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Added MNIST and Entity-Relation extraction tests
Currently work on only a subset of solvers. Closes #4
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| import torch | ||
| import torch.nn.functional as F | ||
|
|
||
| from pytorch_constraints.constraint import constraint | ||
| from pytorch_constraints.sampling_solver import * | ||
|
|
||
| ENTITY_TO_ID = {"O":0,"Loc":1, "Org":2, "Peop":3, "Other":4} | ||
| REL_TO_ID = {"*":0, "Work_For_arg1":1, "Kill_arg1":2, "OrgBased_In_arg1":3, "Live_In_arg1":4, | ||
| "Located_In_arg1":5, "Work_For_arg2":6, "Kill_arg2":7, "OrgBased_In_arg2":8, | ||
| "Live_In_arg2":9, "Located_In_arg2":10} | ||
|
|
||
| def get_solvers(num_samples): | ||
| return [WeightedSamplingSolver(num_samples)] | ||
|
|
||
| class NER_Net(torch.nn.Module): | ||
| '''Simple Named Entity Recognition model''' | ||
|
|
||
| def __init__(self, vocab_size, num_classes, hidden_dim=50, embedding_dim=100): | ||
| super().__init__() | ||
|
|
||
| self.vocab_size = vocab_size | ||
| self.hidden_dim = hidden_dim | ||
| self.embedding_dim = embedding_dim | ||
|
|
||
| # layers | ||
| self.embedding = torch.nn.Embedding(self.vocab_size, self.embedding_dim) | ||
| #self.embedding.weight = torch.nn.Parameter(vocab.vectors) | ||
| self.embedding.weight.data.uniform_(-1.0, 1.0) | ||
|
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||
| self.lstm = torch.nn.LSTM(self.embedding_dim, self.hidden_dim, batch_first=True) | ||
| self.fc = torch.nn.Linear(self.hidden_dim, num_classes) | ||
|
|
||
| # Initialize fully connected layer | ||
| self.fc.bias.data.fill_(0) | ||
| torch.nn.init.xavier_uniform_(self.fc.weight, gain=1) | ||
|
|
||
| def forward(self, s): | ||
| s = self.embedding(s) # dim: batch_size x batch_max_len x embedding_dim | ||
| s, _ = self.lstm(s) # dim: batch_size x batch_max_len x lstm_hidden_dim | ||
| s = self.fc(s) # dim: batch_size*batch_max_len x num_tags | ||
|
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| return s | ||
|
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|
|
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| class RE_Net(torch.nn.Module): | ||
| '''Simple Relation extraction model''' | ||
|
|
||
| def __init__(self, vocab_size, num_classes, hidden_dim=50, embedding_dim=100): | ||
| super().__init__() | ||
|
|
||
| self.vocab_size = vocab_size | ||
| self.hidden_dim = hidden_dim | ||
| self.embedding_dim = embedding_dim | ||
|
|
||
| # layers | ||
| self.embedding = torch.nn.Embedding(self.vocab_size, self.embedding_dim) | ||
| #self.embedding.weight = torch.nn.Parameter(vocab.vectors) | ||
| self.embedding.weight.data.uniform_(-1.0, 1.0) | ||
|
|
||
| self.lstm = torch.nn.LSTM(self.embedding_dim, self.hidden_dim, batch_first=True) | ||
| self.fc = torch.nn.Linear(self.hidden_dim, num_classes) | ||
|
|
||
| # Initialize fully connected layer | ||
| self.fc.bias.data.fill_(0) | ||
| torch.nn.init.xavier_uniform_(self.fc.weight, gain=1) | ||
|
|
||
| def forward(self, s): | ||
| s = self.embedding(s) # dim: batch_size x batch_max_len x embedding_dim | ||
| s, _ = self.lstm(s) # dim: batch_size x batch_max_len x lstm_hidden_dim | ||
| s = self.fc(s) # dim: batch_size*batch_max_len x num_tags | ||
|
|
||
| return s | ||
|
|
||
| def OrgBasedIn_Org_Loc(ne, re): | ||
|
|
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| arg1 = (re==3).nonzero(as_tuple=False) | ||
| arg2 = (re==8).nonzero(as_tuple=False) | ||
|
|
||
| return all(ne[arg1] == 2) and all(ne[arg2] == 1) | ||
|
|
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| def train(constraint): | ||
|
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| ner = NER_Net(vocab_size=3027, num_classes=len(ENTITY_TO_ID)) | ||
| re = RE_Net(vocab_size=3027, num_classes=len(REL_TO_ID)) | ||
|
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| opt = torch.optim.SGD(list(ner.parameters()) + list(re.parameters()), lr=1.0) | ||
|
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| tokens, entities, relations = get_data() | ||
|
|
||
| for i in range(100): | ||
| opt.zero_grad() | ||
|
|
||
| ner_logits = ner(tokens) | ||
| ner_logits = ner_logits.view(-1, ner_logits.shape[2]) | ||
|
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| re_logits = re(tokens) | ||
