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test_ada.py
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test_ada.py
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import os
import time
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from tqdm import tqdm
from models import build_model
from utils.utils import build_dataflow, AverageMeter, \
actnet_acc, get_augmentor, bn_cali_fix, bn_calibration, get_efficient_loss, get_entropy
from utils.video_transforms import *
from utils.video_dataset import VideoDataSet, VideoDataSetLMDB
from utils.video_dataset2 import MultiVideoDataSetOnline
from utils.dataset_config import get_dataset_config
from opts import arg_parser
def load_categories(file_path):
id_to_label = {}
label_to_id = {}
with open(file_path) as f:
cls_id = 0
for label in f.readlines():
label = label.strip()
if label == "":
continue
id_to_label[cls_id] = label
label_to_id[label] = cls_id
cls_id += 1
return id_to_label, label_to_id
def eval_a_batch(data, model, in_channels, num_clips=1, num_crops=1, rand_policy=False, dist=None, deterministic=False,
modality='rgb', softmax=False, threed_data=False):
with torch.no_grad():
batch_size = data.shape[0]
if threed_data:
tmp = torch.chunk(data, num_clips * num_crops, dim=2)
data = torch.cat(tmp, dim=0)
else:
data = data.view((batch_size * num_crops * num_clips, -1) + data.size()[2:])
result, policy, prob, _, _, _ = model(data, is_ada=True, rand_policy=rand_policy, dist=dist, deterministic=deterministic)
if threed_data:
tmp = torch.chunk(result, num_clips * num_crops, dim=0)
result = None
for i in range(len(tmp)):
result = result + tmp[i] if result is not None else tmp[i]
result /= (num_clips * num_crops)
else:
result = result.reshape(batch_size, num_crops * num_clips, -1).mean(dim=1)
return result, policy, prob
def main():
global args
parser = arg_parser()
args = parser.parse_args()
cudnn.benchmark = True
num_classes, train_list_name, val_list_name, test_list_name, filename_seperator, image_tmpl, filter_video, label_file, multilabel = get_dataset_config(args.dataset, args.use_lmdb)
if args.dataset == 'activitynet':
if args.server == 'diva':
datadir = '/store/workspaces/rpanda/sunxm/datasets/activitynet'
elif args.server in ['aimos', 'satori']:
datadir = args.datadir
else:
raise ValueError('server %s is not supported' % args.server)
else:
datadir = args.datadir
data_list_name = val_list_name if args.evaluate else test_list_name
args.num_classes = num_classes
if args.dataset == 'st2stv1' or args.dataset == 'activitynet':
id_to_label, label_to_id = load_categories(os.path.join(datadir, label_file))
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.modality == 'rgb':
args.input_channels = 3
elif args.modality == 'flow':
args.input_channels = 2 * 5
model, arch_name = build_model(args)
mean = model.mean(args.modality)
std = model.std(args.modality)
# overwrite mean and std if they are presented in command
if args.mean is not None:
if args.modality == 'rgb':
if len(args.mean) != 3:
raise ValueError("When training with rgb, dim of mean must be three.")
elif args.modality == 'flow':
if len(args.mean) != 1:
raise ValueError("When training with flow, dim of mean must be three.")
mean = args.mean
if args.std is not None:
if args.modality == 'rgb':
if len(args.std) != 3:
raise ValueError("When training with rgb, dim of std must be three.")
elif args.modality == 'flow':
if len(args.std) != 1:
raise ValueError("When training with flow, dim of std must be three.")
