摘要：使用影象級別標籤的弱監督例項分割，而不是昂貴的畫素級掩碼，仍然未被探索。在本文中，我們通過利用類峰值響應來啟用分類網路（例如掩碼提取）來解決這一具有挑戰性的問題。僅使用影象標籤監控，完全卷積方式的CNN分類器可以生成類響應圖，其指定每個影象位置處的分類置信度。我們觀察到類響應圖中的區域性最大值（即峰值）通常對應於駐留在每個例項內的強視覺提示。受此啟發，我們首先設計一個過程，以激發從類響應圖中出現的峰值。然後，出現的峰值被反向傳播並有效地對映到每個物件例項的高資訊區域，例如例項邊界。我們將從類峰值響應生成的上述對映稱為峰值響應對映（PRM）。 PRM提供了精細的詳細例項級表示，即使使用一些現成的方法也可以提取例項掩碼。據我們所知，我們首次報告了具有挑戰性的影象級監督例項分割任務的結果。大量實驗表明，我們的方法還可以提高弱監督的逐點定位以及語義分割效能，並在流行的基準測試中報告最先進的結果，包括PASCAL VOC 2012和MS COCO。

復現的時候就按照官網上說的那樣：

win10或者≥ubantu14.04

GPU版本：

NVIDIA GPU + CUDA CuDNN

或者CPU可以

還有就是

我是在win10上實現的，沒有GPU加速。

安裝：

1.Install Nest

pip install git+https://github.com/ZhouYanzhao/Nest.git

2.Install PRM via Nest's CLI tool:

nest module install github@ZhouYanzhao/PRM:pytorch prm

測試是否成功：

nest module list --filter prm
 

輸出：

# 3 Nest modules found.
# [0] prm.fc_resnet50 (1.0.0)
# [1] prm.peak_response_mapping (1.0.0)
# [2] prm.prm_visualize (1.0.0)

接下來就是運行了。

1.Install Nest's build-in Pytorch modules

nest module install github@ZhouYanzhao/Nest:pytorch pytorch

2.Download the PASCAL-VOC2012 dataset:在瀏覽器輸入就行解壓到demo裡

http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
 

3.Run the demo experiment via demo/main.ipynb

用jupyter notebook開啟

得到像這樣的：

這裡就是訓練用的，訓練生成的是.pt權重檔案，這是pytorch框架的權重，還有.pkl型別的，其實都一樣。

這裡的config.yml是設定引數用的。

_name: network_trainer
data_loaders:
  _name: fetch_data
  dataset: 
    _name: pascal_voc_classification
    data_dir: ./datasets/VOCdevkit
    year: 2012
  batch_size: 16
  num_workers: 4
  transform:
    _name: image_transform
    image_size: [448, 448]
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
  train_augmentation:
    horizontal_flip: 0.5
  train_splits:
    - trainval
model:
  _name: peak_response_mapping
  backbone:
    _name: fc_resnet50
  win_size: 3
  sub_pixel_locating_factor: 8
  enable_peak_stimulation: true
criterion:
  _name: multilabel_soft_margin_loss
  difficult_samples: yes
optimizer:
  _name: sgd_optimizer
  lr: 0.01
  momentum: 0.9
  weight_decay: 1.0e-4
parameter:
  _name: finetune
  base_lr: 0.01
  groups:
    'features': 0.01
meters:
  loss:
    _name: loss_meter
max_epoch: 20
device: cuda
hooks:
  on_start:
    -
      _name: print_state
      formats:
        - '@CONFIG'
        - 'Model: {model}'
      join_str: '\n'
  on_end_epoch: 
    - 
      _name: print_state
      formats:
        - 'epoch: {epoch_idx}'
        - 'classification_loss: {metrics[trainval_loss]:.4f}'
    -
      _name: checkpoint
      save_dir: './snapshots'
      save_latest: yes

