From aae0be059031df84f72083c050988523f0932800 Mon Sep 17 00:00:00 2001 From: shreshthtuli Date: Mon, 24 Aug 2020 18:11:28 +0530 Subject: [PATCH] Added new toolnet sequence model --- src/GNN/models.py | 83 ++++++++++++++++++ train.py | 66 +++++++++----- ...Metric_Attn_L_NT_Tseq_C_128_3_Trained.ckpt | Bin 0 -> 5603623 bytes 3 files changed, 128 insertions(+), 21 deletions(-) create mode 100644 trained_models/home/Seq_GGCN_Metric_Attn_L_NT_Tseq_C_128_3_Trained.ckpt diff --git a/src/GNN/models.py b/src/GNN/models.py index 4049693..f92d317 100644 --- a/src/GNN/models.py +++ b/src/GNN/models.py @@ -537,3 +537,86 @@ def forward(self, g, goalVec, goalObjectsVec, tool_vec): probNoTool = torch.sigmoid(self.activation(self.p2(probNoTool))).flatten() output = torch.cat(((1-probNoTool)*tools.flatten(), probNoTool), dim=0) return output + + +###################################################################################### + +# The following models are for tool sequence prediction + +class GGCN_Metric_Attn_L_NT_Tseq_C(nn.Module): + """ + The best performing model for the sequential tool prediction task. + Separate likelihood prediction of no tool for more robust tool output considering any tool + to be used or not as prior. + """ + def __init__(self, + in_feats, + n_objects, + n_hidden, + n_classes, + n_layers, + etypes, + activation, + dropout, + embedding, + weighted): + super(GGCN_Metric_Attn_L_NT_Tseq_C, self).__init__() + self.n_classes = n_classes + self.etypes = etypes + self.name = "GGCN_Metric_Attn_L_NT_Tseq_C_" + str(n_hidden) + "_" + str(n_layers) + self.n_hidden = n_hidden + self.layers = nn.ModuleList() + self.layers.append(nn.Linear(in_feats + n_objects*4, n_hidden)) + for i in range(n_layers - 1): + self.layers.append(nn.Linear(n_hidden, n_hidden)) + self.tool_lstm = nn.LSTM(n_hidden, n_hidden) + self.attention = nn.Linear(n_hidden + n_hidden + n_hidden, n_hidden) + self.attention2 = nn.Linear(n_hidden, 1) + self.embed = nn.Linear(PRETRAINED_VECTOR_SIZE, n_hidden) + self.fc1 = nn.Linear(4 * n_hidden, n_hidden) + self.fc2 = nn.Linear(n_hidden, n_hidden) + self.fc3 = nn.Linear(n_hidden, n_hidden) + self.fc4 = nn.Linear(n_hidden, 1) + self.p1 = nn.Linear(3 * n_hidden, n_hidden) + self.p2 = nn.Linear(n_hidden, 1) + self.final = nn.Sigmoid() + self.activation = nn.PReLU() + + def forward(self, g_list, goalVec, goalObjectsVec, tool_vec, t_list): + tool_embedding = self.activation(self.embed(tool_vec)) + t_list = [(tool_embedding[TOOLS.index(i)] if i!='no-tool' else torch.zeros(self.n_hidden)).view(1, -1) for i in t_list] + lstm_hidden = (torch.randn(1, 1, self.n_hidden), torch.randn(1, 1, self.n_hidden)) + goalObjectsVec = self.activation(self.embed(goalObjectsVec)) + goal_embed = self.activation(self.embed(goalVec)) + predicted_tools = [] + for ind,g in enumerate(g_list): + h = g.ndata['feat'] + edgeMatrices = [g.adjacency_matrix(etype=t) for t in self.etypes] + edges = torch.cat(edgeMatrices, 1).to_dense() + h = torch.cat((h, edges), 1) + for i, layer in enumerate(self.layers): + h = self.activation(layer(h)) + if (ind != 0): + lstm_out, lstm_hidden = self.tool_lstm(t_list[ind-1].view(1,1,-1), lstm_hidden) + else: + lstm_out = torch.zeros(1, 1, self.n_hidden) + lstm_out = lstm_out.view(-1) + attn_embedding = torch.cat([h, goalObjectsVec.repeat(h.size(0)).view(h.size(0), -1), lstm_out.repeat(h.size(0)).view(h.size(0), -1)], 1) + attn_embedding = self.activation(self.attention(attn_embedding)) + attn_weights = F.softmax(self.attention2(attn_embedding), dim=0) + scene_embedding = torch.mm(attn_weights.t(), h) + scene_and_goal = torch.cat([scene_embedding, goal_embed.view(1,-1), lstm_out.view(1,-1)], 1) + l = [] + for i in range(NUMTOOLS-1): + final_to_decode = torch.cat([scene_and_goal, tool_embedding[i].view(1, -1)], 1) + h = self.activation(self.fc1(final_to_decode)) + h = self.activation(self.fc2(h)) + h = self.activation(self.fc3(h)) + h = self.final(self.fc4(h)) + l.append(h.flatten()) + tools = torch.stack(l) + probNoTool = self.activation(self.p1(scene_and_goal)) + probNoTool = torch.sigmoid(self.activation(self.p2(probNoTool))).flatten() + output = torch.cat(((1-probNoTool)*tools.flatten(), probNoTool), dim=0) + predicted_tools.append(output) + return predicted_tools \ No newline at end of file diff --git a/train.py b/train.py index ec8c487..bdbb7a2 100644 --- a/train.py +++ b/train.py @@ -233,19 +233,33 @@ def accuracy_score(dset, graphs, model, modelEnc, num_objects = 0, verbose = Fal goal_num, world_num, tools, g, t = graph if 'gcn_seq' in training: actionSeq, graphSeq = g; loss = 0; toolSeq = tools - for i, g in enumerate(graphSeq): - y_pred = model(g, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec) - y_true = torch.zeros(NUMTOOLS) - y_true[TOOLS.index(toolSeq[i])] = 1 - total_test_loss += l(y_pred.view(1,-1), y_true) - y_pred = list(y_pred.reshape(-1)) - # tools_possible = dset.goal_scene_to_tools[(goal_num,world_num)] - tool_predicted = TOOLS[y_pred.index(max(y_pred))] - if tool_predicted == toolSeq[i]: - total_correct += 1 - elif verbose: - print (goal_num, world_num, tool_predicted, toolSeq[i]) - denominator += 1 + if 'Tseq' in model.name: + y_pred = model(graphSeq, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec, tools) + for i in range(len(y_pred)): + y_pred_i = list(y_pred[i].reshape(-1)) + tool_predicted = TOOLS[y_pred_i.index(max(y_pred_i))] + y_true = torch.zeros(NUMTOOLS) + y_true[TOOLS.index(toolSeq[i])] = 1 + total_test_loss += l(y_pred[i].view(1,-1), y_true) + if tool_predicted == toolSeq[i]: + total_correct += 1 + elif verbose: + print (goal_num, world_num, tool_predicted, toolSeq[i]) + denominator += 1 + else: + for i, g in enumerate(graphSeq): + y_pred = model(g, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec) + y_true = torch.zeros(NUMTOOLS) + y_true[TOOLS.index(toolSeq[i])] = 1 + total_test_loss += l(y_pred.view(1,-1), y_true) + y_pred = list(y_pred.reshape(-1)) + # tools_possible = dset.goal_scene_to_tools[(goal_num,world_num)] + tool_predicted = TOOLS[y_pred.index(max(y_pred))] + if tool_predicted == toolSeq[i]: + total_correct += 1 + elif verbose: + print (goal_num, world_num, tool_predicted, toolSeq[i]) + denominator += 1 continue elif 'gcn' in training: y_pred = model(g, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec) @@ -394,13 +408,20 @@ def backprop(data, optimizer, graphs, model, num_objects, modelEnc=None, batch_s for iter_num, graph in tqdm(list(enumerate(graphs)), ncols=80): goal_num, world_num, tools, g, t = graph if 'gcn_seq' in training: - actionSeq, graphSeq = g; loss = 0; toolSeq = tools - for i, g in enumerate(graphSeq): - y_pred = model(g, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec) - y_true = torch.zeros(NUMTOOLS) - y_true[TOOLS.index(tools[i])] = 1 - loss += l(y_pred.view(1,-1), y_true) - if weighted: loss *= (1 if t == data.min_time[(goal_num, world_num)] else 0.5) + actionSeq, graphSeq = g; loss = 0 + if 'Tseq' in model.name: + y_pred = model(graphSeq, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec, tools) + for i in range(len(y_pred)): + y_true = torch.zeros(NUMTOOLS) + y_true[TOOLS.index(tools[i])] = 1 + loss += l(y_pred[i].view(1,-1), y_true) + else: + for i,g in enumerate(graphSeq): + y_pred = model(g, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec) + y_true = torch.zeros(NUMTOOLS) + y_true[TOOLS.index(tools[i])] = 1 + loss += l(y_pred.view(1,-1), y_true) + if weighted: loss *= (1 if t == data.min_time[(goal_num, world_num)] else 0.5) batch_loss += loss elif 'gcn' in training: y_pred = model(g, goal2vec[goal_num], goalObjects2vec[goal_num], tool_vec) @@ -518,7 +539,10 @@ def get_model(model_name): if training == 'gcn' or training == 'gcn_seq': size, layers = (4, 5) if training == 'gcn' else (2, 3) modelEnc = None - if ("Final" not in model_name and "_NT" in model_name) or "Final_W" in model_name: + if "Tseq" in model_name: + model_class = getattr(src.GNN.models, 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