def func(pretrained=False,
tag=None,
root='~/.mxnet/models',
ctx=cpu(0),
**kwargs):
r"""Quantized model.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
tag : str, default is None
Optional length-8 sha1sum of parameter file. If `None`, best parameter file
will be used.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
from ..model_zoo import get_model
from ..model_store import get_model_file
curr_dir = os.path.abspath(os.path.dirname(__file__))
model_name = name.replace('mobilenet1_', 'mobilenet1.')
model_name = model_name.replace('mobilenet0_', 'mobilenet0.')
json_file = os.path.join(curr_dir, '{}-symbol.json'.format(model_name))
base_name = '_'.join(model_name.split('_')[:-1])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
param_file = get_model_file(base_name, tag=tag,
root=root) if pretrained else None
net = get_model('_'.join(model_name.split('_')[:-1]),
prefix=sym_prefix)
classes = getattr(net, 'classes', [])
sym_net = SymbolBlock.imports(json_file, ['data'], None, ctx=ctx)
if param_file:
# directly imports weights saved by save_parameters is not applicable
# so we hack it by load and export once to a temporary params file
import tempfile
net.load_params(param_file)
net.hybridize()
if '512' in base_name:
net(mx.nd.zeros((1, 3, 512, 512)))
elif '300' in base_name:
net(mx.nd.zeros((1, 3, 300, 300)))
else:
net(mx.nd.zeros((1, 3, 224, 224)))
with tempfile.TemporaryDirectory() as tmpdirname:
prefix = os.path.join(tmpdirname, 'tmp')
net.export(prefix, epoch=0)
param_prefix = prefix + '-0000.params'
sym_net.collect_params().load(param_prefix)
net.classes = classes
net.reset_class = _not_impl
net.set_nms = _not_impl
return net
def check_amp_convert_hybrid_block():
# Test conversion for hybrid block on CPU
model_cpu = get_model("resnet50_v1")
model_cpu.collect_params().initialize(ctx=mx.cpu())
model_cpu.hybridize()
model_cpu(mx.nd.random.uniform(0, 1, shape=(1, 3, 224, 224), ctx=mx.cpu()))
converted_model_cpu = amp.convert_hybrid_block(model_cpu)
# Test with real world model, default inputs for convert_hybrid_block
model = get_model("resnet50_v1")
model.collect_params().initialize(ctx=mx.gpu())
model.hybridize()
model(mx.nd.zeros((1, 3, 224, 224)))
converted_model = amp.convert_hybrid_block(model)
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
dtype=np.float32))
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
dtype=np.float32))
# Test with real world model, tweak inputs for convert_hybrid_block
converted_model = amp.convert_hybrid_block(model, target_dtype="float16",
target_dtype_ops=["Convolution"])
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
dtype=np.float32))
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224),
dtype=np.float32))
# Check symbolic block
dir_path = os.path.dirname(os.path.realpath(__file__))
model_path = os.path.join(dir_path, 'model')
if not os.path.isdir(model_path):
os.mkdir(model_path)
prefix, epoch = download_model("imagenet1k-resnet-18", dst_dir=model_path)
net = SymbolBlock.imports(os.path.join(model_path, "imagenet1k-resnet-18-symbol.json"),
input_names=["data", "softmax_label"],
param_file=os.path.join(model_path, "imagenet1k-resnet-18-0000.params"))
net.collect_params().reset_ctx(ctx=mx.gpu())
net.hybridize()
net(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1,)))
converted_model = amp.convert_hybrid_block(net)
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1,)))
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1,)))
# Check symbolic block, tweaked inputs
converted_model = amp.convert_hybrid_block(net, target_dtype="float16", target_dtype_ops=["Convolution"])
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1, )))
result = converted_model.forward(mx.nd.zeros((1, 3, 224, 224)), mx.nd.zeros((1, )))
params = converted_model.collect_params()
assert params["stage2_unit1_conv2_weight"].dtype == np.float32
# Pass cast_optional_params as True to convert_hybrid_block
converted_model = amp.convert_hybrid_block(net, target_dtype="float16", target_dtype_ops=["Convolution"],
cast_optional_params=True)
params = converted_model.collect_params()
assert params["stage2_unit1_conv2_weight"].dtype == np.float16
def func(pretrained=False, tag=None, root='~/.mxnet/models', ctx=cpu(0), **kwargs):
r"""Quantized model.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
tag : str, default is None
Optional length-8 sha1sum of parameter file. If `None`, best parameter file
will be used.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
from ..model_zoo import get_model
from ..model_store import get_model_file
curr_dir = os.path.abspath(os.path.dirname(__file__))
model_name = name.replace('mobilenet1_', 'mobilenet1.')
model_name = model_name.replace('mobilenet0_', 'mobilenet0.')
json_file = os.path.join(curr_dir, '{}-symbol.json'.format(model_name))
base_name = '_'.join(model_name.split('_')[:-1])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
param_file = get_model_file(base_name, tag=tag, root=root) if pretrained else None
net = get_model('_'.join(model_name.split('_')[:-1]), prefix=sym_prefix)
classes = getattr(net, 'classes', [])
sym_net = SymbolBlock.imports(json_file, ['data'], None, ctx=ctx)
if param_file:
# directly imports weights saved by save_parameters is not applicable
# so we hack it by load and export once to a temporary params file
import tempfile
net.load_params(param_file)
net.hybridize()
if '512' in base_name:
net(mx.nd.zeros((1, 3, 512, 512)))
elif '300' in base_name:
net(mx.nd.zeros((1, 3, 300, 300)))
else:
net(mx.nd.zeros((1, 3, 224, 224)))
with tempfile.TemporaryDirectory() as tmpdirname:
prefix = os.path.join(tmpdirname, 'tmp')
net.export(prefix, epoch=0)
param_prefix = prefix + '-0000.params'
sym_net.collect_params().load(param_prefix)
sym_net.classes = classes
sym_net.reset_class = _not_impl
sym_net.set_nms = _not_impl
return sym_net
def load_net(self):
net_cl = SymbolBlock.imports(self.net_path_cl, ['data'],
self.params_path_cl)
net_tl = SymbolBlock.imports(self.net_path_tl, ['data'],
self.params_path_tl)
return net_cl, net_tl
