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train.py
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train.py
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#-*- coding:utf-8 -*-
import os
import argparse
import numpy as np
import time
import sys
import argparse
import paddle
import paddle.fluid as fluid
import cv2
from collections import Counter
from scipy.integrate import simps
from matplotlib import pyplot as plt
#from model.mobilenetv2 import build_model
from model.mobilenetv2 import build_model
from data.WLFW import WLFWDataReader
import data.WLFW
from learning_rate import exponential_with_warmup_decay
from loss.pfld_loss import Loss
os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.99'
pretrain_model = 1 #1 means pretrain_model
epochs = 300
total_step = int(150000 * epochs / 1024)
path = os.getcwd()
def create_reader(rows=224,cols=224):
train_dataset = WLFWDataReader("data/train_data/list.txt")
test_dataset = WLFWDataReader("data/test_data/list.txt")
return train_dataset,test_dataset
def create_model(model='',image_shape=[112,112],class_num=98):
img = fluid.layers.data(name='img', shape=[3] + image_shape, dtype='float32')
landmark = fluid.layers.data(name='landmark', shape=[196],dtype='float32')
attribute = fluid.layers.data(name='attribute', shape=[6],dtype='float32')
euler_angle = fluid.layers.data(name='euler_angle', shape=[3],dtype='float32')
landmarks_pre,angles_pre = build_model(img)
weighted_loss, loss = Loss().PFLDLoss(attribute, landmark, euler_angle, angles_pre, landmarks_pre, 1024)
#loss = Loss().wing_loss(landmark, landmarks_pre, w=10.0, epsilon=2.0, N_LANDMARK = 98)
mse_loss = Loss().mse_loss(landmark, landmarks_pre)
avg_loss = 0.6*weighted_loss + 0.4*mse_loss
print('img.shape = ',img.shape)
print('landmark.shape = ',landmark.shape)
print('euler_angle.shape = ',euler_angle.shape)
print('attribute.shape = ',attribute.shape)
print('loss = ',loss.shape)
print('weighted_loss = ',weighted_loss.shape)
return landmarks_pre,angles_pre,weighted_loss,loss,avg_loss
def compute_nme(preds, target):
""" preds/target:: numpy array, shape is (N, L, 2)
N: batchsize L: num of landmark
"""
N = preds.shape[0]
L = preds.shape[1]
rmse = np.zeros(N)
for i in range(N):
pts_pred, pts_gt = preds[i, ], target[i, ]
if L == 19: # aflw
interocular = 34 # meta['box_size'][i]
elif L == 29: # cofw
interocular = np.linalg.norm(pts_gt[8, ] - pts_gt[9, ])
elif L == 68: # 300w
# interocular
interocular = np.linalg.norm(pts_gt[36, ] - pts_gt[45, ])
elif L == 98:
interocular = np.linalg.norm(pts_gt[60, ] - pts_gt[72, ])
else:
raise ValueError('Number of landmarks is wrong')
rmse[i] = np.sum(np.linalg.norm(pts_pred - pts_gt, axis=1)) / (interocular * L)
return rmse
def compute_auc(errors, failureThreshold, step=0.0001, showCurve=False):
nErrors = len(errors)
xAxis = list(np.arange(0., failureThreshold + step, step))
ced = [float(np.count_nonzero([errors <= x])) / nErrors for x in xAxis]
AUC = simps(ced, x=xAxis) / failureThreshold
failureRate = 1. - ced[-1]
if showCurve:
plt.plot(xAxis, ced)
plt.show()
return AUC, failureRate
def load_model(exe,program,model=''):
if model == 'mobilenetv2':
pretrained_model = path+"/params/mobilenetv2"
def if_exist(var):
return os.path.exists(os.path.join(pretrained_model, var.name))
fluid.io.load_vars(exe, pretrained_model, main_program=program,
predicate=if_exist)
elif model == 'mobilenetv3':
fluid.io.load_params(executor=exe, dirname="", filename=path+'/params/mobilenetv3.params', main_program=program)
def save_model(exe,program,model=''):
if model == 'mobilenetv2':
fluid.io.save_params(executor=exe, dirname=path+"/params/mobilenetv2", main_program=program)
elif model == 'mobilenetv3':
fluid.io.save_params(executor=exe, dirname=path+"/params/mobilenetv2", main_program=program)
def optimizer_setting(lr):
batch_size = 1024
iters = 150000 // batch_size
boundaries = [i * iters for i in [60,100,150]]
values = [ i * lr for i in [1,0.5,0.1,0.05]]
optimizer = fluid.optimizer.Adam(
#momentum=0.9,
learning_rate=exponential_with_warmup_decay(
learning_rate=lr,
boundaries=boundaries,
values=values,
warmup_iter=200,
warmup_factor=0.),
regularization=fluid.regularizer.L2Decay(0.00001), )
return optimizer
def train(model,DataSet):
landmarks_pre,angles_pre,weighted_loss,loss,avg_loss = create_model(model='ResNet')
optimizer = optimizer_setting(0.0002)
optimizer.minimize(loss)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
#fluid.memory_optimize(fluid.default_main_program(),print_log=False, skip_opt_set=set([landmarks_pre.name,angles_pre.name,weighted_loss.name,loss.name]))
if pretrain_model:
load_model(exe,fluid.default_main_program(),model=model)
print("load model succeed")
else:
print("load succeed")
def trainLoop():
batches = DataSet.get_batch_generator(1024, total_step)
for i, imgs, landmarks_gt, attributes_gt, euler_angles_gt in batches:
preTime = time.time()
result = exe.run(fluid.default_main_program(),
feed={'img': imgs,
'landmark': landmarks_gt,
'attribute':attributes_gt,
'euler_angle':euler_angles_gt },
fetch_list=[weighted_loss,loss,landmarks_pre,angles_pre])
nowTime = time.time()
landmarks = result[2]
#print('gt',landmarks_gt.shape)
#print('pre',landmarks.shape)
landmarks = landmarks.reshape(landmarks.shape[0], -1, 2) # landmark
landmarks_gt = landmarks_gt.reshape(landmarks_gt.shape[0], -1, 2)# landmarks_gt
lr = np.array(fluid.global_scope().find_var('learning_rate')
.get_tensor())
if i % 1000 == 0 and i!= 0:
print("Model saved")
save_model(exe,fluid.default_main_program(),model=model)
if i % 2 == 0:
nme_list = []
nme_temp = compute_nme(landmarks, landmarks_gt)
for item in nme_temp:
nme_list.append(item)
# nme
#print('nme: {:.4f}'.format(np.mean(nme_list)))
# auc and failure rate
failureThreshold = 0.1
auc, failure_rate = compute_auc(nme_list, failureThreshold)
#print('auc @ {:.1f} failureThreshold: {:.4f}'.format(auc,failureThreshold))
#print('failure_rate: {:}'.format(failure_rate))
print("step {:d},lr {:.6f},w_loss {:.6f},loss {:.6f},nme: {:.4f},auc {:.1f}, failure_rate: {:}, failureThreshold: {:.4f},step_time: {:.3f}".format(
i,lr[0],result[0][0],result[1][0],np.mean(nme_list),auc,failure_rate,failureThreshold,nowTime - preTime))
trainLoop()
if __name__ == "__main__":
parse = argparse.ArgumentParser(description='')
parse.add_argument('--model', help='model name', nargs='?')
args = parse.parse_args()
model = "mobilenetv2"
train_dataset,test_dataset = create_reader()
train(model,train_dataset)