def regnety(name, pretrained=False, nc=1000):
"""Constructs a RegNetY model."""
is_valid = name in _REGNETY_URLS.keys() and name in _REGNETY_CFGS.keys()
assert is_valid, "RegNetY-{} not found in the model zoo.".format(name)
# Construct the model
cfg = _REGNETY_CFGS[name]
kwargs = {
"stem_type": "simple_stem_in",
"stem_w": 32,
"block_type": "res_bottleneck_block",
"ss": [2, 2, 2, 2],
"bms": [1.0, 1.0, 1.0, 1.0],
"se_r": 0.25,
"nc": nc,
"ds": cfg["ds"],
"ws": cfg["ws"],
"gws": [cfg["g"] for _ in range(4)],
}
model = AnyNet(**kwargs)
# Download and load the weights
if pretrained:
url = os.path.join(_URL_PREFIX, _REGNETY_URLS[name])
ws_path = cache_url(url, _DOWNLOAD_CACHE)
checkpoint.load_checkpoint(ws_path, model)
return model
def resnext(name, pretrained=False, nc=1000):
"""Constructs a ResNeXt model."""
is_valid = name in _RESNEXT_URLS.keys() and name in _RESNEXT_CFGS.keys()
assert is_valid, "ResNet-{} not found in the model zoo.".format(name)
# Construct the model
cfg = _RESNEXT_CFGS[name]
kwargs = {
"stem_type": "res_stem_in",
"stem_w": 64,
"block_type": "res_bottleneck_block",
"ss": [1, 2, 2, 2],
"bms": [0.5, 0.5, 0.5, 0.5],
"se_r": None,
"nc": nc,
"ds": cfg["ds"],
"ws": [256, 512, 1024, 2048],
"gws": [4, 8, 16, 32],
}
model = AnyNet(**kwargs)
# Download and load the weights
if pretrained:
url = os.path.join(_URL_PREFIX, _RESNEXT_URLS[name])
ws_path = cache_url(url, _DOWNLOAD_CACHE)
checkpoint.load_checkpoint(ws_path, model)
return model
def get_network(self, uid):
netinfo = self.data[uid]
config = netinfo['net']
#print(config)
if 'genotype' in config:
#print('geno')
gen = config['genotype']
genotype = Genotype(normal=gen['normal'],
normal_concat=gen['normal_concat'],
reduce=gen['reduce'],
reduce_concat=gen['reduce_concat'])
if '_in' in self.searchspace:
network = NetworkImageNet(config['width'], 1, config['depth'],
config['aux'], genotype)
else:
network = NetworkCIFAR(config['width'], 1, config['depth'],
config['aux'], genotype)
network.drop_path_prob = 0.
#print(config)
#print('genotype')
L = config['depth']
else:
if 'bot_muls' in config and 'bms' not in config:
config['bms'] = config['bot_muls']
del config['bot_muls']
if 'num_gs' in config and 'gws' not in config:
config['gws'] = config['num_gs']
del config['num_gs']
config['nc'] = 1
config['se_r'] = None
config['stem_w'] = 12
L = sum(config['ds'])
if 'ResN' in self.searchspace:
config['stem_type'] = 'res_stem_in'
else:
config['stem_type'] = 'simple_stem_in'
#"res_stem_cifar": ResStemCifar,
#"res_stem_in": ResStemIN,
#"simple_stem_in": SimpleStemIN,
if config['block_type'] == 'double_plain_block':
config['block_type'] = 'vanilla_block'
network = AnyNet(**config)
return_feature_layer(network)
return network
def complexity(cx, params=None):
"""Computes model complexity (if you alter the model, make sure to update)."""
params = RegNet.get_params() if not params else params
return AnyNet.complexity(cx, params)
def complexity(cx, **kwargs):
"""Computes model complexity. If you alter the model, make sure to update."""
kwargs = RegNet.get_args() if not kwargs else kwargs
return AnyNet.complexity(cx, **kwargs)