def RunNet(name):
"""Runs a given net.
Inputs:
name: the name of the net, or a reference to the net.
Returns:
True or an exception.
"""
return C.run_net(StringifyNetName(name))
def RunNet(name, num_iter=1):
"""Runs a given net.
Inputs:
name: the name of the net, or a reference to the net.
num_iter: number of iterations to run
Returns:
True or an exception.
"""
return C.run_net(StringifyNetName(name), num_iter)
def RunNet(name, num_iter=1, allow_fail=False):
"""Runs a given net.
Inputs:
name: the name of the net, or a reference to the net.
num_iter: number of iterations to run
allow_fail: if True, does not assert on net exec failure but returns False
Returns:
True or an exception.
"""
return C.run_net(StringifyNetName(name), num_iter, allow_fail)
C.create_net(net_def.SerializeToString())
C.feed_blob('data', img, device_opts.SerializeToString())
### Debug code
"""
for b in workspace.Blobs():
x = workspace.FetchBlob(b)
if type(x) != str:
print(str(b) + ': ' + str(x.shape))
"""
### End of debug code
print('Running net ' + workspace.GetNetName(net_def) + '...')
C.run_net(workspace.GetNetName(net_def), 1, False)
# Turn it into something we can play with and examine which is in a multi-dimensional array
results = workspace.FetchBlob('prob')
#print("results shape: ", results.shape)
# Quick way to get the top-1 prediction result
# Squeeze out the unnecessary axis. This returns a 1-D array of length 1000
preds = np.squeeze(results)
# Get the prediction and the confidence by finding the maximum value and index of maximum value in preds array
curr_pred, curr_conf = max(enumerate(preds), key=operator.itemgetter(1))
print("Prediction: ", curr_pred)
print("Confidence: ", curr_conf)
# the rest of this is digging through the results
results = np.delete(results, 1)