de-xavier@dexavier-desktop:~/work/abhigyan$ /usr/src/tensorrt/bin/trtexec --onnx=G1.onnx --fp16 --useDLACore=0 --saveEngine=G1xxx.trt --allowGPUFallback --verbose &&&& RUNNING TensorRT.trtexec [TensorRT v8001] # /usr/src/tensorrt/bin/trtexec --onnx=G1.onnx --fp16 --useDLACore=0 --saveEngine=G1xxx.trt --allowGPUFallback --verbose [01/30/2023-11:10:15] [I] === Model Options === [01/30/2023-11:10:15] [I] Format: ONNX [01/30/2023-11:10:15] [I] Model: G1.onnx [01/30/2023-11:10:15] [I] Output: [01/30/2023-11:10:15] [I] === Build Options === [01/30/2023-11:10:15] [I] Max batch: explicit [01/30/2023-11:10:15] [I] Workspace: 16 MiB [01/30/2023-11:10:15] [I] minTiming: 1 [01/30/2023-11:10:15] [I] avgTiming: 8 [01/30/2023-11:10:15] [I] Precision: FP32+FP16 [01/30/2023-11:10:15] [I] Calibration: [01/30/2023-11:10:15] [I] Refit: Disabled [01/30/2023-11:10:15] [I] Sparsity: Disabled [01/30/2023-11:10:15] [I] Safe mode: Disabled [01/30/2023-11:10:15] [I] Restricted mode: Disabled [01/30/2023-11:10:15] [I] Save engine: G1xxx.trt [01/30/2023-11:10:15] [I] Load engine: [01/30/2023-11:10:15] [I] NVTX verbosity: 0 [01/30/2023-11:10:15] [I] Tactic sources: Using default tactic sources [01/30/2023-11:10:15] [I] timingCacheMode: local [01/30/2023-11:10:15] [I] timingCacheFile: [01/30/2023-11:10:15] [I] Input(s)s format: fp32:CHW [01/30/2023-11:10:15] [I] Output(s)s format: fp32:CHW [01/30/2023-11:10:15] [I] Input build shapes: model [01/30/2023-11:10:15] [I] Input calibration shapes: model [01/30/2023-11:10:15] [I] === System Options === [01/30/2023-11:10:15] [I] Device: 0 [01/30/2023-11:10:15] [I] DLACore: 0(With GPU fallback) [01/30/2023-11:10:15] [I] Plugins: [01/30/2023-11:10:15] [I] === Inference Options === [01/30/2023-11:10:15] [I] Batch: Explicit [01/30/2023-11:10:15] [I] Input inference shapes: model [01/30/2023-11:10:15] [I] Iterations: 10 [01/30/2023-11:10:15] [I] Duration: 3s (+ 200ms warm up) [01/30/2023-11:10:15] [I] Sleep time: 0ms [01/30/2023-11:10:15] [I] Streams: 1 [01/30/2023-11:10:15] [I] ExposeDMA: Disabled [01/30/2023-11:10:15] [I] Data transfers: Enabled [01/30/2023-11:10:15] [I] Spin-wait: Disabled [01/30/2023-11:10:15] [I] Multithreading: Disabled [01/30/2023-11:10:15] [I] CUDA Graph: Disabled [01/30/2023-11:10:15] [I] Separate profiling: Disabled [01/30/2023-11:10:15] [I] Time Deserialize: Disabled [01/30/2023-11:10:15] [I] Time Refit: Disabled [01/30/2023-11:10:15] [I] Skip inference: Disabled [01/30/2023-11:10:15] [I] Inputs: [01/30/2023-11:10:15] [I] === Reporting Options === [01/30/2023-11:10:15] [I] Verbose: Enabled [01/30/2023-11:10:15] [I] Averages: 10 inferences [01/30/2023-11:10:15] [I] Percentile: 99 [01/30/2023-11:10:15] [I] Dump refittable layers:Disabled [01/30/2023-11:10:15] [I] Dump output: Disabled [01/30/2023-11:10:15] [I] Profile: Disabled [01/30/2023-11:10:15] [I] Export timing to JSON file: [01/30/2023-11:10:15] [I] Export output to JSON file: [01/30/2023-11:10:15] [I] Export profile to JSON file: [01/30/2023-11:10:15] [I] [01/30/2023-11:10:15] [I] === Device Information === [01/30/2023-11:10:15] [I] Selected Device: Xavier [01/30/2023-11:10:15] [I] Compute Capability: 7.2 [01/30/2023-11:10:15] [I] SMs: 8 [01/30/2023-11:10:15] [I] Compute Clock Rate: 1.377 GHz [01/30/2023-11:10:15] [I] Device Global Memory: 31928 MiB [01/30/2023-11:10:15] [I] Shared Memory per SM: 96 KiB [01/30/2023-11:10:15] [I] Memory Bus Width: 256 bits (ECC disabled) [01/30/2023-11:10:15] [I] Memory Clock Rate: 1.377 GHz [01/30/2023-11:10:15] [I] [01/30/2023-11:10:15] [I] TensorRT version: 8001 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::GridAnchor_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::GridAnchorRect_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::NMS_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::Reorg_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::Region_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::Clip_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::LReLU_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::PriorBox_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::Normalize_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::ScatterND version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::RPROI_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::BatchedNMS_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::BatchedNMSDynamic_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::FlattenConcat_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::CropAndResize version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::DetectionLayer_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::EfficientNMS_ONNX_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::EfficientNMS_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::Proposal version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::ProposalLayer_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::PyramidROIAlign_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::ResizeNearest_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::Split version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::SpecialSlice_TRT version 1 [01/30/2023-11:10:15] [V] [TRT] Registered plugin creator - ::InstanceNormalization_TRT version 1 [01/30/2023-11:10:16] [I] [TRT] [MemUsageChange] Init CUDA: CPU +353, GPU +0, now: CPU 371, GPU 11452 (MiB) [01/30/2023-11:10:16] [I] Start parsing network model [01/30/2023-11:10:16] [I] [TRT] ---------------------------------------------------------------- [01/30/2023-11:10:16] [I] [TRT] Input filename: G1.onnx [01/30/2023-11:10:16] [I] [TRT] ONNX IR version: 0.0.8 [01/30/2023-11:10:16] [I] [TRT] Opset version: 13 [01/30/2023-11:10:16] [I] [TRT] Producer name: pytorch [01/30/2023-11:10:16] [I] [TRT] Producer version: 1.10 [01/30/2023-11:10:16] [I] [TRT] Domain: [01/30/2023-11:10:16] [I] [TRT] Model version: 0 [01/30/2023-11:10:16] [I] [TRT] Doc string: [01/30/2023-11:10:16] [I] [TRT] ---------------------------------------------------------------- [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::GridAnchor_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::GridAnchorRect_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::NMS_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::Reorg_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::Region_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::Clip_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::LReLU_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::PriorBox_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::Normalize_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::ScatterND version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::RPROI_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::BatchedNMS_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::BatchedNMSDynamic_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::FlattenConcat_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::CropAndResize version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::DetectionLayer_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::EfficientNMS_ONNX_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::EfficientNMS_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::Proposal version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::ProposalLayer_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::PyramidROIAlign_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::ResizeNearest_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::Split version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::SpecialSlice_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Plugin creator already registered - ::InstanceNormalization_TRT version 1 [01/30/2023-11:10:16] [V] [TRT] Adding network input: images with dtype: float32, dimensions: (1, 3, 768, 768) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: images for ONNX tensor: images [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.0.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.0.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.2.cv2.conv.weight 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[01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.17.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.17.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.17.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.17.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.17.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.18.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.18.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.20.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.21.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.21.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.23.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.24.m.0.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.24.m.0.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.24.m.1.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.24.m.1.bias [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.24.m.2.weight [01/30/2023-11:10:16] [V] [TRT] Importing initializer: model.24.m.2.bias [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_0 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: images [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.0.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.0.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_0 [Conv] inputs: [images -> (1, 3, 768, 768)[FLOAT]], [model.0.conv.weight -> (32, 3, 6, 6)[FLOAT]], [model.0.conv.bias -> (32)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 3, 768, 768) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_0 for ONNX node: Conv_0 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (6, 6), strides: (2, 2), prepadding: (2, 2), postpadding: (2, 2), dilations: (1, 1), numOutputs: 32 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 32, 384, 384) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 122 for ONNX tensor: 122 [01/30/2023-11:10:16] [V] [TRT] Conv_0 [Conv] outputs: [122 -> (1, 32, 384, 384)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_1 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 122 [01/30/2023-11:10:16] [V] [TRT] Relu_1 [Relu] inputs: [122 -> (1, 32, 384, 384)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_1 for ONNX node: Relu_1 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 123 for ONNX tensor: 123 [01/30/2023-11:10:16] [V] [TRT] Relu_1 [Relu] outputs: [123 -> (1, 32, 384, 384)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_2 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 123 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_2 [Conv] inputs: [123 -> (1, 32, 384, 384)[FLOAT]], [model.1.conv.weight -> (64, 32, 3, 3)[FLOAT]], [model.1.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 32, 384, 384) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_2 for ONNX node: Conv_2 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 124 for ONNX tensor: 124 [01/30/2023-11:10:16] [V] [TRT] Conv_2 [Conv] outputs: [124 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_3 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 124 [01/30/2023-11:10:16] [V] [TRT] Relu_3 [Relu] inputs: [124 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_3 for ONNX node: Relu_3 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 125 for ONNX tensor: 125 [01/30/2023-11:10:16] [V] [TRT] Relu_3 [Relu] outputs: [125 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_4 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 125 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_4 [Conv] inputs: [125 -> (1, 64, 192, 192)[FLOAT]], [model.2.cv1.conv.weight -> (32, 64, 1, 1)[FLOAT]], [model.2.cv1.conv.bias -> (32)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_4 for ONNX node: Conv_4 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 32 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 32, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 126 for ONNX tensor: 126 [01/30/2023-11:10:16] [V] [TRT] Conv_4 [Conv] outputs: [126 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_5 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 126 [01/30/2023-11:10:16] [V] [TRT] Relu_5 [Relu] inputs: [126 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_5 for ONNX node: Relu_5 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 127 for ONNX tensor: 127 [01/30/2023-11:10:16] [V] [TRT] Relu_5 [Relu] outputs: [127 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_6 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 127 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_6 [Conv] inputs: [127 -> (1, 32, 192, 192)[FLOAT]], [model.2.m.0.cv1.conv.weight -> (32, 32, 1, 1)[FLOAT]], [model.2.m.0.cv1.conv.bias -> (32)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 32, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_6 for ONNX node: Conv_6 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 32 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 32, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 128 for ONNX tensor: 128 [01/30/2023-11:10:16] [V] [TRT] Conv_6 [Conv] outputs: [128 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_7 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 128 [01/30/2023-11:10:16] [V] [TRT] Relu_7 [Relu] inputs: [128 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_7 for ONNX node: Relu_7 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 129 for ONNX tensor: 129 [01/30/2023-11:10:16] [V] [TRT] Relu_7 [Relu] outputs: [129 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_8 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 129 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_8 [Conv] inputs: [129 -> (1, 32, 192, 192)[FLOAT]], [model.2.m.0.cv2.conv.weight -> (32, 32, 3, 3)[FLOAT]], [model.2.m.0.cv2.conv.bias -> (32)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 32, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_8 for ONNX node: Conv_8 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 32 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 32, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 130 for ONNX tensor: 130 [01/30/2023-11:10:16] [V] [TRT] Conv_8 [Conv] outputs: [130 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_9 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 130 [01/30/2023-11:10:16] [V] [TRT] Relu_9 [Relu] inputs: [130 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_9 for ONNX node: Relu_9 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 131 for ONNX tensor: 131 [01/30/2023-11:10:16] [V] [TRT] Relu_9 [Relu] outputs: [131 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_10 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 127 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 131 [01/30/2023-11:10:16] [V] [TRT] Add_10 [Add] inputs: [127 -> (1, 32, 192, 192)[FLOAT]], [131 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_10 for ONNX node: Add_10 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 132 for ONNX tensor: 132 [01/30/2023-11:10:16] [V] [TRT] Add_10 [Add] outputs: [132 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_11 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 125 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_11 [Conv] inputs: [125 -> (1, 64, 192, 192)[FLOAT]], [model.2.cv2.conv.weight -> (32, 64, 1, 1)[FLOAT]], [model.2.cv2.conv.bias -> (32)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_11 for ONNX node: Conv_11 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 32 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 32, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 133 for ONNX tensor: 133 [01/30/2023-11:10:16] [V] [TRT] Conv_11 [Conv] outputs: [133 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_12 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 133 [01/30/2023-11:10:16] [V] [TRT] Relu_12 [Relu] inputs: [133 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_12 for ONNX node: Relu_12 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 134 for ONNX tensor: 134 [01/30/2023-11:10:16] [V] [TRT] Relu_12 [Relu] outputs: [134 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_13 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 132 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 134 [01/30/2023-11:10:16] [V] [TRT] Concat_13 [Concat] inputs: [132 -> (1, 32, 192, 192)[FLOAT]], [134 -> (1, 32, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_13 for ONNX node: Concat_13 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 135 for ONNX tensor: 135 [01/30/2023-11:10:16] [V] [TRT] Concat_13 [Concat] outputs: [135 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_14 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 135 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.2.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_14 [Conv] inputs: [135 -> (1, 64, 192, 192)[FLOAT]], [model.2.cv3.conv.weight -> (64, 64, 1, 1)[FLOAT]], [model.2.cv3.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_14 for ONNX node: Conv_14 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 136 for ONNX tensor: 136 [01/30/2023-11:10:16] [V] [TRT] Conv_14 [Conv] outputs: [136 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_15 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 136 [01/30/2023-11:10:16] [V] [TRT] Relu_15 [Relu] inputs: [136 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_15 for ONNX node: Relu_15 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 137 for ONNX tensor: 137 [01/30/2023-11:10:16] [V] [TRT] Relu_15 [Relu] outputs: [137 -> (1, 64, 192, 192)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_16 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 137 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_16 [Conv] inputs: [137 -> (1, 64, 192, 192)[FLOAT]], [model.3.conv.weight -> (128, 64, 3, 3)[FLOAT]], [model.3.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 192, 192) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_16 for ONNX node: Conv_16 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 138 for ONNX tensor: 138 [01/30/2023-11:10:16] [V] [TRT] Conv_16 [Conv] outputs: [138 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_17 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 138 [01/30/2023-11:10:16] [V] [TRT] Relu_17 [Relu] inputs: [138 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_17 for ONNX node: Relu_17 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 139 for ONNX tensor: 139 [01/30/2023-11:10:16] [V] [TRT] Relu_17 [Relu] outputs: [139 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_18 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 139 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_18 [Conv] inputs: [139 -> (1, 128, 96, 96)[FLOAT]], [model.4.cv1.conv.weight -> (64, 128, 1, 1)[FLOAT]], [model.4.cv1.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_18 for ONNX node: Conv_18 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 140 for ONNX tensor: 140 [01/30/2023-11:10:16] [V] [TRT] Conv_18 [Conv] outputs: [140 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_19 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 140 [01/30/2023-11:10:16] [V] [TRT] Relu_19 [Relu] inputs: [140 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_19 for ONNX node: Relu_19 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 141 for ONNX tensor: 141 [01/30/2023-11:10:16] [V] [TRT] Relu_19 [Relu] outputs: [141 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_20 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 141 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_20 [Conv] inputs: [141 -> (1, 64, 96, 96)[FLOAT]], [model.4.m.0.cv1.conv.weight -> (64, 64, 1, 1)[FLOAT]], [model.4.m.0.cv1.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_20 for ONNX node: Conv_20 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 142 for ONNX tensor: 142 [01/30/2023-11:10:16] [V] [TRT] Conv_20 [Conv] outputs: [142 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_21 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 142 [01/30/2023-11:10:16] [V] [TRT] Relu_21 [Relu] inputs: [142 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_21 for ONNX node: Relu_21 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 143 for ONNX tensor: 143 [01/30/2023-11:10:16] [V] [TRT] Relu_21 [Relu] outputs: [143 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_22 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 143 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_22 [Conv] inputs: [143 -> (1, 64, 96, 96)[FLOAT]], [model.4.m.0.cv2.conv.weight -> (64, 64, 3, 3)[FLOAT]], [model.4.m.0.cv2.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_22 for ONNX node: Conv_22 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 144 for ONNX tensor: 144 [01/30/2023-11:10:16] [V] [TRT] Conv_22 [Conv] outputs: [144 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_23 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 144 [01/30/2023-11:10:16] [V] [TRT] Relu_23 [Relu] inputs: [144 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_23 for ONNX node: Relu_23 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 145 for ONNX tensor: 145 [01/30/2023-11:10:16] [V] [TRT] Relu_23 [Relu] outputs: [145 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_24 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 141 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 145 [01/30/2023-11:10:16] [V] [TRT] Add_24 [Add] inputs: [141 -> (1, 64, 96, 96)[FLOAT]], [145 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_24 for ONNX node: Add_24 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 146 for ONNX tensor: 146 [01/30/2023-11:10:16] [V] [TRT] Add_24 [Add] outputs: [146 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_25 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 146 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.1.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.1.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_25 [Conv] inputs: [146 -> (1, 64, 96, 96)[FLOAT]], [model.4.m.1.cv1.conv.weight -> (64, 64, 1, 1)[FLOAT]], [model.4.m.1.cv1.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_25 for ONNX node: Conv_25 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 147 for ONNX tensor: 147 [01/30/2023-11:10:16] [V] [TRT] Conv_25 [Conv] outputs: [147 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_26 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 147 [01/30/2023-11:10:16] [V] [TRT] Relu_26 [Relu] inputs: [147 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_26 for ONNX node: Relu_26 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 148 for ONNX tensor: 148 [01/30/2023-11:10:16] [V] [TRT] Relu_26 [Relu] outputs: [148 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_27 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 148 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.1.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.m.1.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_27 [Conv] inputs: [148 -> (1, 64, 96, 96)[FLOAT]], [model.4.m.1.cv2.conv.weight -> (64, 64, 3, 3)[FLOAT]], [model.4.m.1.cv2.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_27 for ONNX node: Conv_27 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 149 for ONNX tensor: 149 [01/30/2023-11:10:16] [V] [TRT] Conv_27 [Conv] outputs: [149 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_28 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 149 [01/30/2023-11:10:16] [V] [TRT] Relu_28 [Relu] inputs: [149 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_28 for ONNX node: Relu_28 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 150 for ONNX tensor: 150 [01/30/2023-11:10:16] [V] [TRT] Relu_28 [Relu] outputs: [150 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_29 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 146 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 150 [01/30/2023-11:10:16] [V] [TRT] Add_29 [Add] inputs: [146 -> (1, 64, 96, 96)[FLOAT]], [150 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_29 for ONNX node: Add_29 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 151 for ONNX tensor: 151 [01/30/2023-11:10:16] [V] [TRT] Add_29 [Add] outputs: [151 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_30 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 139 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_30 [Conv] inputs: [139 -> (1, 128, 96, 96)[FLOAT]], [model.4.cv2.conv.weight -> (64, 128, 1, 1)[FLOAT]], [model.4.cv2.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_30 for ONNX node: Conv_30 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 152 for ONNX tensor: 152 [01/30/2023-11:10:16] [V] [TRT] Conv_30 [Conv] outputs: [152 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_31 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 152 [01/30/2023-11:10:16] [V] [TRT] Relu_31 [Relu] inputs: [152 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_31 for ONNX node: Relu_31 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 153 for ONNX tensor: 153 [01/30/2023-11:10:16] [V] [TRT] Relu_31 [Relu] outputs: [153 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_32 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 151 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 153 [01/30/2023-11:10:16] [V] [TRT] Concat_32 [Concat] inputs: [151 -> (1, 64, 96, 96)[FLOAT]], [153 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_32 for ONNX node: Concat_32 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 154 for ONNX tensor: 154 [01/30/2023-11:10:16] [V] [TRT] Concat_32 [Concat] outputs: [154 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_33 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 154 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.4.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_33 [Conv] inputs: [154 -> (1, 128, 96, 96)[FLOAT]], [model.4.cv3.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.4.cv3.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_33 for ONNX node: Conv_33 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 155 for ONNX tensor: 155 [01/30/2023-11:10:16] [V] [TRT] Conv_33 [Conv] outputs: [155 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_34 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 155 [01/30/2023-11:10:16] [V] [TRT] Relu_34 [Relu] inputs: [155 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_34 for ONNX node: Relu_34 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 156 for ONNX tensor: 156 [01/30/2023-11:10:16] [V] [TRT] Relu_34 [Relu] outputs: [156 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_35 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 156 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.5.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.5.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_35 [Conv] inputs: [156 -> (1, 128, 96, 96)[FLOAT]], [model.5.conv.weight -> (256, 128, 3, 3)[FLOAT]], [model.5.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_35 for ONNX node: Conv_35 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 157 for ONNX tensor: 157 [01/30/2023-11:10:16] [V] [TRT] Conv_35 [Conv] outputs: [157 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_36 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 157 [01/30/2023-11:10:16] [V] [TRT] Relu_36 [Relu] inputs: [157 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_36 for ONNX node: Relu_36 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 158 for ONNX tensor: 158 [01/30/2023-11:10:16] [V] [TRT] Relu_36 [Relu] outputs: [158 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_37 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 158 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_37 [Conv] inputs: [158 -> (1, 256, 48, 48)[FLOAT]], [model.6.cv1.conv.weight -> (128, 256, 1, 1)[FLOAT]], [model.6.