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few_shot_eval.py
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few_shot_eval.py
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import torch
import numpy as np
from args import *
from utils import *
n_runs = args.n_runs
batch_few_shot_runs = args.batch_fs
assert(n_runs % batch_few_shot_runs == 0)
def define_runs(n_ways, n_shots, n_queries, num_classes, elements_per_class):
shuffle_classes = torch.LongTensor(np.arange(num_classes))
run_classes = torch.LongTensor(n_runs, n_ways).to(args.device)
run_indices = torch.LongTensor(n_runs, n_ways, n_shots + n_queries).to(args.device)
for i in range(n_runs):
run_classes[i] = torch.randperm(num_classes)[:n_ways]
for j in range(n_ways):
run_indices[i,j] = torch.randperm(elements_per_class[run_classes[i, j]])[:n_shots + n_queries]
return run_classes, run_indices
def generate_runs(data, run_classes, run_indices, batch_idx):
n_runs, n_ways, n_samples = run_classes.shape[0], run_classes.shape[1], run_indices.shape[2]
run_classes = run_classes[batch_idx * batch_few_shot_runs : (batch_idx + 1) * batch_few_shot_runs]
run_indices = run_indices[batch_idx * batch_few_shot_runs : (batch_idx + 1) * batch_few_shot_runs]
run_classes = run_classes.unsqueeze(2).unsqueeze(3).repeat(1,1,data.shape[1], data.shape[2])
run_indices = run_indices.unsqueeze(3).repeat(1, 1, 1, data.shape[2])
datas = data.unsqueeze(0).repeat(batch_few_shot_runs, 1, 1, 1)
cclasses = torch.gather(datas, 1, run_classes)
res = torch.gather(cclasses, 2, run_indices)
return res
def ncm(train_features, features, run_classes, run_indices, n_shots, elements_train=None):
with torch.no_grad():
dim = features.shape[2]
targets = torch.arange(args.n_ways).unsqueeze(1).unsqueeze(0).to(args.device)
features = preprocess(train_features, features, elements_train=elements_train)
scores = []
for batch_idx in range(n_runs // batch_few_shot_runs):
runs = generate_runs(features, run_classes, run_indices, batch_idx)
means = torch.mean(runs[:,:,:n_shots], dim = 2)
distances = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, args.n_ways, 1, -1, dim) - means.reshape(batch_few_shot_runs, 1, args.n_ways, 1, dim), dim = 4, p = 2)
winners = torch.min(distances, dim = 2)[1]
scores += list((winners == targets).float().mean(dim = 1).mean(dim = 1).to("cpu").numpy())
return stats(scores, "")
# def transductive_ncm(train_features, features, run_classes, run_indices, n_shots, n_iter_trans = args.transductive_n_iter, n_iter_trans_sinkhorn = args.transductive_n_iter_sinkhorn, temp_trans = args.transductive_temperature, alpha_trans = args.transductive_alpha, cosine = args.transductive_cosine, elements_train=None):
# with torch.no_grad():
# dim = features.shape[2]
# targets = torch.arange(args.n_ways).unsqueeze(1).unsqueeze(0).to(args.device)
# features = preprocess(train_features, features, elements_train=elements_train)
# if cosine:
# features = features / torch.norm(features, dim = 2, keepdim = True)
# scores = []
# for batch_idx in range(n_runs // batch_few_shot_runs):
# runs = generate_runs(features, run_classes, run_indices, batch_idx)
# means = torch.mean(runs[:,:,:n_shots], dim = 2)
# if cosine:
# means = means / torch.norm(means, dim = 2, keepdim = True)
# for _ in range(n_iter_trans):
# if cosine:
# similarities = torch.einsum("bswd,bswd->bsw", runs[:,:,n_shots:].reshape(batch_few_shot_runs, -1, 1, dim), means.reshape(batch_few_shot_runs, 1, args.n_ways, dim))
# soft_sims = torch.softmax(temp_trans * similarities, dim = 2)
# else:
# similarities = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, -1, 1, dim) - means.reshape(batch_few_shot_runs, 1, args.n_ways, dim), dim = 3, p = 2)
# soft_sims = torch.exp( -1 * temp_trans * similarities)
# for _ in range(n_iter_trans_sinkhorn):
# soft_sims = soft_sims / soft_sims.sum(dim = 2, keepdim = True) * args.n_ways
