def get_iterators(data_root, data_shape, batch_size, TorV):
print('Loading Data....')
if TorV == 'train':
batch_iter = image.ImageDetIter(
batch_size=batch_size,
data_shape=(3, data_shape[0], data_shape[1]),
path_imgrec=os.path.join(data_root, TorV+'.rec'),
path_imgidx=os.path.join(data_root, TorV+'.idx'),
shuffle=True,
brightness=0.5,
contrast=0.2,
saturation=0.5,
hue=1.0,
)
elif TorV == 'valid':
batch_iter = image.ImageDetIter(
batch_size=1,
data_shape=(3, data_shape[0], data_shape[1]),
path_imgrec=os.path.join(data_root, 'train.rec'),
path_imgidx=os.path.join(data_root, 'train.idx'),
shuffle=True,
#rand_crop=0.2,
#rand_pad=0.2,
#area_range=(0.8, 1.2),
brightness=0.2,
#contrast=0.2,
saturation=0.5,
#hue=1.0,
)
else:
batch_iter = None
return batch_iter
def get_iterators(data_shape, batch_size, data_dir):
""" 获取训练集和验证集迭代器 """
# lable名和label数
class_names = ['pikachu']
num_classes = len(class_names)
# 训练集迭代器
train_iter = image.ImageDetIter(
batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_dir+'train.rec',
path_imgidx=data_dir+'train.idx',
shuffle=True,
mean=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200
)
# 验证集迭代器
val_iter = image.ImageDetIter(
batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_dir+'val.rec',
shuffle=False,
mean=True,
)
return train_iter, val_iter, class_names, num_classes
def load_data_pikachu(batch_size, edge_size=256): # edge_size：输出图像的宽和高
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'train.rec'),
path_imgidx=os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size), # 输出图像的形状
# shuffle=False, # 以随机顺序读取数据集
# rand_crop=1, # 随机裁剪的概率为1
min_object_covered=0.95, max_attempts=200)
val_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size,
data_shape=(3, edge_size, edge_size), shuffle=False)
return train_iter, val_iter
def load_data_uav(data_dir = '../data/uav', batch_size=4, edge_size=256):
# _download_pikachu(data_dir)
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'train.rec'),
path_imgidx = os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size), # 输出图像的形状
shuffle=True, # 以随机顺序读取数据集
rand_crop=1, # 随机裁剪的概率为1
min_object_covered=0.95, max_attempts=200)
val_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size,
data_shape=(3, edge_size, edge_size), shuffle=False)
return train_iter, val_iter
def load_data_pikaku(batch_size, edg_size=256):
data_dir = './data/pikaku'
download_pikaku(data_dir)
train_iter = image.ImageDetIter(path_imgrec=os.path.join(data_dir,'train.rec'),
path_imgidx=os.path.join(data_dir,'train.idx'),
batch_size=batch_size,
data_shape=(3, edg_size, edg_size),
shuffle=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(path_imgrec=os.path.join(data_dir,'val.rec'),batch_size=batch_size,
data_shape=(3,edg_size,edg_size),shuffle=False)
return train_iter, val_iter
def load_data_pikachu(batch_size, edge_size=256):
data_dir = '../data/pikachu'
_download_pikachu(data_dir)
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'train.rec'),
path_imgidx=os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size), # The shape of the output image
shuffle=True, # Read the data set in random order
rand_crop=1, # The probability of random cropping is 1
min_object_covered=0.95, max_attempts=200)
val_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size,
data_shape=(3, edge_size, edge_size), shuffle=False)
return train_iter, val_iter
def load_data_pikachu(batch_size, edge_size=256): # edge_size：输出图像的宽和高。
data_dir = '~/.mxnet/datasets/pikachu'
_maybe_download_pikachu(data_dir)
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'train.rec'),
path_imgidx=os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size), # 输出图像的形状。
shuffle=True, # 以随机顺序读取数据集。
rand_crop=1, # 随机裁剪的概率为 1。
min_object_covered=0.95, max_attempts=200)
val_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size,
data_shape=(3, edge_size, edge_size), shuffle=False)
return train_iter, val_iter
def __init__(self):
self.net = JanetRes(classes=self.classes, use_bn=True)
self.net.initialize(ctx=self.ctx)
self.trainIter = image.ImageDetIter(
batch_size=self.BATCH_SIZE,
data_shape=(3, 300, 300),
path_imgrec='../DataX/annoTrainX.rec',
path_imgidx='../DataX/annoTrainX.idx',
path_imglist='../DataX/annoTrainX.lst',
path_root='../DataX/',
shuffle=True,
mean=True,
brightness=0.3,
contrast=0.3,
saturation=0.3,
pca_noise=0.3,
hue=0.3)
with autograd.train_mode():
_, _, anchors = self.net(
mx.ndarray.zeros(shape=(self.BATCH_SIZE, 3, 300, 300),
ctx=self.ctx))
self.T = TargetGenV1(anchors=anchors.as_in_context(mx.cpu()),
height=300,
width=300)
self.net.collect_params().reset_ctx(self.ctx)
self.trainer = gluon.Trainer(self.net.collect_params(), 'sgd', {
'learning_rate': 0.1,
'wd': 5e-4
})
def get_iterators(data_shape, batch_size):
train_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_dir + 'train.rec',
path_imgidx=data_dir + 'train.idx',
shuffle=True,
mean=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_dir + 'val.rec',
shuffle=False,
mean=True)
return train_iter, val_iter, class_names, num_class
def load_data_pikachu(batch_size, edge_size=256):
data_dir = '../data/pikachu/'
_download_pikachu(data_dir)
