def extract_features(net_, config):
db = MemoryBlockManager(config.DB_PATH, False)
if config.verbose:
print 'extracting features of %s' % config.dataset
t = time()
num_samples = 10 * db.num_samples
feats = np.zeros((num_samples, get_feature_dims(net_, config.feat_layer)), np.single)
idx = 0
while not db.eof():
batch_data, _ = db.pop_batch(config.batchsize) # batchsize 128
transformed = do_multiview_crop(batch_data, config.crop_dim)
transformed = transformed - config.mean_val
num, chns, rows, cols = transformed.shape
net_.blobs['data'].reshape(*(num, chns, rows, cols))
output = net_.forward(data=transformed.astype(np.float32, copy=False))
target = output[config.feat_layer]
feats[idx:idx+num, ::] = reshape_feature(target)
idx += num
if config.verbose:
print 'feature computation completed (%0.2f sec.)' % (time()-t)
return feats
def extract_features(net_, config):
db = MemoryBlockManager(config.DB_PATH, False)
if config.verbose:
print 'extracting features of %s' % config.dataset
t = time()
num_samples = 10 * db.num_samples
feats = np.zeros((num_samples, get_feature_dims(net_, config.feat_layer)),
np.single)
idx = 0
while not db.eof():
batch_data, _ = db.pop_batch(config.batchsize) # batchsize 128
transformed = do_multiview_crop(batch_data, config.crop_dim)
transformed = transformed - config.mean_val
num, chns, rows, cols = transformed.shape
net_.blobs['data'].reshape(*(num, chns, rows, cols))
output = net_.forward(data=transformed.astype(np.float32, copy=False))
target = output[config.feat_layer]
feats[idx:idx + num, ::] = reshape_feature(target)
idx += num
if config.verbose:
print 'feature computation completed (%0.2f sec.)' % (time() - t)
return feats
