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evaluate_imagenet.py
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evaluate_imagenet.py
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import argparse
import logging
import random
import numpy as np
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from torch.nn import functional as F
import datasets.datasetfactory as df
import model.learner as learner
import model.modelfactory as mf
import utils
from experiment.experiment import experiment
import replay as rep
import datasets.miniimagenet as imgnet
logger = logging.getLogger('experiment')
def main(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
my_experiment = experiment(args.name, args, "../results/", args.commit)
writer = SummaryWriter(my_experiment.path + "tensorboard")
logger = logging.getLogger('experiment')
logger.setLevel(logging.INFO)
total_clases = 10
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
logger.info("Frozen layers = %s", " ".join(frozen_layers))
final_results_all = []
total_clases = args.schedule
for tot_class in total_clases:
lr_list = [0.03, 0.01, 0.003, 0.001, 0.0003, 0.0001, 0.00003, 0.00001, 0.000003]
for aoo in range(0, args.runs):
keep = np.random.choice(list(range(20)), tot_class, replace=False)
#
dataset = imgnet.MiniImagenet(args.dataset_path, mode='test', elem_per_class=30, classes=keep, seed=aoo)
dataset_test = imgnet.MiniImagenet(args.dataset_path, mode='test', elem_per_class=30, test=args.test, classes=keep, seed=aoo)
# Iterators used for evaluation
iterator = torch.utils.data.DataLoader(dataset_test, batch_size=128,
shuffle=True, num_workers=1)
iterator_sorted = torch.utils.data.DataLoader(dataset, batch_size=1,
shuffle=args.iid, num_workers=1)
#
print(args)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
results_mem_size = {}
for mem_size in [args.memory]:
max_acc = -10
max_lr = -10
for lr in lr_list:
print(lr)
# for lr in [0.001, 0.0003, 0.0001, 0.00003, 0.00001]:
maml = torch.load(args.model, map_location='cpu')
if args.scratch:
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config)
# maml = MetaLearingClassification(args, config).to(device).net
maml = maml.to(device)
for name, param in maml.named_parameters():
param.learn = True
for name, param in maml.named_parameters():
# logger.info(name)
if name in frozen_layers:
# logger.info("Freeezing name %s", str(name))
param.learn = False
# logger.info(str(param.requires_grad))
else:
if args.reset:
w = nn.Parameter(torch.ones_like(param))
# logger.info("W shape = %s", str(len(w.shape)))
if len(w.shape) > 1:
torch.nn.init.kaiming_normal_(w)
else:
w = nn.Parameter(torch.zeros_like(param))
param.data = w
param.learn = True
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
torch.nn.init.kaiming_normal_(maml.parameters()[-2])
w = nn.Parameter(torch.zeros_like(maml.parameters()[-1]))
maml.parameters()[-1].data = w
for n, a in maml.named_parameters():
n = n.replace(".", "_")
# logger.info("Name = %s", n)
if n == "vars_14":
w = nn.Parameter(torch.ones_like(a))
# logger.info("W shape = %s", str(w.shape))
torch.nn.init.kaiming_normal_(w)
a.data = w
if n == "vars_15":
w = nn.Parameter(torch.zeros_like(a))
a.data = w
correct = 0
for img, target in iterator:
with torch.no_grad():
img = img.to(device)
target = target.to(device)
logits_q = maml(img, vars=None, bn_training=False, feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q, target).sum().item() / len(img)
logger.info("Pre-epoch accuracy %s", str(correct / len(iterator)))
filter_list = ["vars.0", "vars.1", "vars.2", "vars.3", "vars.4", "vars.5"]
logger.info("Filter list = %s", ",".join(filter_list))
list_of_names = list(
map(lambda x: x[1], list(filter(lambda x: x[0] not in filter_list, maml.named_parameters()))))
list_of_params = list(filter(lambda x: x.learn, maml.parameters()))
list_of_names = list(filter(lambda x: x[1].learn, maml.named_parameters()))
if args.scratch or args.no_freeze:
print("Empty filter list")
list_of_params = maml.parameters()
#
for x in list_of_names:
logger.info("Unfrozen layer = %s", str(x[0]))
opt = torch.optim.Adam(list_of_params, lr=lr)
res_sampler = rep.ReservoirSampler(mem_size)
for _ in range(0, args.epoch):
for img, y in iterator_sorted:
if mem_size > 0:
res_sampler.update_buffer(zip(img, y))
res_sampler.update_observations(len(img))
img = img.to(device)
y = y.to(device)
img2, y2 = res_sampler.sample_buffer(8)
img2 = img2.to(device)
y2 = y2.to(device)
img = torch.cat([img, img2], dim=0)
y = torch.cat([y, y2], dim=0)
else:
img = img.to(device)
y = y.to(device)
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y)
loss.backward()
opt.step()
logger.info("Result after one epoch for LR = %f", lr)
correct = 0
for img, target in iterator:
img = img.to(device)
target = target.to(device)
logits_q = maml(img, vars=None, bn_training=False, feature=False)
pred_q = (logits_q).argmax(dim=1)
# print("Pred=", pred_q)
# print("Target=", target)
correct += torch.eq(pred_q, target).sum().item() / len(img)
logger.info(str(correct / len(iterator)))
if (correct / len(iterator) > max_acc):
max_acc = correct / len(iterator)
max_lr = lr
lr_list = [max_lr]
results_mem_size[mem_size] = (max_acc, max_lr)
logger.info("Final Max Result = %s", str(max_acc))
writer.add_scalar('/finetune/best_' + str(aoo), max_acc, tot_class)
final_results_all.append((tot_class, results_mem_size))
print("A= ", results_mem_size)
logger.info("Final results = %s", str(results_mem_size))
my_experiment.results["Final Results"] = final_results_all
my_experiment.store_json()
print("FINAL RESULTS = ", final_results_all)
writer.close()
if __name__ == '__main__':
argparser = argparse.ArgumentParser()
argparser.add_argument('--epoch', type=int, help='epoch number', default=1)
argparser.add_argument('--seed', type=int, help='epoch number', default=222)
argparser.add_argument('--schedule', type=int, nargs='+', default=[2, 4, 6, 8, 10],
help='Decrease learning rate at these epochs.')
argparser.add_argument('--memory', type=int, help='epoch number', default=0)
argparser.add_argument('--model', type=str, help='epoch number', default="none")
argparser.add_argument('--scratch', action='store_true', default=False)
argparser.add_argument('--dataset', help='Name of experiment', default="omniglot")
argparser.add_argument('--name', help='Name of experiment', default="evaluation")
argparser.add_argument("--commit", action="store_true")
argparser.add_argument("--no-freeze", action="store_true")
argparser.add_argument('--reset', action="store_true")
argparser.add_argument('--test', action="store_true")
argparser.add_argument("--iid", action="store_true")
argparser.add_argument('--dataset-path', help='Name of experiment', default="imagenet")
argparser.add_argument("--rln", type=int, default=6)
argparser.add_argument("--runs", type=int, default=5)
args = argparser.parse_args()
import os
args.name = "/".join([args.dataset, "eval", str(args.epoch).replace(".", "_"), args.name])
main(args)