(self, x):
hidden = self.input(x)
#print(hidden.shape)
hidden = rm.max_pool2d(hidden, stride=1, padding=1)
#print(hidden.shape)
layers = self.hidden._layers
for i in range(self.blocks):
offset = i * (self.depth * 2 + 1)
for j in range(self.depth):
sub = rm.relu(layers[offset + 2 * j](hidden))
#print('{}.{} b {}'.format(i,j,sub.shape))
sub = layers[offset + 2 * j + 1](sub)
#print('{}.{} + {}'.format(i,j,sub.shape))
if self.dropout:
sub = rm.dropout(sub)
hidden = rm.concat(hidden, sub)
#print('{}.{} = {}'.format(i,j,hidden.shape))
offset = (i + 1) * (self.depth * 2 + 1) - 1
hidden = layers[offset](hidden)
#print('{}.{} - {}'.format(i,j,hidden.shape))
hidden = rm.average_pool2d(hidden, stride=2, padding=1)
#print('{}.{} > {}'.format(i,j,hidden.shape))
x = rm.flatten(hidden)
layers = self.fcnn._layers
for i in range(len(layers[:-2])):
x = rm.relu(layers[i](x))
#print(x.shape)
if self.dropout:
x = rm.dropout(x, dropout_ratio=0.5)
z_mean = layers[-2](x)
z_log_var = layers[-1](x)
return z_mean, z_log_var
def forward(self, x):
layers = self.hidden._layers
for i in range(self.depth):
if self.batch_normal:
x = layers[i * 4](x)
x = rm.relu(layers[i * 4 + 1](x))
x = layers[i * 4 + 2](x)
x = rm.relu(layers[i * 4 + 3](x))
else:
x = rm.relu(layers[i * 2](x))
#print(x.shape)
x = rm.relu(layers[i * 2 + 1](x))
#print(x.shape)
if i == self.depth - 1:
x = rm.average_pool2d(x, stride=2, padding=(1, 1))
else:
x = rm.max_pool2d(x, stride=2, padding=(1, 1))
#print(x.shape)
x = rm.flatten(x)
layers = self.fcnn._layers
for i in range(len(layers[:-2])):
x = rm.relu(layers[i](x))
#print(x.shape)
if self.dropout:
x = rm.dropout(x, dropout_ratio=0.5)
z_mean = layers[-2](x)
z_log_var = layers[-1](x)
return z_mean, z_log_var
def test_gpu_node_flatten(a):
set_cuda_active(True)
g1 = Variable(a)
g3 = rm.sum(rm.flatten(g1))
g = g3.grad()
g_g1 = g.get(g1)
g3.to_cpu()
set_cuda_active(False)
c3 = rm.sum(rm.flatten(g1))
c = c3.grad()
c_g1 = c.get(g1)
close(g3, c3)
close(c_g1, g_g1)
def forward(self, x):
t1 = rm.relu(self._l1(x))
t2 = self._sd(self._pool(rm.relu(self._l2(t1))))
t3 = rm.relu(self._l3(t2))
t4 = self._sd(self._pool(rm.relu(self._l4(t3))))
t5 = rm.flatten(t4)
t6 = rm.dropout(rm.relu(self._l5(t5)))
t7 = self._l6(t5)
return t7
def forward(self, x):
t = self.block1(x)
t = self.block2(t)
t = self.block3(t)
t = self.block4(t)
t = self.block5(t)
t = rm.flatten(t)
t = rm.relu(self.fc1(t))
t = self.dropout1(t)
t = rm.relu(self.fc2(t))
t = self.dropout2(t)
t = self.fc3(t)
return t
def forward(self, x, print_parameter=False):
hidden = self.input(x)
if print_parameter:
print('{}'.format('-' * 20))
print('check network')
print(x.shape)
print('{}'.format('-' * 20))
if self.dropout:
hidden = rm.dropout(hidden)
hidden = rm.max_pool2d(hidden, stride=1, padding=1)
if print_parameter:
print(hidden.shape)
print('{}'.format('-' * 20))
