diff --git a/README.MD b/README.MD index 10a0cbf5c3aeb18eef1d463d0e85048196d115ee..a988105a2d13b1df31f7e1b7f778124d8cead8af 100644 --- a/README.MD +++ b/README.MD @@ -2,6 +2,8 @@ ## 1. Introduction +**I've update the code to support both Python2 and Python3, PyTorch 0.4. If you want the old version code please checkout branch [v0.3]()** + This project is a **Simplified** Faster R-CNN implementation based on [chainercv](https://github.com/chainer/chainercv) and other [projects](#acknowledgement) . It aims to: - Simplify the code (*Simple is better than complex*) @@ -43,16 +45,16 @@ VGG16 train on `trainval` and test on `test` split. | This[1] | TITAN Xp | 14-15 fps | 6 fps | | [pytorch-faster-rcnn](https://github.com/ruotianluo/pytorch-faster-rcnn) | TITAN Xp | 15-17fps | 6fps | -[1]: make sure you install cupy correctly and only one program run on the GPU. The training speed is sensitive to your gpu status. see [troubleshooting](troubleshooting) for more info. Morever it's slow in the start of the program. +[1]: make sure you install cupy correctly and only one program run on the GPU. The training speed is sensitive to your gpu status. see [troubleshooting](troubleshooting) for more info. Morever it's slow in the start of the program -- it need time to warm up. It could be faster by removing visualization, logging, averaging loss etc. ## 3. Install dependencies -requires python3 and PyTorch 0.3 +requires PyTorch >=0.4 -- install PyTorch >=0.3 with GPU (code are GPU-only), refer to [official website](http://pytorch.org) +- install PyTorch >=0.4 with GPU (code are GPU-only), refer to [official website](http://pytorch.org) -- install cupy, you can install via `pip install` but it's better to read the [docs](https://docs-cupy.chainer.org/en/latest/install.html#install-cupy-with-cudnn-and-nccl) and make sure the environ is correctly set +- install cupy, you can install via `pip install cupy-cuda80` or(cupy-cuda90,cupy-cuda91). - install other dependencies: `pip install -r requirements.txt ` @@ -60,13 +62,14 @@ requires python3 and PyTorch 0.3 ```Bash cd model/utils/nms/ - python3 build.py build_ext --inplace + python build.py build_ext --inplace + cd - ``` -- start vidom for visualization +- start visdom for visualization ```Bash -nohup python3 -m visdom.server & +nohup python -m visdom.server & ``` @@ -124,7 +127,7 @@ python misc/convert_caffe_pretrain.py This scripts would download pretrained model and converted it to the format compatible with torchvision. -Then you should specify where caffe-pretraind model `vgg16_caffe.pth` stored in `utils/config.py` by setting `caffe_pretrain_path` +Then you could specify where caffe-pretraind model `vgg16_caffe.pth` stored in `utils/config.py` by setting `caffe_pretrain_path`. The default path is ok. If you want to use pretrained model from torchvision, you may skip this step. @@ -139,7 +142,7 @@ mkdir checkpoints/ # folder for snapshots ``` ```bash -python3 train.py train --env='fasterrcnn-caffe' --plot-every=100 --caffe-pretrain +python train.py train --env='fasterrcnn-caffe' --plot-every=100 --caffe-pretrain ``` you may refer to `utils/config.py` for more argument. @@ -156,47 +159,25 @@ Some Key arguments: you may open browser, visit `http://:8097` and see the visualization of training procedure as below: -![visdom](http://7zh43r.com2.z0.glb.clouddn.com/del/visdom-fasterrcnn.png) - -If you're in China and encounter problem with visdom (i.e. timeout, blank screen), you may refer to [visdom issue](https://github.com/facebookresearch/visdom/issues/111#issuecomment-321743890), and see [troubleshooting](#troubleshooting) for