mirror of
https://github.com/deepinsight/insightface.git
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299 lines
11 KiB
Python
299 lines
11 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 logging
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import argparse
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from data import FaceSegIter
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import mxnet as mx
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import mxnet.optimizer as optimizer
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import numpy as np
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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 cv2
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from config import config, default, generate_config
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sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol'))
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import sym_heatmap
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#import sym_fc
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#from symbol import fc
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from optimizer import ONadam
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args = None
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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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[0].asnumpy()[0]
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self.sum_metric += loss
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self.num_inst += 1.0
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class NMEMetric(mx.metric.EvalMetric):
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def __init__(self):
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self.axis = 1
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super(NMEMetric, self).__init__(
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'NME', 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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preds = [preds[-1]]
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for label, pred_label in zip(labels, preds):
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label = label.asnumpy()
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pred_label = pred_label.asnumpy()
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#print('label', np.count_nonzero(label[0][36]))
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#print('acc',label.shape, pred_label.shape)
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#print(label.ndim)
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nme = []
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for b in xrange(pred_label.shape[0]):
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record = [None]*6
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item = []
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if label.ndim==4:
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_heatmap = label[b][36]
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if np.count_nonzero(_heatmap)==0:
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continue
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else:#ndim==3
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#print(label[b])
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if np.count_nonzero(label[b])==0:
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continue
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for p in xrange(pred_label.shape[1]):
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if label.ndim==4:
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heatmap_gt = label[b][p]
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ind_gt = np.unravel_index(np.argmax(heatmap_gt, axis=None), heatmap_gt.shape)
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ind_gt = np.array(ind_gt)
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else:
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ind_gt = label[b][p]
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#ind_gt = ind_gt.astype(np.int)
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#print(ind_gt)
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heatmap_pred = pred_label[b][p]
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heatmap_pred = cv2.resize(heatmap_pred, (config.input_img_size, config.input_img_size))
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ind_pred = np.unravel_index(np.argmax(heatmap_pred, axis=None), heatmap_pred.shape)
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ind_pred = np.array(ind_pred)
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#print(ind_gt.shape)
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#print(ind_pred)
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if p==36:
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#print('b', b, p, ind_gt, np.count_nonzero(heatmap_gt))
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record[0] = ind_gt
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elif p==39:
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record[1] = ind_gt
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elif p==42:
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record[2] = ind_gt
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elif p==45:
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record[3] = ind_gt
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if record[4] is None or record[5] is None:
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record[4] = ind_gt
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record[5] = ind_gt
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else:
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record[4] = np.minimum(record[4], ind_gt)
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record[5] = np.maximum(record[5], ind_gt)
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#print(ind_gt.shape, ind_pred.shape)
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value = np.sqrt(np.sum(np.square(ind_gt - ind_pred)))
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item.append(value)
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_nme = np.mean(item)
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if config.landmark_type=='2d':
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left_eye = (record[0]+record[1])/2
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right_eye = (record[2]+record[3])/2
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_dist = np.sqrt(np.sum(np.square(left_eye - right_eye)))
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#print('eye dist', _dist, left_eye, right_eye)
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_nme /= _dist
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else:
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#_dist = np.sqrt(float(label.shape[2]*label.shape[3]))
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_dist = np.sqrt(np.sum(np.square(record[5] - record[4])))
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#print(_dist)
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_nme /= _dist
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nme.append(_nme)
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#print('nme', nme)
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#nme = np.mean(nme)
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if len(nme)>0:
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self.sum_metric += np.mean(nme)
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self.num_inst += 1.0
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def main(args):
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_seed = 727
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random.seed(_seed)
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np.random.seed(_seed)
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mx.random.seed(_seed)
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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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#ctx = [mx.gpu(0)]
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args.ctx_num = len(ctx)
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args.batch_size = args.per_batch_size*args.ctx_num
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config.per_batch_size = args.per_batch_size
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print('Call with', args, config)
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train_iter = FaceSegIter(path_imgrec = os.path.join(config.dataset_path, 'train.rec'),
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batch_size = args.batch_size,
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per_batch_size = args.per_batch_size,
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aug_level = 1,
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exf = args.exf,
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args = args,
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)
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data_shape = train_iter.get_data_shape()
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#label_shape = train_iter.get_label_shape()
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sym = sym_heatmap.get_symbol(num_classes=config.num_classes)
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if len(args.pretrained)==0:
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#data_shape_dict = {'data' : (args.per_batch_size,)+data_shape, 'softmax_label' : (args.per_batch_size,)+label_shape}
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data_shape_dict = train_iter.get_shape_dict()
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arg_params, aux_params = sym_heatmap.init_weights(sym, data_shape_dict)
