def MeanFieldUpdate(n, bottom_send, bottom_receive, feat_ind, mf_iter, feat_num): ''' Meanfield updating for the features and the attention for one pair of features. bottom_list is a list of observation features derived from the backbone CNN. ''' #generating an attention map concat_f = 'concat_f{}_mf{}'.format(feat_ind, mf_iter) conv_f = 'conv_f{}_mf{}'.format(feat_ind, mf_iter) atten_f = 'atten_f{}_mf{}'.format(feat_ind, mf_iter) norm_atten_f = 'norm_atten_f{}_mf{}'.format(feat_ind, mf_iter) norm_atten_f_tile = 'norm_atten_f_tile{}_mf{}'.format(feat_ind, mf_iter) message_f = 'message_f{}_mf{}'.format(feat_ind, mf_iter) filter_message_f = 'filter_message_f{}_mf{}'.format(feat_ind, mf_iter) message_scaled = 'message_scaled_f{}_mf{}'.format(feat_ind, mf_iter) updated_f = 'updated_f{}_mf{}'.format(feat_ind, mf_iter) n[concat_f] = L.Concat(bottom_send, bottom_receive) #specify parameter names to make them share between different meanfield updating n[atten_f] = L.Convolution(n[concat_f], num_output=1, kernel_size=3, stride=1, pad=1, param=[ dict(name='atten_f{}_w'.format(feat_ind), lr_mult=1, decay_mult=1), dict(name='atten_f{}_b'.format(feat_ind), lr_mult=2, decay_mult=0) ]) n[norm_atten_f] = L.Sigmoid(n[atten_f]) n[norm_atten_f_tile] = L.Tile(net[norm_atten_f], tile_param=dict(axis=1, tiles=feat_num)) n[message_f] = L.Convolution( bottom_send, num_output=feat_num, kernel_size=3, stride=1, pad=1, param=[ dict(name='message_f{}_w'.format(feat_ind), lr_mult=1, decay_mult=1), dict(name='message_f{}_b'.format(feat_ind), lr_mult=2, decay_mult=0) ]) n[filter_message_f] = L.Eltwise(n[message_f], n[norm_atten_f_tile], operation=P.Eltwise.PROD) #scale the messages before adding n[message_scaled] = L.Scale(n[filter_message_f], bias_term=True, in_place=True) n[updated_f] = L.Eltwise(bottom_receive, n[message_scaled], operation=P.Eltwise.SUM)
def decode_features(pixel_spixel_assoc, spixel_feat, spixel_init, num_spixels_h, num_spixels_w, num_spixels, num_channels): num_channels = int(num_channels) # Reshape superpixel features to k_h x k_w spixel_feat_reshaped = L.Reshape( spixel_feat, reshape_param=dict( shape={'dim': [0, 0, num_spixels_h, num_spixels_w]})) # Concatenate neighboring superixel features concat_spixel_feat = L.Convolution( spixel_feat_reshaped, name='concat_spixel_feat_' + str(num_channels), convolution_param=dict(num_output=num_channels * 9, kernel_size=3, stride=1, pad=1, group=num_channels, bias_term=False), param=[{ 'name': 'concat_spixel_feat_' + str(num_channels), 'lr_mult': 0, 'decay_mult': 0 }]) # Spread features to pixels flat_concat_label = L.Reshape( concat_spixel_feat, reshape_param=dict(shape={'dim': [0, 0, 1, num_spixels]})) img_concat_spixel_feat = L.Smear(flat_concat_label, spixel_init) tiled_assoc = L.Tile(pixel_spixel_assoc, tile_param=dict(tiles=num_channels)) weighted_spixel_feat = L.Eltwise( img_concat_spixel_feat, tiled_assoc, eltwise_param=dict(operation=P.Eltwise.PROD)) recon_feat = L.Convolution(weighted_spixel_feat, name='recon_feat_' + str(num_channels), convolution_param=dict(num_output=num_channels, kernel_size=1, stride=1, pad=0, group=num_channels, bias_term=False, weight_filler=dict( type='constant', value=1.0)), param=[{ 'name': 'recon_feat_' + str(num_channels), 'lr_mult': 0, 'decay_mult': 0 }]) return recon_feat
def l2normed(self,vec, dim): #Returns L2-normalized instances of vec; i.e., for each instance x in vec, #computes x / ((x ** 2).sum() ** 0.5). Assumes vec has shape N x dim.""" denom = L.Reduction(vec, axis=1, operation=P.Reduction.SUMSQ) denom = L.Power(denom, power=(-0.5), shift=1e-12) denom = L.Reshape(denom, num_axes=0, axis=-1, shape=dict(dim=[1])) denom = L.Tile(denom, axis=1, tiles=dim) return L.Eltwise(vec, denom, operation=P.Eltwise.PROD)
def normalize(self, bottom, axis=1, numtiles=4096): power = L.Power(bottom, power=2) power_sum = L.Reduction(power, axis=axis, operation=1) sqrt = L.Power(power_sum, power=-0.5, shift=0.00001) if axis == 1: reshape = L.Reshape(sqrt, shape=dict(dim=[-1, 1])) if axis == 2: reshape = L.Reshape(sqrt, shape=dict(dim=[self.batch_size, -1, 1])) tile = L.Tile(reshape, axis=axis, tiles=numtiles) return L.Eltwise(tile, bottom, operation=0)
def build_relational_model_deploy(self, save_tag, visual_feature_dim, language_feature_dim): image_input = L.DummyData( shape=[dict(dim=[21, 1, visual_feature_dim + 2])], ntop=1) setattr(self.n, 'image_data', image_input) image_global = L.DummyData( shape=[dict(dim=[21, 21, visual_feature_dim + 2])], ntop=1) setattr(self.n, 'global_data', image_global) im_model, lang_model = self.get_models() self.silence_count += 1 bottom_tile = L.Tile(image_input, axis=1, tiles=21) bottom_concat = L.Concat(bottom_tile, image_global, axis=2) bottom_visual = im_model(bottom_concat, axis=2) text_input = L.DummyData(shape=[ dict( dim=[self.params['sentence_length'], 21, language_feature_dim]) ], ntop=1) setattr(self.n, 'text_data', text_input) cont_input = L.DummyData( shape=[dict(dim=[self.params['sentence_length'], 21])], ntop=1) setattr(self.n, 'cont_data', cont_input) bottom_text = lang_model(text_input, cont_input) t_reshape = L.Reshape(bottom_text, shape=dict(dim=[self.batch_size, 1, -1])) t_tile = L.Tile(t_reshape, axis=1, tiles=21) self.n.tops['scores'] = self.distance_function(bottom_visual, t_tile)[0] self.write_net(save_tag, self.n)
def normalize(bottom, dim): bottom_relu = L.ReLU(bottom) sum = L.Convolution(bottom_relu, convolution_param = dict(num_output = 1, kernel_size = 1, stride = 1, weight_filler = dict(type = 'constant', value = 1), bias_filler = dict(type = 'constant', value = 0)), param=[{'lr_mult':0, 'decay_mult':0}, {'lr_mult':0, 'decay_mult':0}]) denom = L.Power(sum, power=(-1.0), shift=1e-12) denom = L.Tile(denom, axis=1, tiles=dim) return L.Eltwise(bottom_relu, denom, operation=P.Eltwise.PROD)
def exp_proto(mode, batchsize, T, exp_T, question_vocab_size, exp_vocab_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.exp_att_feature, n.exp, n.exp_out, n.exp_cont_1, n.exp_cont_2 = \ L.Python(module='exp_data_provider_layer', layer='ExpDataProviderLayer', param_str=mode_str, ntop=5) n.exp_embed_ba = L.Embed(n.exp, input_dim=exp_vocab_size, num_output=300, \ weight_filler=dict(type='uniform', min=-0.08, max=0.08)) n.exp_embed = L.TanH(n.exp_embed_ba) # LSTM1 for Explanation n.exp_lstm1 = L.LSTM(\ n.exp_embed, n.exp_cont_1,\ recurrent_param=dict(\ num_output=2048,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) n.exp_lstm1_dropped = L.Dropout(n.exp_lstm1, dropout_param={'dropout_ratio': 0.3}) # Merge with LSTM1 for explanation n.exp_att_resh = L.Reshape( n.exp_att_feature, reshape_param=dict(shape=dict(dim=[1, -1, 2048]))) n.exp_att_tiled = L.Tile(n.exp_att_resh, axis=0, tiles=exp_T) n.exp_eltwise_all = L.Eltwise(n.exp_lstm1_dropped, n.exp_att_tiled, eltwise_param={'operation': P.Eltwise.PROD}) n.exp_eltwise_all_sqrt = L.SignedSqrt(n.exp_eltwise_all) n.exp_eltwise_all_l2 = L.L2Normalize(n.exp_eltwise_all_sqrt) n.exp_eltwise_all_drop = L.Dropout(n.exp_eltwise_all_l2, dropout_param={'dropout_ratio': 0.3}) # LSTM2 for Explanation n.exp_lstm2 = L.LSTM(\ n.exp_eltwise_all_drop, n.exp_cont_2,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) n.exp_lstm2_dropped = L.Dropout(n.exp_lstm2, dropout_param={'dropout_ratio': 0.3}) n.exp_prediction = L.InnerProduct(n.exp_lstm2_dropped, num_output=exp_vocab_size, weight_filler=dict(type='xavier'), axis=2) n.silence_exp_prediction = L.Silence(n.exp_prediction, ntop=0) return n.to_proto()
def l2normed(dim): n = caffe.NetSpec() n.data, n.label = L.Python(module='layers', layer='tripletDataLayer', ntop=2) """Returns L2-normalized instances of vec; i.e., for each instance x in vec, computes x / ((x ** 2).sum() ** 0.5). Assumes vec has shape N x dim.""" n.denom = L.Reduction(n.data, axis=1, operation=P.Reduction.SUMSQ) #denom = L.Power(denom, power=(-0.5)) n.power = L.Power(n.denom, power=(-0.5), shift=1e-12) # For numerical stability n.reshape = L.Reshape(n.power, num_axes=0, axis=-1, shape=dict(dim=[1])) n.tile = L.Tile(n.reshape, axis=1, tiles=dim) n.elwise = L.Eltwise(n.data, n.tile, operation=P.Eltwise.PROD) return n.to_proto()
def concat(n, q_layer, v_layer): # input: q_layer:(N,1024) v_layer:(N,100,2053) n.q_emb_resh1 = L.Reshape( q_layer, reshape_param=dict(shape=dict(dim=[0, 1, cfg.RNN_DIM]))) n.q_emb_tile = L.Tile(n.q_emb_resh1, axis=1, tiles=cfg.RPN_TOPN) n.q_emb_resh = L.Reshape( n.q_emb_tile, reshape_param=dict(shape=dict(dim=[-1, cfg.RNN_DIM]))) n.v_emb_resh = L.Reshape( v_layer, reshape_param=dict(shape=dict( dim=[-1, cfg.SPT_FEAT_DIM + cfg.BOTTOMUP_FEAT_DIM]))) n.qv_fuse = L.Concat(n.q_emb_resh, n.v_emb_resh, concat_param={'axis': 1}) n.qv_fc1 = L.InnerProduct(n.qv_fuse, num_output=512, weight_filler=dict(type='xavier')) n.qv_relu = L.ReLU(n.qv_fc1) return n.qv_relu
def mask_unit(net,input_name,idx,feature_dim,each_dim): #map_num att_map net['mask_conv'+idx]=L.Convolution(net[input_name],kernel_size=1,num_output=1, \ param = [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],\ weight_filler=dict(type="xavier", variance_norm=2), \ bias_filler=dict(type="constant")) #input ~ (-1,1), rescale to range (0,1) net['mask_map'+idx]=L.Sigmoid(net['mask_conv'+idx]) net['tile_map'+idx]=L.Tile(net['mask_map'+idx],tile_param=dict(tiles=feature_dim)) net['masked'+idx]=L.Eltwise(net[input_name],net['tile_map'+idx],\ eltwise_param=dict(operation=0)) net['pooled'+idx]=L.Pooling(net['masked'+idx],pooling_param=dict(pool=1,global_pooling=1)) net['linear'+idx]=L.InnerProduct(net['pooled'+idx], num_output=each_dim, \ param = [dict(lr_mult=1, decay_mult=1), \ dict(lr_mult=2, decay_mult=0)], weight_filler=dict(type="xavier"), bias_filler=dict(type="constant")) return net['linear'+idx]
