def main():
# in_file='/disk2/temp/small_set.txt';
path_to_val = '/disk2/imagenet/val'
# val_gt_file='../../data/ilsvrc12/val.txt'
# synset_words='../../data/ilsvrc12/synset_words.txt'
# deploy_file='../../models/bvlc_reference_caffenet/deploy.prototxt';
# model_file='../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
# mean_file='../../python/caffe/imagenet/ilsvrc_2012_mean.npy';
# out_file_topk='/disk2/octoberExperiments/nn_performance_without_pascal/val_performance_top5_trained.p';
val_gt_file = '/disk2/octoberExperiments/nn_performance_without_pascal/val.txt'
synset_words = '/disk2/octoberExperiments/nn_performance_without_pascal/synset_words.txt'
deploy_file = '/disk2/octoberExperiments/nn_performance_without_pascal/deploy.prototxt'
model_file = '/disk2/octoberExperiments/nn_performance_without_pascal/snapshot_iter_450000.caffemodel'
mean_file = '/disk2/octoberExperiments/nn_performance_without_pascal/mean.npy'
# out_file_topk='/disk2/octoberExperiments/nn_performance_without_pascal/new_val_performance_top5_no_trained.p';
val_gt = imagenet.readLabelsFile(val_gt_file)
im_files = list(zip(*val_gt)[0])
im_files = [os.path.join(path_to_val, im_file) for im_file in im_files]
im_files = im_files[:200]
imagenet_idx_mapped = list(zip(*val_gt)[1])
imagenet_idx_mapped = [int(x) for x in imagenet_idx_mapped]
batch_size = 1000
top_n = 5
gpu_no = 1
getClasses = True
net, transformer = setUpNet(model_file, deploy_file, mean_file, gpu_no)
out_file = 'temp'
in_file_text = 'temp.txt'
with open(in_file_text, 'wb') as f:
for im_file in im_files:
f.write(im_file + '\n')
layers = ['fc7']
saveLayers(net, transformer, in_file_text, layers, out_file)
vals = np.load(out_file + '.npz')
print vals.keys()
for k in vals.keys():
print k, vals[k].shape
# error_bin,pred_classes=getTopNError(net,transformer,im_files,imagenet_idx_mapped,batch_size,top_n,printDebug=True,pred_classes=True)
# print sum(error_bin),len(error_bin)
# print pred_classes[:10]
# pickle.dump([error_bin,pred_classes],open(out_file_topk,'wb'));
return
db_path = 'sqlite://///disk2/novemberExperiments/nn_imagenet/nn_imagenet.db'
all_pascal_id = [
'boat', 'train', 'bicycle', 'chair', 'motorbike', 'aeroplane', 'sofa',
'diningtable', 'bottle', 'tvmonitor', 'bus', 'car'
]
batch_size = 1000
top_n = 1
gpu_no = 1
getClasses = True
ids_labels = imagenet.readLabelsFile(synset_words)
labels = list(zip(*ids_labels)[1])
imagenet_ids = list(zip(*ids_labels)[0])
net, transformer = setUpNet(model_file, deploy_file, mean_file, gpu_no)
for pascal_id in ['car']:
# all_pascal_id:
print pascal_id
out_file_topk = '/disk2/novemberExperiments/nn_imagenet/' + pascal_id + '_pred_no_train.p'
out_file_topk_txt = '/disk2/novemberExperiments/nn_imagenet/' + pascal_id + '_pred_no_train.txt'
mani = Imagenet_Manipulator(db_path)
mani.openSession()
criterion = (Imagenet.class_id_pascal == pascal_id, )
vals = mani.select((Imagenet.img_path, ), criterion, distinct=True)
mani.closeSession()
im_files = [val[0] for val in vals]
imagenet_idx_mapped = [772] * len(im_files)
print len(im_files)
# return
# error_bin=pickle.load(open(out_file_topk,'rb'));
# print sum(error_bin),len(error_bin),sum(error_bin)/float(len(error_bin))
# out_file_topk='/disk2/octoberExperiments/nn_performance_without_pascal/val_performance_top5_trained.p';
# error_bin=pickle.load(open(out_file_topk,'rb'));
# print sum(error_bin),len(error_bin),sum(error_bin)/float(len(error_bin))
# return
# val_gt=imagenet.readLabelsFile(val_gt_file);
# im_files=list(zip(*val_gt)[0]);
# im_files=[os.path.join(path_to_val,im_file) for im_file in im_files]
# imagenet_idx_mapped=list(zip(*val_gt)[1])
# imagenet_idx_mapped=[int(x) for x in imagenet_idx_mapped];
# print idx_mapped_simple[:10],len(idx_mapped_simple);
# print 'getting mapping'
# imagenet_idx_mapped,imagenet_ids_mapped,imagenet_labels_mapped=imagenet.getMappingInfo(im_files,synset_words,val_gt_file)
# print imagenet_idx_mapped[:10],len(imagenet_idx_mapped),type(imagenet_idx_mapped),type(imagenet_idx_mapped[0]),type(idx_mapped_simple),type(idx_mapped_simple[0])
# for idx_idx,idx in enumerate(idx_mapped_simple):
# print idx,imagenet_idx_mapped[idx_idx]
# assert idx==imagenet_idx_mapped[idx_idx]
# return
error_bin, pred_classes = getTopNError(net,
transformer,
im_files,
imagenet_idx_mapped,
batch_size,
top_n,
printDebug=True,
pred_classes=True)
print sum(error_bin), len(error_bin)
print pred_classes[:10]
pickle.dump([error_bin, pred_classes], open(out_file_topk, 'wb'))
pred_classes = pred_classes.ravel()
tuples = []
for class_curr in np.unique(pred_classes):
# print imagenet_ids[class_curr],labels[class_curr];
tuples.append(
(class_curr, labels[class_curr],
sum(pred_classes == class_curr) / float(len(pred_classes))))
tuples = sorted(tuples, key=lambda x: x[2])[::-1]
with open(out_file_topk_txt, 'wb') as f:
for tuple_curr in tuples:
print imagenet_ids[tuple_curr[0]], tuple_curr
f.write(
str(tuple_curr[0]) + '\t' + str(tuple_curr[1]) + '\t' +
str(round(tuple_curr[2] * 100, 2)))
f.write('\n')
