update dir name and delete no use functions

pytorch_object_detection/yolov3_spp/README.md

@@ -18,7 +18,7 @@
   │    └── pascal_voc_classes.json: pascal voc数据集标签
   │ 
   ├── runs: 保存训练过程中生成的所有tensorboard相关文件
-  ├── utils: 搭建训练网络时使用到的工具
+  ├── build_utils: 搭建训练网络时使用到的工具
   │     ├── datasets.py: 数据读取以及预处理方法
   │     ├── img_utils.py: 部分图像处理方法
   │     ├── layers.py: 实现的一些基础层结构

pytorch_object_detection/yolov3_spp/build_utils/datasets.py

@@ -1,11 +1,8 @@
-import glob
 import math
 import os
 import random
 import shutil
-import time
 from pathlib import Path
-from threading import Thread
 
 import cv2
 import numpy as np
@@ -14,7 +11,7 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from utils.utils import xyxy2xywh, xywh2xyxy
+from build_utils.utils import xyxy2xywh, xywh2xyxy
 
 help_url = 'https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data'
 img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.dng']

pytorch_object_detection/yolov3_spp/build_utils/layers.py

@@ -1,6 +1,5 @@
 import torch.nn.functional as F
-import torch
-from utils.utils import *
+from build_utils.utils import *
 
 
 def make_divisible(v, divisor):

pytorch_object_detection/yolov3_spp/build_utils/torch_utils.py

@@ -1,12 +1,10 @@
 import math
-import os
 import time
 from copy import deepcopy
 
 import torch
 import torch.backends.cudnn as cudnn
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 def init_seeds(seed=0):
@@ -18,33 +16,6 @@ def init_seeds(seed=0):
         cudnn.benchmark = True
 
 
-def select_device(device='', apex=False, batch_size=None):
-    # device = 'cpu' or '0' or '0,1,2,3'
-    cpu_request = device.lower() == 'cpu'
-    if device and not cpu_request:  # if device requested other than 'cpu'
-        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
-        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
-
-    cuda = False if cpu_request else torch.cuda.is_available()
-    if cuda:
-        c = 1024 ** 2  # bytes to MB
-        ng = torch.cuda.device_count()
-        if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
-            assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
-        x = [torch.cuda.get_device_properties(i) for i in range(ng)]
-        s = 'Using CUDA ' + ('Apex ' if apex else '')  # apex for mixed precision https://github.com/NVIDIA/apex
-        for i in range(0, ng):
-            if i == 1:
-                s = ' ' * len(s)
-            print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
-                  (s, i, x[i].name, x[i].total_memory / c))
-    else:
-        print('Using CPU')
-
-    print('')  # skip a line
-    return torch.device('cuda:0' if cuda else 'cpu')
-
-
 def time_synchronized():
     torch.cuda.synchronize() if torch.cuda.is_available() else None
     return time.time()
@@ -62,38 +33,6 @@ def initialize_weights(model):
             m.inplace = True
 
 
-def find_modules(model, mclass=nn.Conv2d):
-    # finds layer indices matching module class 'mclass'
-    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
-
-
-def fuse_conv_and_bn(conv, bn):
-    # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    with torch.no_grad():
-        # init
-        fusedconv = torch.nn.Conv2d(conv.in_channels,
-                                    conv.out_channels,
-                                    kernel_size=conv.kernel_size,
-                                    stride=conv.stride,
-                                    padding=conv.padding,
-                                    bias=True)
-
-        # prepare filters
-        w_conv = conv.weight.clone().view(conv.out_channels, -1)
-        w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-        fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
-
-        # prepare spatial bias
-        if conv.bias is not None:
-            b_conv = conv.bias
-        else:
-            b_conv = torch.zeros(conv.weight.size(0))
-        b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
-        fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-        return fusedconv
-
-
 def model_info(model, verbose=False):
     # Plots a line-by-line description of a PyTorch model
     n_p = sum(x.numel() for x in model.parameters())  # number parameters
@@ -115,34 +54,6 @@ def model_info(model, verbose=False):
     print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
 
 
-def load_classifier(name='resnet101', n=2):
-    # Loads a pretrained model reshaped to n-class output
-    import pretrainedmodels  # https://github.com/Cadene/pretrained-models.pytorch#torchvision
-    model = pretrainedmodels.__dict__[name](num_classes=1000, pretrained='imagenet')
-
-    # Display model properties
-    for x in ['model.input_size', 'model.input_space', 'model.input_range', 'model.mean', 'model.std']:
-        print(x + ' =', eval(x))
-
-    # Reshape output to n classes
-    filters = model.last_linear.weight.shape[1]
-    model.last_linear.bias = torch.nn.Parameter(torch.zeros(n))
-    model.last_linear.weight = torch.nn.Parameter(torch.zeros(n, filters))
-    model.last_linear.out_features = n
-    return model
-
-
-def scale_img(img, ratio=1.0, same_shape=True):  # img(16,3,256,416), r=ratio
-    # scales img(bs,3,y,x) by ratio
-    h, w = img.shape[2:]
-    s = (int(h * ratio), int(w * ratio))  # new size
-    img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
-    if not same_shape:  # pad/crop img
-        gs = 64  # (pixels) grid size
-        h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
-    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
-
-
 class ModelEMA:
     """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
     Keep a moving average of everything in the model state_dict (parameters and buffers).