def run_inference_on_image(image):
"""Runs inference on an image.
Parameters
----------
image: String
Image file name.
Returns
-------
Nothing
"""
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
create_graph()
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
start = time.time()
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
print("==================", time.time()-start)
print ("===== TENSORFLOW RESULTS =======")
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path,
uid_lookup_path=label_path)
# Print top 5 predictions from tensorflow.
top_k = predictions.argsort()[-5:][::-1]
print ("===== TENSORFLOW RESULTS =======")
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
def run_tvm(pics, number, repeat):
"""
Runs a single inference and gives back the time
:param pics: The images(s) to run
:param number: The number of times to run the inference
:param repeat: The number of times to repeat the measurement
:return: An array with the time and the result
"""
# combine pictures
arr = np.ndarray(shape=input_shape, dtype=dtype)
p = 0
for ip in pics:
arr[p] = ip.astype(dtype)
p = p + 1
m.set_input("data", tvm.nd.array(arr))
#Actually run inference
time = m.module.time_evaluator("run", ctx, number=number, repeat=repeat)()
#Get output
res = []
if platform == 'MXNet':
for i in range(len(pics)):
res.append(synset[np.argmax(m.get_output(0).asnumpy()[i])])
if platform == 'PyTorch':
# Get top-1 result for TVM
for i in range(len(pics)):
top1_tvm = np.argmax(m.get_output(0).asnumpy()[i])
tvm_class_key = class_id_to_key[top1_tvm]
res.append(key_to_classname[tvm_class_key])
if platform == 'TensorFlow':
pre = np.squeeze(m.get_output(0, tvm.nd.empty(((1, 1008)), "float32")).asnumpy())
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path, uid_lookup_path=label_path)
top_k = pre.argsort()[-5:][::-1]
res = node_lookup.id_to_string(top_k[0])
return [time, res]
m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))
m.set_input(**params)
# execute
m.run()
# get outputs
tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
######################################################################
# Process the output
# ------------------
# Process the model output to human readable text for InceptionV1.
predictions = tvm_output.asnumpy()
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path,
uid_lookup_path=label_path)
# Print top 5 predictions from TVM output.
top_k = predictions.argsort()[-5:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
######################################################################
# Inference on tensorflow
# -----------------------
# Run the corresponding model on tensorflow
def create_graph():
m.set_input(**params)
# execute
m.run()
# get outputs
tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
######################################################################
# Process the output
# ------------------
# Process the model output to human readable text for InceptionV1.
predictions = tvm_output.asnumpy()
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = tf_testing.NodeLookup(
label_lookup_path=os.path.join("./", map_proto),
uid_lookup_path=os.path.join("./", lable_map))
# Print top 5 predictions from TVM output.
top_k = predictions.argsort()[-5:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
######################################################################
# Inference on tensorflow
# -----------------------
# Run the corresponding model on tensorflow
