piiswrong closed pull request #11127: add import_ for SymbolBlock
URL: https://github.com/apache/incubator-mxnet/pull/11127
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/docs/tutorials/gluon/hybrid.md b/docs/tutorials/gluon/hybrid.md
index 3554a15fa3b..5c8372a51f4 100644
--- a/docs/tutorials/gluon/hybrid.md
+++ b/docs/tutorials/gluon/hybrid.md
@@ -117,7 +117,7 @@ x = mx.sym.var('data')
y = net(x)
print(y)
y.save('model.json')
-net.save_params('model.params')
+net.save_parameters('model.params')
```
If your network outputs more than one value, you can use `mx.sym.Group` to
diff --git a/docs/tutorials/gluon/naming.md b/docs/tutorials/gluon/naming.md
index 37b63fa08a9..3606a03dcbd 100644
--- a/docs/tutorials/gluon/naming.md
+++ b/docs/tutorials/gluon/naming.md
@@ -203,12 +203,12 @@ except Exception as e:
Parameter 'model1_dense0_weight' is missing in file 'model.params', which
contains parameters: 'model0_mydense_weight', 'model0_dense1_bias',
'model0_dense1_weight', 'model0_dense0_weight', 'model0_dense0_bias',
'model0_mydense_bias'. Please make sure source and target networks have the
same prefix.
-To solve this problem, we use `save_params`/`load_params` instead of
`collect_params` and `save`/`load`. `save_params` uses model structure, instead
of parameter name, to match parameters.
+To solve this problem, we use `save_parameters`/`load_parameters` instead of
`collect_params` and `save`/`load`. `save_parameters` uses model structure,
instead of parameter name, to match parameters.
```python
-model0.save_params('model.params')
-model1.load_params('model.params')
+model0.save_parameters('model.params')
+model1.load_parameters('model.params')
print(mx.nd.load('model.params').keys())
```
diff --git a/docs/tutorials/gluon/save_load_params.md
b/docs/tutorials/gluon/save_load_params.md
index cd876808a86..f5f48125cc1 100644
--- a/docs/tutorials/gluon/save_load_params.md
+++ b/docs/tutorials/gluon/save_load_params.md
@@ -10,7 +10,7 @@ Parameters of any Gluon model can be saved using the
`save_params` and `load_par
**2. Save/load model parameters AND architecture**
-The Model architecture of `Hybrid` models stays static and don't change during
execution. Therefore both model parameters AND architecture can be saved and
loaded using `export`, `load_checkpoint` and `load` methods.
+The Model architecture of `Hybrid` models stays static and don't change during
execution. Therefore both model parameters AND architecture can be saved and
loaded using `export`, `imports` methods.
Let's look at the above methods in more detail. Let's start by importing the
modules we'll need.
@@ -61,7 +61,7 @@ def build_lenet(net):
net.add(gluon.nn.Dense(512, activation="relu"))
# Second fully connected layer with as many neurons as the number of
classes
net.add(gluon.nn.Dense(num_outputs))
-
+
return net
# Train a given model using MNIST data
@@ -240,18 +240,10 @@ One of the main reasons to serialize model architecture
into a JSON file is to l
### From Python
-Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and
used inside Python frontend using `mx.model.load_checkpoint` and
`gluon.nn.SymbolBlock`. To demonstrate that, let's load the network we
serialized above.
+Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and
used inside Python frontend using `gluon.nn.SymbolBlock`. To demonstrate that,
let's load the network we serialized above.
```python
-# Load the network architecture and parameters
-sym = mx.sym.load('lenet-symbol.json')
-# Create a Gluon Block using the loaded network architecture.
-# 'inputs' parameter specifies the name of the symbol in the computation graph
-# that should be treated as input. 'data' is the default name used for input
when
-# a model architecture is saved to a file.