def SqeezeExcitationLayer(caffe_net, layer_idx, bottom_blob, in_channel, reduced_ch, height, width, bias_term=False): names = ['gPool{}'.format(layer_idx), 'fc{}a'.format(layer_idx), 'fc{}a_relu'.format(layer_idx), 'fc{}b'.format(layer_idx), 'fc{}b_sigmoid'.format(layer_idx), 'tile{}'.format(layer_idx), 'reshape{}'.format(layer_idx), 'eltwise{}'.format(layer_idx), ] start_bottom_blob = bottom_blob caffe_net[names[0]] = L.Pooling(bottom_blob, pool=P.Pooling.AVE, global_pooling=True) caffe_net[names[1]] = L.InnerProduct(caffe_net[names[0]], num_output=reduced_ch, bias_term=bias_term) caffe_net[names[2]] = L.ReLU(caffe_net[names[1]], in_place=True) caffe_net[names[3]] = L.InnerProduct(caffe_net[names[2]], num_output=in_channel, bias_term=bias_term) caffe_net[names[4]] = L.Sigmoid(caffe_net[names[3]]) caffe_net[names[5]] = L.Tile(caffe_net[names[4]], axis = 1, tiles = height*width) caffe_net[names[6]] = L.Reshape(caffe_net[names[5]], reshape_param={'shape':{'dim': [0, in_channel, height, width]}}) caffe_net[names[7]] = L.Eltwise(caffe_net[names[6]], start_bottom_blob, operation=P.Eltwise.PROD ) return caffe_net[names[7]], layer_idx + 1
def generate_model(split, config): n = caffe.NetSpec() batch_size = config.N mode_str = str(dict(split=split, batch_size=batch_size)) n.language, n.cont, n.image, n.spatial, n.label = L.Python(module=config.data_provider, layer=config.data_provider_layer, param_str=mode_str, ntop=5) # the base net (VGG-16) n.conv1_1, n.relu1_1 = conv_relu(n.image, 64, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.pool1 = max_pool(n.relu1_2) n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.pool2 = max_pool(n.relu2_2) n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.pool3 = max_pool(n.relu3_3) n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.pool4 = max_pool(n.relu4_3) n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512, fix_param=config.fix_vgg, finetune=(not config.fix_vgg)) n.pool5 = max_pool(n.relu5_3) # fully conv n.fcn_fc6, n.fcn_relu6 = conv_relu(n.pool5, 4096, ks=7, pad=3) if config.vgg_dropout: n.fcn_drop6 = L.Dropout(n.fcn_relu6, dropout_ratio=0.5, in_place=True) n.fcn_fc7, n.fcn_relu7 = conv_relu(n.fcn_drop6, 4096, ks=1, pad=0) n.fcn_drop7 = L.Dropout(n.fcn_relu7, dropout_ratio=0.5, in_place=True) n.fcn_fc8 = conv(n.fcn_drop7, 1000, ks=1, pad=0) else: n.fcn_fc7, n.fcn_relu7 = conv_relu(n.fcn_relu6, 4096, ks=1, pad=0) n.fcn_fc8 = conv(n.fcn_relu7, 1000, ks=1, pad=0) # embedding n.embed = L.Embed(n.language, input_dim=config.vocab_size, num_output=config.embed_dim, weight_filler=dict(type='uniform', min=-0.08, max=0.08)) # LSTM n.lstm = L.LSTM(n.embed, n.cont, recurrent_param=dict(num_output=config.lstm_dim, weight_filler=dict(type='uniform', min=-0.08, max=0.08), bias_filler=dict(type='constant', value=0))) tops = L.Slice(n.lstm, ntop=config.T, slice_param=dict(axis=0)) for i in range(config.T - 1): n.__setattr__('slice'+str(i), tops[i]) n.__setattr__('silence'+str(i), L.Silence(tops[i], ntop=0)) n.lstm_out = tops[-1] n.lstm_feat = L.Reshape(n.lstm_out, reshape_param=dict(shape=dict(dim=[-1, config.lstm_dim]))) # Tile LSTM feature n.lstm_resh = L.Reshape(n.lstm_feat, reshape_param=dict(shape=dict(dim=[-1, config.lstm_dim, 1, 1]))) n.lstm_tile_1 = L.Tile(n.lstm_resh, axis=2, tiles=config.featmap_H) n.lstm_tile_2 = L.Tile(n.lstm_tile_1, axis=3, tiles=config.featmap_W) # L2 Normalize image and language features n.img_l2norm = L.L2Normalize(n.fcn_fc8) n.lstm_l2norm = L.L2Normalize(n.lstm_tile_2) # Concatenate n.feat_all = L.Concat(n.lstm_l2norm, n.img_l2norm, n.spatial, concat_param=dict(axis=1)) # MLP Classifier over concatenated feature n.fcn_l1, n.fcn_relu1 = conv_relu(n.feat_all, config.mlp_hidden_dims, ks=1, pad=0) if config.mlp_dropout: n.fcn_drop1 = L.Dropout(n.fcn_relu1, dropout_ratio=0.5, in_place=True) n.fcn_scores = conv(n.fcn_drop1, 1, ks=1, pad=0) else: n.fcn_scores = conv(n.fcn_relu1, 1, ks=1, pad=0) # Loss Layer n.loss = L.SigmoidCrossEntropyLoss(n.fcn_scores, n.label) return n.to_proto()
def act_proto(mode, batchsize, exp_vocab_size, use_gt=True): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.img_feature, n.label, n.exp, n.exp_out, n.exp_cont_1, n.exp_cont_2 = \ L.Python(module='activity_data_provider_layer', layer='ActivityDataProviderLayer', param_str=mode_str, ntop=6) # Attention n.att_conv1 = L.Convolution(n.img_feature, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=1, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 1, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att_map = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 1, 14, 14]))) dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature = L.SoftAttention(n.img_feature, n.att_map, dummy) n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 2048]))) # Prediction n.prediction = L.InnerProduct(n.att_feature_resh, num_output=config.NUM_OUTPUT_UNITS, weight_filler=dict(type='xavier'), param=fixed_weights) # Take GT answer or Take the logits of the VQA model and get predicted answer to embed if use_gt: n.exp_emb_ans = L.Embed(n.label, input_dim=config.NUM_OUTPUT_UNITS, num_output=300, weight_filler=dict(type='uniform', min=-0.08, max=0.08)) else: n.vqa_ans = L.ArgMax(n.prediction, axis=1) n.exp_emb_ans = L.Embed(n.vqa_ans, input_dim=config.NUM_OUTPUT_UNITS, num_output=300, weight_filler=dict(type='uniform', min=-0.08, max=0.08)) n.exp_emb_ans_tanh = L.TanH(n.exp_emb_ans) n.exp_emb_ans2 = L.InnerProduct(n.exp_emb_ans_tanh, num_output=2048, weight_filler=dict(type='xavier')) # Merge activity answer and visual feature n.exp_emb_resh = L.Reshape( n.exp_emb_ans2, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) n.exp_emb_tiled_1 = L.Tile(n.exp_emb_resh, axis=2, tiles=14) n.exp_emb_tiled = L.Tile(n.exp_emb_tiled_1, axis=3, tiles=14) n.img_embed = L.Convolution(n.img_feature, kernel_size=1, stride=1, num_output=2048, pad=0, weight_filler=dict(type='xavier')) n.exp_eltwise = L.Eltwise(n.img_embed, n.exp_emb_tiled, eltwise_param={'operation': P.Eltwise.PROD}) n.exp_eltwise_sqrt = L.SignedSqrt(n.exp_eltwise) n.exp_eltwise_l2 = L.L2Normalize(n.exp_eltwise_sqrt) n.exp_eltwise_drop = L.Dropout(n.exp_eltwise_l2, dropout_param={'dropout_ratio': 0.3}) # Attention for Explanation n.exp_att_conv1 = L.Convolution(n.exp_eltwise_drop, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.exp_att_conv1_relu = L.ReLU(n.exp_att_conv1) n.exp_att_conv2 = L.Convolution(n.exp_att_conv1_relu, kernel_size=1, stride=1, num_output=1, pad=0, weight_filler=dict(type='xavier')) n.exp_att_reshaped = L.Reshape( n.exp_att_conv2, reshape_param=dict(shape=dict(dim=[-1, 1, 14 * 14]))) n.exp_att_softmax = L.Softmax(n.exp_att_reshaped, axis=2) n.exp_att_map = L.Reshape( n.exp_att_softmax, reshape_param=dict(shape=dict(dim=[-1, 1, 14, 14]))) exp_dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.exp_att_feature_prev = L.SoftAttention(n.img_feature, n.exp_att_map, exp_dummy) n.exp_att_feature_resh = L.Reshape( n.exp_att_feature_prev, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.exp_att_feature_embed = L.InnerProduct(n.exp_att_feature_resh, num_output=2048, weight_filler=dict(type='xavier')) n.exp_att_feature = L.Eltwise(n.exp_emb_ans2, n.exp_att_feature_embed, eltwise_param={'operation': P.Eltwise.PROD}) n.silence_exp_att = L.Silence(n.exp_att_feature, ntop=0) return n.to_proto()
def build_relational_model(self, param_str, save_tag): data = L.Python(module="data_processing", layer=self.data_layer, param_str=str(param_str), ntop=self.top_size) for key, value in zip(self.params['top_names_dict'].keys(), self.params['top_names_dict'].values()): setattr(self.n, key, data[value]) im_model, lang_model = self.get_models() #bottoms which are always produced bottom_positive = data[self.top_name_dict['features_p']] bottom_negative = data[self.top_name_dict['features_n']] # 'global' is carryover name from MCN -- global == context moment here. global_positive = data[self.top_name_dict['features_global_p']] bottom_positive_tile = L.Tile(bottom_positive, axis=1, tiles=21) bottom_negative_tile = L.Tile(bottom_negative, axis=1, tiles=21) concat_positive = L.Concat(bottom_positive_tile, global_positive, axis=2) concat_negative = L.Concat(bottom_negative_tile, global_positive, axis=2) if self.inter: bottom_inter = data[self.top_name_dict['features_inter']] global_inter = data[self.top_name_dict['features_global_inter']] bottom_inter_tile = L.Tile(bottom_inter, axis=1, tiles=21) concat_inter = L.Concat(bottom_inter_tile, global_inter, axis=2) query = data[self.top_name_dict['BoG']] bottom_positive_feature = im_model(concat_positive, axis=2) bottom_negative_feature = im_model(concat_negative, axis=2) if self.inter: bottom_inter_feature = im_model(concat_inter, axis=2) #'cont' is for LSTM in Caffe -- would not need this if using average Glove features. cont = data[self.top_name_dict['cont']] query = lang_model(query, cont) t_reshape = L.Reshape(query, shape=dict(dim=[self.batch_size, 1, -1])) t_tile = L.Tile(t_reshape, axis=1, tiles=21) #loss function distance_p = self.distance_function(bottom_positive_feature, t_tile) distance_n = self.distance_function(bottom_negative_feature, t_tile) setattr(self.n, 'distance_p', distance_p[0]) setattr(self.n, 'distance_n', distance_n[0]) if self.inter: distance_inter = self.distance_function(bottom_inter_feature, t_tile) setattr(self.n, 'distance_inter', distance_inter[0]) self.n.tops['ranking_loss_inter'] = self.relational_ranking_loss( distance_p[0], distance_inter[0], lw=self.lw_inter) self.n.tops['ranking_loss_intra'] = self.relational_ranking_loss( distance_p[0], distance_n[0], lw=self.lw_intra) if self.args.strong_supervise: self.n.tops[ 'context_supervision_loss'] = self.context_supervision_loss( distance_p[1], lw=self.args.lw_strong_supervision, ind_loss=data[ self.top_name_dict['strong_supervision_loss']]) if self.args.stronger_supervise: #can also assert that the model needs to look at the correct context for the neg moment. self.n.tops[ 'negative_context_supervision_loss'] = self.context_supervision_loss( distance_n[1], lw=self.args.lw_strong_supervision, ind_loss=data[ self.top_name_dict['strong_supervision_loss']]) self.write_net(save_tag, self.n)
