Add yolov5 namespace

.gitignore

@@ -1,3 +1,6 @@
+# vscode
+.vscode
+
 # Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
 *.jpg
 *.jpeg
@@ -26,8 +29,8 @@
 storage.googleapis.com
 runs/*
 data/*
-!data/images/zidane.jpg
-!data/images/bus.jpg
+!yolov5/data/images/zidane.jpg
+!yolov5/data/images/bus.jpg
 !data/coco.names
 !data/coco_paper.names
 !data/coco.data

detect.py → yolov5/detect.py

@@ -7,12 +7,16 @@
 import torch.backends.cudnn as cudnn
 from numpy import random
 
-from models.experimental import attempt_load
-from utils.datasets import LoadStreams, LoadImages
-from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
-    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
-from utils.plots import plot_one_box
-from utils.torch_utils import select_device, load_classifier, time_synchronized
+from yolov5.models.experimental import attempt_load
+from yolov5.utils.datasets import LoadImages, LoadStreams
+from yolov5.utils.general import (apply_classifier, check_img_size,
+                                  check_imshow, check_requirements,
+                                  increment_path, non_max_suppression,
+                                  save_one_box, scale_coords, set_logging,
+                                  strip_optimizer, xyxy2xywh)
+from yolov5.utils.plots import plot_one_box
+from yolov5.utils.torch_utils import (load_classifier, select_device,
+                                      time_synchronized)
 
 
 def detect():

models/common.py → yolov5/models/common.py

@@ -12,10 +12,12 @@
 from PIL import Image
 from torch.cuda import amp
 
-from utils.datasets import letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
-from utils.plots import color_list, plot_one_box
-from utils.torch_utils import time_synchronized
+from yolov5.utils.datasets import letterbox
+from yolov5.utils.general import (increment_path, make_divisible,
+                                  non_max_suppression, save_one_box,
+                                  scale_coords, xyxy2xywh)
+from yolov5.utils.plots import color_list, plot_one_box
+from yolov5.utils.torch_utils import time_synchronized
 
 
 def autopad(k, p=None):  # kernel, padding

models/experimental.py → yolov5/models/experimental.py

@@ -4,8 +4,8 @@
 import torch
 import torch.nn as nn
 
-from models.common import Conv, DWConv
-from utils.google_utils import attempt_download
+from yolov5.models.common import Conv, DWConv
+from yolov5.utils.google_utils import attempt_download
 
 
 class CrossConv(nn.Module):

models/export.py → yolov5/models/export.py

@@ -8,16 +8,14 @@
 import sys
 import time
 
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-
 import torch
 import torch.nn as nn
 
-import models
-from models.experimental import attempt_load
-from utils.activations import Hardswish, SiLU
-from utils.general import colorstr, check_img_size, check_requirements, set_logging
-from utils.torch_utils import select_device
+import yolov5.models
+from yolov5.models.experimental import attempt_load
+from yolov5.utils.activations import Hardswish, SiLU
+from yolov5.utils.general import colorstr, check_img_size, check_requirements, set_logging
+from yolov5.utils.torch_utils import select_device
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()

models/yolo.py → yolov5/models/yolo.py

@@ -2,18 +2,18 @@
 
 import argparse
 import logging
-import sys
 from copy import deepcopy
 
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
 logger = logging.getLogger(__name__)
 
-from models.common import *
-from models.experimental import *
-from utils.autoanchor import check_anchor_order
-from utils.general import make_divisible, check_file, set_logging
-from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
-    select_device, copy_attr
+from yolov5.models.common import *
+from yolov5.models.experimental import *
+from yolov5.utils.autoanchor import check_anchor_order
+from yolov5.utils.general import check_file, make_divisible, set_logging
+from yolov5.utils.torch_utils import (copy_attr, fuse_conv_and_bn,
+                                      initialize_weights, model_info,
+                                      scale_img, select_device,
+                                      time_synchronized)
 
