diff --git a/detect.py b/detect.py
index f3b9b09..ca7b967 100644
--- a/detect.py
+++ b/detect.py
@@ -1,10 +1,19 @@
import argparse
-
+import os
+import platform
+import shutil
+import time
+from pathlib import Path
+
+import cv2
+import torch
import torch.backends.cudnn as cudnn
+from numpy import random
-from models.experimental import *
-from utils.datasets import *
-from utils.utils import *
+from models.experimental import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(save_img=False):
@@ -13,7 +22,7 @@ def detect(save_img=False):
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
- device = torch_utils.select_device(opt.device)
+ device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
@@ -28,7 +37,7 @@ def detect(save_img=False):
# Second-stage classifier
classify = False
if classify:
- modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
+ modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
modelc.to(device).eval()
@@ -58,12 +67,12 @@ def detect(save_img=False):
img = img.unsqueeze(0)
# Inference
- t1 = torch_utils.time_synchronized()
+ t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
- t2 = torch_utils.time_synchronized()
+ t2 = time_synchronized()
# Apply Classifier
if classify:
diff --git a/hubconf.py b/hubconf.py
index bbca702..98416a5 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -6,13 +6,12 @@ Usage:
"""
dependencies = ['torch', 'yaml']
-
import os
import torch
from models.yolo import Model
-from utils import google_utils
+from utils.google_utils import attempt_download
def create(name, pretrained, channels, classes):
@@ -32,7 +31,7 @@ def create(name, pretrained, channels, classes):
model = Model(config, channels, classes)
if pretrained:
ckpt = '%s.pt' % name # checkpoint filename
- google_utils.attempt_download(ckpt) # download if not found locally
+ attempt_download(ckpt) # download if not found locally
state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict() # to FP32
state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter
model.load_state_dict(state_dict, strict=False) # load
diff --git a/models/common.py b/models/common.py
index 7a7272b..5acfeca 100644
--- a/models/common.py
+++ b/models/common.py
@@ -1,6 +1,8 @@
# This file contains modules common to various models
+import math
-from utils.utils import *
+import torch
+import torch.nn as nn
def autopad(k, p=None): # kernel, padding
diff --git a/models/experimental.py b/models/experimental.py
index a22f6bb..1b99ce4 100644
--- a/models/experimental.py
+++ b/models/experimental.py
@@ -1,7 +1,11 @@
# This file contains experimental modules
-from models.common import *
-from utils import google_utils
+import numpy as np
+import torch
+import torch.nn as nn
+
+from models.common import Conv, DWConv
+from utils.google_utils import attempt_download
class CrossConv(nn.Module):
@@ -129,7 +133,7 @@ def attempt_load(weights, map_location=None):
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
- google_utils.attempt_download(w)
+ attempt_download(w)
model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model
if len(model) == 1:
diff --git a/models/export.py b/models/export.py
index 2097df5..7ccd392 100644
--- a/models/export.py
+++ b/models/export.py
@@ -6,8 +6,9 @@ Usage:
import argparse
-from models.common import *
-from utils import google_utils
+import torch
+
+from utils.google_utils import attempt_download
if __name__ == '__main__':
parser = argparse.ArgumentParser()
@@ -22,7 +23,7 @@ if __name__ == '__main__':
img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection
# Load PyTorch model
- google_utils.attempt_download(opt.weights)
+ attempt_download(opt.weights)
model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
model.eval()
model.model[-1].export = True # set Detect() layer export=True
diff --git a/models/yolo.py b/models/yolo.py
index 3cb0a13..baa4e1a 100644
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -1,7 +1,16 @@
import argparse
+import math
from copy import deepcopy
+from pathlib import Path
-from models.experimental import *
+import torch
+import torch.nn as nn
+
+from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat
+from models.experimental import MixConv2d, CrossConv, C3
+from utils.general import check_anchor_order, make_divisible, check_file
+from utils.torch_utils import (
+ time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device)
class Detect(nn.Module):
@@ -75,7 +84,7 @@ class Model(nn.Module):
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
- torch_utils.initialize_weights(self)
+ initialize_weights(self)
self.info()
print('')
@@ -86,7 +95,7 @@ class Model(nn.Module):
f = [None, 3, None] # flips (2-ud, 3-lr)
y = [] # outputs
for si, fi in zip(s, f):
- xi = torch_utils.scale_img(x.flip(fi) if fi else x, si)
+ xi = scale_img(x.flip(fi) if fi else x, si)
yi = self.forward_once(xi)[0] # forward
# cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
yi[..., :4] /= si # de-scale
@@ -111,10 +120,10 @@ class Model(nn.Module):
o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS
except:
o = 0
- t = torch_utils.time_synchronized()
+ t = time_synchronized()
for _ in range(10):
_ = m(x)
- dt.append((torch_utils.time_synchronized() - t) * 100)
+ dt.append((time_synchronized() - t) * 100)
print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
x = m(x) # run
@@ -149,14 +158,14 @@ class Model(nn.Module):
for m in self.model.modules():
if type(m) is Conv:
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability
- m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn) # update conv
+ m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
m.bn = None # remove batchnorm
m.forward = m.fuseforward # update forward
self.info()
return self
def info(self): # print model information
- torch_utils.model_info(self)
+ model_info(self)
def parse_model(d, ch): # model_dict, input_channels(3)