def pj_x(mode, batchsize, exp_T, exp_vocab_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.img_feature, n.label, n.exp, n.exp_out, n.exp_cont_1, n.exp_cont_2 = \ L.Python(module='activity_data_provider_layer', layer='ActivityDataProviderLayer', param_str=mode_str, ntop=6) # Attention n.att_conv1 = L.Convolution(n.img_feature, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=1, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 1, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att_map = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 1, 14, 14]))) dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature = L.SoftAttention(n.img_feature, n.att_map, dummy) n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 2048]))) # Prediction n.prediction = L.InnerProduct(n.att_feature_resh, num_output=config.NUM_OUTPUT_UNITS, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) n.accuracy = L.Accuracy(n.prediction, n.label) # Embed Activity GT answer during training n.exp_emb_ans = L.Embed(n.label, input_dim=config.NUM_OUTPUT_UNITS, num_output=300, \ weight_filler=dict(type='uniform', min=-0.08, max=0.08)) n.exp_emb_ans_tanh = L.TanH(n.exp_emb_ans) n.exp_emb_ans2 = L.InnerProduct(n.exp_emb_ans_tanh, num_output=2048, weight_filler=dict(type='xavier')) # merge activity answer and visual feature n.exp_emb_resh = L.Reshape( n.exp_emb_ans2, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) n.exp_emb_tiled_1 = L.Tile(n.exp_emb_resh, axis=2, tiles=14) n.exp_emb_tiled = L.Tile(n.exp_emb_tiled_1, axis=3, tiles=14) n.img_embed = L.Convolution(n.img_feature, kernel_size=1, stride=1, num_output=2048, pad=0, weight_filler=dict(type='xavier')) n.exp_eltwise = L.Eltwise(n.img_embed, n.exp_emb_tiled, eltwise_param={'operation': P.Eltwise.PROD}) n.exp_eltwise_sqrt = L.SignedSqrt(n.exp_eltwise) n.exp_eltwise_l2 = L.L2Normalize(n.exp_eltwise_sqrt) n.exp_eltwise_drop = L.Dropout(n.exp_eltwise_l2, dropout_param={'dropout_ratio': 0.3}) # Attention for Explanation n.exp_att_conv1 = L.Convolution(n.exp_eltwise_drop, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.exp_att_conv1_relu = L.ReLU(n.exp_att_conv1) n.exp_att_conv2 = L.Convolution(n.exp_att_conv1_relu, kernel_size=1, stride=1, num_output=1, pad=0, weight_filler=dict(type='xavier')) n.exp_att_reshaped = L.Reshape( n.exp_att_conv2, reshape_param=dict(shape=dict(dim=[-1, 1, 14 * 14]))) n.exp_att_softmax = L.Softmax(n.exp_att_reshaped, axis=2) n.exp_att_map = L.Reshape( n.exp_att_softmax, reshape_param=dict(shape=dict(dim=[-1, 1, 14, 14]))) exp_dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.exp_att_feature_prev = L.SoftAttention(n.img_feature, n.exp_att_map, exp_dummy) n.exp_att_feature_resh = L.Reshape( n.exp_att_feature_prev, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.exp_att_feature_embed = L.InnerProduct(n.exp_att_feature_resh, num_output=2048, weight_filler=dict(type='xavier')) n.exp_att_feature = L.Eltwise(n.exp_emb_ans2, n.exp_att_feature_embed, eltwise_param={'operation': P.Eltwise.PROD}) # Embed explanation n.exp_embed_ba = L.Embed(n.exp, input_dim=exp_vocab_size, num_output=300, \ weight_filler=dict(type='uniform', min=-0.08, max=0.08)) n.exp_embed = L.TanH(n.exp_embed_ba) # LSTM1 for Explanation n.exp_lstm1 = L.LSTM(\ n.exp_embed, n.exp_cont_1,\ recurrent_param=dict(\ num_output=2048,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) n.exp_lstm1_dropped = L.Dropout(n.exp_lstm1, dropout_param={'dropout_ratio': 0.3}) # merge with LSTM1 for explanation n.exp_att_resh = L.Reshape( n.exp_att_feature, reshape_param=dict(shape=dict(dim=[1, -1, 2048]))) n.exp_att_tiled = L.Tile(n.exp_att_resh, axis=0, tiles=exp_T) n.exp_eltwise_all = L.Eltwise(n.exp_lstm1_dropped, n.exp_att_tiled, eltwise_param={'operation': P.Eltwise.PROD}) n.exp_eltwise_all_l2 = L.L2Normalize(n.exp_eltwise_all) n.exp_eltwise_all_drop = L.Dropout(n.exp_eltwise_all_l2, dropout_param={'dropout_ratio': 0.3}) # LSTM2 for Explanation n.exp_lstm2 = L.LSTM(\ n.exp_eltwise_all_drop, n.exp_cont_2,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) n.exp_lstm2_dropped = L.Dropout(n.exp_lstm2, dropout_param={'dropout_ratio': 0.3}) n.exp_prediction = L.InnerProduct(n.exp_lstm2_dropped, num_output=exp_vocab_size, weight_filler=dict(type='xavier'), axis=2) n.exp_loss = L.SoftmaxWithLoss(n.exp_prediction, n.exp_out, loss_param=dict(ignore_label=-1), softmax_param=dict(axis=2)) n.exp_accuracy = L.Accuracy(n.exp_prediction, n.exp_out, axis=2, ignore_label=-1) return n.to_proto()
def qlstm(mode, batchsize, T, question_vocab_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.data, n.cont, n.img_feature, n.label = L.Python(\ module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=4)#5 ) # # word embedding (static + dynamic) # n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \ # weight_filler=dict(type='uniform',min=-0.08,max=0.08)) # n.embed_scale = L.Scale(n.embed_ba, n.cont, scale_param=dict(dict(axis=0))) # n.embed_scale_resh = L.Reshape(n.embed_scale,\ # reshape_param=dict(\ # shape=dict(dim=[batchsize,1,T,300]))) # n.glove_scale = L.Scale(n.glove, n.cont, scale_param=dict(dict(axis=0))) # n.glove_scale_resh = L.Reshape(n.glove_scale,\ # reshape_param=dict(\ # shape=dict(dim=[batchsize,1,T,300]))) # concat_word_embed = [n.embed_scale_resh, n.glove_scale_resh] # n.concat_embed = L.Concat(*concat_word_embed, concat_param={'axis': 1}) # N x 2 x T x 300 # char embedding n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=50, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) n.embed_scale = L.Scale(n.embed_ba, n.cont, scale_param=dict(dict(axis=0))) n.embed_scale_resh = L.Reshape(n.embed_scale,\ reshape_param=dict(\ shape=dict(dim=[batchsize,1,T,50]))) # char deep convolution n.char_conv_1 = L.Convolution( n.embed_scale_resh, kernel_h=5, kernel_w=50, stride=1, num_output=256, weight_filler=dict(type='gaussian', std=0.05)) # N x 1 x 100 x 50 -> N x 256 x 96 x 1 n.char_relu_1 = L.ReLU(n.char_conv_1) n.char_pool_1 = L.Pooling( n.char_relu_1, kernel_h=2, kernel_w=1, stride=2, pool=P.Pooling.MAX) # N x 256 x 96 x 1 -> N x 256 x 48 x 1 n.char_conv_2 = L.Convolution( n.char_pool_1, kernel_h=5, kernel_w=1, stride=1, num_output=256, weight_filler=dict(type='gaussian', std=0.05)) # N x 256 x 48 x 1 -> N x 256 x 44 x 1 n.char_relu_2 = L.ReLU(n.char_conv_2) n.char_pool_2 = L.Pooling( n.char_relu_2, kernel_h=2, kernel_w=1, stride=2, pool=P.Pooling.MAX) # N x 256 x 44 x 1 -> N x 256 x 22 x 1 n.char_conv_3 = L.Convolution( n.char_pool_2, kernel_h=3, kernel_w=1, stride=1, num_output=256, weight_filler=dict(type='gaussian', std=0.05)) # N x 256 x 22 x 1 -> N x 256 x 20 x 1 n.char_relu_3 = L.ReLU(n.char_conv_3) n.char_conv_4 = L.Convolution( n.char_relu_3, kernel_h=3, kernel_w=1, stride=1, num_output=256, weight_filler=dict(type='gaussian', std=0.05)) # N x 256 x 20 x 1 -> N x 256 x 18 x 1 n.char_relu_4 = L.ReLU(n.char_conv_4) n.char_conv_5 = L.Convolution( n.char_relu_4, kernel_h=3, kernel_w=1, stride=1, num_output=256, weight_filler=dict(type='gaussian', std=0.05)) # N x 256 x 18 x 1 -> N x 256 x 16 x 1 n.char_relu_5 = L.ReLU(n.char_conv_5) n.char_pool_3 = L.Pooling( n.char_relu_5, kernel_h=2, kernel_w=1, stride=2, pool=P.Pooling.MAX) # N x 256 x 16 x 1 -> N x 256 x 8 x 1 n.vec_reshape = L.Reshape( n.char_pool_3, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) n.concat_vec_dropped = L.Dropout(n.vec_reshape, dropout_param={'dropout_ratio': 0.5}) n.q_emb_tanh_droped_resh_tiled_1 = L.Tile(n.concat_vec_dropped, axis=2, tiles=14) n.q_emb_tanh_droped_resh_tiled = L.Tile(n.q_emb_tanh_droped_resh_tiled_1, axis=3, tiles=14) n.i_emb_tanh_droped_resh = L.Reshape( n.img_feature, reshape_param=dict(shape=dict(dim=[-1, 2048, 14, 14]))) n.blcf = L.CompactBilinear(n.q_emb_tanh_droped_resh_tiled, n.i_emb_tanh_droped_resh, compact_bilinear_param=dict(num_output=16000, sum_pool=False)) n.blcf_sign_sqrt = L.SignedSqrt(n.blcf) n.blcf_sign_sqrt_l2 = L.L2Normalize(n.blcf_sign_sqrt) n.blcf_droped = L.Dropout(n.blcf_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) # multi-channel attention n.att_conv1 = L.Convolution(n.blcf_droped, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=2, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 2, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 2, 14, 14]))) att_maps = L.Slice(n.att, ntop=2, slice_param={'axis': 1}) n.att_map0 = att_maps[0] n.att_map1 = att_maps[1] dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature0 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map0, dummy) n.att_feature1 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map1, dummy) n.att_feature0_resh = L.Reshape( n.att_feature0, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature1_resh = L.Reshape( n.att_feature1, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature = L.Concat(n.att_feature0_resh, n.att_feature1_resh) # merge attention and lstm with compact bilinear pooling n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 4096, 1, 1]))) #n.lstm_12_resh = L.Reshape(n.lstm_12, reshape_param=dict(shape=dict(dim=[-1,2048,1,1]))) n.bc_att_lstm = L.CompactBilinear(n.att_feature_resh, n.concat_vec_dropped, compact_bilinear_param=dict( num_output=16000, sum_pool=False)) n.bc_sign_sqrt = L.SignedSqrt(n.bc_att_lstm) n.bc_sign_sqrt_l2 = L.L2Normalize(n.bc_sign_sqrt) n.bc_dropped = L.Dropout(n.bc_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) n.bc_dropped_resh = L.Reshape( n.bc_dropped, reshape_param=dict(shape=dict(dim=[-1, 16000]))) n.prediction = L.InnerProduct(n.bc_dropped_resh, num_output=3000, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) return n.to_proto()
