def createTrials(self):
dummy_upload = True
image_bucket_name = 'rosch_pose'
seed = 0
meta_pth = '/home/hyo/.skdata/genthor/RoschDataset3_' + cats[0] + '_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/meta.pkl'
meta = cPickle.load(open(meta_pth))
perm = np.random.RandomState(0).permutation(len(meta))
meta_p = meta[perm]
pinv = fast.perminverse(perm)
# decide learning_inds
learning_inds = np.arange(LEARNING_PERIOD)
# decide query_inds
objs = list(set(meta['obj']))
nObj = round(float(NUM_TRAIN) / len(objs)) + 1
query_inds = np.array([], dtype=int)
v = np.arange(len(meta))
for obj in objs:
query_inds = np.append(query_inds, perm[meta_p['obj']==obj][-nObj:])
query_inds = np.sort(query_inds)[:NUM_TRAIN]
query_inds = np.append(query_inds, query_inds[-REPEATS:])
query_inds = np.append(learning_inds, query_inds)
assert len(query_inds) == NUM_TRAIN+REPEATS+LEARNING_PERIOD
print query_inds
urls = publish_images(cats[0], query_inds, image_bucket_name, dummy_upload=dummy_upload)
# construct experiment info
bmeta = meta[query_inds]
imgs = urls
imgData = [{df: bm[df] for df in meta.dtype.names} for bm in bmeta]
meta_pth = '/home/hyo/.skdata/genthor/RoschDataset3_' + cats[1] + '_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/meta.pkl'
meta = cPickle.load(open(meta_pth))
perm = np.random.RandomState(0).permutation(len(meta))
meta_p = meta[perm]
pinv = fast.perminverse(perm)
# decide query_inds
objs = list(set(meta['obj']))
nObj = round(float(NUM_TEST) / len(objs)) + 1
query_inds_here = np.array([], dtype=int)
v = np.arange(len(meta))
for obj in objs:
query_inds_here = np.append(query_inds_here, perm[meta_p['obj']==obj][-nObj:])
query_inds_here = np.sort(query_inds_here)[:NUM_TEST]
query_inds_here = np.append(query_inds_here, query_inds_here[inds_repeat])
assert len(query_inds_here) == NUM_TEST+REPEATS
print query_inds_here
# publish images if needed
urls = publish_images(cats[1], query_inds_here, image_bucket_name, dummy_upload=dummy_upload)
# construct experiment info
bmeta = meta[query_inds_here]
imgs = imgs + urls
imgData = imgData + [{df: bm[df] for df in meta.dtype.names} for bm in bmeta]
self._trials = {'imgFiles': imgs, 'imgData': imgData}
def get_metacol_base(self, ma, perm=None):
assert isinstance(ma, str), ma
if ma in self.dset.meta.dtype.names:
metacol = self.dset.meta[ma][:]
else:
metacol = getattr(self.dset, ma)[:]
if perm is not None:
assert perm.shape == metacol.shape
metacol = metacol[perm]
if hasattr(self, 'subslice'):
metacol = metacol[self.subslice]
try:
metacol + 1
labels_unique = None
except TypeError:
labels_unique = n.unique(metacol)
s = metacol.argsort()
cat_s = metacol[s]
ss = n.array([0] + ((cat_s[1:] != cat_s[:-1]).nonzero()[0] + 1).tolist() + [len(cat_s)])
ssd = ss[1:] - ss[:-1]
labels = n.repeat(n.arange(len(labels_unique)), ssd)
metacol = labels[fast.perminverse(s)]
#labels = n.zeros((mlen, ), dtype='int')
#print(len(labels_unique), "L")
#for label in range(len(labels_unique)):
# labels[metacol == labels_unique[label]] = label
#metacol = labels
return metacol, labels_unique
def publish_images(cat, query_inds, bucket_name, dummy_upload=True):
im_pth = '/home/hyo/.skdata/genthor/RoschDataset3_' + cat + '_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/cache/568ce4d00d2c7901515e71c0f90628db084f9dc6/jpeg/'
meta_pth = '/home/hyo/.skdata/genthor/RoschDataset3_' + cat + '_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/meta.pkl'
meta = cPickle.load(open(meta_pth))
if 'filename' not in meta.dtype.names:
# save filenames
perm = np.random.RandomState(0).permutation(len(meta))
pinv = fast.perminverse(perm)
filename = [im_pth + str(pinv[i]) + '.jpeg' for i in range(len(meta))]
meta = meta.addcols(filename, names='filename')
with open(meta_pth, 'w') as f:
cPickle.dump(meta, f)
# publish to s3
conn = boto.connect_s3()
b = conn.create_bucket(bucket_name)
urls = [ ]
for count, ind in enumerate(query_inds):
name = cat + '_' + str(meta['id'][ind]) + '.jpeg'
url = 'https://s3.amazonaws.com/' + bucket_name + '/' + name
urls.append(url)
if not dummy_upload:
if count % 100 == 0:
print str(count) + ' of ' + str(len(query_inds))
k = b.new_key(name)
k.set_contents_from_filename(meta['filename'][ind], policy='public-read')
return urls
def preprocess_lables(larray): larray = larray[:] labels_unique = np.unique(larray) s = larray.argsort() cat_s = larray[s] ss = np.array([0] + ((cat_s[1:] != cat_s[:-1]).nonzero()[0] + 1).tolist() + [len(cat_s)]) ssd = ss[1:] - ss[:-1] labels = np.repeat(np.arange(len(labels_unique)), ssd) larray = labels[fast.perminverse(s)] return larray.astype(np.int64)
def main():
print "parsing options..."
