├── resources ├── imgs │ ├── blue_box_2_000021.jpg │ ├── blue_box_2_000022.jpg │ ├── blue_box_2_000024.jpg │ ├── blue_box_2_000025.jpg │ ├── blue_box_2_000026.jpg │ ├── blue_box_2_000029.jpg │ ├── blue_box_2_000246.jpg │ ├── yellow_box_1_000051.jpg │ ├── yellow_box_1_000091.jpg │ ├── yellow_box_1_000146.jpg │ └── LICENSE.txt └── model │ ├── LICENSE.txt │ └── deploy_model_algo_1-symbol.json ├── requirements.txt ├── .github └── PULL_REQUEST_TEMPLATE.md ├── CODE_OF_CONDUCT.md ├── src └── ml-inference │ ├── utils.py │ ├── mxnet_model │ ├── mxnet_input_transformer.py │ └── mxnet_model_factory.py │ └── parallize_inference_pool.py ├── LICENSE ├── README.md └── CONTRIBUTING.md /resources/imgs/blue_box_2_000021.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000021.jpg -------------------------------------------------------------------------------- /resources/imgs/blue_box_2_000022.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000022.jpg -------------------------------------------------------------------------------- /resources/imgs/blue_box_2_000024.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000024.jpg -------------------------------------------------------------------------------- /resources/imgs/blue_box_2_000025.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000025.jpg -------------------------------------------------------------------------------- /resources/imgs/blue_box_2_000026.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000026.jpg -------------------------------------------------------------------------------- /resources/imgs/blue_box_2_000029.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000029.jpg -------------------------------------------------------------------------------- /resources/imgs/blue_box_2_000246.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/blue_box_2_000246.jpg -------------------------------------------------------------------------------- /resources/imgs/yellow_box_1_000051.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/yellow_box_1_000051.jpg -------------------------------------------------------------------------------- /resources/imgs/yellow_box_1_000091.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/yellow_box_1_000091.jpg -------------------------------------------------------------------------------- /resources/imgs/yellow_box_1_000146.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/parallelize-ml-inference/HEAD/resources/imgs/yellow_box_1_000146.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # change the mxnet version depending on CUDA version or CPU only 2 | # mxnet-cu101 3 | mxnet 4 | opencv-python 5 | pandas 6 | # If running in greengrass 7 | # greengrasssdk -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | *Issue #, if available:* 2 | 3 | *Description of changes:* 4 | 5 | 6 | By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. 7 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | ## Code of Conduct 2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 4 | opensource-codeofconduct@amazon.com with any additional questions or comments. 5 | -------------------------------------------------------------------------------- /src/ml-inference/utils.py: -------------------------------------------------------------------------------- 1 | import multiprocessing 2 | import logging 3 | import mxnet as mx 4 | 5 | logger = logging.getLogger(__name__) 6 | 7 | 8 | def detect_gpus(): 9 | cpu_count = multiprocessing.cpu_count() 10 | logger.info("{} CPUs detected".format(cpu_count)) 11 | 12 | try: 13 | gpus = mx.test_utils.list_gpus() 14 | except: 15 | gpus = [] 16 | 17 | if len(gpus) == 0: 18 | logger.info("No GPU detected.") 19 | else: 20 | logger.info("{} GPUs detected".format(len(gpus))) 21 | 22 | return gpus 23 | -------------------------------------------------------------------------------- /src/ml-inference/mxnet_model/mxnet_input_transformer.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | from collections import namedtuple 4 | 5 | Batch = namedtuple('Batch', ['data']) 6 | 7 | import mxnet as mx 8 | 9 | def transform_input(filepath, reshape): 10 | # Switch RGB to BGR format (which ImageNet networks take) 11 | img = cv2.cvtColor(cv2.imread(filepath), cv2.COLOR_BGR2RGB) 12 | if img is None: 13 | return [] 14 | 15 | # Resize image to fit network input 16 | img = cv2.resize(img, reshape) 17 | img = np.swapaxes(img, 0, 2) 18 | img = np.swapaxes(img, 1, 2) 19 | img = img[np.newaxis, :] 20 | transformed = Batch([mx.nd.array(img)]) 21 | return transformed 22 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | this software and associated documentation files (the "Software"), to deal in 5 | the Software without restriction, including without limitation the rights to 6 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | -------------------------------------------------------------------------------- /resources/imgs/LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT No Attribution 2 | 3 | Copyright 2019 Amazon Web Services, inc 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of this 6 | software and associated documentation files (the "Software"), to deal in the Software 7 | without restriction, including without limitation the rights to use, copy, modify, 8 | merge, publish, distribute, sublicense, and/or sell copies of the Software, and to 9 | permit persons to whom the Software is furnished to do so. 10 | 11 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, 12 | INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A 13 | PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT 14 | HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION 15 | OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 16 | SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 17 | -------------------------------------------------------------------------------- /resources/model/LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT No Attribution 2 | 3 | Copyright 2019 Amazon Web Services, inc 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of this 6 | software and associated documentation files (the "Software"), to deal in the Software 7 | without restriction, including without limitation the rights to use, copy, modify, 8 | merge, publish, distribute, sublicense, and/or sell copies of the Software, and to 9 | permit persons to whom the Software is furnished to do so. 10 | 11 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, 12 | INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A 13 | PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT 14 | HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION 15 | OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 16 | SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 17 | -------------------------------------------------------------------------------- /src/ml-inference/mxnet_model/mxnet_model_factory.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os, time 3 | import mxnet as mx 4 | import numpy as np 5 | import cv2 6 | 7 | 8 | class MXNetModel: 9 | def __init__(self, param_path, input_shapes, context=mx.cpu(), label_names=[]): 10 | self.logger = logging.getLogger(__name__) 11 | 12 | self.logger.info("MXNet model init") 13 | start_time = time.time() 14 | 15 | self.logger.info('Loading network parameters with prefix: {}'.format(param_path)) 16 | sym, arg_params, aux_params = mx.model.load_checkpoint(param_path, 0) 17 | 18 | self.logger.info('Loading network into MXNet module and binding corresponding parameters') 19 | self.mod = mx.mod.Module(symbol=sym, label_names=label_names, context=context, logger=self.logger) 20 | self.mod.bind(for_training=False, data_shapes=input_shapes) 21 | self.mod.set_params(arg_params, aux_params) 22 | 23 | self.logger.info( 24 | "MXNet model loaded. Took {:10.2f} ms on PID {}".format((time.time() - start_time) * 1000, 25 | str(os.getpid()))) 26 | 27 | def infer(self, inference_input): 28 | self.mod.forward(inference_input) 29 | output = self.mod.get_outputs()[0].asnumpy() 30 | output = np.squeeze(output) 31 | results = [output[0].tolist()] 32 | return results 33 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Parallelize-ml-inference 2 | Sample code snippets for parallelizing across multiple CPU/GPUs on a single machine to speed up deep learning inference 3 | 4 | 5 | 6 | ## Instruction to run the inference script using python multiprocessing 7 | 8 | On a GPU-enabled machine (e.g. a p3 or p2 EC2 instance), make sure GPU drivers are properly installed along with MXNet: 9 | 10 | * Installing Cuda: https://developer.nvidia.com/cuda-downloads 11 | * Installing MXNet: http://mxnet.incubator.apache.org/versions/master/install/index.html 12 | 13 | Alternatively, launch an p3/p2 instance with [AWS Deep Learning AMIs](https://aws.amazon.com/machine-learning/amis/) that have preinstalled CUDA and ML frameworks. 14 | 15 | If you are using a EC2 instance crated from Deep Learning AMI, activate the conda environment: 16 | 17 | ``` 18 | source activate mxnet_p36 19 | ``` 20 | 21 | Download the source code and model artifacts 22 | 23 | ``` 24 | git clone https://github.com/aws-samples/parallelize-ml-inference.git 25 | cd parallelize-ml-inference/resources/model/ 26 | wget https://angelaw-workshop.s3.amazonaws.com/ml/od/model/model.tar.gz 27 | tar -xvzf model.tar.gz 28 | ``` 29 | 30 | Then run the script: 31 | 32 | ``` 33 | cd ~/parallelize-ml-inference 34 | export MXNET_CUDNN_AUTOTUNE_DEFAULT=0 35 | python3 src/parallize_inference_pool.py -n 100 -p 2 36 | ``` 37 | To see the list of supported arguments: 38 | 39 | ``` 40 | python3 src/parallize_inference_pool.py -h 41 | usage: parallize_inference_pool.py [-h] [-n NUMBER_OF_INFERENCE] 42 | [-p NUMBER_OF_PROCESS] 43 | [-m MODEL_PARAM_PATH] 44 | [-i INPUT_DIRECTORY_PATH] 45 | [-o OUTPUT_FILE_PATH] 46 | 47 | optional arguments: 48 | -h, --help show this help message and exit 49 | -n NUMBER_OF_INFERENCE, --number-of-inference NUMBER_OF_INFERENCE 50 | number of inferences to make. 51 | -p NUMBER_OF_PROCESS, --number-of-process NUMBER_OF_PROCESS 52 | size of processes pool. 53 | -m MODEL_PARAM_PATH, --model-param-path MODEL_PARAM_PATH 54 | path + prefix to the model parameter files. Default to 55 | ./resources/model/deploy_model_algo_1 56 | -i INPUT_DIRECTORY_PATH, --input-directory-path INPUT_DIRECTORY_PATH 57 | path to the input file directory to do inference on. 58 | Default is ./resources/imgs/ 59 | -o OUTPUT_FILE_PATH, --output-file-path OUTPUT_FILE_PATH 60 | path to the output file. Default is output.json 61 | ``` 62 | 63 | 64 | 65 | ## License Summary 66 | 67 | This sample code is made available under the MIT-0 license. See the LICENSE file. 68 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing Guidelines 2 | 3 | Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional 4 | documentation, we greatly value feedback and contributions from our community. 5 | 6 | Please read through this document before submitting any issues or pull requests to ensure we have all the necessary 7 | information to effectively respond to your bug report or contribution. 8 | 9 | 10 | ## Reporting Bugs/Feature Requests 11 | 12 | We welcome you to use the GitHub issue tracker to report bugs or suggest features. 13 | 14 | When filing an issue, please check [existing open](https://github.com/aws-samples/parallelize-ml-inference/issues), or [recently closed](https://github.com/aws-samples/parallelize-ml-inference/issues?utf8=%E2%9C%93&q=is%3Aissue%20is%3Aclosed%20), issues to make sure somebody else hasn't already 15 | reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: 16 | 17 | * A reproducible test case or series of steps 18 | * The version of our code being used 19 | * Any modifications you've made relevant to the bug 20 | * Anything unusual about your environment or deployment 21 | 22 | 23 | ## Contributing via Pull Requests 24 | Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that: 25 | 26 | 1. You are working against the latest source on the *master* branch. 27 | 2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already. 28 | 3. You open an issue to discuss any significant work - we would hate for your time to be wasted. 29 | 30 | To send us a pull request, please: 31 | 32 | 1. Fork the repository. 33 | 2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change. 34 | 3. Ensure local tests pass. 35 | 4. Commit to your fork using clear commit messages. 36 | 5. Send us a pull request, answering any default questions in the pull request interface. 37 | 6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation. 38 | 39 | GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and 40 | [creating a pull request](https://help.github.com/articles/creating-a-pull-request/). 41 | 42 | 43 | ## Finding contributions to work on 44 | Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any ['help wanted'](https://github.com/aws-samples/parallelize-ml-inference/labels/help%20wanted) issues is a great place to start. 45 | 46 | 47 | ## Code of Conduct 48 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 49 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 50 | opensource-codeofconduct@amazon.com with any additional questions or comments. 51 | 52 | 53 | ## Security issue notifications 54 | If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue. 55 | 56 | 57 | ## Licensing 58 | 59 | See the [LICENSE](https://github.com/aws-samples/parallelize-ml-inference/blob/master/LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution. 60 | 61 | We may ask you to sign a [Contributor License Agreement (CLA)](http://en.wikipedia.org/wiki/Contributor_License_Agreement) for larger changes. 62 | -------------------------------------------------------------------------------- /src/ml-inference/parallize_inference_pool.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import multiprocessing 3 | import os, json 4 | import time 5 | import argparse 6 | import pandas as pd 7 | import utils 8 | from itertools import cycle 9 | 10 | import mxnet as mx 11 | from mxnet_model.mxnet_model_factory import MXNetModel 12 | from mxnet_model.mxnet_input_transformer import transform_input 13 | 14 | 15 | logging.basicConfig(level=logging.INFO) 16 | logger = logging.getLogger(__name__) 17 | 18 | NUM_OF_PROCESS = 3 19 | IMAGE_DIM = 512 20 | INPUT_SHAPE = [('data', (1, 3, IMAGE_DIM, IMAGE_DIM))] 21 | 22 | ap = argparse.ArgumentParser() 23 | ap.add_argument("-n", "--number-of-inference", help="number of inferences to make. ", default=10) 24 | ap.add_argument("-p", "--number-of-process", help="size of processes pool. ", default=1) 25 | ap.add_argument("-m", "--model-param-path", 26 | help="path + prefix to the model parameter files. Default to ./resources/model/deploy_model_algo_1", 27 | default="./resources/model/deploy_model_algo_1") 28 | ap.add_argument("-i", "--input-directory-path", 29 | help="path to the input file directory to do inference on. Default is ./resources/imgs/ ", 30 | default='./resources/imgs/') 31 | ap.add_argument("-o", "--output-file-path", 32 | help="path to the output file. Default is output.json ", 33 | default='output.json') 34 | 35 | # This model object contains the loaded ML model. 36 | # Each Python child process will have its own independent copy of the Model object. 