def mfb_coatt(mode, batchsize, T, question_vocab_size, folder): n = caffe.NetSpec() mode_str = json.dumps({'mode':mode, 'batchsize':batchsize,'folder':folder}) if mode == 'val': n.data, n.cont, n.img_feature, n.label, n.glove = L.Python( \ module='vqa_data_layer_hdf5', layer='VQADataProviderLayer', \ param_str=mode_str, ntop=5 ) else: n.data, n.cont, n.img_feature, n.label, n.glove = L.Python(\ module='vqa_data_layer_kld_hdf5', layer='VQADataProviderLayer', \ param_str=mode_str, ntop=5 ) n.embed = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \ weight_filler=dict(type='xavier')) n.embed_tanh = L.TanH(n.embed) concat_word_embed = [n.embed_tanh, n.glove] n.concat_embed = L.Concat(*concat_word_embed, concat_param={'axis': 2}) # T x N x 600 # LSTM n.lstm1 = L.LSTM(\ n.concat_embed, n.cont,\ recurrent_param=dict(\ num_output=config.LSTM_UNIT_NUM,\ weight_filler=dict(type='xavier'))) n.lstm1_droped = L.Dropout(n.lstm1,dropout_param={'dropout_ratio':config.LSTM_DROPOUT_RATIO}) n.lstm1_resh = L.Permute(n.lstm1_droped, permute_param=dict(order=[1,2,0])) n.lstm1_resh2 = L.Reshape(n.lstm1_resh, \ reshape_param=dict(shape=dict(dim=[0,0,0,1]))) ''' Question Attention ''' n.qatt_conv1 = L.Convolution(n.lstm1_resh2, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.qatt_relu = L.ReLU(n.qatt_conv1) n.qatt_conv2 = L.Convolution(n.qatt_relu, kernel_size=1, stride=1, num_output=config.NUM_QUESTION_GLIMPSE, pad=0, weight_filler=dict(type='xavier')) n.qatt_reshape = L.Reshape(n.qatt_conv2, reshape_param=dict(shape=dict(dim=[-1,config.NUM_QUESTION_GLIMPSE,config.MAX_WORDS_IN_QUESTION,1]))) # N*NUM_QUESTION_GLIMPSE*15 n.qatt_softmax = L.Softmax(n.qatt_reshape, axis=2) qatt_maps = L.Slice(n.qatt_softmax,ntop=config.NUM_QUESTION_GLIMPSE,slice_param={'axis':1}) dummy_lstm = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) qatt_feature_list = [] for i in xrange(config.NUM_QUESTION_GLIMPSE): if config.NUM_QUESTION_GLIMPSE == 1: n.__setattr__('qatt_feat%d'%i, L.SoftAttention(n.lstm1_resh2, qatt_maps, dummy_lstm)) else: n.__setattr__('qatt_feat%d'%i, L.SoftAttention(n.lstm1_resh2, qatt_maps[i], dummy_lstm)) qatt_feature_list.append(n.__getattr__('qatt_feat%d'%i)) n.qatt_feat_concat = L.Concat(*qatt_feature_list) ''' Image Attention with MFB ''' n.q_feat_resh = L.Reshape(n.qatt_feat_concat,reshape_param=dict(shape=dict(dim=[0,-1,1,1]))) n.i_feat_resh = L.Reshape(n.img_feature,reshape_param=dict(shape=dict(dim=[0,-1,config.IMG_FEAT_WIDTH,config.IMG_FEAT_WIDTH]))) n.iatt_q_proj = L.InnerProduct(n.q_feat_resh, num_output = config.JOINT_EMB_SIZE, weight_filler=dict(type='xavier')) n.iatt_q_resh = L.Reshape(n.iatt_q_proj, reshape_param=dict(shape=dict(dim=[-1,config.JOINT_EMB_SIZE,1,1]))) n.iatt_q_tile1 = L.Tile(n.iatt_q_resh, axis=2, tiles=config.IMG_FEAT_WIDTH) n.iatt_q_tile2 = L.Tile(n.iatt_q_tile1, axis=3, tiles=config.IMG_FEAT_WIDTH) n.iatt_i_conv = L.Convolution(n.i_feat_resh, kernel_size=1, stride=1, num_output=config.JOINT_EMB_SIZE, pad=0, weight_filler=dict(type='xavier')) n.iatt_i_resh1 = L.Reshape(n.iatt_i_conv, reshape_param=dict(shape=dict(dim=[-1,config.JOINT_EMB_SIZE, config.IMG_FEAT_WIDTH,config.IMG_FEAT_WIDTH]))) n.iatt_iq_eltwise = L.Eltwise(n.iatt_q_tile2, n.iatt_i_resh1, eltwise_param=dict(operation=0)) n.iatt_iq_droped = L.Dropout(n.iatt_iq_eltwise, dropout_param={'dropout_ratio':config.MFB_DROPOUT_RATIO}) n.iatt_iq_resh2 = L.Reshape(n.iatt_iq_droped, reshape_param=dict(shape=dict(dim=[-1,config.JOINT_EMB_SIZE,config.IMG_FEAT_SIZE,1]))) n.iatt_iq_permute1 = L.Permute(n.iatt_iq_resh2, permute_param=dict(order=[0,2,1,3])) n.iatt_iq_resh2 = L.Reshape(n.iatt_iq_permute1, reshape_param=dict(shape=dict(dim=[-1,config.IMG_FEAT_SIZE, config.MFB_OUT_DIM,config.MFB_FACTOR_NUM]))) n.iatt_iq_sumpool = L.Pooling(n.iatt_iq_resh2, pool=P.Pooling.SUM, \ pooling_param=dict(kernel_w=config.MFB_FACTOR_NUM, kernel_h=1)) n.iatt_iq_permute2 = L.Permute(n.iatt_iq_sumpool, permute_param=dict(order=[0,2,1,3])) n.iatt_iq_sqrt = L.SignedSqrt(n.iatt_iq_permute2) n.iatt_iq_l2 = L.L2Normalize(n.iatt_iq_sqrt) ## 2 conv layers 1000 -> 512 -> 2 n.iatt_conv1 = L.Convolution(n.iatt_iq_l2, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.iatt_relu = L.ReLU(n.iatt_conv1) n.iatt_conv2 = L.Convolution(n.iatt_relu, kernel_size=1, stride=1, num_output=config.NUM_IMG_GLIMPSE, pad=0, weight_filler=dict(type='xavier')) n.iatt_resh = L.Reshape(n.iatt_conv2, reshape_param=dict(shape=dict(dim=[-1,config.NUM_IMG_GLIMPSE,config.IMG_FEAT_SIZE]))) n.iatt_softmax = L.Softmax(n.iatt_resh, axis=2) n.iatt_softmax_resh = L.Reshape(n.iatt_softmax,reshape_param=dict(shape=dict(dim=[-1,config.NUM_IMG_GLIMPSE,config.IMG_FEAT_WIDTH,config.IMG_FEAT_WIDTH]))) iatt_maps = L.Slice(n.iatt_softmax_resh, ntop=config.NUM_IMG_GLIMPSE,slice_param={'axis':1}) dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) iatt_feature_list = [] for i in xrange(config.NUM_IMG_GLIMPSE): if config.NUM_IMG_GLIMPSE == 1: n.__setattr__('iatt_feat%d'%i, L.SoftAttention(n.i_feat_resh, iatt_maps, dummy)) else: n.__setattr__('iatt_feat%d'%i, L.SoftAttention(n.i_feat_resh, iatt_maps[i], dummy)) n.__setattr__('iatt_feat%d_resh'%i, L.Reshape(n.__getattr__('iatt_feat%d'%i), \ reshape_param=dict(shape=dict(dim=[0,-1])))) iatt_feature_list.append(n.__getattr__('iatt_feat%d_resh'%i)) n.iatt_feat_concat = L.Concat(*iatt_feature_list) n.iatt_feat_concat_resh = L.Reshape(n.iatt_feat_concat, reshape_param=dict(shape=dict(dim=[0,-1,1,1]))) ''' Fine-grained Image-Question MFB fusion ''' n.mfb_q_proj = L.InnerProduct(n.q_feat_resh, num_output=config.JOINT_EMB_SIZE, weight_filler=dict(type='xavier')) n.mfb_i_proj = L.InnerProduct(n.iatt_feat_concat_resh, num_output=config.JOINT_EMB_SIZE, weight_filler=dict(type='xavier')) n.mfb_iq_eltwise = L.Eltwise(n.mfb_q_proj, n.mfb_i_proj, eltwise_param=dict(operation=0)) n.mfb_iq_drop = L.Dropout(n.mfb_iq_eltwise, dropout_param={'dropout_ratio':config.MFB_DROPOUT_RATIO}) n.mfb_iq_resh = L.Reshape(n.mfb_iq_drop, reshape_param=dict(shape=dict(dim=[-1,1,config.MFB_OUT_DIM,config.MFB_FACTOR_NUM]))) n.mfb_iq_sumpool = L.Pooling(n.mfb_iq_resh, pool=P.Pooling.SUM, \ pooling_param=dict(kernel_w=config.MFB_FACTOR_NUM, kernel_h=1)) n.mfb_out = L.Reshape(n.mfb_iq_sumpool,\ reshape_param=dict(shape=dict(dim=[-1,config.MFB_OUT_DIM]))) n.mfb_sign_sqrt = L.SignedSqrt(n.mfb_out) n.mfb_l2 = L.L2Normalize(n.mfb_sign_sqrt) n.prediction = L.InnerProduct(n.mfb_l2, num_output=config.NUM_OUTPUT_UNITS, weight_filler=dict(type='xavier')) if mode == 'val': n.loss = L.SoftmaxWithLoss(n.prediction, n.label) else: n.loss = L.SoftmaxKLDLoss(n.prediction, n.label) return n.to_proto()
def generator_proto(mode, batchsize, T, exp_T, question_vocab_size, exp_vocab_size, use_gt=True): n = caffe.NetSpec() mode_str = json.dumps({'mode':mode, 'batchsize':batchsize}) n.data, n.cont, n.img_feature, n.label, n.exp, n.exp_out, n.exp_cont_1, n.exp_cont_2 = \ L.Python(module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=8) n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08), param=fixed_weights) n.embed = L.TanH(n.embed_ba) # LSTM1 n.lstm1 = L.LSTM(\ n.embed, n.cont,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0)), param=fixed_weights_lstm) tops1 = L.Slice(n.lstm1, ntop=T, slice_param={'axis':0}) for i in range(T-1): n.__setattr__('slice_first'+str(i), tops1[int(i)]) n.__setattr__('silence_data_first'+str(i), L.Silence(tops1[int(i)],ntop=0)) n.lstm1_out = tops1[T-1] n.lstm1_reshaped = L.Reshape(n.lstm1_out,\ reshape_param=dict(\ shape=dict(dim=[-1,1024]))) n.lstm1_reshaped_droped = L.Dropout(n.lstm1_reshaped,dropout_param={'dropout_ratio':0.3}) n.lstm1_droped = L.Dropout(n.lstm1,dropout_param={'dropout_ratio':0.3}) # LSTM2 n.lstm2 = L.LSTM(\ n.lstm1_droped, n.cont,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0)), param=fixed_weights_lstm) tops2 = L.Slice(n.lstm2, ntop=T, slice_param={'axis':0}) for i in range(T-1): n.__setattr__('slice_second'+str(i), tops2[int(i)]) n.__setattr__('silence_data_second'+str(i), L.Silence(tops2[int(i)],ntop=0)) n.lstm2_out = tops2[T-1] n.lstm2_reshaped = L.Reshape(n.lstm2_out,\ reshape_param=dict(\ shape=dict(dim=[-1,1024]))) n.lstm2_reshaped_droped = L.Dropout(n.lstm2_reshaped,dropout_param={'dropout_ratio':0.3}) concat_botom = [n.lstm1_reshaped_droped, n.lstm2_reshaped_droped] n.lstm_12 = L.Concat(*concat_botom) # Tile question feature n.q_emb_resh = L.Reshape(n.lstm_12, reshape_param=dict(shape=dict(dim=[-1,2048,1,1]))) n.q_emb_tiled_1 = L.Tile(n.q_emb_resh, axis=2, tiles=14) n.q_emb_resh_tiled = L.Tile(n.q_emb_tiled_1, axis=3, tiles=14) # Embed image feature n.i_emb = L.Convolution(n.img_feature, kernel_size=1, stride=1, num_output=2048, pad=0, weight_filler=dict(type='xavier'), param=fixed_weights) # Eltwise product and normalization n.eltwise = L.Eltwise(n.q_emb_resh_tiled, n.i_emb, eltwise_param={'operation': P.Eltwise.PROD}) n.eltwise_sqrt = L.SignedSqrt(n.eltwise) n.eltwise_l2 = L.L2Normalize(n.eltwise_sqrt) n.eltwise_drop = L.Dropout(n.eltwise_l2, dropout_param={'dropout_ratio': 0.3}) # Attention for VQA n.att_conv1 = L.Convolution(n.eltwise_drop, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier'), param=fixed_weights) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=1, pad=0, weight_filler=dict(type='xavier'), param=fixed_weights) n.att_reshaped = L.Reshape(n.att_conv2,reshape_param=dict(shape=dict(dim=[-1,1,14*14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att_map = L.Reshape(n.att_softmax,reshape_param=dict(shape=dict(dim=[-1,1,14,14]))) dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature = L.SoftAttention(n.img_feature, n.att_map, dummy) n.att_feature_resh = L.Reshape(n.att_feature, reshape_param=dict(shape=dict(dim=[-1,2048]))) # eltwise product + normalization again for VQA n.i_emb2 = L.InnerProduct(n.att_feature_resh, num_output=2048, weight_filler=dict(type='xavier'), param=fixed_weights) n.eltwise2 = L.Eltwise(n.lstm_12, n.i_emb2, eltwise_param={'operation': P.Eltwise.PROD}) n.eltwise2_sqrt = L.SignedSqrt(n.eltwise2) n.eltwise2_l2 = L.L2Normalize(n.eltwise2_sqrt) n.eltwise2_drop = L.Dropout(n.eltwise2_l2, dropout_param={'dropout_ratio': 0.3}) n.prediction = L.InnerProduct(n.eltwise2_drop, num_output=3000, weight_filler=dict(type='xavier'), param=fixed_weights) # Take GT answer or Take the logits of the VQA model and get predicted answer to embed if use_gt: n.exp_emb_ans = L.Embed(n.label, input_dim=3000, num_output=300, weight_filler=dict(type='uniform', min=-0.08, max=0.08)) else: n.vqa_ans = L.ArgMax(n.prediction, axis=1) n.exp_emb_ans = L.Embed(n.vqa_ans, input_dim=3000, num_output=300, weight_filler=dict(type='uniform', min=-0.08, max=0.08)) n.exp_emb_ans_tanh = L.TanH(n.exp_emb_ans) n.exp_emb_ans2 = L.InnerProduct(n.exp_emb_ans_tanh, num_output=2048, weight_filler=dict(type='xavier')) # Merge VQA answer and visual+textual feature n.exp_emb_resh = L.Reshape(n.exp_emb_ans2, reshape_param=dict(shape=dict(dim=[-1,2048,1,1]))) n.exp_emb_tiled_1 = L.Tile(n.exp_emb_resh, axis=2, tiles=14) n.exp_emb_tiled = L.Tile(n.exp_emb_tiled_1, axis=3, tiles=14) #n.exp_eltwise = L.Eltwise(n.eltwise_drop, n.exp_emb_tiled, eltwise_param={'operation': P.Eltwise.PROD}) n.eltwise_emb = L.Convolution(n.eltwise, kernel_size=1, stride=1, num_output=2048, pad=0, weight_filler=dict(type='xavier')) n.exp_eltwise = L.Eltwise(n.eltwise_emb, n.exp_emb_tiled, eltwise_param={'operation': P.Eltwise.PROD}) n.exp_eltwise_sqrt = L.SignedSqrt(n.exp_eltwise) n.exp_eltwise_l2 = L.L2Normalize(n.exp_eltwise_sqrt) n.exp_eltwise_drop = L.Dropout(n.exp_eltwise_l2, dropout_param={'dropout_ratio': 0.3}) # Attention for Explanation n.exp_att_conv1 = L.Convolution(n.exp_eltwise_drop, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.exp_att_conv1_relu = L.ReLU(n.exp_att_conv1) n.exp_att_conv2 = L.Convolution(n.exp_att_conv1_relu, kernel_size=1, stride=1, num_output=1, pad=0, weight_filler=dict(type='xavier')) n.exp_att_reshaped = L.Reshape(n.exp_att_conv2,reshape_param=dict(shape=dict(dim=[-1,1,14*14]))) n.exp_att_softmax = L.Softmax(n.exp_att_reshaped, axis=2) n.exp_att_map = L.Reshape(n.exp_att_softmax,reshape_param=dict(shape=dict(dim=[-1,1,14,14]))) exp_dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.exp_att_feature_prev = L.SoftAttention(n.img_feature, n.exp_att_map, exp_dummy) n.exp_att_feature_resh = L.Reshape(n.exp_att_feature_prev, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.exp_att_feature_embed = L.InnerProduct(n.exp_att_feature_resh, num_output=2048, weight_filler=dict(type='xavier')) n.exp_lstm12_embed = L.InnerProduct(n.lstm_12, num_output=2048, weight_filler=dict(type='xavier')) n.exp_eltwise2 = L.Eltwise(n.exp_lstm12_embed, n.exp_att_feature_embed, eltwise_param={'operation': P.Eltwise.PROD}) n.exp_att_feature = L.Eltwise(n.exp_emb_ans2, n.exp_eltwise2, eltwise_param={'operation': P.Eltwise.PROD}) n.silence_exp_att = L.Silence(n.exp_att_feature, ntop=0) return n.to_proto()
def qlstm(mode, batchsize, T, question_vocab_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) # n.data, n.cont, n.img_feature, n.label, n.glove = L.Python(\ # module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=5 ) n.data, n.cont, n.img_feature, n.label = L.Python(\ module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=4 ) # word embedding n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) # n.embed = L.TanH(n.embed_ba) n.embed_scale = L.Scale(n.embed_ba, n.cont, scale_param=dict(dict(axis=0))) n.embed_scale_resh = L.Reshape(n.embed_scale,\ reshape_param=dict(\ shape=dict(dim=[batchsize,1,T,300]))) # Convolution n.word_feature_2 = L.Convolution(n.embed_scale_resh, kernel_h=2, kernel_w=300, stride=1, num_output=512, pad_h=1, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_2_g = L.Convolution(n.embed_scale_resh, kernel_h=2, kernel_w=300, stride=1, num_output=512, pad_h=1, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_3 = L.Convolution(n.embed_scale_resh, kernel_h=3, kernel_w=300, stride=1, num_output=512, pad_h=2, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_3_g = L.Convolution(n.embed_scale_resh, kernel_h=3, kernel_w=300, stride=1, num_output=512, pad_h=2, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_4 = L.Convolution(n.embed_scale_resh, kernel_h=4, kernel_w=300, stride=1, num_output=512, pad_h=3, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_4_g = L.Convolution(n.embed_scale_resh, kernel_h=4, kernel_w=300, stride=1, num_output=512, pad_h=3, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_5 = L.Convolution(n.embed_scale_resh, kernel_h=5, kernel_w=300, stride=1, num_output=512, pad_h=4, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_5_g = L.Convolution(n.embed_scale_resh, kernel_h=5, kernel_w=300, stride=1, num_output=512, pad_h=4, pad_w=0, weight_filler=dict(type='xavier')) n.word_2_acti = L.TanH(n.word_feature_2) n.word_3_acti = L.TanH(n.word_feature_3) n.word_4_acti = L.TanH(n.word_feature_4) n.word_5_acti = L.TanH(n.word_feature_5) n.word_2_gate = L.Sigmoid(n.word_feature_2_g) n.word_3_gate = L.Sigmoid(n.word_feature_3_g) n.word_4_gate = L.Sigmoid(n.word_feature_4_g) n.word_5_gate = L.Sigmoid(n.word_feature_5_g) n.word_2 = L.Eltwise(n.word_2_acti, n.word_2_gate, operation=P.Eltwise.PROD) n.word_3 = L.Eltwise(n.word_3_acti, n.word_3_gate, operation=P.Eltwise.PROD) n.word_4 = L.Eltwise(n.word_4_acti, n.word_4_gate, operation=P.Eltwise.PROD) n.word_5 = L.Eltwise(n.word_5_acti, n.word_5_gate, operation=P.Eltwise.PROD) n.word_vec_2 = L.Pooling(n.word_2, kernel_h=T + 1, kernel_w=1, stride=T + 1, pool=P.Pooling.MAX) n.word_vec_3 = L.Pooling(n.word_3, kernel_h=T + 2, kernel_w=1, stride=T + 2, pool=P.Pooling.MAX) n.word_vec_4 = L.Pooling(n.word_4, kernel_h=T + 3, kernel_w=1, stride=T + 3, pool=P.Pooling.MAX) n.word_vec_5 = L.Pooling(n.word_5, kernel_h=T + 4, kernel_w=1, stride=T + 4, pool=P.Pooling.MAX) word_vec = [n.word_vec_2, n.word_vec_3, n.word_vec_4, n.word_vec_5] n.concat_vec = L.Concat(*word_vec, concat_param={'axis': 1}) # N x 4*d_w x 1 x 1 n.concat_vec_dropped = L.Dropout(n.concat_vec, dropout_param={'dropout_ratio': 0.5}) n.q_emb_tanh_droped_resh_tiled_1 = L.Tile(n.concat_vec_dropped, axis=2, tiles=14) n.q_emb_tanh_droped_resh_tiled = L.Tile(n.q_emb_tanh_droped_resh_tiled_1, axis=3, tiles=14) n.i_emb_tanh_droped_resh = L.Reshape( n.img_feature, reshape_param=dict(shape=dict(dim=[-1, 2048, 14, 14]))) n.blcf = L.CompactBilinear(n.q_emb_tanh_droped_resh_tiled, n.i_emb_tanh_droped_resh, compact_bilinear_param=dict(num_output=16000, sum_pool=False)) n.blcf_sign_sqrt = L.SignedSqrt(n.blcf) n.blcf_sign_sqrt_l2 = L.L2Normalize(n.blcf_sign_sqrt) n.blcf_droped = L.Dropout(n.blcf_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) # multi-channel attention n.att_conv1 = L.Convolution(n.blcf_droped, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=2, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 2, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 2, 14, 14]))) att_maps = L.Slice(n.att, ntop=2, slice_param={'axis': 1}) n.att_map0 = att_maps[0] n.att_map1 = att_maps[1] dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature0 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map0, dummy) n.att_feature1 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map1, dummy) n.att_feature0_resh = L.Reshape( n.att_feature0, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature1_resh = L.Reshape( n.att_feature1, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature = L.Concat(n.att_feature0_resh, n.att_feature1_resh) # merge attention and lstm with compact bilinear pooling n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 4096, 1, 1]))) #n.lstm_12_resh = L.Reshape(n.lstm_12, reshape_param=dict(shape=dict(dim=[-1,2048,1,1]))) n.bc_att_lstm = L.CompactBilinear(n.att_feature_resh, n.concat_vec_dropped, compact_bilinear_param=dict( num_output=16000, sum_pool=False)) n.bc_sign_sqrt = L.SignedSqrt(n.bc_att_lstm) n.bc_sign_sqrt_l2 = L.L2Normalize(n.bc_sign_sqrt) n.bc_dropped = L.Dropout(n.bc_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) n.bc_dropped_resh = L.Reshape( n.bc_dropped, reshape_param=dict(shape=dict(dim=[-1, 16000]))) n.prediction = L.InnerProduct(n.bc_dropped_resh, num_output=3000, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) return n.to_proto()
