def _construct_model(self, model_path):
model = get_network('multilabel', NUMCLASS)
statedic = torch.load(model_path, map_location='cpu')
model.load_state_dict(statedic)
model.to(self.device)
model.eval()
return model
def _construct_model(self, model_path):
model = get_network('resnext101_32x8d', 22)
statedic = torch.load(model_path, map_location='cpu')
model.load_state_dict(statedic)
model.to(self.device)
model.eval()
return model
def frozen_model(dictpath):
name = dictpath.split('/')[-1]
epoch = name.split('_')[1]
epoch = epoch.split('.')[0]
model = get_network(MODELNAME, NUMCLASS)
model.load_state_dict(torch.load(dictpath, map_location='cpu'))
model.eval()
model.cuda()
example = torch.randn(1, 3, HEIGHT, WIDTH)
with torch.no_grad():
trace_script_module = torch.jit.trace(model, example.cuda())
trace_script_module.save(os.path.join(MODELSAVEPATH, 'model_{}_frozen.pt'.format(epoch)))
print('[*]! the jit is create now')
from config.value_config import *
from utils.model_select import get_network
import torch
from utils.lr_scheduler import WarmUpLR, get_lr_scheduler
from utils.eval import eval_fuse, displaymetric
import time
from utils.mixup import mixup_data, mixup_criterion
os.environ['CUDA_VISIBLE_DEVICES'] = GPUS
os.makedirs(LOGPATH, exist_ok=True)
summary_writer = SummaryWriter(LOGPATH)
os.makedirs(MODELSAVEPATH, exist_ok=True)
trainloader, testloader = loader(LOADERNAME)
beg = time.time()
net = get_network(MODELNAME, NUMCLASS)
if LOADING:
net.load_state_dict(torch.load(LOADPATH, map_location='cpu'))
net.cuda()
if torch.cuda.device_count() > 1:
net = DataParallel(net)
end = time.time()
print('[*]! model load time is{}'.format(end - beg))
iters = len(trainloader)
optimizer = torch.optim.SGD(net.parameters(),
lr=INITLR,
momentum=0.9,
weight_decay=WD)
scheduler = get_lr_scheduler(optimizer, LRTAG)
warmup = WarmUpLR(optimizer, iters * WARM)
plt.xticks([])
plt.yticks([])
plt.title(title)
return fig
def main(data, label):
tsne = TSNE(n_components=2, init='pca', random_state=0)
t0 = time()
result = tsne.fit_transform(data)
fig = plot_embedding(
result, label,
't-SNE embedding of the digits (time %.2fs)' % (time() - t0))
plt.show(fig)
if __name__ == '__main__':
dirpath = '/home/pc/gitcode/batch-dropblock-network/data/eyeData/bounding_box_test'
state_path = '/home/pc/gitcode/multi_label/vgg16_multi_eye_shape_concetration/epoch139_test_acc0.5077519379844961.pth'
net = get_network('vgg16', 5)
tag = 'classifier.3'
state_dic = torch.load(state_path, map_location='cpu')
net.load_state_dict(state_dic)
net = net.cpu()
net.eval()
hook_feature = HookFeature(dirpath, net)
vecs, label = hook_feature.get_data(tag)
print(vecs)
print(label)
main(vecs, label)
