def _test_image_prediction(self, model_name, epoch, label_name):
try:
data = read_image(VAL_DATA, label_name=label_name)
except:
download_data()
data = read_image(VAL_DATA, label_name=label_name)
mod = load_model(model_name=model_name,
epoch_num=epoch,
data_shapes=data.provide_data,
label_shapes=data.provide_label,
label_names=[
label_name,
])
input_shape = (1, 3, 224, 224)
coreml_model = mxnet_converter.convert(
mod, input_shape={'data': input_shape})
mxnet_acc = []
mxnet_top_5_acc = []
coreml_acc = []
coreml_top_5_acc = []
num_batch = 0
for batch in data:
mod.forward(batch, is_train=False)
mxnet_preds = mod.get_outputs()[0].asnumpy()
data_numpy = batch.data[0].asnumpy()
label_numpy = batch.label[0].asnumpy()
for i in xrange(32):
input_data = {'data': data_numpy[i]}
coreml_predict = coreml_model.predict(
input_data).values()[0].flatten()
mxnet_predict = mxnet_preds[i]
label = label_numpy[i]
mxnet_acc.append(is_correct_top_one(mxnet_predict, label))
mxnet_top_5_acc.append(
is_correct_top_five(mxnet_predict, label))
coreml_acc.append(is_correct_top_one(coreml_predict, label))
coreml_top_5_acc.append(
is_correct_top_five(coreml_predict, label))
num_batch += 1
if (num_batch == 5): break # we only use a subset of the batches.
print("MXNet acc %s" % np.mean(mxnet_acc))
print("Coreml acc %s" % np.mean(coreml_acc))
print("MXNet top 5 acc %s" % np.mean(mxnet_top_5_acc))
print("Coreml top 5 acc %s" % np.mean(coreml_top_5_acc))
self.assertAlmostEqual(np.mean(mxnet_acc),
np.mean(coreml_acc),
delta=1e-4)
self.assertAlmostEqual(np.mean(mxnet_top_5_acc),
np.mean(coreml_top_5_acc),
delta=1e-4)
def _test_image_prediction(self, model_name, epoch, label_name):
try:
data = read_image(VAL_DATA, label_name=label_name)
except:
download_data()
data = read_image(VAL_DATA, label_name=label_name)
mod = load_model(
model_name=model_name,
epoch_num=epoch,
data_shapes=data.provide_data,
label_shapes=data.provide_label,
label_names=[label_name,]
)
input_shape = (1, 3, 224, 224)
coreml_model = mxnet_converter.convert(mod, input_shape={'data': input_shape})
mxnet_acc = []
mxnet_top_5_acc = []
coreml_acc = []
coreml_top_5_acc = []
num_batch = 0
for batch in data:
mod.forward(batch, is_train=False)
mxnet_preds = mod.get_outputs()[0].asnumpy()
data_numpy = batch.data[0].asnumpy()
label_numpy = batch.label[0].asnumpy()
for i in xrange(32):
input_data = {'data': data_numpy[i]}
coreml_predict = coreml_model.predict(input_data).values()[0].flatten()
mxnet_predict = mxnet_preds[i]
label = label_numpy[i]
mxnet_acc.append(is_correct_top_one(mxnet_predict, label))
mxnet_top_5_acc.append(is_correct_top_five(mxnet_predict, label))
coreml_acc.append(is_correct_top_one(coreml_predict, label))
coreml_top_5_acc.append(is_correct_top_five(coreml_predict, label))
num_batch += 1
if (num_batch == 5): break # we only use a subset of the batches.
print("MXNet acc %s" % np.mean(mxnet_acc))
print("Coreml acc %s" % np.mean(coreml_acc))
print("MXNet top 5 acc %s" % np.mean(mxnet_top_5_acc))
print("Coreml top 5 acc %s" % np.mean(coreml_top_5_acc))
self.assertAlmostEqual(np.mean(mxnet_acc), np.mean(coreml_acc), delta=1e-4)
self.assertAlmostEqual(np.mean(mxnet_top_5_acc), np.mean(coreml_top_5_acc), delta=1e-4)
# parse the input data name/shape and label name/shape
input_shape = yaml.safe_load(args.input_shape)
data_shapes = []
for key in input_shape:
# We prepend 1 because the coreml model only accept 1 input data at a time (=batch-size).
shape = (1,)+literal_eval(input_shape[key])
input_shape[key] = shape
data_shapes.append((key, shape))
# if label name is not in input then do not use the label
label_names = [args.label_names,] if args.label_names in input_shape else None
pre_processing_arguments = args.pre_processing_arguments
mod = load_model(
model_name=model_name,
epoch_num=epoch_num,
data_shapes=data_shapes,
label_shapes=None,
label_names=label_names
)
kwargs = {'input_shape': input_shape}
if pre_processing_arguments is not None:
kwargs['preprocessor_args'] = yaml.safe_load(pre_processing_arguments)
coreml_model = convert(model=mod, mode=mode, class_labels=class_labels, **kwargs)
coreml_model.save(output_file)
print("\nSUCCESS\nModel %s has been converted and saved at %s\n" % (model_name, output_file))
# parse the input data name/shape and label name/shape
input_shape = yaml.safe_load(args.input_shape)
data_shapes = []
for key in input_shape:
# We prepend 1 because the coreml model only accept 1 input data at a time.
shape = (1,)+literal_eval(input_shape[key])
input_shape[key] = shape
data_shapes.append((key, shape))
# if label name is not in input then do not use the label
label_names = [args.label_names,] if args.label_names in input_shape else None
pre_processing_arguments = args.pre_processing_arguments
mod = load_model(
model_name=model_name,
epoch_num=epoch_num,
data_shapes=data_shapes,
label_shapes=None,
label_names=label_names
)
kwargs = {'input_shape': input_shape}
if pre_processing_arguments is not None:
kwargs['preprocessor_args'] = yaml.safe_load(pre_processing_arguments)
coreml_model = convert(model=mod, mode=mode, class_labels=class_labels, **kwargs)
coreml_model.save(output_file)
print("\nSUCCESS\nModel %s has been converted and saved at %s\n" % (model_name, output_file))