def get_dataloader(net, train_dataset, val_dataset, short, max_size, batch_size, num_workers): """Get dataloader.""" train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)]) train_loader = mx.gluon.data.DataLoader(train_dataset.transform( FasterRCNNDefaultTrainTransform(short, max_size, net)), batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers) val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)]) val_loader = mx.gluon.data.DataLoader(val_dataset.transform( FasterRCNNDefaultValTransform(short, max_size)), batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers) return train_loader, val_loader
def get_dataloader(net, val_dataset, batch_size, num_workers): """Get dataloader.""" val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)]) val_loader = mx.gluon.data.DataLoader(val_dataset.transform( FasterRCNNDefaultValTransform(net.short, net.max_size)), batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers) return val_loader
def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size, num_workers): """Get dataloader.""" train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(5)]) train_loader = mx.gluon.data.DataLoader(train_dataset.transform( train_transform(net.short, net.max_size)), batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers) val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)]) short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short val_loader = mx.gluon.data.DataLoader(val_dataset.transform( val_transform(short, net.max_size)), batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers) return train_loader, val_loader
def get_frcnn_data_loader(val_dataset, net): """ load data in batches for frcnn model """ batch_size = BATCH_SIZE val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)]) val_loader = mx.gluon.data.DataLoader(val_dataset.transform( FasterRCNNDefaultValTransform(net.short, net.max_size)), batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=0) return val_loader
def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size, num_workers, multi_stage): """Get dataloader.""" train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(6)]) train_loader = mx.gluon.data.DataLoader(train_dataset.transform( train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=multi_stage)), batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers) val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(2)]) val_loader = mx.gluon.data.DataLoader(val_dataset.transform( val_transform(net.short, net.max_size)), batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers) return train_loader, val_loader
def DogDataLoader(net, root='./stanford_dog_dataset', preload_label=True, batch_size=1, shuffle=True, num_workers=0): # dataset train_dataset = DogDetection(root=root, splits='train', preload_label=preload_label) val_dataset = DogDetection(root=root, splits='test', preload_label=preload_label) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) train_batchify = batchify.Tuple(*[batchify.Append() for _ in range(5)]) val_batchify = batchify.Tuple(*[batchify.Append() for _ in range(3)]) train_dataloader = gluon.data.DataLoader(train_dataset.transform( FasterRCNNDefaultTrainTransform(net.short, net.max_size, net)), batch_size=batch_size, shuffle=True, batchify_fn=train_batchify, last_batch='rollover', num_workers=num_workers) val_dataloader = gluon.data.DataLoader(val_dataset.transform( FasterRCNNDefaultValTransform(net.short, net.max_size)), batch_size=batch_size, shuffle=False, batchify_fn=val_batchify, last_batch='keep', num_workers=num_workers) return train_dataloader, val_dataloader, val_metric
def get_dataloader(net, train_dataset, batch_size, num_workers, short=600, max_size=1000): """Get dataloader.""" train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(5)]) train_loader = mx.gluon.data.DataLoader(train_dataset.transform( FasterRCNNDefaultTrainTransform(short, max_size, net)), batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers) return train_loader
def get_dataloader( net, train_dataset, val_dataset, train_transform, val_transform, batch_size, num_shards_per_process, args, ): """Get dataloader.""" train_bfn = batchify.MaskRCNNTrainBatchify(net, num_shards_per_process) train_sampler = gcv.nn.sampler.SplitSortedBucketSampler( train_dataset.get_im_aspect_ratio(), batch_size, num_parts=hvd.size() if args.horovod else 1, part_index=hvd.rank() if args.horovod else 0, shuffle=True, ) train_loader = mx.gluon.data.DataLoader( train_dataset.transform( train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=True)), batch_sampler=train_sampler, batchify_fn=train_bfn, num_workers=args.num_workers, ) val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(2)]) short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short # validation use 1 sample per device val_loader = mx.gluon.data.DataLoader( val_dataset.transform(val_transform(short, net.max_size)), num_shards_per_process, False, batchify_fn=val_bfn, last_batch="keep", num_workers=args.num_workers, ) return train_loader, val_loader
