Python caffe.set_mode_cpu方法代码示例

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def load_caffe(model_desc, model_file):
    """
    Load a caffe model. You must be able to ``import caffe`` to use this
    function.

    Args:
        model_desc (str): path to caffe model description file (.prototxt).
        model_file (str): path to caffe model parameter file (.caffemodel).
    Returns:
        dict: the parameters.
    """
    with change_env('GLOG_minloglevel', '2'):
        import caffe
        caffe.set_mode_cpu()
        net = caffe.Net(model_desc, model_file, caffe.TEST)
    param_dict = CaffeLayerProcessor(net).process()
    logger.info("Model loaded from caffe. Params: " +
                ", ".join(sorted(param_dict.keys())))
    return param_dict 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def prep_net(self, gpu_id, prototxt_path='', caffemodel_path=''):
        import caffe
        print('gpu_id = %d, net_path = %s, model_path = %s' % (gpu_id, prototxt_path, caffemodel_path))
        if gpu_id == -1:
            caffe.set_mode_cpu()
        else:
            caffe.set_device(gpu_id)
            caffe.set_mode_gpu()
        self.gpu_id = gpu_id
        self.net = caffe.Net(prototxt_path, caffemodel_path, caffe.TEST)
        self.net_set = True

        # automatically set cluster centers
        if len(self.net.params[self.pred_ab_layer][0].data[...].shape) == 4 and self.net.params[self.pred_ab_layer][0].data[...].shape[1] == 313:
            print('Setting ab cluster centers in layer: %s' % self.pred_ab_layer)
            self.net.params[self.pred_ab_layer][0].data[:, :, 0, 0] = self.pts_in_hull.T

        # automatically set upsampling kernel
        for layer in self.net._layer_names:
            if layer[-3:] == '_us':
                print('Setting upsampling layer kernel: %s' % layer)
                self.net.params[layer][0].data[:, 0, :, :] = np.array(((.25, .5, .25, 0), (.5, 1., .5, 0), (.25, .5, .25, 0), (0, 0, 0, 0)))[np.newaxis, :, :]

    # ***** Call forward ***** 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def __init__(self, model_weights, model_def, threshold=0.5, GPU_MODE=False):
        if GPU_MODE:
            caffe.set_device(0)
            caffe.set_mode_gpu()
        else:
            caffe.set_mode_cpu()
        self.net = caffe.Net(model_def,  # defines the structure of the model
                        model_weights,  # contains the trained weights
                        caffe.TEST)  # use test mode (e.g., don't perform dropout)
        self.threshold = threshold
        self.transformer = caffe.io.Transformer({'data': self.net.blobs['data'].data.shape})
        self.transformer.set_transpose('data', (2, 0, 1))
        self.transformer.set_mean('data', np.array([127.0, 127.0, 127.0]))  # mean pixel
        self.transformer.set_raw_scale('data',
                                  255)  # the reference model operates on images in [0,255] range instead of [0,1]
        self.transformer.set_channel_swap('data', (2, 1, 0))  # the reference model has channels in BGR order instead of RGB
        image_resize = 300
        self.net.blobs['data'].reshape(1, 3, image_resize, image_resize) 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def predict(sound_file, prototxt, model, output_path):

  image_files = wav_to_images(sound_file, output_path)

  caffe.set_mode_cpu()
  net = caffe.Classifier(prototxt, model,
                         #image_dims=(224, 224)
                         #channel_swap=(2,1,0),
                         raw_scale=255 # convert 0..255 values into range 0..1
                         #caffe.TEST
                        )

  input_images = np.array([caffe.io.load_image(image_file, color=False) for image_file in image_files["melfilter"]])
  #input_images = np.swapaxes(input_images, 1, 3)

  #prediction = net.forward_all(data=input_images)["prob"]

  prediction = net.predict(input_images, False)  # predict takes any number of images, and formats them for the Caffe net automatically

  print prediction
  print 'prediction shape:', prediction[0].shape
  print 'predicted class:', prediction[0].argmax()
  print image_files

  return prediction 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def load_caffe(model_desc, model_file):
    """
    Load a caffe model. You must be able to ``import caffe`` to use this
    function.
    Args:
        model_desc (str): path to caffe model description file (.prototxt).
        model_file (str): path to caffe model parameter file (.caffemodel).
    Returns:
        dict: the parameters.
    """
    with change_env('GLOG_minloglevel', '2'):
        import caffe
        caffe.set_mode_cpu()
        net = caffe.Net(model_desc, model_file, caffe.TEST)
    param_dict = CaffeLayerProcessor(net).process()
    logger.info("Model loaded from caffe. Params: " +
                ", ".join(sorted(param_dict.keys())))
    return param_dict 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def __init__(self,params):

        self.dimension = params['dimension']
        self.dataset = params['dataset']
        self.pooling = params['pooling']
        # Read image lists
        with open(params['query_list'],'r') as f:
            self.query_names = f.read().splitlines()

        with open(params['frame_list'],'r') as f:
            self.database_list = f.read().splitlines()

