def script_temp():
path_to_val = "/disk2/imagenet/val"
ext = "JPEG"
out_dir = "/disk2/novemberExperiments/nn_imagenet_try"
if not os.path.exists(out_dir):
os.mkdir(out_dir)
in_file_pre = "list_of_ims_for_nn"
in_file_pre = os.path.join(out_dir, in_file_pre)
path_to_classify = ".."
trainFlag = False
# caffe_model='/home/maheenrashid/Downloads/caffe/caffe-rc2/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
caffe_model = "/disk2/octoberExperiments/nn_performance_without_pascal/snapshot_iter_450000.caffemodel"
caffe_deploy = "/disk2/octoberExperiments/nn_performance_without_pascal/deploy.prototxt"
caffe_mean = "/disk2/octoberExperiments/nn_performance_without_pascal/mean.npy"
gpu_no = 0
layers = ["pool5", "fc6", "fc7"]
out_file = "nn_non_trained"
out_file = os.path.join(out_dir, out_file)
db_path_out = "sqlite://///disk2/novemberExperiments/nn_imagenet/nn_imagenet.db"
synset_words = "../../data/ilsvrc12/synset_words.txt"
val_gt_file = "../../data/ilsvrc12/val.txt"
idx_chosen = pickle.load(open("/disk2/novemberExperiments/nn_imagenet/equal_mix_ids.p", "rb"))
im_files_gt_classes = imagenet.selectTestSetByID(val_gt_file, idx_chosen, path_to_val=path_to_val)
im_files = list(zip(*im_files_gt_classes)[0])
gt_classes = list(zip(*im_files_gt_classes)[1])
print len(im_files)
print len(gt_classes)
print len(set(gt_classes))
per_file = len(im_files)
# in_files,_=writeInputImageFiles(im_files,in_file_pre,per_file);
in_files = [in_file_pre + "_" + str(0) + ".txt"]
print in_files
out_files = []
for idx, in_file_curr in enumerate(in_files):
out_file_curr = out_file + "_" + str(idx)
out_files.append(
caffe_wrapper.saveFeaturesOfLayers(
in_file_curr,
path_to_classify,
gpu_no,
layers,
ext=ext,
out_file=out_file_curr,
meanFile=caffe_mean,
deployFile=caffe_deploy,
modelFile=caffe_model,
)
)
print in_files
print out_files
file_list_all = []
for in_file_curr in in_files:
with open(in_file_curr, "rb") as f:
file_list = f.readlines()
file_list_all.extend([file_curr.strip("\n") for file_curr in file_list])
print len(file_list_all)
imagenet_idx_mapped, imagenet_ids_mapped, imagenet_labels_mapped = imagenet.getMappingInfo(
file_list_all, synset_words, val_gt_file
)
print "about to combine"
t = time.time()
val_combined = combineDeepFeaturesFromFiles(out_files, layers)
print time.time() - t
for layer_curr in layers:
print "about to nn for ", layer_curr
t = time.time()
indices, distances = nearest_neighbor.doCosineDistanceNN(val_combined[layer_curr], numberOfN=None)
print time.time() - t
# break;
# return
print indices.shape
print distances.shape
print "writing to db"
mani = Imagenet_Manipulator(db_path_out)
mani.openSession()
for idx in range(len(file_list_all)):
if idx % 100 == 0:
print layer_curr, idx, len(file_list_all)
idx_out_file = idx / per_file
out_file_layers = out_file + "_" + str(idx_out_file) + ".npz"
mani.insert(
idx,
file_list_all[idx],
layer_curr,
out_file_layers,
trainFlag,
imagenet_idx_mapped[idx],
imagenet_ids_mapped[idx],
caffe_model,
class_label_imagenet=imagenet_labels_mapped[idx],
neighbor_index=indices[idx],
neighbor_distance=distances[idx],
)
mani.closeSession()