37 | # Allowing each child process to do inference using its own copy 38 | model = None 39 | 40 | 41 | def init_worker(gpus, model_param_path): 42 | """ 43 | This gets called to initialize the worker process. Here we load the ML model into GPU memory. 44 | Each worker process will pull an GPU ID from a queue of available IDs (e.g. [0, 1, 2, 3]) to ensure that multiple 45 | GPUs are consumed evenly. 46 | """ 47 | global model 48 | if not gpus.empty(): 49 | gpu_id = gpus.get() 50 | logger.info("Using GPU {} on pid {}".format(gpu_id, os.getpid())) 51 | ctx = mx.gpu(gpu_id) 52 | else: 53 | logger.info("Using CPU only on pid {}".format(os.getpid())) 54 | ctx = mx.cpu() 55 | model = MXNetModel(param_path=model_param_path, 56 | input_shapes=INPUT_SHAPE, 57 | context=ctx) 58 | 59 | 60 | def process_input_by_worker_process(input_file_path): 61 | logger.debug("processing input {} on pid {}".format(input, str(os.getpid()))) 62 | 63 | start_time = time.time() 64 | transformed_input = transform_input(input_file_path, reshape=(IMAGE_DIM, IMAGE_DIM)) 65 | transform_end_time = time.time() 66 | output = model.infer(transformed_input) 67 | inference_end_time = time.time() 68 | return {"file": input_file_path, 69 | "result": output, 70 | "transformTime": (transform_end_time - start_time) * 1000, 71 | "inferenceTime": (inference_end_time - transform_end_time) * 1000, 72 | } 73 | 74 | 75 | def run_inference_in_process_pool(model_param_path, input_paths, num_process, output_path): 76 | # If GPUs are available, create a queue that loops through the GPU IDs. 77 | # For example, if there are 4 worker processes and 4 GPUs, the queue contains [0, 1, 2, 3] 78 | # If there are 4 worker processes and 2 GPUs the queue contains [0, 1, 0 ,1] 79 | gpus = utils.detect_gpus() 80 | gpu_ids = multiprocessing.Queue() 81 | if len(gpus) > 0: 82 | gpu_id_cycle_iterator = cycle(gpus) 83 | for i in range(num_process): 84 | gpu_ids.put(next(gpu_id_cycle_iterator)) 85 | 86 | # Initialize process pool 87 | process_pool = multiprocessing.Pool(processes=num_process, initializer=init_worker, initargs=(gpu_ids, model_param_path)) 88 | 89 | start = time.time() 90 | 91 | # Feed inputs to process pool to do inference 92 | pool_output = process_pool.map(process_input_by_worker_process, input_paths) 93 | 94 | logger.info('Processed {} images on {} processes for {:10.4f} seconds '.format(len(input_paths), num_process, 95 | time.time() - start)) 96 | write_output_file(pool_output, output_path) 97 | 98 | df = pd.read_json(output_path, lines=True) 99 | logger.info('Per input timing stats (unit: ms): \n {}'.format(df.describe())) 100 | 101 | 102 | def write_output_file(output, file_path): 103 | with open(file_path, 'w') as f: 104 | for line in output: 105 | f.write(json.dumps(line)) 106 | f.write('\n') 107 | 108 | 109 | 110 | 111 | def generate_input_path_list(input_directory_path, num_inference): 112 | """ 113 | :return: A list of files from the input directory, repeated enough times to have at least 'num_inference' elements 114 | """ 115 | input_files = os.listdir(input_directory_path) 116 | input_paths = [] 117 | for file in input_files: 118 | input_paths.append(os.path.join(input_directory_path, file)) 119 | repeated_inputs = [] 120 | while len(repeated_inputs) < num_inference: 121 | repeated_inputs += input_paths 122 | return repeated_inputs 123 | 124 | 125 | def main(): 126 | # Parse command line arguments 127 | args = vars(ap.parse_args()) 128 | num_inference = int(args["number_of_inference"]) 129 | num_process = int(args["number_of_process"]) 130 | model_param_path = args["model_param_path"] 131 | input_directory_path = args["input_directory_path"] 132 | output_path = args["output_file_path"] 133 | logger.info("parameters: \n{}".format(args)) 134 | 135 | # Read a list of files from the input directory. Repeat them until we generate enough number of inputs 136 | repeated_inputs = generate_input_path_list(input_directory_path, num_inference) 137 | logger.info("will process {} images".format(len(repeated_inputs))) 138 | 139 | run_inference_in_process_pool(model_param_path, repeated_inputs, num_process, output_path) 140 | 141 | 142 | if __name__ == "__main__": 143 | main() 144 | -------------------------------------------------------------------------------- /resources/model/deploy_model_algo_1-symbol.json: -------------------------------------------------------------------------------- 1 | { 2 | "nodes": [ 3 | { 4 | "op": "null", 5 | "name": "data", 6 | "inputs": [] 7 | }, 8 | { 9 | "op": "_copy", 10 | "name": "id", 11 | "inputs": [[0, 0, 0]] 12 | }, 13 | { 14 | "op": "null", 15 | "name": "bn_data_gamma", 16 | "attrs": { 17 | "eps": "2e-05", 18 | "fix_gamma": "True", 19 | "momentum": "0.9" 20 | }, 21 | "inputs": [] 22 | }, 23 | { 24 | "op": "null", 25 | "name": "bn_data_beta", 26 | "attrs": { 27 | "eps": "2e-05", 28 | "fix_gamma": "True", 29 | "momentum": "0.9" 30 | }, 31 | "inputs": [] 32 | }, 33 | { 34 | "op": "null", 35 | "name": "bn_data_moving_mean", 36 | "attrs": { 37 | "__init__": "[\"zero\", {}]", 38 | "eps": "2e-05", 39 | "fix_gamma": "True", 40 | "momentum": "0.9" 41 | }, 42 | "inputs": [] 43 | }, 44 | { 45 | "op": "null", 46 | "name": "bn_data_moving_var", 47 | "attrs": { 48 | "__init__": "[\"one\", {}]", 49 | "eps": "2e-05", 50 | "fix_gamma": "True", 51 | "momentum": "0.9" 52 | }, 53 | "inputs": [] 54 | }, 55 | { 56 | "op": "BatchNorm", 57 | "name": "bn_data", 58 | "attrs": { 59 | "eps": "2e-05", 60 | "fix_gamma": "True", 61 | "momentum": "0.9" 62 | }, 63 | "inputs": [[1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 1], [5, 0, 1]] 64 | }, 65 | { 66 | "op": "null", 67 | "name": "conv0_weight", 68 | "attrs": { 69 | "kernel": "(7, 7)", 70 | "no_bias": "True", 71 | "num_filter": "64", 72 | "pad": "(3, 3)", 73 | "stride": "(2, 2)", 74 | "workspace": "256" 75 | }, 76 | "inputs": [] 77 | }, 78 | { 79 | "op": "Convolution", 80 | "name": "conv0", 81 | "attrs": { 82 | "kernel": "(7, 7)", 83 | "no_bias": "True", 84 | "num_filter": "64", 85 | "pad": "(3, 3)", 86 | "stride": "(2, 2)", 87 | "workspace": "256" 88 | }, 89 | "inputs": [[6, 0, 0], [7, 0, 0]] 90 | }, 91 | { 92 | "op": "null", 93 | "name": "bn0_gamma", 94 | "attrs": { 95 | "eps": "2e-05", 96 | "fix_gamma": "False", 97 | "momentum": "0.9" 98 | }, 99 | "inputs": [] 100 | }, 101 | { 102 | "op": "null", 103 | "name": "bn0_beta", 104 | "attrs": { 105 | "eps": "2e-05", 106 | "fix_gamma": "False", 107 | "momentum": "0.9" 108 | }, 109 | "inputs": [] 110 | }, 111 | { 112 | "op": "null", 113 | "name": "bn0_moving_mean", 114 | "attrs": { 115 | "__init__": "[\"zero\", {}]", 116 | "eps": "2e-05", 117 | "fix_gamma": "False", 118 | "momentum": "0.9" 119 | }, 120 | "inputs": [] 121 | }, 122 | { 123 | "op": "null", 124 | "name": "bn0_moving_var", 125 | "attrs": { 126 | "__init__": "[\"one\", {}]", 127 | "eps": "2e-05", 128 | "fix_gamma": "False", 129 | "momentum": "0.9" 130 | }, 131 | "inputs": [] 132 | }, 133 | { 134 | "op": "BatchNorm", 135 | "name": "bn0", 136 | "attrs": { 137 | "eps": "2e-05", 138 | "fix_gamma": "False", 139 | "momentum": "0.9" 140 | }, 141 | "inputs": [[8, 0, 0], [9, 0, 0], [10, 0, 0], [11, 0, 1], [12, 0, 1]] 142 | }, 143 | { 144 | "op": "Activation", 145 | "name": "relu0", 146 | "attrs": {"act_type": "relu"}, 147 | "inputs": [[13, 0, 0]] 148 | }, 149 | { 150 | "op": "Pooling", 151 | "name": "pooling0", 152 | "attrs": { 153 | "kernel": "(3, 3)", 154 | "pad": "(1, 1)", 155 | "pool_type": "max", 156 | "stride": "(2, 2)" 157 | }, 158 | "inputs": [[14, 0, 0]] 159 | }, 160 | { 161 | "op": "null", 162 | "name": "stage1_unit1_bn1_gamma", 163 | "attrs": { 164 | "eps": "2e-05", 165 | "fix_gamma": "False", 166 | "momentum": "0.9" 167 | }, 168 | "inputs": [] 169 | }, 170 | { 171 | "op": "null", 172 | "name": "stage1_unit1_bn1_beta", 173 | "attrs": { 174 | "eps": "2e-05", 175 | "fix_gamma": "False", 176 | "momentum": "0.9" 177 | }, 178 | "inputs": [] 179 | }, 180 | { 181 | "op": "null", 182 | "name": "stage1_unit1_bn1_moving_mean", 183 | "attrs": { 184 | "__init__": "[\"zero\", {}]", 185 | "eps": "2e-05", 186 | "fix_gamma": "False", 187 | "momentum": "0.9" 188 | }, 189 | "inputs": [] 190 | }, 191 | { 192 | "op": "null", 193 | "name": "stage1_unit1_bn1_moving_var", 194 | "attrs": { 195 | "__init__": "[\"one\", {}]", 196 | "eps": "2e-05", 197 | "fix_gamma": "False", 198 | "momentum": "0.9" 199 | }, 200 | "inputs": [] 201 | }, 202 | { 203 | "op": "BatchNorm", 204 | "name": "stage1_unit1_bn1", 205 | "attrs": { 206 | "eps": "2e-05", 207 | "fix_gamma": "False", 208 | "momentum": "0.9" 209 | }, 210 | "inputs": [[15, 0, 0], [16, 0, 0], [17, 0, 0], [18, 0, 1], [19, 0, 1]] 211 | }, 212 | { 213 | "op": "Activation", 214 | "name": "stage1_unit1_relu1", 215 | "attrs": {"act_type": "relu"}, 216 | "inputs": [[20, 0, 0]] 217 | }, 218 | { 219 | "op": "null", 220 | "name": "stage1_unit1_conv1_weight", 221 | "attrs": { 222 | "kernel": "(1, 1)", 223 | "no_bias": "True", 224 | "num_filter": "64", 225 | "pad": "(0, 0)", 226 | "stride": "(1, 1)", 227 | "workspace": "256" 228 | }, 229 | "inputs": [] 230 | }, 231 | { 232 | "op": "Convolution", 233 | "name": "stage1_unit1_conv1", 234 | "attrs": { 235 | "kernel": "(1, 1)", 236 | "no_bias": "True", 237 | "num_filter": "64", 238 | "pad": "(0, 0)", 239 | "stride": "(1, 1)", 240 | "workspace": "256" 241 | }, 242 | "inputs": [[21, 0, 0], [22, 0, 0]] 243 | }, 244 | { 245 | "op": "null", 246 | "name": "stage1_unit1_bn2_gamma", 247 | "attrs": { 248 | "eps": "2e-05", 249 | "fix_gamma": "False", 250 | "momentum": "0.9" 251 | }, 252 | "inputs": [] 253 | }, 254 | { 255 | "op": "null", 256 | "name": "stage1_unit1_bn2_beta", 257 | "attrs": { 258 | "eps": "2e-05", 259 | "fix_gamma": "False", 260 | "momentum": "0.9" 261 | }, 262 | "inputs": [] 263 | }, 264 | { 265 | "op": "null", 266 | "name": "stage1_unit1_bn2_moving_mean", 267 | "attrs": { 268 | "__init__": "[\"zero\", {}]", 269 | "eps": "2e-05", 270 | "fix_gamma": "False", 271 | "momentum": "0.9" 272 | }, 273 | "inputs": [] 274 | }, 275 | { 276 | "op": "null", 277 | "name": "stage1_unit1_bn2_moving_var", 278 | "attrs": { 279 | "__init__": "[\"one\", {}]", 280 | "eps": "2e-05", 281 | "fix_gamma": "False", 282 | "momentum": "0.9" 283 | }, 284 | "inputs": [] 285 | }, 286 | { 287 | "op": "BatchNorm", 288 | "name": "stage1_unit1_bn2", 289 | "attrs": { 290 | "eps": "2e-05", 291 | "fix_gamma": "False", 292 | "momentum": "0.9" 293 | }, 294 | "inputs": [[23, 0, 0], [24, 0, 0], [25, 0, 0], [26, 0, 1], [27, 0, 1]] 295 | }, 296 | { 297 | "op": "Activation", 298 | "name": "stage1_unit1_relu2", 299 | "attrs": {"act_type": "relu"}, 300 | "inputs": [[28, 0, 0]] 301 | }, 302 | { 303 | "op": "null", 304 | "name": "stage1_unit1_conv2_weight", 305 | "attrs": { 306 | "kernel": "(3, 3)", 307 | "no_bias": "True", 308 | "num_filter": "64", 309 | "pad": "(1, 1)", 310 | "stride": "(1, 1)", 311 | "workspace": "256" 312 | }, 313 | "inputs": [] 314 | }, 315 | { 316 | "op": "Convolution", 317 | "name": "stage1_unit1_conv2", 318 | "attrs": { 319 | "kernel": "(3, 3)", 320 | "no_bias": "True", 321 | "num_filter": "64", 322 | "pad": "(1, 1)", 323 | "stride": "(1, 1)", 324 | "workspace": "256" 325 | }, 326 | "inputs": [[29, 0, 0], [30, 0, 0]] 327 | }, 328 | { 329 | "op": "null", 330 | "name": "stage1_unit1_bn3_gamma", 331 | "attrs": { 332 | "eps": "2e-05", 333 | "fix_gamma": "False", 334 | "momentum": "0.9" 335 | }, 336 | "inputs": [] 337 | }, 338 | { 339 | "op": "null", 340 | "name": "stage1_unit1_bn3_beta", 341 | "attrs": { 342 | "eps": "2e-05", 343 | "fix_gamma": "False", 344 | "momentum": "0.9" 345 | }, 346 | "inputs": [] 347 | }, 348 | { 349 | "op": "null", 350 | "name": "stage1_unit1_bn3_moving_mean", 351 | "attrs": { 352 | "__init__": "[\"zero\", {}]", 353 | "eps": "2e-05", 354 | "fix_gamma": "False", 355 | "momentum": "0.9" 356 | }, 357 | "inputs": [] 358 | }, 359 | { 360 | "op": "null", 361 | "name": "stage1_unit1_bn3_moving_var", 362 | "attrs": { 363 | "__init__": "[\"one\", {}]", 364 | "eps": "2e-05", 365 | "fix_gamma": "False", 366 | "momentum": "0.9" 367 | }, 368 | "inputs": [] 369 | }, 370 | { 371 | "op": "BatchNorm", 372 | "name": "stage1_unit1_bn3", 373 | "attrs": { 374 | "eps": "2e-05", 375 | "fix_gamma": "False", 376 | "momentum": "0.9" 377 | }, 378 | "inputs": [[31, 0, 0], [32, 0, 0], [33, 0, 0], [34, 0, 1], [35, 0, 1]] 379 | }, 380 | { 381 | "op": "Activation", 382 | "name": "stage1_unit1_relu3", 383 | "attrs": {"act_type": "relu"}, 384 | "inputs": [[36, 0, 0]] 385 | }, 386 | { 387 | "op": "null", 388 | "name": "stage1_unit1_conv3_weight", 389 | "attrs": { 390 | "kernel": "(1, 1)", 391 | "no_bias": "True", 392 | "num_filter": "256", 393 | "pad": "(0, 0)", 394 | "stride": "(1, 1)", 395 | "workspace": "256" 396 | }, 397 | "inputs": [] 398 | }, 399 | { 400 | "op": "Convolution", 401 | "name": "stage1_unit1_conv3", 402 | "attrs": { 403 | "kernel": "(1, 1)", 404 | "no_bias": "True", 405 | "num_filter": "256", 406 | "pad": "(0, 0)", 407 | "stride": "(1, 1)", 408 | "workspace": "256" 409 | }, 410 | "inputs": [[37, 0, 0], [38, 0, 0]] 411 | }, 412 | { 413 | "op": "null", 414 | "name": "stage1_unit1_sc_weight", 415 | "attrs": { 416 | "kernel": "(1, 1)", 417 | "no_bias": "True", 418 | "num_filter": "256", 419 | "stride": "(1, 1)", 420 | "workspace": "256" 421 | }, 422 | "inputs": [] 423 | }, 424 | { 425 | "op": "Convolution", 426 | "name": "stage1_unit1_sc", 427 | "attrs": { 428 | "kernel": "(1, 1)", 429 | "no_bias": "True", 430 | "num_filter": "256", 431 | "stride": "(1, 1)", 432 | "workspace": "256" 433 | }, 434 | "inputs": [[21, 0, 0], [40, 0, 0]] 435 | }, 436 | { 437 | "op": "elemwise_add", 438 | "name": "_plus0", 439 | "inputs": [[39, 0, 0], [41, 0, 0]] 440 | }, 441 | { 442 | "op": "null", 443 | "name": "stage1_unit2_bn1_gamma", 444 | "attrs": { 445 | "eps": "2e-05", 446 | "fix_gamma": "False", 447 | "momentum": "0.9" 448 | }, 449 | "inputs": [] 450 | }, 451 | { 452 | "op": "null", 453 | "name": "stage1_unit2_bn1_beta", 454 | "attrs": { 455 | "eps": "2e-05", 456 | "fix_gamma": "False", 457 | "momentum": "0.9" 458 | }, 459 | "inputs": [] 460 | }, 461 | { 462 | "op": "null", 463 | "name": "stage1_unit2_bn1_moving_mean", 464 | "attrs": { 465 | "__init__": "[\"zero\", {}]", 466 | "eps": "2e-05", 467 | "fix_gamma": "False", 468 | "momentum": "0.9" 469 | }, 470 | "inputs": [] 471 | }, 472 | { 473 | "op": "null", 474 | "name": "stage1_unit2_bn1_moving_var", 475 | "attrs": { 476 | "__init__": "[\"one\", {}]", 477 | "eps": "2e-05", 478 | "fix_gamma": "False", 479 | "momentum": "0.9" 480 | }, 481 | "inputs": [] 482 | }, 483 | { 484 | "op": "BatchNorm", 485 | "name": "stage1_unit2_bn1", 486 | "attrs": { 487 | "eps": "2e-05", 488 | "fix_gamma": "False", 489 | "momentum": "0.9" 490 | }, 491 | "inputs": [[42, 0, 0], [43, 0, 0], [44, 0, 0], [45, 0, 1], [46, 0, 1]] 492 | }, 493 | { 494 | "op": "Activation", 495 | "name": "stage1_unit2_relu1", 496 | "attrs": {"act_type": "relu"}, 497 | "inputs": [[47, 0, 0]] 498 | }, 499 | { 500 | "op": "null", 501 | "name": "stage1_unit2_conv1_weight", 502 | "attrs": { 503 | "kernel": "(1, 1)", 504 | "no_bias": "True", 505 | "num_filter": "64", 506 | "pad": "(0, 0)", 507 | "stride": "(1, 1)", 508 | "workspace": "256" 509 | }, 510 | "inputs": [] 511 | }, 512 | { 513 | "op": "Convolution", 514 | "name": "stage1_unit2_conv1", 515 | "attrs": { 516 | "kernel": "(1, 1)", 517 | "no_bias": "True", 518 | "num_filter": "64", 519 | "pad": "(0, 0)", 520 | "stride": "(1, 1)", 521 | "workspace": "256" 522 | }, 523 | "inputs": [[48, 0, 0], [49, 0, 0]] 524 | }, 525 | { 526 | "op": "null", 527 | "name": "stage1_unit2_bn2_gamma", 528 | "attrs": { 529 | "eps": "2e-05", 530 | "fix_gamma": "False", 531 | "momentum": "0.9" 532 | }, 533 | "inputs": [] 534 | }, 535 | { 536 | "op": "null", 537 | "name": "stage1_unit2_bn2_beta", 538 | "attrs": { 539 | "eps": "2e-05", 540 | "fix_gamma": "False", 541 | "momentum": "0.9" 542 | }, 543 | "inputs": [] 544 | }, 545 | { 546 | "op": "null", 547 | "name": "stage1_unit2_bn2_moving_mean", 548 | "attrs": { 549 | "__init__": "[\"zero\", {}]", 550 | "eps": "2e-05", 551 | "fix_gamma": "False", 552 | "momentum": "0.9" 553 | }, 554 | "inputs": [] 555 | }, 556 | { 557 | "op": "null", 558 | "name": "stage1_unit2_bn2_moving_var", 559 | "attrs": { 560 | "__init__": "[\"one\", {}]", 561 | "eps": "2e-05", 562 | "fix_gamma": "False", 563 | "momentum": "0.9" 564 | }, 565 | "inputs": [] 566 | }, 567 | { 568 | "op": "BatchNorm", 569 | "name": "stage1_unit2_bn2", 570 | "attrs": { 571 | "eps": "2e-05", 572 | "fix_gamma": "False", 573 | "momentum": "0.9" 574 | }, 575 | "inputs": [[50, 0, 0], [51, 0, 0], [52, 0, 0], [53, 0, 1], [54, 0, 1]] 576 | }, 577 | { 578 | "op": "Activation", 579 | "name": "stage1_unit2_relu2", 580 | "attrs": {"act_type": "relu"}, 581 | "inputs": [[55, 0, 0]] 582 | }, 583 | { 584 | "op": "null", 585 | "name": "stage1_unit2_conv2_weight", 586 | "attrs": { 587 | "kernel": "(3, 3)", 588 | "no_bias": "True", 589 | "num_filter": "64", 590 | "pad": "(1, 1)", 591 | "stride": "(1, 1)", 592 | "workspace": "256" 593 | }, 594 | "inputs": [] 595 | }, 596 | { 597 | "op": "Convolution", 598 | "name": "stage1_unit2_conv2", 599 | "attrs": { 600 | "kernel": "(3, 3)", 601 | "no_bias": "True", 602 | "num_filter": "64", 603 | "pad": "(1, 1)", 604 | "stride": "(1, 1)", 605 | "workspace": "256" 606 | }, 607 | "inputs": [[56, 0, 0], [57, 0, 0]] 608 | }, 609 | { 610 | "op": "null", 611 | "name": "stage1_unit2_bn3_gamma", 612 | "attrs": { 613 | "eps": "2e-05", 614 | "fix_gamma": "False", 615 | "momentum": "0.9" 616 | }, 617 | "inputs": [] 618 | }, 619 | { 620 | "op": "null", 621 | "name": "stage1_unit2_bn3_beta", 622 | "attrs": { 623 | "eps": "2e-05", 624 | "fix_gamma": "False", 625 | "momentum": "0.9" 626 | }, 627 | "inputs": [] 628 | }, 629 | { 630 | "op": "null", 631 | "name": "stage1_unit2_bn3_moving_mean", 632 | "attrs": { 633 | "__init__": "[\"zero\", {}]", 634 | "eps": "2e-05", 635 | "fix_gamma": "False", 636 | "momentum": "0.9" 637 | }, 638 | "inputs": [] 639 | }, 640 | { 641 | "op": "null", 642 | "name": "stage1_unit2_bn3_moving_var", 643 | "attrs": { 644 | "__init__": "[\"one\", {}]", 645 | "eps": "2e-05", 646 | "fix_gamma": "False", 647 | "momentum": "0.9" 648 | }, 649 | "inputs": [] 650 | }, 651 | { 652 | "op": "BatchNorm", 653 | "name": "stage1_unit2_bn3", 654 | "attrs": { 655 | "eps": "2e-05", 656 | "fix_gamma": "False", 657 | "momentum": "0.9" 658 | }, 659 | "inputs": [[58, 0, 0], [59, 0, 0], [60, 0, 0], [61, 0, 1], [62, 0, 1]] 660 | }, 661 | { 662 | "op": "Activation", 663 | "name": "stage1_unit2_relu3", 664 | "attrs": {"act_type": "relu"}, 665 | "inputs": [[63, 0, 0]] 666 | }, 667 | { 668 | "op": "null", 669 | "name": "stage1_unit2_conv3_weight", 670 | "attrs": { 671 | "kernel": "(1, 1)", 672 | "no_bias": "True", 673 | "num_filter": "256", 674 | "pad": "(0, 0)", 675 | "stride": "(1, 1)", 676 | "workspace": "256" 677 | }, 678 | "inputs": [] 679 | }, 680 | { 681 | "op": "Convolution", 682 | "name": "stage1_unit2_conv3", 683 | "attrs": { 684 | "kernel": "(1, 1)", 685 | "no_bias": "True", 686 | "num_filter": "256", 687 | "pad": "(0, 0)", 688 | "stride": "(1, 1)", 689 | "workspace": "256" 690 | }, 691 | "inputs": [[64, 0, 0], [65, 0, 0]] 692 | }, 693 | { 694 | "op": "elemwise_add", 695 | "name": "_plus1", 696 | "inputs": [[66, 0, 0], [42, 0, 0]] 697 | }, 698 | { 699 | "op": "null", 700 | "name": "stage1_unit3_bn1_gamma", 701 | "attrs": { 702 | "eps": "2e-05", 703 | "fix_gamma": "False", 704 | "momentum": "0.9" 705 | }, 706 | "inputs": [] 707 | }, 708 | { 709 | "op": "null", 710 | "name": "stage1_unit3_bn1_beta", 711 | "attrs": { 712 | "eps": "2e-05", 713 | "fix_gamma": "False", 714 | "momentum": "0.9" 715 | }, 716 | "inputs": [] 717 | }, 718 | { 719 | "op": "null", 720 | "name": "stage1_unit3_bn1_moving_mean", 721 | "attrs": { 722 | "__init__": "[\"zero\", {}]", 723 | "eps": "2e-05", 724 | "fix_gamma": "False", 725 | "momentum": "0.9" 726 | }, 727 | "inputs": [] 728 | }, 729 | { 730 | "op": "null", 731 | "name": "stage1_unit3_bn1_moving_var", 732 | "attrs": { 733 | "__init__": "[\"one\", {}]", 734 | "eps": "2e-05", 735 | "fix_gamma": "False", 736 | "momentum": "0.9" 737 | }, 738 | "inputs": [] 739 | }, 740 | { 741 | "op": "BatchNorm", 742 | "name": "stage1_unit3_bn1", 743 | "attrs": { 744 | "eps": "2e-05", 745 | "fix_gamma": "False", 746 | "momentum": "0.9" 747 | }, 748 | "inputs": [[67, 0, 0], [68, 0, 0], [69, 0, 0], [70, 0, 1], [71, 0, 1]] 749 | }, 750 | { 751 | "op": "Activation", 752 | "name": "stage1_unit3_relu1", 753 | "attrs": {"act_type": "relu"}, 754 | "inputs": [[72, 0, 0]] 755 | }, 756 | { 757 | "op": "null", 758 | "name": "stage1_unit3_conv1_weight", 759 | "attrs": { 760 | "kernel": "(1, 1)", 761 | "no_bias": "True", 762 | "num_filter": "64", 763 | "pad": "(0, 0)", 764 | "stride": "(1, 1)", 765 | "workspace": "256" 766 | }, 767 | "inputs": [] 768 | }, 769 | { 770 | "op": "Convolution", 771 | "name": "stage1_unit3_conv1", 772 | "attrs": { 773 | "kernel": "(1, 1)", 774 | "no_bias": "True", 775 | "num_filter": "64", 776 | "pad": "(0, 0)", 777 | "stride": "(1, 1)", 778 | "workspace": "256" 779 | }, 780 | "inputs": [[73, 0, 0], [74, 0, 0]] 781 | }, 782 | { 783 | "op": "null", 784 | "name": "stage1_unit3_bn2_gamma", 785 | "attrs": { 786 | "eps": "2e-05", 787 | "fix_gamma": "False", 788 | "momentum": "0.9" 789 | }, 790 | "inputs": [] 791 | }, 792 | { 793 | "op": "null", 794 | "name": "stage1_unit3_bn2_beta", 795 | "attrs": { 796 | "eps": "2e-05", 797 | "fix_gamma": "False", 798 | "momentum": "0.9" 799 | }, 800 | "inputs": [] 801 | }, 802 | { 803 | "op": "null", 804 | "name": "stage1_unit3_bn2_moving_mean", 805 | "attrs": { 806 | "__init__": "[\"zero\", {}]", 807 | "eps": "2e-05", 808 | "fix_gamma": "False", 809 | "momentum": "0.9" 810 | }, 811 | "inputs": [] 812 | }, 813 | { 814 | "op": "null", 815 | "name": "stage1_unit3_bn2_moving_var", 816 | "attrs": { 817 | "__init__": "[\"one\", {}]", 818 | "eps": "2e-05", 819 | "fix_gamma": "False", 820 | "momentum": "0.9" 821 | }, 822 | "inputs": [] 823 | }, 824 | { 825 | "op": "BatchNorm", 826 | "name": "stage1_unit3_bn2", 827 | "attrs": { 828 | "eps": "2e-05", 829 | "fix_gamma": "False", 830 | "momentum": "0.9" 831 | }, 832 | "inputs": [[75, 0, 0], [76, 0, 0], [77, 0, 0], [78, 0, 1], [79, 0, 1]] 833 | }, 834 | { 835 | "op": "Activation", 836 | "name": "stage1_unit3_relu2", 837 | "attrs": {"act_type": "relu"}, 838 | "inputs": [[80, 0, 0]] 839 | }, 840 | { 841 | "op": "null", 842 | "name": "stage1_unit3_conv2_weight", 843 | "attrs": { 844 | "kernel": "(3, 3)", 845 | "no_bias": "True", 846 | "num_filter": "64", 847 | "pad": "(1, 1)", 848 | "stride": "(1, 1)", 849 | "workspace": "256" 850 | }, 851 | "inputs": [] 852 | }, 853 | { 854 | "op": "Convolution", 855 | "name": "stage1_unit3_conv2", 856 | "attrs": { 857 | "kernel": "(3, 3)", 858 | "no_bias": "True", 859 | "num_filter": "64", 860 | "pad": "(1, 1)", 861 | "stride": "(1, 1)", 862 | "workspace": "256" 863 | }, 864 | "inputs": [[81, 0, 0], [82, 0, 0]] 865 | }, 866 | { 867 | "op": "null", 868 | "name": "stage1_unit3_bn3_gamma", 869 | "attrs": { 870 | "eps": "2e-05", 871 | "fix_gamma": "False", 872 | "momentum": "0.9" 873 | }, 874 | "inputs": [] 875 | }, 876 | { 877 | "op": "null", 878 | "name": "stage1_unit3_bn3_beta", 879 | "attrs": { 880 | "eps": "2e-05", 881 | "fix_gamma": "False", 882 | "momentum": "0.9" 883 | }, 884 | "inputs": [] 885 | }, 886 | { 887 | "op": "null", 888 | "name": "stage1_unit3_bn3_moving_mean", 889 | "attrs": { 890 | "__init__": "[\"zero\", {}]", 891 | "eps": "2e-05", 892 | "fix_gamma": "False", 893 | "momentum": "0.9" 894 | }, 895 | "inputs": [] 896 | }, 897 | { 898 | "op": "null", 899 | "name": "stage1_unit3_bn3_moving_var", 900 | "attrs": { 901 | "__init__": "[\"one\", {}]", 902 | "eps": "2e-05", 903 | "fix_gamma": "False", 904 | "momentum": "0.9" 905 | }, 906 | "inputs": [] 907 | }, 908 | { 909 | "op": "BatchNorm", 910 | "name": "stage1_unit3_bn3", 911 | "attrs": { 912 | "eps": "2e-05", 913 | "fix_gamma": "False", 914 | "momentum": "0.9" 915 | }, 916 | "inputs": [[83, 0, 0], [84, 0, 0], [85, 0, 0], [86, 0, 1], [87, 0, 1]] 917 | }, 918 | { 919 | "op": "Activation", 920 | "name": "stage1_unit3_relu3", 921 | "attrs": {"act_type": "relu"}, 922 | "inputs": [[88, 0, 0]] 923 | }, 924 | { 925 | "op": "null", 926 | "name": "stage1_unit3_conv3_weight", 927 | "attrs": { 928 | "kernel": "(1, 1)", 929 | "no_bias": "True", 930 | "num_filter": "256", 931 | "pad": "(0, 0)", 932 | "stride": "(1, 1)", 933 | "workspace": "256" 934 | }, 935 | "inputs": [] 936 | }, 937 | { 938 | "op": "Convolution", 939 | "name": "stage1_unit3_conv3", 940 | "attrs": { 941 | "kernel": "(1, 1)", 942 | "no_bias": "True", 943 | "num_filter": "256", 944 | "pad": "(0, 0)", 945 | "stride": "(1, 1)", 946 | "workspace": "256" 947 | }, 948 | "inputs": [[89, 0, 0], [90, 0, 0]] 949 | }, 950 | { 951 | "op": "elemwise_add", 952 | "name": "_plus2", 953 | "inputs": [[91, 0, 0], [67, 0, 0]] 954 | }, 955 | { 956 | "op": "null", 957 | "name": "stage2_unit1_bn1_gamma", 958 | "attrs": { 959 | "eps": "2e-05", 960 | "fix_gamma": "False", 961 | "momentum": "0.9" 962 | }, 963 | "inputs": [] 964 | }, 965 | { 966 | "op": "null", 967 | "name": "stage2_unit1_bn1_beta", 968 | "attrs": { 969 | "eps": "2e-05", 970 | "fix_gamma": "False", 971 | "momentum": "0.9" 972 | }, 973 | "inputs": [] 974 | }, 975 | { 976 | "op": "null", 977 | "name": "stage2_unit1_bn1_moving_mean", 978 | "attrs": { 979 | "__init__": "[\"zero\", {}]", 980 | "eps": "2e-05", 981 | "fix_gamma": "False", 982 | "momentum": "0.9" 983 | }, 984 | "inputs": [] 985 | }, 986 | { 987 | "op": "null", 988 | "name": "stage2_unit1_bn1_moving_var", 989 | "attrs": { 990 | "__init__": "[\"one\", {}]", 991 | "eps": "2e-05", 992 | "fix_gamma": "False", 993 | "momentum": "0.9" 994 | }, 995 | "inputs": [] 996 | }, 997 | { 998 | "op": "BatchNorm", 999 | "name": "stage2_unit1_bn1", 1000 | "attrs": { 1001 | "eps": "2e-05", 1002 | "fix_gamma": "False", 1003 | "momentum": "0.9" 1004 | }, 1005 | "inputs": [[92, 0, 0], [93, 0, 0], [94, 0, 0], [95, 0, 1], [96, 0, 1]] 1006 | }, 1007 | { 1008 | "op": "Activation", 1009 | "name": "stage2_unit1_relu1", 1010 | "attrs": {"act_type": "relu"}, 1011 | "inputs": [[97, 0, 0]] 1012 | }, 1013 | { 1014 | "op": "null", 1015 | "name": "stage2_unit1_conv1_weight", 1016 | "attrs": { 1017 | "kernel": "(1, 1)", 1018 | "no_bias": "True", 1019 | "num_filter": "128", 1020 | "pad": "(0, 0)", 1021 | "stride": "(1, 1)", 1022 | "workspace": "256" 1023 | }, 1024 | "inputs": [] 1025 | }, 1026 | { 1027 | "op": "Convolution", 1028 | "name": "stage2_unit1_conv1", 1029 | "attrs": { 1030 | "kernel": "(1, 1)", 1031 | "no_bias": "True", 1032 | "num_filter": "128", 1033 | "pad": "(0, 0)", 1034 | "stride": "(1, 1)", 1035 | "workspace": "256" 1036 | }, 1037 | "inputs": [[98, 0, 0], 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1084 | "attrs": { 1085 | "eps": "2e-05", 1086 | "fix_gamma": "False", 1087 | "momentum": "0.9" 1088 | }, 1089 | "inputs": [[100, 0, 0], [101, 0, 0], [102, 0, 0], [103, 0, 1], [104, 0, 1]] 1090 | }, 1091 | { 1092 | "op": "Activation", 1093 | "name": "stage2_unit1_relu2", 1094 | "attrs": {"act_type": "relu"}, 1095 | "inputs": [[105, 0, 0]] 1096 | }, 1097 | { 1098 | "op": "null", 1099 | "name": "stage2_unit1_conv2_weight", 1100 | "attrs": { 1101 | "kernel": "(3, 3)", 1102 | "no_bias": "True", 1103 | "num_filter": "128", 1104 | "pad": "(1, 1)", 1105 | "stride": "(2, 2)", 1106 | "workspace": "256" 1107 | }, 1108 | "inputs": [] 1109 | }, 1110 | { 1111 | "op": "Convolution", 1112 | "name": "stage2_unit1_conv2", 1113 | "attrs": { 1114 | "kernel": "(3, 3)", 1115 | "no_bias": "True", 1116 | "num_filter": "128", 1117 | "pad": "(1, 1)", 1118 | "stride": "(2, 2)", 1119 | "workspace": "256" 1120 | }, 1121 | "inputs": [[106, 0, 0], [107, 0, 0]] 1122 | }, 1123 | { 1124 | "op": "null", 1125 | "name": 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"False", 1171 | "momentum": "0.9" 1172 | }, 1173 | "inputs": [[108, 0, 0], [109, 0, 0], [110, 0, 0], [111, 0, 1], [112, 0, 1]] 1174 | }, 1175 | { 1176 | "op": "Activation", 1177 | "name": "stage2_unit1_relu3", 1178 | "attrs": {"act_type": "relu"}, 1179 | "inputs": [[113, 0, 0]] 1180 | }, 1181 | { 1182 | "op": "null", 1183 | "name": "stage2_unit1_conv3_weight", 1184 | "attrs": { 1185 | "kernel": "(1, 1)", 1186 | "no_bias": "True", 1187 | "num_filter": "512", 1188 | "pad": "(0, 0)", 1189 | "stride": "(1, 1)", 1190 | "workspace": "256" 1191 | }, 1192 | "inputs": [] 1193 | }, 1194 | { 1195 | "op": "Convolution", 1196 | "name": "stage2_unit1_conv3", 1197 | "attrs": { 1198 | "kernel": "(1, 1)", 1199 | "no_bias": "True", 1200 | "num_filter": "512", 1201 | "pad": "(0, 0)", 1202 | "stride": "(1, 1)", 1203 | "workspace": "256" 1204 | }, 1205 | "inputs": [[114, 0, 0], [115, 0, 0]] 1206 | }, 1207 | { 1208 | "op": "null", 1209 | "name": "stage2_unit1_sc_weight", 1210 | "attrs": { 1211 | "kernel": 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1258 | "name": "stage2_unit2_bn1_moving_mean", 1259 | "attrs": { 1260 | "__init__": "[\"zero\", {}]", 1261 | "eps": "2e-05", 1262 | "fix_gamma": "False", 1263 | "momentum": "0.9" 1264 | }, 1265 | "inputs": [] 1266 | }, 1267 | { 1268 | "op": "null", 1269 | "name": "stage2_unit2_bn1_moving_var", 1270 | "attrs": { 1271 | "__init__": "[\"one\", {}]", 1272 | "eps": "2e-05", 1273 | "fix_gamma": "False", 1274 | "momentum": "0.9" 1275 | }, 1276 | "inputs": [] 1277 | }, 1278 | { 1279 | "op": "BatchNorm", 1280 | "name": "stage2_unit2_bn1", 1281 | "attrs": { 1282 | "eps": "2e-05", 1283 | "fix_gamma": "False", 1284 | "momentum": "0.9" 1285 | }, 1286 | "inputs": [[119, 0, 0], [120, 0, 0], [121, 0, 0], [122, 0, 1], [123, 0, 1]] 1287 | }, 1288 | { 1289 | "op": "Activation", 1290 | "name": "stage2_unit2_relu1", 1291 | "attrs": {"act_type": "relu"}, 1292 | "inputs": [[124, 0, 0]] 1293 | }, 1294 | { 1295 | "op": "null", 1296 | "name": "stage2_unit2_conv1_weight", 1297 | "attrs": { 1298 | "kernel": "(1, 1)", 1299 | "no_bias": "True", 1300 | "num_filter": "128", 1301 | "pad": "(0, 0)", 1302 | "stride": "(1, 1)", 1303 | "workspace": "256" 1304 | }, 1305 | "inputs": [] 1306 | }, 1307 | { 1308 | "op": "Convolution", 1309 | "name": "stage2_unit2_conv1", 1310 | "attrs": { 1311 | "kernel": "(1, 1)", 1312 | "no_bias": "True", 1313 | "num_filter": "128", 1314 | "pad": "(0, 0)", 1315 | "stride": "(1, 1)", 1316 | "workspace": "256" 1317 | }, 1318 | "inputs": [[125, 0, 0], [126, 0, 0]] 1319 | }, 1320 | { 1321 | "op": "null", 1322 | "name": "stage2_unit2_bn2_gamma", 1323 | "attrs": { 1324 | "eps": "2e-05", 1325 | "fix_gamma": "False", 1326 | "momentum": "0.9" 1327 | }, 1328 | "inputs": [] 1329 | }, 1330 | { 1331 | "op": "null", 1332 | "name": "stage2_unit2_bn2_beta", 1333 | "attrs": { 1334 | "eps": "2e-05", 1335 | "fix_gamma": "False", 1336 | "momentum": "0.9" 1337 | }, 1338 | "inputs": [] 1339 | }, 1340 | { 1341 | "op": "null", 1342 | "name": "stage2_unit2_bn2_moving_mean", 1343 | "attrs": { 1344 | "__init__": "[\"zero\", {}]", 1345 | "eps": "2e-05", 1346 | "fix_gamma": "False", 1347 | "momentum": "0.9" 1348 | }, 1349 | "inputs": [] 1350 | }, 1351 | { 1352 | "op": "null", 1353 | "name": "stage2_unit2_bn2_moving_var", 1354 | "attrs": { 1355 | "__init__": "[\"one\", {}]", 1356 | "eps": "2e-05", 1357 | "fix_gamma": "False", 1358 | "momentum": "0.9" 1359 | }, 1360 | "inputs": [] 1361 | }, 1362 | { 1363 | "op": "BatchNorm", 1364 | "name": "stage2_unit2_bn2", 1365 | "attrs": { 1366 | "eps": "2e-05", 1367 | "fix_gamma": "False", 1368 | "momentum": "0.9" 1369 | }, 1370 | "inputs": [[127, 0, 0], [128, 0, 0], [129, 0, 0], [130, 0, 1], [131, 0, 1]] 1371 | }, 1372 | { 1373 | "op": "Activation", 1374 | "name": "stage2_unit2_relu2", 1375 | "attrs": {"act_type": "relu"}, 1376 | "inputs": [[132, 0, 0]] 1377 | }, 1378 | { 1379 | "op": "null", 1380 | "name": "stage2_unit2_conv2_weight", 1381 | "attrs": { 1382 | "kernel": "(3, 3)", 1383 | "no_bias": "True", 1384 | "num_filter": "128", 1385 | "pad": "(1, 1)", 1386 | "stride": "(1, 1)", 1387 | "workspace": "256" 1388 | }, 1389 | "inputs": [] 1390 | }, 1391 | { 1392 | "op": "Convolution", 1393 | "name": "stage2_unit2_conv2", 1394 | "attrs": { 1395 | "kernel": "(3, 3)", 1396 | "no_bias": "True", 1397 | "num_filter": "128", 1398 | "pad": "(1, 1)", 1399 | "stride": "(1, 1)", 1400 | "workspace": "256" 1401 | }, 1402 | "inputs": [[133, 0, 0], [134, 0, 0]] 1403 | }, 1404 | { 1405 | "op": "null", 1406 | "name": "stage2_unit2_bn3_gamma", 1407 | "attrs": { 1408 | "eps": "2e-05", 1409 | "fix_gamma": "False", 1410 | "momentum": "0.9" 1411 | }, 1412 | "inputs": [] 1413 | }, 1414 | { 1415 | "op": "null", 1416 | "name": "stage2_unit2_bn3_beta", 1417 | "attrs": { 1418 | "eps": "2e-05", 1419 | "fix_gamma": "False", 1420 | "momentum": "0.9" 1421 | }, 1422 | "inputs": [] 1423 | }, 1424 | { 1425 | "op": "null", 1426 | "name": "stage2_unit2_bn3_moving_mean", 1427 | "attrs": { 1428 | "__init__": "[\"zero\", {}]", 1429 | "eps": "2e-05", 1430 | "fix_gamma": "False", 1431 | "momentum": "0.9" 1432 | }, 1433 | "inputs": [] 1434 | }, 1435 | { 1436 | "op": "null", 1437 | "name": "stage2_unit2_bn3_moving_var", 1438 | "attrs": { 1439 | "__init__": "[\"one\", {}]", 1440 | "eps": "2e-05", 1441 | "fix_gamma": "False", 1442 | "momentum": "0.9" 1443 | }, 1444 | "inputs": [] 1445 | }, 1446 | { 1447 | "op": "BatchNorm", 1448 | "name": "stage2_unit2_bn3", 1449 | "attrs": { 1450 | "eps": "2e-05", 1451 | "fix_gamma": "False", 1452 | "momentum": "0.9" 1453 | }, 1454 | "inputs": [[135, 0, 0], [136, 0, 0], [137, 0, 0], [138, 0, 1], [139, 0, 1]] 1455 | }, 1456 | { 1457 | "op": "Activation", 1458 | "name": "stage2_unit2_relu3", 1459 | "attrs": {"act_type": "relu"}, 1460 | "inputs": [[140, 0, 0]] 1461 | }, 1462 | { 1463 | "op": "null", 1464 | "name": "stage2_unit2_conv3_weight", 1465 | "attrs": { 1466 | "kernel": "(1, 1)", 1467 | "no_bias": "True", 1468 | "num_filter": "512", 1469 | "pad": "(0, 0)", 1470 | "stride": "(1, 1)", 1471 | "workspace": "256" 1472 | }, 1473 | "inputs": [] 1474 | }, 1475 | { 1476 | "op": "Convolution", 1477 | "name": "stage2_unit2_conv3", 1478 | "attrs": { 1479 | "kernel": "(1, 1)", 1480 | "no_bias": "True", 1481 | "num_filter": "512", 1482 | "pad": "(0, 0)", 1483 | "stride": "(1, 1)", 1484 | "workspace": "256" 1485 | }, 1486 | "inputs": [[141, 0, 0], [142, 0, 0]] 1487 | }, 1488 | { 1489 | "op": "elemwise_add", 1490 | "name": "_plus4", 1491 | "inputs": [[143, 0, 0], [119, 0, 0]] 1492 | }, 1493 | { 1494 | "op": "null", 1495 | "name": "stage2_unit3_bn1_gamma", 1496 | "attrs": { 1497 | "eps": "2e-05", 1498 | "fix_gamma": "False", 1499 | "momentum": "0.9" 1500 | }, 1501 | "inputs": [] 1502 | }, 1503 | { 1504 | "op": "null", 1505 | "name": "stage2_unit3_bn1_beta", 1506 | "attrs": { 1507 | "eps": "2e-05", 1508 | "fix_gamma": "False", 1509 | "momentum": "0.9" 1510 | }, 1511 | "inputs": [] 1512 | }, 1513 | { 1514 | "op": "null", 1515 | "name": "stage2_unit3_bn1_moving_mean", 1516 | "attrs": { 1517 | "__init__": "[\"zero\", {}]", 1518 | "eps": "2e-05", 1519 | "fix_gamma": "False", 1520 | "momentum": "0.9" 1521 | }, 1522 | "inputs": [] 1523 | }, 1524 | { 1525 | "op": "null", 1526 | "name": "stage2_unit3_bn1_moving_var", 1527 | "attrs": { 1528 | "__init__": "[\"one\", {}]", 1529 | "eps": "2e-05", 1530 | "fix_gamma": "False", 1531 | "momentum": "0.9" 1532 | }, 1533 | "inputs": [] 1534 | }, 1535 | { 1536 | "op": "BatchNorm", 1537 | "name": "stage2_unit3_bn1", 1538 | "attrs": { 1539 | "eps": "2e-05", 1540 | "fix_gamma": "False", 1541 | "momentum": "0.9" 1542 | }, 1543 | "inputs": [[144, 0, 0], [145, 0, 0], [146, 0, 0], [147, 0, 1], [148, 0, 1]] 1544 | }, 1545 | { 1546 | "op": "Activation", 1547 | "name": "stage2_unit3_relu1", 1548 | "attrs": {"act_type": "relu"}, 1549 | "inputs": [[149, 0, 0]] 1550 | }, 1551 | { 1552 | "op": "null", 1553 | "name": "stage2_unit3_conv1_weight", 1554 | "attrs": { 1555 | "kernel": "(1, 1)", 1556 | "no_bias": "True", 1557 | "num_filter": "128", 1558 | "pad": "(0, 0)", 1559 | "stride": "(1, 1)", 1560 | "workspace": "256" 1561 | }, 1562 | "inputs": [] 1563 | }, 1564 | { 1565 | "op": "Convolution", 1566 | "name": "stage2_unit3_conv1", 1567 | "attrs": { 1568 | "kernel": "(1, 1)", 1569 | "no_bias": "True", 1570 | "num_filter": "128", 1571 | "pad": "(0, 0)", 1572 | "stride": "(1, 1)", 1573 | "workspace": "256" 1574 | }, 1575 | "inputs": [[150, 0, 0], [151, 0, 0]] 1576 | }, 1577 | { 1578 | "op": "null", 1579 | "name": "stage2_unit3_bn2_gamma", 1580 | "attrs": { 1581 | "eps": "2e-05", 1582 | "fix_gamma": "False", 1583 | "momentum": "0.9" 1584 | }, 1585 | "inputs": [] 1586 | }, 1587 | { 1588 | "op": "null", 1589 | "name": "stage2_unit3_bn2_beta", 1590 | "attrs": { 1591 | "eps": "2e-05", 1592 | "fix_gamma": "False", 1593 | "momentum": "0.9" 1594 | }, 1595 | "inputs": [] 1596 | }, 1597 | { 1598 | "op": "null", 1599 | "name": "stage2_unit3_bn2_moving_mean", 1600 | "attrs": { 1601 | "__init__": "[\"zero\", {}]", 1602 | "eps": "2e-05", 1603 | "fix_gamma": "False", 1604 | "momentum": "0.9" 1605 | }, 1606 | "inputs": [] 1607 | }, 1608 | { 1609 | "op": "null", 1610 | "name": "stage2_unit3_bn2_moving_var", 1611 | "attrs": { 1612 | "__init__": "[\"one\", {}]", 1613 | "eps": "2e-05", 1614 | "fix_gamma": "False", 1615 | "momentum": "0.9" 1616 | }, 1617 | "inputs": [] 1618 | }, 1619 | { 1620 | "op": "BatchNorm", 1621 | "name": "stage2_unit3_bn2", 1622 | "attrs": { 1623 | "eps": "2e-05", 1624 | "fix_gamma": "False", 1625 | "momentum": "0.9" 1626 | }, 1627 | "inputs": [[152, 0, 0], [153, 0, 0], [154, 0, 0], [155, 0, 1], [156, 0, 1]] 1628 | }, 1629 | { 1630 | "op": "Activation", 1631 | "name": "stage2_unit3_relu2", 1632 | "attrs": {"act_type": "relu"}, 1633 | "inputs": [[157, 0, 0]] 1634 | }, 1635 | { 1636 | "op": "null", 1637 | "name": "stage2_unit3_conv2_weight", 1638 | "attrs": { 1639 | "kernel": "(3, 3)", 1640 | "no_bias": "True", 1641 | "num_filter": "128", 1642 | "pad": "(1, 1)", 1643 | "stride": "(1, 1)", 1644 | "workspace": "256" 1645 | }, 1646 | "inputs": [] 1647 | }, 1648 | { 1649 | "op": "Convolution", 1650 | "name": "stage2_unit3_conv2", 1651 | "attrs": { 1652 | "kernel": "(3, 3)", 1653 | "no_bias": "True", 1654 | "num_filter": "128", 1655 | "pad": "(1, 1)", 1656 | "stride": "(1, 1)", 1657 | "workspace": "256" 1658 | }, 1659 | "inputs": [[158, 0, 0], [159, 0, 0]] 1660 | }, 1661 | { 1662 | "op": "null", 1663 | "name": "stage2_unit3_bn3_gamma", 1664 | "attrs": { 1665 | "eps": "2e-05", 1666 | "fix_gamma": "False", 1667 | "momentum": "0.9" 1668 | }, 1669 | "inputs": [] 1670 | }, 1671 | { 1672 | "op": "null", 1673 | "name": "stage2_unit3_bn3_beta", 1674 | "attrs": { 1675 | "eps": "2e-05", 1676 | "fix_gamma": "False", 1677 | "momentum": "0.9" 1678 | }, 1679 | "inputs": [] 1680 | }, 1681 | { 1682 | "op": "null", 1683 | "name": "stage2_unit3_bn3_moving_mean", 1684 | "attrs": { 1685 | "__init__": "[\"zero\", {}]", 1686 | "eps": "2e-05", 1687 | "fix_gamma": "False", 1688 | "momentum": "0.9" 1689 | }, 1690 | "inputs": [] 1691 | }, 1692 | { 1693 | "op": "null", 1694 | "name": "stage2_unit3_bn3_moving_var", 1695 | "attrs": { 1696 | "__init__": "[\"one\", {}]", 1697 | "eps": "2e-05", 1698 | "fix_gamma": "False", 1699 | "momentum": "0.9" 1700 | }, 1701 | "inputs": [] 1702 | }, 1703 | { 1704 | "op": "BatchNorm", 1705 | "name": "stage2_unit3_bn3", 1706 | "attrs": { 1707 | "eps": "2e-05", 1708 | "fix_gamma": "False", 1709 | "momentum": "0.9" 1710 | }, 1711 | "inputs": [[160, 0, 0], [161, 0, 0], [162, 0, 0], [163, 0, 1], [164, 0, 1]] 1712 | }, 1713 | { 1714 | "op": "Activation", 1715 | "name": "stage2_unit3_relu3", 1716 | "attrs": {"act_type": "relu"}, 1717 | "inputs": [[165, 0, 0]] 1718 | }, 1719 | { 1720 | "op": "null", 1721 | "name": "stage2_unit3_conv3_weight", 1722 | "attrs": { 1723 | "kernel": "(1, 1)", 1724 | "no_bias": "True", 1725 | "num_filter": "512", 1726 | "pad": "(0, 0)", 1727 | "stride": "(1, 1)", 1728 | "workspace": "256" 1729 | }, 1730 | "inputs": [] 1731 | }, 1732 | { 1733 | "op": "Convolution", 1734 | "name": "stage2_unit3_conv3", 1735 | "attrs": { 1736 | "kernel": "(1, 1)", 1737 | "no_bias": "True", 1738 | "num_filter": "512", 1739 | "pad": "(0, 0)", 1740 | "stride": "(1, 1)", 1741 | "workspace": "256" 1742 | }, 1743 | "inputs": [[166, 0, 0], [167, 0, 0]] 1744 | }, 1745 | { 1746 | "op": "elemwise_add", 1747 | "name": "_plus5", 1748 | "inputs": [[168, 0, 0], [144, 0, 0]] 1749 | }, 1750 | { 1751 | "op": "null", 1752 | "name": "stage2_unit4_bn1_gamma", 1753 | "attrs": { 1754 | "eps": "2e-05", 1755 | "fix_gamma": "False", 1756 | "momentum": "0.9" 1757 | }, 1758 | "inputs": [] 1759 | }, 1760 | { 1761 | "op": "null", 1762 | "name": "stage2_unit4_bn1_beta", 1763 | "attrs": { 1764 | "eps": "2e-05", 1765 | "fix_gamma": "False", 1766 | "momentum": "0.9" 1767 | }, 1768 | "inputs": [] 1769 | }, 1770 | { 1771 | "op": "null", 1772 | "name": "stage2_unit4_bn1_moving_mean", 1773 | "attrs": { 1774 | "__init__": "[\"zero\", {}]", 1775 | "eps": "2e-05", 1776 | "fix_gamma": "False", 1777 | "momentum": "0.9" 1778 | }, 1779 | "inputs": [] 1780 | }, 1781 | { 1782 | "op": "null", 1783 | "name": "stage2_unit4_bn1_moving_var", 1784 | "attrs": { 1785 | "__init__": "[\"one\", {}]", 1786 | "eps": "2e-05", 1787 | "fix_gamma": "False", 1788 | "momentum": "0.9" 1789 | }, 1790 | "inputs": [] 1791 | }, 1792 | { 1793 | "op": "BatchNorm", 1794 | "name": "stage2_unit4_bn1", 1795 | "attrs": { 1796 | "eps": "2e-05", 1797 | "fix_gamma": "False", 1798 | "momentum": "0.9" 1799 | }, 1800 | "inputs": [[169, 0, 0], [170, 0, 0], [171, 0, 0], [172, 0, 1], [173, 0, 1]] 1801 | }, 1802 | { 1803 | "op": "Activation", 1804 | "name": "stage2_unit4_relu1", 1805 | "attrs": {"act_type": "relu"}, 1806 | "inputs": [[174, 0, 0]] 1807 | }, 1808 | { 1809 | "op": "null", 1810 | "name": "stage2_unit4_conv1_weight", 1811 | "attrs": { 1812 | "kernel": "(1, 1)", 1813 | "no_bias": "True", 1814 | "num_filter": "128", 1815 | "pad": "(0, 0)", 1816 | "stride": "(1, 1)", 1817 | "workspace": "256" 1818 | }, 1819 | "inputs": [] 1820 | }, 1821 | { 1822 | "op": "Convolution", 1823 | "name": "stage2_unit4_conv1", 1824 | "attrs": { 1825 | "kernel": "(1, 1)", 1826 | "no_bias": "True", 1827 | "num_filter": "128", 1828 | "pad": "(0, 0)", 1829 | "stride": "(1, 1)", 1830 | "workspace": "256" 1831 | }, 1832 | "inputs": [[175, 0, 0], [176, 0, 0]] 1833 | }, 1834 | { 1835 | "op": "null", 1836 | "name": "stage2_unit4_bn2_gamma", 1837 | "attrs": { 1838 | "eps": "2e-05", 1839 | "fix_gamma": "False", 1840 | "momentum": "0.9" 1841 | }, 1842 | "inputs": [] 1843 | }, 1844 | { 1845 | "op": "null", 1846 | "name": "stage2_unit4_bn2_beta", 1847 | "attrs": { 1848 | "eps": "2e-05", 1849 | "fix_gamma": "False", 1850 | "momentum": "0.9" 1851 | }, 1852 | "inputs": [] 1853 | }, 1854 | { 1855 | "op": "null", 1856 | "name": "stage2_unit4_bn2_moving_mean", 1857 | "attrs": { 1858 | "__init__": "[\"zero\", {}]", 1859 | "eps": "2e-05", 1860 | "fix_gamma": "False", 1861 | "momentum": "0.9" 1862 | }, 1863 | "inputs": [] 1864 | }, 1865 | { 1866 | "op": "null", 1867 | "name": "stage2_unit4_bn2_moving_var", 1868 | "attrs": { 1869 | "__init__": "[\"one\", {}]", 1870 | "eps": "2e-05", 1871 | "fix_gamma": "False", 1872 | "momentum": "0.9" 1873 | }, 1874 | "inputs": [] 1875 | }, 1876 | { 1877 | "op": "BatchNorm", 1878 | "name": "stage2_unit4_bn2", 1879 | "attrs": { 1880 | "eps": "2e-05", 1881 | "fix_gamma": "False", 1882 | "momentum": "0.9" 1883 | }, 1884 | "inputs": [[177, 0, 0], [178, 0, 0], [179, 0, 0], [180, 0, 1], [181, 0, 1]] 1885 | }, 1886 | { 1887 | "op": "Activation", 1888 | "name": "stage2_unit4_relu2", 1889 | "attrs": {"act_type": "relu"}, 1890 | "inputs": [[182, 0, 0]] 1891 | }, 1892 | { 1893 | "op": "null", 1894 | "name": "stage2_unit4_conv2_weight", 1895 | "attrs": { 1896 | "kernel": "(3, 3)", 1897 | "no_bias": "True", 1898 | "num_filter": "128", 1899 | "pad": "(1, 1)", 1900 | "stride": "(1, 1)", 1901 | "workspace": "256" 1902 | }, 1903 | "inputs": [] 1904 | }, 1905 | { 1906 | "op": "Convolution", 1907 | "name": "stage2_unit4_conv2", 1908 | "attrs": { 1909 | "kernel": "(3, 3)", 1910 | "no_bias": "True", 1911 | "num_filter": "128", 1912 | "pad": "(1, 1)", 1913 | "stride": "(1, 1)", 1914 | "workspace": "256" 1915 | }, 1916 | "inputs": [[183, 0, 0], [184, 0, 0]] 1917 | }, 1918 | { 1919 | "op": "null", 1920 | "name": "stage2_unit4_bn3_gamma", 1921 | "attrs": { 1922 | "eps": "2e-05", 1923 | "fix_gamma": "False", 1924 | "momentum": "0.9" 1925 | }, 1926 | "inputs": [] 1927 | }, 1928 | { 1929 | "op": "null", 1930 | "name": "stage2_unit4_bn3_beta", 1931 | "attrs": { 1932 | "eps": "2e-05", 1933 | "fix_gamma": "False", 1934 | "momentum": "0.9" 1935 | }, 1936 | "inputs": [] 1937 | }, 1938 | { 1939 | "op": "null", 1940 | "name": "stage2_unit4_bn3_moving_mean", 1941 | "attrs": { 1942 | "__init__": "[\"zero\", {}]", 1943 | "eps": "2e-05", 1944 | "fix_gamma": "False", 1945 | "momentum": "0.9" 1946 | }, 1947 | "inputs": [] 1948 | }, 1949 | { 1950 | "op": "null", 1951 | "name": "stage2_unit4_bn3_moving_var", 1952 | "attrs": { 1953 | "__init__": "[\"one\", {}]", 1954 | "eps": "2e-05", 1955 | "fix_gamma": "False", 1956 | "momentum": "0.9" 1957 | }, 1958 | "inputs": [] 1959 | }, 1960 | { 1961 | "op": "BatchNorm", 1962 | "name": "stage2_unit4_bn3", 1963 | "attrs": { 1964 | "eps": "2e-05", 1965 | "fix_gamma": "False", 1966 | "momentum": "0.9" 1967 | }, 1968 | "inputs": [[185, 0, 0], [186, 0, 0], [187, 0, 0], [188, 0, 1], [189, 0, 1]] 1969 | }, 1970 | { 1971 | "op": "Activation", 1972 | "name": "stage2_unit4_relu3", 1973 | "attrs": {"act_type": "relu"}, 1974 | "inputs": [[190, 0, 0]] 1975 | }, 1976 | { 1977 | "op": "null", 1978 | "name": "stage2_unit4_conv3_weight", 1979 | "attrs": { 1980 | "kernel": "(1, 1)", 1981 | "no_bias": "True", 1982 | "num_filter": "512", 1983 | "pad": "(0, 0)", 1984 | "stride": "(1, 1)", 