I am trying to get the gradient with respect to a specific images (sitting in
adv_loader). These images are loaded in adv_loader. I tried just taking the
code that was calculating the gradient in the backprop step Yolo3 doesn't seem
to have a grad attribute. Any ideas on how to get that?
```
net =
gcv.model_zoo.yolo3_mobilenet0_25_custom(pretrained_base=False,classes=classes,ctx=ctx)
import time
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())
```
---
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