-deserialized_net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('data'))
-# Load the parameters
-deserialized_net.collect_params().load('lenet-0001.params', ctx=ctx)
+deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'],
"lenet-0001.params")
```
`deserialized_net` now contains the network we deserialized from files. Let's
test the deserialized network to make sure it works.
diff --git a/example/gluon/dcgan.py b/example/gluon/dcgan.py
index 3233f430eea..8ac9c522cf5 100644
--- a/example/gluon/dcgan.py
+++ b/example/gluon/dcgan.py
@@ -229,8 +229,8 @@ def transformer(data, label):
logging.info('time: %f' % (time.time() - tic))
if check_point:
- netG.save_params(os.path.join(outf,'generator_epoch_%d.params' %epoch))
- netD.save_params(os.path.join(outf,'discriminator_epoch_%d.params' %
epoch))
+ netG.save_parameters(os.path.join(outf,'generator_epoch_%d.params'
%epoch))
+ netD.save_parameters(os.path.join(outf,'discriminator_epoch_%d.params'
% epoch))
-netG.save_params(os.path.join(outf, 'generator.params'))
-netD.save_params(os.path.join(outf, 'discriminator.params'))
+netG.save_parameters(os.path.join(outf, 'generator.params'))
+netD.save_parameters(os.path.join(outf, 'discriminator.params'))
diff --git a/example/gluon/embedding_learning/train.py
b/example/gluon/embedding_learning/train.py
index 46f76b55614..b8a5bf2716c 100644
--- a/example/gluon/embedding_learning/train.py
+++ b/example/gluon/embedding_learning/train.py
@@ -246,7 +246,7 @@ def train(epochs, ctx):
if val_accs[0] > best_val:
best_val = val_accs[0]
logging.info('Saving %s.' % opt.save_model_prefix)
- net.save_params('%s.params' % opt.save_model_prefix)
+ net.save_parameters('%s.params' % opt.save_model_prefix)
return best_val
diff --git a/example/gluon/image_classification.py
b/example/gluon/image_classification.py
index 6e2f1d6a78d..b21e943f17f 100644
--- a/example/gluon/image_classification.py
+++ b/example/gluon/image_classification.py
@@ -122,7 +122,7 @@ def get_model(model, ctx, opt):
net = models.get_model(model, **kwargs)
if opt.resume:
- net.load_params(opt.resume)
+ net.load_parameters(opt.resume)
elif not opt.use_pretrained:
if model in ['alexnet']:
net.initialize(mx.init.Normal())
@@ -176,12 +176,12 @@ def update_learning_rate(lr, trainer, epoch, ratio,
steps):
def save_checkpoint(epoch, top1, best_acc):
if opt.save_frequency and (epoch + 1) % opt.save_frequency == 0:
fname = os.path.join(opt.prefix, '%s_%d_acc_%.4f.params' % (opt.model,
epoch, top1))
- net.save_params(fname)
+ net.save_parameters(fname)
logger.info('[Epoch %d] Saving checkpoint to %s with Accuracy: %.4f',
epoch, fname, top1)
if top1 > best_acc[0]:
best_acc[0] = top1
fname = os.path.join(opt.prefix, '%s_best.params' % (opt.model))
- net.save_params(fname)
+ net.save_parameters(fname)
logger.info('[Epoch %d] Saving checkpoint to %s with Accuracy: %.4f',
epoch, fname, top1)
def train(opt, ctx):
@@ -267,7 +267,7 @@ def main():
optimizer = 'sgd',
optimizer_params = {'learning_rate': opt.lr, 'wd': opt.wd,
'momentum': opt.momentum, 'multi_precision': True},
initializer = mx.init.Xavier(magnitude=2))
- mod.save_params('image-classifier-%s-%d-final.params'%(opt.model,
opt.epochs))
+ mod.save_parameters('image-classifier-%s-%d-final.params'%(opt.model,
opt.epochs))
else:
if opt.mode == 'hybrid':
net.hybridize()
diff --git a/example/gluon/mnist.py b/example/gluon/mnist.py
index 198d7ca5ab2..6aea3abc504 100644
--- a/example/gluon/mnist.py
+++ b/example/gluon/mnist.py
@@ -117,7 +117,7 @@ def train(epochs, ctx):
name, val_acc = test(ctx)
print('[Epoch %d] Validation: %s=%f'%(epoch, name, val_acc))
- net.save_params('mnist.params')
+ net.save_parameters('mnist.params')
if __name__ == '__main__':
diff --git a/example/gluon/style_transfer/main.py
b/example/gluon/style_transfer/main.py
index cab8211bc9c..dde992ae700 100644
--- a/example/gluon/style_transfer/main.py