layers = self.hidden._layers
blocks = self.blocks if isinstance(self.blocks, int) else len(
self.blocks)
for i in range(blocks):
offset = i * (self.depth * 2 + 1)
for j in range(self.depth):
sub = rm.leaky_relu(layers[offset + 2 * j](hidden))
if print_parameter:
print('{}.{} b {}'.format(i, j, sub.shape))
sub = layers[offset + 2 * j + 1](sub)
if print_parameter:
print('{}.{} + {}'.format(i, j, sub.shape))
if self.dropout:
sub = rm.dropout(sub)
hidden = rm.concat(hidden, sub)
if print_parameter:
print('{}.{} = {}'.format(i, j, hidden.shape))
offset = (i + 1) * (self.depth * 2 + 1) - 1
hidden = layers[offset](hidden)
if print_parameter:
print('{}.{} * {}'.format(i, j, hidden.shape))
if self.dropout:
if print_parameter:
print('dropout')
hidden = rm.dropout(hidden)
hidden = rm.average_pool2d(hidden,
padding=1,
stride=(1, 2) if self.keep_v else 2)
if print_parameter:
print('{}.{} @ {}'.format(i, j, hidden.shape))
print('{}'.format('-' * 20))
x = rm.flatten(hidden)
if print_parameter:
print(' >>> {} prameters'.format(x.shape))
return x
def forward(self, x, check_network=False):
hidden = x
if check_network:
print('^ {}'.format(hidden.shape))
hidden = self.first(hidden)
if check_network:
print('^ {}'.format(hidden.shape))
layers = self.parameters
for i, layer in enumerate(layers):
if check_network:
print('^ {}'.format(hidden.shape))
hidden = layer(hidden)
if self.batchnormal:
if i % 2 == 1:
hidden = self.act(hidden)
else:
hidden = self.act(hidden)
if check_network:
print('^ {}'.format(hidden.shape))
return rm.flatten(hidden)
def forward(self, x):
i = 0
t = self.base[i](x)
i += 1
t = rm.relu(self.base[i](t))
i += 1
t = rm.max_pool2d(t, filter=3, stride=2, padding=1)
for j in self.layer_per_block[:-1]:
for k in range(j):
tmp = t
t = self.base[i](t)
i += 1
t = rm.concat(tmp, t)
t = self.base[i](t)
i += 1
for j in range(self.layer_per_block[-1]):
tmp = t
t = self.base[i](t)
i += 1
t = rm.concat(tmp, t)
t = rm.average_pool2d(t, filter=7, stride=1)
t = rm.flatten(t)
t = self.fc(t)
return t
def forward(self, x):
n = x.shape[0]
t = x
# Vgg 3rd Block
t = rm.relu(self.conv3_1(t))
t = rm.relu(self.conv3_2(t))
t = rm.relu(self.conv3_3(t))
t = self.pool3(t)
# Vgg 4th Block
t = rm.relu(self.conv4_1(t))
t = rm.relu(self.conv4_2(t))
t = rm.relu(self.conv4_3(t))
# Normalize and compute location, confidence and priorbox aspect ratio
conv4_norm = self.norm(t)
conv4_norm_loc = self.conv4_3_mbox_loc(conv4_norm)
conv4_norm_loc_flat = rm.flatten(conv4_norm_loc.transpose(0, 2, 3, 1))
conv4_norm_conf = self.conv4_3_mbox_conf(conv4_norm)
conv4_norm_conf_flat = rm.flatten(conv4_norm_conf.transpose(
0, 2, 3, 1))
t = self.pool4(t)
# Vgg 5th Block
t = rm.relu(self.conv5_1(t))
t = rm.relu(self.conv5_2(t))
t = rm.relu(self.conv5_3(t))
t = self.pool5(t)
# Vgg 6, 7th Block
t = rm.relu(self.fc6(t))
t = rm.relu(self.fc7(t))