因為用的是CPU，所以batch_size設定成的是4，設定成16，發現卡的不行。然後就開始訓練了。

訓練了三天多，要不是因為無故斷電了，還能訓練，最後的loss下降到0.03

[2018-07-29 15:41:05,699] epoch: 0 | classification_loss: 0.1172
[2018-07-29 15:41:05,918] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-29 20:29:30,672] epoch: 1 | classification_loss: 0.0758
[2018-07-29 20:29:30,812] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-30 01:17:34,042] epoch: 2 | classification_loss: 0.0645
[2018-07-30 01:17:34,214] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-30 06:04:50,313] epoch: 3 | classification_loss: 0.0566
[2018-07-30 06:04:50,485] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-30 11:03:36,152] epoch: 4 | classification_loss: 0.0498
[2018-07-30 11:03:36,293] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-30 16:09:31,468] epoch: 5 | classification_loss: 0.0444
[2018-07-30 16:09:31,683] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-30 21:07:03,611] epoch: 6 | classification_loss: 0.0409
[2018-07-30 21:07:03,751] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-31 01:55:32,188] epoch: 7 | classification_loss: 0.0414
[2018-07-31 01:55:32,422] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-31 06:43:39,614] epoch: 8 | classification_loss: 0.0388
[2018-07-31 06:43:39,761] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-31 11:32:24,822] epoch: 9 | classification_loss: 0.0351
[2018-07-31 11:32:25,025] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-31 16:20:31,651] epoch: 10 | classification_loss: 0.0351
[2018-07-31 16:20:31,858] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt
[2018-07-31 21:08:55,310] epoch: 11 | classification_loss: 0.0330
[2018-07-31 21:08:55,508] checkpoint created at C:\Users\Mars\PRM-pytorch\demo\snapshots\model_latest.pt

然後是匯入模型：

這回問題很大了！因為儲存的是模型的引數，需要再建立一個網路，再把引數賦值進去，但匯入的時候發現，有問題，新建的模型需要的是‘module.0.features.0.weight’類似命名的，而生成的權重檔案都是'0.features.0.weight'這樣的命名的，所以我得到的權重檔案缺少了‘module.’這幾個字元，先想著用notepad++直接改，但是讀取的時候根本識別不了。

KeyError: ‘unexpected key “module.encoder.embedding.weight”搜到了一個類似的問題，這個問題說的正是我遇到的反例，生成的模型沒有‘module.’，而生成的模型裡多了‘module.’這幾個字元，所以大家把權重檔案中的前面那幾個字元去掉了。

# 匯入已經儲存了的權重檔案
state_dict = torch.load('myfile.pth.tar')
# 建立一個沒有`module.`的權重
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
    name = k[7:] # remove `module.`
    new_state_dict[name] = v
# load
model.load_state_dict(new_state_dict)

而我的情況恰好相反，所以我需要加上這幾個字元：

先把字串變成list，然後在前面新增`module.`

n_state = state['model']
new_state_dict = OrderedDict()
for k, v in n_state.items():
    temp_list = list(k)
    temp_list.insert(0,'module.')
    name = ''.join(temp_list)
    new_state_dict[name] = v
# load params
# print(new_state_dict)
model.load_state_dict(new_state_dict)

問題解決！

然後就都是一些小問題了。這是全部的程式碼：

%matplotlib inline
from matplotlib import pyplot as plt
import torch
import torch.nn as nn
from nest import modules
import numpy as np
import os
import PIL.Image
import json
import scipy.misc
import torch.nn.functional as F
import PIL.Image
from collections import OrderedDict
import warnings
warnings.filterwarnings("ignore")
class_names = modules.pascal_voc_object_categories()
image_size = 448
# image pre-processor
transformer = modules.image_transform(
    image_size = [image_size, image_size],
    augmentation = dict(),
    mean = [0.485, 0.456, 0.406],
    std = [0.229, 0.224, 0.225])
backbone = modules.fc_resnet50(num_classes=20, pretrained=False)
model = modules.peak_response_mapping(backbone)
model = nn.DataParallel(model)
state = torch.load('11.pt')
# model.load_state_dict(state['model'])
# model = model.module.cuda()
# a = state['model']
# b = np.array(a)
# print(state.items())
n_state = state['model']
new_state_dict = OrderedDict()
for k, v in n_state.items():
    temp_list = list(k)
    temp_list.insert(0,'module.')
    name = ''.join(temp_list)
    new_state_dict[name] = v
# load params
# print(new_state_dict)
model.load_state_dict(new_state_dict)
model = model.module.cpu()
idx = 4
raw_img = PIL.Image.open('F://python//PRM-pytorch//demo//data//sample%d.jpg' % idx).convert('RGB')
input_var = transformer(raw_img).unsqueeze(0).cpu().requires_grad_()
with open('F://python//PRM-pytorch//demo//data//sample%d.json' % idx, 'r') as f:
    proposals = list(map(modules.rle_decode, json.load(f)))
# plot raw image
# plt.figure(figsize=(5,5))
# plt.imshow(raw_img)

model = model.eval()
print('Object categories in the image:')
confidence = model(input_var)
for idx in range(len(class_names)):
    if confidence.data[0, idx] > 0:
        print('    [class_idx: %d] %s (%.2f)' % (idx, class_names[idx], confidence[0, idx]))
        