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_37 for ONNX node: Conv_37 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 159 for ONNX tensor: 159 [01/30/2023-11:10:16] [V] [TRT] Conv_37 [Conv] outputs: [159 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_38 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 159 [01/30/2023-11:10:16] [V] [TRT] Relu_38 [Relu] inputs: [159 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_38 for ONNX node: Relu_38 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 160 for ONNX tensor: 160 [01/30/2023-11:10:16] [V] [TRT] Relu_38 [Relu] outputs: [160 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_39 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 160 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_39 [Conv] inputs: [160 -> (1, 128, 48, 48)[FLOAT]], [model.6.m.0.cv1.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.6.m.0.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_39 for ONNX node: Conv_39 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 161 for ONNX tensor: 161 [01/30/2023-11:10:16] [V] [TRT] Conv_39 [Conv] outputs: [161 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_40 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 161 [01/30/2023-11:10:16] [V] [TRT] Relu_40 [Relu] inputs: [161 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_40 for ONNX node: Relu_40 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 162 for ONNX tensor: 162 [01/30/2023-11:10:16] [V] [TRT] Relu_40 [Relu] outputs: [162 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_41 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 162 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_41 [Conv] inputs: [162 -> (1, 128, 48, 48)[FLOAT]], [model.6.m.0.cv2.conv.weight -> (128, 128, 3, 3)[FLOAT]], [model.6.m.0.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_41 for ONNX node: Conv_41 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 163 for ONNX tensor: 163 [01/30/2023-11:10:16] [V] [TRT] Conv_41 [Conv] outputs: [163 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_42 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 163 [01/30/2023-11:10:16] [V] [TRT] Relu_42 [Relu] inputs: [163 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_42 for ONNX node: Relu_42 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 164 for ONNX tensor: 164 [01/30/2023-11:10:16] [V] [TRT] Relu_42 [Relu] outputs: [164 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_43 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 160 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 164 [01/30/2023-11:10:16] [V] [TRT] Add_43 [Add] inputs: [160 -> (1, 128, 48, 48)[FLOAT]], [164 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_43 for ONNX node: Add_43 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 165 for ONNX tensor: 165 [01/30/2023-11:10:16] [V] [TRT] Add_43 [Add] outputs: [165 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_44 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 165 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.1.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.1.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_44 [Conv] inputs: [165 -> (1, 128, 48, 48)[FLOAT]], [model.6.m.1.cv1.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.6.m.1.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_44 for ONNX node: Conv_44 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 166 for ONNX tensor: 166 [01/30/2023-11:10:16] [V] [TRT] Conv_44 [Conv] outputs: [166 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_45 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 166 [01/30/2023-11:10:16] [V] [TRT] Relu_45 [Relu] inputs: [166 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_45 for ONNX node: Relu_45 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 167 for ONNX tensor: 167 [01/30/2023-11:10:16] [V] [TRT] Relu_45 [Relu] outputs: [167 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_46 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 167 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.1.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.1.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_46 [Conv] inputs: [167 -> (1, 128, 48, 48)[FLOAT]], [model.6.m.1.cv2.conv.weight -> (128, 128, 3, 3)[FLOAT]], [model.6.m.1.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_46 for ONNX node: Conv_46 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 168 for ONNX tensor: 168 [01/30/2023-11:10:16] [V] [TRT] Conv_46 [Conv] outputs: [168 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_47 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 168 [01/30/2023-11:10:16] [V] [TRT] Relu_47 [Relu] inputs: [168 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_47 for ONNX node: Relu_47 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 169 for ONNX tensor: 169 [01/30/2023-11:10:16] [V] [TRT] Relu_47 [Relu] outputs: [169 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_48 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 165 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 169 [01/30/2023-11:10:16] [V] [TRT] Add_48 [Add] inputs: [165 -> (1, 128, 48, 48)[FLOAT]], [169 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_48 for ONNX node: Add_48 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 170 for ONNX tensor: 170 [01/30/2023-11:10:16] [V] [TRT] Add_48 [Add] outputs: [170 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_49 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 170 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.2.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.2.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_49 [Conv] inputs: [170 -> (1, 128, 48, 48)[FLOAT]], [model.6.m.2.cv1.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.6.m.2.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_49 for ONNX node: Conv_49 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 171 for ONNX tensor: 171 [01/30/2023-11:10:16] [V] [TRT] Conv_49 [Conv] outputs: [171 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_50 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 171 [01/30/2023-11:10:16] [V] [TRT] Relu_50 [Relu] inputs: [171 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_50 for ONNX node: Relu_50 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 172 for ONNX tensor: 172 [01/30/2023-11:10:16] [V] [TRT] Relu_50 [Relu] outputs: [172 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_51 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 172 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.2.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.m.2.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_51 [Conv] inputs: [172 -> (1, 128, 48, 48)[FLOAT]], [model.6.m.2.cv2.conv.weight -> (128, 128, 3, 3)[FLOAT]], [model.6.m.2.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_51 for ONNX node: Conv_51 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 173 for ONNX tensor: 173 [01/30/2023-11:10:16] [V] [TRT] Conv_51 [Conv] outputs: [173 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_52 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 173 [01/30/2023-11:10:16] [V] [TRT] Relu_52 [Relu] inputs: [173 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_52 for ONNX node: Relu_52 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 174 for ONNX tensor: 174 [01/30/2023-11:10:16] [V] [TRT] Relu_52 [Relu] outputs: [174 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_53 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 170 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 174 [01/30/2023-11:10:16] [V] [TRT] Add_53 [Add] inputs: [170 -> (1, 128, 48, 48)[FLOAT]], [174 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_53 for ONNX node: Add_53 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 175 for ONNX tensor: 175 [01/30/2023-11:10:16] [V] [TRT] Add_53 [Add] outputs: [175 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_54 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 158 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_54 [Conv] inputs: [158 -> (1, 256, 48, 48)[FLOAT]], [model.6.cv2.conv.weight -> (128, 256, 1, 1)[FLOAT]], [model.6.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_54 for ONNX node: Conv_54 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 176 for ONNX tensor: 176 [01/30/2023-11:10:16] [V] [TRT] Conv_54 [Conv] outputs: [176 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_55 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 176 [01/30/2023-11:10:16] [V] [TRT] Relu_55 [Relu] inputs: [176 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_55 for ONNX node: Relu_55 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 177 for ONNX tensor: 177 [01/30/2023-11:10:16] [V] [TRT] Relu_55 [Relu] outputs: [177 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_56 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 175 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 177 [01/30/2023-11:10:16] [V] [TRT] Concat_56 [Concat] inputs: [175 -> (1, 128, 48, 48)[FLOAT]], [177 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_56 for ONNX node: Concat_56 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 178 for ONNX tensor: 178 [01/30/2023-11:10:16] [V] [TRT] Concat_56 [Concat] outputs: [178 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_57 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 178 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.6.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_57 [Conv] inputs: [178 -> (1, 256, 48, 48)[FLOAT]], [model.6.cv3.conv.weight -> (256, 256, 1, 1)[FLOAT]], [model.6.cv3.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_57 for ONNX node: Conv_57 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 179 for ONNX tensor: 179 [01/30/2023-11:10:16] [V] [TRT] Conv_57 [Conv] outputs: [179 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_58 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 179 [01/30/2023-11:10:16] [V] [TRT] Relu_58 [Relu] inputs: [179 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_58 for ONNX node: Relu_58 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 180 for ONNX tensor: 180 [01/30/2023-11:10:16] [V] [TRT] Relu_58 [Relu] outputs: [180 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_59 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 180 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.7.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.7.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_59 [Conv] inputs: [180 -> (1, 256, 48, 48)[FLOAT]], [model.7.conv.weight -> (512, 256, 3, 3)[FLOAT]], [model.7.conv.bias -> (512)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_59 for ONNX node: Conv_59 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 512 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 181 for ONNX tensor: 181 [01/30/2023-11:10:16] [V] [TRT] Conv_59 [Conv] outputs: [181 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_60 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 181 [01/30/2023-11:10:16] [V] [TRT] Relu_60 [Relu] inputs: [181 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_60 for ONNX node: Relu_60 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 182 for ONNX tensor: 182 [01/30/2023-11:10:16] [V] [TRT] Relu_60 [Relu] outputs: [182 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_61 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 182 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_61 [Conv] inputs: [182 -> (1, 512, 24, 24)[FLOAT]], [model.8.cv1.conv.weight -> (256, 512, 1, 1)[FLOAT]], [model.8.cv1.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_61 for ONNX node: Conv_61 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 183 for ONNX tensor: 183 [01/30/2023-11:10:16] [V] [TRT] Conv_61 [Conv] outputs: [183 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_62 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 183 [01/30/2023-11:10:16] [V] [TRT] Relu_62 [Relu] inputs: [183 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_62 for ONNX node: Relu_62 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 184 for ONNX tensor: 184 [01/30/2023-11:10:16] [V] [TRT] Relu_62 [Relu] outputs: [184 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_63 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 184 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_63 [Conv] inputs: [184 -> (1, 256, 24, 24)[FLOAT]], [model.8.m.0.cv1.conv.weight -> (256, 256, 1, 1)[FLOAT]], [model.8.m.0.cv1.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_63 for ONNX node: Conv_63 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 185 for ONNX tensor: 185 [01/30/2023-11:10:16] [V] [TRT] Conv_63 [Conv] outputs: [185 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_64 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 185 [01/30/2023-11:10:16] [V] [TRT] Relu_64 [Relu] inputs: [185 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_64 for ONNX node: Relu_64 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 186 for ONNX tensor: 186 [01/30/2023-11:10:16] [V] [TRT] Relu_64 [Relu] outputs: [186 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_65 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 186 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_65 [Conv] inputs: [186 -> (1, 256, 24, 24)[FLOAT]], [model.8.m.0.cv2.conv.weight -> (256, 256, 3, 3)[FLOAT]], [model.8.m.0.cv2.