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YOLOv5 v4.0 Release (#1837) 

* Update C3 module

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* Update datasets.py
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35
README.md
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@ -4,28 +4,32 @@
![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg) ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
<img src="https://user-images.githubusercontent.com/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. <img src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.
- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP. - **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP. - **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). - **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).
- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
- **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
## Pretrained Checkpoints ## Pretrained Checkpoints
| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | GFLOPS | | Model | size | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>V100</sub> | FPS<sub>V100</sub> || params | GFLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | |---------- |------ |------ |------ |------ | -------- | ------| ------ |------ | :------: |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases) | 37.0 | 37.0 | 56.2 | **2.4ms** | **416** || 7.5M | 17.5 | [YOLOv5s](https://github.com/ultralytics/yolov5/releases) |640 |36.8 |36.8 |55.6 |**2.2ms** |**455** ||7.3M |17.0
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases) | 44.3 | 44.3 | 63.2 | 3.4ms | 294 || 21.8M | 52.3 | [YOLOv5m](https://github.com/ultralytics/yolov5/releases) |640 |44.5 |44.5 |63.1 |2.9ms |345 ||21.4M |51.3
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases) | 47.7 | 47.7 | 66.5 | 4.4ms | 227 || 47.8M | 117.2 | [YOLOv5l](https://github.com/ultralytics/yolov5/releases) |640 |48.1 |48.1 |66.4 |3.8ms |264 ||47.0M |115.4
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) | **49.2** | **49.2** | **67.7** | 6.9ms | 145 || 89.0M | 221.5 | [YOLOv5x](https://github.com/ultralytics/yolov5/releases) |640 |**50.1** |**50.1** |**68.7** |6.0ms |167 ||87.7M |218.8
| | | | | | || | | | | | | | | || |
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms | 39 || 89.0M | 801.0 | [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA |832 |**51.9** |**51.9** |**69.6** |24.9ms |40 ||87.7M |1005.3
<!---
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases) |640 |49.0 |49.0 |67.4 |4.1ms |244 ||77.2M |117.7
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases) |1280 |53.0 |53.0 |70.8 |12.3ms |81 ||77.2M |117.7
--->
** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` ** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
@ -33,6 +37,7 @@ This repository represents Ultralytics open-source research into future object d
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` ** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment`
## Requirements ## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
@ -106,7 +111,7 @@ import torch
from PIL import Image from PIL import Image
# Model # Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) # for PIL/cv2/np inputs and NMS model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
# Images # Images
img1 = Image.open('zidane.jpg') img1 = Image.open('zidane.jpg')
@ -114,13 +119,13 @@ img2 = Image.open('bus.jpg')
imgs = [img1, img2] # batched list of images imgs = [img1, img2] # batched list of images
# Inference # Inference
prediction = model(imgs, size=640) # includes NMS result = model(imgs)
``` ```
## Training ## Training
Download [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
```bash ```bash
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40 yolov5m 40

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data/coco.yaml
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@ -18,7 +18,7 @@ test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://compe
nc: 80 nc: 80
# class names # class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
@ -26,7 +26,7 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'] 'hair drier', 'toothbrush' ]
# Print classes # Print classes
# with open('data/coco.yaml') as f: # with open('data/coco.yaml') as f:

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data/coco128.yaml
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@ -17,7 +17,7 @@ val: ../coco128/images/train2017/ # 128 images
nc: 80 nc: 80
# class names # class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
@ -25,4 +25,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'] 'hair drier', 'toothbrush' ]

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data/voc.yaml
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@ -17,5 +17,5 @@ val: ../VOC/images/val/ # 4952 images
nc: 20 nc: 20
# class names # class names
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]

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models/common.py
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@ -30,7 +30,7 @@ class Conv(nn.Module):
super(Conv, self).__init__() super(Conv, self).__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2) self.bn = nn.BatchNorm2d(c2)
self.act = nn.Hardswish() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
def forward(self, x): def forward(self, x):
return self.act(self.bn(self.conv(x))) return self.act(self.bn(self.conv(x)))
@ -105,9 +105,39 @@ class Focus(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__() super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act) self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
# self.contract = Contract(gain=2)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
# return self.conv(self.contract(x))
class Contract(nn.Module):
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
s = self.gain
x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
class Expand(nn.Module):
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
s = self.gain
x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
class Concat(nn.Module): class Concat(nn.Module):
@ -253,20 +283,13 @@ class Detections:
return x return x
class Flatten(nn.Module):
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Classify(nn.Module): class Classify(nn.Module):
# Classification head, i.e. x(b,c1,20,20) to x(b,c2) # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
super(Classify, self).__init__() super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
self.flat = Flatten() self.flat = nn.Flatten()
def forward(self, x): def forward(self, x):
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list