std = args.std
model = model.cuda()
model.eval()
if args.threed_data:
dummy_data = (args.input_channels, args.groups, args.input_size, args.input_size)
else:
dummy_data = (args.input_channels * args.groups, args.input_size, args.input_size)
# model_summary = torchsummary.summary(model, input_size=dummy_data)
# flops, params = extract_total_flops_params(model_summary)
# flops = int(flops.replace(',', '')) * (args.num_clips * args.num_crops)
model = torch.nn.DataParallel(model).cuda()
if args.pretrained is not None:
print("=> using pre-trained model '{}'".format(args.pretrained))
checkpoint = torch.load(args.pretrained)
model.load_state_dict(checkpoint['state_dict'])
print('the epoch is %d' % checkpoint['epoch'])
else:
print("=> creating model '{}'".format(arch_name))
# augmentor
if args.disable_scaleup:
scale_size = args.input_size
else:
scale_size = int(args.input_size / 0.875 + 0.5)
# augments = []
# if args.num_crops == 1:
# augments += [
# GroupScale(scale_size),
# GroupCenterCrop(args.input_size)
# ]
# else:
# flip = True if args.num_crops == 10 else False
# augments += [
# GroupOverSample(args.input_size, scale_size, num_crops=args.num_crops, flip=flip),
# ]
# augments += [
# Stack(threed_data=args.threed_data),
# ToTorchFormatTensor(num_clips_crops=args.num_clips * args.num_crops),
# GroupNormalize(mean=mean, std=std, threed_data=args.threed_data)
# ]
#
# augmentor = transforms.Compose(augments)
augmentor = get_augmentor(False, args.input_size, mean, std, args.disable_scaleup,
threed_data=args.threed_data, version=args.augmentor_ver,
scale_range=args.scale_range)
# Data loading code
data_list = os.path.join(datadir, data_list_name)
sample_offsets = list(range(-args.num_clips // 2 + 1, args.num_clips // 2 + 1))
print("Image is scaled to {} and crop {}".format(scale_size, args.input_size))
print("Number of crops: {}".format(args.num_crops))
print("Number of clips: {}, offset from center with {}".format(args.num_clips, sample_offsets))
if args.use_pyav:
video_data_cls = MultiVideoDataSetOnline
else:
video_data_cls = VideoDataSetLMDB if args.use_lmdb else VideoDataSet
val_dataset = video_data_cls(args.datadir, data_list, args.groups, args.frames_per_group,
num_clips=args.num_clips, modality=args.modality,
image_tmpl=image_tmpl, dense_sampling=args.dense_sampling,
fixed_offset=not args.random_sampling,
transform=augmentor, is_train=False, test_mode=not args.evaluate,
seperator=filename_seperator, filter_video=filter_video)
data_loader = build_dataflow(val_dataset, is_train=False, batch_size=args.batch_size,
workers=args.workers)
if args.bn_calibrate:
print('BN Calibration')
train_list = os.path.join(datadir, train_list_name)
train_augmentor = get_augmentor(True, args.input_size, mean, std, threed_data=args.threed_data,
version=args.augmentor_ver, scale_range=args.scale_range)
train_dataset = video_data_cls(args.datadir, train_list, args.groups, args.frames_per_group,
num_clips=args.num_clips,
modality=args.modality, image_tmpl=image_tmpl,
dense_sampling=args.dense_sampling,
transform=train_augmentor, is_train=True, test_mode=False,
seperator=filename_seperator, filter_video=filter_video)
train_loader = build_dataflow(train_dataset, is_train=True, batch_size=args.batch_size,
workers=args.workers)
model = bn_cali_fix(model)
bn_calibration(train_loader, model, args.gpu)
log_folder = os.path.join(args.logdir, arch_name)
if not os.path.exists(log_folder):
os.makedirs(log_folder)
batch_time = AverageMeter()
if args.evaluate:
logfile = open(os.path.join(log_folder, 'evaluate_log.log'), 'a')
else:
logfile = open(os.path.join(log_folder,
'test_{}crops_{}clips_{}.csv'.format(args.num_crops,
args.num_clips,
args.input_size))
, 'w')
total_outputs = 0
outputs = torch.zeros((len(data_loader) * args.batch_size, num_classes))
num_frames = args.groups * args.frames_per_group
decision_space = len(args.skip_list) + len(args.bit_width_family)
decision_space = decision_space + 1 if args.use_fp_as_bb else decision_space
policys = torch.zeros((len(data_loader) * args.batch_size * num_frames, decision_space))
probs = torch.zeros((len(data_loader) * args.batch_size * num_frames, decision_space))
if args.evaluate:
if multilabel:
labels = torch.zeros((len(data_loader) * args.batch_size, num_classes), dtype=torch.long)
else:
labels = torch.zeros((len(data_loader) * args.batch_size), dtype=torch.long)
else:
labels = [None] * len(data_loader) * args.batch_size
# switch to evaluate mode
model.eval()
total_batches = len(data_loader)