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_65 for ONNX node: Conv_65 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 187 for ONNX tensor: 187 [01/30/2023-11:10:16] [V] [TRT] Conv_65 [Conv] outputs: [187 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_66 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 187 [01/30/2023-11:10:16] [V] [TRT] Relu_66 [Relu] inputs: [187 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_66 for ONNX node: Relu_66 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 188 for ONNX tensor: 188 [01/30/2023-11:10:16] [V] [TRT] Relu_66 [Relu] outputs: [188 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Add_67 [Add] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 184 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 188 [01/30/2023-11:10:16] [V] [TRT] Add_67 [Add] inputs: [184 -> (1, 256, 24, 24)[FLOAT]], [188 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Add_67 for ONNX node: Add_67 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 189 for ONNX tensor: 189 [01/30/2023-11:10:16] [V] [TRT] Add_67 [Add] outputs: [189 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_68 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 182 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_68 [Conv] inputs: [182 -> (1, 512, 24, 24)[FLOAT]], [model.8.cv2.conv.weight -> (256, 512, 1, 1)[FLOAT]], [model.8.cv2.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_68 for ONNX node: Conv_68 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 190 for ONNX tensor: 190 [01/30/2023-11:10:16] [V] [TRT] Conv_68 [Conv] outputs: [190 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_69 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 190 [01/30/2023-11:10:16] [V] [TRT] Relu_69 [Relu] inputs: [190 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_69 for ONNX node: Relu_69 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 191 for ONNX tensor: 191 [01/30/2023-11:10:16] [V] [TRT] Relu_69 [Relu] outputs: [191 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_70 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 189 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 191 [01/30/2023-11:10:16] [V] [TRT] Concat_70 [Concat] inputs: [189 -> (1, 256, 24, 24)[FLOAT]], [191 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_70 for ONNX node: Concat_70 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 192 for ONNX tensor: 192 [01/30/2023-11:10:16] [V] [TRT] Concat_70 [Concat] outputs: [192 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_71 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 192 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.8.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_71 [Conv] inputs: [192 -> (1, 512, 24, 24)[FLOAT]], [model.8.cv3.conv.weight -> (512, 512, 1, 1)[FLOAT]], [model.8.cv3.conv.bias -> (512)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_71 for ONNX node: Conv_71 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 193 for ONNX tensor: 193 [01/30/2023-11:10:16] [V] [TRT] Conv_71 [Conv] outputs: [193 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_72 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 193 [01/30/2023-11:10:16] [V] [TRT] Relu_72 [Relu] inputs: [193 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_72 for ONNX node: Relu_72 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 194 for ONNX tensor: 194 [01/30/2023-11:10:16] [V] [TRT] Relu_72 [Relu] outputs: [194 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_73 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 194 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.9.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.9.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_73 [Conv] inputs: [194 -> (1, 512, 24, 24)[FLOAT]], [model.9.cv1.conv.weight -> (256, 512, 1, 1)[FLOAT]], [model.9.cv1.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_73 for ONNX node: Conv_73 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 195 for ONNX tensor: 195 [01/30/2023-11:10:16] [V] [TRT] Conv_73 [Conv] outputs: [195 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_74 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 195 [01/30/2023-11:10:16] [V] [TRT] Relu_74 [Relu] inputs: [195 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_74 for ONNX node: Relu_74 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 196 for ONNX tensor: 196 [01/30/2023-11:10:16] [V] [TRT] Relu_74 [Relu] outputs: [196 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: MaxPool_75 [MaxPool] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 196 [01/30/2023-11:10:16] [V] [TRT] MaxPool_75 [MaxPool] inputs: [196 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: MaxPool_75 for ONNX node: MaxPool_75 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 197 for ONNX tensor: 197 [01/30/2023-11:10:16] [V] [TRT] MaxPool_75 [MaxPool] outputs: [197 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: MaxPool_76 [MaxPool] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 197 [01/30/2023-11:10:16] [V] [TRT] MaxPool_76 [MaxPool] inputs: [197 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: MaxPool_76 for ONNX node: MaxPool_76 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 198 for ONNX tensor: 198 [01/30/2023-11:10:16] [V] [TRT] MaxPool_76 [MaxPool] outputs: [198 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: MaxPool_77 [MaxPool] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 198 [01/30/2023-11:10:16] [V] [TRT] MaxPool_77 [MaxPool] inputs: [198 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: MaxPool_77 for ONNX node: MaxPool_77 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 199 for ONNX tensor: 199 [01/30/2023-11:10:16] [V] [TRT] MaxPool_77 [MaxPool] outputs: [199 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_78 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 196 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 197 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 198 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 199 [01/30/2023-11:10:16] [V] [TRT] Concat_78 [Concat] inputs: [196 -> (1, 256, 24, 24)[FLOAT]], [197 -> (1, 256, 24, 24)[FLOAT]], [198 -> (1, 256, 24, 24)[FLOAT]], [199 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_78 for ONNX node: Concat_78 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 200 for ONNX tensor: 200 [01/30/2023-11:10:16] [V] [TRT] Concat_78 [Concat] outputs: [200 -> (1, 1024, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_79 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 200 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.9.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.9.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_79 [Conv] inputs: [200 -> (1, 1024, 24, 24)[FLOAT]], [model.9.cv2.conv.weight -> (512, 1024, 1, 1)[FLOAT]], [model.9.cv2.conv.bias -> (512)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 1024, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_79 for ONNX node: Conv_79 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 201 for ONNX tensor: 201 [01/30/2023-11:10:16] [V] [TRT] Conv_79 [Conv] outputs: [201 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_80 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 201 [01/30/2023-11:10:16] [V] [TRT] Relu_80 [Relu] inputs: [201 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_80 for ONNX node: Relu_80 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 202 for ONNX tensor: 202 [01/30/2023-11:10:16] [V] [TRT] Relu_80 [Relu] outputs: [202 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_81 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 202 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.10.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.10.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_81 [Conv] inputs: [202 -> (1, 512, 24, 24)[FLOAT]], [model.10.conv.weight -> (256, 512, 1, 1)[FLOAT]], [model.10.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_81 for ONNX node: Conv_81 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 203 for ONNX tensor: 203 [01/30/2023-11:10:16] [V] [TRT] Conv_81 [Conv] outputs: [203 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_82 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 203 [01/30/2023-11:10:16] [V] [TRT] Relu_82 [Relu] inputs: [203 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_82 for ONNX node: Relu_82 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 204 for ONNX tensor: 204 [01/30/2023-11:10:16] [V] [TRT] Relu_82 [Relu] outputs: [204 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Resize_83 [Resize] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 204 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 468 [01/30/2023-11:10:16] [V] [TRT] Resize_83 [Resize] inputs: [204 -> (1, 256, 24, 24)[FLOAT]], [optional input, not set], [468 -> (4)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Resize_83 for ONNX node: Resize_83 [01/30/2023-11:10:16] [V] [TRT] Running resize layer with: Transformation mode: asymmetric Resize mode: nearest [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 209 for ONNX tensor: 209 [01/30/2023-11:10:16] [V] [TRT] Resize_83 [Resize] outputs: [209 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_84 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 209 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 180 [01/30/2023-11:10:16] [V] [TRT] Concat_84 [Concat] inputs: [209 -> (1, 256, 48, 48)[FLOAT]], [180 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_84 for ONNX node: Concat_84 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 210 for ONNX tensor: 210 [01/30/2023-11:10:16] [V] [TRT] Concat_84 [Concat] outputs: [210 -> (1, 512, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_85 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 210 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_85 [Conv] inputs: [210 -> (1, 512, 48, 48)[FLOAT]], [model.13.cv1.conv.weight -> (128, 512, 1, 1)[FLOAT]], [model.13.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_85 for ONNX node: Conv_85 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 211 for ONNX tensor: 211 [01/30/2023-11:10:16] [V] [TRT] Conv_85 [Conv] outputs: [211 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_86 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 211 [01/30/2023-11:10:16] [V] [TRT] Relu_86 [Relu] inputs: [211 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_86 for ONNX node: Relu_86 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 212 for ONNX tensor: 212 [01/30/2023-11:10:16] [V] [TRT] Relu_86 [Relu] outputs: [212 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_87 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 212 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_87 [Conv] inputs: [212 -> (1, 128, 48, 48)[FLOAT]], [model.13.m.0.cv1.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.13.m.0.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_87 for ONNX node: Conv_87 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 213 for ONNX tensor: 213 [01/30/2023-11:10:16] [V] [TRT] Conv_87 [Conv] outputs: [213 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_88 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 213 [01/30/2023-11:10:16] [V] [TRT] Relu_88 [Relu] inputs: [213 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_88 for ONNX node: Relu_88 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 214 for ONNX tensor: 214 [01/30/2023-11:10:16] [V] [TRT] Relu_88 [Relu] outputs: [214 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_89 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 214 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_89 [Conv] inputs: [214 -> (1, 128, 48, 48)[FLOAT]], [model.13.m.0.cv2.conv.weight -> (128, 128, 3, 3)[FLOAT]], [model.13.m.0.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_89 for ONNX node: Conv_89 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 215 for ONNX tensor: 215 [01/30/2023-11:10:16] [V] [TRT] Conv_89 [Conv] outputs: [215 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_90 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 215 [01/30/2023-11:10:16] [V] [TRT] Relu_90 [Relu] inputs: [215 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_90 for ONNX node: Relu_90 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 216 for ONNX tensor: 216 [01/30/2023-11:10:16] [V] [TRT] Relu_90 [Relu] outputs: [216 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_91 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 210 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_91 [Conv] inputs: [210 -> (1, 512, 48, 48)[FLOAT]], [model.13.cv2.conv.weight -> (128, 512, 1, 1)[FLOAT]], [model.13.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_91 for ONNX node: Conv_91 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 217 for ONNX tensor: 217 [01/30/2023-11:10:16] [V] [TRT] Conv_91 [Conv] outputs: [217 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_92 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 217 [01/30/2023-11:10:16] [V] [TRT] Relu_92 [Relu] inputs: [217 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_92 for ONNX node: Relu_92 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 218 for ONNX tensor: 218 [01/30/2023-11:10:16] [V] [TRT] Relu_92 [Relu] outputs: [218 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_93 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 216 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 218 [01/30/2023-11:10:16] [V] [TRT] Concat_93 [Concat] inputs: [216 -> (1, 128, 48, 48)[FLOAT]], [218 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_93 for ONNX node: Concat_93 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 219 for ONNX tensor: 219 [01/30/2023-11:10:16] [V] [TRT] Concat_93 [Concat] outputs: [219 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_94 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 219 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.13.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_94 [Conv] inputs: [219 -> (1, 256, 48, 48)[FLOAT]], [model.13.cv3.conv.weight -> (256, 256, 1, 1)[FLOAT]], [model.13.cv3.