+++ b/example/gluon/style_transfer/main.py
@@ -55,7 +55,7 @@ def train(args):
style_model.initialize(init=mx.initializer.MSRAPrelu(), ctx=ctx)
if args.resume is not None:
print('Resuming, initializing using weight from
{}.'.format(args.resume))
- style_model.load_params(args.resume, ctx=ctx)
+ style_model.load_parameters(args.resume, ctx=ctx)
print('style_model:',style_model)
# optimizer and loss
trainer = gluon.Trainer(style_model.collect_params(), 'adam',
@@ -121,14 +121,14 @@ def train(args):
str(count) + "_" + str(time.ctime()).replace(' ', '_') +
"_" + str(
args.content_weight) + "_" + str(args.style_weight) +
".params"
save_model_path = os.path.join(args.save_model_dir,
save_model_filename)
- style_model.save_params(save_model_path)
+ style_model.save_parameters(save_model_path)
print("\nCheckpoint, trained model saved at", save_model_path)
# save model
save_model_filename = "Final_epoch_" + str(args.epochs) + "_" +
str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".params"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
- style_model.save_params(save_model_path)
+ style_model.save_parameters(save_model_path)
print("\nDone, trained model saved at", save_model_path)
@@ -143,7 +143,7 @@ def evaluate(args):
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
- style_model.load_params(args.model, ctx=ctx)
+ style_model.load_parameters(args.model, ctx=ctx)
# forward
style_model.set_target(style_image)
output = style_model(content_image)
diff --git a/example/gluon/super_resolution.py
b/example/gluon/super_resolution.py
index 38c3bec8949..0f2f21f3c0a 100644
--- a/example/gluon/super_resolution.py
+++ b/example/gluon/super_resolution.py
@@ -168,13 +168,13 @@ def train(epoch, ctx):
print('training mse at epoch %d: %s=%f'%(i, name, acc))
test(ctx)
- net.save_params('superres.params')
+ net.save_parameters('superres.params')
def resolve(ctx):
from PIL import Image
if isinstance(ctx, list):
ctx = [ctx[0]]
- net.load_params('superres.params', ctx=ctx)
+ net.load_parameters('superres.params', ctx=ctx)
img = Image.open(opt.resolve_img).convert('YCbCr')
y, cb, cr = img.split()
data = mx.nd.expand_dims(mx.nd.expand_dims(mx.nd.array(y), axis=0), axis=0)
diff --git a/example/gluon/tree_lstm/main.py b/example/gluon/tree_lstm/main.py
index d2fe464638a..ad5d59f7a47 100644
--- a/example/gluon/tree_lstm/main.py
+++ b/example/gluon/tree_lstm/main.py
@@ -138,7 +138,7 @@ def test(ctx, data_iter, best, mode='validation',
num_iter=-1):
if test_r >= best:
best = test_r
logging.info('New optimum found: {}. Checkpointing.'.format(best))
- net.save_params('childsum_tree_lstm_{}.params'.format(num_iter))
+
net.save_parameters('childsum_tree_lstm_{}.params'.format(num_iter))
test(ctx, test_iter, -1, 'test')
return best
diff --git a/example/gluon/word_language_model/train.py
b/example/gluon/word_language_model/train.py
index 9e152636bb0..7f0a916b79b 100644
--- a/example/gluon/word_language_model/train.py
+++ b/example/gluon/word_language_model/train.py
@@ -185,7 +185,7 @@ def train():
if val_L < best_val:
best_val = val_L
test_L = eval(test_data)
- model.save_params(args.save)
+ model.save_parameters(args.save)
print('test loss %.2f, test ppl %.2f'%(test_L, math.exp(test_L)))
else:
args.lr = args.lr*0.25
@@ -193,6 +193,6 @@ def train():
if __name__ == '__main__':
train()
- model.load_params(args.save, context)
+ model.load_parameters(args.save, context)
test_L = eval(test_data)
print('Best test loss %.2f, test ppl %.2f'%(test_L, math.exp(test_L)))
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index f107da3c8da..77c6f88f111 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -16,7 +16,7 @@
# under the License.
# coding: utf-8
-# pylint: disable= arguments-differ
+# pylint: disable= arguments-differ, too-many-lines
"""Base container class for all neural network models."""
