From ab9e3392855651b3ff622fb0efe034c840f58c44 Mon Sep 17 00:00:00 2001
From: z1069614715 <1069614715@qq.com>
Date: Thu, 9 Feb 2023 19:24:25 +0800
Subject: [PATCH] v1.5

---
 README.md                                     |  15 ++++++---
 model/__pycache__/__init__.cpython-38.pyc     | Bin 482 -> 479 bytes
 model/__pycache__/convnext.cpython-38.pyc     | Bin 9323 -> 9320 bytes
 model/__pycache__/cspnet.cpython-38.pyc       | Bin 27163 -> 27160 bytes
 model/__pycache__/densenet.cpython-38.pyc     | Bin 12416 -> 12413 bytes
 model/__pycache__/dpn.cpython-38.pyc          | Bin 9911 -> 9908 bytes
 .../__pycache__/efficientnetv2.cpython-38.pyc | Bin 31851 -> 31852 bytes
 model/__pycache__/ghostnet.cpython-38.pyc     | Bin 7899 -> 7896 bytes
 model/__pycache__/mnasnet.cpython-38.pyc      | Bin 10663 -> 10660 bytes
 model/__pycache__/mobilenetv2.cpython-38.pyc  | Bin 7648 -> 7645 bytes
 model/__pycache__/mobilenetv3.cpython-38.pyc  | Bin 10891 -> 10888 bytes
 model/__pycache__/repghost.cpython-38.pyc     | Bin 14224 -> 14221 bytes
 model/__pycache__/repvgg.cpython-38.pyc       | Bin 12997 -> 12994 bytes
 model/__pycache__/resnest.cpython-38.pyc      | Bin 13947 -> 13944 bytes
 model/__pycache__/resnet.cpython-38.pyc       | Bin 13873 -> 13870 bytes
 model/__pycache__/sequencer.cpython-38.pyc    | Bin 15308 -> 15305 bytes
 model/__pycache__/shufflenetv2.cpython-38.pyc | Bin 8967 -> 8964 bytes
 model/__pycache__/vgg.cpython-38.pyc          | Bin 9327 -> 9324 bytes
 model/__pycache__/vovnet.cpython-38.pyc       | Bin 7733 -> 7730 bytes
 model/efficientnetv2.py                       |   2 +-
 predict.py                                    |  16 ++++++++--
 utils/__pycache__/__init__.cpython-38.pyc     | Bin 140 -> 137 bytes
 utils/__pycache__/utils.cpython-38.pyc        | Bin 34789 -> 34887 bytes
 utils/__pycache__/utils_aug.cpython-38.pyc    | Bin 6324 -> 6321 bytes
 utils/__pycache__/utils_model.cpython-38.pyc  | Bin 3077 -> 3090 bytes
 utils/utils.py                                |  15 +++++----
 utils/utils_model.py                          |  30 +++++++++---------
 27 files changed, 48 insertions(+), 30 deletions(-)

diff --git a/README.md b/README.md
index d070aad..8280d8a 100644
--- a/README.md
+++ b/README.md
@@ -52,7 +52,7 @@ image classifier implement in pytoch.
 - **简单的安装过程**  
   1. 安装好pytorch, torchvision(pytorch==1.12.0+ torchvision==0.13.0+)  
     可以在[pytorch](https://pytorch.org/get-started/previous-versions/)官网找到对应的命令进行安装.
-  2. pip install -r requirements.txt
+  2. pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
 
 - **人性化的设定**
   1. 大部分可视化数据(混淆矩阵,tsne,每个类别的指标)都会以csv或者log的格式保存到本地,方便后期美工图像.
@@ -245,7 +245,7 @@ image classifier implement in pytoch.
     是否采用测试阶段的数据增强.  
   - **cam_visual**  
     default: False  
-    是否进行热力图可视化.  
+    是否进行热力图可视化.(目前只支持在cpu上进行可视化热力图)  
   - **cam_type**  
     type: string, default: GradCAMPlusPlus, choices: ['GradCAM', 'HiResCAM', 'ScoreCAM', 'GradCAMPlusPlus', 'AblationCAM', 'XGradCAM', 'EigenCAM', 'FullGrad']  
     热力图可视化的类型.  
@@ -533,7 +533,7 @@ image classifier implement in pytoch.
 