# soft_sims = soft_sims / soft_sims.sum(dim = 1, keepdim = True) * args.n_queries
# new_means = ((runs[:,:,:n_shots].mean(dim = 2) * n_shots + torch.einsum("rsw,rsd->rwd", soft_sims, runs[:,:,n_shots:].reshape(runs.shape[0], -1, runs.shape[3])))) / runs.shape[2]
# if cosine:
# new_means = new_means / torch.norm(new_means, dim = 2, keepdim = True)
# means = means * alpha_trans + (1 - alpha_trans) * new_means
# if cosine:
# means = means / torch.norm(means, dim = 2, keepdim = True)
# if cosine:
# winners = torch.max(similarities.reshape(runs.shape[0], runs.shape[1], runs.shape[2] - n_shots, -1), dim = 3)[1]
# else:
# winners = torch.min(similarities.reshape(runs.shape[0], runs.shape[1], runs.shape[2] - n_shots, -1), dim = 3)[1]
# scores += list((winners == targets).float().mean(dim = 1).mean(dim = 1).to("cpu").numpy())
# return stats(scores, "")
# def kmeans(train_features, features, run_classes, run_indices, n_shots, elements_train=None):
# with torch.no_grad():
# dim = features.shape[2]
# targets = torch.arange(args.n_ways).unsqueeze(1).unsqueeze(0).to(args.device)
# features = preprocess(train_features, features, elements_train=elements_train)
# scores = []
# for batch_idx in range(n_runs // batch_few_shot_runs):
# runs = generate_runs(features, run_classes, run_indices, batch_idx)
# means = torch.mean(runs[:,:,:n_shots], dim = 2)
# for i in range(500):
# similarities = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, -1, 1, dim) - means.reshape(batch_few_shot_runs, 1, args.n_ways, dim), dim = 3, p = 2)
# new_allocation = (similarities == torch.min(similarities, dim = 2, keepdim = True)[0]).float()
# new_allocation = new_allocation / new_allocation.sum(dim = 1, keepdim = True)
# allocation = new_allocation
# means = (runs[:,:,:n_shots].mean(dim = 2) * n_shots + torch.einsum("rsw,rsd->rwd", allocation, runs[:,:,n_shots:].reshape(runs.shape[0], -1, runs.shape[3])) * args.n_queries) / runs.shape[2]
# winners = torch.min(similarities.reshape(runs.shape[0], runs.shape[1], runs.shape[2] - n_shots, -1), dim = 3)[1]
# scores += list((winners == targets).float().mean(dim = 1).mean(dim = 1).to("cpu").numpy())
# return stats(scores, "")
def softkmeans(train_features, features, run_classes, run_indices, n_shots, transductive_temperature_softkmeans=args.transductive_temperature_softkmeans, elements_train=None):
with torch.no_grad():
dim = features.shape[2]
targets = torch.arange(args.n_ways).unsqueeze(1).unsqueeze(0).to(args.device)
features = preprocess(train_features, features, elements_train=elements_train)
scores = []
for batch_idx in range(n_runs // batch_few_shot_runs):
runs = generate_runs(features, run_classes, run_indices, batch_idx)
runs = postprocess(runs)
means = torch.mean(runs[:,:,:n_shots], dim = 2)
for i in range(30):
similarities = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, -1, 1, dim) - means.reshape(batch_few_shot_runs, 1, args.n_ways, dim), dim = 3, p = 2)
soft_allocations = F.softmax(-similarities.pow(2)*args.transductive_temperature_softkmeans, dim=2)
means = torch.sum(runs[:,:,:n_shots], dim = 2) + torch.einsum("rsw,rsd->rwd", soft_allocations, runs[:,:,n_shots:].reshape(runs.shape[0], -1, runs.shape[3]))
means = means/(n_shots+soft_allocations.sum(dim = 1).reshape(batch_few_shot_runs, -1, 1))
winners = torch.min(similarities, dim = 2)[1]
winners = winners.reshape(batch_few_shot_runs, args.n_ways, -1)
scores += list((winners == targets).float().mean(dim = 1).mean(dim = 1).to("cpu").numpy())
return stats(scores, "")
def ncm_cosine(train_features, features, run_classes, run_indices, n_shots, elements_train=None):
with torch.no_grad():
dim = features.shape[2]
targets = torch.arange(args.n_ways).unsqueeze(1).unsqueeze(0).to(args.device)
features = preprocess(train_features, features, elements_train=elements_train)
features = sphering(features)
scores = []
