我正在尝试获取关于特定图像的渐变（位于 adv_loader 中）。这些图像在 adv_loader 中加载。我尝试只使用在反向传播步骤中计算梯度的代码，但 Yolo3 似乎没有 grad 属性。关于如何获得它的任何想法？
import mxnet as mx
import gluoncv as gcv
import numpy as np
import matplotlib.pyplot as plt
from mxnet import gluon
from gluoncv.loss import YOLOV3Loss
import time
ctx = [mx.gpu(i) for i in range(mx.context.num_gpus())] if mx.context.num_gpus()>0 else [mx.cpu()]
#Datasets
train = gcv.data.RecordFileDetection('./data/train.rec',coord_normalized=False)
val = gcv.data.RecordFileDetection('./data/val.rec',coord_normalized=False)
classes = ['Passenger Vehicle','Small Car','Bus']
net = gcv.model_zoo.yolo3_mobilenet0_25_custom(pretrained_base=False,classes=classes,ctx=ctx)
net.collect_params().initialize(force_reinit=True,ctx=ctx)
import multiprocessing as mp
### The following looks nasty, and I apologize for the difficulty but basically what we are doing is
# transforming our data so it is in the correct format for yolo3
batch_size = 32 #This can be changed, but it determines how often we update our model.
num_workers = mp.cpu_count()//2
### We will import the following to reduce what i need to type (I am not sure about you but I like being lazy)
from mxnet import autograd
### 416 is our width/height we want the network to train on
sizes = 328
train_transform = gcv.data.transforms.presets.yolo.YOLO3DefaultTrainTransform(sizes,sizes, net)
# return stacked images, center_targets, scale_targets, gradient weights, objectness_targets, class_targets
# additionally, return padded ground truth bboxes, so there are 7 components returned by dataloader
batchify_fn = gcv.data.batchify.Tuple(*([gcv.data.batchify.Stack() for _ in range(6)] + [gcv.data.batchify.Pad(axis=0, pad_val=-1) for _ in range(1)]))
train_loader = mx.gluon.data.DataLoader(train.transform(train_transform), batch_size, shuffle=True,
batchify_fn=batchify_fn, last_batch='rollover',num_workers=num_workers,prefetch=num_workers + num_workers//2)
val_batchify_fn = gcv.data.batchify.Tuple(gcv.data.batchify.Stack(), gcv.data.batchify.Pad(pad_val=-1))
val_transform = gcv.data.transforms.presets.yolo.YOLO3DefaultValTransform(sizes,sizes)
val_loader = mx.gluon.data.DataLoader(
val.transform(val_transform),
batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep',num_workers=num_workers,prefetch=num_workers + num_workers//2)
adv_loader = mx.gluon.data.DataLoader(train.transform(train_transform), batch_size, shuffle=True,
batchify_fn=batchify_fn, last_batch='rollover',num_workers=num_workers,prefetch=num_workers + num_workers//2) #this is a placeholder for data that will generate advarsaial examples
#How we will validate our model
def validate(net, val_data, ctx, eval_metric):
"""Test on validation dataset."""
eval_metric.reset()
# set nms threshold and topk constraint
net.set_nms(nms_thresh=0.45, nms_topk=455)
mx.nd.waitall()
net.hybridize()
for batch in val_data:
data = mx.gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0, even_split=False)
label = mx.gluon.utils.split_and_load(batch[1], ctx_list=ctx,batch_axis=0, even_split=False)
det_bboxes = []
det_ids = []
det_scores = []
gt_bboxes = []
gt_ids = []
gt_difficults = []
for x, y in zip(data, label):
# get prediction results
ids, scores, bboxes = net(x)
det_ids.append(ids)
det_scores.append(scores)
# clip to image size
det_bboxes.append(bboxes.clip(0, batch[0].shape[2]))
# split ground truths
gt_ids.append(y.slice_axis(axis=-1, begin=4, end=5))
gt_bboxes.append(y.slice_axis(axis=-1, begin=0, end=4))
gt_difficults.append(y.slice_axis(axis=-1, begin=5, end=6) if y.shape[-1] > 5 else None)
# update metric
eval_metric.update(det_bboxes, det_ids, det_scores, gt_bboxes, gt_ids, gt_difficults)
return eval_metric.get()
eval_metric = gcv.utils.metrics.voc_detection.VOCMApMetric(class_names=classes)
nepochs=11
net.initialize(force_reinit=True)
net.collect_params().reset_ctx(ctx)
#Grab a trainer or optimizer to perform the optimization
trainer = mx.gluon.Trainer(net.collect_params(),'adam',
{'learning_rate':0.001},
kvstore='device')
for i in range(nepochs):
now = time.time()
mx.nd.waitall()
net.hybridize(static_alloc=True,static_shape=True)
for ixl,batch in enumerate(train_loader):
data = mx.gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
# objectness, center_targets, scale_targets, weights, class_targets
fixed_targets = [mx.gluon.utils.split_and_load(batch[it], ctx_list=ctx, batch_axis=0) for it in range(1, 6)]
gt_boxes = mx.gluon.utils.split_and_load(batch[6], ctx_list=ctx, batch_axis=0)
sum_losses = []
with autograd.record():
for ix, x in enumerate(data):
obj_loss, center_loss, scale_loss, cls_loss = net(x, gt_boxes[ix], *[ft[ix] for ft in fixed_targets])
sum_losses.append(obj_loss + center_loss + scale_loss + cls_loss)
autograd.backward(sum_losses)
trainer.step(batch_size)
for ixl,batch in enumerate(adv_loader):
data = mx.gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
# objectness, center_targets, scale_targets, weights, class_targets
fixed_targets = [mx.gluon.utils.split_and_load(batch[it], ctx_list=ctx, batch_axis=0) for it in range(1, 6)]
gt_boxes = mx.gluon.utils.split_and_load(batch[6], ctx_list=ctx, batch_axis=0)
sum_losses = []
with autograd.record():
for ix, x in enumerate(data):
obj_loss, center_loss, scale_loss, cls_loss = net(x, gt_boxes[ix], *[ft[ix] for ft in fixed_targets])
sum_losses.append(obj_loss + center_loss + scale_loss + cls_loss)
autograd.backward(sum_losses,retain_graph=True)
print(net.grad.asnumpy())
我在这里被告知 AttributeError: 'YOLOV3' object has no attribute 'grad'。我正在使用此文档https://mxnet.apache.org/versions/1.6/api/python/docs/tutorials/packages/autograd/index.html 知道如何获取图像并将其传递给渐变yolo损失函数？