# Confirmed here.
# Normalize and compute location, confidence and priorbox aspect ratio
fc7_mbox_loc = self.fc7_mbox_loc(t)
fc7_mbox_loc_flat = rm.flatten(fc7_mbox_loc.transpose(0, 2, 3, 1))
fc7_mbox_conf = self.fc7_mbox_conf(t)
fc7_mbox_conf_flat = rm.flatten(fc7_mbox_conf.transpose(0, 2, 3, 1))
t = rm.relu(self.conv8_1(t))
t = rm.relu(self.conv8_2(t))
# Normalize and compute location, confidence and priorbox aspect ratio
conv8_mbox_loc = self.conv8_2_mbox_loc(t)
conv8_mbox_loc_flat = rm.flatten(conv8_mbox_loc.transpose(0, 2, 3, 1))
conv8_mbox_conf = self.conv8_2_mbox_conf(t)
conv8_mbox_conf_flat = rm.flatten(conv8_mbox_conf.transpose(
0, 2, 3, 1))
t = rm.relu(self.conv9_1(t))
t = rm.relu(self.conv9_2(t))
# Normalize and compute location, confidence and priorbox aspect ratio
conv9_mbox_loc = self.conv9_2_mbox_loc(t)
conv9_mbox_loc_flat = rm.flatten(conv9_mbox_loc.transpose(0, 2, 3, 1))
conv9_mbox_conf = self.conv9_2_mbox_conf(t)
conv9_mbox_conf_flat = rm.flatten(conv9_mbox_conf.transpose(
0, 2, 3, 1))
t = rm.relu(self.conv10_1(t))
t = rm.relu(self.conv10_2(t))
conv10_mbox_loc = self.conv10_2_mbox_loc(t)
conv10_mbox_loc_flat = rm.flatten(conv10_mbox_loc.transpose(
0, 2, 3, 1))
conv10_mbox_conf = self.conv10_2_mbox_conf(t)
conv10_mbox_conf_flat = rm.flatten(
conv10_mbox_conf.transpose(0, 2, 3, 1))
t = rm.relu(self.conv10_1(t))
t = rm.relu(self.conv10_2(t))
conv11_mbox_loc = self.conv11_2_mbox_loc(t)
conv11_mbox_loc_flat = rm.flatten(conv11_mbox_loc.transpose(
0, 2, 3, 1))
conv11_mbox_conf = self.conv11_2_mbox_conf(t)
conv11_mbox_conf_flat = rm.flatten(
conv11_mbox_conf.transpose(0, 2, 3, 1))
mbox_loc = rm.concat([
conv4_norm_loc_flat, fc7_mbox_loc_flat, conv8_mbox_loc_flat,
conv9_mbox_loc_flat, conv10_mbox_loc_flat, conv11_mbox_loc_flat
])
mbox_conf = rm.concat([
conv4_norm_conf_flat, fc7_mbox_conf_flat, conv8_mbox_conf_flat,
conv9_mbox_conf_flat, conv10_mbox_conf_flat, conv11_mbox_conf_flat
])
mbox_loc = mbox_loc.reshape((n, -1, 4))
mbox_conf = mbox_conf.reshape((n, -1, self.num_class))
predictions = rm.concat([mbox_loc, mbox_conf], axis=2)
return predictions
def forward(self, x):
n = x.shape[0]
t = x
t = self.pool3(
rm.relu(
self.conv3_3(rm.relu(self.conv3_2(rm.relu(self.conv3_1(t)))))))
t = rm.relu(
self.conv4_3(rm.relu(self.conv4_2(rm.relu(self.conv4_1(t))))))
# Normalize and compute location, confidence and priorbox aspect ratio
conv4_norm = self.norm(t)
#conv4_norm = t
conv4_norm_loc = self.conv4_3_mbox_loc(conv4_norm)
conv4_norm_loc_flat = rm.flatten(conv4_norm_loc)
conv4_norm_conf = self.conv4_3_mbox_conf(conv4_norm)
