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evaluate.py
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import torch.optim as optim
import torch.nn as nn
import torch
from filelock import FileLock
import time
import os
from dataset import get_dataset, get_dataloader, DATASET_CONFIGS, AVAILABLE_TRANSFORMS
from train import train, train_parallel, test, test_ensemble
from models import get_model
from config import config
from decoder import MixedPredConvLightDecoder, get_baseline_decoder
from utils import save_checkpoint, load_checkpoint
from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR
def prepare_alpha_beta(target_num_blocks=1, num_blocks=1, num_decoders=3):
if config.eval_base_dir:
# load alpha and beta
alpha_path = os.path.join(config.eval_base_dir, 'alpha.pt')
alpha = torch.load(alpha_path)
if config.no_beta:
beta = None
else:
beta_path = os.path.join(config.eval_base_dir, 'beta.pt')
beta = torch.load(beta_path)
else:
# prepare baseline (uniform split)
alpha = torch.zeros((num_blocks-1, 2))
alpha[:, 0] = -1
selected_layers = torch.linspace(-1, num_blocks-1, target_num_blocks+1)[1:-1].long()
alpha[selected_layers, 0] = 1.
beta = None
return alpha, beta
def evaluate_hps(arch, alpha, beta, train_dataset, test_dataset,
target_num_blocks=0, valid_dataset=None,
decoder=None):
decoder = MixedPredConvLightDecoder
if beta is None:
decoder = get_baseline_decoder(config.baseline_dec_type)
n_gpus = torch.cuda.device_count()
model = get_model(arch, 1, cuda=False, return_devices=False, decoder=decoder)[0]
if config.eval_continuous:
block_indices, decoder_configs = [], []
else:
block_indices, decoder_configs = model.update_model(
alpha, beta, num_blocks=target_num_blocks
)
device = 'cuda:0'
model = model.to(device)
if config.optim == 'adam':
optimizers = [optim.Adam(model.block_parameters(j), lr=config.lr, weight_decay=config.weight_decay)
for j in range(model.num_blocks)]
elif config.optim == 'sgd':
optimizers = [optim.SGD(model.block_parameters(j), lr=config.lr, momentum=0., weight_decay=config.weight_decay)
for j in range(model.num_blocks)]
elif config.optim == 'momentum':
optimizers = [optim.SGD(model.block_parameters(j), lr=config.lr, momentum=config.momentum, weight_decay=config.weight_decay)
for j in range(model.num_blocks)]
else:
raise NotImplementedError
if config.lr_scheduler == 'cosine':
print("Using cosine annealing lr")
schedulers = [CosineAnnealingLR(optimizers[j], config.train_iters, config.lr_min)
for j in range(len(optimizers))]
elif config.lr_scheduler == 'multistep':
milestones = config.lr_decay_milestones
print("Using multistep lr with milestones at {}".format(milestones))
schedulers = [MultiStepLR(optimizers[j], milestones)
for j in range(len(optimizers))]
else:
raise NotImplementedError
print("Train model with found backward path ({} blocks)".format(model.num_blocks))
dconfig = DATASET_CONFIGS[config.dataset]
train_loader = get_dataloader(train_dataset, config.batch_size, shuffle=True,
classes_per_batch=config.classes_per_batch)
if valid_dataset:
valid_loader = get_dataloader(valid_dataset, config.valid_batch_size)
else:
valid_loader = None
test_loader = get_dataloader(test_dataset, config.valid_batch_size)
if config.test:
load_checkpoint(model, device, target_num_blocks, is_best=True)
elif config.eval_continuous:
sd = train(model, optimizers, train_loader, config.train_iters, alpha=alpha, beta=beta,
scheduler=schedulers, valid_loader=valid_loader, valid_freq=config.valid_freq)
elif n_gpus == 1 or target_num_blocks == 1:
sd = train(model, optimizers, train_loader, config.train_iters,
scheduler=schedulers, valid_loader=valid_loader, mixed_precision=config.fp16,
valid_freq=config.valid_freq)
elif n_gpus > 0:
model.to('cpu')
devices = [torch.device('cuda:{}'.format(i % n_gpus)) for i in range(model.num_blocks)]
sd = train_parallel(model, optimizers, train_loader, config.train_iters,
schedulers, devices, valid_loader=valid_loader,
mixed_precision=config.fp16, valid_freq=config.valid_freq)
model.to(device)
else:
raise NotImplementedError
if not config.test:
save_checkpoint(sd, target_num_blocks, is_best=True)
if not config.test:
test_loss, err1, err5 = test(model, test_loader, device=device, mixed_precision=config.fp16)
else:
test_loss, err1, err5 = test_ensemble(model, test_loader, device=device, num_ensemble=config.num_ensemble, mixed_precision=config.fp16)
print("Test error: {:.2f}% (top1) / {:.2f}% (top5)".format(err1*100, err5*100))
if not config.test or config.num_ensemble == 1:
results = {'block_indices': block_indices, 'decoder_configs':decoder_configs,
'loss': test_loss, 'err@1': err1, 'err@5': err5,
}
else:
results = None
return results
if __name__ == '__main__':
import os
import torch
import json
import numpy as np
import random
os.environ['PYTHONHASHSEED']=str(config.seed)
torch.manual_seed(config.seed)
np.random.seed(config.seed)
random.seed(config.seed)
# prepare datasets
train_dataset, valid_dataset = get_dataset(config.dataset, train=True, download=True, return_valid=True)
test_dataset = get_dataset(config.dataset, train=False, download=False)
# evaluate found backward path
arch = config.name.split('-')[0]
model = get_model(arch, 1, cuda=False, return_devices=False)[0]
num_blocks = model.num_blocks
num_decoders = model.num_decoders
if config.eval_continuous:
lst_num_blocks = [num_blocks]
else:
lst_num_blocks = range(num_blocks, -1, -1) if config.target_num_blocks == 0 else [config.target_num_blocks]
for i in lst_num_blocks:
alpha, beta = prepare_alpha_beta(i, num_blocks, num_decoders)
results = evaluate_hps(arch, alpha, beta, train_dataset, test_dataset,
target_num_blocks=i, valid_dataset=valid_dataset)
if results is not None:
result_base_dir = os.path.join(config.result_dir, config.dataset, str(config.feat_mult), config.name)
result_dir = os.path.join(result_base_dir, str(i)) if not config.eval_continuous else config.result_dir
if not os.path.exists(result_dir):
os.makedirs(result_dir)
result_path = os.path.join(result_dir, 'results.json')
lock = FileLock(result_path + ".lock")
with lock:
with open(result_path, 'w') as f:
json.dump(results, f)
while os.stat(result_path).st_size == 0:
time.time(1)
print('write results in {} with size {}'.format(result_path, os.stat(result_path).st_size))