train_data = image.ImageDetIter(path_imgrec=data_dir + 'train.rec',
path_imgidx=data_dir + 'train.idx',
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_data = image.ImageDetIter(path_imgrec=data_dir + 'val.rec',
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=False)
return (train_data, val_data)
def __init__(self, batchSize):
self.net = Brunette(classes=self.classes)
self.net.initialize(mx.init.Xavier(magnitude=2), ctx=self.ctx)
self.batchSize = batchSize
self.trainIter = image.ImageDetIter(batch_size=self.batchSize,
data_shape=(3, 300, 300),
path_imgrec='utils/TrainY.rec',
path_imgidx='utils/TrainY.idx',
shuffle=True,
mean=True,
brightness=0.3,
contrast=0.3,
saturation=0.3,
pca_noise=0.3,
hue=0.3)
# with autograd.train_mode():
# _, _, anchors = self.net(mx.ndarray.zeros(shape=(self.batchSize, 3, 300, 300), ctx=self.ctx))
# self.T = TargetGenV2(anchors=anchors.as_in_context(mx.cpu()), height=300, width=300)
print("2")
self.net.collect_params().reset_ctx(self.ctx)
self.trainer = gluon.Trainer(self.net.collect_params(), 'sgd', {
'learning_rate': 0.1,
'wd': 3e-4
})
def load_data_test(batch_size, data_dir, fname):
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, '%s.rec' % fname),
path_imgidx=os.path.join(data_dir, '%s.idx' % fname),
batch_size=batch_size,
shuffle=True, # Read the dataset in random order
data_shape=(config.channel, config.img_h, config.img_w))
return train_iter
def __init__(self, batchSize):
self.net = SSDv1()
self.net.initialize(init=Xavier(), ctx=self.ctx)
self.batchSize = batchSize
self.trainIter = image.ImageDetIter(batch_size=self.batchSize, data_shape=(3, 300, 300),
path_imgrec='utils/TrainY.rec', path_imgidx='utils/TrainY.idx',)
self.trainer = gluon.Trainer(self.net.collect_params(), 'sgd', {'learning_rate': 0.1, 'wd': 5e-4})
def load_data_pikachu(batch_size, edge_size):
data_dir = "../data/pikachu"
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, "train.rec"),
path_imgidx=os.path.join(data_dir, "train.idx"),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(path_imgrec=os.path.join(
data_dir, "val.rec"),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=False)
return train_iter, val_iter
def load_data_pikachu(batch_size, edge_size=256):
"""Download the pikachu dataest and then load into memory."""
data_dir = '../data/pikachu/'
_download_pikachu(data_dir)
train_iter = image.ImageDetIter(path_imgrec=data_dir + 'train.rec',
path_imgidx=data_dir + 'train.idx',
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(path_imgrec=data_dir + 'val.rec',
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=False)
return train_iter, val_iter
def load_data_uav(data_dir='../data/uav', batch_size=4, edge_size=256):
# _download_pikachu(data_dir)
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'train.rec'),
path_imgidx=os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size), # shape of output image
shuffle=True, # read data in randomly
rand_crop=1, # prob of cropping randomly
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(path_imgrec=os.path.join(
data_dir, 'val.rec'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=False)
return train_iter, val_iter
def get_iterators(data_shape, batch_size):
class_names = ['pikachu']
num_class = len(class_names)
train_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec='./data/pikachu_train.rec',
path_imgidx='./data/pikachu_train.idx',
shuffle=True,
mean=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec='./data/pikachu_val.rec',
shuffle=False,
mean=True)
return train_iter, val_iter, class_names, num_class
def get_iterators(data_shape, batch_size):
data_dir = '/Users/liudiwen/.mxnet/datasets/data/pikachu/'
class_names = ['pikachu']
num_class = len(class_names)
train_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_dir + 'train.rec',
path_imgidx=data_dir + 'train.idx',
shuffle=True,
mean=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_dir + 'val.rec',
shuffle=False,
mean=True)
return train_iter, val_iter, class_names, num_class
def get_iterators(data_shape, batch_size):
# class_names = ["RBC", "WBC", "Platelets"]
class_names = ["HUMAN", "WBC", "Platelets"]
num_class = len(class_names)
train_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape[0],
data_shape[1]),
path_imgrec=data_dir + 'train.rec',
path_imgidx=data_dir + 'train.idx',
shuffle=True,
mean=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
val_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape[0], data_shape[1]),
path_imgrec=data_dir + 'val.rec',
shuffle=False,
mean=True)
return train_iter, val_iter, class_names, num_class
def data_iterator(batch_size, data_shape, data_root):
#os.chdir(data_root)
train_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_root + '/train.rec',
path_imgidx=data_root + '/train.idx',
shuffle=True,
mean=True,
std=True,
rand_crop=1,
min_object_covered=0.9,
max_attempts=200)
val_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec=data_root + '/val.rec',
path_imgidx=data_root + '/val.idx',
shuffle=False,
mean=True,
std=True)
return train_iter, val_iter
def load_data_pikachu(batch_size, edge_size=256):
"""Download the pikachu dataest and then load into memory."""
