Python caffe.set_mode_gpu方法代碼示例

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
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 ***** 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
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) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def _initialize_caffe(deploy_file, input_weight_file, training_mean_pickle, inference_width,
            inference_height):
    """
    Initializes Caffe to prepare to run some data through the model for inference.
    """
    caffe.set_mode_gpu()
    net = caffe.Net(deploy_file, input_weight_file, caffe.TEST)

    # input preprocessing: 'data' is the name of the input blob == net.inputs[0]
    transformer = caffe.io.Transformer({"data": net.blobs["data"].data.shape})
    # PIL.Image loads the data with the channel last.
    transformer.set_transpose("data", (2, 0, 1))
    # Mean pixel.
    transformer.set_mean("data", np.load(training_mean_pickle).mean(1).mean(1))
    # The reference model operates on images in [0, 255] range instead of [0, 1].
    transformer.set_raw_scale("data", 255)
    # The reference model has channels in BGR order instead of RGB.
    transformer.set_channel_swap("data", (2, 1, 0))

    net.blobs["data"].reshape(1, 3, inference_height, inference_width)

    return (net, transformer) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def __init__(self, solver_prototxt, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        caffe.set_mode_gpu()
        caffe.set_device(0)
        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def train(solver_proto_path, snapshot_solver_path=None, init_weights=None, GPU_ID=0):
	"""
	Train the defined net. While we did not use this function for our final net, we used the caffe executable for multi-gpu use, this was used for prototyping
	"""

	import time
	t0 = time.time()
	caffe.set_mode_gpu()
	caffe.set_device(GPU_ID)
	solver = caffe.get_solver(solver_proto_path)
	if snapshot_solver_path is not None:
		solver.solve(snapshot_solver_path) # train from previous solverstate
	else:
		if init_weights is not None:
			solver.net.copy_from(init_weights) # for copying weights from a model without solverstate		
		solver.solve() # train form scratch

	t1 = time.time()
	print 'Total training time: ', t1-t0, ' sec'
	model_dir = "calc_" +  time.strftime("%d-%m-%Y_%I%M%S")
	moveModel(model_dir=model_dir) # move all the model files to a directory  
	print "Moved model to model/"+model_dir 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def __init__(self, net_proto, net_weights, device_id, input_size=None):
        caffe.set_mode_gpu()
        caffe.set_device(device_id)
        self._net = caffe.Net(net_proto, net_weights, caffe.TEST)

        input_shape = self._net.blobs['data'].data.shape

        if input_size is not None:
            input_shape = input_shape[:2] + input_size

        transformer = caffe.io.Transformer({'data': input_shape})

        if self._net.blobs['data'].data.shape[1] == 3:
            transformer.set_transpose('data', (2, 0, 1))  # move image channels to outermost dimension
            transformer.set_mean('data', np.array([104, 117, 123]))  # subtract the dataset-mean value in each channel
        else:
            pass # non RGB data need not use transformer

        self._transformer = transformer

        self._sample_shape = self._net.blobs['data'].data.shape 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def load_nets(args, cur_gpu):
    # initialize solver and feature net,
    # RNN should be initialized before CNN, because CNN cudnn conv layers
    # may assume using all available memory
    caffe.set_mode_gpu()
    caffe.set_device(cur_gpu)
    solver = caffe.SGDSolver(args.solver)
    if args.snapshot:
        print "Restoring history from {}".format(args.snapshot)
        solver.restore(args.snapshot)
    rnn = solver.net
    if args.weights:
        rnn.copy_from(args.weights)
    feature_net = caffe.Net(args.feature_net, args.feature_param, caffe.TEST)

    # apply bbox regression normalization on the net weights
    with open(args.bbox_mean, 'rb') as f:
        bbox_means = cPickle.load(f)
    with open(args.bbox_std, 'rb') as f:
        bbox_stds = cPickle.load(f)
    feature_net.params['bbox_pred_vid'][0].data[...] = \
        feature_net.params['bbox_pred_vid'][0].data * bbox_stds[:, np.newaxis]
    feature_net.params['bbox_pred_vid'][1].data[...] = \
        feature_net.params['bbox_pred_vid'][1].data * bbox_stds + bbox_means
    return solver, feature_net, rnn, bbox_means, bbox_stds 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def load_nets(args, cur_gpu):
    # initialize solver and feature net,
    # RNN should be initialized before CNN, because CNN cudnn conv layers
    # may assume using all available memory
    caffe.set_mode_gpu()
    caffe.set_device(cur_gpu)
    solver = caffe.SGDSolver(args.solver)
    if args.snapshot:
        print "Restoring history from {}".format(args.snapshot)
        solver.restore(args.snapshot)
    net = solver.net
    if args.weights:
        print "Copying weights from {}".format(args.weights)
        net.copy_from(args.weights)

    return solver, net 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def load_models(args):

    # load rnn model
    caffe.set_mode_gpu()
    if args.gpus is None:
        caffe.set_device(args.job_id - 1)
    else:
        assert args.job_id <= len(args.gpus)
        caffe.set_device(args.gpus[args.job_id-1])
    if args.lstm_param is not '':
        rnn_net = caffe.Net(args.lstm_def, args.lstm_param, caffe.TEST)
        print 'Loaded RNN network from {:s}.'.format(args.lstm_def)
    else:
        rnn_net = caffe.Net(args.lstm_def, caffe.TEST)
        print 'WARNING: dummy RNN network created.'