        # Parameters needed
        self.layer = params['layer']
        self.save_db_feats = params['database_feats']

        # Init network
        if params['gpu']:
            caffe.set_mode_gpu()
            caffe.set_device(0)
        else:
            caffe.set_mode_cpu()
        print "Extracting from:", params['net_proto']
        cfg.TEST.HAS_RPN = True
        self.net = caffe.Net(params['net_proto'], params['net'], caffe.TEST) 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def create_classifier(pretrained_model):
    """Creates a model from saved caffemodel file and returns a classifier."""
    # Creates model from saved .caffemodel.

    # The following file are shipped inside caffe-doc Debian package
    model_def = os.path.join("/", "usr", "share", "doc", "caffe-doc",
                             "models","bvlc_reference_caffenet",
                             "deploy.prototxt")
    image_dims = [ 256, 256 ]
    # The following file are shipped inside python3-caffe-cpu Debian package
    mean = np.load(os.path.join('/', 'usr', 'lib', 'python3',
                                'dist-packages', 'caffe', 'imagenet',
                                'ilsvrc_2012_mean.npy'))
    channel_swap = [2, 1, 0]
    raw_scale = 255.0

    caffe.set_mode_cpu()
    classifier = caffe.Classifier(model_def, pretrained_model,
                                  image_dims=image_dims, mean=mean,
                                  raw_scale=raw_scale,
                                  channel_swap=channel_swap)
    return classifier 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def setUp(self):
        self.num_output = 13
        net_f = simple_net_file(self.num_output)
        f = tempfile.NamedTemporaryFile(delete=False)
        f.write("""net: '""" + net_f + """'
        test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
        weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
        display: 100 max_iter: 100 snapshot_after_train: false""")
        f.close()
        self.solver = caffe.SGDSolver(f.name)
        # also make sure get_solver runs
        caffe.get_solver(f.name)
        caffe.set_mode_cpu()
        # fill in valid labels
        self.solver.net.blobs['label'].data[...] = \
                np.random.randint(self.num_output,
                    size=self.solver.net.blobs['label'].data.shape)
        self.solver.test_nets[0].blobs['label'].data[...] = \
                np.random.randint(self.num_output,
                    size=self.solver.test_nets[0].blobs['label'].data.shape)
        os.remove(f.name)
        os.remove(net_f) 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def setUp(self):
        self.num_output = 13
        net_f = simple_net_file(self.num_output)
        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
        f.write("""net: '""" + net_f + """'
        test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
        weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
        display: 100 max_iter: 100 snapshot_after_train: false
        snapshot_prefix: "model" """)
        f.close()
        self.solver = caffe.SGDSolver(f.name)
        # also make sure get_solver runs
        caffe.get_solver(f.name)
        caffe.set_mode_cpu()
        # fill in valid labels
        self.solver.net.blobs['label'].data[...] = \
                np.random.randint(self.num_output,
                    size=self.solver.net.blobs['label'].data.shape)
        self.solver.test_nets[0].blobs['label'].data[...] = \
                np.random.randint(self.num_output,
                    size=self.solver.test_nets[0].blobs['label'].data.shape)
        os.remove(f.name)
        os.remove(net_f) 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def __init__(self, weights_path, image_net_proto, device_id=-1):
        if device_id >= 0:
            caffe.set_mode_gpu()
            caffe.set_device(device_id)
        else:
            caffe.set_mode_cpu()
        # Setup image processing net.
        phase = caffe.TEST
        self.image_net = caffe.Net(image_net_proto, weights_path, phase)
        image_data_shape = self.image_net.blobs['data'].data.shape
        self.transformer = caffe.io.Transformer({'data': image_data_shape})
        channel_mean = np.zeros(image_data_shape[1:])
        channel_mean_values = [104, 117, 123]
        assert channel_mean.shape[0] == len(channel_mean_values)
        for channel_index, mean_val in enumerate(channel_mean_values):
            channel_mean[channel_index, ...] = mean_val
        self.transformer.set_mean('data', channel_mean)
        self.transformer.set_channel_swap('data', (2, 1, 0))  # BGR
        self.transformer.set_transpose('data', (2, 0, 1)) 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def load_network(proto_txt, caffe_model, device):
    if 'gpu' in device:
        caffe.set_mode_gpu()
        device_id = int(device.split('gpu')[-1])
        caffe.set_device(device_id)
    else:
        caffe.set_mode_cpu()
    # load network
    net = caffe.Net(proto_txt, caffe_model, caffe.TEST)