data_dir = '../data/pikachu'
_download_pikachu(data_dir)
#aug = image.CreateDetAugmenter(data_shape = (3,edge_size,edge_size),mean=True,std= True)
train_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'train.rec'),
path_imgidx=os.path.join(data_dir, 'train.idx'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200,
)
val_iter = image.ImageDetIter(
path_imgrec=os.path.join(data_dir, 'val.rec'),
batch_size=batch_size,
data_shape=(3, edge_size, edge_size),
shuffle=False,
)
return train_iter, val_iter
def load_dataset(self,dataName):
if dataName == "toy":
self.trainIter = image.ImageDetIter(
batch_size = self.batchSize,
data_shape = (3, self.dataShape, self.dataShape),
path_imgrec = os.path.join( os.path.join(self.dataRoot,dataName), 'train.rec'),
path_imgidx = os.path.join( os.path.join(self.dataRoot,dataName), 'train.idx'),
shuffle = True,
mean = True,
std = True,
rand_crop = 1,
min_object_covered = 0.95,
max_attempts = 200)
self.validIter = image.ImageDetIter(
batch_size=self.batchSize,
data_shape=(3,self.dataShape,self.dataShape),
path_imgrec=os.path.join( os.path.join(self.dataRoot,dataName), 'val.rec'),
shuffle=False, mean=True, std=True)
self.classNames = 'pikachu,dummy'.split(',')
self.numClass = len(self.classNames)
return True
return False
def get_iterators(rec_prefix, data_shape, batch_size):
class_names = ['papercup']
num_class = len(class_names)
train_iter = image.ImageDetIter(
batch_size=batch_size,
data_shape=data_shape,
path_imgrec=rec_prefix + '_train.rec',
path_imgidx=rec_prefix + '_train.idx',
aug_list=None,
shuffle=True,
mean=True,
std=True,
rand_crop=1,
rand_gray=0.2,
rand_mirror=True,
rand_pad=0.4,
pad_val=(rgb_mean[0], rgb_mean[1], rgb_mean[2]),
min_object_covered=0.95,
max_attempts=200,
brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.05,
aspect_ratio_range=(0.8, 1.2),
# pca_noise=0.01,
)
valid_iter = image.ImageDetIter(
batch_size=batch_size,
data_shape=data_shape,
path_imgrec=rec_prefix + '_val.rec',
shuffle=False,
# mean=True,
# std=True
)
return train_iter, valid_iter, class_names, num_class
def load_ImageDetIter(path, batch_size, h, w):
print('Loading ImageDetIter ' + path)
batch_iter = image.ImageDetIter(batch_size, (3, h, w),
path_imgrec=path+'.rec',
path_imgidx=path+'.idx',
shuffle=True,
pca_noise=0.1,
brightness=0.5,
saturation=0.5,
contrast=0.5,
hue=1.0
#rand_crop=0.2,
#rand_pad=0.2,
#area_range=(0.8, 1.2),
)
return batch_iter
def get_iterators(data_shape, batch_size):
train_iter = image.ImageDetIter(batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec='./REC_Data/voc2012.rec',
path_imgidx='./REC_Data/voc2012.idx',
shuffle=True,
mean=True,
rand_crop=0,
min_object_covered=0.95,
max_attempts=200)
# val_iter = image.ImageDetIter(
# batch_size=batch_size,
# data_shape=(3, data_shape, data_shape),
# path_imgrec=data_dir+'val.rec',
# shuffle=False,
# mean=True)
return train_iter
#计算每一项差的绝对值
output = F.abs(pred-labels)
#计算平滑损失
smooth_output = F.smooth_l1(output,sigma)
return F.mean(smooth_output,axis=self.axis)
if __name__ == '__main__':
#获取人群数据
batch_size = 64
edge_size = 256
param_filename = 'ssd_params'
train_iter = image.ImageDetIter(path_imgrec='../data/mydataset.rec',
path_imgidx='../data/mydataset.idx',
batch_size=batch_size,
