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train_naive_triplet.py
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train_naive_triplet.py
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from __future__ import division
from data import load_dataset
import time
import argparse
import numpy as np
from torch import nn, optim
import torch.nn.functional as F
import torch
import itertools
from dim_red.support_func import loss_permutation,\
loss_top_1_in_lat_top_k, normalize_numpy,\
get_nearestneighbors, sanitize, forward_pass, Normalize,\
get_nearestneighbors_partly, write_ivecs, save_transformed_data
def repeat(l, r):
return list(itertools.chain.from_iterable(itertools.repeat(x, r) for x in l))
def pairwise_NNs_inner(x):
dots = torch.mm(x, x.t())
n = x.shape[0]
dots.view(-1)[::(n+1)].fill_(-1) # Trick to fill diagonal with -1
_, I = torch.max(dots, 1) # max inner prod -> min distance
return I
def triplet_optimize(xt, xv, gt_nn, xq, gt, net, args, lambda_uniform, kpos, rank_negative, val_k, margin):
lr_schedule = [float(x.rstrip().lstrip()) for x in args.lr_schedule.split(",")]
assert args.epochs % len(lr_schedule) == 0
lr_schedule = repeat(lr_schedule, args.epochs // len(lr_schedule))
print("Lr schedule", lr_schedule)
N = gt_nn.shape[0]
xt_var = torch.from_numpy(xt).to(args.device)
# prepare optimizer
optimizer = optim.SGD(net.parameters(), lr_schedule[0], momentum=args.momentum)
pdist = nn.PairwiseDistance(2)
all_logs = []
for epoch in range(args.epochs):
# Update learning rate
args.lr = lr_schedule[epoch]
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
t0 = time.time()
# Sample positives for triplet
rank_pos = np.random.choice(kpos, size=N)
positive_idx = gt_nn[np.arange(N), rank_pos]
# Sample negatives for triplet
net.eval()
print(" Forward pass")
xl_net = forward_pass(net, xt, 1024)
print(" Distances")
I = get_nearestneighbors(xl_net, xl_net, rank_negative, args.device, needs_exact=False)
negative_idx = I[:, -1]
# training pass
print(" Train")
net.train()
avg_triplet, avg_uniform, avg_loss = 0, 0, 0
offending = idx_batch = 0
# process dataset in a random order
perm = np.random.permutation(N)
t1 = time.time()
for i0 in range(0, N, args.batch_size):
i1 = min(i0 + args.batch_size, N)
n = i1 - i0
data_idx = perm[i0:i1]
# anchor, positives, negatives
ins = xt_var[data_idx]
pos = xt_var[positive_idx[data_idx]]
neg = xt_var[negative_idx[data_idx]]
# do the forward pass (+ record gradients)
ins, pos, neg = net(ins), net(pos), net(neg)
# triplet loss
per_point_loss = pdist(ins, pos) - pdist(ins, neg) + margin
per_point_loss = F.relu(per_point_loss)
loss_triplet = per_point_loss.mean()
offending += torch.sum(per_point_loss.data > 0).item()
# entropy loss
I = pairwise_NNs_inner(ins.data)
distances = pdist(ins, ins[I])
loss_uniform = - torch.log(distances).mean()
# combined loss
loss = loss_triplet + lambda_uniform * loss_uniform
# collect some stats
avg_triplet += loss_triplet.data.item()
avg_uniform += loss_uniform.data.item()
avg_loss += loss.data.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
idx_batch += 1
avg_triplet /= idx_batch
avg_uniform /= idx_batch
avg_loss /= idx_batch
logs = {
'epoch': epoch,
'loss_triplet': avg_triplet,
'loss_uniform': avg_uniform,
'loss': avg_loss,
'offending': offending,
'lr': args.lr
}
all_logs.append(logs)
t2 = time.time()
# maybe perform a validation run
if (epoch + 1) % args.val_freq == 0 or epoch == args.epochs - 1:
yt = forward_pass(net, xt, 1024)
yv = forward_pass(net, xv, 1024)
logs['perm'] = loss_permutation(xt, yt, args, k=val_k, size=10**4)
logs['train_top1_k'] = loss_top_1_in_lat_top_k(xt, xt, yt, yt, args, 2, val_k, size=10**5, name="TRAIN")
logs['valid_top1_k'] = loss_top_1_in_lat_top_k(xv, xt, yv, yt, args, 1, val_k, size=10**5, name="VALID")
yq = forward_pass(net, xq, 1024)
logs['query_top1_k'] = loss_top_1_in_lat_top_k(xq, xt, yq, yt, args, 1, val_k, size=10**4, name="QUERY_tr")
logs['query_top1_2k'] = loss_top_1_in_lat_top_k(xq, xt, yq, yt, args, 1, 2*val_k, size=10**4, name="QUERY_tr")
net.train()
t3 = time.time()
# synthetic logging
print('epoch %d, times: [hn %.2f s epoch %.2f s val %.2f s] lr = %f loss = %g = %g + lam * %g, offending %d' %
(epoch, t1 - t0, t2 - t1, t3 - t2, args.lr, avg_loss, avg_triplet, avg_uniform, offending))
logs['times'] = (t1 - t0, t2 - t1, t3 - t2)
return all_logs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# ray.init(num_cpus=cpus)
def aa(*args, **kwargs):
group.add_argument(*args, **kwargs)
group = parser.add_argument_group('dataset options')
aa("--database", default="deep1b") # can be "bigann", "deep1b" or "*.fvecs"
aa("--size_base", type=int, default=int(1e6),
help="size of evaluation dataset")