| re_logits = re_logits.view(-1, re_logits.shape[2]) | ||
|
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| re_loss = F.cross_entropy(re_logits, relations.view(-1)) | ||
| closs = constraint(ner_logits, re_logits) | ||
| loss = 0.05 * closs + 10 * re_loss | ||
|
|
||
| loss.backward() | ||
| opt.step() | ||
|
|
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| return ner, re | ||
|
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||
|
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| def test_entity_relation(): | ||
|
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| tokens, entities, relations = get_data() | ||
| for solver in get_solvers(num_samples=200): | ||
|
|
||
| cons = constraint(OrgBasedIn_Org_Loc, solver) | ||
| ner, re = train(cons) | ||
|
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||
| re = torch.argmax(torch.softmax(re(tokens).view(-1, 11), dim=-1), dim=-1) | ||
| ner = torch.argmax(torch.softmax(ner(tokens).view(-1, 5), dim=-1), dim=-1) | ||
|
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| assert (ner[re == 3] == 2).all() and (ner[re == 8] == 1).all() | ||
|
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| def get_data(): | ||
|
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| tokens = torch.tensor([[ 32, 1973, 2272, 15, 3, 0, 0, 5, 0, 389, 0, 12, | ||
| 7, 823, 4, 2636, 4, 0, 114, 5, 3, 2701, 6]]) | ||
| entities = torch.LongTensor([0, 0, 0, 0, 0, 0, 2, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0]) | ||
| relations = torch.LongTensor([0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) | ||
|
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| return tokens, entities, relations |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,199 @@ | ||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| import torch.optim as optim | ||
|
|
||
| from pytorch_constraints.constraint import constraint | ||
| from pytorch_constraints.tnorm_solver import * | ||
| from pytorch_constraints.sampling_solver import WeightedSamplingSolver | ||
| from pytorch_constraints.circuit_solver import SemanticLossCircuitSolver | ||
|
|
||
| def get_solvers(num_samples): | ||
| return [WeightedSamplingSolver(num_samples), SemanticLossCircuitSolver(), ProductTNormLogicSolver()] | ||
|
|
||
| class Net(nn.Module): | ||
| def __init__(self): | ||
| super(Net, self).__init__() | ||
| self.conv1 = nn.Conv2d(1, 32, 3, 1) | ||
| self.conv2 = nn.Conv2d(32, 64, 3, 1) | ||
| self.dropout1 = nn.Dropout2d(0.25) | ||
| self.dropout2 = nn.Dropout2d(0.5) | ||
| self.fc1 = nn.Linear(9216, 128) | ||
| self.fc2 = nn.Linear(128, 20) | ||
|
|
||
| def forward(self, x): | ||
| x = self.conv1(x) | ||
| x = F.relu(x) | ||
| x = self.conv2(x) | ||
| x = F.relu(x) | ||
| x = F.max_pool2d(x, 2) | ||
| x = self.dropout1(x) | ||
| x = torch.flatten(x, 1) | ||
| x = self.fc1(x) | ||
| x = F.relu(x) | ||
| x = self.dropout2(x) | ||
| x = self.fc2(x) | ||
|
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| return x.reshape(10,2) | ||
|
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| def train(constraint=None, epoch=100): | ||
|
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| net = Net() | ||
| X, y = get_mnist_data() | ||
| optimizer = optim.Adadelta(net.parameters(), lr=1.0) | ||
|
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| for i in range(epoch): | ||
|
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| optimizer.zero_grad() | ||
|
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| output = net(X) | ||
| loss = F.cross_entropy(output[:,1].reshape(1,10), y) | ||
|
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| if constraint: | ||
| loss += constraint(output) | ||
|
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| loss.backward() | ||
| optimizer.step() | ||
|
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| return net | ||
|
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| def only_one(x): | ||
| #return sum(x) == 1 | ||
| return (x[0] == 1 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 1 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 1 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 1 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 1 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 1 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 1 and x[7] == 0 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 1 and x[8] == 0 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 1 and x[9] == 0) or\ | ||
