class Trainer:
def __init__(self, experiment, label_type, lr, weight_decay, num_directions, batch_size, num_epochs, threshold, hidden_size,
dropout):
self.experiment = experiment
self.label_type = label_type
self.lr = lr
self.weight_decay = weight_decay
self.num_directions = num_directions
self.batch_size = batch_size
self.num_epochs = num_epochs
with open("public_data/vocab/word2id.pkl", 'rb') as infile:
self.word2id = pickle.load(infile)
self.train_dataset = BinaryPairDataset("train", label_type, threshold)
self.valid_dataset = BinaryPairDataset("valid", label_type, threshold)
self.orig_labels = self.train_dataset.orig_labels
self.collection = IR_Dataset("train", label_type, threshold, "above_threshold")
self.queries = IR_Dataset("valid", label_type, threshold, "above_threshold")
with open("public_data/vocab/word2id.pkl", 'rb') as infile:
self.word2id = pickle.load(infile)
with open("public_data/embeddings/word_embeddings_%s.pkl" %self.experiment, 'rb') as infile:
embeddings = pickle.load(infile)
self.model = Encoder(embeddings=embeddings, dropout=dropout,
hidden_size=hidden_size, num_directions=num_directions)
if torch.cuda.is_available():
self.model = self.model.cuda()
self.float = torch.cuda.FloatTensor
self.long = torch.cuda.LongTensor
else:
self.float = torch.FloatTensor
self.long = torch.LongTensor
log('============ EXPERIMENT ID ============')
id = str(uuid.uuid4())[:8]
log("Files will be stored with id %s" % id)
self.save_as = "_".join([self.experiment, label_type, str(threshold), "pair_cel", id])
with open("public_data/models/configuration_classifier_%s.pkl" % self.save_as, 'wb') as out:
pickle.dump({"NUM_TRAIN": len(self.train_dataset.df), "NUM_VALID": len(self.valid_dataset.df),
"DROPOUT": dropout, "HIDDEN_SIZE": hidden_size, "NUM_DIR": num_directions,
"EMB_SHAPE": embeddings.shape, "LABEL_TYPE": label_type,
"THRESHOLD": threshold, "LABELS": self.orig_labels}, out)
self.run_training()
def train(self, train_loader):
"""
Trains the model
:param train_loader: training documents
:return: training acc & loss
"""
self.model.train()
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, self.model.parameters()), lr=self.lr,
weight_decay=self.weight_decay)
loss_func = torch.nn.CosineEmbeddingLoss()
sum_loss = 0
for batch_id, (text_a, text_b, label) in enumerate(train_loader):
last_hidden_a = self.model(text_a.type(self.long))
last_hidden_b = self.model(text_b.type(self.long))
for i in range(len(label)):
if label[i] == 0.0:
label[i] = -1.0
cur_loss = loss_func(last_hidden_a, last_hidden_b, label.type(self.float))
sum_loss += cur_loss.data
cur_loss.backward()
optimizer.step()
optimizer.zero_grad()
train_loss = sum_loss/len(self.train_dataset.df)
return train_loss
def validate(self, valid_loader):
"""
Validates model
:param valid_loader: validation documents
:return: validation acc & loss
"""
self.model.eval()
loss_func = torch.nn.CosineEmbeddingLoss()
sum_loss = 0
for batch_id, (text_a, text_b, label) in enumerate(valid_loader):
last_hidden_a = self.model(text_a.type(self.long))
last_hidden_b = self.model(text_b.type(self.long))
for i in range(len(label)):
if label[i] == 0.0:
label[i] = -1.0
cur_loss = loss_func(last_hidden_a, last_hidden_b, label.type(self.float))
sum_loss += cur_loss.data
valid_loss = sum_loss / len(self.valid_dataset.df)
return valid_loss
def validate_ir(self, collection_loader, queries_loader):
"""
Computes MAP on last hidden states during classifier training
:param collection_loader: documents in the collection
:param queries_loader: query documents
:return: mean average precision
"""
collection = extract_from_model(doc_repr="hidden", sequences=collection_loader, model=self.model)
queries = extract_from_model(doc_repr="hidden", sequences=queries_loader, model=self.model)
index = build_index(collection=collection)
