def get_overfeat_features(imgs, weights_path, typ, layer=None, cache=None):
"""Returns features at layer for given image(s) from OverFeat model.
Small (fast) network: 22 layers
Large (accurate) network: 25 layers
Args:
imgs: Iterable of images each of shape (h,w,c)
weights_path: Path to the OverFeat weights
typ: 0 for small, 1 for large version of OverFeat
layer: The layer to extract features from
cache: Dict containing descs/other cached values
"""
if cache is None:
cache = {}
if 'overfeat_descs' not in cache:
# Initialize network
print('Loading OverFeat ({}) model...'.format(typ))
overfeat.init(weights_path, typ)
# Determine feature layer if none specified
if layer is None:
if overfeat.get_n_layers() == 22: # small
layer = 19 # 16 also recommended
else: # large
# Layer used by Zhang et al.
layer = 22
# Determine resize dim
if typ == 0:
resize = 231 # small network
else:
resize = 221 # large network
# Allocate for feature descriptors
descs = []
# Run images through network
print('Running images through OverFeat, extracting features '
'at layer {}.'.format(layer))
for idx, img in enumerate(imgs):
if (idx + 1) % 100 == 0:
print('Processing image {}...'.format(idx + 1))
# Preprocess image
img = overfeat_preprocess(img, resize)
# Run through model
_ = overfeat.fprop(img)
# Retrieve feature output
desc = overfeat.get_output(layer)
descs.append(desc)
# Free network
overfeat.free()
# NumPy-ify
descs = np.asarray(descs)
cache.update(overfeat_descs=descs)
else:
descs = cache['overfeat_descs']
return descs, cache
def run_overfeat(image, layer=None):
'''
runs an image through overfeat. returns the 1,000-length likelihoods
vector and N-length layer in the net as copied and formatted numpy arrays.
layer
None: means return 4,096-length feature vector just prior to the output layer.
int: return that layer instead.
'''
if not layer:
# get layer just before output layer
feature_layer = overfeat.get_n_layers() - 2
overfeat_likelihoods = overfeat.fprop(image)
overfeat_features = overfeat.get_output(feature_layer)
# flatten and copy. NOTE: copy() is intentional, don't delete.
formatted_features = copy(overfeat_features.flatten())
formatted_likelihoods = copy(overfeat_likelihoods.flatten())
return formatted_likelihoods, formatted_features
def run_overfeat(image, layer=None):
'''
runs an image through overfeat. returns the 1,000-length likelihoods
vector and N-length layer in the net as copied and formatted numpy arrays.
layer
None: means return 4,096-length feature vector just prior to the output layer.
int: return that layer instead.
'''
if not layer:
# get layer just before output layer
feature_layer = overfeat.get_n_layers() - 2
overfeat_likelihoods = overfeat.fprop(image)
overfeat_features = overfeat.get_output(feature_layer)
# flatten and copy. NOTE: copy() is intentional, don't delete.
formatted_features = copy(overfeat_features.flatten())
formatted_likelihoods = copy(overfeat_likelihoods.flatten())
return formatted_likelihoods, formatted_features
def process_through_net(photo, feature_layer=None):
if not feature_layer:
feature_layer = overfeat.get_n_layers() - 2
likelihoods = copy(overfeat.fprop(photo).flatten())
features = copy(overfeat.get_output(feature_layer).flatten())
return likelihoods, features