__all__ = ['Block', 'HybridBlock', 'SymbolBlock']
@@ -307,7 +307,7 @@ def _collect_params_with_prefix(self, prefix=''):
ret.update(child._collect_params_with_prefix(prefix + name))
return ret
- def save_params(self, filename):
+ def save_parameters(self, filename):
"""Save parameters to file.
filename : str
@@ -317,8 +317,23 @@ def save_params(self, filename):
arg_dict = {key : val._reduce() for key, val in params.items()}
ndarray.save(filename, arg_dict)
- def load_params(self, filename, ctx=None, allow_missing=False,
- ignore_extra=False):
+ def save_params(self, filename):
+ """[Deprecated] Please use save_parameters.
+
+ Save parameters to file.
+
+ filename : str
+ Path to file.
+ """
+ warnings.warn("save_params is deprecated. Please use save_parameters.")
+ try:
+ self.collect_params().save(filename, strip_prefix=self.prefix)
+ except ValueError as e:
+ raise ValueError('%s\nsave_params is deprecated. Using ' \
+ 'save_parameters may resolve this
error.'%e.message)
+
+ def load_parameters(self, filename, ctx=None, allow_missing=False,
+ ignore_extra=False):
"""Load parameters from file.
filename : str
@@ -357,6 +372,25 @@ def load_params(self, filename, ctx=None,
allow_missing=False,
name, filename,
_brief_print_list(self._params.keys())))
params[name]._load_init(loaded[name], ctx)
+ def load_params(self, filename, ctx=None, allow_missing=False,
+ ignore_extra=False):
+ """[Deprecated] Please use load_parameters.
+
+ Load parameters from file.
+
+ filename : str
+ Path to parameter file.
+ ctx : Context or list of Context, default cpu()
+ Context(s) initialize loaded parameters on.
+ allow_missing : bool, default False
+ Whether to silently skip loading parameters not represents in the
file.
+ ignore_extra : bool, default False
+ Whether to silently ignore parameters from the file that are not
+ present in this Block.
+ """
+ warnings.warn("load_params is deprecated. Please use load_parameters.")
+ self.load_parameters(filename, ctx, allow_missing, ignore_extra)
+
def register_child(self, block, name=None):
"""Registers block as a child of self. :py:class:`Block` s assigned to
self as
attributes will be registered automatically."""
@@ -770,8 +804,8 @@ def infer_type(self, *args):
self._infer_attrs('infer_type', 'dtype', *args)
def export(self, path, epoch=0):
- """Export HybridBlock to json format that can be loaded by
`mxnet.mod.Module`
- or the C++ interface.
+ """Export HybridBlock to json format that can be loaded by
+ `SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.
.. note:: When there are only one input, it will have name `data`.
When there
Are more than one inputs, they will be named as `data0`,
`data1`, etc.
@@ -885,6 +919,50 @@ class SymbolBlock(HybridBlock):
>>> x = mx.nd.random.normal(shape=(16, 3, 224, 224))
>>> print(feat_model(x))
"""
+ @staticmethod
+ def imports(symbol_file, input_names, param_file=None, ctx=None):
+ """Import model previously saved by `HybridBlock.export` or
+ `Module.save_checkpoint` as a SymbolBlock for use in Gluon.
+
+ Parameters
+ ----------
+ symbol_file : str
+ Path to symbol file.
+ input_names : list of str
+ List of input variable names
+ param_file : str, optional
+ Path to parameter file.
+ ctx : Context, default None
+ The context to initialize SymbolBlock on.
+
+ Returns
+ -------
+ SymbolBlock
+ SymbolBlock loaded from symbol and parameter files.
