def forward(self, x, t): hierarchical_losses = {} y, hy = self.model(x, save_hierarchy=True) loss = F.softmax_cross_entropy(y, t) loss_final = loss # Save current inference if self.keep_inference: self.y = y self.hy = hy hy = sorted(hy.items(), key=lambda x: x[0], reverse=True) dt = F.expand_dims(chainer.Variable(t.astype(cp.float32)), axis=1) for level, volume in hy: dt = F.max_pooling_3d(dt, ksize=2, stride=2) hl = F.softmax_cross_entropy( volume, chainer.Variable(dt[:, 0, ...].data.astype(cp.int32))) hierarchical_losses[level] = hl loss += hl # Report losses metrics = { 'loss_l{}'.format(k): v for k, v in hierarchical_losses.items() } metrics['loss_l{}'.format(max(hierarchical_losses) + 1)] = loss_final metrics['loss'] = loss reporter.report(metrics, self.observer) return loss
def predict(self, loading, fnc, net_type, spatial_map, **kwargs): batch_size, channel, x, y, z = spatial_map.shape xp = chainer.cuda.get_array_module(spatial_map) spatial_map = spatial_map.astype(xp.float32) / 3000.0 spatial_map[spatial_map > 2.0] = 2.0 spatial_map[spatial_map < -2.0] = -2.0 sm = spatial_map.reshape(batch_size * channel, 1, x, y, z) sm = F.pad(sm, ((0, 0), (0, 0), (6, 5), (1, 0), (6, 6)), 'constant', constant_values=0) sm = F.max_pooling_3d(F.relu(self.bn1(self.conv_3d_1(sm))), ksize=2) # 64 x 64 sm = F.max_pooling_3d(F.relu(self.bn2(self.conv_3d_2(sm))), ksize=2) # 32 x 32 sm = F.max_pooling_3d(F.relu(self.bn3(self.conv_3d_3(sm))), ksize=2) # 16 x 16 sm = F.max_pooling_3d(F.relu(self.bn4(self.conv_3d_4(sm))), ksize=4) # 8 x 8 sm = sm.reshape(batch_size, channel, 32) h = self.gn1(F.concat([net_type, sm], axis=2)) e = F.expand_dims(fnc, 3) # adj = (xp.abs(fnc) > 1e-5).astype(np.float32) # adj_mask = F.tile(F.expand_dims(adj, 3), (1, 1, 1, self.edge_dim)) for layer in range(self.num_layer): e = self['eup{}'.format(layer)](e, h) h = self['int{}'.format(layer)](h, e) h_mean = F.mean(h, axis=1) h_max = F.mean(h, axis=1) h = F.concat([loading * 10.0, h_mean, h_max], 1) # h = self.gn2(h) # h = F.concat([loading * 10.0, h.reshape(batch_size, -1)], 1) h = F.relu(self.n1(h)) out = self.n2(h) return out
def __call__(self, x): dropout_ratio = CONFIG.fc_dropout_rate batch_size, sequence_len, ch, w, h_ = x.shape if CONFIG.conv_type == "3D": x = x.transpose(0, 2, 1, 3, 4) elif CONFIG.conv_type == "2D": x = x.reshape(-1, ch, w, h_) elif CONFIG.conv_type == "k": x = x.reshape(-1, 2, ch, w, h_) x = x.transpose(0, 2, 1, 3, 4) h = self.conv5_1(x) if self.use_bn: h = self.bn5_1(h) h = F.relu(h) h = self.conv5_2(x) if self.use_bn: h = self.bn5_2(h) h = F.relu(h) if CONFIG.conv_type == "3D": # batch_size, ch, sequence_length, w, h >> batch_size, sequence_length, ch, w, h h = h.transpose(0, 2, 1, 3, 4) # batch_size, sequence_length, ch, w, h >> batch_size*sequence_len, ch, w, h h = h.reshape(batch_size * sequence_len, ch, w, h_) h = F.max_pooling_2d(h, ksize=h.shape[-1], stride=1, pad=0) elif CONFIG.conv_type == "2D": h = F.max_pooling_2d(h, ksize=h.shape[-1], stride=1, pad=0) elif CONFIG.conv_type == "k": # batch_size, ch, sequence_length, w, h h = F.max_pooling_3d(h, ksize=(h.shape[2], h.shape[3], h.shape[4]), stride=1, pad=0) h = self.fc1(h) h = F.relu(h) if dropout_ratio > 0: h = F.dropout(h, dropout_ratio) h = self.fc2(h) h = F.relu(h) h = self.fc3(h) h = h.reshape(batch_size, -1, 128) return h
def test_max_pooling_3d_invalid(self): (x, ksize) = self._get_data(2) with self.assertRaises(ValueError): functions.max_pooling_3d(x, ksize)
def test_max_pooling_3d(self): (x, ksize) = self._get_data(3) testing.assert_allclose( functions.max_pooling_nd(x, ksize).data, functions.max_pooling_3d(x, ksize).data)
def test_max_pooling_3d_invalid(self): (x, ksize) = self._get_data(2) with self.assertRaises(ValueError): functions.max_pooling_3d(x, ksize)
def test_max_pooling_3d(self): (x, ksize) = self._get_data(3) testing.assert_allclose( functions.max_pooling_nd(x, ksize).data, functions.max_pooling_3d(x, ksize).data)