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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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model = mx.mod.Module(
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context = ctx,
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symbol = sym,
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label_names = train_iter.get_label_names(),
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)
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#lr = 1.0e-3
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#lr = 2.5e-4
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_rescale_grad = 1.0/args.ctx_num
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#_rescale_grad = 1.0/args.batch_size
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#lr = args.lr
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#opt = optimizer.Nadam(learning_rate=args.lr, wd=args.wd, rescale_grad=_rescale_grad, clip_gradient=5.0)
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if args.optimizer=='onadam':
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opt = ONadam(learning_rate=args.lr, wd=args.wd, rescale_grad=_rescale_grad, clip_gradient=5.0)
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elif args.optimizer=='nadam':
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opt = optimizer.Nadam(learning_rate=args.lr, rescale_grad=_rescale_grad)
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elif args.optimizer=='rmsprop':
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opt = optimizer.RMSProp(learning_rate=args.lr, rescale_grad=_rescale_grad)
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elif args.optimizer=='adam':
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opt = optimizer.Adam(learning_rate=args.lr, rescale_grad=_rescale_grad)
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else:
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opt = optimizer.SGD(learning_rate=args.lr, momentum=0.9, wd=args.wd, rescale_grad=_rescale_grad)
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initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2)
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_cb = mx.callback.Speedometer(args.batch_size, args.frequent)
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_metric = LossValueMetric()
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#_metric = NMEMetric()
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#_metric2 = AccMetric()
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#eval_metrics = [_metric, _metric2]
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eval_metrics = [_metric]
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lr_steps = [int(x) for x in args.lr_step.split(',')]
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print('lr-steps', lr_steps)
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global_step = [0]
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def val_test():
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all_layers = sym.get_internals()
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vsym = all_layers['heatmap_output']
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vmodel = mx.mod.Module(symbol=vsym, context=ctx, label_names = None)
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#model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
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vmodel.bind(data_shapes=[('data', (args.batch_size,)+data_shape)])
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arg_params, aux_params = model.get_params()
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vmodel.set_params(arg_params, aux_params)
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for target in config.val_targets:
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_file = os.path.join(config.dataset_path, '%s.rec'%target)
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if not os.path.exists(_file):
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continue
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val_iter = FaceSegIter(path_imgrec = _file,
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batch_size = args.batch_size,
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#batch_size = 4,
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aug_level = 0,
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args = args,
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)
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_metric = NMEMetric()
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val_metric = mx.metric.create(_metric)
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val_metric.reset()
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val_iter.reset()
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for i, eval_batch in enumerate(val_iter):
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#print(eval_batch.data[0].shape, eval_batch.label[0].shape)
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batch_data = mx.io.DataBatch(eval_batch.data)
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model.forward(batch_data, is_train=False)
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model.update_metric(val_metric, eval_batch.label)
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nme_value = val_metric.get_name_value()[0][1]
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print('[%d][%s]NME: %f'%(global_step[0], target, nme_value))
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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 *= 0.2
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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>0 and mbatch%args.verbose==0:
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val_test()
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if args.ckpt==1:
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msave = mbatch//args.verbose
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print('saving', msave)
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arg, aux = model.get_params()
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mx.model.save_checkpoint(args.prefix, msave, model.symbol, arg, aux)
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if mbatch==lr_steps[-1]:
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if args.ckpt==2:
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#msave = mbatch//args.verbose
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msave = 1
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print('saving', msave)
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arg, aux = model.get_params()
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mx.model.save_checkpoint(args.prefix, msave, model.symbol, arg, aux)
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sys.exit(0)
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train_iter = mx.io.PrefetchingIter(train_iter)
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model.fit(train_iter,
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begin_epoch = 0,
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num_epoch = 9999,
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#eval_data = val_iter,
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eval_data = None,
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eval_metric = eval_metrics,
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kvstore = 'device',
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optimizer = opt,
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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 = None,
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)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Train face alignment')
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# general
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parser.add_argument('--network', help='network name', default=default.network, type=str)
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parser.add_argument('--dataset', help='dataset name', default=default.dataset, type=str)
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args, rest = parser.parse_known_args()
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generate_config(args.network, args.dataset)
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parser.add_argument('--prefix', default=default.prefix, help='directory to save model.')
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parser.add_argument('--pretrained', default=default.pretrained, help='')
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parser.add_argument('--optimizer', default='nadam', help='')
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parser.add_argument('--lr', type=float, default=default.lr, help='')
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parser.add_argument('--wd', type=float, default=default.wd, help='')
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parser.add_argument('--per-batch-size', type=int, default=default.per_batch_size, help='')
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parser.add_argument('--lr-step', help='learning rate steps (in epoch)', default=default.lr_step, type=str)
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parser.add_argument('--ckpt', type=int, default=1, help='')
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parser.add_argument('--norm', type=int, default=0, help='')
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parser.add_argument('--exf', type=int, default=1, help='')
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parser.add_argument('--frequent', type=int, default=default.frequent, help='')
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parser.add_argument('--verbose', type=int, default=default.verbose, help='')
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args = parser.parse_args()
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main(args)
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