mirror of
https://github.com/deepinsight/insightface.git
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330 lines
12 KiB
Python
330 lines
12 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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import math
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import random
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import logging
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import pickle
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import numpy as np
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import sklearn
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from data import FaceImageIter
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import mxnet as mx
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from mxnet import ndarray as nd
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import argparse
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import mxnet.optimizer as optimizer
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sys.path.append(os.path.join(os.path.dirname(__file__), 'common'))
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#import face_image
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import fresnet
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import fmobilenet
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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AGE=100
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args = None
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class AccMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(AccMetric, self).__init__(
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'acc', axis=self.axis,
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output_names=None, label_names=None)
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self.losses = []
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self.count = 0
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def update(self, labels, preds):
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self.count+=1
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label = labels[0].asnumpy()[:,0:1]
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pred_label = preds[-1].asnumpy()[:,0:2]
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pred_label = np.argmax(pred_label, axis=self.axis)
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pred_label = pred_label.astype('int32').flatten()
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label = label.astype('int32').flatten()
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assert label.shape==pred_label.shape
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self.sum_metric += (pred_label.flat == label.flat).sum()
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self.num_inst += len(pred_label.flat)
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class LossValueMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(LossValueMetric, self).__init__(
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'lossvalue', axis=self.axis,
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output_names=None, label_names=None)
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self.losses = []
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def update(self, labels, preds):
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loss = preds[-1].asnumpy()[0]
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self.sum_metric += loss
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self.num_inst += 1.0
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gt_label = preds[-2].asnumpy()
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#print(gt_label)
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class MAEMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(MAEMetric, self).__init__(
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'MAE', axis=self.axis,
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output_names=None, label_names=None)
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self.losses = []
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self.count = 0
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def update(self, labels, preds):
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self.count+=1
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label = labels[0].asnumpy()
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label_age = np.count_nonzero(label[:,1:], axis=1)
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pred_age = np.zeros( label_age.shape, dtype=np.int)
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#pred_age = np.zeros( label_age.shape, dtype=np.float32)
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pred = preds[-1].asnumpy()
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for i in xrange(AGE):
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_pred = pred[:,2+i*2:4+i*2]
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_pred = np.argmax(_pred, axis=1)
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#pred = pred[:,1]
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pred_age += _pred
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#pred_age = pred_age.astype(np.int)
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mae = np.mean(np.abs(label_age - pred_age))
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self.sum_metric += mae
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self.num_inst += 1.0
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class CUMMetric(mx.metric.EvalMetric):
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def __init__(self, n=5):
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self.axis = 1
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self.n = n
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super(CUMMetric, self).__init__(
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'CUM_%d'%n, axis=self.axis,
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output_names=None, label_names=None)
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self.losses = []
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self.count = 0
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def update(self, labels, preds):
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self.count+=1
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label = labels[0].asnumpy()
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label_age = np.count_nonzero(label[:,1:], axis=1)
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pred_age = np.zeros( label_age.shape, dtype=np.int)
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pred = preds[-1].asnumpy()
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for i in xrange(AGE):
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_pred = pred[:,2+i*2:4+i*2]
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_pred = np.argmax(_pred, axis=1)
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#pred = pred[:,1]
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pred_age += _pred
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diff = np.abs(label_age - pred_age)
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cum = np.sum( (diff<self.n) )
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self.sum_metric += cum
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self.num_inst += len(label_age)
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def parse_args():
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parser = argparse.ArgumentParser(description='Train face network')
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# general
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parser.add_argument('--data-dir', default='', help='training set directory')
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parser.add_argument('--prefix', default='../model/model', help='directory to save model.')
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parser.add_argument('--pretrained', default='', help='pretrained model to load')
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parser.add_argument('--ckpt', type=int, default=1, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')
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parser.add_argument('--loss-type', type=int, default=4, help='loss type')
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parser.add_argument('--verbose', type=int, default=2000, help='do verification testing and model saving every verbose batches')
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parser.add_argument('--max-steps', type=int, default=0, help='max training batches')
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parser.add_argument('--end-epoch', type=int, default=100000, help='training epoch size.')
