net_google= caffe.Classifier( MODEL_GOOGLE_DEPLOY_FILE, MODEL_GOOGLE_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
net_vgg = caffe.Classifier( MODEL_VGG_DEPLOY_FILE, MODEL_VGG_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
src_google = net_google.blobs['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()
import pdb; pdb.set_trace()
caffe.set_mode_cpu()
net_vgg = caffe.Classifier( MODEL_VGG_DEPLOY_FILE, MODEL_VGG_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
src_vgg = net_vgg.blobs['data']
src_vgg.reshape(1, 3, MODEL_ORIGINAL_INPUT_SIZE[0], MODEL_ORIGINAL_INPUT_SIZE[1])
filenames=['%s' % entry.strip().split(' ')[0] for entry in open('%s/%s' % (DATASET_ROOT, DATASET_LIST))]
end_blob_name = ['pool1', 'pool2', 'pool3', 'pool4', 'pool5', 'fc6_conv', 'relu6_conv']
copy_to_blob_name= ['deconv2_1', 'deconv3_1', 'deconv4_1', 'deconv5_1', 'fc6_deconv', 'relu6_deconv', 'fc7_deconv']
decon_layer_name = ['unpool1', 'unpool2', 'unpool3', 'unpool4', 'unpool5', 'fc6_deconv', 'relu6_deconv']
iter_idx = [0, 1, 2, 3, 4, 5, 6]
print 'Start deconvolution'
for file_id, filename in enumerate(filenames):
im = utils.load_image( '%s/%s' %(DATASET_ROOT, filename) )
im = im.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
im = utils.preprocess(net_vgg, im); src_vgg.data[:] = im[:]
print 'Done load image'
for layer_idx, end_blob, copy_to, deconv_layer in zip(iter_idx, end_blob_name, copy_to_blob_name, decon_layer_name):
net_vgg.forward(end=end_blob)
net_vgg.blobs[copy_to].data[...] = np.copy(net_vgg.blobs[end_blob].data)
net_vgg.forward(start=deconv_layer)
recon = np.copy(net_vgg.blobs['deconv1_1'].data[0])
recon = utils.deprocess(net_vgg, recon)
min_val, max_val = recon.flatten().min(), recon.flatten().max()
print "{}, layer {}, dim: {}={}, mean: {}, min_val: {}, max_val: {}".format( \
filename, end_blob, net_vgg.blobs[end_blob].data[0].shape, \
np.prod(net_vgg.blobs[end_blob].data[0].shape), \
net_google= caffe.Classifier( MODEL_GOOGLE_DEPLOY_FILE, MODEL_GOOGLE_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
net_vgg = caffe.Classifier( MODEL_VGG_DEPLOY_FILE, MODEL_VGG_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
src_google = net_google.blobs['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/val/%s' % (DATASET_ROOT, entry.strip().split(' ')[0]) for entry in open('%s' % DATASET_INPUT_LIST)]
labels = [entry.strip().split(' ')[1] for entry in open('%s' % DATASET_INPUT_LIST)]
hit_count, hit5_count, tic_global = 0, 0, datetime.datetime.now()
for n, fname in enumerate(filenames):
try:
tic_load = datetime.datetime.now()
im = utils.load_image( fname )
toc_load = datetime.datetime.now(); elapsed_load = toc_load - tic_load
tic_resize = datetime.datetime.now()
im = im.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
toc_resize = datetime.datetime.now(); elapsed_resize = toc_resize - tic_resize
im = utils.preprocess(net_google, im)
tic_jittering = datetime.datetime.now()
except:
print 'error: filename: ', fname
if FEATURE_JITTER == 10:
im_jittered = utils.oversample(im, MODEL_INPUT_SIZE)
src_google.data[:] = im_jittered
src_vgg.data[:] = im_jittered
else: src_google.data[0], src_vgg.data[0] = im[:], im[:]
toc_jittering = datetime.datetime.now(); elapsed_jittering = toc_jittering - tic_jittering
#import pdb; pdb.set_trace()
caffe.set_device(0)
caffe.set_mode_gpu()
net = caffe.Classifier(
MODEL_GOOGLE_DEPLOY_FILE, MODEL_GOOGLE_WEIGHT_FILE,
mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
src = net.blobs['data']
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,:,:,:]
if __name__ == '__main__':
#import pdb; pdb.set_trace()
entries=[entry.strip().split(' ') for entry in open('%s/PAIRS/CDVS_1M_PAIRS/%s' % (DATASET_ROOT, DATASET_LIST))]
