testbed/openvinotoolkit__datumaro/datumaro/plugins/openvino_launcher.py

# Copyright (C) 2019-2020 Intel Corporation
#
# SPDX-License-Identifier: MIT

# pylint: disable=exec-used

import cv2
import logging as log
import numpy as np
import os.path as osp
import shutil

from openvino.inference_engine import IECore

from datumaro.components.cli_plugin import CliPlugin
from datumaro.components.launcher import Launcher


class OpenVinoImporter(CliPlugin):
    @classmethod
    def build_cmdline_parser(cls, **kwargs):
        parser = super().build_cmdline_parser(**kwargs)
        parser.add_argument('-d', '--description', required=True,
            help="Path to the model description file (.xml)")
        parser.add_argument('-w', '--weights', required=True,
            help="Path to the model weights file (.bin)")
        parser.add_argument('-i', '--interpreter', required=True,
            help="Path to the network output interprter script (.py)")
        parser.add_argument('--device', default='CPU',
            help="Target device (default: %(default)s)")
        return parser

    @staticmethod
    def copy_model(model_dir, model):
        shutil.copy(model['description'],
            osp.join(model_dir, osp.basename(model['description'])))
        model['description'] = osp.basename(model['description'])

        shutil.copy(model['weights'],
            osp.join(model_dir, osp.basename(model['weights'])))
        model['weights'] = osp.basename(model['weights'])

        shutil.copy(model['interpreter'],
            osp.join(model_dir, osp.basename(model['interpreter'])))
        model['interpreter'] = osp.basename(model['interpreter'])


class InterpreterScript:
    def __init__(self, path):
        with open(path, 'r') as f:
            script = f.read()

        context = {}
        exec(script, context, context)

        process_outputs = context.get('process_outputs')
        if not callable(process_outputs):
            raise Exception("Can't find 'process_outputs' function in "
                "the interpreter script")
        self.__dict__['process_outputs'] = process_outputs

        get_categories = context.get('get_categories')
        assert get_categories is None or callable(get_categories)
        if get_categories:
            self.__dict__['get_categories'] = get_categories

    @staticmethod
    def get_categories():
        return None

    @staticmethod
    def process_outputs(inputs, outputs):
        raise NotImplementedError(
            "Function should be implemented in the interpreter script")


class OpenVinoLauncher(Launcher):
    cli_plugin = OpenVinoImporter

    def __init__(self, description, weights, interpreter,
            plugins_path=None, device=None, model_dir=None):
        model_dir = model_dir or ''
        if not osp.isfile(description):
            description = osp.join(model_dir, description)
        if not osp.isfile(description):
            raise Exception('Failed to open model description file "%s"' % \
                (description))

        if not osp.isfile(weights):
            weights = osp.join(model_dir, weights)
        if not osp.isfile(weights):
            raise Exception('Failed to open model weights file "%s"' % \
                (weights))

        if not osp.isfile(interpreter):
            interpreter = osp.join(model_dir, interpreter)
        if not osp.isfile(interpreter):
            raise Exception('Failed to open model interpreter script file "%s"' % \
                (interpreter))

        self._interpreter = InterpreterScript(interpreter)

        self._device = device or 'CPU'

        self._ie = IECore()
        if hasattr(self._ie, 'read_network'):
            self._network = self._ie.read_network(description, weights)
        else: # backward compatibility
            from openvino.inference_engine import IENetwork
            self._network = IENetwork.from_ir(description, weights)
        self._check_model_support(self._network, self._device)
        self._load_executable_net()

    def _check_model_support(self, net, device):
        supported_layers = set(self._ie.query_network(net, device))
        not_supported_layers = set(net.layers) - supported_layers
        if len(not_supported_layers) != 0:
            log.error("The following layers are not supported " \
                "by the plugin for device '%s': %s." % \
                (device, ', '.join(not_supported_layers)))
            raise NotImplementedError(
                "Some layers are not supported on the device")

    def _load_executable_net(self, batch_size=1):
        network = self._network

        iter_inputs = iter(network.inputs)
        self._input_blob_name = next(iter_inputs)
        self._output_blob_name = next(iter(network.outputs))

        # NOTE: handling for the inclusion of `image_info` in OpenVino2019
        self._require_image_info = 'image_info' in network.inputs
        if self._input_blob_name == 'image_info':
            self._input_blob_name = next(iter_inputs)

        input_type = network.inputs[self._input_blob_name]
        self._input_layout = input_type if isinstance(input_type, list) else input_type.shape

        self._input_layout[0] = batch_size
        network.reshape({self._input_blob_name: self._input_layout})
        self._batch_size = batch_size

        self._net = self._ie.load_network(network=network, num_requests=1,
            device_name=self._device)

    def infer(self, inputs):
        assert len(inputs.shape) == 4, \
            "Expected an input image in (N, H, W, C) format, got %s" % \
            (inputs.shape)
        assert inputs.shape[3] == 3, "Expected BGR input, got %s" % inputs.shape

        n, c, h, w = self._input_layout
        if inputs.shape[1:3] != (h, w):
            resized_inputs = np.empty((n, h, w, c), dtype=inputs.dtype)
            for inp, resized_input in zip(inputs, resized_inputs):
                cv2.resize(inp, (w, h), resized_input)
            inputs = resized_inputs
        inputs = inputs.transpose((0, 3, 1, 2)) # NHWC to NCHW
        inputs = {self._input_blob_name: inputs}
        if self._require_image_info:
            info = np.zeros([1, 3])
            info[0, 0] = h
            info[0, 1] = w
            info[0, 2] = 1.0 # scale
            inputs['image_info'] = info

        results = self._net.infer(inputs)
        if len(results) == 1:
            return results[self._output_blob_name]
        else:
            return results

    def launch(self, inputs):
        batch_size = len(inputs)
        if self._batch_size < batch_size:
            self._load_executable_net(batch_size)

        outputs = self.infer(inputs)
        results = self.process_outputs(inputs, outputs)
        return results

    def categories(self):
        return self._interpreter.get_categories()

    def process_outputs(self, inputs, outputs):
        return self._interpreter.process_outputs(inputs, outputs)