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ndp_test.py
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ndp_test.py
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from base_config import BaseConfigByEpoch
from model_map import get_model_fn
from data.data_factory import create_dataset, load_cuda_data
from torch.nn.modules.loss import CrossEntropyLoss
from utils.engine import Engine
from utils.misc import torch_accuracy, AvgMeter
from collections import OrderedDict
import torch
from tqdm import tqdm
import time
from builder import ConvBuilder
from utils.misc import log_important, extract_deps_from_weights_file
from base_config import get_baseconfig_for_test
from data.data_factory import num_val_examples
SPEED_TEST_SAMPLE_IGNORE_RATIO = 0.5
TEST_BATCH_SIZE = 100
OVERALL_LOG_FILE = 'overall_test_log.txt'
DETAIL_LOG_FILE = 'detail_test_log.txt'
def run_eval(val_data, max_iters, net, criterion, discrip_str, dataset_name):
pbar = tqdm(range(max_iters))
top1 = AvgMeter()
top5 = AvgMeter()
losses = AvgMeter()
pbar.set_description('Validation' + discrip_str)
total_net_time = 0
with torch.no_grad():
for iter_idx, i in enumerate(pbar):
start_time = time.time()
data, label = load_cuda_data(val_data, dataset_name=dataset_name)
data_time = time.time() - start_time
net_time_start = time.time()
pred = net(data)
net_time_end = time.time()
if iter_idx >= SPEED_TEST_SAMPLE_IGNORE_RATIO * max_iters:
total_net_time += net_time_end - net_time_start
loss = criterion(pred, label)
acc, acc5 = torch_accuracy(pred, label, (1, 5))
top1.update(acc.item())
top5.update(acc5.item())
losses.update(loss.item())
pbar_dic = OrderedDict()
pbar_dic['data-time'] = '{:.2f}'.format(data_time)
pbar_dic['top1'] = '{:.5f}'.format(top1.mean)
pbar_dic['top5'] = '{:.5f}'.format(top5.mean)
pbar_dic['loss'] = '{:.5f}'.format(losses.mean)
pbar.set_postfix(pbar_dic)
metric_dic = {'top1':torch.tensor(top1.mean),
'top5':torch.tensor(top5.mean),
'loss':torch.tensor(losses.mean)}
# reduced_metirc_dic = reduce_loss_dict(metric_dic)
reduced_metirc_dic = metric_dic #TODO note this
return reduced_metirc_dic, total_net_time
def val_during_train(epoch, iteration, tb_tags,
engine, model, val_data, criterion, descrip_str,
dataset_name, test_batch_size, tb_writer):
model.eval()
num_examples = num_val_examples(dataset_name)
assert num_examples % test_batch_size == 0
val_iters = num_examples // test_batch_size
eval_dict, total_net_time = run_eval(val_data, val_iters, model, criterion, descrip_str,
dataset_name=dataset_name)
val_top1_value = eval_dict['top1'].item()
val_top5_value = eval_dict['top5'].item()
val_loss_value = eval_dict['loss'].item()
for tag, value in zip(tb_tags, [val_top1_value, val_top5_value, val_loss_value]):
tb_writer.add_scalars(tag, {'Val': value}, iteration)
engine.log(
'val at epoch {}, top1={:.5f}, top5={:.5f}, loss={:.6f}'.format(epoch, val_top1_value,
val_top5_value,
val_loss_value))
model.train()
def get_criterion(cfg):
return CrossEntropyLoss() #TODO note this
def ding_test(cfg:BaseConfigByEpoch, net=None, val_dataloader=None, show_variables=False, convbuilder=None,
init_hdf5=None, extra_msg=None, weights_dict=None):
with Engine(local_rank=0, for_val_only=True) as engine:
engine.setup_log(
name='test', log_dir='./', file_name=DETAIL_LOG_FILE)
if convbuilder is None:
convbuilder = ConvBuilder(base_config=cfg)
if net is None:
net_fn = get_model_fn(cfg.dataset_name, cfg.network_type)
model = net_fn(cfg, convbuilder).cuda()
else:
model = net.cuda()
if val_dataloader is None:
val_data = create_dataset(cfg.dataset_name, cfg.dataset_subset,
global_batch_size=cfg.global_batch_size, distributed=False)
num_examples = num_val_examples(cfg.dataset_name)
assert num_examples % cfg.global_batch_size == 0
val_iters = num_val_examples(cfg.dataset_name) // cfg.global_batch_size
print('batchsize={}, {} iters'.format(cfg.global_batch_size, val_iters))
criterion = get_criterion(cfg).cuda()
engine.register_state(
scheduler=None, model=model, optimizer=None)
if show_variables:
engine.show_variables()
assert not engine.distributed
if weights_dict is not None:
engine.load_from_weights_dict(weights_dict)
else:
if cfg.init_weights:
engine.load_checkpoint(cfg.init_weights)
if init_hdf5:
engine.load_hdf5(init_hdf5)
# engine.save_by_order('smi2_by_order.hdf5')
# engine.load_by_order('smi2_by_order.hdf5')
# engine.save_hdf5('model_files/stami2_lrs4Z.hdf5')
model.eval()
eval_dict, total_net_time = run_eval(val_data, val_iters, model, criterion, 'TEST', dataset_name=cfg.dataset_name)
val_top1_value = eval_dict['top1'].item()
val_top5_value = eval_dict['top5'].item()
val_loss_value = eval_dict['loss'].item()
msg = '{},{},{},top1={:.5f},top5={:.5f},loss={:.7f},total_net_time={}'.format(cfg.network_type, init_hdf5 or cfg.init_weights, cfg.dataset_subset,
val_top1_value, val_top5_value, val_loss_value, total_net_time)
if extra_msg is not None:
msg += ', ' + extra_msg
log_important(msg, OVERALL_LOG_FILE)
return eval_dict
def general_test(network_type, weights, builder=None, net=None, dataset_name=None, weights_dict=None,
batch_size=None):
if weights is None or weights == 'None':
init_weights = None
init_hdf5 = None
elif weights.endswith('.hdf5'):
init_weights = None
init_hdf5 = weights
else:
init_weights = weights
init_hdf5 = None
if init_hdf5 is not None:
deps = extract_deps_from_weights_file(init_hdf5)
else:
deps = None
if deps is None and ('wrnc16' in network_type or 'wrnh16' in network_type):
from constants import wrn_origin_deps_flattened
deps = wrn_origin_deps_flattened(2, 8)
if network_type == 'sres50':
from constants import RESNET50_ORIGIN_DEPS_FLATTENED
from rr.resrep_scripts import calculate_resnet_50_flops
flops_ratio = calculate_resnet_50_flops(deps) / calculate_resnet_50_flops(RESNET50_ORIGIN_DEPS_FLATTENED)
extra_msg = 'flops_r={:.4f}'.format(flops_ratio)
else:
extra_msg = None
if batch_size is None:
batch_size = TEST_BATCH_SIZE
test_config = get_baseconfig_for_test(network_type=network_type, dataset_subset='val', global_batch_size=batch_size,
init_weights=init_weights, deps=deps, dataset_name=dataset_name)
return ding_test(cfg=test_config, net=net, show_variables=True, init_hdf5=init_hdf5, convbuilder=builder,
extra_msg=extra_msg, weights_dict=weights_dict)
if __name__ == '__main__':
# from seg_model.psp_resnet import resnet50
# net = resnet50(pretrained=False)
# general_test(network_type='xx', weights='model_files/pspback50_origin.hdf5', net=net, dataset_name='imagenet_standard')
import sys
general_test(network_type=sys.argv[1], weights=sys.argv[2])