#import pdb; pdb.set_trace()
print 'Load data from: ', DATASET_LIST
LMDB_NAME = '%s/%s' % (LMDB_PATH, LMDB_FILENAME)
os.system('rm -rf %s' % (LMDB_NAME))
env = lmdb.open('%s' %(LMDB_NAME), map_size=1e12)
#import pdb; pdb.set_trace()
n = 0
for entry in entries:
try:
docid_i, docid_j, dist, label = entry[0], entry[1], entry[2], entry[3]
im_i = utils.load_image( '%s/%s' %(DATASET_ROOT, docid_i))
im_i = im_i.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
im_i = np.uint8(im_i)
im_i = np.rollaxis(im_i, 2)[::-1]
im_j = utils.load_image( '%s/%s' %(DATASET_ROOT, docid_j))
im_j = im_j.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
im_j = np.uint8(im_j)
im_j = np.rollaxis(im_j, 2)[::-1]
im = np.vstack((im_i, im_j))
datum = caffe.proto.caffe_pb2.Datum()
datum.channels = im.shape[0]
datum.height = im.shape[1]
datum.width = im.shape[2]
datum.data = im.tobytes()
datum.label = int(label)
str_id = '{:0>10d}'.format(n)
src_google = net_google.blobs['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])
#import pdb; pdb.set_trace()
for nn in range(5):
DATASET_LIST = '%s_%02d.txt' % (SET_ID, nn)
print 'Start ', DATASET_LIST
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))
for n, fname in enumerate(filenames):
try:
im = utils.load_image( '%s/%s/%s' %(DATASET_ROOT, SET_ID, fname) )
im = im.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
im = utils.preprocess(net_google, im)
except:
print 'ERROR: filename: ', fname
traceback.print_exc(file=sys.stdout)
sys.exit(-1)
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)
src_vgg = net_vgg.blobs["data"]
src_vgg.reshape(FEATURE_JITTER, 3, MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1])
import pdb
pdb.set_trace()
for nn in range(5):
DATASET_LIST = "%s_%02d.txt" % (SET_ID, nn)
print "Start ", DATASET_LIST
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))
for n, fname in enumerate(filenames):
try:
im = utils.load_image("%s/%s/%s" % (DATASET_ROOT, SET_ID, fname))
im = im.resize(MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS)
im = utils.preprocess(net_google, im)
except:
print "ERROR: filename: ", fname
traceback.print_exc(file=sys.stdout)
sys.exit(-1)
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
net_google= caffe.Classifier( MODEL_GOOGLE_DEPLOY_FILE, MODEL_GOOGLE_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
net_vgg = caffe.Classifier( MODEL_VGG_DEPLOY_FILE, MODEL_VGG_WEIGHT_FILE, mean = MODEL_MEAN_VALUE, channel_swap = (2, 1, 0) )
src_google = net_google.blobs['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() 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/oxbuild_images/%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) % 1 == 0: print 'End of ', n+1; sys.stdout.flush()
fname__ = "/".join(fname_)
dic_ref[n], dic_idx[fname__] = fname__, n
#import pdb; pdb.set_trace()
if len(dic_ref) <> len(dic_idx):
print 'len(dic_ref) len(dic_idx) mismatched'
sys.exit(-1)
database['dic_ref'] = dic_ref
database['dic_idx'] = dic_idx
#import pdb; pdb.set_trace()
for n, fname in enumerate(filenames):
if n >= NUM_SAMPLE: break
try:
im = utils.load_image( '%s' %(fname) )
im = im.resize( MODEL_ORIGINAL_INPUT_SIZE, PIL.Image.ANTIALIAS )
im = utils.preprocess(net_google, im)
except:
print 'ERROR: filename: ', fname
im_jittered = utils.oversample(im, MODEL_INPUT_SIZE)
src_google.data[:], src_vgg.data[:] = im_jittered, im_jittered
net_google.forward(end='pool5/7x7_s1')
net_vgg.forward(end='fc6')
feat_vgg = np.reshape(net_vgg.blobs['fc6'].data, (1,10*4096))
feat_google = np.reshape(np.squeeze(net_google.blobs['pool5/7x7_s1'].data), (1,10*1024))
feat = np.hstack((feat_vgg,feat_google))
fea = (np.packbits(np.uint8(feat > 0), axis=1)).astype(np.uint16)
fea_shift = fea << 8