solution. +![visdom](http://7zh43r.com2.z0.glb.clouddn.com/del/visdom-fasterrcnn.png) ## Troubleshooting -- visdom - - Some js files in visdom was blocked in China, see simple solution [here](https://github.com/chenyuntc/PyTorch-book/blob/master/README.md#visdom打不开及其解决方案) - - Also, `updata=append` doesn't work due to a bug brought in latest version, see [issue](https://github.com/facebookresearch/visdom/issues/233) and [fix](https://github.com/facebookresearch/visdom/pull/234/files) - - You don't need to build from source, modifying related files would be OK. - dataloader: `received 0 items of ancdata` see [discussion](https://github.com/pytorch/pytorch/issues/973#issuecomment-346405667), It's alreadly fixed in [train.py](https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/master/train.py#L17-L22). So I think you are free from this problem. -- cupy `numpy.core._internal.AxisError: axis 1 out of bounds [0, 1)` - - bug of cupy, see [issue](https://github.com/cupy/cupy/issues/793), fix via [pull request](https://github.com/cupy/cupy/pull/749) - - You don't need to build from source, modifying related files would be OK. - -- VGG: Slow in construction - - VGG16 is slow in construction(i.e. 9 seconds),it could be speed up by this [PR](https://github.com/pytorch/vision/pull/377) - - You don't need to build from source, modifying related files would be OK. - -- About the speed - - One strange thing is that, even the code doesn't use chainer, but if I remove `from chainer import cuda`, the speed drops a lot (train 6.5->6.1,test 14.5->10), because Chainer replaces the default allocator of CuPy by its memory pool implementation. But ever since V4.0, cupy use memory pool as default. However you need to build from souce if you are gona use the latest version of cupy (uninstall cupy -> git clone -> git checkout v4.0 -> setup.py install) @_@ +- Windows support + + I don't have windows machine with GPU to debug and test it. It's welcome if anyone could make a pull request and test it. - Another simple fix: add `from chainer import cuda` at the begining of `train.py`. in such case,you'll need to `pip install chainer` first. ## More - [ ] training on coco - [ ] resnet - [ ] Maybe；replace cupy with THTensor+cffi? - [ ] Maybe：Convert all numpy code to tensor? -- [ ] check python2-compatibility +- [x] python2-compatibility ## Acknowledgement This work builds on many excellent works, which include: @@ -211,7 +192,7 @@ Licensed under MIT, see the LICENSE for more detail. Contribution Welcome. -If you encounter any problem, feel free to open an issue. +If you encounter any problem, feel free to open an issue, but too busy lately. Correct me if anything is wrong or unclear. diff --git a/data/dataset.py b/data/dataset.py index b76f546e409896a451442af383b8d29837915334..aeaf12fcbdc0909acd00974b558a50572ca004f3 100644 --- a/data/dataset.py +++ b/data/dataset.py @@ -1,8 +1,10 @@ +from __future__ import absolute_import +from __future__ import division import torch as t -from .voc_dataset import VOCBboxDataset +from data.voc_dataset import VOCBboxDataset from skimage import transform as sktsf from torchvision import transforms as tvtsf -from . import util +from data import util import numpy as np from utils.config import opt diff --git a/data/util.py b/data/util.py index 01c3169c78ef356c9bbedf4f1dd3ff6d2704fc3b..3a3fbc1c80a9863ca1b60c541c6fced90598cb7a 100644 --- a/data/util.py +++ b/data/util.py @@ -167,7 +167,7 @@ def crop_bbox( if allow_outside_center: mask = np.ones(bbox.shape[0], dtype=bool) else: - center = (bbox[:, :2] + bbox[:, 2:]) / 2 + center = (bbox[:, :2] + bbox[:, 2:]) / 2.0 mask = np.logical_and(crop_bb[:2] <= center, center < crop_bb[2:]) \ .all(axis=1) diff --git a/misc/convert_caffe_pretrain.py b/misc/convert_caffe_pretrain.py index 805b9bdf3756c54a08c3d72bf6f9fb30fe047086..a2d3d9367e396e74261cbf59a065618b4f52b247 100644 --- a/misc/convert_caffe_pretrain.py +++ b/misc/convert_caffe_pretrain.py @@ -15,6 +15,8 @@ sd['classifier.3.bias'] = sd['classifier.4.bias'] del sd['classifier.4.weight'] del sd['classifier.4.bias'] - +import os # speicify the path to save -torch.save(sd, "vgg16_caffe.pth") \ No newline at end of file +if not os.path.exists('checkpoints'): + os.makedirs('checkpoints') +torch.save(sd, "checkpoints/vgg16_caffe.pth") \ No newline at end of file diff --git a/misc/train_fast.py b/misc/train_fast.py index 0201550143ad7e17fc635f51c4ae3f011187153f..33240ec5c4966f24ddac5cfd34ce4460616f05c3 100644 --- a/misc/train_fast.py +++ b/misc/train_fast.py @@ -7,7 +7,6 @@ from tqdm import tqdm from utils.config import opt from data.dataset import Dataset, TestDataset from model import FasterRCNNVGG16 -from torch.autograd import Variable from torch.utils import data as data_ from trainer import FasterRCNNTrainer from utils import array_tool as at @@ -68,7 +67,6 @@ def train(**kwargs): for ii, (img, bbox_, label_, scale, ori_img) in tqdm(enumerate(dataloader)): scale = at.scalar(scale) img, bbox, label = img.cuda().float(), bbox_.cuda(), label_.cuda() - img, bbox, label = Variable(img), Variable(bbox), Variable(label) losses = trainer.train_step(img, bbox, label, scale) if (ii + 1) % opt.plot_every == 0: diff --git a/model/__init__.py b/model/__init__.py index 70c38e32d720c7910b2764d1263887f42e6347c1..ce66f96fddd0e6539e9cc59c9e6a2f75ef3813fb 100644 --- a/model/__init__.py +++ b/model/__init__.py @@ -1 +1 @@ -from .faster_rcnn_vgg16 import FasterRCNNVGG16 +from model.faster_rcnn_vgg16 import FasterRCNNVGG16 diff --git a/model/faster_rcnn.py b/model/faster_rcnn.py index 7aefafb2d81f7f8ff28f344ed2d9f59db092a4fb..5dba41ef8cfb889255a90c31872a3b6ff200a172 100644 --- a/model/faster_rcnn.py +++ b/model/faster_rcnn.py @@ -1,3 +1,4 @@ +from __future__ import absolute_import from __future__ import division import torch as t import numpy as np @@ -12,6 +13,12 @@ from torch.nn import functional as F from utils.config import opt +def nograd(f): + def new_f(*args,**kwargs): + with t.no_grad(): + return f(*args,**kwargs) + return new_f + class FasterRCNN(nn.Module): """Base class for Faster R-CNN. @@ -176,6 +183,7 @@ class FasterRCNN(nn.Module): score = np.concatenate(score, axis=0).astype(np.float32) return bbox, label, score + @nograd def predict(self, imgs,sizes=None,visualize=False): """Detect objects from images. @@ -220,7 +228,7 @@ class FasterRCNN(nn.Module): labels = list() scores = list() for img, size in zip(prepared_imgs, sizes): - img = t.autograd.Variable(at.totensor(img).float()[None], volatile=True) + img = at.totensor(img[None]).float() scale = img.shape[3] / size[1] roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale) # We are assuming that batch size is 1. @@ -246,7 +254,7 @@ class FasterRCNN(nn.Module): cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0]) cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1]) - prob = at.tonumpy(F.softmax(at.tovariable(roi_score), dim=1)) + prob = at.tonumpy(F.softmax(at.totensor(roi_score), dim=1)) raw_cls_bbox = at.tonumpy(cls_bbox) raw_prob = at.tonumpy(prob) diff --git a/model/faster_rcnn_vgg16.py b/model/faster_rcnn_vgg16.py index de1eda2515db5efab5ab91081566587f2cbe363a..edf02e356549d5572055f6d39a484a0658c25b2d 100644 --- a/model/faster_rcnn_vgg16.py +++ b/model/faster_rcnn_vgg16.py @@ -1,3 +1,4 @@ +from __future__ import absolute_import import torch as t from torch import nn from torchvision.models import vgg16 @@ -136,7 +137,7 @@ class VGG16RoIHead(nn.Module): indices_and_rois = t.cat([roi_indices[:, None], rois], dim=1) # NOTE: important: yx->xy xy_indices_and_rois = indices_and_rois[:, [0, 2, 1, 4, 3]] - indices_and_rois = t.autograd.Variable(xy_indices_and_rois.contiguous()) + indices_and_rois = xy_indices_and_rois.contiguous() pool = self.roi(x, indices_and_rois) pool = pool.view(pool.size(0), -1) diff --git a/model/roi_module.py b/model/roi_module.py index 6b45f89f2ed5223e71e4ff8ced0435eff3885b63..9218af84d9c32ddbd203e27c5d4d1d933dba9899 100644 --- a/model/roi_module.py +++ b/model/roi_module.py @@ -27,10 +27,6 @@ def GET_BLOCKS(N, K=CUDA_NUM_THREADS): class RoI(Function): - """ - NOTE：only CUDA-compatible - """ - def __init__(self, outh, outw, spatial_scale): self.forward_fn = load_kernel('roi_forward', kernel_forward) self.backward_fn = load_kernel('roi_backward', kernel_backward) @@ -104,8 +100,9 @@ def test_roi_module(): # pytorch version module = RoIPooling2D(outh, outw, spatial_scale) - x = t.autograd.Variable(bottom_data, requires_grad=True) - rois = t.autograd.Variable(bottom_rois) + x = bottom_data.requires_grad_() + rois = bottom_rois.detach() + output = module(x, rois) output.sum().backward() diff --git a/model/utils/creator_tool.py b/model/utils/creator_tool.py index 91e19dcc373fb17c978f3ac768ebd7593fc812a3..d9a0d0e9d424df21e62a1f58a7663b88e294a104 100644 --- a/model/utils/creator_tool.py +++ b/model/utils/creator_tool.py @@ -38,7 +38,7 @@ class ProposalTargetCreator(object): self.pos_ratio = pos_ratio self.pos_iou_thresh = pos_iou_thresh self.neg_iou_thresh_hi = neg_iou_thresh_hi - self.neg_iou_thresh_lo = neg_iou_thresh_lo # NOTE: py-faster-rcnn默认的值是0.1 + self.neg_iou_thresh_lo = neg_iou_thresh_lo # NOTE:default 0.1 in py-faster-rcnn def __call__(self, roi, bbox, label, loc_normalize_mean=(0., 0., 0., 0.), diff --git a/model/utils/nms/non_maximum_suppression.py b/model/utils/nms/non_maximum_suppression.py index c488b52f08e5c32dab4571de3f1955401e1301e7..f1765946556ee2744a9fa13500a5fa9886a77c47 100644 --- a/model/utils/nms/non_maximum_suppression.py +++ b/model/utils/nms/non_maximum_suppression.py @@ -167,7 +167,7 @@ def _call_nms_kernel(bbox, thresh): threads = (threads_per_block, 1, 1) mask_dev = cp.zeros((n_bbox * col_blocks,), dtype=np.uint64) - bbox = cp.ascontiguousarray(bbox, dtype=np.float32) # NOTE: 变成连续的 + bbox = cp.ascontiguousarray(bbox, dtype=np.float32) kern = _load_kernel('nms_kernel', _nms_gpu_code) kern(blocks, threads, args=(cp.int32(n_bbox), cp.float32(thresh), bbox, mask_dev)) diff --git a/train.py b/train.py index 7c595568c77283bd9f2786777ce60735f2d3e879..83f3159f67d4ce771832114a1ca8001b6c252e4b 100644 --- a/train.py +++ b/train.py @@ -1,3 +1,4 @@ +from __future__ import absolute_import import os import ipdb @@ -7,7 +8,6 @@ from tqdm import tqdm from utils.config import opt from data.dataset import Dataset, TestDataset, inverse_normalize from model import FasterRCNNVGG16 -from torch.autograd import Variable from torch.utils import data as data_ from trainer import FasterRCNNTrainer from utils import array_tool as at @@ -28,7 +28,7 @@ def eval(dataloader, faster_rcnn, test_num=10000): pred_bboxes, pred_labels, pred_scores = list(), list(), list() gt_bboxes, gt_labels, gt_difficults = list(), list(), list() for ii, (imgs, sizes, gt_bboxes_, gt_labels_, gt_difficults_) in tqdm(enumerate(dataloader)): - sizes = [sizes[0][0], sizes[1][0]] + sizes = [sizes[0][0].item(), sizes[1][0].item()] pred_bboxes_, pred_labels_, pred_scores_ = faster_rcnn.predict(imgs, [sizes]) gt_bboxes += list(gt_bboxes_.numpy()) gt_labels += list(gt_labels_.numpy()) @@ -68,7 +68,6 @@ def train(**kwargs): if opt.load_path: trainer.load(opt.load_path) print('load pretrained model from %s' % opt.load_path) - trainer.vis.text(dataset.db.label_names, win='labels') best_map = 0 for epoch in range(opt.epoch): @@ -76,7 +75,6 @@ def train(**kwargs): for ii, (img, bbox_, label_, scale) in tqdm(enumerate(dataloader)): scale = at.scalar(scale) img, bbox, label = img.cuda().float(), bbox_.cuda(), label_.cuda() - img, bbox, label = Variable(img), Variable(bbox), Variable(label) trainer.train_step(img, bbox, label, scale) if (ii + 1) % opt.plot_every == 0: @@ -106,6 +104,12 @@ def train(**kwargs): # roi confusion matrix trainer.vis.img('roi_cm', at.totensor(trainer.roi_cm.conf, False).float()) eval_result = eval(test_dataloader, faster_rcnn, test_num=opt.test_num) + trainer.vis.plot('test_map', eval_result['map']) + lr_ = trainer.faster_rcnn.optimizer.param_groups[0]['lr'] + log_info = 'lr:{}, map:{},loss:{}'.format(str(lr_), + str(eval_result['map']), + str(trainer.get_meter_data())) + trainer.vis.log(log_info) if eval_result['map'] > best_map: best_map = eval_result['map'] @@ -114,12 +118,6 @@ def train(**kwargs): trainer.load(best_path) trainer.faster_rcnn.scale_lr(opt.lr_decay) - trainer.vis.plot('test_map', eval_result['map']) - lr_ = trainer.faster_rcnn.optimizer.param_groups[0]['lr'] - log_info = 'lr:{}, map:{},loss:{}'.format(str(lr_), - str(eval_result['map']), - str(trainer.get_meter_data())) - trainer.vis.log(log_info) if epoch == 13: break diff --git a/trainer.py b/trainer.py index 29c142bb493d08a87e3ea26405f43767dd7f65d8..913d59ff14d8444df9fd9dd74f637d3b2ac10ad4 100644 --- a/trainer.py +++ b/trainer.py @@ -1,3 +1,5 @@ +from __future__ import absolute_import +import os from collections import namedtuple import time from torch.nn import functional as F @@ -5,7 +7,6 @@ from model.utils.creator_tool import AnchorTargetCreator, ProposalTargetCreator from torch import nn import torch as t -from torch.autograd import Variable from utils import array_tool as at from utils.vis_tool import Visualizer @@ -126,8 +127,8 @@ class FasterRCNNTrainer(nn.Module): at.tonumpy(bbox), anchor, img_size) - gt_rpn_label = at.tovariable(gt_rpn_label).long() - gt_rpn_loc = at.tovariable(gt_rpn_loc) + gt_rpn_label = at.totensor(gt_rpn_label).long() + gt_rpn_loc = at.totensor(gt_rpn_loc) rpn_loc_loss = _fast_rcnn_loc_loss( rpn_loc, gt_rpn_loc, @@ -145,8 +146,8 @@ class FasterRCNNTrainer(nn.Module): roi_cls_loc = roi_cls_loc.view(n_sample, -1, 4) roi_loc = roi_cls_loc[t.arange(0, n_sample).long().cuda(), \ at.totensor(gt_roi_label).long()] - gt_roi_label = at.tovariable(gt_roi_label).long() - gt_roi_loc = at.tovariable(gt_roi_loc) + gt_roi_label = at.totensor(gt_roi_label).long() + gt_roi_loc = at.totensor(gt_roi_loc) roi_loc_loss = _fast_rcnn_loc_loss( roi_loc.contiguous(), @@ -199,6 +200,10 @@ class FasterRCNNTrainer(nn.Module): for k_, v_ in kwargs.items(): save_path += '_%s' % v_ + save_dir = os.path.dirname(save_path) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + t.save(save_dict, save_path) self.vis.save([self.vis.env]) return save_path @@ -236,7 +241,6 @@ def _smooth_l1_loss(x, t, in_weight, sigma): diff = in_weight * (x - t) abs_diff = diff.abs() flag = (abs_diff.data < (1. / sigma2)).float() - flag = Variable(flag) y = (flag * (sigma2 / 2.) * (diff ** 2) + (1 - flag) * (abs_diff - 0.5 / sigma2)) return y.sum() @@ -248,7 +252,7 @@ def _fast_rcnn_loc_loss(pred_loc, gt_loc, gt_label, sigma): # NOTE: unlike origin implementation, # we don't need inside_weight and outside_weight, they can calculate by gt_label in_weight[(gt_label > 0).view(-1, 1).expand_as(in_weight).cuda()] = 1 - loc_loss = _smooth_l1_loss(pred_loc, gt_loc, Variable(in_weight), sigma) + loc_loss = _smooth_l1_loss(pred_loc, gt_loc, in_weight.detach(), sigma) # Normalize by total number of negtive and positive rois. - loc_loss /= (gt_label >= 0).sum() # ignore gt_label==-1 for rpn_loss + loc_loss /= ((gt_label >= 0).sum().float()) # ignore gt_label==-1 for rpn_loss return loc_loss diff --git a/utils/array_tool.py b/utils/array_tool.py index 6c75fe0d9b2252858f1b1ede813e86db2080b3a8..12798a20cdba363cb16002fbc4c52dcc4a00e7a1 100644 --- a/utils/array_tool.py +++ b/utils/array_tool.py @@ -8,39 +8,22 @@ import numpy as np def tonumpy(data): if isinstance(data, np.ndarray): return data - if isinstance(data, t._TensorBase): - return data.cpu().numpy() - if isinstance(data, t.autograd.Variable): - return tonumpy(data.data) + if isinstance(data, t.Tensor): + return data.detach().cpu().numpy() def totensor(data, cuda=True): if isinstance(data, np.ndarray): tensor = t.from_numpy(data) - if isinstance(data, t._TensorBase): - tensor = data - if isinstance(data, t.autograd.Variable): - tensor = data.data + if isinstance(data, t.Tensor): + tensor = data.detach() if cuda: tensor = tensor.cuda() return tensor -def tovariable(data): - if isinstance(data, np.ndarray): - return tovariable(totensor(data)) - if isinstance(data, t._TensorBase): - return t.autograd.Variable(data) - if isinstance(data, t.autograd.Variable): - return data - else: - raise ValueError("UnKnow data type: %s, input should be {np.ndarray,Tensor,Variable}" %type(data)) - - def scalar(data): if isinstance(data, np.ndarray): return data.reshape(1)[0] - if isinstance(data, t._TensorBase): - return data.view(1)[0] - if isinstance(data, t.autograd.Variable): - return data.data.view(1)[0] + if isinstance(data, t.Tensor): + return data.view(1)[0].item() \ No newline at end of file diff --git a/utils/config.py b/utils/config.py index 89d7bf1d300c5a8dca2ac611d9b7dd05284a62c6..cb7609f47dd23da020123e4021b4f2d676cdabca 100644 --- a/utils/config.py +++ b/utils/config.py @@ -48,7 +48,7 @@ class Config: load_path = None caffe_pretrain = False # use caffe pretrained model instead of torchvision - caffe_pretrain_path = 'checkpoints/vgg16-caffe.pth' + caffe_pretrain_path = 'checkpoints/vgg16_caffe.pth' def _parse(self, kwargs): state_dict = self._state_dict() diff --git a/utils/vis_tool.py b/utils/vis_tool.py index 6878474558ff8c3683d27f068f60a80209363d68..9cf651a020c9a1a7cebd601f3c33a552267eaef7 100644 --- a/utils/vis_tool.py +++ b/utils/vis_tool.py @@ -133,7 +133,7 @@ def fig2data(fig): brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it - @param fig： a matplotlib figure + @param fig: a matplotlib figure @return a numpy 3D array of RGBA values """ # draw the renderer @@ -178,7 +178,7 @@ class Visualizer(object): self.vis = visdom.Visdom(env=env, **kwargs) self._vis_kw = kwargs - # e.g.（’loss',23） the 23th value of loss + # e.g.('loss',23) the 23th value of loss self.index = {} self.log_text = '' @@ -221,7 +221,7 @@ class Visualizer(object): self.img('input_imgs',t.Tensor(3,64,64)) self.img('input_imgs',t.Tensor(100,1,64,64)) self.img('input_imgs',t.Tensor(100,3,64,64),nrows=10) - ！！！don‘t ~~self.img('input_imgs',t.Tensor(100,64,64),nrows=10)~~！！！ + !!don't ~~self.img('input_imgs',t.Tensor(100,64,64),nrows=10)~~!! """ self.vis.images(t.Tensor(img_).cpu().numpy(), win=name,