+
+ Examples
+ --------
+ >>> net1 = gluon.model_zoo.vision.resnet18_v1(
+ ... prefix='resnet', pretrained=True)
+ >>> net1.hybridize()
+ >>> x = mx.nd.random.normal(shape=(1, 3, 32, 32))
+ >>> out1 = net1(x)
+ >>> net1.export('net1', epoch=1)
+ >>>
+ >>> net2 = gluon.SymbolBlock.imports(
+ ... 'net1-symbol.json', ['data'], 'net1-0001.params')
+ >>> out2 = net2(x)
+ """
+ sym = symbol.load(symbol_file)
+ if isinstance(input_names, str):
+ input_names = [input_names]
+ inputs = [symbol.var(i) for i in input_names]
+ ret = SymbolBlock(sym, inputs)
+ if param_file is not None:
+ ret.collect_params().load(param_file, ctx=ctx)
+ return ret
+
+
def __init__(self, outputs, inputs, params=None):
super(SymbolBlock, self).__init__(prefix=None, params=None)
self._prefix = ''
diff --git a/python/mxnet/gluon/model_zoo/vision/alexnet.py
b/python/mxnet/gluon/model_zoo/vision/alexnet.py
index 55499470460..fdb006258c2 100644
--- a/python/mxnet/gluon/model_zoo/vision/alexnet.py
+++ b/python/mxnet/gluon/model_zoo/vision/alexnet.py
@@ -83,5 +83,5 @@ def alexnet(pretrained=False, ctx=cpu(),
net = AlexNet(**kwargs)
if pretrained:
from ..model_store import get_model_file
- net.load_params(get_model_file('alexnet', root=root), ctx=ctx)
+ net.load_parameters(get_model_file('alexnet', root=root), ctx=ctx)
return net
diff --git a/python/mxnet/gluon/model_zoo/vision/densenet.py
b/python/mxnet/gluon/model_zoo/vision/densenet.py
index 835336739a6..b03f5ce8d52 100644
--- a/python/mxnet/gluon/model_zoo/vision/densenet.py
+++ b/python/mxnet/gluon/model_zoo/vision/densenet.py
@@ -141,7 +141,7 @@ def get_densenet(num_layers, pretrained=False, ctx=cpu(),
net = DenseNet(num_init_features, growth_rate, block_config, **kwargs)
if pretrained:
from ..model_store import get_model_file
- net.load_params(get_model_file('densenet%d'%(num_layers), root=root),
ctx=ctx)
+ net.load_parameters(get_model_file('densenet%d'%(num_layers),
root=root), ctx=ctx)
return net
def densenet121(**kwargs):
diff --git a/python/mxnet/gluon/model_zoo/vision/inception.py
b/python/mxnet/gluon/model_zoo/vision/inception.py
index 6d75050b83f..7c54691f1b5 100644
--- a/python/mxnet/gluon/model_zoo/vision/inception.py
+++ b/python/mxnet/gluon/model_zoo/vision/inception.py
@@ -216,5 +216,5 @@ def inception_v3(pretrained=False, ctx=cpu(),
net = Inception3(**kwargs)
if pretrained:
from ..model_store import get_model_file
- net.load_params(get_model_file('inceptionv3', root=root), ctx=ctx)
+ net.load_parameters(get_model_file('inceptionv3', root=root), ctx=ctx)
return net
diff --git a/python/mxnet/gluon/model_zoo/vision/mobilenet.py
b/python/mxnet/gluon/model_zoo/vision/mobilenet.py
index 5b4c9a8e615..1a2c9b94619 100644
--- a/python/mxnet/gluon/model_zoo/vision/mobilenet.py
+++ b/python/mxnet/gluon/model_zoo/vision/mobilenet.py
@@ -213,7 +213,7 @@ def get_mobilenet(multiplier, pretrained=False, ctx=cpu(),
version_suffix = '{0:.2f}'.format(multiplier)
if version_suffix in ('1.00', '0.50'):
version_suffix = version_suffix[:-1]
- net.load_params(
+ net.load_parameters(
get_model_file('mobilenet%s' % version_suffix, root=root), ctx=ctx)
return net