    # load feature model
    feature_net = caffe.Net(args.def_file, args.param, caffe.TEST)
    print 'Loaded feature network from {:s}.'.format(args.def_file)

    return feature_net, rnn_net 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def solve(proto, snapshot, gpus, timing, uid, rank):
    caffe.set_mode_gpu()
    caffe.set_device(gpus[rank])
    caffe.set_solver_count(len(gpus))
    caffe.set_solver_rank(rank)
    caffe.set_multiprocess(True)

    solver = caffe.SGDSolver(proto)
    if snapshot and len(snapshot) != 0:
        solver.restore(snapshot)

    nccl = caffe.NCCL(solver, uid)
    nccl.bcast()

    if timing and rank == 0:
        time(solver, nccl)
    else:
        solver.add_callback(nccl)

    if solver.param.layer_wise_reduce:
        solver.net.after_backward(nccl)
    solver.step(solver.param.max_iter) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def __init__(self, net_proto, net_weights, device_id, input_size=None):
        caffe.set_mode_gpu()
        caffe.set_device(device_id)
        self._net = caffe.Net(net_proto, net_weights, caffe.TEST)

        input_shape = self._net.blobs['data'].data.shape

        if input_size is not None:
            input_shape = input_shape[:2] + input_size

        transformer = caffe.io.Transformer({'data': input_shape})

        #if self._net.blobs['data'].data.shape[1] == 3:
            #printf
         #   transformer.set_transpose('data', (2, 0, 1))  # move image channels to outermost dimension
         #   transformer.set_mean('data', np.array([104, 117, 123]))  # subtract the dataset-mean value in each channel
        #else:
        #    pass # non RGB data need not use transformer

        self._transformer = transformer

        self._sample_shape = self._net.blobs['data'].data.shape 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
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) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def get_net(caffemodel, deploy_file, use_gpu=True):
    """
    Returns an instance of caffe.Net

    Arguments:
    caffemodel -- path to a .caffemodel file
    deploy_file -- path to a .prototxt file

    Keyword arguments:
    use_gpu -- if True, use the GPU for inference
    """
    if use_gpu:
        caffe.set_mode_gpu()

    # load a new model
    return caffe.Net(deploy_file, caffemodel, caffe.TEST) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def loadDNN(useGpu = False):
    global net,W_in,H_in,H_out,W_out,lm_lab_l_rs
    if useGpu:    
        gpu_id = 0
        caffe.set_mode_gpu()
        caffe.set_device(gpu_id)
    net = caffe.Net('colorization_deploy_v0.prototxt', 'colorization_release_v0.caffemodel', caffe.TEST)
    print '\n done loading network! \n'

    (H_in,W_in) = net.blobs['data_l'].data.shape[2:] # get input shape
    (H_out,W_out) = net.blobs['class8_ab'].data.shape[2:] # get output shape
    net.blobs['Trecip'].data[...] = 6/np.log(10) # 1/T, set annealing temperature
    
    l_mean = sio.loadmat('ilsvrc_2012_mean.mat')
    lm = np.array(l_mean['mean_data'])
    lm = lm/np.max(lm)
    lm_lab = color.rgb2lab(lm)
    lm_lab_l = lm_lab[:,:,0]
    lm_lab_l = lm_lab_l - np.mean(np.mean(lm_lab_l)) + 50
    lm_lab_l =  Image.fromarray(lm_lab_l)    
    lm_lab_l_rs = lm_lab_l.resize((W_in,H_in), Image.ANTIALIAS) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import set_mode_gpu [as 別名]
def main_image(args):
    """Adversarial example for ImageNet classification model."""

    if args.gpu >= 0:
        caffe.set_mode_gpu()
        caffe.set_device(args.gpu)

    net = caffe.Net(args.model_def, args.model_weights, caffe.TEST)

    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
    transformer.set_mean('data', args.mean)
    transformer.set_transpose('data', (2,0,1))
    transformer.set_channel_swap('data', (2,1,0))
    transformer.set_raw_scale('data', 255.0)

    image = caffe.io.load_image(args.image)
    image = transformer.preprocess('data', image)

    print "Prediction of original image"
    Predict(net, image, do_top_5=True, label_names=args.label_names)

    adv_func_args = GetAdvFuncArgs(args, net, image)
    adversarial_image_data, added_noise_data = adv_attacks[args.attack_method](**adv_func_args)

    print "Prediction of adversarial image"
    Predict(net, adversarial_image_data, do_top_5=True, label_names=args.label_names)

    adversarial_image = np.squeeze(adversarial_image_data[0,:,:,:]) # CxHxW
    adversarial_image = np.transpose( adversarial_image, [1,2,0] ) # HxWxC
    cv2.imwrite("adversarial_example.png", adversarial_image + args.mean)

    added_noise = np.transpose( np.squeeze(added_noise_data[0,:,:,:]), [1,2,0] )
    cv2.imwrite("perturbation.png", added_noise + args.mean)