| (x[0] == 0 and x[1] == 0 and x[2] == 0 and x[3] == 0 and x[4] == 0 and x[5] == 0 and x[6] == 0 and x[7] == 0 and x[8] == 0 and x[9] == 1) | ||
|
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| def test_only_one_mnist(): | ||
|
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| X, y = get_mnist_data() | ||
|
|
||
| for solver in get_solvers(num_samples=200): | ||
| only_one_constraint = constraint(only_one, solver) | ||
| net = train(only_one_constraint) | ||
|
|
||
| assert(torch.argmax(torch.softmax(net(X), dim=-1), dim=-1).sum().item() == 1) | ||
|
|
||
| def get_mnist_data(): | ||
| X = torch.tensor([[[ | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, 0.2249, 1.5996, 2.7960, 1.5996, 0.2122, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| 0.1867, 2.6051, 2.7833, 2.7833, 2.7833, 2.5924, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.2631, | ||
| 2.4651, 2.7960, 2.7833, 2.6178, 2.5415, 2.7833, 0.3013, | ||
| -0.3478, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.2969, 0.3395, 2.4269, | ||
| 2.7833, 2.7960, 2.7833, 2.1469, 0.6450, 2.7833, 2.7960, | ||
| 1.1286, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, 1.6505, 2.7833, 2.7833, | ||
| 2.7833, 2.7960, 2.7833, 2.7833, 0.7977, 1.9814, 2.7960, | ||
| 1.7014, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, 0.2249, 2.6051, 2.7960, 2.7960, | ||
| 1.9942, 1.0268, 2.7960, 2.4778, 0.1740, 0.5813, 2.8215, | ||
| 1.7141, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, 0.1867, 2.6051, 2.7833, 2.7833, 1.8541, | ||
| -0.2715, 0.5304, 1.1159, -0.1569, -0.4242, -0.4242, 2.7960, | ||
| 2.6687, 0.2122, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, 0.0595, 1.6759, 2.7960, 2.5415, 2.2233, 0.6450, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 2.7960, | ||
| 2.7833, 1.6759, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.3351, 1.8414, 2.7833, 2.6306, 0.4795, -0.1824, -0.0678, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 2.7960, | ||
| 2.7833, 2.0578, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| 0.3013, 2.7833, 2.7833, 0.3777, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 2.7960, | ||
| 2.7833, 2.0578, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| 2.0960, 2.7960, 1.9942, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 2.8215, | ||
| 2.7960, 2.0705, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.5431, | ||
| 2.7069, 2.7833, 1.0013, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 2.7960, | ||
| 2.7833, 1.4596, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.6577, | ||
| 2.7833, 2.5033, -0.1060, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.3351, 1.2941, 2.7960, | ||
| 1.9432, -0.2715, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.6577, | ||
| 2.7833, 2.4142, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.3351, 1.2432, 2.7833, 2.4396, | ||
| 0.4795, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.6577, | ||
| 2.7833, 1.4214, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, 0.1867, 1.6759, 2.7833, 1.7778, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.6704, | ||
| 2.7960, 2.4396, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, 1.0268, 2.6051, 2.7960, 1.6378, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.6577, | ||
| 2.7833, 2.7451, 1.4341, 0.1867, -0.0551, 0.6577, 1.8414, | ||
| 2.4396, 2.7960, 2.4142, 1.7014, 0.2886, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, 0.6577, | ||
| 2.7833, 2.7833, 2.7833, 2.4906, 2.3124, 2.7833, 2.7833, | ||
| 2.7833, 2.0705, 1.2305, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.0678, | ||
| 2.1087, 2.7833, 2.7833, 2.7960, 2.7833, 2.7833, 2.5415, | ||
| 1.4214, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.1060, 1.2050, 2.7833, 2.7960, 2.7833, 1.3705, 0.0467, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242], | ||
| [-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, | ||
| -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242]]]]) | ||
| y = torch.tensor([0]) | ||
|
|
||
| return X,y |