def test_constructor (self):
here = os.path.dirname(__file__)
filename = os.path.join(here, '..', 'cfg', 'yolov3.cfg')
cfg = net_config(filename)
assert len(cfg) == 108
print(cfg)
with pytest.raises(IOError):
filename = ''
cfg = net_config(filename)
def load(self, cfg_filename, weights=None):
'''
Load network model from config file in INI fmt
'''
model = net_config(cfg_filename)
self.batch = model.get('net1', 'batch', 1)
self.w = model.get('net1', 'width', 416)
self.h = model.get('net1', 'height', 416)
self.c = model.get('net1', 'channels', 3)
# TODO: add other network parameters
input_shape = (self.batch, self.w, self.h, self.c)
self._net = [ Input_layer(input_shape=input_shape) ]
print('layer filters size input output')
for i, layer in enumerate(model):
layer_t = re.split(r'\d+', layer)[0]
params = dict(model.get_params(layer))
layer_params = {}
for k, v in params.items():
try:
val = eval(v)
except NameError:
val = v
except:
raise DataVariableError('Type variable not recognized! Possible variables are only [int, float, string, vector<float>].')
layer_params[k] = val
if layer_t == 'shortcut':
_from = model.get(layer, 'from', 0)
self._net.append( self.LAYERS[layer_t](input_shape=input_shape, **layer_params)([self._net[-1], self._net[_from]]) )
elif layer_t == 'route':
_layers = model.get(layer, 'layers', [])
self._net.append( self.LAYERS[layer_t](input_shape=input_shape, **layer_params)(self._net[_layers]) )
else:
self._net.append( self.LAYERS[layer_t](input_shape=input_shape, **layer_params)(self._net[-1]) )
print('{:>4d} {}'.format(i, self._net[-1]), flush=True, end='\n')
#if model.get(layer, 'batch_normalize', 0): # wrong because it add a new layer and so the shortcut is broken
# self._net.append( BatchNorm_layer()(self._net[-1]) )
# print('{:>4d} {}'.format(i, self._net[-1]), flush=True, end='\n')
return self
if weights is not None:
self.load_weights(weights)
def load(self, cfg_filename, weights=None):
'''
Load network model from config file in INI fmt
'''
model = net_config(cfg_filename)
self.batch = model.get('net0', 'batch', 1)
self.w = model.get('net0', 'width', 416)
self.h = model.get('net0', 'height', 416)
self.c = model.get('net0', 'channels', 3)
# TODO: add other network parameters
input_shape = (self.batch, self.w, self.h, self.c)
self._net = [Input_layer(input_shape=input_shape)]
print(
'layer filters size input output'
)
for i, layer in enumerate(model):
layer_t = re.split(r'\d+', layer)[0]
params = model.get_params(layer)
if layer_t == 'shortcut':
_from = model.get(layer, 'from', 0)
self._net.append(self.LAYERS[layer_t](
input_shape=input_shape,
**params)([self._net[-1], self._net[_from]]))
elif layer_t == 'route':
_layers = model.get(layer, 'layers', [])
self._net.append(self.LAYERS[layer_t](input_shape=input_shape,
**params)(
self._net[_layers]))
else:
self._net.append(self.LAYERS[layer_t](input_shape=input_shape,
**params)(self._net[-1]))
input_shape = self._net[-1].out_shape
print('{:>4d} {}'.format(i, self._net[-1]), end='\n') # flush=True
sys.stdout.flush() # compatibility with pythonn 2.7
# if model.get(layer, 'batch_normalize', 0): # wrong because it add a new layer and so the shortcut is broken
# self._net.append( BatchNorm_layer()(self._net[-1]) )
# print('{:>4d} {}'.format(i, self._net[-1]), flush=True, end='\n')
if weights is not None:
self.load_weights(weights)
return self
def test_getter (self):
here = os.path.dirname(__file__)
filename = os.path.join(here, '..', 'cfg', 'yolov3.cfg')
cfg = net_config(filename)
with pytest.raises(DataVariableError):
res = cfg.get('net', 'batch', 42)
assert res == 42
assert cfg.get('net0', 'batch', 42) == 1
assert cfg.get('convolutional1', 'stride', 3) == 1