     1. 在predict.py中的--cam_type参数,可以选择各种各样的热力图计算方法,如果遇到这个效果不好可以进行更换,当然效果不好除了跟热力图的计算方法有关系之外还有是你选择的层.
     2. 在第一点中说到的层,热力图可视化需要选择层进行计算,这个是在predict.py中的cam_visual方法定义中进行定义.
-      例如这样 cam_model = cam_visual(model, test_transform, DEVICE, model.cam_layer(), opt) 这个model.cam_layer()就是选择的层,默认就是卷积特征的最后一个block,这个可以在model文件夹中的每个模型文件中的模型进行修改.
+      例如这样 cam_model = cam_visual(model, test_transform, DEVICE, model.cam_layer(), opt) 这个model.cam_layer()就是选择的层,默认就是卷积特征的最后一个layer,这个可以在model文件夹中的每个模型文件中的模型进行修改.
   
   13. 关于类别平衡的问题.
 
@@ -679,4 +679,11 @@ image classifier implement in pytoch.
 
 1. predict.py支持检测灰度图,其读取后会检测是否为RGB通道,不是的话会进行转换.
 2. 更新readme.md.
-3. 修复一些bug.
\ No newline at end of file
+3. 修复一些bug.
+
+### pytorch-classifier v1.5 更新日志
+
+1. 更新readme.md
+2. 修改predict.py中的读取模型方式.
+3. 修复predict.py中批量预测保存图片时候的内存溢出问题.
+4. 修复predict.py中热力图可视化的报错问题,但是目前只支持cpu状态下可视化热力图.
\ No newline at end of file
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delta 34
ocmX?Eex{r|l$V!_0SNA_ZQIDL#KK{upPN^rU!1bph-JDt0Jy^nZU6uP

diff --git a/model/__pycache__/shufflenetv2.cpython-38.pyc b/model/__pycache__/shufflenetv2.cpython-38.pyc
index 77d7ce96bf0b3c17cf1009a76d8fbed030ee9243..74cfcaeaef4b39889422912885b72dd3b23f996d 100644
GIT binary patch
delta 31
lcmZp7YjNWa<>lpK00OzxCmXrrSy+wqQxZ!y7qLv00|0h!2ps?b

delta 34
ocmZp1Yj@)g<>lpK00M{d&W+shEF7l#xp^h}#VMQfS*FSX0FJx~6#xJL

diff --git a/model/__pycache__/vgg.cpython-38.pyc b/model/__pycache__/vgg.cpython-38.pyc
index be54e01fc4f3e110c1ffab74129a67120ea53808..6e3fee56d4056ae4e75aa310ca9562000d85ad80 100644
GIT binary patch
delta 31
lcmaFw@y3HYl$V!_0SM$$pKRpb#m=g!pORRz`7HY)832_z39kSE

delta 34
ocmaFk@!o?wl$V!_0SGcDb#CO|#m=FlpPN^rU!1b}6#F6>0JeGx>i_@%

diff --git a/model/__pycache__/vovnet.cpython-38.pyc b/model/__pycache__/vovnet.cpython-38.pyc
index 61e939ea8194a78c63390290e3f8ff7ddb9ad5ab..06f25de71edc2cc285ab587a4b54d0df8d45c4f2 100644
GIT binary patch
delta 31
lcmdmLv&n`#l$V!_0SM$$pKRn#W?|LQPf0A<T*k6r5&(Zn2#f##

delta 34
ocmdmFv(<(>l$V!_0SM;J?A*wm%)+6spPN^rU!1bJm}S2t0HVhV1ONa4

diff --git a/model/efficientnetv2.py b/model/efficientnetv2.py
index acdbc76..addf4b5 100644
--- a/model/efficientnetv2.py
+++ b/model/efficientnetv2.py
@@ -383,7 +383,7 @@ def forward_features(self, x, need_fea=False):
             return x
 
     def cam_layer(self):
-        return self.features[-1]
+        return self.features[-5:]
     
     def switch_to_deploy(self):
         new_block = []
diff --git a/predict.py b/predict.py
index 98ef390..f7eecac 100644
--- a/predict.py
+++ b/predict.py
@@ -6,8 +6,10 @@
 os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 import matplotlib.pyplot as plt
 import numpy as np
+from copy import deepcopy
 from utils import utils_aug
 from utils.utils import predict_single_image, cam_visual, dict_to_PrettyTable, select_device, model_fuse
+from utils.utils_model import select_model
 