data_shape=(3,edge_size,edge_size),
shuffle= True,
rand_crop=1, #随机裁剪的概率为1
min_object_covered = 0.95,
max_attempts=200)
ctx = gb.try_gpu()
ssd = TinySSD(num_classes=1)
ssd.initialize(init = init.Xavier(),ctx=ctx)
trainer = gluon.Trainer(ssd.collect_params(),'sgd',{'learning_rate':0.2,'wd':5e-4})
focal_loss = focal_SoftMaxCrossEntropyLoss()
smooth_l1 = smooth_L1Loss()
while True:
#由于不是DataLoader类，每次需要我们重置指针
train_iter.reset()
start = time.time()
from SimpleYOLO.YOLO2.metric import LossRecorder
from projects.pikachu.config import Config
batch_size = 32
ctx = mx.gpu(0)
data_h, data_w = Config.size
class_names = Config.classes
num_class = len(Config.classes)
anchors = Config.anchors
train_data = image.ImageDetIter(
data_shape=(3, data_h, data_w),
std=np.array([255, 255, 255]),
path_imgrec=Config.train_rec,
path_imgidx=Config.train_idx,
batch_size=batch_size,
shuffle=True,
rand_mirror=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=100
)
test_data = image.ImageDetIter(
data_shape=(3, data_h, data_w),
std=np.array([255, 255, 255]),
path_imgrec=Config.val_rec,
batch_size=batch_size,
shuffle=False
)
sce_loss = gluon.loss.SoftmaxCrossEntropyLoss(from_logits=False)
l1_loss = gluon.loss.L1Loss()
mean_red, mean_blue, mean_green = 123, 117, 104
rgb_mean = nd.array([123, 117, 104])
rgb_std = nd.array([58.395, 57.12, 57.375])
maxEpoch = 200
data_dir = 'data\\'
#1--create data iter
#trainIter = iterator.DetRecordIter(trainRecPath,batchSize,(3, dataShape, dataShape),\
# mean_pixels=(mean_red, mean_green, mean_blue), std_pixel = ( rgb_std[0], rgb_std[1], rgb_std[2] ))
trainIter = image.ImageDetIter(batch_size=batchSize,
data_shape=(3, dataShape, dataShape),
path_imgrec=data_dir + 'train.rec',
path_imgidx=data_dir + 'train.idx',
shuffle=True,
mean=True,
std=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
batch = trainIter.next()
label, data = batch.label, batch.data
print(label[0].shape,
"batchSize, maxObjectNum,classId,xmin,ymin,xmax,ymax,difficult")
print(data[0].shape, "batchSize,C,H,W")
#2--load net symbol
#2.1-load pretrained net (feature part)
pretrained = vision.get_model('resnet18_v1', pretrained=True).features
net = nn.HybridSequential()
X, anchors[i], class_preds[i], bbox_preds[i] = blk_forward(
X, getattr(self, 'blk_%d' % i), sizes[i], ratios[i],
getattr(self, 'class_predictor_%d' % i),
getattr(self, 'bbox_predictor_%d' % i))
return (nd.concat(*anchors, dim=1),
concat_preds(class_preds).reshape(0, -1, self.num_classes + 1),
concat_preds(bbox_preds))
batch_size = 32
img_size = 256
train_iter = image.ImageDetIter(
path_imgrec='D:\d2l-zh20200904\data\pikachu\\train.rec',
path_imgidx='D:\d2l-zh20200904\data\pikachu\\train.idx',
batch_size=batch_size,
data_shape=(3, img_size, img_size),
shuffle=True,
rand_crop=1,
min_object_covered=0.95,
max_attempts=200)
# test_iter = image.ImageDetIter(path_imgrec='D:\d2l-zh20200904\data\pikachu\\val.rec', batch_size=batch_size,
# data_shape=(3, img_size, img_size), shuffle=False)
softmaxloss = gloss.SoftmaxCrossEntropyLoss()
l1loss = gloss.L1Loss()
def ssdLoss(class_preds, bbox_preds, class_labels, bbox_labels, bbox_mask):
class_loss = softmaxloss(class_preds, class_labels)
bbox_loss = l1loss(bbox_preds * bbox_mask, bbox_labels * bbox_mask)
return class_loss + bbox_loss
