def ScSPM_caltech101_l1():
# path to the Caltech101 dataset
# data_path =
imd = img_dataset(data_path, maxdim=300, online=True, color=param_set.get("color"))
imgs = imd()
y = imd.y
# define the architecture
n_atoms = 1024
patch_shape = (16, 16)
step_size = 6
n_descriptors = int(1e6)
# the l1 penalty parameter
_lambda = 0.15
normalizer = l2_normalizer
metadata = {'name': "ScSPM_caltech101",
'desc': "ScSPM using sparse coding on top of SIFT features",
'n_atoms': n_atoms,
'lambda': _lambda,
'patch_shape': patch_shape,
'step_size': step_size,
'n_descriptors': n_descriptors,
'pooling': 'max pooling',
'normalization': 'l2'}
wm = workspace_manager(metadata=metadata)
wm.set_metadata(metadata)
wm.save("labels.npy", y)
sp = spatial_pyramid("ScSPM", workspace=wm, mmap=False)
n_jobs = 8
# the sift extractor of the
# initial phase of dictionary learning
feature_extractor = sift_extractor(n_descriptors=n_descriptors, patch_shape=patch_shape, mmap=False, n_jobs=n_jobs)
se = sparse_encoder(algorithm='lasso', params={'lambda': n_nonzero_coefs}, n_jobs=n_jobs)
odc = online_dictionary_coder(n_atoms=n_atoms, sparse_coder=se,
batch_size=int(1e3), verbose=True, mmap=False, n_jobs=n_jobs)
# learn the dictionary
sp.dict_learn(imgs, feature_extractor=feature_extractor, dict_learner=odc)
se.n_jobs = 1
# extract ScSPM features
sc_spm = sc_spm_extractor(feature_extractor=dsift_extractor(step_size=step_size, patch_size=patch_shape[0]),
levels=(1, 2, 4), sparse_coder=se, pooling_operator=sc_max_pooling(),
normalizer=normalizer)
sp.extract(imgs, pyramid_feat_extractor=sc_spm, n_jobs=n_jobs)
def build_hmp_net(param_set,workspace=None):
# path to the Caltech101 dataset
data_path = "Caltech101"
imd = img_dataset(data_path, maxdim=300, online=True, color=param_set.get("color"))
imgs = imd()
y = imd.y
workspace.save("labels.npy", y)
param_set["dataset_path"] = data_path
hmp_net = hmp_network(dict_learners=param_set.get("dict_learners"), n_atoms=param_set.get("n_atoms"),
filter_sizes=param_set.get("filter_sizes"), n_nonzero_coefs=param_set.get("n_nonzero_coefs"),
step_sizes=param_set.get("step_sizes"), pooling_sizes=param_set.get("pooling_sizes"),
spp_pooler = param_set.get('spp_pooler'), pooling_steps=param_set.get("pooling_steps"),
spm_levels = param_set.get("spm_levels"), rebuild_spp = param_set.get('rebuild_spp'),
sparse_coders=param_set.get("sparse_coders"), feature_encoders=param_set.get("feature_encoders"),
workspace=workspace,pretrained_dicts = param_set.get('pretrained_dicts'),
n_training_patches=param_set.get("n_training_patches"), imgs=imgs,mmap=False,n_jobs=param_set.get("n_jobs"))
hmp_net.build_hierarchy()
print "finished!"