def load_variables(chkpoint, base_dir=BASE_DIR):
manifest_path = os.path.join(base_dir, chkpoint, "manifest.json")
if not os.path.exists(manifest_path):
print(
'Weights for checkpoint %s are not downloaded. Downloading to %s ...'
% (chkpoint, base_dir))
from posenet.converter.wget import download
download(chkpoint, base_dir)
assert os.path.exists(manifest_path)
manifest = open(manifest_path)
variables = json.load(manifest)
manifest.close()
state_dict = {}
for x in variables:
torch_name = to_torch_name(x)
if not torch_name:
continue
filename = variables[x]["filename"]
byte = open(os.path.join(base_dir, chkpoint, filename), 'rb').read()
fmt = str(int(len(byte) / struct.calcsize('f'))) + 'f'
d = struct.unpack(fmt, byte)
d = np.array(d, dtype=np.float32)
shape = variables[x]["shape"]
if len(shape) == 4:
tpt = (2, 3, 0, 1) if 'depthwise' in filename else (3, 2, 0, 1)
d = np.reshape(d, shape).transpose(tpt)
state_dict[torch_name] = torch.Tensor(d)
return state_dict
def load_variables(chkpoint, base_dir=BASE_DIR):
manifest_path = os.path.join(base_dir, chkpoint, "manifest.json")
if not os.path.exists(manifest_path):
print(
'Weights for checkpoint %s are not downloaded. Downloading to %s ...'
% (chkpoint, base_dir))
from posenet.converter.wget import download
download(chkpoint, base_dir)
assert os.path.exists(manifest_path)
f = open(manifest_path)
variables = json.load(f)
f.close()
# with tf.variable_scope(None, 'MobilenetV1'):
for x in variables:
filename = variables[x]["filename"]
byte = open(os.path.join(base_dir, chkpoint, filename), 'rb').read()
fmt = str(int(len(byte) / struct.calcsize('f'))) + 'f'
d = struct.unpack(fmt, byte)
d = tf.cast(d, tf.float32)
d = tf.reshape(d, variables[x]["shape"])
variables[x]["x"] = tf.Variable(d, name=x)
return variables