def model_init_par():
# model
backbone = resnet50()
classifier = BaseClassifier(nattr=6)
model = FeatClassifier(backbone, classifier)
# load
checkpoint = torch.load(
'/home/sohaibrabbani/PycharmProjects/Strong_Baseline_of_Pedestrian_Attribute_Recognition/exp_result/custom/custom/img_model/ckpt_max.pth'
)
# unfolded load
# state_dict = checkpoint['state_dicts']
# new_state_dict = OrderedDict()
# for k, v in state_dict.items():
# name = k[7:]
# new_state_dict[name] = v
# model.load_state_dict(new_state_dict)
# one-liner load
# if torch.cuda.is_available():
# model = torch.nn.DataParallel(model).cuda()
# model.load_state_dict(checkpoint['state_dicts'])
# else:
model.load_state_dict({
k.replace('module.', ''): v
for k, v in checkpoint['state_dicts'].items()
})
# cuda eval
model.cuda()
model.eval()
# valid_transform
height, width = 256, 192
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
valid_transform = T.Compose(
[T.Resize((height, width)),
T.ToTensor(), normalize])
return model, valid_transform
def model_init_par():
# model
backbone = resnet50()
classifier = BaseClassifier(nattr=6)
model = FeatClassifier(backbone, classifier)
# load
checkpoint = torch.load(
'/home/deep/PycharmProjects/pedestrian-attribute-recognition/exp_result/custom/custom/img_model/ckpt_max.pth')
model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint['state_dicts'].items()})
# cuda eval
model.cuda()
model.eval()
# valid_transform
height, width = 256, 192
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
valid_transform = T.Compose([
T.Resize((height, width)),
T.ToTensor(),
normalize
])
return model, valid_transform
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
backbone = resnet50()
classifier = BaseClassifier(nattr=35)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
print("reloading pretrained models")
exp_dir = os.path.join('exp_result', args.dataset)
model_path = os.path.join(exp_dir, args.dataset, 'img_model')
model.load_state_dict(
torch.load(
'/home/sohaibrabbani/PycharmProjects/Strong_Baseline_of_Pedestrian_Attribute_Recognition/pedestrian_model/rap2_ckpt_max.pth'
)['state_dicts'])
# model = get_reload_weight(model_path, model)
model.eval()
preds_probs = []
gt_list = []
with torch.no_grad():
for step, (imgs, gt_label, imgname) in enumerate(tqdm(valid_loader)):
imgs = imgs.cuda()
gt_label = gt_label.cuda()
gt_list.append(gt_label.cpu().numpy())
gt_label[gt_label == -1] = 0
valid_logits = model(imgs)
valid_probs = torch.sigmoid(valid_logits)
preds_probs.append(valid_probs.cpu().numpy())
gt_label = np.concatenate(gt_list, axis=0)
preds_probs = np.concatenate(preds_probs, axis=0)
valid_result = get_pedestrian_metrics(gt_label, preds_probs)
print(
f'Evaluation on test set, \n',
'ma: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
valid_result.ma, np.mean(valid_result.label_pos_recall),
np.mean(valid_result.label_neg_recall)),
'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
valid_result.instance_acc, valid_result.instance_prec,
valid_result.instance_recall, valid_result.instance_f1))
def __init__(self):
device = torch.device('cpu')
FORCE_TO_CPU = True
parser = argument_parser()
args = parser.parse_args(['PETA', '--model=dpn107'])
visenv_name = 'PETA'
exp_dir = os.path.join('exp_result', visenv_name)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(
model_dir, 'ckpt_max_e0384293_2020-09-17_18-35-21.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
_, predict_tsfm = get_transform(args)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=predict_tsfm)
args.att_list = valid_set.attr_id
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter,
nattr=valid_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available() and not FORCE_TO_CPU:
model = torch.nn.DataParallel(model).cuda()
ckpt = torch.load(save_model_path)
print(f'Model is served with GPU ')
else:
model = torch.nn.DataParallel(model)
ckpt = torch.load(save_model_path,
map_location=torch.device('cpu'))
print(f'Model is served with CPU ')
model.load_state_dict(ckpt['state_dicts'])
model.eval()
# from torchsummary import summary
# summary(model, input_size=(3, 256, 192))
print('Total number of parameters: ',
sum(p.numel() for p in model.parameters() if p.requires_grad))
self.args = args
self.predict_tsfm = predict_tsfm
self.model = model
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter, nattr=valid_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available() and not FORCE_TO_CPU:
model = torch.nn.DataParallel(model).cuda()
ckpt = torch.load(save_model_path)
print(f'Model is served with GPU ')
else:
model = torch.nn.DataParallel(model)
ckpt = torch.load(save_model_path, map_location=torch.device('cpu'))
print(f'Model is served with CPU ')
model.load_state_dict(ckpt['state_dicts'])
model.eval()
macs, params = get_model_complexity_info(model, (3, 256, 192),
as_strings=True,
print_per_layer_stat=False,
verbose=False)
data.append([trained_model, macs, params])
df = pd.DataFrame(data, columns=['model','macs','params'])
df.to_csv('flops.csv')
