def load_test_model():
if not os.path.isfile(DUMPED_MODEL) and not os.path.isfile(os.path.join(DATASET_BASE, "models", DUMPED_MODEL)):
print("No trained model file!")
return
main_model = f_model(model_path=DUMPED_MODEL).cuda(GPU_ID)
color_model = c_model().cuda(GPU_ID)
pooling_model = p_model().cuda(GPU_ID)
extractor = FeatureExtractor(main_model, color_model, pooling_model)
return extractor
def load_test_model(with_clsf=False):
if not os.path.isfile(DUMPED_MODEL) and not os.path.isfile(
os.path.join(DATASET_BASE, "models", DUMPED_MODEL)):
print("No trained model file!")
return
main_model = f_model(model_path=DUMPED_MODEL).cuda(GPU_ID)
color_model = c_model().cuda(GPU_ID)
pooling_model = p_model().cuda(GPU_ID)
if not with_clsf:
extractor = FeatureExtractor(main_model, color_model, pooling_model)
else:
extractor = FeatureExtractorWithClassification(main_model, color_model,
pooling_model)
return extractor
# -*- coding: utf-8 -*-
import torch
import os
from myconfig import cfg
#from utils import FeatureExtractor,data_transform_test
from utils import *
from torch.autograd import Variable
from data import Fashion_attr_prediction, Fashion_inshop
from net import f_model, c_model, p_model
import numpy as np
print(cfg.DUMPED_MODEL)
main_model = f_model(model_path=cfg.DUMPED_MODEL).cpu()
color_model = c_model().cpu()
pooling_model = p_model().cpu()
extractor = FeatureExtractor(main_model, color_model, pooling_model)
def dump_dataset(loader, deep_feats, color_feats, labels):
for batch_idx, (data, data_path) in enumerate(loader):
data = Variable(data).cpu()
deep_feat, color_feat = extractor(data)
for i in range(len(data_path)):
path = data_path[i]
feature_n = deep_feat[i].squeeze()
color_feature_n = color_feat[i]
# dump_feature(feature, path)
deep_feats.append(feature_n)
color_feats.append(color_feature_n)
labels.append(path)
# -*- coding: utf-8 -*-
import os
from config import *
from utils import *
from torch.autograd import Variable
from data import Fashion_attr_prediction, Fashion_inshop
from net import f_model, c_model, p_model
main_model = f_model(model_path=DUMPED_MODEL).cuda(GPU_ID)
color_model = c_model().cuda(GPU_ID)
pooling_model = p_model().cuda(GPU_ID)
extractor = FeatureExtractor(main_model, color_model, pooling_model)
def dump_dataset(loader, deep_feats, color_feats, labels):
for batch_idx, (data, data_path) in enumerate(loader):
data = Variable(data).cuda(GPU_ID)
deep_feat, color_feat = extractor(data)
for i in range(len(data_path)):
path = data_path[i]
feature_n = deep_feat[i].squeeze()
color_feature_n = color_feat[i]
# dump_feature(feature, path)
deep_feats.append(feature_n)
color_feats.append(color_feature_n)
labels.append(path)
if batch_idx % LOG_INTERVAL == 0:
print("{} / {}".format(batch_idx * EXTRACT_BATCH_SIZE,
# -*- coding: utf-8 -*-
import torch
import os
from myconfig import cfg
#from utils import FeatureExtractor,data_transform_test
from utils import *
from torch.autograd import Variable
from data import Fashion_attr_prediction, Fashion_inshop
from net import f_model, c_model, p_model
import numpy as np
print(cfg.DUMPED_MODEL)
main_model = f_model(model_path=cfg.DUMPED_MODEL).cuda(cfg.GPU_ID)
color_model = c_model().cuda(cfg.GPU_ID)
pooling_model = p_model().cuda(cfg.GPU_ID)
extractor = FeatureExtractor(main_model, color_model, pooling_model)
def dump_dataset(loader, deep_feats, color_feats, labels):
for batch_idx, (data, data_path) in enumerate(loader):
data = Variable(data).cuda(cfg.GPU_ID)
deep_feat, color_feat = extractor(data)
for i in range(len(data_path)):
path = data_path[i]
feature_n = deep_feat[i].squeeze()
color_feature_n = color_feat[i]
# dump_feature(feature, path)
deep_feats.append(feature_n)
color_feats.append(color_feature_n)
labels.append(path)