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_94 for ONNX node: Conv_94 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 220 for ONNX tensor: 220 [01/30/2023-11:10:16] [V] [TRT] Conv_94 [Conv] outputs: [220 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_95 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 220 [01/30/2023-11:10:16] [V] [TRT] Relu_95 [Relu] inputs: [220 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_95 for ONNX node: Relu_95 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 221 for ONNX tensor: 221 [01/30/2023-11:10:16] [V] [TRT] Relu_95 [Relu] outputs: [221 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_96 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 221 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.14.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.14.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_96 [Conv] inputs: [221 -> (1, 256, 48, 48)[FLOAT]], [model.14.conv.weight -> (128, 256, 1, 1)[FLOAT]], [model.14.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_96 for ONNX node: Conv_96 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 222 for ONNX tensor: 222 [01/30/2023-11:10:16] [V] [TRT] Conv_96 [Conv] outputs: [222 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_97 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 222 [01/30/2023-11:10:16] [V] [TRT] Relu_97 [Relu] inputs: [222 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_97 for ONNX node: Relu_97 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 223 for ONNX tensor: 223 [01/30/2023-11:10:16] [V] [TRT] Relu_97 [Relu] outputs: [223 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Resize_98 [Resize] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 223 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 468 [01/30/2023-11:10:16] [V] [TRT] Resize_98 [Resize] inputs: [223 -> (1, 128, 48, 48)[FLOAT]], [optional input, not set], [468 -> (4)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Resize_98 for ONNX node: Resize_98 [01/30/2023-11:10:16] [V] [TRT] Running resize layer with: Transformation mode: asymmetric Resize mode: nearest [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 228 for ONNX tensor: 228 [01/30/2023-11:10:16] [V] [TRT] Resize_98 [Resize] outputs: [228 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_99 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 228 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 156 [01/30/2023-11:10:16] [V] [TRT] Concat_99 [Concat] inputs: [228 -> (1, 128, 96, 96)[FLOAT]], [156 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_99 for ONNX node: Concat_99 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 229 for ONNX tensor: 229 [01/30/2023-11:10:16] [V] [TRT] Concat_99 [Concat] outputs: [229 -> (1, 256, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_100 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 229 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_100 [Conv] inputs: [229 -> (1, 256, 96, 96)[FLOAT]], [model.17.cv1.conv.weight -> (64, 256, 1, 1)[FLOAT]], [model.17.cv1.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_100 for ONNX node: Conv_100 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 230 for ONNX tensor: 230 [01/30/2023-11:10:16] [V] [TRT] Conv_100 [Conv] outputs: [230 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_101 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 230 [01/30/2023-11:10:16] [V] [TRT] Relu_101 [Relu] inputs: [230 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_101 for ONNX node: Relu_101 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 231 for ONNX tensor: 231 [01/30/2023-11:10:16] [V] [TRT] Relu_101 [Relu] outputs: [231 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_102 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 231 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_102 [Conv] inputs: [231 -> (1, 64, 96, 96)[FLOAT]], [model.17.m.0.cv1.conv.weight -> (64, 64, 1, 1)[FLOAT]], [model.17.m.0.cv1.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_102 for ONNX node: Conv_102 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 232 for ONNX tensor: 232 [01/30/2023-11:10:16] [V] [TRT] Conv_102 [Conv] outputs: [232 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_103 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 232 [01/30/2023-11:10:16] [V] [TRT] Relu_103 [Relu] inputs: [232 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_103 for ONNX node: Relu_103 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 233 for ONNX tensor: 233 [01/30/2023-11:10:16] [V] [TRT] Relu_103 [Relu] outputs: [233 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_104 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 233 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_104 [Conv] inputs: [233 -> (1, 64, 96, 96)[FLOAT]], [model.17.m.0.cv2.conv.weight -> (64, 64, 3, 3)[FLOAT]], [model.17.m.0.cv2.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_104 for ONNX node: Conv_104 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 234 for ONNX tensor: 234 [01/30/2023-11:10:16] [V] [TRT] Conv_104 [Conv] outputs: [234 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_105 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 234 [01/30/2023-11:10:16] [V] [TRT] Relu_105 [Relu] inputs: [234 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_105 for ONNX node: Relu_105 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 235 for ONNX tensor: 235 [01/30/2023-11:10:16] [V] [TRT] Relu_105 [Relu] outputs: [235 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_106 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 229 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_106 [Conv] inputs: [229 -> (1, 256, 96, 96)[FLOAT]], [model.17.cv2.conv.weight -> (64, 256, 1, 1)[FLOAT]], [model.17.cv2.conv.bias -> (64)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_106 for ONNX node: Conv_106 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 64, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 236 for ONNX tensor: 236 [01/30/2023-11:10:16] [V] [TRT] Conv_106 [Conv] outputs: [236 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_107 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 236 [01/30/2023-11:10:16] [V] [TRT] Relu_107 [Relu] inputs: [236 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_107 for ONNX node: Relu_107 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 237 for ONNX tensor: 237 [01/30/2023-11:10:16] [V] [TRT] Relu_107 [Relu] outputs: [237 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_108 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 235 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 237 [01/30/2023-11:10:16] [V] [TRT] Concat_108 [Concat] inputs: [235 -> (1, 64, 96, 96)[FLOAT]], [237 -> (1, 64, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_108 for ONNX node: Concat_108 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 238 for ONNX tensor: 238 [01/30/2023-11:10:16] [V] [TRT] Concat_108 [Concat] outputs: [238 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_109 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 238 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.17.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_109 [Conv] inputs: [238 -> (1, 128, 96, 96)[FLOAT]], [model.17.cv3.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.17.cv3.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_109 for ONNX node: Conv_109 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 239 for ONNX tensor: 239 [01/30/2023-11:10:16] [V] [TRT] Conv_109 [Conv] outputs: [239 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_110 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 239 [01/30/2023-11:10:16] [V] [TRT] Relu_110 [Relu] inputs: [239 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_110 for ONNX node: Relu_110 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 240 for ONNX tensor: 240 [01/30/2023-11:10:16] [V] [TRT] Relu_110 [Relu] outputs: [240 -> (1, 128, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_111 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 240 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.18.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.18.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_111 [Conv] inputs: [240 -> (1, 128, 96, 96)[FLOAT]], [model.18.conv.weight -> (128, 128, 3, 3)[FLOAT]], [model.18.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_111 for ONNX node: Conv_111 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 241 for ONNX tensor: 241 [01/30/2023-11:10:16] [V] [TRT] Conv_111 [Conv] outputs: [241 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_112 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 241 [01/30/2023-11:10:16] [V] [TRT] Relu_112 [Relu] inputs: [241 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_112 for ONNX node: Relu_112 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 242 for ONNX tensor: 242 [01/30/2023-11:10:16] [V] [TRT] Relu_112 [Relu] outputs: [242 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_113 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 242 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 223 [01/30/2023-11:10:16] [V] [TRT] Concat_113 [Concat] inputs: [242 -> (1, 128, 48, 48)[FLOAT]], [223 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_113 for ONNX node: Concat_113 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 243 for ONNX tensor: 243 [01/30/2023-11:10:16] [V] [TRT] Concat_113 [Concat] outputs: [243 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_114 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 243 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_114 [Conv] inputs: [243 -> (1, 256, 48, 48)[FLOAT]], [model.20.cv1.conv.weight -> (128, 256, 1, 1)[FLOAT]], [model.20.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_114 for ONNX node: Conv_114 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 244 for ONNX tensor: 244 [01/30/2023-11:10:16] [V] [TRT] Conv_114 [Conv] outputs: [244 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_115 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 244 [01/30/2023-11:10:16] [V] [TRT] Relu_115 [Relu] inputs: [244 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_115 for ONNX node: Relu_115 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 245 for ONNX tensor: 245 [01/30/2023-11:10:16] [V] [TRT] Relu_115 [Relu] outputs: [245 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_116 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 245 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_116 [Conv] inputs: [245 -> (1, 128, 48, 48)[FLOAT]], [model.20.m.0.cv1.conv.weight -> (128, 128, 1, 1)[FLOAT]], [model.20.m.0.cv1.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_116 for ONNX node: Conv_116 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 246 for ONNX tensor: 246 [01/30/2023-11:10:16] [V] [TRT] Conv_116 [Conv] outputs: [246 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_117 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 246 [01/30/2023-11:10:16] [V] [TRT] Relu_117 [Relu] inputs: [246 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_117 for ONNX node: Relu_117 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 247 for ONNX tensor: 247 [01/30/2023-11:10:16] [V] [TRT] Relu_117 [Relu] outputs: [247 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_118 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 247 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_118 [Conv] inputs: [247 -> (1, 128, 48, 48)[FLOAT]], [model.20.m.0.cv2.conv.weight -> (128, 128, 3, 3)[FLOAT]], [model.20.m.0.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_118 for ONNX node: Conv_118 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 248 for ONNX tensor: 248 [01/30/2023-11:10:16] [V] [TRT] Conv_118 [Conv] outputs: [248 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_119 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 248 [01/30/2023-11:10:16] [V] [TRT] Relu_119 [Relu] inputs: [248 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_119 for ONNX node: Relu_119 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 249 for ONNX tensor: 249 [01/30/2023-11:10:16] [V] [TRT] Relu_119 [Relu] outputs: [249 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_120 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 243 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_120 [Conv] inputs: [243 -> (1, 256, 48, 48)[FLOAT]], [model.20.cv2.conv.weight -> (128, 256, 1, 1)[FLOAT]], [model.20.cv2.conv.bias -> (128)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_120 for ONNX node: Conv_120 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 128, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 250 for ONNX tensor: 250 [01/30/2023-11:10:16] [V] [TRT] Conv_120 [Conv] outputs: [250 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_121 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 250 [01/30/2023-11:10:16] [V] [TRT] Relu_121 [Relu] inputs: [250 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_121 for ONNX node: Relu_121 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 251 for ONNX tensor: 251 [01/30/2023-11:10:16] [V] [TRT] Relu_121 [Relu] outputs: [251 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_122 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 249 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 251 [01/30/2023-11:10:16] [V] [TRT] Concat_122 [Concat] inputs: [249 -> (1, 128, 48, 48)[FLOAT]], [251 -> (1, 128, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_122 for ONNX node: Concat_122 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 252 for ONNX tensor: 252 [01/30/2023-11:10:16] [V] [TRT] Concat_122 [Concat] outputs: [252 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_123 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 252 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.20.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_123 [Conv] inputs: [252 -> (1, 256, 48, 48)[FLOAT]], [model.20.cv3.conv.weight -> (256, 256, 1, 1)[FLOAT]], [model.20.cv3.