@@ -245,7 +245,7 @@ def get_mobilenet_v2(multiplier, pretrained=False,
ctx=cpu(),
version_suffix = '{0:.2f}'.format(multiplier)
if version_suffix in ('1.00', '0.50'):
version_suffix = version_suffix[:-1]
- net.load_params(
+ net.load_parameters(
get_model_file('mobilenetv2_%s' % version_suffix, root=root),
ctx=ctx)
return net
diff --git a/python/mxnet/gluon/model_zoo/vision/resnet.py
b/python/mxnet/gluon/model_zoo/vision/resnet.py
index 5ee67b510a8..da279b89583 100644
--- a/python/mxnet/gluon/model_zoo/vision/resnet.py
+++ b/python/mxnet/gluon/model_zoo/vision/resnet.py
@@ -386,8 +386,8 @@ def get_resnet(version, num_layers, pretrained=False,
ctx=cpu(),
net = resnet_class(block_class, layers, channels, **kwargs)
if pretrained:
from ..model_store import get_model_file
- net.load_params(get_model_file('resnet%d_v%d'%(num_layers, version),
- root=root), ctx=ctx)
+ net.load_parameters(get_model_file('resnet%d_v%d'%(num_layers,
version),
+ root=root), ctx=ctx)
return net
def resnet18_v1(**kwargs):
diff --git a/python/mxnet/gluon/model_zoo/vision/squeezenet.py
b/python/mxnet/gluon/model_zoo/vision/squeezenet.py
index 09f62a52074..aaff4c36dfa 100644
--- a/python/mxnet/gluon/model_zoo/vision/squeezenet.py
+++ b/python/mxnet/gluon/model_zoo/vision/squeezenet.py
@@ -132,7 +132,7 @@ def get_squeezenet(version, pretrained=False, ctx=cpu(),
net = SqueezeNet(version, **kwargs)
if pretrained:
from ..model_store import get_model_file
- net.load_params(get_model_file('squeezenet%s'%version, root=root),
ctx=ctx)
+ net.load_parameters(get_model_file('squeezenet%s'%version, root=root),
ctx=ctx)
return net
def squeezenet1_0(**kwargs):
diff --git a/python/mxnet/gluon/model_zoo/vision/vgg.py
b/python/mxnet/gluon/model_zoo/vision/vgg.py
index dbae5385898..a3b1685b413 100644
--- a/python/mxnet/gluon/model_zoo/vision/vgg.py
+++ b/python/mxnet/gluon/model_zoo/vision/vgg.py
@@ -114,8 +114,8 @@ def get_vgg(num_layers, pretrained=False, ctx=cpu(),
if pretrained:
from ..model_store import get_model_file
batch_norm_suffix = '_bn' if kwargs.get('batch_norm') else ''
- net.load_params(get_model_file('vgg%d%s'%(num_layers,
batch_norm_suffix),
- root=root), ctx=ctx)
+ net.load_parameters(get_model_file('vgg%d%s'%(num_layers,
batch_norm_suffix),
+ root=root), ctx=ctx)
return net
def vgg11(**kwargs):
diff --git a/tests/python/unittest/test_gluon.py
b/tests/python/unittest/test_gluon.py
index ced3063448b..71e65ec6810 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -202,20 +202,20 @@ def forward(self, x):
net1.collect_params().initialize()
net2(mx.nd.zeros((3, 5)))
- net1.save_params('net1.params')
+ net1.save_parameters('net1.params')
net3 = Net(prefix='net3_')
- net3.load_params('net1.params', mx.cpu())
+ net3.load_parameters('net1.params', mx.cpu())
net4 = Net(prefix='net4_')
net5 = Net(prefix='net5_', in_units=5, params=net4.collect_params())
net4.collect_params().initialize()
net5(mx.nd.zeros((3, 5)))
- net4.save_params('net4.params')
+ net4.save_parameters('net4.params')
net6 = Net(prefix='net6_')
- net6.load_params('net4.params', mx.cpu())
+ net6.load_parameters('net4.params', mx.cpu())
@with_seed()
