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52 lines
1.9 KiB
Python

# Copyright (C) 2018 Intel Corporation
#
# SPDX-License-Identifier: MIT
import json
import cv2
import os
import subprocess
from cvat.apps.auto_annotation.inference_engine import make_plugin, make_network
class ModelLoader():
def __init__(self, model, weights):
self._model = model
self._weights = weights
IE_PLUGINS_PATH = os.getenv("IE_PLUGINS_PATH")
if not IE_PLUGINS_PATH:
raise OSError("Inference engine plugin path env not found in the system.")
plugin = make_plugin()
network = make_network(self._model, self._weights)
supported_layers = plugin.get_supported_layers(network)
not_supported_layers = [l for l in network.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
raise Exception("Following layers are not supported by the plugin for specified device {}:\n {}".
format(plugin.device, ", ".join(not_supported_layers)))
self._input_blob_name = next(iter(network.inputs))
self._output_blob_name = next(iter(network.outputs))
self._net = plugin.load(network=network, num_requests=2)
input_type = network.inputs[self._input_blob_name]
self._input_layout = input_type if isinstance(input_type, list) else input_type.shape
def infer(self, image):
_, _, h, w = self._input_layout
in_frame = image if image.shape[:-1] == (h, w) else cv2.resize(image, (w, h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
results = self._net.infer(inputs={self._input_blob_name: in_frame})
if len(results) == 1:
return results[self._output_blob_name].copy()
else:
return results.copy()
def load_label_map(labels_path):
with open(labels_path, "r") as f:
return json.load(f)["label_map"]