def get_vgg16_output(self, step2h5list):
res = h5util.read_from_h5(step2h5list['vgg16_output'][0])
meta = res['meta']
label = res['label']
del res['meta']
del res['label']
return res, meta, label
def model_layers(data_iter, config, pipeline, step2h5list, output_files):
data_stats = h5util.read_from_h5(step2h5list['data_stats'][0])
channel_mean = data_stats['channel_mean']
datalen = data_stats['datalen']
model = pipeline.vgg16()
prepare_for_vgg16 = pipeline.prepare_for_vgg16
imgprep_config = pipeline.get_config('imgprep')
vgg16 = pipeline.vgg16()
h5out = h5py.File(output_files[0], 'w')
idx = 0
layer_names = ['fc1', 'fc2']
name2dset = {}
all_meta = []
for X, Y, meta in data_iter:
all_meta.append(meta)
img = X[0]
prep_img = prepare_for_vgg16(img,
imgprep_config,
channel_mean=channel_mean)
prep_img = np.reshape(prep_img, [1] + list(prep_img.shape))
layers = vgg16.get_model_layers(sess=pipeline.session,
imgs=prep_img,
layer_names=layer_names)
if idx == 0:
for layer, name in zip(layers, layer_names):
dset_shape = tuple([datalen] + list(layer.shape[1:]))
name2dset[name] = h5out.create_dataset(name,
shape=dset_shape,
dtype=layer.dtype)
for layer, name in zip(layers, layer_names):
name2dset[name][idx, :] = layer[0, :]
idx += 1
h5util.write_meta(h5out, all_meta)
def get_datastat(self, name, step2h5list):
if name in ['codeword_num_features']:
if None is self._datastats_codewords:
h5 = h5py.File(step2h5list['compute_vgg16_codewords'][0],'r')
self._datastats_codewords = {}
self._datastats_codewords['codeword_num_features'] = h5['codewords'].shape[1]
return self._datastats_codewords['codeword_num_features']
if None is self._datastats:
self._datastats = h5util.read_from_h5(step2h5list['compute_channel_mean'][0])
assert name in self._datastats, "name=%s not in datasats, keys are: %r" % (name, self._datastats.keys())
return self._datastats[name]