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_123 for ONNX node: Conv_123 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 253 for ONNX tensor: 253 [01/30/2023-11:10:16] [V] [TRT] Conv_123 [Conv] outputs: [253 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_124 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 253 [01/30/2023-11:10:16] [V] [TRT] Relu_124 [Relu] inputs: [253 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_124 for ONNX node: Relu_124 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 254 for ONNX tensor: 254 [01/30/2023-11:10:16] [V] [TRT] Relu_124 [Relu] outputs: [254 -> (1, 256, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_125 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 254 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.21.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.21.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_125 [Conv] inputs: [254 -> (1, 256, 48, 48)[FLOAT]], [model.21.conv.weight -> (256, 256, 3, 3)[FLOAT]], [model.21.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_125 for ONNX node: Conv_125 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 255 for ONNX tensor: 255 [01/30/2023-11:10:16] [V] [TRT] Conv_125 [Conv] outputs: [255 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_126 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 255 [01/30/2023-11:10:16] [V] [TRT] Relu_126 [Relu] inputs: [255 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_126 for ONNX node: Relu_126 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 256 for ONNX tensor: 256 [01/30/2023-11:10:16] [V] [TRT] Relu_126 [Relu] outputs: [256 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_127 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 256 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 204 [01/30/2023-11:10:16] [V] [TRT] Concat_127 [Concat] inputs: [256 -> (1, 256, 24, 24)[FLOAT]], [204 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_127 for ONNX node: Concat_127 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 257 for ONNX tensor: 257 [01/30/2023-11:10:16] [V] [TRT] Concat_127 [Concat] outputs: [257 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_128 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 257 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_128 [Conv] inputs: [257 -> (1, 512, 24, 24)[FLOAT]], [model.23.cv1.conv.weight -> (256, 512, 1, 1)[FLOAT]], [model.23.cv1.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_128 for ONNX node: Conv_128 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 258 for ONNX tensor: 258 [01/30/2023-11:10:16] [V] [TRT] Conv_128 [Conv] outputs: [258 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_129 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 258 [01/30/2023-11:10:16] [V] [TRT] Relu_129 [Relu] inputs: [258 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_129 for ONNX node: Relu_129 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 259 for ONNX tensor: 259 [01/30/2023-11:10:16] [V] [TRT] Relu_129 [Relu] outputs: [259 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_130 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 259 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.m.0.cv1.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.m.0.cv1.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_130 [Conv] inputs: [259 -> (1, 256, 24, 24)[FLOAT]], [model.23.m.0.cv1.conv.weight -> (256, 256, 1, 1)[FLOAT]], [model.23.m.0.cv1.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_130 for ONNX node: Conv_130 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 260 for ONNX tensor: 260 [01/30/2023-11:10:16] [V] [TRT] Conv_130 [Conv] outputs: [260 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_131 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 260 [01/30/2023-11:10:16] [V] [TRT] Relu_131 [Relu] inputs: [260 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_131 for ONNX node: Relu_131 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 261 for ONNX tensor: 261 [01/30/2023-11:10:16] [V] [TRT] Relu_131 [Relu] outputs: [261 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_132 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 261 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.m.0.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.m.0.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_132 [Conv] inputs: [261 -> (1, 256, 24, 24)[FLOAT]], [model.23.m.0.cv2.conv.weight -> (256, 256, 3, 3)[FLOAT]], [model.23.m.0.cv2.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_132 for ONNX node: Conv_132 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (1, 1), postpadding: (1, 1), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 262 for ONNX tensor: 262 [01/30/2023-11:10:16] [V] [TRT] Conv_132 [Conv] outputs: [262 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_133 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 262 [01/30/2023-11:10:16] [V] [TRT] Relu_133 [Relu] inputs: [262 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_133 for ONNX node: Relu_133 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 263 for ONNX tensor: 263 [01/30/2023-11:10:16] [V] [TRT] Relu_133 [Relu] outputs: [263 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_134 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 257 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.cv2.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.cv2.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_134 [Conv] inputs: [257 -> (1, 512, 24, 24)[FLOAT]], [model.23.cv2.conv.weight -> (256, 512, 1, 1)[FLOAT]], [model.23.cv2.conv.bias -> (256)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_134 for ONNX node: Conv_134 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 256, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 264 for ONNX tensor: 264 [01/30/2023-11:10:16] [V] [TRT] Conv_134 [Conv] outputs: [264 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_135 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 264 [01/30/2023-11:10:16] [V] [TRT] Relu_135 [Relu] inputs: [264 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_135 for ONNX node: Relu_135 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 265 for ONNX tensor: 265 [01/30/2023-11:10:16] [V] [TRT] Relu_135 [Relu] outputs: [265 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Concat_136 [Concat] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 263 [01/30/2023-11:10:16] [V] [TRT] Searching for input: 265 [01/30/2023-11:10:16] [V] [TRT] Concat_136 [Concat] inputs: [263 -> (1, 256, 24, 24)[FLOAT]], [265 -> (1, 256, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Concat_136 for ONNX node: Concat_136 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 266 for ONNX tensor: 266 [01/30/2023-11:10:16] [V] [TRT] Concat_136 [Concat] outputs: [266 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_137 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 266 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.cv3.conv.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.23.cv3.conv.bias [01/30/2023-11:10:16] [V] [TRT] Conv_137 [Conv] inputs: [266 -> (1, 512, 24, 24)[FLOAT]], [model.23.cv3.conv.weight -> (512, 512, 1, 1)[FLOAT]], [model.23.cv3.conv.bias -> (512)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_137 for ONNX node: Conv_137 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 267 for ONNX tensor: 267 [01/30/2023-11:10:16] [V] [TRT] Conv_137 [Conv] outputs: [267 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Relu_138 [Relu] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 267 [01/30/2023-11:10:16] [V] [TRT] Relu_138 [Relu] inputs: [267 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Registering layer: Relu_138 for ONNX node: Relu_138 [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 268 for ONNX tensor: 268 [01/30/2023-11:10:16] [V] [TRT] Relu_138 [Relu] outputs: [268 -> (1, 512, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_139 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 240 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.24.m.0.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.24.m.0.bias [01/30/2023-11:10:16] [V] [TRT] Conv_139 [Conv] inputs: [240 -> (1, 128, 96, 96)[FLOAT]], [model.24.m.0.weight -> (21, 128, 1, 1)[FLOAT]], [model.24.m.0.bias -> (21)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 128, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_139 for ONNX node: Conv_139 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 21 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 21, 96, 96) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 269_0 for ONNX tensor: 269 [01/30/2023-11:10:16] [V] [TRT] Conv_139 [Conv] outputs: [269 -> (1, 21, 96, 96)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_193 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 254 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.24.m.1.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.24.m.1.bias [01/30/2023-11:10:16] [V] [TRT] Conv_193 [Conv] inputs: [254 -> (1, 256, 48, 48)[FLOAT]], [model.24.m.1.weight -> (21, 256, 1, 1)[FLOAT]], [model.24.m.1.bias -> (21)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 256, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_193 for ONNX node: Conv_193 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 21 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 21, 48, 48) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 335_1 for ONNX tensor: 335 [01/30/2023-11:10:16] [V] [TRT] Conv_193 [Conv] outputs: [335 -> (1, 21, 48, 48)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Parsing node: Conv_247 [Conv] [01/30/2023-11:10:16] [V] [TRT] Searching for input: 268 [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.24.m.2.weight [01/30/2023-11:10:16] [V] [TRT] Searching for input: model.24.m.2.bias [01/30/2023-11:10:16] [V] [TRT] Conv_247 [Conv] inputs: [268 -> (1, 512, 24, 24)[FLOAT]], [model.24.m.2.weight -> (21, 512, 1, 1)[FLOAT]], [model.24.m.2.bias -> (21)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Convolution input dimensions: (1, 512, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering layer: Conv_247 for ONNX node: Conv_247 [01/30/2023-11:10:16] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 21 [01/30/2023-11:10:16] [V] [TRT] Convolution output dimensions: (1, 21, 24, 24) [01/30/2023-11:10:16] [V] [TRT] Registering tensor: 401_2 for ONNX tensor: 401 [01/30/2023-11:10:16] [V] [TRT] Conv_247 [Conv] outputs: [401 -> (1, 21, 24, 24)[FLOAT]], [01/30/2023-11:10:16] [V] [TRT] Marking 269_0 as output: 269 [01/30/2023-11:10:16] [V] [TRT] Marking 335_1 as output: 335 [01/30/2023-11:10:16] [V] [TRT] Marking 401_2 as output: 401 [01/30/2023-11:10:16] [I] Finish parsing network model [01/30/2023-11:10:16] [I] [TRT] [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 399, GPU 11507 (MiB) [01/30/2023-11:10:16] [W] [TRT] Default DLA is enabled but layer Resize_83 is not supported on DLA, falling back to GPU. [01/30/2023-11:10:16] [W] [TRT] Default DLA is enabled but layer Resize_98 is not supported on DLA, falling back to GPU. [01/30/2023-11:10:16] [I] [TRT] [MemUsageSnapshot] Builder begin: CPU 399 MiB, GPU 11507 MiB [01/30/2023-11:10:16] [V] [TRT] Applying generic optimizations to the graph for inference. [01/30/2023-11:10:16] [V] [TRT] Original: 142 layers [01/30/2023-11:10:16] [V] [TRT] After dead-layer removal: 142 layers [01/30/2023-11:10:16] [V] [TRT] After Myelin optimization: 142 layers [01/30/2023-11:10:17] [V] [TRT] After DLA optimization: 11 layers [01/30/2023-11:10:17] [V] [TRT] After scale fusion: 11 layers [01/30/2023-11:10:17] [V] [TRT] After vertical fusions: 11 layers [01/30/2023-11:10:17] [V] [TRT] After dupe layer removal: 11 layers [01/30/2023-11:10:17] [V] [TRT] After final dead-layer removal: 11 layers [01/30/2023-11:10:17] [V] [TRT] After tensor merging: 11 layers [01/30/2023-11:10:17] [V] [TRT] After concat removal: 11 layers [01/30/2023-11:10:17] [V] [TRT] Graph construction and optimization completed in 0.639844 seconds. [01/30/2023-11:10:17] [I] [TRT] ---------- Layers Running on DLA ---------- [01/30/2023-11:10:17] [I] [TRT] [DlaLayer] {ForeignNode[Conv_0...Relu_82]} [01/30/2023-11:10:17] [I] [TRT] [DlaLayer] {ForeignNode[Concat_84...Relu_97]} [01/30/2023-11:10:17] [I] [TRT] [DlaLayer] {ForeignNode[Concat_99...Conv_247]} [01/30/2023-11:10:17] [I] [TRT] ---------- Layers Running on GPU ---------- [01/30/2023-11:10:17] [I] [TRT] [GpuLayer] Resize_83 [01/30/2023-11:10:17] [I] [TRT] [GpuLayer] Resize_98 [01/30/2023-11:10:17] [V] [TRT] Using cublas a tactic source [01/30/2023-11:10:17] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +197, GPU +237, now: CPU 627, GPU 11782 (MiB) [01/30/2023-11:10:17] [V] [TRT] Using cuDNN as a tactic source [01/30/2023-11:10:19] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +307, GPU +400, now: CPU 934, GPU 12182 (MiB) [01/30/2023-11:10:19] [W] [TRT] Detected invalid timing cache, setup a local cache instead [01/30/2023-11:10:19] [V] [TRT] Constructing optimization profile number 0 [1/1]. [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Float(1769472,589824,768,1) -> Half(1769472,589824,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: images to nvm (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 0.727428 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 1.136 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 0.727428 [01/30/2023-11:10:19] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Float(1769472,589824,768,1) -> Half(589824,1:4,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: images to nvm (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 0.68158 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 0.799364 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 0.68158 [01/30/2023-11:10:19] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Float(1769472,589824,768,1) -> Half(589824,589824:16,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: images to nvm (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 5.40545 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 6.4862 