stonedl3 commented on issue #9766: DeepLearning on Imagenet with mxnet issues translating .lst to .rec files
URL: https://github.com/apache/incubator-mxnet/issues/9766#issuecomment-366445508
 
 
   Here is the script used to train alexnet
   
   # USAGE
   # python train_alexnet.py --checkpoints checkpoints --prefix alexnet
   # python train_alexnet.py --checkpoints checkpoints --prefix alexnet --start-epoch 25
   
   # import the necessary packages
   from config import imagenet_alexnet_config as config
   from pyimagesearch.nn.mxconv import MxAlexNet
   import mxnet as mx
   import argparse
   import logging
   import json
   import os
   
   # construct the argument parse and parse the arguments
   ap = argparse.ArgumentParser()
   ap.add_argument("-c", "--checkpoints", required=True,
   	help="path to output checkpoint directory")
   ap.add_argument("-p", "--prefix", required=True,
   	help="name of model prefix")
   ap.add_argument("-s", "--start-epoch", type=int, default=0,
   	help="epoch to restart training at")
   args = vars(ap.parse_args())
   
   # set the logging level and output file
   logging.basicConfig(level=logging.DEBUG,
   	filename="training_{}.log".format(args["start_epoch"]),
   	filemode="w")
   
   # load the RGB means for the training set, then determine the batch
   # size
   means = json.loads(open(config.DATASET_MEAN).read())
   batchSize = config.BATCH_SIZE * config.NUM_DEVICES
   
   # construct the training image iterator
   trainIter = mx.io.ImageRecordIter(
   	path_imgrec=config.TRAIN_MX_REC,
   	data_shape=(3, 224, 224),
   	batch_size=batchSize,
   	rand_crop=True,
   	rand_mirror=True,
   	rotate=15,
   	max_shear_ratio=0.1,
   	mean_r=means["R"],
   	mean_g=means["G"],
   	mean_b=means["B"],
   	preprocess_threads=config.NUM_DEVICES * 2)
   
   # construct the validation image iterator
   valIter = mx.io.ImageRecordIter(
   	path_imgrec=config.VAL_MX_REC,
   	data_shape=(3, 224, 224),
   	batch_size=batchSize,
   	mean_r=means["R"],
   	mean_g=means["G"],
   	mean_b=means["B"])
   
   # initialize the optimizer
   opt = mx.optimizer.SGD(learning_rate=1e-2, momentum=0.9, wd=0.0005,
   	rescale_grad=1.0 / batchSize)
   
   # construct the checkpoints path, initialize the model argument and
   # auxiliary parameters
   checkpointsPath = os.path.sep.join([args["checkpoints"],
   	args["prefix"]])
   argParams = None
   auxParams = None
   
   # if there is no specific model starting epoch supplied, then
   # initialize the network
   if args["start_epoch"] <= 0:
   	# build the LeNet architecture
   	print("[INFO] building network...")
   	model = MxAlexNet.build(config.NUM_CLASSES)
   
   # otherwise, a specific checkpoint was supplied
   else:
   	# load the checkpoint from disk
   	print("[INFO] loading epoch {}...".format(args["start_epoch"]))
   	model = mx.model.FeedForward.load(checkpointsPath,
   		args["start_epoch"])
   
   	# update the model and parameters
   	argParams = model.arg_params
   	auxParams = model.aux_params
   	model = model.symbol
   
   # compile the model
   model = mx.mod.Module(
   	context=[mx.gpu(0), mx.gpu(1)],
   	symbol=model)
   
   # initialize the callbacks and evaluation metrics
   batchEndCBs = [mx.callback.Speedometer(batchSize, 500)]
   epochEndCBs = [mx.callback.do_checkpoint(checkpointsPath)]
   metrics = [mx.metric.Accuracy(), mx.metric.TopKAccuracy(top_k=5),
   	mx.metric.CrossEntropy()]
   
   # train the network
   print("[INFO] training network...")
   model.fit(
   	train_data=trainIter,
   	eval_data=valIter,
   	eval_metric=metrics,
   	batch_end_callback=batchEndCBs,
   	epoch_end_callback=epochEndCBs,
   	initializer=mx.initializer.Xavier(),
   	arg_params=argParams,
   	aux_params=auxParams,
   	optimizer=opt,
   	num_epoch=65,
   	begin_epoch=args["start_epoch"])

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