def generate(self, num_samples):
"""Grabs input/label pairs from MNIST"""
ctx = mxnet.cpu()
# Load a random image from the test dataset
sample_data = mxnet.gluon.data.DataLoader(
mxnet.gluon.data.vision.CIFAR10(train=False),
1,
shuffle=self.config.get('shuffle', True))
samples = []
for i, (data, label) in zip(range(num_samples), sample_data):
if i == num_samples:
break
data_np = numpy.copy(data.as_in_context(ctx).asnumpy())
# gluon data is in NHWC format
shape = util.LabelledShape(dim_iter=zip('NHWC', data_np.shape),
dtype='uint8')
data_nt = util.LabelledTensor(data_np,
shape).with_layout(self.DATA_LAYOUT)
label = int(label.asnumpy()[0])
samples.append(
DatasetSample(inputs={'data': data_nt},
outputs={'label': label}))
return samples
def _load_cmsis_params(mod, param_file, param_shapes):
with open(param_file) as f:
cmsis_params = json.load(f)
params = {}
for formal_param in mod['main'].params[1:]: # exclude data
name = formal_param.name_hint
_LOG.debug('name %r %r %r', name, _CMSIS_PARAM_SHAPES[name].dtype,
len(cmsis_params[name]))
cmsis_tensor = util.LabelledTensor(data=np.array(
cmsis_params[name],
dtype=_CMSIS_PARAM_SHAPES[name].dtype,
copy=True).reshape(_CMSIS_PARAM_SHAPES[name].shape),
shape=_CMSIS_PARAM_SHAPES[name])
if name == 'conv0_bias':
cmsis_tensor = _CMSIS_PARAM_SHAPES[name].gen_zero_tensor()
param_shape = param_shapes[name]
relay_shape = util.LabelledShape(
zip(param_shape.layout,
[x.value for x in formal_param.checked_type.shape]),
dtype=param_shape.dtype)
assert param_shape.dims == relay_shape.dims
param = cmsis_tensor.resize(param_shape)
data = param.data
if name == 'mean_data':
data = relay_shape.gen_zero_tensor().data
params[name] = tvm.nd.array(data, tvm.cpu(0))
return params
def get_sample_points(n, data_layout='NHWC'):
"""Grabs a single input/label pair from MNIST"""
ctx = mxnet.cpu()
# Load a random image from the test dataset
sample_data = mxnet.gluon.data.DataLoader(
mxnet.gluon.data.vision.CIFAR10(train=False),
1)#,
# shuffle=True)
samples = []
for i, (data, label) in zip(range(n), sample_data):
if i == n:
break
data_np = numpy.copy(data.as_in_context(ctx).asnumpy())
# gluon data is in NHWC format
shape = util.LabelledShape(dim_iter=zip('NHWC', data_np.shape), dtype='uint8')
data_nt = util.LabelledTensor(data_np, shape).with_layout(data_layout)
label = int(label.asnumpy()[0])
samples.append({'data': data_nt, 'label': label})
return samples