src.reshape(FEATURE_JITTER, 3, MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1])
for n, fname in enumerate(entries):
try:
query_filename = fname[0]
ref_filename = fname[1]
label = int(fname[2])
inputs_q = utils.load_image( os.path.join(prefix, query_filename) )
inputs_ref= utils.load_image( os.path.join(prefix, ref_filename) )
if FEATURE_JITTER == 10:
inputs_q = inputs_q.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
inputs_ref= inputs_ref.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
inputs_q = utils.preprocess(net, inputs_q)
inputs_ref= utils.preprocess(net, inputs_ref)
inputs_q = utils.oversample(inputs_q, MODEL_INPUT_SIZE)
inputs_ref= utils.oversample(inputs_ref, MODEL_INPUT_SIZE)
else:
inputs_q = inputs_q.resize( MODEL_INPUT_SIZE, PIL.Image.ANTIALIAS )
inputs_ref= inputs_ref.resize( MODEL_INPUT_SIZE, PIL.Image.ANTIALIAS )
inputs_q = utils.preprocess(net, inputs_q)
inputs_ref= utils.preprocess(net, inputs_ref)
inputs_q = inputs_q[np.newaxis,:,:,:]
inputs_ref= inputs_ref[np.newaxis,:,:,:]
src.data[:] = inputs_q
net.forward(end='pool5/7x7_s1')
feat_q = np.squeeze(net.blobs['pool5/7x7_s1'].data).copy()
src.data[:] = inputs_ref
net.forward(end='pool5/7x7_s1')
feat_ref= np.squeeze(net.blobs['pool5/7x7_s1'].data)
src_google.reshape(FEATURE_JITTER, 3, MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1])
src_vgg = net_vgg.blobs['data']
src_vgg.reshape(FEATURE_JITTER, 3, MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1])
filenames=['%s' % entry.strip().split(' ')[0] for entry in open('%s/%s' % (DATASET_ROOT, DATASET_LIST))]
feat_vgg = np.squeeze(np.zeros((len(filenames), FEATURE_JITTER, FEATURE_DIM_VGG), dtype=np.float32))
feat_google= np.squeeze(np.zeros((len(filenames), FEATURE_JITTER, FEATURE_DIM_GOOGLE), dtype=np.float32))
import pdb; pdb.set_trace()
for n, fname in enumerate(filenames):
try:
im = utils.load_image( '%s/jpg/%s' %(DATASET_ROOT, fname) )
im = im.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
im = utils.preprocess(net_google, im)
except:
print 'error: filename: ', fname
if FEATURE_JITTER == 10:
im_jittered = utils.oversample(im, MODEL_INPUT_SIZE)
src_google.data[:], src_vgg.data[:] = im_jittered, im_jittered
else: src_google.data[:], src_vgg.data[:] = im[:], im[:]
net_google.forward(end='pool5/7x7_s1')
net_vgg.forward(end='fc6')
feat_vgg[n] = net_vgg.blobs['fc6'].data
feat_google[n] = np.squeeze(net_google.blobs['pool5/7x7_s1'].data)
if (n+1) % 10 == 0: print 'End of ', n+1; sys.stdout.flush()
# save mat format
sio.savemat('%s_vggoogle_fc6_pool5_7x7_s1.mat' % DATASET_LIST, {'filenames': filenames, 'feat_vgg': feat_vgg, 'feat_google': feat_google})
src = net.blobs['data']
src.reshape(FEATURE_JITTER, 3, 224, 224)
# load holidays image file list
filenames=[entry.strip() for entry in open('%s/%s' % (DATASET_ROOT, DATASET_INPUT_LIST ))]
# load gt list
entries = [entry.strip().split(' ') for entry in open('%s/%s' % (DATASET_ROOT, DATASET_GT_LIST))]
# set feature set
feat, n = np.squeeze(np.zeros((len(filenames), FEATURE_JITTER, FEATURE_DIM), dtype=np.float32)), 0
import pdb; pdb.set_trace()
for fname in filenames:
# load img
#im = PIL.Image.open('%s/holidays/jpg/%s' % (DATASET_ROOT, fname))
im = PIL.Image.open('%s/full/%s' % (DATASET_ROOT, fname))
im = im.resize(MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
# preprocessing
im = utils.preprocess(net, im)
if FEATURE_JITTER == 10:
src.data[:] = utils.oversample(im, MODEL_INPUT_SIZE)
else: src.data[:] = im[:]
net.forward(end=layer_name[2])
dst = net.blobs[layer_name[2]]
feat[n] = dst.data.reshape(1, FEATURE_DIM)
n+=1
if n % 10 == 0: print 'End of %d' % n
# save mat format
sio.savemat('ukb_vgg16_fc7.mat', {'feat': feat})
