-
jamesguo
- 使用TensorRT 6.0.1 OnnxParser parse错误为
In node 0 (importModel): INVALID_GRAPH: Assertion failed: tensors.count(input_name) - 使用TensorRT源码编译后的onnx2trt 命令行转换,没有报错
没法找到具体原因
- 使用TensorRT 6.0.1 OnnxParser parse错误为
-
jamesguo
模型
class TFUnetCleanModel: def __init__(self, image_size, image_channel, n_class, layer_count): self.image_channel = image_channel self.n_class = n_class self.image_size = image_size self.init_weight() self.predicts = self.build_model(layer_count=layer_count) def init_weight(self): with tf.name_scope('inputs'): self.image_feature = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.image_channel], name='image_feature') def convolution(self, input_, num_filters, kernel_size): conv = tf.layers.conv2d(input_, num_filters, kernel_size, padding="same", activation=tf.nn.relu) conv = tf.layers.batch_normalization(conv) return conv def max_pool(self, input_, pool_size, stride_size): conv = tf.layers.max_pooling2d(input_, pool_size, stride_size, padding='same') return conv def upsample_and_concat(self, layer_upper, layer_down, output_channels): deconv = tf.layers.conv2d_transpose(layer_upper, output_channels, kernel_size=(2, 2), strides=(2, 2)) deconv_output = tf.concat([layer_down, deconv], -1) return deconv_output def build_model(self, layer_count, features_root=64): """ Creates a new convolutional unet for the given parametrization. :param layer_count: number of layers in the net :param features_root: number of features in the first layer """ last_down_input = self.image_feature down_layers = OrderedDict() for layer in range(0, layer_count): with tf.name_scope("down_conv_{}".format(str(layer))): num_filters = 2 ** layer * features_root last_down_input = self.convolution(last_down_input, num_filters, (3, 3)) last_down_input = self.convolution(last_down_input, num_filters, (3, 3)) down_layers[layer] = last_down_input last_down_input = self.max_pool(last_down_input, pool_size=(2, 2), stride_size=(2, 2)) print("down_conv_{}.shape:{}".format(str(layer), last_down_input.get_shape())) num_filters = 2 ** layer_count * features_root last_up_input = self.convolution(last_down_input, num_filters, (3, 3)) last_up_input = self.convolution(last_up_input, num_filters, (3, 3)) print("last_up_input.shape:{}".format(last_up_input.get_shape())) for layer in range(layer_count, 0, -1): with tf.name_scope("up_conv_{}".format(str(layer - 1))): num_filters = 2 ** (layer - 1) * features_root last_up_input = self.upsample_and_concat(last_up_input, down_layers[layer - 1], num_filters) print("up_sample_{}.shape:{}".format(str(layer), last_up_input.get_shape())) last_up_input = self.convolution(last_up_input, num_filters, (3, 3)) last_up_input = self.convolution(last_up_input, num_filters, (3, 3)) # last_up_input = tf.nn.dropout(last_up_input, rate=1 - self.keep_prob) print("up_conv_{}.shape:{}".format(str(layer), last_up_input.get_shape())) last_up_input = self.convolution(last_up_input, 16, (3, 3)) return last_up_input
-
jamesguo
TensorRT 5.1.5 CUDA 10.0 TensorFlow 1.13.1
tensorflow 模型转uff出错
uff.model.exceptions.UffException: Transpose permutation has op ConcatV2, expected Const. Only constant permuations are supported in UFF.
但是根据官方文档
ConcatV2应该是支持的 -
-
-