def qlstm(mode, batchsize, T, question_vocab_size): #prototxt 없이 network 생성시 사용 n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) #지정된 Python 모듈 형식 #https://stackoverflow.com/questions/41344168/what-is-a-python-layer-in-caffe #해당 클래스를 바탕으로 Layer를 생성하며 #리턴된 변수에 값을 채워넣으면 자동으로 Run된다. #여기서 만들어진 Class 내부에서 실질적인 databatch load가 이루어짐. #Glove = Global vectors for word representation #https://www.aclweb.org/anthology/D14-1162 #Pretrained 된 GloveVector를 Concat에 사용. #img_feature는 이미 Resnet512 통과후 L2를 적용한 Preprocessing이 끝난 상태의 Feature Vector. n.data, n.cont, n.img_feature, n.label, n.glove = L.Python(\ module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=5 ) #module = python 파일이름 #layer = layer형식이 맞춰진 python class #param_str = json으로 Data Load시 사용된 파라미터, 내부 class에 self.param_str = modestr 로 저장된다 #ntop = 각 setup , forward backward의 top 변수의 크기 #보통 textual Embed의 뜻은 => texture -> number #Embed 3000개의 Vector종류를 #300개로 compact하게 표현함 n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) #Tanh 적용 n.embed = L.TanH(n.embed_ba) #Glove Data와 Concat concat_word_embed = [n.embed, n.glove] n.concat_embed = L.Concat(*concat_word_embed, concat_param={'axis': 2}) # T x N x 600 # LSTM1 n.lstm1 = L.LSTM(\ n.concat_embed, n.cont,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) tops1 = L.Slice(n.lstm1, ntop=T, slice_param={'axis': 0}) for i in xrange(T - 1): n.__setattr__('slice_first' + str(i), tops1[int(i)]) n.__setattr__('silence_data_first' + str(i), L.Silence(tops1[int(i)], ntop=0)) n.lstm1_out = tops1[T - 1] n.lstm1_reshaped = L.Reshape(n.lstm1_out,\ reshape_param=dict(\ shape=dict(dim=[-1,1024]))) n.lstm1_reshaped_droped = L.Dropout(n.lstm1_reshaped, dropout_param={'dropout_ratio': 0.3}) n.lstm1_droped = L.Dropout(n.lstm1, dropout_param={'dropout_ratio': 0.3}) # LSTM2 n.lstm2 = L.LSTM(\ n.lstm1_droped, n.cont,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) tops2 = L.Slice(n.lstm2, ntop=T, slice_param={'axis': 0}) #https://www.programcreek.com/python/example/107865/caffe.NetSpec 참조. # give top2[~] the name specified by argument `slice_second` #변수 부여 기능 for i in xrange(T - 1): n.__setattr__('slice_second' + str(i), tops2[int(i)]) n.__setattr__('silence_data_second' + str(i), L.Silence(tops2[int(i)], ntop=0)) #마지막 LSTM output을 사용. n.lstm2_out = tops2[T - 1] n.lstm2_reshaped = L.Reshape(n.lstm2_out,\ reshape_param=dict(\ shape=dict(dim=[-1,1024]))) n.lstm2_reshaped_droped = L.Dropout(n.lstm2_reshaped, dropout_param={'dropout_ratio': 0.3}) concat_botom = [n.lstm1_reshaped_droped, n.lstm2_reshaped_droped] n.lstm_12 = L.Concat(*concat_botom) #lstm1의 output => 1024 reshape뒤 dropout #lstm2의 output => 1024 reshape뒤 dropout #concat n.q_emb_tanh_droped_resh = L.Reshape( n.lstm_12, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) #L.Tile 차원을 자동으로 안맞춰주므로 차원맞춤 함수. 2048,1 (tile=14, axis=1) =>2048,14 n.q_emb_tanh_droped_resh_tiled_1 = L.Tile(n.q_emb_tanh_droped_resh, axis=2, tiles=14) n.q_emb_tanh_droped_resh_tiled = L.Tile(n.q_emb_tanh_droped_resh_tiled_1, axis=3, tiles=14) n.i_emb_tanh_droped_resh = L.Reshape( n.img_feature, reshape_param=dict(shape=dict(dim=[-1, 2048, 14, 14]))) n.blcf = L.CompactBilinear(n.q_emb_tanh_droped_resh_tiled, n.i_emb_tanh_droped_resh, compact_bilinear_param=dict(num_output=16000, sum_pool=False)) n.blcf_sign_sqrt = L.SignedSqrt(n.blcf) n.blcf_sign_sqrt_l2 = L.L2Normalize(n.blcf_sign_sqrt) #논문 그림과 달리 Dropout 추가 n.blcf_droped = L.Dropout(n.blcf_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) # multi-channel attention n.att_conv1 = L.Convolution(n.blcf_droped, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) #논문 그림과 달리 output dim이 2 n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=2, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 2, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) #softmax로 attentionmap 생성 #14x14 Softmax map이 2개 생성 n.att = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 2, 14, 14]))) #두가지 att_map을 각각 Slice att_maps = L.Slice(n.att, ntop=2, slice_param={'axis': 1}) n.att_map0 = att_maps[0] n.att_map1 = att_maps[1] dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature0 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map0, dummy) n.att_feature1 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map1, dummy) n.att_feature0_resh = L.Reshape( n.att_feature0, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature1_resh = L.Reshape( n.att_feature1, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature = L.Concat(n.att_feature0_resh, n.att_feature1_resh) #각각 ATT를 곱한값을 연산뒤 Concat한다. # merge attention and lstm with compact bilinear pooling n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 4096, 1, 1]))) #그뒤 4096으로 Reshape n.lstm_12_resh = L.Reshape( n.lstm_12, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) #논문과 달리 가로축 세로축 inputVector크기가 다름 #논문 2048 2048 #코드 4096 2048 n.bc_att_lstm = L.CompactBilinear(n.att_feature_resh, n.lstm_12_resh, compact_bilinear_param=dict( num_output=16000, sum_pool=False)) #SignedSqrt n.bc_sign_sqrt = L.SignedSqrt(n.bc_att_lstm) #L2_Normalize n.bc_sign_sqrt_l2 = L.L2Normalize(n.bc_sign_sqrt) #Dropout n.bc_dropped = L.Dropout(n.bc_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) n.bc_dropped_resh = L.Reshape( n.bc_dropped, reshape_param=dict(shape=dict(dim=[-1, 16000]))) #FullyConnected n.prediction = L.InnerProduct(n.bc_dropped_resh, num_output=3000, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) return n.to_proto()
def net(): n = caffe.NetSpec() n.data = L.Input(input_param=dict(shape=dict(dim=data_shape))) n.dataout = L.Tile(n.data, axis=3, tiles=3) return n.to_proto()
def qlstm(mode, batchsize, T, T_c, question_c_vocab_size, question_vocab_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.data, n.cont, n.data1, n.cont1, n.img_feature, n.label = L.Python(\ module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=6)#5 ) # char embedding n.embed_c = L.Embed(n.data1, input_dim=question_c_vocab_size, num_output=15, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) n.embed_c_scale = L.Scale(n.embed_c, n.cont1, scale_param=dict(dict(axis=0))) n.embed_c_scale_resh = L.Reshape( n.embed_c_scale, reshape_param=dict(shape=dict(dim=[batchsize, 1, T_c * T, -1]))) # N x 1 x T_c x d_c tops = L.Slice(n.embed_c_scale_resh, ntop=T, slice_param={'axis': 2}) for i in xrange(T): n.__setattr__('slice_' + str(i + 1), tops[int(i)]) # char conv n.c_feature_1 = L.Convolution( n.slice_1, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_2 = L.Convolution( n.slice_2, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_3 = L.Convolution( n.slice_3, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_4 = L.Convolution( n.slice_4, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_5 = L.Convolution( n.slice_5, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_6 = L.Convolution( n.slice_6, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_7 = L.Convolution( n.slice_7, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_8 = L.Convolution( n.slice_8, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_9 = L.Convolution( n.slice_9, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_10 = L.Convolution( n.slice_10, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_11 = L.Convolution( n.slice_11, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_12 = L.Convolution( n.slice_12, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_13 = L.Convolution( n.slice_13, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_14 = L.Convolution( n.slice_14, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_15 = L.Convolution( n.slice_15, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_16 = L.Convolution( n.slice_16, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_17 = L.Convolution( n.slice_17, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_18 = L.Convolution( n.slice_18, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_19 = L.Convolution( n.slice_19, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_20 = L.Convolution( n.slice_20, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_21 = L.Convolution( n.slice_21, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_feature_22 = L.Convolution( n.slice_22, convolution_param={ 'kernel_h': 3, 'kernel_w': 15, 'stride': 1, 'num_output': 150, 'pad_h': 1, 'pad_w': 0, 'weight_filler': dict(type='xavier') }, param=[dict(name="conv_c_w"), dict(name="conv_c_b")]) n.c_vec_1 = L.Pooling(n.c_feature_1, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_2 = L.Pooling(n.c_feature_2, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_3 = L.Pooling(n.c_feature_3, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_4 = L.Pooling(n.c_feature_4, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_5 = L.Pooling(n.c_feature_5, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_6 = L.Pooling(n.c_feature_6, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_7 = L.Pooling(n.c_feature_7, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_8 = L.Pooling(n.c_feature_8, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_9 = L.Pooling(n.c_feature_9, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_10 = L.Pooling(n.c_feature_10, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_11 = L.Pooling(n.c_feature_11, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_12 = L.Pooling(n.c_feature_12, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_13 = L.Pooling(n.c_feature_13, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_14 = L.Pooling(n.c_feature_14, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_15 = L.Pooling(n.c_feature_15, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_16 = L.Pooling(n.c_feature_16, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_17 = L.Pooling(n.c_feature_17, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_18 = L.Pooling(n.c_feature_18, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_19 = L.Pooling(n.c_feature_19, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_20 = L.Pooling(n.c_feature_20, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_21 = L.Pooling(n.c_feature_21, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_vec_22 = L.Pooling(n.c_feature_22, kernel_h=T_c, kernel_w=1, stride=T_c, pool=P.Pooling.MAX) n.c_embed_1 = L.Reshape( n.c_vec_1, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_2 = L.Reshape( n.c_vec_2, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_3 = L.Reshape( n.c_vec_3, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_4 = L.Reshape( n.c_vec_4, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_5 = L.Reshape( n.c_vec_5, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_6 = L.Reshape( n.c_vec_6, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_7 = L.Reshape( n.c_vec_7, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_8 = L.Reshape( n.c_vec_8, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_9 = L.Reshape( n.c_vec_9, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_10 = L.Reshape( n.c_vec_10, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_11 = L.Reshape( n.c_vec_11, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_12 = L.Reshape( n.c_vec_12, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_13 = L.Reshape( n.c_vec_13, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_14 = L.Reshape( n.c_vec_14, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_15 = L.Reshape( n.c_vec_15, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_16 = L.Reshape( n.c_vec_16, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_17 = L.Reshape( n.c_vec_17, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_18 = L.Reshape( n.c_vec_18, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_19 = L.Reshape( n.c_vec_19, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_20 = L.Reshape( n.c_vec_20, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_21 = L.Reshape( n.c_vec_21, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) n.c_embed_22 = L.Reshape( n.c_vec_22, reshape_param=dict(shape=dict(dim=[batchsize, 1, 150]))) concat_c_embed = [n.c_embed_1, n.c_embed_2, n.c_embed_3, n.c_embed_4, n.c_embed_5, n.c_embed_6, n.c_embed_7, n.c_embed_8, n.c_embed_9, n.c_embed_10,\ n.c_embed_11, n.c_embed_12, n.c_embed_13, n.c_embed_14, n.c_embed_15, n.c_embed_16, n.c_embed_17, n.c_embed_18, n.c_embed_19, n.c_embed_20, n.c_embed_21, n.c_embed_22] n.concat_char_embed = L.Concat(*concat_c_embed, concat_param={'axis': 1}) # N x T x d_c # word embedding n.embed_w = L.Embed(n.data, input_dim=question_vocab_size, num_output=150, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) # N x T x d_w # combine word and char embedding concat_word_embed = [n.embed_w, n.concat_char_embed] n.concat_embed = L.Concat(*concat_word_embed, concat_param={'axis': 2}) # N x T x (d_c+d_w) n.embed_scale = L.Scale(n.concat_embed, n.cont, scale_param=dict(dict(axis=0))) n.embed_scale_resh = L.Reshape( n.embed_scale, reshape_param=dict(shape=dict( dim=[batchsize, 1, T, -1]))) # N x 1 x T x (d_c+d_w) # n.glove_scale = L.Scale(n.glove, n.cont, scale_param=dict(dict(axis=0))) # n.glove_scale_resh = L.Reshape(n.glove_scale,\ # reshape_param=dict(\ # shape=dict(dim=[batchsize,1,T,300]))) # concat_word_embed = [n.embed_scale_resh, n.glove_scale_resh] # n.concat_embed = L.Concat(*concat_word_embed, concat_param={'axis': 1}) # N x 2 x T x 300 # convolution n.word_feature_2 = L.Convolution( n.embed_scale_resh, kernel_h=2, kernel_w=300, stride=1, num_output=512, pad_h=1, pad_w=0, weight_filler=dict(type='xavier')) # N x C x ? x 1 n.word_feature_3 = L.Convolution(n.embed_scale_resh, kernel_h=3, kernel_w=300, stride=1, num_output=512, pad_h=2, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_4 = L.Convolution(n.embed_scale_resh, kernel_h=4, kernel_w=300, stride=1, num_output=512, pad_h=3, pad_w=0, weight_filler=dict(type='xavier')) n.word_feature_5 = L.Convolution(n.embed_scale_resh, kernel_h=5, kernel_w=300, stride=1, num_output=512, pad_h=4, pad_w=0, weight_filler=dict(type='xavier')) n.word_relu_2 = L.ReLU(n.word_feature_2) n.word_relu_3 = L.ReLU(n.word_feature_3) n.word_relu_4 = L.ReLU(n.word_feature_4) n.word_relu_5 = L.ReLU(n.word_feature_5) n.word_vec_2 = L.Pooling(n.word_relu_2, kernel_h=T + 1, kernel_w=1, stride=T + 1, pool=P.Pooling.MAX) # N x C x 1 x 1 n.word_vec_3 = L.Pooling(n.word_relu_3, kernel_h=T + 2, kernel_w=1, stride=T + 2, pool=P.Pooling.MAX) n.word_vec_4 = L.Pooling(n.word_relu_4, kernel_h=T + 3, kernel_w=1, stride=T + 3, pool=P.Pooling.MAX) n.word_vec_5 = L.Pooling(n.word_relu_5, kernel_h=T + 4, kernel_w=1, stride=T + 4, pool=P.Pooling.MAX) word_vec = [n.word_vec_2, n.word_vec_3, n.word_vec_4, n.word_vec_5] n.concat_vec = L.Concat(*word_vec, concat_param={'axis': 1}) # N x 4C x 1 x 1 n.concat_vec_dropped = L.Dropout(n.concat_vec, dropout_param={'dropout_ratio': 0.5}) n.q_emb_tanh_droped_resh_tiled_1 = L.Tile(n.concat_vec_dropped, axis=2, tiles=14) n.q_emb_tanh_droped_resh_tiled = L.Tile(n.q_emb_tanh_droped_resh_tiled_1, axis=3, tiles=14) n.i_emb_tanh_droped_resh = L.Reshape( n.img_feature, reshape_param=dict(shape=dict(dim=[-1, 2048, 14, 14]))) n.blcf = L.CompactBilinear(n.q_emb_tanh_droped_resh_tiled, n.i_emb_tanh_droped_resh, compact_bilinear_param=dict(num_output=16000, sum_pool=False)) n.blcf_sign_sqrt = L.SignedSqrt(n.blcf) n.blcf_sign_sqrt_l2 = L.L2Normalize(n.blcf_sign_sqrt) n.blcf_droped = L.Dropout(n.blcf_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) # multi-channel attention n.att_conv1 = L.Convolution(n.blcf_droped, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=2, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 2, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 2, 14, 14]))) att_maps = L.Slice(n.att, ntop=2, slice_param={'axis': 1}) n.att_map0 = att_maps[0] n.att_map1 = att_maps[1] dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature0 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map0, dummy) n.att_feature1 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map1, dummy) n.att_feature0_resh = L.Reshape( n.att_feature0, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature1_resh = L.Reshape( n.att_feature1, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature = L.Concat(n.att_feature0_resh, n.att_feature1_resh) # merge attention and lstm with compact bilinear pooling n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 4096, 1, 1]))) #n.lstm_12_resh = L.Reshape(n.lstm_12, reshape_param=dict(shape=dict(dim=[-1,2048,1,1]))) n.bc_att_lstm = L.CompactBilinear(n.att_feature_resh, n.concat_vec_dropped, compact_bilinear_param=dict( num_output=16000, sum_pool=False)) n.bc_sign_sqrt = L.SignedSqrt(n.bc_att_lstm) n.bc_sign_sqrt_l2 = L.L2Normalize(n.bc_sign_sqrt) n.bc_dropped = L.Dropout(n.bc_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) n.bc_dropped_resh = L.Reshape( n.bc_dropped, reshape_param=dict(shape=dict(dim=[-1, 16000]))) n.prediction = L.InnerProduct(n.bc_dropped_resh, num_output=3000, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) return n.to_proto()
def qlstm(mode, batchsize, T, question_vocab_size, embed_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.data, n.cont, n.img_feature, n.label = L.Python(\ module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=4 ) # word embedding (static + dynamic) n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=embed_size, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) n.embed_scale = L.Scale(n.embed_ba, n.cont, scale_param=dict(dict(axis=0))) # N x T x d_w n.embed_scale_resh = L.Reshape( n.embed_scale, reshape_param=dict(shape=dict(dim=[batchsize, T, embed_size, 1]))) # avg of word embedding n.embed_avg = L.Convolution(n.embed_scale_resh, convolution_param={ 'kernel_size': 1, 'num_output': 1, 'bias_term': False, 'weight_filler': dict(type='constant', value=1) }, param=dict(lr_mult=0, decay_mult=0)) # N x 1 x d_w x 1 n.embed_avg_resh = L.Reshape( n.embed_avg, reshape_param=dict(shape=dict(dim=[batchsize, embed_size, 1, 1]))) n.q_emb_tanh_droped_resh_tiled_1 = L.Tile(n.embed_avg_resh, axis=2, tiles=14) n.q_emb_tanh_droped_resh_tiled = L.Tile(n.q_emb_tanh_droped_resh_tiled_1, axis=3, tiles=14) n.i_emb_tanh_droped_resh = L.Reshape( n.img_feature, reshape_param=dict(shape=dict(dim=[-1, 2048, 14, 14]))) n.blcf = L.CompactBilinear(n.q_emb_tanh_droped_resh_tiled, n.i_emb_tanh_droped_resh, compact_bilinear_param=dict(num_output=16000, sum_pool=False)) n.blcf_sign_sqrt = L.SignedSqrt(n.blcf) n.blcf_sign_sqrt_l2 = L.L2Normalize(n.blcf_sign_sqrt) n.blcf_droped = L.Dropout(n.blcf_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) # multi-channel attention n.att_conv1 = L.Convolution(n.blcf_droped, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=2, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 2, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 2, 14, 14]))) att_maps = L.Slice(n.att, ntop=2, slice_param={'axis': 1}) n.att_map0 = att_maps[0] n.att_map1 = att_maps[1] dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature0 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map0, dummy) n.att_feature1 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map1, dummy) n.att_feature0_resh = L.Reshape( n.att_feature0, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature1_resh = L.Reshape( n.att_feature1, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature = L.Concat(n.att_feature0_resh, n.att_feature1_resh) # merge attention and lstm with compact bilinear pooling n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 4096, 1, 1]))) #n.lstm_12_resh = L.Reshape(n.lstm_12, reshape_param=dict(shape=dict(dim=[-1,2048,1,1]))) n.bc_att_lstm = L.CompactBilinear(n.att_feature_resh, n.embed_avg_resh, compact_bilinear_param=dict( num_output=16000, sum_pool=False)) n.bc_sign_sqrt = L.SignedSqrt(n.bc_att_lstm) n.bc_sign_sqrt_l2 = L.L2Normalize(n.bc_sign_sqrt) n.bc_dropped = L.Dropout(n.bc_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) n.bc_dropped_resh = L.Reshape( n.bc_dropped, reshape_param=dict(shape=dict(dim=[-1, 16000]))) n.prediction = L.InnerProduct(n.bc_dropped_resh, num_output=3000, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) return n.to_proto()