print('Temperature of Policy Net: ', model.module.policy_net.temperature)
with torch.no_grad(), tqdm(total=total_batches) as t_bar:
end = time.time()
for i, (video, label) in enumerate(data_loader):
output, policy, prob = eval_a_batch(video, model, args.input_channels, num_clips=args.num_clips, deterministic=args.deterministic,
num_crops=args.num_crops, rand_policy=args.rand_policy, dist=args.dist, modality=args.modality, softmax=True, threed_data=args.threed_data)
batch_size = output.shape[0]
outputs[total_outputs:total_outputs + batch_size, :] = output
policys[total_outputs * num_frames:(total_outputs + batch_size) * num_frames] = policy.view(-1, decision_space)
probs[total_outputs * num_frames:(total_outputs + batch_size) * num_frames] = prob.view(-1, decision_space)
if multilabel:
labels[total_outputs:total_outputs + batch_size, :] = label
else:
labels[total_outputs:total_outputs + batch_size] = label
total_outputs += video.shape[0]
batch_time.update(time.time() - end)
end = time.time()
t_bar.update(1)
outputs = outputs[:total_outputs]
labels = labels[:total_outputs]
print("Predicted {} videos.".format(total_outputs), flush=True)
if args.rand_policy:
npy_prefix = 'rand'
else:
npy_prefix = os.path.basename(args.pretrained).split(".")[0]
np.save(os.path.join(log_folder, '{}_{}crops_{}clips_{}_details_{}.npy'.format("val" if args.evaluate else "test", args.num_crops, args.num_clips, args.input_size, npy_prefix)), outputs)
np.save(os.path.join(log_folder, '{}_{}crops_{}clips.npy'.format('labels', args.num_crops, args.num_clips)), labels)
if not args.evaluate:
json_output = {
'version': "VERSION 1.3",
'results': {},
'external_data': {'used': False, 'details': 'none'}
}
prob = F.softmax(outputs, dim=1).data.cpu().numpy().copy()
predictions = np.argsort(prob, axis=1)
for ii in range(len(predictions)):
temp = predictions[ii][::-1][:5]
preds = [str(pred) for pred in temp]
if args.dataset == 'st2stv1':
print("{};{}".format(labels[ii], id_to_label[int(preds[0])]), file=logfile)
elif args.dataset == 'activitynet':
video_id = labels[ii].replace("v_", "")
if video_id not in json_output['results']:
json_output['results'][video_id] = []
for jj in range(num_classes):
tmp = {'label': id_to_label[predictions[ii][::-1][jj]],
'score': prob[ii, predictions[ii][::-1][jj]].item()}
json_output['results'][video_id].append(tmp)
else:
print("{};{}".format(labels[ii], ";".join(preds)), file=logfile)
if args.dataset == 'activitynet':
json.dump(json_output, logfile, indent=4)
else:
acc, mAP = actnet_acc(outputs, labels)
top1, top5 = acc
print(args.pretrained, file=logfile)
print('Val@{}({}) (# crops = {}, # clips = {}): \tTop@1: {:.4f}\tTop@5: {:.4f}\tmAP: {:.4f}\tFLOPs: {:,}\tParams:{} '.format(
args.input_size, scale_size, args.num_crops, args.num_clips, top1, top5, mAP, 0, 0), flush=True)
print('Val@{}({}) (# crops = {}, # clips = {}): \tTop@1: {:.4f}\tTop@5: {:.4f}\tmAP: {:.4f}\tFLOPs: {:,}\tParams:{} '.format(
args.input_size, scale_size, args.num_crops, args.num_clips, top1, top5, mAP, 0, 0), flush=True, file=logfile)
print('Policy Dist: ', policys.mean(0), flush=True)
print('Policy Dist: ', policys.mean(0), flush=True, file=logfile)
bit_width_family = [min(bit, 8) for bit in args.bit_width_family]
if args.use_fp_as_bb:
bit_width_family = [8] + bit_width_family
codebook1 = ((torch.tensor(bit_width_family)) ** 2).float() / 64.
if len(args.skip_list) != 0:
codebook2 = - (torch.tensor(args.skip_list).float() - 1) * 0 / 64.
codebook = torch.cat([codebook1, codebook2]).float()
else:
codebook = codebook1.float()
efficient_loss = get_efficient_loss(policys, codebook)
if 'resnet' in args.backbone_net and args.depth == 18:
flops = efficient_loss * (29.1 - 1.92) + 1.92
elif 'resnet' in args.backbone_net and args.depth == 50:
flops = efficient_loss * (65.76 - 1.92) + 1.92
else:
raise ValueError
policy_flops = 0.779 * min(args.p_bit, 8) ** 2 / 64. + 0.119
total_flops = policy_flops + flops
print('FLOPs (current epoch):\tBackbone: {:.2f}G,\tPolicyNet: {:.2f}G,\tTotal: {:.2f}G '.format(flops,
policy_flops,
total_flops),
flush=True)
print('FLOPs (current epoch):\tBackbone: {:.2f}G,\tPolicyNet: {:.2f}G,\tTotal: {:.2f}G '.format(flops,
policy_flops,
total_flops),
flush=True, file=logfile)
entropy = get_entropy(probs)
print('Entropy of the Validation Set: {:.2f}'.format(entropy.mean()), flush=True)
print('Entropy of the Validation Set: {:.2f}'.format(entropy.mean()), flush=True, file=logfile)
logfile.close()
if __name__ == '__main__':
main()