 def set_seed(seed):
     random.seed(seed)
@@ -27,6 +29,7 @@ def parse_opt():
     parser.add_argument('--device', type=str, default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
 
     opt = parser.parse_known_args()[0]
+    
     if not os.path.exists(os.path.join(opt.save_path, 'best.pt')):
         raise Exception('best.pt not found. please check your --save_path folder')
     ckpt = torch.load(os.path.join(opt.save_path, 'best.pt'))
@@ -34,8 +37,13 @@ def parse_opt():
     if opt.half and DEVICE.type == 'cpu':
         raise Exception('half inference only supported GPU.')
     if opt.half and opt.cam_visual:
+        raise Exception('cam visual only supported cpu. please set device=cpu.')
+    if (opt.device != 'cpu') and opt.cam_visual:
         raise Exception('cam visual only supported FP32.')
-    model = ckpt['model'].float()
+    with open(opt.label_path) as f:
+        CLASS_NUM = len(f.readlines())
+    model = select_model(ckpt['model'].name, CLASS_NUM)
+    model.load_state_dict(ckpt['model'].float().state_dict(), strict=False)
     model_fuse(model)
     model = (model.half() if opt.half else model)
     model.to(DEVICE)
@@ -59,7 +67,7 @@ def parse_opt():
     opt, DEVICE, model, test_transform, label = parse_opt()
 
     if opt.cam_visual:
-        cam_model = cam_visual(model, test_transform, DEVICE, model.cam_layer(), opt)
+        cam_model = cam_visual(model, test_transform, DEVICE, opt)
 
     if os.path.isdir(opt.source):
         save_path = os.path.join(opt.save_path, 'predict', datetime.datetime.strftime(datetime.datetime.now(),'%Y_%m_%d_%H_%M_%S'))
@@ -71,7 +79,7 @@ def parse_opt():
             
             plt.figure(figsize=(6, 6))
             if opt.cam_visual:
-                cam_output = cam_model(os.path.join(opt.source, file), pred)
+                cam_output = cam_model(os.path.join(opt.source, file))
                 plt.imshow(cam_output)
             else:
                 plt.imshow(plt.imread(os.path.join(opt.source, file)))
@@ -79,6 +87,8 @@ def parse_opt():
             plt.title('predict label:{}\npredict probability:{:.4f}'.format(label[pred], float(pred_result[pred])))
             plt.tight_layout()
             plt.savefig(os.path.join(save_path, file))
+            plt.clf()
+            plt.close()
         
         with open(os.path.join(save_path, 'result.csv'), 'w+') as f:
             f.write('img_path,pred_class,pred_class_probability\n')
diff --git a/utils/__pycache__/__init__.cpython-38.pyc b/utils/__pycache__/__init__.cpython-38.pyc
index 935f6cd527c41f85cf2294993f1a7bdfdef68aeb..b4c7fcc4ef8acc74a5c2dbc82b6513bfce71e71a 100644
GIT binary patch
delta 28
icmeBS>}2E)<>lpK00OzxClk4CSoQQ%5=$of+X4VlCI*uL

delta 31
lcmeBV>|x{%<>lpK0D`$|J0^15a2V?6=9TCdr%d#*1pseZ2ps?b

diff --git a/utils/__pycache__/utils.cpython-38.pyc b/utils/__pycache__/utils.cpython-38.pyc
index 67a88268fb00739ffcab6bf383767d5336a1970f..a73cf7d2ba722557f9ed79ca785d47ae142ab40f 100644
GIT binary patch
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diff --git a/utils/__pycache__/utils_aug.cpython-38.pyc b/utils/__pycache__/utils_aug.cpython-38.pyc
index 15166b0fa299fd5fb53b136c406312b9f035f97d..f2238e33f59d2664fd97199e6f804190c04993f9 100644
GIT binary patch
delta 760
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delta 807
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diff --git a/utils/__pycache__/utils_model.cpython-38.pyc b/utils/__pycache__/utils_model.cpython-38.pyc
index 59c04d0f38b6fa7818096f567da50b5c858b33a7..f27a018ffa038d7f727928a8570e57b273a2a790 100644
GIT binary patch
delta 108
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H!(9mg=58H_