@@ -776,7 +776,7 @@ def test_export():
model = gluon.model_zoo.vision.resnet18_v1(
prefix='resnet', ctx=ctx, pretrained=True)
model.hybridize()
- data = mx.nd.random.normal(shape=(1, 3, 224, 224))
+ data = mx.nd.random.normal(shape=(1, 3, 32, 32))
out = model(data)
model.export('gluon')
@@ -794,6 +794,22 @@ def test_export():
assert_almost_equal(out.asnumpy(), out2.asnumpy())
+@with_seed()
+def test_import():
+ ctx = mx.context.current_context()
+ net1 = gluon.model_zoo.vision.resnet18_v1(
+ prefix='resnet', ctx=ctx, pretrained=True)
+ net1.hybridize()
+ data = mx.nd.random.normal(shape=(1, 3, 32, 32))
+ out1 = net1(data)
+
+ net1.export('net1', epoch=1)
+
+ net2 = gluon.SymbolBlock.imports(
+ 'net1-symbol.json', ['data'], 'net1-0001.params', ctx)
+ out2 = net2(data)
+
+ assert_almost_equal(out1.asnumpy(), out2.asnumpy())
@with_seed()
def test_hybrid_stale_cache():
@@ -910,7 +926,7 @@ def test_fill_shape_load():
net1.hybridize()
net1.initialize(ctx=ctx)
net1(mx.nd.ones((2,3,5,7), ctx))
- net1.save_params('net_fill.params')
+ net1.save_parameters('net_fill.params')
net2 = nn.HybridSequential()
with net2.name_scope():
@@ -919,7 +935,7 @@ def test_fill_shape_load():
nn.Dense(10))
net2.hybridize()
net2.initialize()
- net2.load_params('net_fill.params', ctx)
+ net2.load_parameters('net_fill.params', ctx)
assert net2[0].weight.shape[1] == 3, net2[0].weight.shape[1]
assert net2[1].gamma.shape[0] == 64, net2[1].gamma.shape[0]
assert net2[2].weight.shape[1] == 3072, net2[2].weight.shape[1]
@@ -1065,12 +1081,12 @@ def test_req():
@with_seed()
def test_save_load():
net = mx.gluon.model_zoo.vision.get_resnet(1, 18, pretrained=True)
- net.save_params('test_save_load.params')
+ net.save_parameters('test_save_load.params')
net = mx.gluon.model_zoo.vision.get_resnet(1, 18)
net.output = mx.gluon.nn.Dense(1000)
- net.load_params('test_save_load.params')
+ net.load_parameters('test_save_load.params')
@with_seed()
def test_symbol_block_save_load():
@@ -1095,10 +1111,10 @@ def hybrid_forward(self, F, x):
net1.initialize(mx.init.Normal())
net1.hybridize()
net1(mx.nd.random.normal(shape=(1, 3, 32, 32)))
- net1.save_params('./test_symbol_block_save_load.params')
+ net1.save_parameters('./test_symbol_block_save_load.params')
net2 = Net()
- net2.load_params('./test_symbol_block_save_load.params', ctx=mx.cpu())
+ net2.load_parameters('./test_symbol_block_save_load.params', ctx=mx.cpu())
@with_seed()
@@ -1252,6 +1268,22 @@ def test_summary():
assert_raises(AssertionError, net.summary, mx.nd.ones((32, 3, 224, 224)))
+@with_seed()
+def test_legacy_save_params():
+ net = gluon.nn.HybridSequential(prefix='')
+ with net.name_scope():
+ net.add(gluon.nn.Conv2D(10, (3, 3)))
+ net.add(gluon.nn.Dense(50))
+ net.initialize()
+ net(mx.nd.ones((1,1,50,50)))
+ a = net(mx.sym.var('data'))
+ a.save('test.json')
+ net.save_params('test.params')
+ model = gluon.nn.SymbolBlock(outputs=mx.sym.load_json(open('test.json',
'r').read()),
+ inputs=mx.sym.var('data'))
+ model.load_params('test.params', ctx=mx.cpu())
+
+
if __name__ == '__main__':
import nose
nose.runmodule()
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