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