 try:
     import thop  # for FLOPS computation

test.py → yolov5/test.py

@@ -9,13 +9,17 @@
 import yaml
 from tqdm import tqdm
 
-from models.experimental import attempt_load
-from utils.datasets import create_dataloader
-from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
-    box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
-from utils.metrics import ap_per_class, ConfusionMatrix
-from utils.plots import plot_images, output_to_target, plot_study_txt
-from utils.torch_utils import select_device, time_synchronized
+from yolov5.models.experimental import attempt_load
+from yolov5.utils.datasets import create_dataloader
+from yolov5.utils.general import (box_iou, check_dataset, check_file,
+                                  check_img_size, check_requirements,
+                                  coco80_to_coco91_class, colorstr,
+                                  increment_path, non_max_suppression,
+                                  scale_coords, set_logging, xywh2xyxy,
+                                  xyxy2xywh)
+from yolov5.utils.metrics import ConfusionMatrix, ap_per_class
+from yolov5.utils.plots import output_to_target, plot_images, plot_study_txt
+from yolov5.utils.torch_utils import select_device, time_synchronized
 
 
 def test(data,

train.py → yolov5/train.py

@@ -21,19 +21,26 @@
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
-import test  # import test.py to get mAP after each epoch
-from models.experimental import attempt_load
-from models.yolo import Model
-from utils.autoanchor import check_anchors
-from utils.datasets import create_dataloader
-from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
-    fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
-    check_requirements, print_mutation, set_logging, one_cycle, colorstr
-from utils.google_utils import attempt_download
-from utils.loss import ComputeLoss
-from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
-from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
-from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
+import yolov5.test as test  # import test.py to get mAP after each epoch
+from yolov5.models.experimental import attempt_load
+from yolov5.models.yolo import Model
+from yolov5.utils.autoanchor import check_anchors
+from yolov5.utils.datasets import create_dataloader
+from yolov5.utils.general import (check_dataset, check_file, check_git_status,
+                                  check_img_size, check_requirements, colorstr,
+                                  fitness, get_latest_run, increment_path,
+                                  init_seeds, labels_to_class_weights,
+                                  labels_to_image_weights, one_cycle,
+                                  print_mutation, set_logging, strip_optimizer)
+from yolov5.utils.google_utils import attempt_download
+from yolov5.utils.loss import ComputeLoss
+from yolov5.utils.plots import (plot_evolution, plot_images, plot_labels,
+                                plot_results)
+from yolov5.utils.torch_utils import (ModelEMA, intersect_dicts, is_parallel,
+                                      select_device,
+                                      torch_distributed_zero_first)
+from yolov5.utils.wandb_logging.wandb_utils import (WandbLogger,
+                                                    check_wandb_resume)
 
 logger = logging.getLogger(__name__)
 

utils/autoanchor.py → yolov5/utils/autoanchor.py

@@ -6,7 +6,7 @@
 from scipy.cluster.vq import kmeans
 from tqdm import tqdm
 
-from utils.general import colorstr
+from yolov5.utils.general import colorstr
 
 
 def check_anchor_order(m):

utils/datasets.py → yolov5/utils/datasets.py

@@ -20,9 +20,11 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
-    resample_segments, clean_str
-from utils.torch_utils import torch_distributed_zero_first
+from yolov5.utils.general import (check_requirements, clean_str,
+                                  resample_segments, segment2box,
+                                  segments2boxes, xyn2xy, xywh2xyxy,
+                                  xywhn2xyxy, xyxy2xywh)
+from yolov5.utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
 help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'

utils/general.py → yolov5/utils/general.py

@@ -18,9 +18,9 @@
 import torchvision
 import yaml
 
-from utils.google_utils import gsutil_getsize
-from utils.metrics import fitness
-from utils.torch_utils import init_torch_seeds
+from yolov5.utils.google_utils import gsutil_getsize
+from yolov5.utils.metrics import fitness
+from yolov5.utils.torch_utils import init_torch_seeds
 
 # Settings
 torch.set_printoptions(linewidth=320, precision=5, profile='long')

utils/loss.py → yolov5/utils/loss.py

@@ -3,8 +3,8 @@
 import torch
 import torch.nn as nn
 
-from utils.general import bbox_iou
-from utils.torch_utils import is_parallel
+from yolov5.utils.general import bbox_iou
+from yolov5.utils.torch_utils import is_parallel
 
 
 def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441

utils/plots.py → yolov5/utils/plots.py

@@ -18,8 +18,8 @@
 from PIL import Image, ImageDraw, ImageFont
 from scipy.signal import butter, filtfilt
 
-from utils.general import xywh2xyxy, xyxy2xywh
-from utils.metrics import fitness
+from yolov5.utils.general import xywh2xyxy, xyxy2xywh
+from yolov5.utils.metrics import fitness
 
 # Settings
 matplotlib.rc('font', **{'size': 11})