delta 95
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diff --git a/utils/utils.py b/utils/utils.py
index 75688dc..63b2654 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -16,6 +16,7 @@
 from sklearn.manifold import TSNE
 from pytorch_grad_cam import GradCAM, HiResCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, FullGrad
 from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients
 from pytorch_grad_cam.utils.image import show_cam_on_image
 from collections import OrderedDict
 from .utils_aug import rand_bbox
@@ -655,22 +656,22 @@ def predict_single_image(path, model, test_transform, DEVICE, half=False):
     return int(pred_result.argmax()), pred_result
 
 class cam_visual:
-    def __init__(self, model, test_transform, DEVICE, target_layers, opt):
+    def __init__(self, model, test_transform, DEVICE, opt):
         self.test_transform = test_transform
         self.DEVICE = DEVICE
         self.opt = opt
 
-        self.cam_model = eval(opt.cam_type)(model=deepcopy(model), target_layers=[target_layers], use_cuda=torch.cuda.is_available())
+        self.cam_model = eval(opt.cam_type)(model=model, target_layers=model.cam_layer(), use_cuda=(DEVICE.type != 'cpu'))
+        self.model = model
     
-    def __call__(self, path, label):
+    def __call__(self, path):
         pil_img = Image.open(path)
         tensor_img = self.test_transform(pil_img).unsqueeze(0).to(self.DEVICE)
-        targets = [ClassifierOutputTarget(label)]
-
+        
         if len(tensor_img.shape) == 5:
-            grayscale_cam_list = [self.cam_model(input_tensor=tensor_img[:, i], targets=targets) for i in range(tensor_img.size(1))]
+            grayscale_cam_list = [self.cam_model(input_tensor=tensor_img[:, i], targets=None) for i in range(tensor_img.size(1))]
         else:
-            grayscale_cam_list = [self.cam_model(input_tensor=tensor_img, targets=targets)]
+            grayscale_cam_list = [self.cam_model(input_tensor=tensor_img, targets=None)]
         
         grayscale_cam = np.concatenate(grayscale_cam_list, 0).mean(0)
         grayscale_cam = cv2.resize(grayscale_cam, pil_img.size)
diff --git a/utils/utils_model.py b/utils/utils_model.py
index d9fc7f6..4cfd4ec 100644
--- a/utils/utils_model.py
+++ b/utils/utils_model.py
@@ -3,7 +3,7 @@
 import model as models
 from thop import clever_format, profile
 
-def select_model(name, num_classes, input_shape, channels, pretrained=False):
+def select_model(name, num_classes, input_shape=None, channels=None, pretrained=False):
     if 'shufflenet_v2' in name:
         model = eval('models.{}(pretrained={})'.format(name, pretrained))
         model.fc = nn.Sequential(
@@ -118,22 +118,22 @@ def select_model(name, num_classes, input_shape, channels, pretrained=False):
     else:
         raise 'Unsupported Model Name.'
 
+    if input_shape and channels:
     # 计算参数量和flops
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    dummy_input = torch.randn(1, channels, input_shape[0], input_shape[1]).to(device)
-    flops, params = profile(model.to(device), (dummy_input,), verbose=False)
-    #--------------------------------------------------------#
-    #   flops * 2是因为profile没有将卷积作为两个operations
-    #   有些论文将卷积算乘法、加法两个operations。此时乘2
-    #   有些论文只考虑乘法的运算次数，忽略加法。此时不乘2
-    # --------------------------------------------------------#
-    # flops = flops * 2
-    flops, params = clever_format([flops, params], "%.3f")
-    print('Select Model: {}'.format(name))
-    print('Total FLOPS: %s' % (flops))
-    print('Total params: %s' % (params))
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        dummy_input = torch.randn(1, channels, input_shape[0], input_shape[1]).to(device)
+        flops, params = profile(model.to(device), (dummy_input,), verbose=False)
+        #--------------------------------------------------------#
+        #   flops * 2是因为profile没有将卷积作为两个operations
+        #   有些论文将卷积算乘法、加法两个operations。此时乘2
+        #   有些论文只考虑乘法的运算次数，忽略加法。此时不乘2
+        # --------------------------------------------------------#
+        # flops = flops * 2
+        flops, params = clever_format([flops, params], "%.3f")
+        print('Select Model: {}'.format(name))
+        print('Total FLOPS: %s' % (flops))
+        print('Total params: %s' % (params))
     model.name = name
-
     return model
 
 if __name__ == '__main__':