    # tansformer
    mu = np.load(osp.join(CAFFE_ROOT, 'models', 'ResNet', 'ResNet_mean.npy'))
    mu = mu.mean(1).mean(1)
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
    transformer.set_transpose('data', (2, 0, 1))  # move image channels to outermost dimension
    transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel
    transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]
    transformer.set_channel_swap('data', (2, 1, 0))  # swap channels from RGB to BGR
    # reshape input
    net.blobs['data'].reshape(BS, 3, 224, 224)

    return net, transformer 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def __init__(self, config_entry: dict, *args, **kwargs):
        super().__init__(config_entry, *args, **kwargs)

        self._delayed_model_loading = kwargs.get('delayed_model_loading', False)
        caffe_launcher_config = LauncherConfigValidator(
            'Caffe_Launcher', fields=self.parameters(), delayed_model_loading=self._delayed_model_loading
        )
        caffe_launcher_config.validate(self.config)
        self._do_reshape = False

        if not self._delayed_model_loading:
            self.model, self.weights = self.automatic_model_search()
            self.network = caffe.Net(str(self.model), str(self.weights), caffe.TEST)
        self.allow_reshape_input = self.get_value_from_config('allow_reshape_input')

        match = re.match(DEVICE_REGEX, self.get_value_from_config('device').lower())
        if match.group('device') == 'gpu':
            caffe.set_mode_gpu()
            identifier = match.group('identifier') or 0
            caffe.set_device(int(identifier))
        elif match.group('device') == 'cpu':
            caffe.set_mode_cpu()
        self._batch = self.get_value_from_config('batch') 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def load_caffe(img_p, layers=50):
    caffe.set_mode_cpu()

    prototxt = "data/ResNet-%d-deploy.prototxt" % layers
    caffemodel = "data/ResNet-%d-model.caffemodel" % layers
    net = caffe.Net(prototxt, caffemodel, caffe.TEST)

    net.blobs['data'].data[0] = img_p.transpose((2, 0, 1))
    assert net.blobs['data'].data[0].shape == (3, 224, 224)
    net.forward()

    caffe_prob = net.blobs['prob'].data[0]
    print_prob(caffe_prob)

    return net


# returns the top1 string 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def read_caffemodel(prototxt_fname, caffemodel_fname):
    """Return a caffe_pb2.NetParameter object that defined in a binary
    caffemodel file
    """
    if use_caffe:
        caffe.set_mode_cpu()
        net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST)
        layer_names = net._layer_names
        layers = net.layers
        return (layers, layer_names)
    else:
        proto = caffe_pb2.NetParameter()
        with open(caffemodel_fname, 'rb') as f:
            proto.ParseFromString(f.read())
        return (get_layers(proto), None) 

# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import set_mode_cpu [as 别名]
def load_weigths_from_caffe(self, protofile, caffemodel):
        caffe.set_mode_cpu()
        net = caffe.Net(protofile, caffemodel, caffe.TEST)
        for name, layer in self.models.items():
            if isinstance(layer, nn.Conv2d):
                caffe_weight = net.params[name][0].data
                layer.weight.data = torch.from_numpy(caffe_weight)
                if len(net.params[name]) > 1:
                    caffe_bias = net.params[name][1].data
                    layer.bias.data = torch.from_numpy(caffe_bias)
                continue
            if isinstance(layer, nn.BatchNorm2d):
                caffe_means = net.params[name][0].data
                caffe_var = net.params[name][1].data
                layer.running_mean = torch.from_numpy(caffe_means)
                layer.running_var = torch.from_numpy(caffe_var)
                # find the scale layer
                top_name_of_bn = self.layer_map_to_top[name][0]
                scale_name = ''
                for caffe_layer in self.net_info['layers']:
                    if caffe_layer['type'] == 'Scale' and caffe_layer['bottom'][0] == top_name_of_bn:
                        scale_name = caffe_layer['name']
                        break
                if scale_name != '':
                    caffe_weight = net.params[scale_name][0].data
                    layer.weight.data = torch.from_numpy(caffe_weight)
                    if len(net.params[name]) > 1:
                        caffe_bias = net.params[scale_name][1].data
                        layer.bias.data = torch.from_numpy(caffe_bias)