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 5.40545 [01/30/2023-11:10:19] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Half(1769472,589824,768,1) -> Half(589824,1:4,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 0.259624 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 0.408888 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 0.259624 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Half(1769472,589824,768,1) -> Half(589824,589824:16,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 0.989984 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 5.40375 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 0.989984 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Half(589824,1:4,768,1) -> Half(1769472,589824,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 7.37257 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 0.333132 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 0 Time: 0.333132 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Half(589824,1:4,768,1) -> Half(589824,589824:16,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:10:19] [V] [TRT] Tactic: 1002 Time: 2.76776 [01/30/2023-11:10:19] [V] [TRT] Tactic: 0 Time: 5.55174 [01/30/2023-11:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 2.76776 [01/30/2023-11:10:19] [V] [TRT] *************** Autotuning Reformat:Half(589824,589824:16,768,1) -> Half(1769472,589824,768,1) *************** [01/30/2023-11:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:10:20] [V] [TRT] Tactic: 1002 Time: 8.35369 [01/30/2023-11:10:20] [V] [TRT] Tactic: 0 Time: 0.9499 [01/30/2023-11:10:20] [V] [TRT] Fastest Tactic: 0 Time: 0.9499 [01/30/2023-11:10:20] [V] [TRT] *************** Autotuning Reformat:Half(589824,589824:16,768,1) -> Half(589824,1:4,768,1) *************** [01/30/2023-11:10:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:10:20] [V] [TRT] Tactic: 1002 Time: 2.77254 [01/30/2023-11:10:20] [V] [TRT] Tactic: 0 Time: 0.523584 [01/30/2023-11:10:20] [V] [TRT] Fastest Tactic: 0 Time: 0.523584 [01/30/2023-11:10:20] [V] [TRT] *************** Autotuning format combination: Half(1769472,589824,768,1) -> Half(1179648,9216,96,1), Half(786432,3072,64,1), Half(196608,768,32,1) *************** [01/30/2023-11:10:20] [V] [TRT] --------------- Timing Runner: {ForeignNode[Conv_0...Relu_82]} (DLA) [01/30/2023-11:10:43] [V] [TRT] Tactic: 3 Time: 175.724 [01/30/2023-11:10:43] [V] [TRT] Fastest Tactic: 3 Time: 175.724 [01/30/2023-11:10:43] [V] [TRT] *************** Autotuning format combination: Half(1769472,589824,768,1) -> Half(73728,9216:16,96,1), Half(36864,2304:16,48,1), Half(9216,576:16,24,1) *************** [01/30/2023-11:10:43] [V] [TRT] --------------- Timing Runner: {ForeignNode[Conv_0...Relu_82]} (DLA) [01/30/2023-11:11:05] [V] [TRT] Tactic: 3 Time: 174.405 [01/30/2023-11:11:05] [V] [TRT] Fastest Tactic: 3 Time: 174.405 [01/30/2023-11:11:05] [V] [TRT] *************** Autotuning format combination: Half(589824,1:4,768,1) -> Half(1179648,9216,96,1), Half(786432,3072,64,1), Half(196608,768,32,1) *************** [01/30/2023-11:11:05] [V] [TRT] --------------- Timing Runner: {ForeignNode[Conv_0...Relu_82]} (DLA) [01/30/2023-11:11:17] [V] [TRT] Tactic: 3 Time: 93.2642 [01/30/2023-11:11:17] [V] [TRT] Fastest Tactic: 3 Time: 93.2642 [01/30/2023-11:11:17] [V] [TRT] *************** Autotuning format combination: Half(589824,1:4,768,1) -> Half(73728,9216:16,96,1), Half(36864,2304:16,48,1), Half(9216,576:16,24,1) *************** [01/30/2023-11:11:17] [V] [TRT] --------------- Timing Runner: {ForeignNode[Conv_0...Relu_82]} (DLA) [01/30/2023-11:11:29] [V] [TRT] Tactic: 3 Time: 91.7388 [01/30/2023-11:11:29] [V] [TRT] Fastest Tactic: 3 Time: 91.7388 [01/30/2023-11:11:29] [V] [TRT] *************** Autotuning format combination: Half(589824,589824:16,768,1) -> Half(1179648,9216,96,1), Half(786432,3072,64,1), Half(196608,768,32,1) *************** [01/30/2023-11:11:29] [V] [TRT] --------------- Timing Runner: {ForeignNode[Conv_0...Relu_82]} (DLA) [01/30/2023-11:11:52] [V] [TRT] Tactic: 3 Time: 172.64 [01/30/2023-11:11:52] [V] [TRT] Fastest Tactic: 3 Time: 172.64 [01/30/2023-11:11:52] [V] [TRT] *************** Autotuning format combination: Half(589824,589824:16,768,1) -> Half(73728,9216:16,96,1), Half(36864,2304:16,48,1), Half(9216,576:16,24,1) *************** [01/30/2023-11:11:52] [V] [TRT] --------------- Timing Runner: {ForeignNode[Conv_0...Relu_82]} (DLA) [01/30/2023-11:12:14] [V] [TRT] Tactic: 3 Time: 171.033 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 3 Time: 171.033 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(1179648,9216,96,1) -> Half(73728,9216:16,96,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.282992 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 1.47455 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.282992 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(73728,9216:16,96,1) -> Half(1179648,9216,96,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 1.2265 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.859404 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.859404 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(786432,3072,64,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.165888 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.727552 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.165888 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(36864,2304:16,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.15204 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.309228 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.15204 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Float(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.129804 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.113788 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.113788 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Half(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.10508 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.036612 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.036612 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Half(9216,576:16,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.056684 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.183176 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.056684 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Float(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.114044 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.095368 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.095368 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Half(196608,768,32,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.063504 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.087804 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.063504 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Half(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.165632 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.083964 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.083964 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Float(147456,576,24,1) -> Half(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.11982 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.120964 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.11982 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Float(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.067212 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.104324 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.067212 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Half(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.06836 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.0648 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.0648 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(147456,576,24,1) -> Float(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.119948 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.104188 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.104188 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Float(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.114312 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.095744 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.095744 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Half(147456,576,24,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.166652 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.08434 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.08434 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning format combination: Float(147456,576,24,1) -> Float(589824,2304,48,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Resize_83 (Resize) [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.212988 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.212988 [01/30/2023-11:12:14] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Resize Tactic: 0 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning format combination: Half(147456,576,24,1) -> Half(589824,2304,48,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Resize_83 (Resize) [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.211832 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 0 Time: 0.211832 [01/30/2023-11:12:14] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Resize Tactic: 0 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Float(589824,2304,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.18316 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.40152 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.18316 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Float(589824,2304,48,1) -> Half(589824,2304,48,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.279076 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.4004 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.279076 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Float(589824,2304,48,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.146004 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.556612 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.146004 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(589824,2304,48,1) -> Float(589824,2304,48,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.224128 [01/30/2023-11:12:14] [V] [TRT] Tactic: 0 Time: 0.273764 [01/30/2023-11:12:14] [V] [TRT] Fastest Tactic: 1002 Time: 0.224128 [01/30/2023-11:12:14] [V] [TRT] *************** Autotuning Reformat:Half(589824,2304,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:14] [V] [TRT] Tactic: 1002 Time: 0.138596 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.156672 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 1002 Time: 0.138596 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(589824,2304,48,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.111612 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.540508 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 1002 Time: 0.111612 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Float(589824,2304,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.211136 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.303796 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 1002 Time: 0.211136 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Float(589824,2304,48,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.144468 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.556216 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 1002 Time: 0.144468 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(786432,3072,64,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.291704 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.54258 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 1002 Time: 0.291704 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(589824,2304,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.16816 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.072632 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 0 Time: 0.072632 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(589824,2304,48,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.111968 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.540888 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 1002 Time: 0.111968 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(36864,2304:16,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:15] [V] [TRT] Tactic: 1002 Time: 0.48202 [01/30/2023-11:12:15] [V] [TRT] Tactic: 0 Time: 0.221184 [01/30/2023-11:12:15] [V] [TRT] Fastest Tactic: 0 Time: 0.221184 [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(786432,3072,64,1) -> Half(36864,2304:16,48,1) *************** [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning Reformat:Half(36864,2304:16,48,1) -> Half(786432,3072,64,1) *************** [01/30/2023-11:12:15] [V] [TRT] *************** Autotuning format combination: Half(786432,3072,64,1), Half(786432,3072,64,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:15] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_84...Relu_97]} (DLA) [01/30/2023-11:12:16] [V] [TRT] Tactic: 3 Time: 10.7814 [01/30/2023-11:12:16] [V] [TRT] Fastest Tactic: 3 Time: 10.7814 [01/30/2023-11:12:16] [V] [TRT] *************** Autotuning format combination: Half(786432,3072,64,1), Half(786432,3072,64,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:16] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_84...Relu_97]} (DLA) [01/30/2023-11:12:18] [V] [TRT] Tactic: 3 Time: 10.5414 [01/30/2023-11:12:18] [V] [TRT] Fastest Tactic: 3 Time: 10.5414 [01/30/2023-11:12:18] [V] [TRT] *************** Autotuning format combination: Half(36864,2304:16,48,1), Half(36864,2304:16,48,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:18] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_84...Relu_97]} (DLA) [01/30/2023-11:12:19] [V] [TRT] Tactic: 3 Time: 9.87656 [01/30/2023-11:12:19] [V] [TRT] Fastest Tactic: 3 Time: 