def visualize_epoch(self, model, data_loader, priorbox, writer, epoch, use_gpu):
model.eval()
img_index = random.randint(0, len(data_loader.dataset)-1)
#img_index = 1
# get img
image = data_loader.dataset.pull_image(img_index)
anno = data_loader.dataset.pull_anno(img_index)
# visualize archor box
viz_prior_box(writer, priorbox, image, epoch)
# get preproc
preproc = data_loader.dataset.preproc
preproc.add_writer(writer, epoch)
# preproc.p = 0.6
# preproc image & visualize preprocess prograss
images = Variable(preproc(image, anno)[0].unsqueeze(0), volatile=True)
if use_gpu:
images = images.cuda()
# visualize feature map in base and extras
base_out = viz_module_feature_maps(writer, model.base, images, module_name='base', epoch=epoch)
extras_out = viz_module_feature_maps(writer, model.extras, base_out, module_name='extras', epoch=epoch)
# visualize feature map in feature_extractors
viz_feature_maps(writer, model(images, 'feature'), module_name='feature_extractors', epoch=epoch)
model.train()
images.requires_grad = True
images.volatile=False
base_out = viz_module_grads(writer, model, model.base, images, images, preproc.means, module_name='base', epoch=epoch)
def visualize_epoch(self, model, data_loader, priorbox, writer, epoch, use_gpu):
model.eval()
img_index = random.randint(0, len(data_loader.dataset)-1)
# get img
image = data_loader.dataset.pull_image(img_index)
anno = data_loader.dataset.pull_anno(img_index)
# get preproc
preproc = data_loader.dataset.preproc
preproc.add_writer(writer, epoch)
# visualize archor box
viz_prior_box(writer, priorbox, image, epoch)
# preproc image & visualize preprocess prograss
images = Variable(preproc(image, anno)[0].unsqueeze(0), volatile=True)
if use_gpu:
images = images.cuda()