top_doc_ids, _ = search_index(index=index, k=len(self.collection.df), queries=queries)
map = compute_mean_avg_prec(top_doc_ids=top_doc_ids, train_df=self.collection.df,
test_df=self.queries.df, label_type=self.label_type)
return map
def run_training(self):
"""
Runs the training and validation, computes the MAP, plots the results
"""
train_loader = DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True)
valid_loader = DataLoader(self.valid_dataset, batch_size=self.batch_size, shuffle=True)
collection_loader = DataLoader(self.collection, batch_size=64, shuffle=False)
queries_loader = DataLoader(self.queries, batch_size=64, shuffle=False)
log('============ IR DATA ============')
log("Corpus examples: %d, query examples: %d" %(len(self.collection.df), len(self.queries.df)))
log('============ TRAINING & VALIDATION ============')
log("%d original classes, %d training examples, %d validation examples" % (
len(self.orig_labels), len(self.train_dataset.df), len(self.valid_dataset.df)))
train_losses = []
valid_losses = []
mean_avg_precs = []
best_map = -1
for epoch in range(self.num_epochs):
log("Epoch: %d/%d ..." % (epoch, self.num_epochs))
train_loss = self.train(train_loader)
valid_loss = self.validate(valid_loader)
train_losses.append(train_loss)
valid_losses.append(valid_loss)
map = self.validate_ir(collection_loader, queries_loader)
mean_avg_precs.append(map)
if map > best_map:
best_map = map
torch.save(self.model.state_dict(), "public_data/models/experiment_%s/classifier_%s.pkl"
% (self.save_as.split("_")[0], self.save_as))
log("TrainLoss: %.3f " %train_loss + "ValidLoss: %.3f " %valid_loss + "MAP: %.3f " %map )
plot_training(train_losses, valid_losses, "loss", "_".join([self.save_as, "loss"]))
plot_ir(values=mean_avg_precs, save_as=self.save_as, title="Mean Average Precision", name="map")
return train_losses, valid_losses
}:
setattr(model_args, key, getattr(dataset, key))
# iterations per epoch
setattr(args, 'iter_per_epoch',
math.ceil(dataset.num_data_points['train'] / args.batch_size))
print("{} iter per epoch.".format(args.iter_per_epoch))
# ----------------------------------------------------------------------------
# setup the model
# ----------------------------------------------------------------------------
encoder = Encoder(model_args)
decoder = Decoder(model_args, encoder)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()),
lr=args.lr)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=args.lr_decay_rate)
if args.load_path != '':
encoder.load_state_dict(components['encoder'])
decoder.load_state_dict(components['decoder'])
optimizer.load_state_dict(components['optimizer'])
# cuda enabled optimizer, see:
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.to(args.device)
print("Loaded model from {}".format(args.load_path))
'max_ans_len'
}:
setattr(model_args, key, getattr(dataset, key))
# iterations per epoch
setattr(args, 'iter_per_epoch',
math.floor(dataset.num_data_points['train'] / args.batch_size))
print("{} iter per epoch.".format(args.iter_per_epoch))
# ----------------------------------------------------------------------------
# setup the model
# ----------------------------------------------------------------------------
encoder = Encoder(model_args)
decoder = Decoder(model_args, encoder)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()),
lr=args.lr,
weight_decay=args.weight_decay)
encoder.word_embed.init_embedding('data/glove/glove6b_init_300d_1.0.npy')
start_epoch = 0
if args.load_path != '':
components = torch.load(args.load_path)
encoder.load_state_dict(components.get('encoder', components))
decoder.load_state_dict(components.get('decoder', components))
optimizer.load_state_dict(components.get('optimizer', components))
start_epoch = components['epoch']
print("Loaded model from {}".format(args.load_path))
print("Decoder: {}".format(args.decoder))
args_for_save = encoder.args