9.87656 [01/30/2023-11:12:19] [V] [TRT] *************** Autotuning format combination: Half(36864,2304:16,48,1), Half(36864,2304:16,48,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:19] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_84...Relu_97]} (DLA) [01/30/2023-11:12:20] [V] [TRT] Tactic: 3 Time: 9.65976 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 3 Time: 9.65976 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Float(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.133268 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.150924 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.133268 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Half(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.100728 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.050152 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.050152 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.06474 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.265924 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.06474 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Float(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.144092 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.131024 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.131024 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.057072 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.119936 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.057072 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Half(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.255868 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.117392 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.117392 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Float(294912,2304,48,1) -> Half(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.113716 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.16074 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.113716 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Float(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.07808 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.144344 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.07808 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Half(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.077828 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.086732 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.077828 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(294912,2304,48,1) -> Float(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.122696 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.144352 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.122696 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Float(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.144472 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.1313 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.1313 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Half(294912,2304,48,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.2562 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.118112 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.118112 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning format combination: Float(294912,2304,48,1) -> Float(1179648,9216,96,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Resize_98 (Resize) [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.30902 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.30902 [01/30/2023-11:12:20] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Resize Tactic: 0 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning format combination: Half(294912,2304,48,1) -> Half(1179648,9216,96,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Resize_98 (Resize) [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.306824 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 0 Time: 0.306824 [01/30/2023-11:12:20] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Resize Tactic: 0 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Float(1179648,9216,96,1) -> Half(1179648,9216,96,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.372604 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 0.583896 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.372604 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Float(1179648,9216,96,1) -> Half(73728,9216:16,96,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:20] [V] [TRT] Tactic: 1002 Time: 0.272524 [01/30/2023-11:12:20] [V] [TRT] Tactic: 0 Time: 1.12212 [01/30/2023-11:12:20] [V] [TRT] Fastest Tactic: 1002 Time: 0.272524 [01/30/2023-11:12:20] [V] [TRT] *************** Autotuning Reformat:Half(1179648,9216,96,1) -> Float(1179648,9216,96,1) *************** [01/30/2023-11:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.421836 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.533228 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.421836 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(1179648,9216,96,1) -> Half(73728,9216:16,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Float(294912,2304,48,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.078308 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.164816 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.078308 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Float(294912,2304,48,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.079952 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.275292 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.079952 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.07792 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.085228 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.07792 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.152688 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.266604 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.152688 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(294912,2304,48,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.077312 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.086532 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.077312 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(294912,2304,48,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.066364 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.267388 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.066364 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.058588 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.120548 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.058588 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 223 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.070912 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.0431 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.0431 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 0 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Float(147456,576,24,1) -> Half(196608,768,32,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.052988 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.091756 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.052988 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Float(147456,576,24,1) -> Half(9216,576:16,24,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.049772 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.144904 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.049772 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Half(196608,768,32,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.052456 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.0512 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.0512 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 0 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Half(9216,576:16,24,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.157128 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.141 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.141 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 0 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(147456,576,24,1) -> Half(196608,768,32,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.052596 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.05002 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.05002 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 0 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(147456,576,24,1) -> Half(9216,576:16,24,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.042424 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.139784 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.042424 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Half(196608,768,32,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.048216 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.066428 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.048216 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 1002 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Half(9216,576:16,24,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: 204 to nvm (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.045888 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.028212 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.028212 [01/30/2023-11:12:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: Reformat Tactic: 0 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Float(1179648,9216,96,1) -> Half(1179648,9216,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.37324 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.583136 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.37324 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Float(1179648,9216,96,1) -> Half(73728,9216:16,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.271856 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 1.13059 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.271856 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(1179648,9216,96,1) -> Half(73728,9216:16,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.20648 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 1.10198 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.20648 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(73728,9216:16,96,1) -> Half(1179648,9216,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.905152 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.518876 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.518876 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(1179648,9216,96,1) -> Half(73728,9216:16,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(73728,9216:16,96,1) -> Half(1179648,9216,96,1) *************** [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(393216,3072,64,1) -> Half(18432,2304:16,48,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.123704 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.217268 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 0.123704 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(18432,2304:16,48,1) -> Half(393216,3072,64,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.206068 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.096848 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.096848 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(196608,768,32,1) -> Half(9216,576:16,24,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.127832 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.114528 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.114528 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning Reformat:Half(9216,576:16,24,1) -> Half(196608,768,32,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [01/30/2023-11:12:21] [V] [TRT] Tactic: 1002 Time: 0.102668 [01/30/2023-11:12:21] [V] [TRT] Tactic: 0 Time: 0.053568 [01/30/2023-11:12:21] [V] [TRT] Fastest Tactic: 0 Time: 0.053568 [01/30/2023-11:12:21] [V] [TRT] *************** Autotuning format combination: Half(1179648,9216,96,1), Half(1179648,9216,96,1), Half(393216,3072,64,1), Half(196608,768,32,1) -> Half(193536,9216,96,1), Half(64512,3072,64,1), Half(16128,768,32,1) *************** [01/30/2023-11:12:21] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_99...Conv_247]} (DLA) [01/30/2023-11:12:25] [V] [TRT] Tactic: 3 Time: 32.0604 [01/30/2023-11:12:25] [V] [TRT] Fastest Tactic: 3 Time: 32.0604 [01/30/2023-11:12:25] [V] [TRT] *************** Autotuning format combination: Half(1179648,9216,96,1), Half(1179648,9216,96,1), Half(393216,3072,64,1), Half(196608,768,32,1) -> Half(18432,9216:16,96,1), Half(4608,2304:16,48,1), Half(1152,576:16,24,1) *************** [01/30/2023-11:12:25] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_99...Conv_247]} (DLA) [01/30/2023-11:12:29] [V] [TRT] Tactic: 3 Time: 31.7931 [01/30/2023-11:12:29] [V] [TRT] Fastest Tactic: 3 Time: 31.7931 [01/30/2023-11:12:29] [V] [TRT] *************** Autotuning format combination: Half(73728,9216:16,96,1), Half(73728,9216:16,96,1), Half(18432,2304:16,48,1), Half(9216,576:16,24,1) -> Half(193536,9216,96,1), Half(64512,3072,64,1), Half(16128,768,32,1) *************** [01/30/2023-11:12:29] [V] [TRT] --------------- Timing Runner: {ForeignNode[Concat_99...Conv_247]} (DLA) Module_id 33 Severity 2 : NVMEDIA_DLA 684 Module_id 33 Severity 2 : Failed to bind input tensor. err : 0x00000b Module_id 33 Severity 2 : NVMEDIA_DLA 2866 Module_id 33 Severity 2 : Failed to bind input tensor args. status: 0x000007 [01/30/2023-11:12:30] [V] [TRT] Deleting timing cache: 57 entries, 5 hits [01/30/2023-11:12:30] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +0, now: CPU 984, GPU 12360 (MiB) [01/30/2023-11:12:30] [E] Error[1]: [nvdlaUtils.cpp::submit::198] Error Code 1: DLA (Failure to submit program to DLA engine.) [01/30/2023-11:12:30] [E] Error[2]: [builder.cpp::buildSerializedNetwork::417] Error Code 2: Internal Error (Assertion enginePtr != nullptr failed.) Segmentation fault (core dumped)