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train_model.py
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train_model.py
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import json
from data import load_data, get_epoch
import model
import torch
from torch import nn
import os
from random import shuffle
import argparse
import logging
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
logger.setLevel(logging.INFO)
def train_epoch(model, data, config):
model.train()
n_iter = 0
epoch_x, epoch_y, lengths_x = get_epoch(data["train_x"], data["train_y"], config["batch_size"], is_train=True)
epoch_loss = 0
corrects = 0
criterion = nn.CrossEntropyLoss()
# corrects_neg, corrects_pos = 0, 0
for batch_x, batch_y, length_x in zip(epoch_x, epoch_y, lengths_x):
batch_x = torch.LongTensor(batch_x)
batch_y = torch.LongTensor(batch_y)
lengths_x = torch.LongTensor(length_x)
if config["cuda"]:
batch_x, batch_y, lengths_x = batch_x.cuda(), batch_y.cuda(), lengths_x.cuda()
optimizer.zero_grad()
pred = model(batch_x)['logits']
loss = criterion(pred, batch_y)
n_iter += 1
epoch_loss += float(loss)
loss.backward()
optimizer.step()
batch_corrects = int((torch.max(pred, 1)[1].view(batch_y.size()).data == batch_y.data).sum())
corrects += batch_corrects
# if n_iter % 200 == 0:
# eval()
# model.train()
return epoch_loss / len(data["train_y"]), corrects / len(data["train_y"]) * 100
def eval_epoch(model, data, config):
model.eval()
n_iter = 0
epoch_x, epoch_y, lengths_x = get_epoch(data["valid_x"], data["valid_y"], config["batch_size"], is_train=False)
epoch_loss = 0
corrects = 0
criterion = nn.CrossEntropyLoss()
for batch_x, batch_y, length_x in zip(epoch_x, epoch_y, lengths_x):
batch_x = torch.LongTensor(batch_x)
batch_y = torch.LongTensor(batch_y)
lengths_x = torch.LongTensor(length_x)
if config["cuda"]:
batch_x, batch_y, lengths_x = batch_x.cuda(), batch_y.cuda(), lengths_x.cuda()
# optimizer.zero_grad()
pred = model(batch_x)['logits']
loss = criterion(pred, batch_y)
n_iter += 1
epoch_loss += float(loss)
batch_corrects = int((torch.max(pred, 1)[1].view(batch_y.size()).data == batch_y.data).sum())
corrects += batch_corrects
# if n_iter % 200 == 0:
# eval()
# model.train()
del batch_x, batch_y, pred, loss
return epoch_loss / len(data["valid_y"]), corrects / len(data["valid_y"]) * 100
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config", type=str, required=True)
args = parser.parse_args()
with open(args.config) as fp:
config = json.load(fp)
data = load_data(config=config)
print(config)
model = model.CnnClassifier(ngram_sizes=config["ngram_sizes"], embedding_dim=config["embedding_dim"],
num_filters=config["num_filters"], padding_idx=data["word_to_idx"]["@@PAD@@"],
num_classes=len(data["classes"]), vocab_size=len(data["vocab"]))
if "cuda" not in config:
config["cuda"] = False
if config["cuda"]:
model = model.cuda()
parameters = filter(lambda p: p.requires_grad, model.parameters())
if "learning_rate" in config:
optimizer = torch.optim.Adam(parameters, config["learning_rate"])
else:
optimizer = torch.optim.Adam(parameters)
if not os.path.exists(config["model_path"]):
os.makedirs(config["model_path"])
print("\t".join(["Epoch", "Loss", "Acc", "Eval", "Acc", "Best"]))
metrics = {"loss": [], "acc": [], "eval": [], "eval_acc": [], "best": -1}
with open(config["model_path"] + "/config.json", "w") as fp:
json.dump(config, fp)
with open(config["model_path"] + "/w2i.json", "w") as fp:
json.dump(data["word_to_idx"], fp)
for I in range(config["num_epochs"]):
loss, acc = train_epoch(model=model, data=data, config=config)
eval_loss, eval_acc = eval_epoch(model=model, data=data, config=config)
metrics["loss"].append(loss)
metrics["acc"].append(acc)
metrics["eval"].append(eval_loss)
metrics["eval_acc"].append(eval_acc)
if eval_acc > metrics["best"]:
metrics["best"] = eval_acc
torch.save(model, config["model_path"] + "/model")
print(f"{I}\t{loss:.5f}\t{acc:.2f}\t{eval_loss:.5f}\t{eval_acc:.2f}\t{metrics['best']:.2f}")
with open(config["model_path"] + "/metrics.json", "w") as fp:
json.dump(metrics, fp)