model = serial.load('/data/lisatmp/goodfeli/darpa_s3c.pkl')
preprocessor = serial.load(
'/data/lisatmp/goodfeli/darpa_imagenet_patch_6x6_train_preprocessor.pkl'
)
patchifier = ExtractGridPatches(patch_shape=(size, size),
patch_stride=(1, 1))
preprocessor.items.insert(0, patchifier)
extractor = FeatureExtractor(model=model, preprocessor=preprocessor)
xavier = '/data/lisatmp/glorotxa/train'
thumbnail = '/data/lisatmp/goodfeli/darpa_imagenet'
feature = '/data/lisatmp/goodfeli/darpa_imagenet_features'
from galatea.darpa_imagenet.utils import explore_images
for img_path in explore_images(xavier, '.JPEG'):
print img_path
thumbnail_path = img_path.replace(xavier, thumbnail)
thumbnail_path = thumbnail_path.replace('.JPEG', '.npy')
if os.path.exists(thumbnail_path):
feature_path = thumbnail_path.replace(thumbnail, feature)
if not os.path.exists(feature_path):
print 'making ' + feature_path
X = np.load(thumbnail_path)
X = extractor(X)
np.save(feature_path, X)
else:
print 'No thumbnail!'
report.write(img_path)
from galatea.darpa_imagenet.utils import explore_images
from pylearn2.utils import serial
from pylearn2.utils import image
import numpy as np
import os
import time
input_path = '/Tmp/glorotxa/train'
output_path = '/Tmp/goodfeli/darpa_imagenet'
image_shape = (32,32)
created_subdirs = set([])
for image_path in explore_images(input_path):
thumbnail_path = image_path.replace(input_path,output_path)
thumbnail_path = thumbnail_path.replace('.JPEG','.npy')
t1 = time.time()
e = os.path.exists(thumbnail_path)
t2 = time.time()
print t2-t1
if e:
continue
thumbnail_subdir = '/'.join(thumbnail_path.split('/')[:-1])
if thumbnail_subdir not in created_subdirs:
serial.mkdir(thumbnail_subdir)
created_subdirs = created_subdirs.union([thumbnail_subdir])
from galatea.darpa_imagenet.utils import explore_images
from pylearn2.utils import serial
from pylearn2.utils import image
import numpy as np
import os
import time
input_path = '/data/lisatmp/glorotxa/val'
output_path = '/data/lisatmp/goodfeli/darpa_imagenet_valid'
image_shape = (32, 32)
created_subdirs = set([])
for image_path in explore_images(input_path, '.JPEG'):
thumbnail_path = image_path.replace(input_path, output_path)
thumbnail_path = thumbnail_path.replace('.JPEG', '.npy')
t1 = time.time()
e = os.path.exists(thumbnail_path)
t2 = time.time()
print t2 - t1
if e:
continue
thumbnail_subdir = '/'.join(thumbnail_path.split('/')[:-1])
if thumbnail_subdir not in created_subdirs:
serial.mkdir(thumbnail_subdir)
created_subdirs = created_subdirs.union([thumbnail_subdir])
from galatea.darpa_imagenet.utils import explore_images
from pylearn2.utils import serial
from pylearn2.utils import image
import numpy as np
import os
import time
input_path = '/data/lisatmp/glorotxa/train'
output_path = '/data/lisatmp/goodfeli/darpa_imagenet'
image_shape = (32, 32)
created_subdirs = set([])
for image_path in explore_images(input_path):
thumbnail_path = image_path.replace(input_path, output_path)
thumbnail_path = thumbnail_path.replace('.JPEG', '.npy')
t1 = time.time()
e = os.path.exists(thumbnail_path)
t2 = time.time()
print t2 - t1
if e:
continue
thumbnail_subdir = '/'.join(thumbnail_path.split('/')[:-1])
if thumbnail_subdir not in created_subdirs:
serial.mkdir(thumbnail_subdir)
created_subdirs = created_subdirs.union([thumbnail_subdir])
model = serial.load('/data/lisatmp/goodfeli/darpa_s3c.pkl')
preprocessor = serial.load('/data/lisatmp/goodfeli/darpa_imagenet_patch_6x6_train_preprocessor.pkl')
patchifier = ExtractGridPatches( patch_shape = (size,size), patch_stride = (1,1) )
preprocessor.items.insert(0,patchifier)
extractor = FeatureExtractor( model = model, preprocessor = preprocessor)
xavier = '/data/lisatmp/glorotxa/val'
thumbnail = '/data/lisatmp/goodfeli/darpa_imagenet_valid_thumb'
feature = '/data/lisatmp/goodfeli/darpa_imagenet_valid_features'
from galatea.darpa_imagenet.utils import explore_images
for img_path in explore_images(xavier,'.JPEG'):
print img_path
thumbnail_path = img_path.replace(xavier,thumbnail)
thumbnail_path = thumbnail_path.replace('.JPEG','.npy')
if os.path.exists(thumbnail_path):
feature_path = thumbnail_path.replace(thumbnail,feature)
if not os.path.exists(feature_path):
print 'making '+feature_path
X = np.load(thumbnail_path)
X = extractor(X)
np.save(feature_path,X)
else:
print 'No thumbnail!'
report.write(img_path)