def speed_test(): args = parse_args() # context list ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()] ctx = [mx.cpu()] if not ctx else ctx # Get net net = gcv.model_zoo.get_model(args.network, pretrained=False, pretrained_base=False) net.load_parameters(args.pretrained) net.set_nms(0.45, 200) net.collect_params().reset_ctx(ctx=ctx) if not os.path.exists(args.save_dir): os.mkdir(args.save_dir) # Dataset val_dataset = COCOInstance(root='/home/tutian/dataset', skip_empty=False) val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(2)]) # val_bfn = Tuple(Stack(), Pad(pad_val=-1)) val_loader = gluon.data.DataLoader(val_dataset.transform( YOLO3UsdSegCocoValTransform(416, 416, 50, 'coco')), 1, False, batchify_fn=val_bfn, last_batch='keep', num_workers=1) # Some preparation total_time_net = 0 total_time_post = 0 total_time_cpu = 0 total_time_predot = 0 total_time_dot = 0 total_time_genmask = 0 total_time_calculate = 0 colors = { i: plt.get_cmap('hsv')(i / len(CLASSES)) for i in range(len(CLASSES)) } img_ids = sorted(val_dataset.coco.getImgIds()) mx.nd.waitall() net.hybridize() save_images = False # print(image_list_batch) with tqdm(total=5000) as pbar: for ibt, batch in enumerate(val_loader): batch = split_and_load(batch, ctx_list=ctx) for x, im_info in zip(*batch): # get prediction results t1 = time.time() ids, scores, bboxes, coefs = net(x) t_c0 = time.time() mx.nd.waitall() t_c1 = time.time() t2 = time.time() # Post process t_cpu0 = time.time() bboxes = bboxes.asnumpy()[0] ids = ids.asnumpy()[0] scores = scores.asnumpy()[0] coefs = coefs.asnumpy()[0] im_info = im_info.asnumpy()[0] t_cpu1 = time.time() total_time_cpu += (t_cpu1 - t_cpu0) t_cpu0 = time.time() im_height, im_width = [int(i) for i in im_info] valid = np.where(((ids >= 0) & (scores >= 0.45)))[0] ids = ids[valid] scores = scores[valid] bboxes = bboxes[valid] coefs = coefs[valid] coefs = coefs * sqrt_var + x_mean t_cpu1 = time.time() total_time_predot += (t_cpu1 - t_cpu0) t_cpu0 = time.time() masks = np.dot(coefs, bases) t_cpu1 = time.time() total_time_dot += (t_cpu1 - t_cpu0) t_cpu0 = time.time() bboxes, masks = generate_bbox_mask(coefs, bboxes, im_height, im_width) t_cpu1 = time.time() total_time_genmask += (t_cpu1 - t_cpu0) t3 = time.time() if ibt >= 500: total_time_net += (t2 - t1) total_time_calculate += (t_c1 - t_c0) total_time_post += (t3 - t2) # Save the masks if save_images: fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot(1, 1, 1) ax.imshow( Image.open('/disk1/data/coco/val2017/' + str(img_ids[ibt]).zfill(12) + '.jpg')) for bbox, mask, idt in zip(bboxes, masks, ids): idt = int(idt) # bbox rect = plt.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], fill=False, edgecolor=colors[idt], linewidth=2) ax.add_patch(rect) # mask mask_channel = np.zeros((im_height, im_width, 4)) for i in range(4): mask_channel[:, :, i] = mask * colors[idt][i] ax.imshow(mask_channel, alpha=0.5) plt.axis('off') plt.gca().xaxis.set_major_locator(NullLocator()) plt.gca().yaxis.set_major_locator(NullLocator()) plt.subplots_adjust(top=0.995, bottom=0.005, right=0.995, left=0.005, hspace=0, wspace=0) plt.savefig( os.path.join(args.save_dir, str(img_ids[ibt]).zfill(12) + '.jpg')) plt.close() pbar.update(1) print("network speed ", 4500 / total_time_net, "fps") print("Sync speed ", 4500 / total_time_calculate, "fps") print("post process speed ", 5000 / total_time_post, "fps") print("GPU to CPU speed ", 5000 / total_time_cpu, "fps") print("Pre-dot speed ", 5000 / total_time_predot, "fps") print("np.dot speed ", 5000 / total_time_dot, "fps") print("Gen-Mask speed ", 5000 / total_time_genmask, "fps") print("total speed ", 5000 / (total_time_net + total_time_post), "fps")
num_workers = 0 # init model net = get_model("faster_rcnn_resnet50_v1b_voc", pretrained=False, pretrained_base=False) net.load_parameters(model_path) net.collect_params().reset_ctx(ctx) # load val dataset val_dataset = gdata.VOCDetection(root="/Users/rensike/Files/temp/voc_mini", splits=[(0, 'val')]) eval_metric = VOCMApMetric(iou_thresh=0.5, class_names=val_dataset.classes) # load val dataloader val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)]) val_data_loader = mx.gluon.data.DataLoader(val_dataset.transform( FasterRCNNDefaultValTransform(net.short, net.max_size)), 1, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers) # do evaluate eval_metric.reset() net.hybridize(static_alloc=True) map_name, mean_ap = validate(net, val_data_loader, ctx, eval_metric) val_msg = '\n'.join(['{}={}'.format(k, v) for k, v in zip(map_name, mean_ap)]) print('Validation: \n{}'.format(val_msg))