parser = OptionParser()
parser.add_option("-f", "--feature-dir", dest="feature_dir")
parser.add_option("-n", "--Nsubsample", type="int", dest="Nsub", default=None)
parser.add_option("-m", "--metric", dest="metric", default='pn')
(options, args) = parser.parse_args()
feature_dir = options.feature_dir
Nsub = options.Nsub
metric = options.metric
fds = feature_dir.split(',')
features = cPickle.load(open(fds[0]))
#features = assemble_feature_batches(fds[0])
perm = np.random.RandomState(0).permutation(features.shape[0])
pinv = fast.perminverse(perm)
features = features[pinv]
if len(fds) > 1:
features2 = cPickle.load(open(fds[1]))
# features2 = assemble_feature_batches(fds[1])
perm = np.random.RandomState(0).permutation(features2.shape[0])
pinv = fast.perminverse(perm)
features = np.append( features, features2[pinv], axis=1 )
if Nsub is not None:
features = features[pinv][:,perm[:Nsub]]
RR = {}
if 'p' in metric:
print "Compute hvm performance..."
RR['p'] = compute_perf(features, var_levels=['V6'])
if 'n' in metric:
print "Compute hvm neural fit..."
RR['n'] = compute_nfit(features)
cPickle.dump( RR, open(fds[0] + "result_hvm_" + str(Nsub) + ".p", "wb") )
def assemble_feature_batches(feat_dir, N=None, seed=0, batch_range=None, shape_reduce=None, perm=None):
bns = get_batches(feat_dir)
if batch_range is not None:
bns = bns[batch_range[0]: batch_range[1]]
data = []
for x in bns:
ft = unpickle(os.path.join(feat_dir, 'data_batch_%d' % x))['data']
if shape_reduce is not None:
nf0, nf1 = ft.shape
s = int(np.sqrt(nf1 / float(shape_reduce)))
ft = ft.reshape((nf0, shape_reduce, s, s))
ft1 = ft.mean(2).mean(2)
ft2 = ft.sum(1).reshape((nf0, s * s))
if N is not None:
nf1 = ft1.shape[1]
nf2 = ft2.shape[1]
rng = np.random.RandomState(seed=seed)
units1 = rng.permutation(nf1)[: N/2]
units2 = rng.permutation(nf2)[: N - len(units1)]
ft = np.column_stack([ft1[:, units1], ft2[:, units2]])
else:
ft = np.column_stack([ft1, ft2])
elif N is not None:
print('subsetting batch %d' % x)
ft = ft[:, np.random.RandomState(seed=seed).permutation(ft.shape[1])[:N]]
#data.append(unpickle(os.path.join(feat_dir, 'data_batch_%d' % x))['data'])
data.append(ft)
data = np.row_stack(data)
if perm == 'random':
_perm = np.random.RandomState(0).permutation(len(data))
pinv = fast.perminverse(_perm)
data = data[pinv]
elif perm == None:
pass
else:
raise ValueError("unknown perm type: %s" % perm)
return data
if '.p' in feature_file:
features = cPickle.load(open(feature_file))
elif '.h5' in feature_file:
f_feat = h5py.File(feature_file, 'r')
bns = ['bn'+str(i) for i in range(23)]
F = f_feat[bns[0]][:].copy()
for bn in bns[1:]:
Fbn = f_feat[bn][:].copy()
F = np.append(F, Fbn, axis=0)
if 'hvm' in feature_file:
F = F[:5760]
features = F
else:
print "Incorrect feature file extension!"
pinv = fast.perminverse(perm)
if len(features.shape) > 2:
print "reshaping..."
features = np.reshape(features, (features.shape[0], features.shape[1]*features.shape[2]*features.shape[3]))
if (nfeat is not None) and (features.shape[1] > nfeat):
print "Select " + str(nfeat) + " features..."
perm = np.random.RandomState(0).permutation(features.shape[1])
features = features[:,perm[:nfeat]]
if features.shape[1] > 5760:
print "random select..."
perm = np.random.RandomState(0).permutation(features.shape[1])
features = features[:,perm[:5760]]
features = features[pinv]
## hvm tasks
#print "Computing for hvm tasks..."
from rosch_analysis import rosch_analysis
import numpy as np
import yamutils.fast as fast
import cPickle
import dldata.stimulus_sets.synthetic.synthetic_datasets as sd
dataset = sd.RoschDataset3_simple74testFewer()
features = cPickle.load(open("/om/user/hyo/caffe/features/catInet_roschSimple74testFewer_fc7.p"))
perm = np.random.RandomState(0).permutation(len(features))
pinv = fast.perminverse(perm)
features = features[pinv]
# start = 792320
# end = 792320 + 23*256
R = rosch_analysis(features, hvm_dataset, ntest=5, do_centroids=False, var_levels=["V6"])