def qlstm(mode, batchsize, T, question_vocab_size): n = caffe.NetSpec() mode_str = json.dumps({'mode': mode, 'batchsize': batchsize}) n.data, n.cont, n.img_feature, n.label, n.glove = L.Python(\ module='vqa_data_provider_layer', layer='VQADataProviderLayer', param_str=mode_str, ntop=5 ) n.embed_ba = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \ weight_filler=dict(type='uniform',min=-0.08,max=0.08)) n.embed = L.TanH(n.embed_ba) concat_word_embed = [n.embed, n.glove] n.concat_embed = L.Concat(*concat_word_embed, concat_param={'axis': 2}) # T x N x 600 # LSTM1 n.lstm1 = L.LSTM(\ n.concat_embed, n.cont,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) tops1 = L.Slice(n.lstm1, ntop=T, slice_param={'axis': 0}) for i in xrange(T - 1): n.__setattr__('slice_first' + str(i), tops1[int(i)]) n.__setattr__('silence_data_first' + str(i), L.Silence(tops1[int(i)], ntop=0)) n.lstm1_out = tops1[T - 1] n.lstm1_reshaped = L.Reshape(n.lstm1_out,\ reshape_param=dict(\ shape=dict(dim=[-1,1024]))) n.lstm1_reshaped_droped = L.Dropout(n.lstm1_reshaped, dropout_param={'dropout_ratio': 0.3}) n.lstm1_droped = L.Dropout(n.lstm1, dropout_param={'dropout_ratio': 0.3}) # LSTM2 n.lstm2 = L.LSTM(\ n.lstm1_droped, n.cont,\ recurrent_param=dict(\ num_output=1024,\ weight_filler=dict(type='uniform',min=-0.08,max=0.08),\ bias_filler=dict(type='constant',value=0))) tops2 = L.Slice(n.lstm2, ntop=T, slice_param={'axis': 0}) for i in xrange(T - 1): n.__setattr__('slice_second' + str(i), tops2[int(i)]) n.__setattr__('silence_data_second' + str(i), L.Silence(tops2[int(i)], ntop=0)) n.lstm2_out = tops2[T - 1] n.lstm2_reshaped = L.Reshape(n.lstm2_out,\ reshape_param=dict(\ shape=dict(dim=[-1,1024]))) n.lstm2_reshaped_droped = L.Dropout(n.lstm2_reshaped, dropout_param={'dropout_ratio': 0.3}) concat_botom = [n.lstm1_reshaped_droped, n.lstm2_reshaped_droped] n.lstm_12 = L.Concat(*concat_botom) n.q_emb_tanh_droped_resh = L.Reshape( n.lstm_12, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) n.q_emb_tanh_droped_resh_tiled_1 = L.Tile(n.q_emb_tanh_droped_resh, axis=2, tiles=14) n.q_emb_tanh_droped_resh_tiled = L.Tile(n.q_emb_tanh_droped_resh_tiled_1, axis=3, tiles=14) n.i_emb_tanh_droped_resh = L.Reshape( n.img_feature, reshape_param=dict(shape=dict(dim=[-1, 2048, 14, 14]))) n.blcf = L.CompactBilinear(n.q_emb_tanh_droped_resh_tiled, n.i_emb_tanh_droped_resh, compact_bilinear_param=dict(num_output=16000, sum_pool=False)) n.blcf_sign_sqrt = L.SignedSqrt(n.blcf) n.blcf_sign_sqrt_l2 = L.L2Normalize(n.blcf_sign_sqrt) n.blcf_droped = L.Dropout(n.blcf_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) # multi-channel attention n.att_conv1 = L.Convolution(n.blcf_droped, kernel_size=1, stride=1, num_output=512, pad=0, weight_filler=dict(type='xavier')) n.att_conv1_relu = L.ReLU(n.att_conv1) n.att_conv2 = L.Convolution(n.att_conv1_relu, kernel_size=1, stride=1, num_output=2, pad=0, weight_filler=dict(type='xavier')) n.att_reshaped = L.Reshape( n.att_conv2, reshape_param=dict(shape=dict(dim=[-1, 2, 14 * 14]))) n.att_softmax = L.Softmax(n.att_reshaped, axis=2) n.att = L.Reshape(n.att_softmax, reshape_param=dict(shape=dict(dim=[-1, 2, 14, 14]))) att_maps = L.Slice(n.att, ntop=2, slice_param={'axis': 1}) n.att_map0 = att_maps[0] n.att_map1 = att_maps[1] dummy = L.DummyData(shape=dict(dim=[batchsize, 1]), data_filler=dict(type='constant', value=1), ntop=1) n.att_feature0 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map0, dummy) n.att_feature1 = L.SoftAttention(n.i_emb_tanh_droped_resh, n.att_map1, dummy) n.att_feature0_resh = L.Reshape( n.att_feature0, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature1_resh = L.Reshape( n.att_feature1, reshape_param=dict(shape=dict(dim=[-1, 2048]))) n.att_feature = L.Concat(n.att_feature0_resh, n.att_feature1_resh) # merge attention and lstm with compact bilinear pooling n.att_feature_resh = L.Reshape( n.att_feature, reshape_param=dict(shape=dict(dim=[-1, 4096, 1, 1]))) n.lstm_12_resh = L.Reshape( n.lstm_12, reshape_param=dict(shape=dict(dim=[-1, 2048, 1, 1]))) n.bc_att_lstm = L.CompactBilinear(n.att_feature_resh, n.lstm_12_resh, compact_bilinear_param=dict( num_output=16000, sum_pool=False)) n.bc_sign_sqrt = L.SignedSqrt(n.bc_att_lstm) n.bc_sign_sqrt_l2 = L.L2Normalize(n.bc_sign_sqrt) n.bc_dropped = L.Dropout(n.bc_sign_sqrt_l2, dropout_param={'dropout_ratio': 0.1}) n.bc_dropped_resh = L.Reshape( n.bc_dropped, reshape_param=dict(shape=dict(dim=[-1, 16000]))) n.prediction = L.InnerProduct(n.bc_dropped_resh, num_output=3000, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.prediction, n.label) return n.to_proto()
def lrcn_reinforce(self, save_name, RL_loss='lstm_classification', lw=20): data_inputs = self.data_inputs param_str = self.param_str ss_tag = 'reg_' #reg sentences will be the first part of the batch if self.separate_sents: if not 'batch_size' in param_str.keys(): param_str['batch_size'] = 100 self.slice_point = param_str['batch_size'] / 2 self.batch_size = param_str['batch_size'] param_str_loss = {} param_str_loss['vocab'] = param_str['vocabulary'] param_str_loss['avoid_words'] = ['red', 'small'] if self.baseline: param_str_loss['baseline'] = True data_input = 'fc8' data_tops = self.python_input_layer(data_inputs['module'], data_inputs['layer'], param_str) self.rename_tops(data_tops, data_inputs['param_str']['top_names']) feature_name = 'fc8' self.n.tops[feature_name] = L.InnerProduct( self.n.tops[param_str['image_data_key']], num_output=1000, weight_filler=self.uniform_weight_filler(-.08, .08), bias_filler=self.constant_filler(0), param=self.init_params([[1, 1], [2, 0]])) if self.cc: #If class conditional data_top = self.n.tops['fc8'] class_top = self.n.tops[param_str['data_label_feat']] self.n.tops['class_input'] = L.Concat(data_top, class_top, axis=1) data_input = 'class_input' else: self.silence(self.n.tops[param_str['data_label_feat']]) bottom_sent = self.n.tops[param_str['text_data_key']] bottom_cont = self.n.tops[param_str['text_marker_key']] #prep for caption model bottom_cont_slice = L.Slice(bottom_cont, ntop=self.T, axis=0) self.rename_tops(bottom_cont_slice, ['bottom_cont_%d' % i for i in range(self.T)]) if not self.separate_sents: bottom_sent_slice = L.Slice(bottom_sent, ntop=self.T, axis=0) self.rename_tops(bottom_sent_slice, ['input_sent_%d' % i for i in range(self.T)]) target_sentence = self.n.tops['target_sentence'] else: bottom_sents = L.Slice(bottom_sent, slice_point=[self.slice_point], axis=1, ntop=2) self.rename_tops(bottom_sents, ['reg_input_sent', 'rl_input_sent']) reg_bottom_sents_slice = L.Slice(self.n.tops['reg_input_sent'], axis=0, ntop=20) rl_bottom_sents_slice = L.Slice(self.n.tops['rl_input_sent'], axis=0, ntop=20) self.silence([rl_bottom_sents_slice[i] for i in range(1, self.T)]) self.n.tops['input_sent_0'] = L.Concat(reg_bottom_sents_slice[0], rl_bottom_sents_slice[0], axis=1) self.rename_tops( reg_bottom_sents_slice, ['reg_input_sent_%d' % i for i in range(1, self.T)]) self.rename_tops(reg_bottom_sents_slice, ['reg_input_sent_%d' % i for i in range(self.T)]) slice_target_sentence = L.Slice(self.n.tops['target_sentence'], slice_point=[self.slice_point], axis=1, ntop=2) self.rename_tops(slice_target_sentence, ['reg_target_sentence', 'rl_target_sentence']) self.silence(self.n.tops['rl_target_sentence']) target_sentence = self.n.tops['reg_target_sentence'] self.n.tops['lstm1_h0'] = self.dummy_data_layer( [1, self.N, self.lstm_dim], 0) self.n.tops['lstm1_c0'] = self.dummy_data_layer( [1, self.N, self.lstm_dim], 0) self.n.tops['lstm2_h0'] = self.dummy_data_layer( [1, self.N, self.lstm_dim], 0) self.n.tops['lstm2_c0'] = self.dummy_data_layer( [1, self.N, self.lstm_dim], 0) self.make_caption_model(static_input=data_input) #prep bottoms for loss predict_tops = [self.n.tops['predict_%d' % i] for i in range(self.T)] self.n.tops['predict_concat'] = L.Concat(*predict_tops, axis=0) if self.separate_sents: word_sample_tops = [ self.n.tops['rl_word_sample_reshape_%d' % i] for i in range(1, self.T + 1) ] self.n.tops['word_sample_concat'] = L.Concat(*word_sample_tops, axis=0) concat_predict_tops = L.Slice(self.n.tops['predict_concat'], slice_point=[self.slice_point], axis=1, ntop=2) reg_predict = concat_predict_tops[0] RL_predict = concat_predict_tops[1] bottom_cont_tops = L.Slice(bottom_cont, slice_point=[self.slice_point], axis=1, ntop=2) self.silence(bottom_cont_tops[0]) label_tops = L.Slice(self.n.tops[param_str['data_label']], slice_point=[self.slice_point], axis=0, ntop=2) self.silence(label_tops[0]) self.rename_tops([bottom_cont_tops[1], label_tops[1]], ['rl_bottom_cont', 'rl_label_top']) label_top = self.n.tops['rl_label_top'] bottom_cont = self.n.tops['rl_bottom_cont'] else: word_sample_tops = [ self.n.tops['word_sample_reshape_%d' % i] for i in range(1, self.T + 1) ] self.n.tops['word_sample_concat'] = L.Concat(*word_sample_tops, axis=0) reg_predict = self.n.tops['predict_concat'] RL_predict = self.n.tops['predict_concat'] label_top = self.n.tops[param_str['data_label']] #RL loss if RL_loss == 'lstm_classification': self.n.tops['embed_classification'] = self.embed( self.n.tops['word_sample_concat'], 1000, input_dim=self.vocab_size, bias_term=False, learning_param=self.init_params([[0, 0]])) self.n.tops['lstm_classification'] = self.lstm( self.n.tops['embed_classification'], bottom_cont, learning_param_lstm=self.init_params([[0, 0], [0, 0], [0, 0]]), lstm_hidden=1000) self.n.tops['predict_classification'] = L.InnerProduct( self.n.tops['lstm_classification'], num_output=200, axis=2) self.n.tops['probs_classification'] = L.Softmax( self.n.tops['predict_classification'], axis=2) #classification reward layer: classification, word_sample_concat (to get sentence length), #data label should be single stream; even though trained with 20 stream... self.n.tops['reward'] = self.python_layer([ self.n.tops['probs_classification'], self.n.tops['word_sample_concat'], label_top ], 'loss_layers', 'sequenceClassificationLoss', param_str_loss) self.n.tops['reward_reshape'] = L.Reshape(self.n.tops['reward'], shape=dict(dim=[1, -1])) self.n.tops['reward_tile'] = L.Tile(self.n.tops['reward_reshape'], axis=0, tiles=self.T) #softmax with sampled words as "correct" word self.n.tops['sample_loss'] = self.softmax_per_inst_loss( RL_predict, self.n.tops['word_sample_concat'], axis=2) self.n.tops['sample_reward'] = L.Eltwise(self.n.tops['sample_loss'], self.n.tops['reward_tile'], propagate_down=[1, 0], operation=0) avoid_lw = 100 self.n.tops['normalized_reward'] = L.Power( self.n.tops['sample_reward'], scale=(1. / self.N) * avoid_lw) self.n.tops['sum_rewards'] = L.Reduction( self.n.tops['normalized_reward'], loss_weight=[1]) self.n.tops['sentence_loss'] = self.softmax_loss(reg_predict, target_sentence, axis=2, loss_weight=20) self.write_net(save_name)