conv4_norm_conf_flat = rm.flatten(conv4_norm_conf)
conv4_priorbox = self.conv4_3_priorbox(conv4_norm)
t = self.pool4(t)
t = self.pool5(
rm.relu(
self.conv5_3(rm.relu(self.conv5_2(rm.relu(self.conv5_1(t)))))))
t = rm.relu(self.fc6(t))
t = rm.relu(self.fc7(t))
# Normalize and compute location, confidence and priorbox aspect ratio
fc7_mbox_loc = self.fc7_mbox_loc(t)
fc7_mbox_loc_flat = rm.flatten(fc7_mbox_loc)
fc7_mbox_conf = self.fc7_mbox_conf(t)
fc7_mbox_conf_flat = rm.flatten(fc7_mbox_conf)
fc7_priorbox = self.fc7_priorbox(t)
t = rm.relu(self.conv8_2(rm.relu(self.conv8_1(t))))
# Normalize and compute location, confidence and priorbox aspect ratio
conv8_mbox_loc = self.conv8_2_mbox_loc(t)
conv8_mbox_loc_flat = rm.flatten(conv8_mbox_loc)
conv8_mbox_conf = self.conv8_2_mbox_conf(t)
conv8_mbox_conf_flat = rm.flatten(conv8_mbox_conf)
conv8_priorbox = self.conv8_2_priorbox(t)
t = rm.relu(self.conv9_2(rm.relu(self.conv9_1(t))))
# Normalize and compute location, confidence and priorbox aspect ratio
conv9_mbox_loc = self.conv9_2_mbox_loc(t)
conv9_mbox_loc_flat = rm.flatten(conv9_mbox_loc)
conv9_mbox_conf = self.conv9_2_mbox_conf(t)
conv9_mbox_conf_flat = rm.flatten(conv9_mbox_conf)
conv9_priorbox = self.conv9_2_priorbox(t)
t = rm.relu(self.conv10_2(rm.relu(self.conv10_1(t))))
conv10_mbox_loc = self.conv10_2_mbox_loc(t)
conv10_mbox_loc_flat = rm.flatten(conv10_mbox_loc)
conv10_mbox_conf = self.conv10_2_mbox_conf(t)
conv10_mbox_conf_flat = rm.flatten(conv10_mbox_conf)
conv10_priorbox = self.conv10_2_priorbox(t)
t = rm.average_pool2d(t)
t = rm.flatten(t)
pool11_mbox_loc_flat = self.pool11_mbox_loc(t)
pool11_mbox_conf_flat = self.pool11_mbox_conf(t)
pool11_reshaped = t.reshape((t.shape[0], 256, 1, 1))
pool11_priorbox = self.pool11_priorbox(pool11_reshaped)
mbox_loc = rm.concat([
conv4_norm_loc_flat, fc7_mbox_loc_flat, conv8_mbox_loc_flat,
conv9_mbox_loc_flat, conv10_mbox_loc_flat, pool11_mbox_loc_flat
])
mbox_conf = rm.concat([
conv4_norm_conf_flat, fc7_mbox_conf_flat, conv8_mbox_conf_flat,
conv9_mbox_conf_flat, conv10_mbox_conf_flat, pool11_mbox_conf_flat
])
mbox_priorbox = np.concatenate([
conv4_priorbox, fc7_priorbox, conv8_priorbox, conv9_priorbox,
conv10_priorbox, pool11_priorbox
],
axis=1)
num_boxes = mbox_loc.shape[-1] // 4
mbox_loc = mbox_loc.reshape((n, 4, num_boxes))
mbox_conf = mbox_conf.reshape((n, self.num_class, num_boxes))
predictions = rm.concat([
mbox_loc, mbox_conf,
np.broadcast_to(mbox_priorbox.transpose((0, 2, 1)),
(mbox_conf.shape[0], mbox_priorbox.shape[2],
mbox_priorbox.shape[1]))
])
return predictions