@@ -228,7 +237,7 @@ if __name__ == '__main__':
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
opt = parser.parse_args()
opt.cfg = check_file(opt.cfg) # check file
- device = torch_utils.select_device(opt.device)
+ device = select_device(opt.device)
# Create model
model = Model(opt.cfg).to(device)
diff --git a/test.py b/test.py
index c997157..7d4df08 100644
--- a/test.py
+++ b/test.py
@@ -1,8 +1,21 @@
import argparse
+import glob
import json
+import os
+import shutil
+from pathlib import Path
-from models.experimental import *
-from utils.datasets import *
+import numpy as np
+import torch
+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_file, check_img_size, compute_loss, non_max_suppression,
+ scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class)
+from utils.torch_utils import select_device, time_synchronized
def test(data,
@@ -26,7 +39,7 @@ def test(data,
device = next(model.parameters()).device # get model device
else: # called directly
- device = torch_utils.select_device(opt.device, batch_size=batch_size)
+ device = select_device(opt.device, batch_size=batch_size)
merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels
if save_txt:
out = Path('inference/output')
@@ -85,18 +98,18 @@ def test(data,
# Disable gradients
with torch.no_grad():
# Run model
- t = torch_utils.time_synchronized()
+ t = time_synchronized()
inf_out, train_out = model(img, augment=augment) # inference and training outputs
- t0 += torch_utils.time_synchronized() - t
+ t0 += time_synchronized() - t
# Compute loss
if training: # if model has loss hyperparameters
loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # GIoU, obj, cls
# Run NMS
- t = torch_utils.time_synchronized()
+ t = time_synchronized()
output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge)
- t1 += torch_utils.time_synchronized() - t
+ t1 += time_synchronized() - t
# Statistics per image
for si, pred in enumerate(output):
diff --git a/train.py b/train.py
index 7d99374..4b8f1f7 100644
--- a/train.py
+++ b/train.py
@@ -1,19 +1,32 @@
import argparse
-
+import glob
+import math
+import os
+import time
+from pathlib import Path
+from random import random
+
+import numpy as np
import torch.distributed as dist
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
+import yaml
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
import test # import test.py to get mAP after each epoch
from models.yolo import Model
-from utils import google_utils
-from utils.datasets import *
-from utils.utils import *
+from utils.datasets import create_dataloader
+from utils.general import (
+ check_img_size, torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors,
+ labels_to_image_weights, compute_loss, plot_images, fitness, strip_optimizer, plot_results,
+ get_latest_run, check_git_status, check_file, increment_dir, print_mutation)
+from utils.google_utils import attempt_download
+from utils.torch_utils import init_seeds, ModelEMA, select_device
# Hyperparameters
hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
@@ -119,7 +132,7 @@ def train(hyp, opt, device, tb_writer=None):
# Load Model
with torch_distributed_zero_first(rank):
- google_utils.attempt_download(weights)
+ attempt_download(weights)
start_epoch, best_fitness = 0, 0.0
if weights.endswith('.pt'): # pytorch format
ckpt = torch.load(weights, map_location=device) # load checkpoint
@@ -167,7 +180,7 @@ def train(hyp, opt, device, tb_writer=None):
print('Using SyncBatchNorm()')
# Exponential moving average
- ema = torch_utils.ModelEMA(model) if rank in [-1, 0] else None
+ ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
@@ -438,7 +451,7 @@ if __name__ == '__main__':
with open(opt.hyp) as f:
hyp.update(yaml.load(f, Loader=yaml.FullLoader)) # update hyps
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
- device = torch_utils.select_device(opt.device, batch_size=opt.batch_size)
+ device = select_device(opt.device, batch_size=opt.batch_size)
opt.total_batch_size = opt.batch_size
opt.world_size = 1
diff --git a/utils/datasets.py b/utils/datasets.py
index 5002e6d..169a518 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -14,7 +14,7 @@ from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm
-from utils.utils import xyxy2xywh, xywh2xyxy, torch_distributed_zero_first
+from utils.general import xyxy2xywh, xywh2xyxy, torch_distributed_zero_first
help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
diff --git a/utils/utils.py b/utils/general.py
similarity index 99%
rename from utils/utils.py
rename to utils/general.py
index 9d412bc..ca1225a 100755
--- a/utils/utils.py
+++ b/utils/general.py
@@ -18,10 +18,11 @@ import torch
import torch.nn as nn
import torchvision
import yaml
+from scipy.cluster.vq import kmeans
from scipy.signal import butter, filtfilt
from tqdm import tqdm
-from . import torch_utils # torch_utils, google_utils
+from utils.torch_utils import init_seeds, is_parallel
# Set printoptions
torch.set_printoptions(linewidth=320, precision=5, profile='long')
@@ -47,7 +48,7 @@ def torch_distributed_zero_first(local_rank: int):
def init_seeds(seed=0):
random.seed(seed)
np.random.seed(seed)
- torch_utils.init_seeds(seed=seed)
+ init_seeds(seed=seed)
def get_latest_run(search_dir='./runs'):
@@ -505,7 +506,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
def build_targets(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- det = model.module.model[-1] if torch_utils.is_parallel(model) else model.model[-1] # Detect() module
+ det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
na, nt = det.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
@@ -779,7 +780,6 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
# Kmeans calculation
- from scipy.cluster.vq import kmeans
print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
s = wh.std(0) # sigmas for whitening
k, dist = kmeans(wh / s, n, iter=30) # points, mean distance