        
        
model = model.inference()
visual_cues = model(input_var)
if visual_cues is None:
    print('No class peak response detected')
else:
    confidence, class_response_maps, class_peak_responses, peak_response_maps = visual_cues
    _, class_idx = torch.max(confidence, dim=1)
    class_idx = class_idx.item()
    num_plots = 2 + len(peak_response_maps)
    f, axarr = plt.subplots(1, num_plots, figsize=(num_plots * 4, 4))
    axarr[0].imshow(scipy.misc.imresize(raw_img, (image_size, image_size), interp='bicubic'))
    axarr[0].set_title('Image')
    axarr[0].axis('off')
    axarr[1].imshow(class_response_maps[0, class_idx].cpu(), interpolation='bicubic')
    axarr[1].set_title('Class Response Map ("%s")' % class_names[class_idx])
    axarr[1].axis('off')
    for idx, (prm, peak) in enumerate(sorted(zip(peak_response_maps, class_peak_responses), key=lambda v: v[-1][-1])):
        axarr[idx + 2].imshow(prm.cpu(), cmap=plt.cm.jet)
        axarr[idx + 2].set_title('Peak Response Map ("%s")' % (class_names[peak[1].item()]))
        axarr[idx + 2].axis('off')        
# predict instance masks via proposal retrieval
instance_list = model(input_var, retrieval_cfg=dict(proposals=proposals, param=(0.95, 1e-5, 0.8)))

# visualization
if instance_list is None:
    print('No object detected')
else:
    # peak response maps are merged if they select similar proposals
    vis = modules.prm_visualize(instance_list, class_names=class_names)
    f, axarr = plt.subplots(1, 3, figsize=(12, 5))
    axarr[0].imshow(scipy.misc.imresize(raw_img, (image_size, image_size), interp='bicubic'))
    axarr[0].set_title('Image')
    axarr[0].axis('off')
    axarr[1].imshow(vis[0])
    axarr[1].set_title('Prediction')
    axarr[1].axis('off')
    axarr[2].imshow(vis[1])
    axarr[2].set_title('Peak Response Maps')
    axarr[2].axis('off')
    plt.show()

結果：

其他圖片的測試：

其實我在這裡就有了問題，就是作者在進行畫mask的時候用到了提前計算好的proposals，儲存成了.json檔案，

.json檔案我遇到過，就是用labelme進行標註的時候生成的標籤，但是我比較這兩個檔案不一樣。

標籤檔案是這樣的：

{
  "flags": {},
  "shapes": [
    {
      "label": "car",
      "line_color": null,
      "fill_color": null,
      "points": [
        [
          64,
          77
        ],
        [
          50,
          91
        ],
        [
          41,
          102
        ],
        [
          37,
          115
        ],
        [
          36,
          130
        ],
        [
          36,
          138
        ],
        [
          37,
          153
        ],
        [
          40,
          163
        ],
        [
          47,
          167
        ],
        [
          55,
          167
        ],
        [
          60,
          165
        ],
        [
          63,
          157
        ],
        [
          64,
          154
        ],
        [
          110,
          160
        ],
        [
          113,
          167
        ],
        [
          120,
          174
        ],
        [
          127,
          178
        ],
        [
          146,
          178
        ],
        [
          155,
          174
        ],
        [
          159,
          167
        ],
        [
          160,
          165
        ],
        [
          218,
          164
        ],
        [
          220,
          170
        ],
        [
          232,
          175
        ],
        [
          247,
          174
        ],
        [
          255,
          169
        ],
        [
          258,
          163
        ],
        [
          258,
          161
        ],
        [
          267,
          160
        ],
        [
          268,
          155
        ],
        [
          267,
          154
        ],
        [
          269,
          141
        ],
        [
          268,
          131
        ],
        [
          264,
          121
        ],
        [
          260,
          113
        ],
        [
          252,
          109
        ],
        [
          238,
          103
        ],
        [
          224,
          99
        ],
        [
          217,
          98
        ],
        [
          219,
          96
        ],
        [
          213,
          93
        ],
        [
          208,
          91
        ],
        [
          194,
          80
        ],
        [
          185,
          74
        ],
        [
          175,
          71
        ],
        [
          162,
          69
        ],
        [
          140,
          68
        ],
        [
          122,
          67
        ],
        [
          106,
          67
        ],
        [
          93,
          67
        ],
        [
          80,
          70
        ]
      ]
    }
  ],
  "lineColor": [
    0,
    255,
    0,
    128
  ],
  "fillColor": [
    255,
    0,
    0,
    128
  ],
  "imagePath": "..\\..\\..\\get_img\\China_Vehicle_Raw\\0001_6.jpg",
  "imageData": 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