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ab_train.py
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ab_train.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
import json
import csv
import math, random, sys
import numpy as np
import argparse
import os
from structgen import *
from tqdm import tqdm
def evaluate(model, loader, args):
model.eval()
val_nll = val_tot = 0.
val_rmsd = []
with torch.no_grad():
for hbatch in tqdm(loader):
hX, hS, hL, hmask = completize(hbatch)
for i in range(len(hbatch)):
L = hmask[i:i+1].sum().long().item()
if L > 0:
out = model.log_prob(hS[i:i+1, :L], [hL[i]], hmask[i:i+1, :L])
nll, X_pred = out.nll, out.X_cdr
val_nll += nll.item() * hL[i].count(args.cdr_type)
val_tot += hL[i].count(args.cdr_type)
l, r = hL[i].index(args.cdr_type), hL[i].rindex(args.cdr_type)
rmsd = compute_rmsd(X_pred[:, :, 1, :], hX[i:i+1, l:r+1, 1, :], hmask[i:i+1, l:r+1]) # alpha carbon
val_rmsd.append(rmsd.item())
return math.exp(val_nll / val_tot), sum(val_rmsd) / len(val_rmsd)
parser = argparse.ArgumentParser()
parser.add_argument('--train_path', default='data/sabdab/hcdr3_cluster/train_data.jsonl')
parser.add_argument('--val_path', default='data/sabdab/hcdr3_cluster/val_data.jsonl')
parser.add_argument('--test_path', default='data/sabdab/hcdr3_cluster/test_data.jsonl')
parser.add_argument('--save_dir', default='ckpts/tmp')
parser.add_argument('--load_model', default=None)
parser.add_argument('--cdr_type', default='3')
parser.add_argument('--hidden_size', type=int, default=256)
parser.add_argument('--batch_tokens', type=int, default=100)
parser.add_argument('--k_neighbors', type=int, default=9)
parser.add_argument('--block_size', type=int, default=8)
parser.add_argument('--update_freq', type=int, default=1)
parser.add_argument('--depth', type=int, default=4)
parser.add_argument('--vocab_size', type=int, default=21)
parser.add_argument('--num_rbf', type=int, default=16)
parser.add_argument('--dropout', type=float, default=0.1)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--clip_norm', type=float, default=5.0)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--seed', type=int, default=7)
parser.add_argument('--anneal_rate', type=float, default=0.9)
parser.add_argument('--print_iter', type=int, default=50)
args = parser.parse_args()
print(args)
os.makedirs(args.save_dir, exist_ok=True)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
loaders = []
for path in [args.train_path, args.val_path, args.test_path]:
data = AntibodyDataset(path, cdr_type=args.cdr_type)
loader = StructureLoader(data.data, batch_tokens=args.batch_tokens, interval_sort=int(args.cdr_type))
loaders.append(loader)
loader_train, loader_val, loader_test = loaders
model = HierarchicalDecoder(args).cuda()
optimizer = torch.optim.Adam(model.parameters())
if args.load_model:
model_ckpt, opt_ckpt, model_args = torch.load(args.load_model)
model = HierarchicalDecoder(model_args).cuda()
optimizer = torch.optim.Adam(model.parameters())
model.load_state_dict(model_ckpt)
optimizer.load_state_dict(opt_ckpt)
print('Training:{}, Validation:{}, Test:{}'.format(
len(loader_train.dataset), len(loader_val.dataset), len(loader_test.dataset))
)
best_ppl, best_epoch = 100, -1
for e in range(args.epochs):
model.train()
meter = 0
for i, hbatch in enumerate(tqdm(loader_train)):
optimizer.zero_grad()
hchain = completize(hbatch)
if hchain[-1].sum().item() == 0:
continue
loss, snll = model(*hchain)
loss.backward()
optimizer.step()
meter += snll.exp().item()
if (i + 1) % args.print_iter == 0:
meter /= args.print_iter
print(f'[{i + 1}] Train PPL = {meter:.3f}')
meter = 0
val_ppl, val_rmsd = evaluate(model, loader_val, args)
ckpt = (model.state_dict(), optimizer.state_dict(), args)
torch.save(ckpt, os.path.join(args.save_dir, f"model.ckpt.{e}"))
print(f'Epoch {e}, Val PPL = {val_ppl:.3f}, Val RMSD = {val_rmsd:.3f}')
if val_ppl < best_ppl:
best_ppl = val_ppl
best_epoch = e
if best_epoch >= 0:
best_ckpt = os.path.join(args.save_dir, f"model.ckpt.{best_epoch}")
model.load_state_dict(torch.load(best_ckpt)[0])
test_ppl, test_rmsd = evaluate(model, loader_test, args)
print(f'Test PPL = {test_ppl:.3f}, Test RMSD = {test_rmsd:.3f}')