for batch_idx in range(n_runs // batch_few_shot_runs):
runs = generate_runs(features, run_classes, run_indices, batch_idx)
means = torch.mean(runs[:,:,:n_shots], dim = 2)
means = sphering(means)
distances = torch.einsum("bwysd,bwysd->bwys",runs[:,:,n_shots:].reshape(batch_few_shot_runs, args.n_ways, 1, -1, dim), means.reshape(batch_few_shot_runs, 1, args.n_ways, 1, dim))
winners = torch.max(distances, dim = 2)[1]
scores += list((winners == targets).float().mean(dim = 1).mean(dim = 1).to("cpu").numpy())
return stats(scores, "")
def get_features(model, loader, n_aug = args.sample_aug):
model.eval()
for augs in range(n_aug):
all_features, offset, max_offset = [], 1000000, 0
for batch_idx, (data, target) in enumerate(loader):
with torch.no_grad():
data, target = data.to(args.device), target.to(args.device)
_, features = model(data)
all_features.append(features)
offset = min(min(target), offset)
max_offset = max(max(target), max_offset)
num_classes = max_offset - offset + 1
print(".", end='')
if augs == 0:
features_total = torch.cat(all_features, dim = 0).reshape(num_classes, -1, all_features[0].shape[1])
else:
features_total += torch.cat(all_features, dim = 0).reshape(num_classes, -1, all_features[0].shape[1])
return features_total / n_aug
def eval_few_shot(train_features, val_features, novel_features, val_run_classes, val_run_indices, novel_run_classes, novel_run_indices, n_shots, transductive = False,elements_train=None):
if transductive:
return softkmeans(train_features, val_features, val_run_classes, val_run_indices, n_shots, elements_train=elements_train), softkmeans(train_features, novel_features, novel_run_classes, novel_run_indices, n_shots, elements_train=elements_train)
else:
return ncm(train_features, val_features, val_run_classes, val_run_indices, n_shots, elements_train=elements_train), ncm(train_features, novel_features, novel_run_classes, novel_run_indices, n_shots, elements_train=elements_train)
def update_few_shot_meta_data(model, train_clean, novel_loader, val_loader, few_shot_meta_data):
if "M" in args.preprocessing or args.save_features != '':
train_features = get_features(model, train_clean)
else:
train_features = torch.Tensor(0,0,0)
val_features = get_features(model, val_loader)
novel_features = get_features(model, novel_loader)
res = []
for i in range(len(args.n_shots)):
res.append(evaluate_shot(i, train_features, val_features, novel_features, few_shot_meta_data, model = model))
return res
def evaluate_shot(index, train_features, val_features, novel_features, few_shot_meta_data, model = None, transductive = False):
(val_acc, val_conf), (novel_acc, novel_conf) = eval_few_shot(train_features, val_features, novel_features, few_shot_meta_data["val_run_classes"][index], few_shot_meta_data["val_run_indices"][index], few_shot_meta_data["novel_run_classes"][index], few_shot_meta_data["novel_run_indices"][index], args.n_shots[index], transductive = transductive, elements_train=few_shot_meta_data["elements_train"])
if val_acc > few_shot_meta_data["best_val_acc"][index]:
if val_acc > few_shot_meta_data["best_val_acc_ever"][index]:
few_shot_meta_data["best_val_acc_ever"][index] = val_acc
if args.save_model != "":
if len(args.devices) == 1:
torch.save(model.state_dict(), args.save_model + str(args.n_shots[index]))
else:
torch.save(model.module.state_dict(), args.save_model + str(args.n_shots[index]))
if args.save_features != "":
if args.base != '':
torch.save({"base": train_features, "val": val_features, "novel": novel_features}, args.save_features + str(args.n_shots[index]))
else:
torch.save(torch.cat([train_features, val_features, novel_features], dim = 0), args.save_features + str(args.n_shots[index]))
few_shot_meta_data["best_val_acc"][index] = val_acc
few_shot_meta_data["best_novel_acc"][index] = novel_acc
return val_acc, val_conf, novel_acc, novel_conf
print("eval_few_shot, ", end='')