我使用 PyCaffe 编写了一个简单的脚本来测试模型，但我发现它非常慢!即使在 GPU 上!我的测试集有 82K 个大小为 256x256 的样本，当我运行下面给出的代码时，需要几个小时才能完成。
我什至使用了成批图像而不是单个图像，但没有任何变化。目前，它已经运行了近5个小时，只处理了50K个样本!我应该怎么做才能让它更快？
我可以完全避免使用 transformer.preprocessing 吗？如果是这样怎么办？
这是片段:
#run on gpu
caffe.set_mode_gpu()
#Extract mean from the mean image file
mean_blobproto_new = caffe.proto.caffe_pb2.BlobProto()
f = open(args.mean, 'rb')
mean_blobproto_new.ParseFromString(f.read())
mean_image = caffe.io.blobproto_to_array(mean_blobproto_new)
f.close()
predicted_lables = []
true_labels = []
misclassified =[]
class_names = ['unsafe','safe']
count = 0
correct = 0
batch=[]
plabe_ls = []
batch_size = 50
net1 = caffe.Net(args.proto, args.model, caffe.TEST)
transformer = caffe.io.Transformer({'data': net1.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', mean_image[0].mean(1).mean(1))
transformer.set_raw_scale('data', 255)
transformer.set_channel_swap('data', (2,1,0))
net1.blobs['data'].reshape(batch_size, 3,224, 224)
data_blob_shape = net1.blobs['data'].data.shape
data_blob_shape = list(data_blob_shape)
i=0
mu = np.array([ 104, 117, 123])#imagenet mean
#check and see if its lmdb or leveldb
if(args.db_type.lower() == 'lmdb'):
lmdb_env = lmdb.open(args.db_path)
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
for key, value in lmdb_cursor:
count += 1
datum = caffe.proto.caffe_pb2.Datum()
datum.ParseFromString(value)
label = int(datum.label)
image = caffe.io.datum_to_array(datum).astype(np.uint8)
if(count % 5000 == 0):
print('count: ',count)
if(i < batch_size):
i+=1
inf= key,image,label
batch.append(inf)
if(i >= batch_size):
#process n image
ims=[]
for x in range(len(batch)):
ims.append(transformer.preprocess('data',batch[x][1]))# - mean_image[0].mean(1).mean(1) )
net1.blobs['data'].data[...] = ims[:]
out_1 = net1.forward()
plbl = np.asarray( out_1['pred'])
plbl = plbl.argmax(axis=1)
for j in range(len(batch)):
if (plbl[j] == batch[j][2]):
correct+=1
else:
misclassified.append(batch[j][0])
predicted_lables.append(plbl[j])
true_labels.append(batch[j][2])
batch.clear()
i=0
更新:
通过替换
for x in range(len(batch)):
ims.append(transformer.preprocess('data',batch[x][1]))
net1.blobs['data'].data[...] = ims[:]
与
for x in range(len(batch)):
img = batch[x][1]
ims.append(img[:,0:224,0:224])
在不到一分钟的时间内处理了 82K 个样本。罪魁祸首确实是预处理方法，我不知道为什么它会这样!
无论如何，我不能这样使用平均文件。我试过了
ims.append(img[:,0:224,0:224] - mean.mean(1).mean(1))
同样，但遇到此错误:
ValueError: operands could not be broadcast together with shapes (3,224,224) (3,)
我还需要找到一种更好的裁剪图像的方法，我不知道是否需要将其调整回 224？或者我应该像咖啡一样使用农裁剪？
最佳答案
我终于成功了!这是运行速度更快的代码:
predicted_lables=[]
true_labels = []
misclassified =[]
class_names = ['unsafe','safe']
count =0
correct = 0
batch = []
plabe_ls = []
batch_size = 50
cropx = 224
cropy = 224
i = 0
# Extract mean from the mean image file
mean_blobproto_new = caffe.proto.caffe_pb2.BlobProto()
f = open(args.mean, 'rb')
mean_blobproto_new.ParseFromString(f.read())
mean_image = caffe.io.blobproto_to_array(mean_blobproto_new)
f.close()
caffe.set_mode_gpu()
net1 = caffe.Net(args.proto, args.model, caffe.TEST)
net1.blobs['data'].reshape(batch_size, 3, 224, 224)
data_blob_shape = net1.blobs['data'].data.shape
#check and see if its lmdb or leveldb
if(args.db_type.lower() == 'lmdb'):
lmdb_env = lmdb.open(args.db_path)
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
for key, value in lmdb_cursor:
count += 1
datum = caffe.proto.caffe_pb2.Datum()
datum.ParseFromString(value)
label = int(datum.label)
image = caffe.io.datum_to_array(datum).astype(np.float32)
#key,image,label
#buffer n image
if(count % 5000 == 0):
print('{0} samples processed so far'.format(count))
if(i < batch_size):
i += 1
inf= key,image,label
batch.append(inf)
#print(key)
if(i >= batch_size):
#process n image
ims=[]
for x in range(len(batch)):
img = batch[x][1]
#img has c,h,w shape! its already gone through transpose
#and channel swap when it was being saved into lmdb!
#method I: crop the both the image and mean file
#ims.append(img[:,0:224,0:224] - mean_image[0][:,0:224,0:224] )
#Method II : resize the image to the desired size(crop size)
#img = caffe.io.resize_image(img.transpose(2,1,0), (224, 224))
#Method III : use center crop just like caffe does in test time
#center crop
c,w,h = img.shape
startx = h//2 - cropx//2
starty = w//2 - cropy//2
img = img[:, startx:startx + cropx, starty:starty + cropy]
#transpose the image so we can subtract from mean
img = img.transpose(2,1,0)
img -= mean_image[0].mean(1).mean(1)
#transpose back to the original state
img = img.transpose(2,1,0)
ims.append(img)
net1.blobs['data'].data[...] = ims[:]
out_1 = net1.forward()
plabe_ls = out_1['pred']
plbl = np.asarray(plabe_ls)
plbl = plbl.argmax(axis=1)
for j in range(len(batch)):
if (plbl[j] == batch[j][2]):
correct += 1
else:
misclassified.append(batch[j][0])
predicted_lables.append(plbl[j])
true_labels.append(batch[j][2])
batch.clear()
i = 0
虽然我没有得到准确的准确度，但非常接近(满分 98.65 我得到 98.61%!我不知道是什么导致了这种差异!)
更新
transformer.preprocess 完成时间过长的原因是它的 resize_image() 方法。 resize_image 需要图像采用 H,W,C, 的形式，而图像已经被转置和 channel 交换(以 c,w,h 的形式) 在我的例子中(我正在读取一个 lmdb 数据集)，这导致 resize_image() 采取最慢的调整图像大小的方法，因此处理每个图像需要 0.6 秒。
现在知道这一点，将图像转换为正确的尺寸，就可以解决这个问题。意思是我必须这样做:
ims.append(transformer.preprocess('data',img.transpose(2,1,0)))
请注意，它仍然比上述方法慢，但比以前快得多!
关于Caffe 的 transformer.preprocessing 完成时间太长，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45294732/