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parser.add_argument('--network', default='r50', help='specify network')
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parser.add_argument('--image-size', default='112,112', help='specify input image height and width')
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parser.add_argument('--version-input', type=int, default=1, help='network input config')
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parser.add_argument('--version-output', type=str, default='GAP', help='network embedding output config')
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parser.add_argument('--version-act', type=str, default='prelu', help='network activation config')
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parser.add_argument('--multiplier', type=float, default=1.0, help='')
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parser.add_argument('--lr', type=float, default=0.1, help='start learning rate')
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parser.add_argument('--lr-steps', type=str, default='', help='steps of lr changing')
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parser.add_argument('--wd', type=float, default=0.0005, help='weight decay')
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parser.add_argument('--bn-mom', type=float, default=0.9, help='bn mom')
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parser.add_argument('--mom', type=float, default=0.9, help='momentum')
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parser.add_argument('--per-batch-size', type=int, default=128, help='batch size in each context')
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parser.add_argument('--rand-mirror', type=int, default=1, help='if do random mirror in training')
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parser.add_argument('--cutoff', type=int, default=0, help='cut off aug')
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parser.add_argument('--color', type=int, default=0, help='color jittering aug')
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parser.add_argument('--ce-loss', default=False, action='store_true', help='if output ce loss')
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args = parser.parse_args()
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return args
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def get_symbol(args, arg_params, aux_params):
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data_shape = (args.image_channel,args.image_h,args.image_w)
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image_shape = ",".join([str(x) for x in data_shape])
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margin_symbols = []
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if args.network[0]=='m':
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fc1 = fmobilenet.get_symbol(AGE*2+2,
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multiplier = args.multiplier,
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version_input=args.version_input,
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version_output=args.version_output)
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else:
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fc1 = fresnet.get_symbol(AGE*2+2, args.num_layers,
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version_input=args.version_input,
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version_output=args.version_output)
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label = mx.symbol.Variable('softmax_label')
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gender_label = mx.symbol.slice_axis(data = label, axis=1, begin=0, end=1)
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gender_label = mx.symbol.reshape(gender_label, shape=(args.per_batch_size,))
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gender_fc1 = mx.symbol.slice_axis(data = fc1, axis=1, begin=0, end=2)
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#gender_fc7 = mx.sym.FullyConnected(data=gender_fc1, num_hidden=2, name='gender_fc7')
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gender_softmax = mx.symbol.SoftmaxOutput(data=gender_fc1, label = gender_label, name='gender_softmax', normalization='valid', use_ignore=True, ignore_label = 9999)
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outs = [gender_softmax]
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for i in range(AGE):
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age_label = mx.symbol.slice_axis(data = label, axis=1, begin=i+1, end=i+2)
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age_label = mx.symbol.reshape(age_label, shape=(args.per_batch_size,))
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age_fc1 = mx.symbol.slice_axis(data = fc1, axis=1, begin=2+i*2, end=4+i*2)
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#age_fc7 = mx.sym.FullyConnected(data=age_fc1, num_hidden=2, name='age_fc7_%i'%i)
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age_softmax = mx.symbol.SoftmaxOutput(data=age_fc1, label = age_label, name='age_softmax_%d'%i, normalization='valid', grad_scale=1)
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outs.append(age_softmax)
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outs.append(mx.sym.BlockGrad(fc1))
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out = mx.symbol.Group(outs)
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return (out, arg_params, aux_params)
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def train_net(args):
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ctx = []
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cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()
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if len(cvd)>0:
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for i in xrange(len(cvd.split(','))):
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ctx.append(mx.gpu(i))
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if len(ctx)==0:
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ctx = [mx.cpu()]
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print('use cpu')
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else:
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print('gpu num:', len(ctx))
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prefix = args.prefix
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prefix_dir = os.path.dirname(prefix)
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if not os.path.exists(prefix_dir):
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os.makedirs(prefix_dir)
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end_epoch = args.end_epoch
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args.ctx_num = len(ctx)
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args.num_layers = int(args.network[1:])
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print('num_layers', args.num_layers)
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if args.per_batch_size==0:
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args.per_batch_size = 128
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args.batch_size = args.per_batch_size*args.ctx_num
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args.rescale_threshold = 0
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args.image_channel = 3
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data_dir_list = args.data_dir.split(',')
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assert len(data_dir_list)==1
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data_dir = data_dir_list[0]
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path_imgrec = None
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path_imglist = None
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image_size = [int(x) for x in args.image_size.split(',')]
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assert len(image_size)==2
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assert image_size[0]==image_size[1]
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args.image_h = image_size[0]