1985 | "workspace": "256" 1986 | }, 1987 | "inputs": [] 1988 | }, 1989 | { 1990 | "op": "Convolution", 1991 | "name": "stage2_unit4_conv3", 1992 | "attrs": { 1993 | "kernel": "(1, 1)", 1994 | "no_bias": "True", 1995 | "num_filter": "512", 1996 | "pad": "(0, 0)", 1997 | "stride": "(1, 1)", 1998 | "workspace": "256" 1999 | }, 2000 | "inputs": [[191, 0, 0], [192, 0, 0]] 2001 | }, 2002 | { 2003 | "op": "elemwise_add", 2004 | "name": "_plus6", 2005 | "inputs": [[193, 0, 0], [169, 0, 0]] 2006 | }, 2007 | { 2008 | "op": "null", 2009 | "name": "stage3_unit1_bn1_gamma", 2010 | "attrs": { 2011 | "eps": "2e-05", 2012 | "fix_gamma": "False", 2013 | "momentum": "0.9" 2014 | }, 2015 | "inputs": [] 2016 | }, 2017 | { 2018 | "op": "null", 2019 | "name": "stage3_unit1_bn1_beta", 2020 | "attrs": { 2021 | "eps": "2e-05", 2022 | "fix_gamma": "False", 2023 | "momentum": "0.9" 2024 | }, 2025 | "inputs": [] 2026 | }, 2027 | { 2028 | "op": "null", 2029 | "name": "stage3_unit1_bn1_moving_mean", 2030 | "attrs": { 2031 | "__init__": "[\"zero\", {}]", 2032 | "eps": "2e-05", 2033 | "fix_gamma": "False", 2034 | "momentum": "0.9" 2035 | }, 2036 | "inputs": [] 2037 | }, 2038 | { 2039 | "op": "null", 2040 | "name": "stage3_unit1_bn1_moving_var", 2041 | "attrs": { 2042 | "__init__": "[\"one\", {}]", 2043 | "eps": "2e-05", 2044 | "fix_gamma": "False", 2045 | "momentum": "0.9" 2046 | }, 2047 | "inputs": [] 2048 | }, 2049 | { 2050 | "op": "BatchNorm", 2051 | "name": "stage3_unit1_bn1", 2052 | "attrs": { 2053 | "eps": "2e-05", 2054 | "fix_gamma": "False", 2055 | "momentum": "0.9" 2056 | }, 2057 | "inputs": [[194, 0, 0], [195, 0, 0], [196, 0, 0], [197, 0, 1], [198, 0, 1]] 2058 | }, 2059 | { 2060 | "op": "Activation", 2061 | "name": "stage3_unit1_relu1", 2062 | "attrs": {"act_type": "relu"}, 2063 | "inputs": [[199, 0, 0]] 2064 | }, 2065 | { 2066 | "op": "null", 2067 | "name": "stage3_unit1_conv1_weight", 2068 | "attrs": { 2069 | "kernel": "(1, 1)", 2070 | "no_bias": "True", 2071 | "num_filter": "256", 2072 | "pad": "(0, 0)", 2073 | "stride": "(1, 1)", 2074 | "workspace": "256" 2075 | }, 2076 | "inputs": [] 2077 | }, 2078 | { 2079 | "op": "Convolution", 2080 | "name": "stage3_unit1_conv1", 2081 | "attrs": { 2082 | "kernel": "(1, 1)", 2083 | "no_bias": "True", 2084 | "num_filter": "256", 2085 | "pad": "(0, 0)", 2086 | "stride": "(1, 1)", 2087 | "workspace": "256" 2088 | }, 2089 | "inputs": [[200, 0, 0], [201, 0, 0]] 2090 | }, 2091 | { 2092 | "op": "null", 2093 | "name": "stage3_unit1_bn2_gamma", 2094 | "attrs": { 2095 | "eps": "2e-05", 2096 | "fix_gamma": "False", 2097 | "momentum": "0.9" 2098 | }, 2099 | "inputs": [] 2100 | }, 2101 | { 2102 | "op": "null", 2103 | "name": "stage3_unit1_bn2_beta", 2104 | "attrs": { 2105 | "eps": "2e-05", 2106 | "fix_gamma": "False", 2107 | "momentum": "0.9" 2108 | }, 2109 | "inputs": [] 2110 | }, 2111 | { 2112 | "op": "null", 2113 | "name": "stage3_unit1_bn2_moving_mean", 2114 | "attrs": { 2115 | "__init__": "[\"zero\", {}]", 2116 | "eps": "2e-05", 2117 | "fix_gamma": "False", 2118 | "momentum": "0.9" 2119 | }, 2120 | "inputs": [] 2121 | }, 2122 | { 2123 | "op": "null", 2124 | "name": "stage3_unit1_bn2_moving_var", 2125 | "attrs": { 2126 | "__init__": "[\"one\", {}]", 2127 | "eps": "2e-05", 2128 | "fix_gamma": "False", 2129 | "momentum": "0.9" 2130 | }, 2131 | "inputs": [] 2132 | }, 2133 | { 2134 | "op": "BatchNorm", 2135 | "name": "stage3_unit1_bn2", 2136 | "attrs": { 2137 | "eps": "2e-05", 2138 | "fix_gamma": "False", 2139 | "momentum": "0.9" 2140 | }, 2141 | "inputs": [[202, 0, 0], [203, 0, 0], [204, 0, 0], [205, 0, 1], [206, 0, 1]] 2142 | }, 2143 | { 2144 | "op": "Activation", 2145 | "name": "stage3_unit1_relu2", 2146 | "attrs": {"act_type": "relu"}, 2147 | "inputs": [[207, 0, 0]] 2148 | }, 2149 | { 2150 | "op": "null", 2151 | "name": "stage3_unit1_conv2_weight", 2152 | "attrs": { 2153 | "kernel": "(3, 3)", 2154 | "no_bias": "True", 2155 | "num_filter": "256", 2156 | "pad": "(1, 1)", 2157 | "stride": "(2, 2)", 2158 | "workspace": "256" 2159 | }, 2160 | "inputs": [] 2161 | }, 2162 | { 2163 | "op": "Convolution", 2164 | "name": "stage3_unit1_conv2", 2165 | "attrs": { 2166 | "kernel": "(3, 3)", 2167 | "no_bias": "True", 2168 | "num_filter": "256", 2169 | "pad": "(1, 1)", 2170 | "stride": "(2, 2)", 2171 | "workspace": "256" 2172 | }, 2173 | "inputs": [[208, 0, 0], [209, 0, 0]] 2174 | }, 2175 | { 2176 | "op": "null", 2177 | "name": "stage3_unit1_bn3_gamma", 2178 | "attrs": { 2179 | "eps": "2e-05", 2180 | "fix_gamma": "False", 2181 | "momentum": "0.9" 2182 | }, 2183 | "inputs": [] 2184 | }, 2185 | { 2186 | "op": "null", 2187 | "name": "stage3_unit1_bn3_beta", 2188 | "attrs": { 2189 | "eps": "2e-05", 2190 | "fix_gamma": "False", 2191 | "momentum": "0.9" 2192 | }, 2193 | "inputs": [] 2194 | }, 2195 | { 2196 | "op": "null", 2197 | "name": "stage3_unit1_bn3_moving_mean", 2198 | "attrs": { 2199 | "__init__": "[\"zero\", {}]", 2200 | "eps": "2e-05", 2201 | "fix_gamma": "False", 2202 | "momentum": "0.9" 2203 | }, 2204 | "inputs": [] 2205 | }, 2206 | { 2207 | "op": "null", 2208 | "name": "stage3_unit1_bn3_moving_var", 2209 | "attrs": { 2210 | "__init__": "[\"one\", {}]", 2211 | "eps": "2e-05", 2212 | "fix_gamma": "False", 2213 | "momentum": "0.9" 2214 | }, 2215 | "inputs": [] 2216 | }, 2217 | { 2218 | "op": "BatchNorm", 2219 | "name": "stage3_unit1_bn3", 2220 | "attrs": { 2221 | "eps": "2e-05", 2222 | "fix_gamma": "False", 2223 | "momentum": "0.9" 2224 | }, 2225 | "inputs": [[210, 0, 0], [211, 0, 0], [212, 0, 0], [213, 0, 1], [214, 0, 1]] 2226 | }, 2227 | { 2228 | "op": "Activation", 2229 | "name": "stage3_unit1_relu3", 2230 | "attrs": {"act_type": "relu"}, 2231 | "inputs": [[215, 0, 0]] 2232 | }, 2233 | { 2234 | "op": "null", 2235 | "name": "stage3_unit1_conv3_weight", 2236 | "attrs": { 2237 | "kernel": "(1, 1)", 2238 | "no_bias": "True", 2239 | "num_filter": "1024", 2240 | "pad": "(0, 0)", 2241 | "stride": "(1, 1)", 2242 | "workspace": "256" 2243 | }, 2244 | "inputs": [] 2245 | }, 2246 | { 2247 | "op": "Convolution", 2248 | "name": "stage3_unit1_conv3", 2249 | "attrs": { 2250 | "kernel": "(1, 1)", 2251 | "no_bias": "True", 2252 | "num_filter": "1024", 2253 | "pad": "(0, 0)", 2254 | "stride": "(1, 1)", 2255 | "workspace": "256" 2256 | }, 2257 | "inputs": [[216, 0, 0], [217, 0, 0]] 2258 | }, 2259 | { 2260 | "op": "null", 2261 | "name": "stage3_unit1_sc_weight", 2262 | "attrs": { 2263 | "kernel": "(1, 1)", 2264 | "no_bias": "True", 2265 | "num_filter": "1024", 2266 | "stride": "(2, 2)", 2267 | "workspace": "256" 2268 | }, 2269 | "inputs": [] 2270 | }, 2271 | { 2272 | "op": "Convolution", 2273 | "name": "stage3_unit1_sc", 2274 | "attrs": { 2275 | "kernel": "(1, 1)", 2276 | "no_bias": "True", 2277 | "num_filter": "1024", 2278 | "stride": "(2, 2)", 2279 | "workspace": "256" 2280 | }, 2281 | "inputs": [[200, 0, 0], [219, 0, 0]] 2282 | }, 2283 | { 2284 | "op": "elemwise_add", 2285 | "name": "_plus7", 2286 | "inputs": [[218, 0, 0], [220, 0, 0]] 2287 | }, 2288 | { 2289 | "op": "null", 2290 | "name": "stage3_unit2_bn1_gamma", 2291 | "attrs": { 2292 | "eps": "2e-05", 2293 | "fix_gamma": "False", 2294 | "momentum": "0.9" 2295 | }, 2296 | "inputs": [] 2297 | }, 2298 | { 2299 | "op": "null", 2300 | "name": "stage3_unit2_bn1_beta", 2301 | "attrs": { 2302 | "eps": "2e-05", 2303 | "fix_gamma": "False", 2304 | "momentum": "0.9" 2305 | }, 2306 | "inputs": [] 2307 | }, 2308 | { 2309 | "op": "null", 2310 | "name": "stage3_unit2_bn1_moving_mean", 2311 | "attrs": { 2312 | "__init__": "[\"zero\", {}]", 2313 | "eps": "2e-05", 2314 | "fix_gamma": "False", 2315 | "momentum": "0.9" 2316 | }, 2317 | "inputs": [] 2318 | }, 2319 | { 2320 | "op": "null", 2321 | "name": "stage3_unit2_bn1_moving_var", 2322 | "attrs": { 2323 | "__init__": "[\"one\", {}]", 2324 | "eps": "2e-05", 2325 | "fix_gamma": "False", 2326 | "momentum": "0.9" 2327 | }, 2328 | "inputs": [] 2329 | }, 2330 | { 2331 | "op": "BatchNorm", 2332 | "name": "stage3_unit2_bn1", 2333 | "attrs": { 2334 | "eps": "2e-05", 2335 | "fix_gamma": "False", 2336 | "momentum": "0.9" 2337 | }, 2338 | "inputs": [[221, 0, 0], [222, 0, 0], [223, 0, 0], [224, 0, 1], [225, 0, 1]] 2339 | }, 2340 | { 2341 | "op": "Activation", 2342 | "name": "stage3_unit2_relu1", 2343 | "attrs": {"act_type": "relu"}, 2344 | "inputs": [[226, 0, 0]] 2345 | }, 2346 | { 2347 | "op": "null", 2348 | "name": "stage3_unit2_conv1_weight", 2349 | "attrs": { 2350 | "kernel": "(1, 1)", 2351 | "no_bias": "True", 2352 | "num_filter": "256", 2353 | "pad": "(0, 0)", 2354 | "stride": "(1, 1)", 2355 | "workspace": "256" 2356 | }, 2357 | "inputs": [] 2358 | }, 2359 | { 2360 | "op": "Convolution", 2361 | "name": "stage3_unit2_conv1", 2362 | "attrs": { 2363 | "kernel": "(1, 1)", 2364 | "no_bias": "True", 2365 | "num_filter": "256", 2366 | "pad": "(0, 0)", 2367 | "stride": "(1, 1)", 2368 | "workspace": "256" 2369 | }, 2370 | "inputs": [[227, 0, 0], [228, 0, 0]] 2371 | }, 2372 | { 2373 | "op": "null", 2374 | "name": "stage3_unit2_bn2_gamma", 2375 | "attrs": { 2376 | "eps": "2e-05", 2377 | "fix_gamma": "False", 2378 | "momentum": "0.9" 2379 | }, 2380 | "inputs": [] 2381 | }, 2382 | { 2383 | "op": "null", 2384 | "name": "stage3_unit2_bn2_beta", 2385 | "attrs": { 2386 | "eps": "2e-05", 2387 | "fix_gamma": "False", 2388 | "momentum": "0.9" 2389 | }, 2390 | "inputs": [] 2391 | }, 2392 | { 2393 | "op": "null", 2394 | "name": "stage3_unit2_bn2_moving_mean", 2395 | "attrs": { 2396 | "__init__": "[\"zero\", {}]", 2397 | "eps": "2e-05", 2398 | "fix_gamma": "False", 2399 | "momentum": "0.9" 2400 | }, 2401 | "inputs": [] 2402 | }, 2403 | { 2404 | "op": "null", 2405 | "name": "stage3_unit2_bn2_moving_var", 2406 | "attrs": { 2407 | "__init__": "[\"one\", {}]", 2408 | "eps": "2e-05", 2409 | "fix_gamma": "False", 2410 | "momentum": "0.9" 2411 | }, 2412 | "inputs": [] 2413 | }, 2414 | { 2415 | "op": "BatchNorm", 2416 | "name": "stage3_unit2_bn2", 2417 | "attrs": { 2418 | "eps": "2e-05", 2419 | "fix_gamma": "False", 2420 | "momentum": "0.9" 2421 | }, 2422 | "inputs": [[229, 0, 0], [230, 0, 0], [231, 0, 0], [232, 0, 1], [233, 0, 1]] 2423 | }, 2424 | { 2425 | "op": "Activation", 2426 | "name": "stage3_unit2_relu2", 2427 | "attrs": {"act_type": "relu"}, 2428 | "inputs": [[234, 0, 0]] 2429 | }, 2430 | { 2431 | "op": "null", 2432 | "name": "stage3_unit2_conv2_weight", 2433 | "attrs": { 2434 | "kernel": "(3, 3)", 2435 | "no_bias": "True", 2436 | "num_filter": "256", 2437 | "pad": "(1, 1)", 2438 | "stride": "(1, 1)", 2439 | "workspace": "256" 2440 | }, 2441 | "inputs": [] 2442 | }, 2443 | { 2444 | "op": "Convolution", 2445 | "name": "stage3_unit2_conv2", 2446 | "attrs": { 2447 | "kernel": "(3, 3)", 2448 | "no_bias": "True", 2449 | "num_filter": "256", 2450 | "pad": "(1, 1)", 2451 | "stride": "(1, 1)", 2452 | "workspace": "256" 2453 | }, 2454 | "inputs": [[235, 0, 0], [236, 0, 0]] 2455 | }, 2456 | { 2457 | "op": "null", 2458 | "name": "stage3_unit2_bn3_gamma", 2459 | "attrs": { 2460 | "eps": "2e-05", 2461 | "fix_gamma": "False", 2462 | "momentum": "0.9" 2463 | }, 2464 | "inputs": [] 2465 | }, 2466 | { 2467 | "op": "null", 2468 | "name": "stage3_unit2_bn3_beta", 2469 | "attrs": { 2470 | "eps": "2e-05", 2471 | "fix_gamma": "False", 2472 | "momentum": "0.9" 2473 | }, 2474 | "inputs": [] 2475 | }, 2476 | { 2477 | "op": "null", 2478 | "name": "stage3_unit2_bn3_moving_mean", 2479 | "attrs": { 2480 | "__init__": "[\"zero\", {}]", 2481 | "eps": "2e-05", 2482 | "fix_gamma": "False", 2483 | "momentum": "0.9" 2484 | }, 2485 | "inputs": [] 2486 | }, 2487 | { 2488 | "op": "null", 2489 | "name": "stage3_unit2_bn3_moving_var", 2490 | "attrs": { 2491 | "__init__": "[\"one\", {}]", 2492 | "eps": "2e-05", 2493 | "fix_gamma": "False", 2494 | "momentum": "0.9" 2495 | }, 2496 | "inputs": [] 2497 | }, 2498 | { 2499 | "op": "BatchNorm", 2500 | "name": "stage3_unit2_bn3", 2501 | "attrs": { 2502 | "eps": "2e-05", 2503 | "fix_gamma": "False", 2504 | "momentum": "0.9" 2505 | }, 2506 | "inputs": [[237, 0, 0], [238, 0, 0], [239, 0, 0], [240, 0, 1], [241, 0, 1]] 2507 | }, 2508 | { 2509 | "op": "Activation", 2510 | "name": "stage3_unit2_relu3", 2511 | "attrs": {"act_type": "relu"}, 2512 | "inputs": [[242, 0, 0]] 2513 | }, 2514 | { 2515 | "op": "null", 2516 | "name": "stage3_unit2_conv3_weight", 2517 | "attrs": { 2518 | "kernel": "(1, 1)", 2519 | "no_bias": "True", 2520 | "num_filter": "1024", 2521 | "pad": "(0, 0)", 2522 | "stride": "(1, 1)", 2523 | "workspace": "256" 2524 | }, 2525 | "inputs": [] 2526 | }, 2527 | { 2528 | "op": "Convolution", 2529 | "name": "stage3_unit2_conv3", 2530 | "attrs": { 2531 | "kernel": "(1, 1)", 2532 | "no_bias": "True", 2533 | "num_filter": "1024", 2534 | "pad": "(0, 0)", 2535 | "stride": "(1, 1)", 2536 | "workspace": "256" 2537 | }, 2538 | "inputs": [[243, 0, 0], [244, 0, 0]] 2539 | }, 2540 | { 2541 | "op": "elemwise_add", 2542 | "name": "_plus8", 2543 | "inputs": [[245, 0, 0], [221, 0, 0]] 2544 | }, 2545 | { 2546 | "op": "null", 2547 | "name": "stage3_unit3_bn1_gamma", 2548 | "attrs": { 2549 | "eps": "2e-05", 2550 | "fix_gamma": "False", 2551 | "momentum": "0.9" 2552 | }, 2553 | "inputs": [] 2554 | }, 2555 | { 2556 | "op": "null", 2557 | "name": "stage3_unit3_bn1_beta", 2558 | "attrs": { 2559 | "eps": "2e-05", 2560 | "fix_gamma": "False", 2561 | "momentum": "0.9" 2562 | }, 2563 | "inputs": [] 2564 | }, 2565 | { 2566 | "op": "null", 2567 | "name": "stage3_unit3_bn1_moving_mean", 2568 | "attrs": { 2569 | "__init__": "[\"zero\", {}]", 2570 | "eps": "2e-05", 2571 | "fix_gamma": "False", 2572 | "momentum": "0.9" 2573 | }, 2574 | "inputs": [] 2575 | }, 2576 | { 2577 | "op": "null", 2578 | "name": "stage3_unit3_bn1_moving_var", 2579 | "attrs": { 2580 | "__init__": "[\"one\", {}]", 2581 | "eps": "2e-05", 2582 | "fix_gamma": "False", 2583 | "momentum": "0.9" 2584 | }, 2585 | "inputs": [] 2586 | }, 2587 | { 2588 | "op": "BatchNorm", 2589 | "name": "stage3_unit3_bn1", 2590 | "attrs": { 2591 | "eps": "2e-05", 2592 | "fix_gamma": "False", 2593 | "momentum": "0.9" 2594 | }, 2595 | "inputs": [[246, 0, 0], [247, 0, 0], [248, 0, 0], [249, 0, 1], [250, 0, 1]] 2596 | }, 2597 | { 2598 | "op": "Activation", 2599 | "name": "stage3_unit3_relu1", 2600 | "attrs": {"act_type": "relu"}, 2601 | "inputs": [[251, 0, 0]] 2602 | }, 2603 | { 2604 | "op": "null", 2605 | "name": "stage3_unit3_conv1_weight", 2606 | "attrs": { 2607 | "kernel": "(1, 1)", 2608 | "no_bias": "True", 2609 | "num_filter": "256", 2610 | "pad": "(0, 0)", 2611 | "stride": "(1, 1)", 2612 | "workspace": "256" 2613 | }, 2614 | "inputs": [] 2615 | }, 2616 | { 2617 | "op": "Convolution", 2618 | "name": "stage3_unit3_conv1", 2619 | "attrs": { 2620 | "kernel": "(1, 1)", 2621 | "no_bias": "True", 2622 | "num_filter": "256", 2623 | "pad": "(0, 0)", 2624 | "stride": "(1, 1)", 2625 | "workspace": "256" 2626 | }, 2627 | "inputs": [[252, 0, 0], [253, 0, 0]] 2628 | }, 2629 | { 2630 | "op": "null", 2631 | "name": "stage3_unit3_bn2_gamma", 2632 | "attrs": { 2633 | "eps": "2e-05", 2634 | "fix_gamma": "False", 2635 | "momentum": "0.9" 2636 | }, 2637 | "inputs": [] 2638 | }, 2639 | { 2640 | "op": "null", 2641 | "name": "stage3_unit3_bn2_beta", 2642 | "attrs": { 2643 | "eps": "2e-05", 2644 | "fix_gamma": "False", 2645 | "momentum": "0.9" 2646 | }, 2647 | "inputs": [] 2648 | }, 2649 | { 2650 | "op": "null", 2651 | "name": "stage3_unit3_bn2_moving_mean", 2652 | "attrs": { 2653 | "__init__": "[\"zero\", {}]", 2654 | "eps": "2e-05", 2655 | "fix_gamma": "False", 2656 | "momentum": "0.9" 2657 | }, 2658 | "inputs": [] 2659 | }, 2660 | { 2661 | "op": "null", 2662 | "name": "stage3_unit3_bn2_moving_var", 2663 | "attrs": { 2664 | "__init__": "[\"one\", {}]", 2665 | "eps": "2e-05", 2666 | "fix_gamma": "False", 2667 | "momentum": "0.9" 2668 | }, 2669 | "inputs": [] 2670 | }, 2671 | { 2672 | "op": "BatchNorm", 2673 | "name": "stage3_unit3_bn2", 2674 | "attrs": { 2675 | "eps": "2e-05", 2676 | "fix_gamma": "False", 2677 | "momentum": "0.9" 2678 | }, 2679 | "inputs": [[254, 0, 0], [255, 0, 0], [256, 0, 0], [257, 0, 1], [258, 0, 1]] 2680 | }, 2681 | { 2682 | "op": "Activation", 2683 | "name": "stage3_unit3_relu2", 2684 | "attrs": {"act_type": "relu"}, 2685 | "inputs": [[259, 0, 0]] 2686 | }, 2687 | { 2688 | "op": "null", 2689 | "name": "stage3_unit3_conv2_weight", 2690 | "attrs": { 2691 | "kernel": "(3, 3)", 2692 | "no_bias": "True", 2693 | "num_filter": "256", 2694 | "pad": "(1, 1)", 2695 | "stride": "(1, 1)", 2696 | "workspace": "256" 2697 | }, 2698 | "inputs": [] 2699 | }, 2700 | { 2701 | "op": "Convolution", 2702 | "name": "stage3_unit3_conv2", 2703 | "attrs": { 2704 | "kernel": "(3, 3)", 2705 | "no_bias": "True", 2706 | "num_filter": "256", 2707 | "pad": "(1, 1)", 2708 | "stride": "(1, 1)", 2709 | "workspace": "256" 2710 | }, 2711 | "inputs": [[260, 0, 0], [261, 0, 0]] 2712 | }, 2713 | { 2714 | "op": "null", 2715 | "name": "stage3_unit3_bn3_gamma", 2716 | "attrs": { 2717 | "eps": "2e-05", 2718 | "fix_gamma": "False", 2719 | "momentum": "0.9" 2720 | }, 2721 | "inputs": [] 2722 | }, 2723 | { 2724 | "op": "null", 2725 | "name": "stage3_unit3_bn3_beta", 2726 | "attrs": { 2727 | "eps": "2e-05", 2728 | "fix_gamma": "False", 2729 | "momentum": "0.9" 2730 | }, 2731 | "inputs": [] 2732 | }, 2733 | { 2734 | "op": "null", 2735 | "name": "stage3_unit3_bn3_moving_mean", 2736 | "attrs": { 2737 | "__init__": "[\"zero\", {}]", 2738 | "eps": "2e-05", 2739 | "fix_gamma": "False", 2740 | "momentum": "0.9" 2741 | }, 2742 | "inputs": [] 2743 | }, 2744 | { 2745 | "op": "null", 2746 | "name": "stage3_unit3_bn3_moving_var", 2747 | "attrs": { 2748 | "__init__": "[\"one\", {}]", 2749 | "eps": "2e-05", 2750 | "fix_gamma": "False", 2751 | "momentum": "0.9" 2752 | }, 2753 | "inputs": [] 2754 | }, 2755 | { 2756 | "op": "BatchNorm", 2757 | "name": "stage3_unit3_bn3", 2758 | "attrs": { 2759 | "eps": "2e-05", 2760 | "fix_gamma": "False", 2761 | "momentum": "0.9" 2762 | }, 2763 | "inputs": [[262, 0, 0], [263, 0, 0], [264, 0, 0], [265, 0, 1], [266, 0, 1]] 2764 | }, 2765 | { 2766 | "op": "Activation", 2767 | "name": "stage3_unit3_relu3", 2768 | "attrs": {"act_type": "relu"}, 2769 | "inputs": [[267, 0, 0]] 2770 | }, 2771 | { 2772 | "op": "null", 2773 | "name": "stage3_unit3_conv3_weight", 2774 | "attrs": { 2775 | "kernel": "(1, 1)", 2776 | "no_bias": "True", 2777 | "num_filter": "1024", 2778 | "pad": "(0, 0)", 2779 | "stride": "(1, 1)", 2780 | "workspace": "256" 2781 | }, 2782 | "inputs": [] 2783 | }, 2784 | { 2785 | "op": "Convolution", 2786 | "name": "stage3_unit3_conv3", 2787 | "attrs": { 2788 | "kernel": "(1, 1)", 2789 | "no_bias": "True", 2790 | "num_filter": "1024", 2791 | "pad": "(0, 0)", 2792 | "stride": "(1, 1)", 2793 | "workspace": "256" 2794 | }, 2795 | "inputs": [[268, 0, 0], [269, 0, 0]] 2796 | }, 2797 | { 2798 | "op": "elemwise_add", 2799 | "name": "_plus9", 2800 | "inputs": [[270, 0, 0], [246, 0, 0]] 2801 | }, 2802 | { 2803 | "op": "null", 2804 | "name": "stage3_unit4_bn1_gamma", 2805 | "attrs": { 2806 | "eps": "2e-05", 2807 | "fix_gamma": "False", 2808 | "momentum": "0.9" 2809 | }, 2810 | "inputs": [] 2811 | }, 2812 | { 2813 | "op": "null", 2814 | "name": "stage3_unit4_bn1_beta", 2815 | "attrs": { 2816 | "eps": "2e-05", 2817 | "fix_gamma": "False", 2818 | "momentum": "0.9" 2819 | }, 2820 | "inputs": [] 2821 | }, 2822 | { 2823 | "op": "null", 2824 | "name": "stage3_unit4_bn1_moving_mean", 2825 | "attrs": { 2826 | "__init__": "[\"zero\", {}]", 2827 | "eps": "2e-05", 2828 | "fix_gamma": "False", 2829 | "momentum": "0.9" 2830 | }, 2831 | "inputs": [] 2832 | }, 2833 | { 2834 | "op": "null", 2835 | "name": "stage3_unit4_bn1_moving_var", 2836 | "attrs": { 2837 | "__init__": "[\"one\", {}]", 2838 | "eps": "2e-05", 2839 | "fix_gamma": "False", 2840 | "momentum": "0.9" 2841 | }, 2842 | "inputs": [] 2843 | }, 2844 | { 2845 | "op": "BatchNorm", 2846 | "name": "stage3_unit4_bn1", 2847 | "attrs": { 2848 | "eps": "2e-05", 2849 | "fix_gamma": "False", 2850 | "momentum": "0.9" 2851 | }, 2852 | "inputs": [[271, 0, 0], [272, 0, 0], [273, 0, 0], [274, 0, 1], [275, 0, 1]] 2853 | }, 2854 | { 2855 | "op": "Activation", 2856 | "name": "stage3_unit4_relu1", 2857 | "attrs": {"act_type": "relu"}, 2858 | "inputs": [[276, 0, 0]] 2859 | }, 2860 | { 2861 | "op": "null", 2862 | "name": "stage3_unit4_conv1_weight", 2863 | "attrs": { 2864 | "kernel": "(1, 1)", 2865 | "no_bias": "True", 2866 | "num_filter": "256", 2867 | "pad": "(0, 0)", 2868 | "stride": "(1, 1)", 2869 | "workspace": "256" 2870 | }, 2871 | "inputs": [] 2872 | }, 2873 | { 2874 | "op": "Convolution", 2875 | "name": "stage3_unit4_conv1", 2876 | "attrs": { 2877 | "kernel": "(1, 1)", 2878 | "no_bias": "True", 2879 | "num_filter": "256", 2880 | "pad": "(0, 0)", 2881 | "stride": "(1, 1)", 2882 | "workspace": "256" 2883 | }, 2884 | "inputs": [[277, 0, 0], [278, 0, 0]] 2885 | }, 2886 | { 2887 | "op": "null", 2888 | "name": "stage3_unit4_bn2_gamma", 2889 | "attrs": { 2890 | "eps": "2e-05", 2891 | "fix_gamma": "False", 2892 | "momentum": "0.9" 2893 | }, 2894 | "inputs": [] 2895 | }, 2896 | { 2897 | "op": "null", 2898 | "name": "stage3_unit4_bn2_beta", 2899 | "attrs": { 2900 | "eps": "2e-05", 2901 | "fix_gamma": "False", 2902 | "momentum": "0.9" 2903 | }, 2904 | "inputs": [] 2905 | }, 2906 | { 2907 | "op": "null", 2908 | "name": "stage3_unit4_bn2_moving_mean", 2909 | "attrs": { 2910 | "__init__": "[\"zero\", {}]", 2911 | "eps": "2e-05", 2912 | "fix_gamma": "False", 2913 | "momentum": "0.9" 2914 | }, 2915 | "inputs": [] 2916 | }, 2917 | { 2918 | "op": "null", 2919 | "name": "stage3_unit4_bn2_moving_var", 2920 | "attrs": { 2921 | "__init__": "[\"one\", {}]", 2922 | "eps": "2e-05", 2923 | "fix_gamma": "False", 2924 | "momentum": "0.9" 2925 | }, 2926 | "inputs": [] 2927 | }, 2928 | { 2929 | "op": "BatchNorm", 2930 | "name": "stage3_unit4_bn2", 2931 | "attrs": { 2932 | "eps": "2e-05", 2933 | "fix_gamma": "False", 2934 | "momentum": "0.9" 2935 | }, 2936 | "inputs": [[279, 0, 0], [280, 0, 0], [281, 0, 0], [282, 0, 1], [283, 0, 1]] 2937 | }, 2938 | { 2939 | "op": "Activation", 2940 | "name": "stage3_unit4_relu2", 2941 | "attrs": {"act_type": "relu"}, 2942 | "inputs": [[284, 0, 0]] 2943 | }, 2944 | { 2945 | "op": "null", 2946 | "name": "stage3_unit4_conv2_weight", 2947 | "attrs": { 2948 | "kernel": "(3, 3)", 2949 | "no_bias": "True", 2950 | "num_filter": "256", 2951 | "pad": "(1, 1)", 2952 | "stride": "(1, 1)", 2953 | "workspace": "256" 2954 | }, 2955 | "inputs": [] 2956 | }, 2957 | { 2958 | "op": "Convolution", 2959 | "name": "stage3_unit4_conv2", 2960 | "attrs": { 2961 | "kernel": "(3, 3)", 2962 | "no_bias": "True", 2963 | "num_filter": "256", 2964 | "pad": "(1, 1)", 2965 | "stride": "(1, 1)", 2966 | "workspace": "256" 2967 | }, 2968 | "inputs": [[285, 0, 0], [286, 0, 0]] 2969 | }, 2970 | { 2971 | "op": "null", 2972 | "name": "stage3_unit4_bn3_gamma", 2973 | "attrs": { 2974 | "eps": "2e-05", 2975 | "fix_gamma": "False", 2976 | "momentum": "0.9" 2977 | }, 2978 | "inputs": [] 2979 | }, 2980 | { 2981 | "op": "null", 2982 | "name": "stage3_unit4_bn3_beta", 2983 | "attrs": { 2984 | "eps": "2e-05", 2985 | "fix_gamma": "False", 2986 | "momentum": "0.9" 2987 | }, 2988 | "inputs": [] 2989 | }, 2990 | { 2991 | "op": "null", 2992 | "name": "stage3_unit4_bn3_moving_mean", 2993 | "attrs": { 2994 | "__init__": "[\"zero\", {}]", 2995 | "eps": "2e-05", 2996 | "fix_gamma": "False", 2997 | "momentum": "0.9" 2998 | }, 2999 | "inputs": [] 3000 | }, 3001 | { 3002 | "op": "null", 3003 | "name": "stage3_unit4_bn3_moving_var", 3004 | "attrs": { 3005 | "__init__": "[\"one\", {}]", 3006 | "eps": "2e-05", 3007 | "fix_gamma": "False", 3008 | "momentum": "0.9" 3009 | }, 3010 | "inputs": [] 3011 | }, 3012 | { 3013 | "op": "BatchNorm", 3014 | "name": "stage3_unit4_bn3", 3015 | "attrs": { 3016 | "eps": "2e-05", 3017 | "fix_gamma": "False", 3018 | "momentum": "0.9" 3019 | }, 3020 | "inputs": [[287, 0, 0], [288, 0, 0], [289, 0, 0], [290, 0, 1], [291, 0, 1]] 3021 | }, 3022 | { 3023 | "op": "Activation", 3024 | "name": "stage3_unit4_relu3", 3025 | "attrs": {"act_type": "relu"}, 3026 | "inputs": [[292, 0, 0]] 3027 | }, 3028 | { 3029 | "op": "null", 3030 | "name": "stage3_unit4_conv3_weight", 3031 | "attrs": { 3032 | "kernel": "(1, 1)", 3033 | "no_bias": "True", 3034 | "num_filter": "1024", 3035 | "pad": "(0, 0)", 3036 | "stride": "(1, 1)", 3037 | "workspace": "256" 3038 | }, 3039 | "inputs": [] 3040 | }, 3041 | { 3042 | "op": "Convolution", 3043 | "name": "stage3_unit4_conv3", 3044 | "attrs": { 3045 | "kernel": "(1, 1)", 3046 | "no_bias": "True", 3047 | "num_filter": "1024", 3048 | "pad": "(0, 0)", 3049 | "stride": "(1, 1)", 3050 | "workspace": "256" 3051 | }, 3052 | "inputs": [[293, 0, 0], [294, 0, 0]] 3053 | }, 3054 | { 3055 | "op": "elemwise_add", 3056 | "name": "_plus10", 3057 | "inputs": [[295, 0, 0], [271, 0, 0]] 3058 | }, 3059 | { 3060 | "op": "null", 3061 | "name": "stage3_unit5_bn1_gamma", 3062 | "attrs": { 3063 | "eps": "2e-05", 3064 | "fix_gamma": "False", 3065 | "momentum": "0.9" 3066 | }, 3067 | "inputs": [] 3068 | }, 3069 | { 3070 | "op": "null", 3071 | "name": "stage3_unit5_bn1_beta", 3072 | "attrs": { 3073 | "eps": "2e-05", 3074 | "fix_gamma": "False", 3075 | "momentum": "0.9" 3076 | }, 3077 | "inputs": [] 3078 | }, 3079 | { 3080 | "op": "null", 3081 | "name": "stage3_unit5_bn1_moving_mean", 3082 | "attrs": { 3083 | "__init__": "[\"zero\", {}]", 3084 | "eps": "2e-05", 3085 | "fix_gamma": "False", 3086 | "momentum": "0.9" 3087 | }, 3088 | "inputs": [] 3089 | }, 3090 | { 3091 | "op": "null", 3092 | "name": "stage3_unit5_bn1_moving_var", 3093 | "attrs": { 3094 | "__init__": "[\"one\", {}]", 3095 | "eps": "2e-05", 3096 | "fix_gamma": "False", 3097 | "momentum": "0.9" 3098 | }, 3099 | "inputs": [] 3100 | }, 3101 | { 3102 | "op": "BatchNorm", 3103 | "name": "stage3_unit5_bn1", 3104 | "attrs": { 3105 | "eps": "2e-05", 3106 | "fix_gamma": "False", 3107 | "momentum": "0.9" 3108 | }, 3109 | "inputs": [[296, 0, 0], [297, 0, 0], [298, 0, 0], [299, 0, 1], [300, 0, 1]] 3110 | }, 3111 | { 3112 | "op": "Activation", 3113 | "name": "stage3_unit5_relu1", 3114 | "attrs": {"act_type": "relu"}, 3115 | "inputs": [[301, 0, 0]] 3116 | }, 3117 | { 3118 | "op": "null", 3119 | "name": "stage3_unit5_conv1_weight", 3120 | "attrs": { 3121 | "kernel": "(1, 1)", 3122 | "no_bias": "True", 3123 | "num_filter": "256", 3124 | "pad": "(0, 0)", 3125 | "stride": "(1, 1)", 3126 | "workspace": "256" 3127 | }, 3128 | "inputs": [] 3129 | }, 3130 | { 3131 | "op": "Convolution", 3132 | "name": "stage3_unit5_conv1", 3133 | "attrs": { 3134 | "kernel": "(1, 1)", 3135 | "no_bias": "True", 3136 | "num_filter": "256", 3137 | "pad": "(0, 0)", 3138 | "stride": "(1, 1)", 3139 | "workspace": "256" 3140 | }, 3141 | "inputs": [[302, 0, 0], [303, 0, 0]] 3142 | }, 3143 | { 3144 | "op": "null", 3145 | "name": "stage3_unit5_bn2_gamma", 3146 | "attrs": { 3147 | "eps": "2e-05", 3148 | "fix_gamma": "False", 3149 | "momentum": "0.9" 3150 | }, 3151 | "inputs": [] 3152 | }, 3153 | { 3154 | "op": "null", 3155 | "name": "stage3_unit5_bn2_beta", 3156 | "attrs": { 3157 | "eps": "2e-05", 3158 | "fix_gamma": "False", 3159 | "momentum": "0.9" 3160 | }, 3161 | "inputs": [] 3162 | }, 3163 | { 3164 | "op": "null", 3165 | "name": "stage3_unit5_bn2_moving_mean", 3166 | "attrs": { 3167 | "__init__": "[\"zero\", {}]", 3168 | "eps": "2e-05", 3169 | "fix_gamma": "False", 3170 | "momentum": "0.9" 3171 | }, 3172 | "inputs": [] 3173 | }, 3174 | { 3175 | "op": "null", 3176 | "name": "stage3_unit5_bn2_moving_var", 3177 | "attrs": { 3178 | "__init__": "[\"one\", {}]", 3179 | "eps": "2e-05", 3180 | "fix_gamma": "False", 3181 | "momentum": "0.9" 3182 | }, 3183 | "inputs": [] 3184 | }, 3185 | { 3186 | "op": "BatchNorm", 3187 | "name": "stage3_unit5_bn2", 3188 | "attrs": { 3189 | "eps": "2e-05", 3190 | "fix_gamma": "False", 3191 | "momentum": "0.9" 3192 | }, 3193 | "inputs": [[304, 0, 0], [305, 0, 0], [306, 0, 0], [307, 0, 1], [308, 0, 1]] 3194 | }, 3195 | { 3196 | "op": "Activation", 3197 | "name": "stage3_unit5_relu2", 3198 | "attrs": {"act_type": "relu"}, 3199 | "inputs": [[309, 0, 0]] 3200 | }, 3201 | { 3202 | "op": "null", 3203 | "name": "stage3_unit5_conv2_weight", 3204 | "attrs": { 3205 | "kernel": "(3, 3)", 3206 | "no_bias": "True", 3207 | "num_filter": "256", 3208 | "pad": "(1, 1)", 3209 | "stride": "(1, 1)", 3210 | "workspace": "256" 3211 | }, 3212 | "inputs": [] 3213 | }, 3214 | { 3215 | "op": "Convolution", 3216 | "name": "stage3_unit5_conv2", 3217 | "attrs": { 3218 | "kernel": "(3, 3)", 3219 | "no_bias": "True", 3220 | "num_filter": "256", 3221 | "pad": "(1, 1)", 3222 | "stride": "(1, 1)", 3223 | "workspace": "256" 3224 | }, 3225 | "inputs": [[310, 0, 0], [311, 0, 0]] 3226 | }, 3227 | { 3228 | "op": "null", 3229 | "name": "stage3_unit5_bn3_gamma", 3230 | "attrs": { 3231 | "eps": "2e-05", 3232 | "fix_gamma": "False", 3233 | "momentum": "0.9" 3234 | }, 3235 | "inputs": [] 3236 | }, 3237 | { 3238 | "op": "null", 3239 | "name": "stage3_unit5_bn3_beta", 3240 | "attrs": { 3241 | "eps": "2e-05", 3242 | "fix_gamma": "False", 3243 | "momentum": "0.9" 3244 | }, 3245 | "inputs": [] 3246 | }, 3247 | { 3248 | "op": "null", 3249 | "name": "stage3_unit5_bn3_moving_mean", 3250 | "attrs": { 3251 | "__init__": "[\"zero\", {}]", 3252 | "eps": "2e-05", 3253 | "fix_gamma": "False", 3254 | "momentum": "0.9" 3255 | }, 3256 | "inputs": [] 3257 | }, 3258 | { 3259 | "op": "null", 3260 | "name": "stage3_unit5_bn3_moving_var", 3261 | "attrs": { 3262 | "__init__": "[\"one\", {}]", 3263 | "eps": "2e-05", 3264 | "fix_gamma": "False", 3265 | "momentum": "0.9" 3266 | }, 3267 | "inputs": [] 3268 | }, 3269 | { 3270 | "op": "BatchNorm", 3271 | "name": "stage3_unit5_bn3", 3272 | "attrs": { 3273 | "eps": "2e-05", 3274 | "fix_gamma": "False", 3275 | "momentum": "0.9" 3276 | }, 3277 | "inputs": [[312, 0, 0], [313, 0, 0], [314, 0, 0], [315, 0, 1], [316, 0, 1]] 3278 | }, 3279 | { 3280 | "op": "Activation", 3281 | "name": "stage3_unit5_relu3", 3282 | "attrs": {"act_type": "relu"}, 3283 | "inputs": [[317, 0, 0]] 3284 | }, 3285 | { 3286 | "op": "null", 3287 | "name": "stage3_unit5_conv3_weight", 3288 | "attrs": { 3289 | "kernel": "(1, 1)", 3290 | "no_bias": "True", 3291 | "num_filter": "1024", 3292 | "pad": "(0, 0)", 3293 | "stride": "(1, 1)", 3294 | "workspace": "256" 3295 | }, 3296 | "inputs": [] 3297 | }, 3298 | { 3299 | "op": "Convolution", 3300 | "name": "stage3_unit5_conv3", 3301 | "attrs": { 3302 | "kernel": "(1, 1)", 3303 | "no_bias": "True", 3304 | "num_filter": "1024", 3305 | "pad": "(0, 0)", 3306 | "stride": "(1, 1)", 3307 | "workspace": "256" 3308 | }, 3309 | "inputs": [[318, 0, 0], [319, 0, 0]] 3310 | }, 3311 | { 3312 | "op": "elemwise_add", 3313 | "name": "_plus11", 3314 | "inputs": [[320, 0, 0], [296, 0, 0]] 3315 | }, 3316 | { 3317 | "op": "null", 3318 | "name": "stage3_unit6_bn1_gamma", 3319 | "attrs": { 3320 | "eps": "2e-05", 3321 | "fix_gamma": "False", 3322 | "momentum": "0.9" 3323 | }, 3324 | "inputs": [] 3325 | }, 3326 | { 3327 | "op": "null", 3328 | "name": "stage3_unit6_bn1_beta", 3329 | "attrs": { 3330 | "eps": "2e-05", 3331 | "fix_gamma": "False", 3332 | "momentum": "0.9" 3333 | }, 3334 | "inputs": [] 3335 | }, 3336 | { 3337 | "op": "null", 3338 | "name": "stage3_unit6_bn1_moving_mean", 3339 | "attrs": { 3340 | "__init__": "[\"zero\", {}]", 3341 | "eps": "2e-05", 3342 | "fix_gamma": "False", 3343 | "momentum": "0.9" 3344 | }, 3345 | "inputs": [] 3346 | }, 3347 | { 3348 | "op": "null", 3349 | "name": "stage3_unit6_bn1_moving_var", 3350 | "attrs": { 3351 | "__init__": "[\"one\", {}]", 3352 | "eps": "2e-05", 3353 | "fix_gamma": "False", 3354 | "momentum": "0.9" 3355 | }, 3356 | "inputs": [] 3357 | }, 3358 | { 3359 | "op": "BatchNorm", 3360 | "name": "stage3_unit6_bn1", 3361 | "attrs": { 3362 | "eps": "2e-05", 3363 | "fix_gamma": "False", 3364 | "momentum": "0.9" 3365 | }, 3366 | "inputs": [[321, 0, 0], [322, 0, 0], [323, 0, 0], [324, 0, 1], [325, 0, 1]] 3367 | }, 3368 | { 3369 | "op": "Activation", 3370 | "name": "stage3_unit6_relu1", 3371 | "attrs": {"act_type": "relu"}, 3372 | "inputs": [[326, 0, 0]] 3373 | }, 3374 | { 3375 | "op": "null", 3376 | "name": "stage3_unit6_conv1_weight", 3377 | "attrs": { 3378 | "kernel": "(1, 1)", 3379 | "no_bias": "True", 3380 | "num_filter": "256", 3381 | "pad": "(0, 0)", 3382 | "stride": "(1, 1)", 3383 | "workspace": "256" 3384 | }, 3385 | "inputs": [] 3386 | }, 3387 | { 3388 | "op": "Convolution", 3389 | "name": "stage3_unit6_conv1", 3390 | "attrs": { 3391 | "kernel": "(1, 1)", 3392 | "no_bias": "True", 3393 | "num_filter": "256", 3394 | "pad": "(0, 0)", 3395 | "stride": "(1, 1)", 3396 | "workspace": "256" 3397 | }, 3398 | "inputs": [[327, 0, 0], [328, 0, 0]] 3399 | }, 3400 | { 3401 | "op": "null", 3402 | "name": "stage3_unit6_bn2_gamma", 3403 | "attrs": { 3404 | "eps": "2e-05", 3405 | "fix_gamma": "False", 3406 | "momentum": "0.9" 3407 | }, 3408 | "inputs": [] 3409 | }, 3410 | { 3411 | "op": "null", 3412 | "name": "stage3_unit6_bn2_beta", 3413 | "attrs": { 3414 | "eps": "2e-05", 3415 | "fix_gamma": "False", 3416 | "momentum": "0.9" 3417 | }, 3418 | "inputs": [] 3419 | }, 3420 | { 3421 | "op": "null", 3422 | "name": "stage3_unit6_bn2_moving_mean", 3423 | "attrs": { 3424 | "__init__": "[\"zero\", {}]", 3425 | "eps": "2e-05", 3426 | "fix_gamma": "False", 3427 | "momentum": "0.9" 3428 | }, 3429 | "inputs": [] 3430 | }, 3431 | { 3432 | "op": "null", 3433 | "name": "stage3_unit6_bn2_moving_var", 3434 | "attrs": { 3435 | "__init__": "[\"one\", {}]", 3436 | "eps": "2e-05", 3437 | "fix_gamma": "False", 3438 | "momentum": "0.9" 3439 | }, 3440 | "inputs": [] 3441 | }, 3442 | { 3443 | "op": "BatchNorm", 3444 | "name": "stage3_unit6_bn2", 3445 | "attrs": { 3446 | "eps": "2e-05", 3447 | "fix_gamma": "False", 3448 | "momentum": "0.9" 3449 | }, 3450 | "inputs": [[329, 0, 0], [330, 0, 0], [331, 0, 0], [332, 0, 1], [333, 0, 1]] 3451 | }, 3452 | { 3453 | "op": "Activation", 3454 | "name": "stage3_unit6_relu2", 3455 | "attrs": {"act_type": "relu"}, 3456 | "inputs": [[334, 0, 0]] 3457 | }, 3458 | { 3459 | "op": "null", 3460 | "name": "stage3_unit6_conv2_weight", 3461 | "attrs": { 3462 | "kernel": "(3, 3)", 3463 | "no_bias": "True", 3464 | "num_filter": "256", 3465 | "pad": "(1, 1)", 3466 | "stride": "(1, 1)", 3467 | "workspace": "256" 3468 | }, 3469 | "inputs": [] 3470 | }, 3471 | { 3472 | "op": "Convolution", 3473 | "name": "stage3_unit6_conv2", 3474 | "attrs": { 3475 | "kernel": "(3, 3)", 3476 | "no_bias": "True", 3477 | "num_filter": "256", 3478 | "pad": "(1, 1)", 3479 | "stride": "(1, 1)", 3480 | "workspace": "256" 3481 | }, 3482 | "inputs": [[335, 0, 0], [336, 0, 0]] 3483 | }, 3484 | { 3485 | "op": "null", 3486 | "name": "stage3_unit6_bn3_gamma", 3487 | "attrs": { 3488 | "eps": "2e-05", 3489 | "fix_gamma": "False", 3490 | "momentum": "0.9" 3491 | }, 3492 | "inputs": [] 3493 | }, 3494 | { 3495 | "op": "null", 3496 | "name": "stage3_unit6_bn3_beta", 3497 | "attrs": { 3498 | "eps": "2e-05", 3499 | "fix_gamma": "False", 3500 | "momentum": "0.9" 3501 | }, 3502 | "inputs": [] 3503 | }, 3504 | { 3505 | "op": "null", 3506 | "name": "stage3_unit6_bn3_moving_mean", 3507 | "attrs": { 3508 | "__init__": "[\"zero\", {}]", 3509 | "eps": "2e-05", 3510 | "fix_gamma": "False", 3511 | "momentum": "0.9" 3512 | }, 3513 | "inputs": [] 3514 | }, 3515 | { 3516 | "op": "null", 3517 | "name": "stage3_unit6_bn3_moving_var", 3518 | "attrs": { 3519 | "__init__": "[\"one\", {}]", 3520 | "eps": "2e-05", 3521 | "fix_gamma": "False", 3522 | "momentum": "0.9" 3523 | }, 3524 | "inputs": [] 3525 | }, 3526 | { 3527 | "op": "BatchNorm", 3528 | "name": "stage3_unit6_bn3", 3529 | "attrs": { 3530 | "eps": "2e-05", 3531 | "fix_gamma": "False", 3532 | "momentum": "0.9" 3533 | }, 3534 | "inputs": [[337, 0, 0], [338, 0, 0], [339, 0, 0], [340, 0, 1], [341, 0, 1]] 3535 | }, 3536 | { 3537 | "op": "Activation", 3538 | "name": "stage3_unit6_relu3", 3539 | "attrs": {"act_type": "relu"}, 3540 | "inputs": [[342, 0, 0]] 3541 | }, 3542 | { 3543 | "op": "null", 3544 | "name": "stage3_unit6_conv3_weight", 3545 | "attrs": { 3546 | "kernel": "(1, 1)", 3547 | "no_bias": "True", 3548 | "num_filter": "1024", 3549 | "pad": "(0, 0)", 3550 | "stride": "(1, 1)", 3551 | "workspace": "256" 3552 | }, 3553 | "inputs": [] 3554 | }, 3555 | { 3556 | "op": "Convolution", 3557 | "name": "stage3_unit6_conv3", 3558 | "attrs": { 3559 | "kernel": "(1, 1)", 3560 | "no_bias": "True", 3561 | "num_filter": "1024", 3562 | "pad": "(0, 0)", 3563 | "stride": "(1, 1)", 3564 | "workspace": "256" 3565 | }, 3566 | "inputs": [[343, 0, 0], [344, 0, 0]] 3567 | }, 3568 | { 3569 | "op": "elemwise_add", 3570 | "name": "_plus12", 3571 | "inputs": [[345, 0, 0], [321, 0, 0]] 3572 | }, 3573 | { 3574 | "op": "null", 3575 | "name": "_plus12_cls_pred_conv_weight", 3576 | "attrs": { 3577 | "kernel": "(3, 3)", 3578 | "num_filter": "12", 3579 | "pad": "(1, 1)", 3580 | "stride": "(1, 1)" 3581 | }, 3582 | "inputs": [] 3583 | }, 3584 | { 3585 | "op": "null", 3586 | "name": "_plus12_cls_pred_conv_bias", 3587 | "attrs": { 3588 | "__init__": "[\"constant\", {\"value\": 0.0}]", 3589 | "__lr_mult__": "2.0" 3590 | }, 3591 | "inputs": [] 3592 | }, 3593 | { 3594 | "op": "Convolution", 3595 | "name": "_plus12_cls_pred_conv", 3596 | "attrs": { 3597 | "kernel": "(3, 3)", 3598 | "num_filter": "12", 3599 | "pad": "(1, 1)", 3600 | "stride": "(1, 1)" 3601 | }, 3602 | "inputs": [[346, 0, 0], [347, 0, 0], [348, 0, 0]] 3603 | }, 3604 | { 3605 | "op": "transpose", 3606 | "name": "transpose1", 3607 | "attrs": {"axes": "(0, 2, 3, 1)"}, 3608 | "inputs": [[349, 0, 0]] 3609 | }, 3610 | { 3611 | "op": "Flatten", 3612 | "name": "flatten2", 3613 | "inputs": [[350, 0, 0]] 3614 | }, 3615 | { 3616 | "op": "null", 3617 | "name": "stage4_unit1_bn1_gamma", 3618 | "attrs": { 3619 | "eps": "2e-05", 3620 | "fix_gamma": "False", 3621 | "momentum": "0.9" 3622 | }, 3623 | "inputs": [] 3624 | }, 3625 | { 3626 | "op": "null", 3627 | "name": "stage4_unit1_bn1_beta", 3628 | "attrs": { 3629 | "eps": "2e-05", 3630 | "fix_gamma": "False", 3631 | "momentum": "0.9" 3632 | }, 3633 | "inputs": [] 3634 | }, 3635 | { 3636 | "op": "null", 3637 | "name": "stage4_unit1_bn1_moving_mean", 3638 | "attrs": { 3639 | "__init__": "[\"zero\", {}]", 3640 | "eps": "2e-05", 3641 | "fix_gamma": "False", 3642 | "momentum": "0.9" 3643 | }, 3644 | "inputs": [] 3645 | }, 3646 | { 3647 | "op": "null", 3648 | "name": "stage4_unit1_bn1_moving_var", 3649 | "attrs": { 3650 | "__init__": "[\"one\", {}]", 3651 | "eps": "2e-05", 3652 | "fix_gamma": "False", 3653 | "momentum": "0.9" 3654 | }, 3655 | "inputs": [] 3656 | }, 3657 | { 3658 | "op": "BatchNorm", 3659 | "name": "stage4_unit1_bn1", 3660 | "attrs": { 3661 | "eps": "2e-05", 3662 | "fix_gamma": "False", 3663 | "momentum": "0.9" 3664 | }, 3665 | "inputs": [[346, 0, 0], [352, 0, 0], [353, 0, 0], [354, 0, 1], [355, 0, 1]] 3666 | }, 3667 | { 3668 | "op": "Activation", 3669 | "name": "stage4_unit1_relu1", 3670 | "attrs": {"act_type": "relu"}, 3671 | "inputs": [[356, 0, 0]] 3672 | }, 3673 | { 3674 | "op": "null", 3675 | "name": "stage4_unit1_conv1_weight", 3676 | "attrs": { 3677 | "kernel": "(1, 1)", 3678 | "no_bias": "True", 3679 | "num_filter": "512", 3680 | "pad": "(0, 0)", 3681 | "stride": "(1, 1)", 3682 | "workspace": "256" 3683 | }, 3684 | "inputs": [] 3685 | }, 3686 | { 3687 | "op": "Convolution", 3688 | "name": "stage4_unit1_conv1", 3689 | "attrs": { 3690 | "kernel": "(1, 1)", 3691 | "no_bias": "True", 3692 | "num_filter": "512", 3693 | "pad": "(0, 0)", 3694 | "stride": "(1, 1)", 3695 | "workspace": "256" 3696 | }, 3697 | "inputs": [[357, 0, 0], [358, 0, 0]] 3698 | }, 3699 | { 3700 | "op": "null", 3701 | "name": "stage4_unit1_bn2_gamma", 3702 | "attrs": { 3703 | "eps": "2e-05", 3704 | "fix_gamma": "False", 3705 | "momentum": "0.9" 3706 | }, 3707 | "inputs": [] 3708 | }, 3709 | { 3710 | "op": "null", 3711 | "name": "stage4_unit1_bn2_beta", 3712 | "attrs": { 3713 | "eps": "2e-05", 3714 | "fix_gamma": "False", 3715 | "momentum": "0.9" 3716 | }, 3717 | "inputs": [] 3718 | }, 3719 | { 3720 | "op": "null", 3721 | "name": "stage4_unit1_bn2_moving_mean", 3722 | "attrs": { 3723 | "__init__": "[\"zero\", {}]", 3724 | "eps": "2e-05", 3725 | "fix_gamma": "False", 3726 | "momentum": "0.9" 3727 | }, 3728 | "inputs": [] 3729 | }, 3730 | { 3731 | "op": "null", 3732 | "name": "stage4_unit1_bn2_moving_var", 3733 | "attrs": { 3734 | "__init__": "[\"one\", {}]", 3735 | "eps": "2e-05", 3736 | "fix_gamma": "False", 3737 | "momentum": "0.9" 3738 | }, 3739 | "inputs": [] 3740 | }, 3741 | { 3742 | "op": "BatchNorm", 3743 | "name": "stage4_unit1_bn2", 3744 | "attrs": { 3745 | "eps": "2e-05", 3746 | "fix_gamma": "False", 3747 | "momentum": "0.9" 3748 | }, 3749 | "inputs": [[359, 0, 0], [360, 0, 0], [361, 0, 0], [362, 0, 1], [363, 0, 1]] 3750 | }, 3751 | { 3752 | "op": "Activation", 3753 | "name": "stage4_unit1_relu2", 3754 | "attrs": {"act_type": "relu"}, 3755 | "inputs": [[364, 0, 0]] 3756 | }, 3757 | { 3758 | "op": "null", 3759 | "name": "stage4_unit1_conv2_weight", 3760 | "attrs": { 3761 | "kernel": "(3, 3)", 3762 | "no_bias": "True", 3763 | "num_filter": "512", 3764 | "pad": "(1, 1)", 3765 | "stride": "(2, 2)", 3766 | "workspace": "256" 3767 | }, 3768 | "inputs": [] 3769 | }, 3770 | { 3771 | "op": "Convolution", 3772 | "name": "stage4_unit1_conv2", 3773 | "attrs": { 3774 | "kernel": "(3, 3)", 3775 | "no_bias": "True", 3776 | "num_filter": "512", 3777 | "pad": "(1, 1)", 3778 | "stride": "(2, 2)", 3779 | "workspace": "256" 3780 | }, 3781 | "inputs": [[365, 0, 0], [366, 0, 0]] 3782 | }, 3783 | { 3784 | "op": "null", 3785 | "name": "stage4_unit1_bn3_gamma", 3786 | "attrs": { 3787 | "eps": "2e-05", 3788 | "fix_gamma": "False", 3789 | "momentum": "0.9" 3790 | }, 3791 | "inputs": [] 3792 | }, 3793 | { 3794 | "op": "null", 3795 | "name": "stage4_unit1_bn3_beta", 3796 | "attrs": { 3797 | "eps": "2e-05", 3798 | "fix_gamma": "False", 3799 | "momentum": "0.9" 3800 | }, 3801 | "inputs": [] 3802 | }, 3803 | { 3804 | "op": "null", 3805 | "name": "stage4_unit1_bn3_moving_mean", 3806 | "attrs": { 3807 | "__init__": "[\"zero\", {}]", 3808 | "eps": "2e-05", 3809 | "fix_gamma": "False", 3810 | "momentum": "0.9" 3811 | }, 3812 | "inputs": [] 3813 | }, 3814 | { 3815 | "op": "null", 3816 | "name": "stage4_unit1_bn3_moving_var", 3817 | "attrs": { 3818 | "__init__": "[\"one\", {}]", 3819 | "eps": "2e-05", 3820 | "fix_gamma": "False", 3821 | "momentum": "0.9" 3822 | }, 3823 | "inputs": [] 3824 | }, 3825 | { 3826 | "op": "BatchNorm", 3827 | "name": "stage4_unit1_bn3", 3828 | "attrs": { 3829 | "eps": "2e-05", 3830 | "fix_gamma": "False", 3831 | "momentum": "0.9" 3832 | }, 3833 | "inputs": [[367, 0, 0], [368, 0, 0], [369, 0, 0], [370, 0, 1], [371, 0, 1]] 3834 | }, 3835 | { 3836 | "op": "Activation", 3837 | "name": "stage4_unit1_relu3", 3838 | "attrs": {"act_type": "relu"}, 3839 | "inputs": [[372, 0, 0]] 3840 | }, 3841 | { 3842 | "op": "null", 3843 | "name": "stage4_unit1_conv3_weight", 3844 | "attrs": { 3845 | "kernel": "(1, 1)", 3846 | "no_bias": "True", 3847 | "num_filter": "2048", 3848 | "pad": "(0, 0)", 3849 | "stride": "(1, 1)", 3850 | "workspace": "256" 3851 | }, 3852 | "inputs": [] 3853 | }, 3854 | { 3855 | "op": "Convolution", 3856 | "name": "stage4_unit1_conv3", 3857 | "attrs": { 3858 | "kernel": "(1, 1)", 3859 | "no_bias": "True", 3860 | "num_filter": "2048", 3861 | "pad": "(0, 0)", 3862 | "stride": "(1, 1)", 3863 | "workspace": "256" 3864 | }, 3865 | "inputs": [[373, 0, 0], [374, 0, 0]] 3866 | }, 3867 | { 3868 | "op": "null", 3869 | "name": "stage4_unit1_sc_weight", 3870 | "attrs": { 3871 | "kernel": "(1, 1)", 3872 | "no_bias": "True", 3873 | "num_filter": "2048", 3874 | "stride": "(2, 2)", 3875 | "workspace": "256" 3876 | }, 3877 | "inputs": [] 3878 | }, 3879 | { 3880 | "op": "Convolution", 3881 | "name": "stage4_unit1_sc", 3882 | "attrs": { 3883 | "kernel": "(1, 1)", 3884 | "no_bias": "True", 3885 | "num_filter": "2048", 3886 | "stride": "(2, 2)", 3887 | "workspace": "256" 3888 | }, 3889 | "inputs": [[357, 0, 0], [376, 0, 0]] 3890 | }, 3891 | { 3892 | "op": "elemwise_add", 3893 | "name": "_plus13", 3894 | "inputs": [[375, 0, 0], [377, 0, 0]] 3895 | }, 3896 | { 3897 | "op": "null", 3898 | "name": "stage4_unit2_bn1_gamma", 3899 | "attrs": { 3900 | "eps": "2e-05", 3901 | "fix_gamma": "False", 3902 | "momentum": "0.9" 3903 | }, 3904 | "inputs": [] 3905 | }, 3906 | { 3907 | "op": "null", 3908 | "name": "stage4_unit2_bn1_beta", 3909 | "attrs": { 3910 | "eps": "2e-05", 3911 | "fix_gamma": "False", 3912 | "momentum": "0.9" 3913 | }, 3914 | "inputs": [] 3915 | }, 3916 | { 3917 | "op": "null", 3918 | "name": "stage4_unit2_bn1_moving_mean", 3919 | "attrs": { 3920 | "__init__": "[\"zero\", {}]", 3921 | "eps": "2e-05", 3922 | "fix_gamma": "False", 3923 | "momentum": "0.9" 3924 | }, 3925 | "inputs": [] 3926 | }, 3927 | { 3928 | "op": "null", 3929 | "name": "stage4_unit2_bn1_moving_var", 3930 | "attrs": { 3931 | "__init__": "[\"one\", {}]", 3932 | "eps": "2e-05", 3933 | "fix_gamma": "False", 3934 | "momentum": "0.9" 3935 | }, 3936 | "inputs": [] 3937 | }, 3938 | { 3939 | "op": "BatchNorm", 3940 | "name": "stage4_unit2_bn1", 3941 | "attrs": { 3942 | "eps": "2e-05", 3943 | "fix_gamma": "False", 3944 | "momentum": "0.9" 3945 | }, 3946 | "inputs": [[378, 0, 0], [379, 0, 0], [380, 0, 0], [381, 0, 1], [382, 0, 1]] 3947 | }, 3948 | { 3949 | "op": "Activation", 3950 | "name": "stage4_unit2_relu1", 3951 | "attrs": {"act_type": "relu"}, 3952 | "inputs": [[383, 0, 0]] 3953 | }, 3954 | { 3955 | "op": "null", 3956 | "name": "stage4_unit2_conv1_weight", 3957 | "attrs": { 3958 | "kernel": "(1, 1)", 3959 | "no_bias": "True", 3960 | "num_filter": "512", 3961 | "pad": "(0, 0)", 3962 | "stride": "(1, 1)", 3963 | "workspace": "256" 3964 | }, 3965 | "inputs": [] 3966 | }, 3967 | { 3968 | "op": "Convolution", 3969 | "name": "stage4_unit2_conv1", 3970 | "attrs": { 3971 | "kernel": "(1, 1)", 3972 | "no_bias": "True", 3973 | "num_filter": "512", 3974 | "pad": "(0, 0)", 3975 | "stride": "(1, 1)", 3976 | "workspace": "256" 3977 | }, 3978 | "inputs": [[384, 0, 0], [385, 0, 0]] 3979 | }, 3980 | { 3981 | "op": "null", 3982 | "name": "stage4_unit2_bn2_gamma", 3983 | "attrs": { 3984 | "eps": "2e-05", 3985 | "fix_gamma": "False", 3986 | "momentum": "0.9" 3987 | }, 3988 | "inputs": [] 3989 | }, 3990 | { 3991 | "op": "null", 3992 | "name": "stage4_unit2_bn2_beta", 3993 | "attrs": { 3994 | "eps": "2e-05", 3995 | "fix_gamma": "False", 3996 | "momentum": "0.9" 3997 | }, 3998 | "inputs": [] 3999 | }, 4000 | { 4001 | "op": "null", 4002 | "name": "stage4_unit2_bn2_moving_mean", 4003 | "attrs": { 4004 | "__init__": "[\"zero\", {}]", 4005 | "eps": "2e-05", 4006 | "fix_gamma": "False", 4007 | "momentum": "0.9" 4008 | }, 4009 | "inputs": [] 4010 | }, 4011 | { 4012 | "op": "null", 4013 | "name": "stage4_unit2_bn2_moving_var", 4014 | "attrs": { 4015 | "__init__": "[\"one\", {}]", 4016 | "eps": "2e-05", 4017 | "fix_gamma": "False", 4018 | "momentum": "0.9" 4019 | }, 4020 | "inputs": [] 4021 | }, 4022 | { 4023 | "op": "BatchNorm", 4024 | "name": "stage4_unit2_bn2", 4025 | "attrs": { 4026 | "eps": "2e-05", 4027 | "fix_gamma": "False", 4028 | "momentum": "0.9" 4029 | }, 4030 | "inputs": [[386, 0, 0], [387, 0, 0], [388, 0, 0], [389, 0, 1], [390, 0, 1]] 4031 | }, 4032 | { 4033 | "op": "Activation", 4034 | "name": "stage4_unit2_relu2", 4035 | "attrs": {"act_type": "relu"}, 4036 | "inputs": [[391, 0, 0]] 4037 | }, 4038 | { 4039 | "op": "null", 4040 | "name": "stage4_unit2_conv2_weight", 4041 | "attrs": { 4042 | "kernel": "(3, 3)", 4043 | "no_bias": "True", 4044 | "num_filter": "512", 4045 | "pad": "(1, 1)", 4046 | "stride": "(1, 1)", 4047 | "workspace": "256" 4048 | }, 4049 | "inputs": [] 4050 | }, 4051 | { 4052 | "op": "Convolution", 4053 | "name": "stage4_unit2_conv2", 4054 | "attrs": { 4055 | "kernel": "(3, 3)", 4056 | "no_bias": "True", 4057 | "num_filter": "512", 4058 | "pad": "(1, 1)", 4059 | "stride": "(1, 1)", 4060 | "workspace": "256" 4061 | }, 4062 | "inputs": [[392, 0, 0], [393, 0, 0]] 4063 | }, 4064 | { 4065 | "op": "null", 4066 | "name": "stage4_unit2_bn3_gamma", 4067 | "attrs": { 4068 | "eps": "2e-05", 4069 | "fix_gamma": "False", 4070 | "momentum": "0.9" 4071 | }, 4072 | "inputs": [] 4073 | }, 4074 | { 4075 | "op": "null", 4076 | "name": "stage4_unit2_bn3_beta", 4077 | "attrs": { 4078 | "eps": "2e-05", 4079 | "fix_gamma": "False", 4080 | "momentum": "0.9" 4081 | }, 4082 | "inputs": [] 4083 | }, 4084 | { 4085 | "op": "null", 4086 | "name": "stage4_unit2_bn3_moving_mean", 4087 | "attrs": { 4088 | "__init__": "[\"zero\", {}]", 4089 | "eps": "2e-05", 4090 | "fix_gamma": "False", 4091 | "momentum": "0.9" 4092 | }, 4093 | "inputs": [] 4094 | }, 4095 | { 4096 | "op": "null", 4097 | "name": "stage4_unit2_bn3_moving_var", 4098 | "attrs": { 4099 | "__init__": "[\"one\", {}]", 4100 | "eps": "2e-05", 4101 | "fix_gamma": "False", 4102 | "momentum": "0.9" 4103 | }, 4104 | "inputs": [] 4105 | }, 4106 | { 4107 | "op": "BatchNorm", 4108 | "name": "stage4_unit2_bn3", 4109 | "attrs": { 4110 | "eps": "2e-05", 4111 | "fix_gamma": "False", 4112 | "momentum": "0.9" 4113 | }, 4114 | "inputs": [[394, 0, 0], [395, 0, 0], [396, 0, 0], [397, 0, 1], [398, 0, 1]] 4115 | }, 4116 | { 4117 | "op": "Activation", 4118 | "name": "stage4_unit2_relu3", 4119 | "attrs": {"act_type": "relu"}, 4120 | "inputs": [[399, 0, 0]] 4121 | }, 4122 | { 4123 | "op": "null", 4124 | "name": "stage4_unit2_conv3_weight", 4125 | "attrs": { 4126 | "kernel": "(1, 1)", 4127 | "no_bias": "True", 4128 | "num_filter": "2048", 4129 | "pad": "(0, 0)", 4130 | "stride": "(1, 1)", 4131 | "workspace": "256" 4132 | }, 4133 | "inputs": [] 4134 | }, 4135 | { 4136 | "op": "Convolution", 4137 | "name": "stage4_unit2_conv3", 4138 | "attrs": { 4139 | "kernel": "(1, 1)", 4140 | "no_bias": "True", 4141 | "num_filter": "2048", 4142 | "pad": "(0, 0)", 4143 | "stride": "(1, 1)", 4144 | "workspace": "256" 4145 | }, 4146 | "inputs": [[400, 0, 0], [401, 0, 0]] 4147 | }, 4148 | { 4149 | "op": "elemwise_add", 4150 | "name": "_plus14", 4151 | "inputs": [[402, 0, 0], [378, 0, 0]] 4152 | }, 4153 | { 4154 | "op": "null", 4155 | "name": "stage4_unit3_bn1_gamma", 4156 | "attrs": { 4157 | "eps": "2e-05", 4158 | "fix_gamma": "False", 4159 | "momentum": "0.9" 4160 | }, 4161 | "inputs": [] 4162 | }, 4163 | { 4164 | "op": "null", 4165 | "name": "stage4_unit3_bn1_beta", 4166 | "attrs": { 4167 | "eps": "2e-05", 4168 | "fix_gamma": "False", 4169 | "momentum": "0.9" 4170 | }, 4171 | "inputs": [] 4172 | }, 4173 | { 4174 | "op": "null", 4175 | "name": "stage4_unit3_bn1_moving_mean", 4176 | "attrs": { 4177 | "__init__": "[\"zero\", {}]", 4178 | "eps": "2e-05", 4179 | "fix_gamma": "False", 4180 | "momentum": "0.9" 4181 | }, 4182 | "inputs": [] 4183 | }, 4184 | { 4185 | "op": "null", 4186 | "name": "stage4_unit3_bn1_moving_var", 4187 | "attrs": { 4188 | "__init__": "[\"one\", {}]", 4189 | "eps": "2e-05", 4190 | "fix_gamma": "False", 4191 | "momentum": "0.9" 4192 | }, 4193 | "inputs": [] 4194 | }, 4195 | { 4196 | "op": "BatchNorm", 4197 | "name": "stage4_unit3_bn1", 4198 | "attrs": { 4199 | "eps": "2e-05", 4200 | "fix_gamma": "False", 4201 | "momentum": "0.9" 4202 | }, 4203 | "inputs": [[403, 0, 0], [404, 0, 0], [405, 0, 0], [406, 0, 1], [407, 0, 1]] 4204 | }, 4205 | { 4206 | "op": "Activation", 4207 | "name": "stage4_unit3_relu1", 4208 | "attrs": {"act_type": "relu"}, 4209 | "inputs": [[408, 0, 0]] 4210 | }, 4211 | { 4212 | "op": "null", 4213 | "name": "stage4_unit3_conv1_weight", 4214 | "attrs": { 4215 | "kernel": "(1, 1)", 4216 | "no_bias": "True", 4217 | "num_filter": "512", 4218 | "pad": "(0, 0)", 4219 | "stride": "(1, 1)", 4220 | "workspace": "256" 4221 | }, 4222 | "inputs": [] 4223 | }, 4224 | { 4225 | "op": "Convolution", 4226 | "name": "stage4_unit3_conv1", 4227 | "attrs": { 4228 | "kernel": "(1, 1)", 4229 | "no_bias": "True", 4230 | "num_filter": "512", 4231 | "pad": "(0, 0)", 4232 | "stride": "(1, 1)", 4233 | "workspace": "256" 4234 | }, 4235 | "inputs": [[409, 0, 0], [410, 0, 0]] 4236 | }, 4237 | { 4238 | "op": "null", 4239 | "name": "stage4_unit3_bn2_gamma", 4240 | "attrs": { 4241 | "eps": "2e-05", 4242 | "fix_gamma": "False", 4243 | "momentum": "0.9" 4244 | }, 4245 | "inputs": [] 4246 | }, 4247 | { 4248 | "op": "null", 4249 | "name": "stage4_unit3_bn2_beta", 4250 | "attrs": { 4251 | "eps": "2e-05", 4252 | "fix_gamma": "False", 4253 | "momentum": "0.9" 4254 | }, 4255 | "inputs": [] 4256 | }, 4257 | { 4258 | "op": "null", 4259 | "name": "stage4_unit3_bn2_moving_mean", 4260 | "attrs": { 4261 | "__init__": "[\"zero\", {}]", 4262 | "eps": "2e-05", 4263 | "fix_gamma": "False", 4264 | "momentum": "0.9" 4265 | }, 4266 | "inputs": [] 4267 | }, 4268 | { 4269 | "op": "null", 4270 | "name": "stage4_unit3_bn2_moving_var", 4271 | "attrs": { 4272 | "__init__": "[\"one\", {}]", 4273 | "eps": "2e-05", 4274 | "fix_gamma": "False", 4275 | "momentum": "0.9" 4276 | }, 4277 | "inputs": [] 4278 | }, 4279 | { 4280 | "op": "BatchNorm", 4281 | "name": "stage4_unit3_bn2", 4282 | "attrs": { 4283 | "eps": "2e-05", 4284 | "fix_gamma": "False", 4285 | "momentum": "0.9" 4286 | }, 4287 | "inputs": [[411, 0, 0], [412, 0, 0], [413, 0, 0], [414, 0, 1], [415, 0, 1]] 4288 | }, 4289 | { 4290 | "op": "Activation", 4291 | "name": "stage4_unit3_relu2", 4292 | "attrs": {"act_type": "relu"}, 4293 | "inputs": [[416, 0, 0]] 4294 | }, 4295 | { 4296 | "op": "null", 4297 | "name": "stage4_unit3_conv2_weight", 4298 | "attrs": { 4299 | "kernel": "(3, 3)", 4300 | "no_bias": "True", 4301 | "num_filter": "512", 4302 | "pad": "(1, 1)", 4303 | "stride": "(1, 1)", 4304 | "workspace": "256" 4305 | }, 4306 | "inputs": [] 4307 | }, 4308 | { 4309 | "op": "Convolution", 4310 | "name": "stage4_unit3_conv2", 4311 | "attrs": { 4312 | "kernel": "(3, 3)", 4313 | "no_bias": "True", 4314 | "num_filter": "512", 4315 | "pad": "(1, 1)", 4316 | "stride": "(1, 1)", 4317 | "workspace": "256" 4318 | }, 4319 | "inputs": [[417, 0, 0], [418, 0, 0]] 4320 | }, 4321 | { 4322 | "op": "null", 4323 | "name": "stage4_unit3_bn3_gamma", 4324 | "attrs": { 4325 | "eps": "2e-05", 4326 | "fix_gamma": "False", 4327 | "momentum": "0.9" 4328 | }, 4329 | "inputs": [] 4330 | }, 4331 | { 4332 | "op": "null", 4333 | "name": "stage4_unit3_bn3_beta", 4334 | "attrs": { 4335 | "eps": "2e-05", 4336 | "fix_gamma": "False", 4337 | "momentum": "0.9" 4338 | }, 4339 | "inputs": [] 4340 | }, 4341 | { 4342 | "op": "null", 4343 | "name": "stage4_unit3_bn3_moving_mean", 4344 | "attrs": { 4345 | "__init__": "[\"zero\", {}]", 4346 | "eps": "2e-05", 4347 | "fix_gamma": "False", 4348 | "momentum": "0.9" 4349 | }, 4350 | "inputs": [] 4351 | }, 4352 | { 4353 | "op": "null", 4354 | "name": "stage4_unit3_bn3_moving_var", 4355 | "attrs": { 4356 | "__init__": "[\"one\", {}]", 4357 | "eps": "2e-05", 4358 | "fix_gamma": "False", 4359 | "momentum": "0.9" 4360 | }, 4361 | "inputs": [] 4362 | }, 4363 | { 4364 | "op": "BatchNorm", 4365 | "name": "stage4_unit3_bn3", 4366 | "attrs": { 4367 | "eps": "2e-05", 4368 | "fix_gamma": "False", 4369 | "momentum": "0.9" 4370 | }, 4371 | "inputs": [[419, 0, 0], [420, 0, 0], [421, 0, 0], [422, 0, 1], [423, 0, 1]] 4372 | }, 4373 | { 4374 | "op": "Activation", 4375 | "name": "stage4_unit3_relu3", 4376 | "attrs": {"act_type": "relu"}, 4377 | "inputs": [[424, 0, 0]] 4378 | }, 4379 | { 4380 | "op": "null", 4381 | "name": "stage4_unit3_conv3_weight", 4382 | "attrs": { 4383 | "kernel": "(1, 1)", 4384 | "no_bias": "True", 4385 | "num_filter": "2048", 4386 | "pad": "(0, 0)", 4387 | "stride": "(1, 1)", 4388 | "workspace": "256" 4389 | }, 4390 | "inputs": [] 4391 | }, 4392 | { 4393 | "op": "Convolution", 4394 | "name": "stage4_unit3_conv3", 4395 | "attrs": { 4396 | "kernel": "(1, 1)", 4397 | "no_bias": "True", 4398 | "num_filter": "2048", 4399 | "pad": "(0, 0)", 4400 | "stride": "(1, 1)", 4401 | "workspace": "256" 4402 | }, 4403 | "inputs": [[425, 0, 0], [426, 0, 0]] 4404 | }, 4405 | { 4406 | "op": "elemwise_add", 4407 | "name": "_plus15", 4408 | "inputs": [[427, 0, 0], [403, 0, 0]] 4409 | }, 4410 | { 4411 | "op": "null", 4412 | "name": "_plus15_cls_pred_conv_weight", 4413 | "attrs": { 4414 | "kernel": "(3, 3)", 4415 | "num_filter": "18", 4416 | "pad": "(1, 1)", 4417 | "stride": "(1, 1)" 4418 | }, 4419 | "inputs": [] 4420 | }, 4421 | { 4422 | "op": "null", 4423 | "name": "_plus15_cls_pred_conv_bias", 4424 | "attrs": { 4425 | "__init__": "[\"constant\", {\"value\": 0.0}]", 4426 | "__lr_mult__": "2.0" 4427 | }, 4428 | "inputs": [] 4429 | }, 4430 | { 4431 | "op": "Convolution", 4432 | "name": "_plus15_cls_pred_conv", 4433 | "attrs": { 4434 | "kernel": "(3, 3)", 4435 | "num_filter": "18", 4436 | "pad": "(1, 1)", 4437 | "stride": "(1, 1)" 4438 | }, 4439 | "inputs": [[428, 0, 0], [429, 0, 0], [430, 0, 0]] 4440 | }, 4441 | { 4442 | "op": "transpose", 4443 | "name": "transpose3", 4444 | "attrs": {"axes": "(0, 2, 3, 1)"}, 4445 | "inputs": [[431, 0, 0]] 4446 | }, 4447 | { 4448 | "op": "Flatten", 4449 | "name": "flatten5", 4450 | "inputs": [[432, 0, 0]] 4451 | }, 4452 | { 4453 | "op": "null", 4454 | "name": "multi_feat_2_conv_1x1_conv_weight", 4455 | "attrs": { 4456 | "kernel": "(1, 1)", 4457 | "num_filter": "256", 4458 | "pad": "(0, 0)", 4459 | "stride": "(1, 1)" 4460 | }, 4461 | "inputs": [] 4462 | }, 4463 | { 4464 | "op": "null", 4465 | "name": "multi_feat_2_conv_1x1_conv_bias", 4466 | "attrs": { 4467 | "__init__": "[\"constant\", {\"value\": 0.0}]", 4468 | "__lr_mult__": "2.0" 4469 | }, 4470 | "inputs": [] 4471 | }, 4472 | { 4473 | "op": "Convolution", 4474 | "name": "multi_feat_2_conv_1x1_conv", 4475 | "attrs": { 4476 | "kernel": "(1, 1)", 4477 | "num_filter": "256", 4478 | "pad": "(0, 0)", 4479 | "stride": "(1, 1)" 4480 | }, 4481 | "inputs": [[428, 0, 0], [434, 0, 0], [435, 0, 0]] 4482 | }, 4483 | { 4484 | "op": "Activation", 4485 | "name": "multi_feat_2_conv_1x1_relu", 4486 | "attrs": {"act_type": "relu"}, 4487 | "inputs": [[436, 0, 0]] 4488 | }, 4489 | { 4490 | "op": "null", 4491 | "name": "multi_feat_2_conv_3x3_conv_weight", 4492 | "attrs": { 4493 | "kernel": "(3, 3)", 4494 | "num_filter": "512", 4495 | "pad": "(1, 1)", 4496 | "stride": "(2, 2)" 4497 | }, 4498 | "inputs": [] 4499 | }, 4500 | { 4501 | "op": "null", 4502 | "name": "multi_feat_2_conv_3x3_conv_bias", 4503 | "attrs": { 4504 | "__init__": "[\"constant\", {\"value\": 0.0}]", 4505 | "__lr_mult__": "2.0" 4506 | }, 4507 | "inputs": [] 4508 | }, 4509 | { 4510 | "op": "Convolution", 4511 | "name": "multi_feat_2_conv_3x3_conv", 4512 | "attrs": { 4513 | "kernel": "(3, 3)", 4514 | "num_filter": "512", 4515 | "pad": "(1, 1)", 4516 | "stride": "(2, 2)" 4517 | }, 4518 | "inputs": [[437, 0, 0], [438, 0, 0], [439, 0, 0]] 4519 | }, 4520 | { 4521 | "op": "Activation", 4522 | "name": "multi_feat_2_conv_3x3_relu", 4523 | "attrs": {"act_type": "relu"}, 4524 | "inputs": [[440, 0, 0]] 4525 | }, 4526 | { 4527 | "op": "null", 4528 | "name": "multi_feat_2_conv_3x3_relu_cls_pred_conv_weight", 4529 | "attrs": { 4530 | "kernel": "(3, 3)", 4531 | "num_filter": "18", 4532 | "pad": "(1, 1)", 4533 | "stride": "(1, 1)" 4534 | }, 4535 | "inputs": [] 4536 | }, 4537 | { 4538 | "op": "null", 4539 | "name": "multi_feat_2_conv_3x3_relu_cls_pred_conv_bias", 4540 | "attrs": { 4541 | "__init__": "[\"constant\", {\"value\": 0.0}]", 4542 | "__lr_mult__": "2.0" 4543 | }, 4544 | "inputs": [] 4545 | }, 4546 | { 4547 | "op": "Convolution", 4548 | "name": "multi_feat_2_conv_3x3_relu_cls_pred_conv", 4549 | "attrs": { 4550 | "kernel": "(3, 3)", 4551 | "num_filter": "18", 4552 | "pad": "(1, 1)", 4553 | "stride": "(1, 1)" 4554 | }, 4555 | "inputs": [[441, 0, 0], [442, 0, 0], [443, 0, 0]] 4556 | }, 4557 | { 4558 | "op": "transpose", 4559 | "name": "transpose5", 4560 | "attrs": {"axes": "(0, 2, 3, 1)"}, 4561 | "inputs": [[444, 0, 0]] 4562 | }, 4563 | { 4564 | "op": "Flatten", 4565 | "name": "flatten8", 4566 | "inputs": [[445, 0, 0]] 4567 | }, 4568 | { 4569 | "op": "null", 4570 | "name": "multi_feat_3_conv_1x1_conv_weight", 4571 | "attrs": { 4572 | "kernel": "(1, 1)", 4573 | "num_filter": "128", 4574 | "pad": "(0, 0)", 4575 | "stride": "(1, 1)" 4576 | }, 4577 | "inputs": [] 4578 | }, 4579 | { 4580 | "op": "null", 4581 | "name": "multi_feat_3_conv_1x1_conv_bias", 4582 | "attrs": { 4583 | "__init__": "[\"constant\", {\"value\": 0.0}]", 4584 | "__lr_mult__": "2.0" 4585 | }, 4586 | "inputs": [] 4587 | }, 4588 | { 4589 | "op": "Convolution", 4590 | "name": "multi_feat_3_conv_1x1_conv", 4591 | "attrs": { 4592 | "kernel": "(1, 1)", 4593 | "num_filter": "128", 4594 | "pad": "(0, 0)", 4595 | "stride": "(1, 1)" 4596 | }, 4597 | "inputs": [[441, 0, 0], [447, 0, 0], [448, 0, 0]] 4598 | }, 4599 | { 4600 | "op": "Activation", 4601 | "name": "multi_feat_3_conv_1x1_relu", 4602 | "attrs": {"act_type": "relu"}, 4603 | "inputs": [[449, 0, 0]] 4604 | }, 4605 | { 4606 | "op": "null", 4607 | "name": "multi_feat_3_conv_3x3_conv_weight", 4608 | "attrs": { 4609 | "kernel": "(3, 3)", 4610 | "num_filter": "256", 4611 | "pad": "(1, 1)", 4612 | "stride": "(2, 2)" 4613 | }, 4614 | "inputs": [] 4615 | }, 4616 | { 4617 | "op": "null", 4618 | "name": "multi_feat_3_conv_3x3_conv_bias", 4619 | "attrs": { 4620 | "__init__": "[\"constant\", {\"value\": 0.0}]", 4621 | "__lr_mult__": "2.0" 4622 | 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