aa("--num_learn", type=int, default=int(5e5),
help="nb of learning vectors")
group = parser.add_argument_group('Model hyperparameters')
aa("--dint", type=int, default=1024,
help="size of hidden states")
aa("--dout", type=int, default=16,
help="output dimension")
aa("--lambda_uniform", type=float, default=0.05,
help="weight of the uniformity loss")
group = parser.add_argument_group('Training hyperparameters')
aa("--batch_size", type=int, default=256)
aa("--epochs", type=int, default=40)
aa("--momentum", type=float, default=0.9)
aa("--rank_positive", type=int, default=10,
help="this number of vectors are considered positives")
aa("--rank_negative", type=int, default=50,
help="these are considered negatives")
group = parser.add_argument_group('Computation params')
aa("--seed", type=int, default=1234)
aa("--checkpoint_dir", type=str, default="",
help="checkpoint directory")
aa("--init_name", type=str, default="",
help="checkpoint to load from")
aa("--save_best_criterion", type=str, default="",
help="for example r2=4,rank=10")
aa("--quantizer_train", type=str, default="")
aa("--lr_schedule", type=str, default="0.1,0.1,0.05,0.01")
aa("--optim", type=str, default="sgd")
aa("--device", choices=["cuda", "cpu", "auto"], default="auto")
aa("--val_freq", type=int, default=10,
help="frequency of validation calls")
aa("--validation_quantizers", type=str, default="",
help="r2 values to try in validation")
aa("--print_results", type=int, default=0)
args = parser.parse_args()
if args.device == "auto":
args.device = "cuda" if torch.cuda.is_available() else "cpu"
np.random.seed(args.seed)
torch.manual_seed(args.seed)
print(args)
name_for_c = "naive"
results_file_name = "/home/shekhale/results/nns_graphs/" + args.database + "/train_results_" + name_for_c + ".txt"
if args.print_results > 0:
with open(results_file_name, "a") as rfile:
rfile.write("\n\n")
rfile.write("START TRAINING \n")
print ("load dataset %s" % args.database)
(_, xb, xq, gt) = load_dataset(args.database, args.device, size=args.size_base, test=False, mnt=True)
base_size = xb.shape[0]
dim = xb.shape[1]
lam = args.lambda_uniform
xt = xb
# GIST's vectors have different norm
if args.database != "gist":
xt = normalize_numpy(xt, args)
xb = normalize_numpy(xb, args)
xq = normalize_numpy(xq, args)
print(xb.shape)
print(xt.shape)
xt = sanitize(xt)
xb = sanitize(xb)
xq = sanitize(xq)
print ("computing training ground truth")
xt_gt = get_nearestneighbors_partly(xt, xt, args.rank_positive, device=args.device, bs=10**5, needs_exact=True)
print ("build network")
net = nn.Sequential(
nn.Linear(in_features=dim, out_features=args.dint, bias=True),
nn.BatchNorm1d(args.dint),
nn.ReLU(),
nn.Linear(in_features=args.dint, out_features=args.dint, bias=True),
nn.BatchNorm1d(args.dint),
nn.ReLU(),
nn.Linear(in_features=args.dint, out_features=args.dout, bias=True),
Normalize()
)
net.to(args.device)
val_k = 2 * args.dout
all_logs = triplet_optimize(xt, xt, xt_gt, xq, gt, net, args, lam, args.rank_positive, args.rank_negative, val_k, 0)
yb = forward_pass(net, xb, 1024)
if args.print_results > 0:
with open(results_file_name, "a") as rfile:
rfile.write("\n")
rfile.write(
"Triplet, DATABASE %s, xt_size = %d, batch_size = %d, lat_dim = %d, k = %d, lam_u = %.5f, r_pos = %d,"
" r_neg = %d , dint = %d, optim %s, margin = %.5f \n" %
(args.database, xt.shape[0], args.batch_size, args.dout, val_k, lam, args.rank_positive,
args.rank_negative, args.dint, args.optim, 0))
# rfile.write("\n")
log = all_logs[-1]
rfile.write(
"last perm = %.4f, train_top1_k = %.3f, valid_top1_k = %.3f, query_top1_k = %.3f,"
" query_top1_2k = %.3f \n" %
(log['perm'], log['train_top1_k'], log['valid_top1_k'], log['query_top1_k'],
log['query_top1_2k']))
rfile.write(
"last logs: epochs %d, loss_uniform = %.6f, loss_triplet = %.6f, loss = %.6f, offending = %d,"
" times %f %f %f \n" %
(log['epoch'] + 1, log['loss_uniform'], log['loss_triplet'], log['loss'], log['offending'],
log['times'][0], log['times'][1], log['times'][2]))
rfile.write("------------------------------------------------------ \n")
modelPath = "/mnt/data/shekhale/models/nns_graphs/" + args.database + "/" + args.database
k_nn = get_nearestneighbors_partly(xb, xb, 1000, args.device, bs=3*10**5, needs_exact=True)
write_ivecs(modelPath + "_knn_1k.ivecs", k_nn)
k_nn_latent = get_nearestneighbors_partly(yb, yb, 1000, args.device, bs=3*10**5, needs_exact=True)
write_ivecs(modelPath + "_knn_low_1k_" + name_for_c + ".ivecs", k_nn_latent)
save_transformed_data(xb, net, args.database + "/" + args.database + "_base_" + name_for_c + ".fvecs",
args.device)
save_transformed_data(xq, net, args.database + "/" + args.database + "_query_" + name_for_c + ".fvecs",
args.device)