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args.image_w = image_size[1]
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print('image_size', image_size)
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path_imgrec = os.path.join(data_dir, "train.rec")
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path_imgrec_val = os.path.join(data_dir, "val.rec")
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print('Called with argument:', args)
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data_shape = (args.image_channel,image_size[0],image_size[1])
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mean = None
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begin_epoch = 0
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base_lr = args.lr
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base_wd = args.wd
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base_mom = args.mom
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if len(args.pretrained)==0:
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arg_params = None
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aux_params = None
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sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
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else:
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vec = args.pretrained.split(',')
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print('loading', vec)
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_, arg_params, aux_params = mx.model.load_checkpoint(vec[0], int(vec[1]))
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sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
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#label_name = 'softmax_label'
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#label_shape = (args.batch_size,)
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model = mx.mod.Module(
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context = ctx,
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symbol = sym,
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)
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val_dataiter = None
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train_dataiter = FaceImageIter(
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batch_size = args.batch_size,
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data_shape = data_shape,
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path_imgrec = path_imgrec,
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shuffle = True,
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rand_mirror = args.rand_mirror,
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mean = mean,
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cutoff = args.cutoff,
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color_jittering = args.color,
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)
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val_dataiter = FaceImageIter(
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batch_size = args.batch_size,
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data_shape = data_shape,
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path_imgrec = path_imgrec_val,
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shuffle = False,
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rand_mirror = False,
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mean = mean,
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)
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metric = mx.metric.CompositeEvalMetric([AccMetric(), MAEMetric(), CUMMetric()])
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if args.network[0]=='r' or args.network[0]=='y':
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initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style
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elif args.network[0]=='i' or args.network[0]=='x':
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initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2) #inception
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else:
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initializer = mx.init.Xavier(rnd_type='uniform', factor_type="in", magnitude=2)
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_rescale = 1.0/args.ctx_num
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opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)
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#opt = optimizer.Nadam(learning_rate=base_lr, wd=base_wd, rescale_grad=_rescale)
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som = 20
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_cb = mx.callback.Speedometer(args.batch_size, som)
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lr_steps = [int(x) for x in args.lr_steps.split(',')]
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global_step = [0]
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def _batch_callback(param):
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_cb(param)
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global_step[0]+=1
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mbatch = global_step[0]
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for _lr in lr_steps:
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if mbatch==_lr:
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opt.lr *= 0.1
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print('lr change to', opt.lr)
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break
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if mbatch%1000==0:
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print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch)
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if mbatch==lr_steps[-1]:
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arg, aux = model.get_params()
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all_layers = model.symbol.get_internals()
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_sym = all_layers['fc1_output']
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mx.model.save_checkpoint(args.prefix, 0, _sym, arg, aux)
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sys.exit(0)
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epoch_cb = None
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train_dataiter = mx.io.PrefetchingIter(train_dataiter)
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print('start fitting')
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model.fit(train_dataiter,
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begin_epoch = begin_epoch,
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num_epoch = end_epoch,
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eval_data = val_dataiter,
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eval_metric = metric,
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kvstore = 'device',
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optimizer = opt,
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#optimizer_params = optimizer_params,
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initializer = initializer,
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arg_params = arg_params,
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aux_params = aux_params,
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allow_missing = True,
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batch_end_callback = _batch_callback,
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epoch_end_callback = epoch_cb )
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def main():
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#time.sleep(3600*6.5)
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global args
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args = parse_args()
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train_net(args)
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if __name__ == '__main__':
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main()
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