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prune.py
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import time
import copy
import argparse
import torch
import torch.nn as nn
import models
import dataset
import util
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--name', required=True)
parser.add_argument('--zeroshot', action='store_true')
parser.add_argument('--epochs', default=50)
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--patience', type=int, default=5)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--alpha', type=float, default=0.2)
parser.add_argument('--l', type=int, default=2)
parser.add_argument('--filename')
args = parser.parse_args()
if 'atis' in args.name:
args.dataset = 'atis'
elif 'snips' in args.name:
args.dataset = 'snips'
if 'intent' in args.name:
args.model = 'intent'
elif 'slot' in args.name:
args.model = 'slot'
elif 'joint' in args.name:
args.model = 'joint'
print(f"seed {util.rep(args.seed)}")
cuda = torch.cuda.is_available()
train, valid, test, num_words, num_intent, num_slot, wordvecs = dataset.load(args.dataset, batch_size=8, seq_len=50)
model = util.load_model(args.model, num_words, num_intent, num_slot, args.dropout, wordvecs)
model.load_state_dict(torch.load(args.name))
criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
if cuda:
model = model.cuda()
if len(args.name.split('/')) > 1:
nameprefix = args.name.split('/')[-1]
else:
nameprefix = args.name
filename = args.filename if args.filename else f"results/{nameprefix}_{'zeroshot' if args.zeroshot else 'retrain'}_l{args.l}_alpha{args.alpha}.csv"
if args.model == 'intent':
open(filename, 'w').close() # clear the file
f = open(filename, "a")
while sum(model.filter_sizes) > 0:
_, test_acc = util.valid_intent(model, test, criterion, cuda)
print(f"{sum(model.filter_sizes)}, {test_acc:.5f}", file=f, flush=True)
if sum(model.filter_sizes) > 10:
model.prune(5, args.l)
else:
model.prune(1, args.l)
if not args.zeroshot:
optimizer = torch.optim.Adam(model.parameters())
best_epoch = 0
best_valid_loss, _ = util.valid_intent(model, valid, criterion, cuda)
best_model = copy.deepcopy(model)
epoch = 1
while epoch <= best_epoch + args.patience:
train_loss, train_acc = util.train_intent(model, train, criterion, optimizer, cuda)
valid_loss, valid_acc = util.valid_intent(model, valid, criterion, cuda)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_epoch = epoch
best_model = copy.deepcopy(model)
epoch += 1
model = best_model
elif args.model == 'slot':
open(filename, 'w').close() # clear the file
f = open(filename, "a")
while sum(model.filter_sizes) > 0:
_, test_f1 = util.valid_slot(model, test, criterion, cuda)
print(f"{sum(model.filter_sizes)}, {test_f1:.5f}", file=f, flush=True)
if sum(model.filter_sizes) > 10:
model.prune(5, args.l)
else:
model.prune(1, args.l)
if not args.zeroshot:
optimizer = torch.optim.Adam(model.parameters())
best_epoch = 0
best_valid_loss, _ = util.valid_slot(model, valid, criterion, cuda)
best_model = copy.deepcopy(model)
epoch = 1
while epoch <= best_epoch + args.patience:
train_loss, train_f1 = util.train_slot(model, train, criterion, optimizer, cuda)
valid_loss, valid_f1 = util.valid_slot(model, valid, criterion, cuda)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_epoch = epoch
best_model = copy.deepcopy(model)
epoch += 1
model = best_model
elif args.model == 'joint':
open(filename, 'w').close() # clear the file
f = open(filename, "a")
while sum(model.filter_sizes) > 0:
_, (_, test_acc), (_, test_f1) = util.valid_joint(model, test, criterion, cuda, args.alpha)
print(f"{sum(model.filter_sizes)}, {test_acc:.5f}, {test_f1:.5f}", file=f, flush=True)
if sum(model.filter_sizes) > 10:
model.prune(5, args.l)
else:
model.prune(1, args.l)
if not args.zeroshot:
optimizer = torch.optim.Adam(model.parameters())
best_epoch = 0
best_valid_loss, (_, _), (_, _) = util.valid_joint(model, valid, criterion, cuda, args.alpha)
best_model = copy.deepcopy(model)
epoch = 1
while epoch <= best_epoch + args.patience:
train_loss, (_, _), (_, _) = util.train_joint(model, train, criterion, optimizer, cuda, args.alpha)
valid_loss, (_, _), (_, _) = util.valid_joint(model, valid, criterion, cuda, args.alpha)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_epoch = epoch
best_model = copy.deepcopy(model)
epoch += 1
model = best_model