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models/experimental.py
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@ -105,8 +105,8 @@ class Ensemble(nn.ModuleList):
for module in self: for module in self:
y.append(module(x, augment)[0]) y.append(module(x, augment)[0])
# y = torch.stack(y).max(0)[0] # max ensemble # y = torch.stack(y).max(0)[0] # max ensemble
# y = torch.cat(y, 1) # nms ensemble # y = torch.stack(y).mean(0) # mean ensemble
y = torch.stack(y).mean(0) # mean ensemble y = torch.cat(y, 1) # nms ensemble
return y, None # inference, train output return y, None # inference, train output

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models/hub/anchors.yaml Normal file
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@ -0,0 +1,58 @@
# Default YOLOv5 anchors for COCO data
# P5 -------------------------------------------------------------------------------------------------------------------
# P5-640:
anchors_p5_640:
- [ 10,13, 16,30, 33,23 ] # P3/8
- [ 30,61, 62,45, 59,119 ] # P4/16
- [ 116,90, 156,198, 373,326 ] # P5/32
# P6 -------------------------------------------------------------------------------------------------------------------
# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387
anchors_p6_640:
- [ 9,11, 21,19, 17,41 ] # P3/8
- [ 43,32, 39,70, 86,64 ] # P4/16
- [ 65,131, 134,130, 120,265 ] # P5/32
- [ 282,180, 247,354, 512,387 ] # P6/64
# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792
anchors_p6_1280:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187
anchors_p6_1920:
- [ 28,41, 67,59, 57,141 ] # P3/8
- [ 144,103, 129,227, 270,205 ] # P4/16
- [ 209,452, 455,396, 358,812 ] # P5/32
- [ 653,922, 1109,570, 1387,1187 ] # P6/64
# P7 -------------------------------------------------------------------------------------------------------------------
# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372
anchors_p7_640:
- [ 11,11, 13,30, 29,20 ] # P3/8
- [ 30,46, 61,38, 39,92 ] # P4/16
- [ 78,80, 146,66, 79,163 ] # P5/32
- [ 149,150, 321,143, 157,303 ] # P6/64
- [ 257,402, 359,290, 524,372 ] # P7/128
# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818
anchors_p7_1280:
- [ 19,22, 54,36, 32,77 ] # P3/8
- [ 70,83, 138,71, 75,173 ] # P4/16
- [ 165,159, 148,334, 375,151 ] # P5/32
- [ 334,317, 251,626, 499,474 ] # P6/64
- [ 750,326, 534,814, 1079,818 ] # P7/128
# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227
anchors_p7_1920:
- [ 29,34, 81,55, 47,115 ] # P3/8
- [ 105,124, 207,107, 113,259 ] # P4/16
- [ 247,238, 222,500, 563,227 ] # P5/32
- [ 501,476, 376,939, 749,711 ] # P6/64
- [ 1126,489, 801,1222, 1618,1227 ] # P7/128

54
models/hub/yolov5-p2.yaml Normal file
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@ -0,0 +1,54 @@
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors: 3
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[ -1, 3, C3, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[ -1, 9, C3, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[ -1, 9, C3, [ 512 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
[ -1, 3, C3, [ 1024, False ] ], # 9
]
# YOLOv5 head
head:
[ [ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 3, C3, [ 512, False ] ], # 13
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small)
[ -1, 1, Conv, [ 128, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2
[ -1, 1, C3, [ 128, False ] ], # 21 (P2/4-xsmall)
[ -1, 1, Conv, [ 128, 3, 2 ] ],
[ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P3
[ -1, 3, C3, [ 256, False ] ], # 24 (P3/8-small)
[ -1, 1, Conv, [ 256, 3, 2 ] ],
[ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
[ -1, 3, C3, [ 512, False ] ], # 27 (P4/16-medium)
[ -1, 1, Conv, [ 512, 3, 2 ] ],
[ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5
[ -1, 3, C3, [ 1024, False ] ], # 30 (P5/32-large)
[ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
]

56
models/hub/yolov5-p6.yaml Normal file
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@ -0,0 +1,56 @@
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors: 3
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[ -1, 3, C3, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[ -1, 9, C3, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[ -1, 9, C3, [ 512 ] ],
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
[ -1, 3, C3, [ 768 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
[ -1, 3, C3, [ 1024, False ] ], # 11
]
# YOLOv5 head
head:
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
[ -1, 3, C3, [ 768, False ] ], # 15
[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 3, C3, [ 512, False ] ], # 19
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
[ -1, 1, Conv, [ 256, 3, 2 ] ],
[ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
[ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
[ -1, 1, Conv, [ 512, 3, 2 ] ],
[ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
[ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
[ -1, 1, Conv, [ 768, 3, 2 ] ],
[ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
[ -1, 3, C3, [ 1024, False ] ], # 32 (P5/64-xlarge)
[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
]

67
models/hub/yolov5-p7.yaml Normal file
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@ -0,0 +1,67 @@
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors: 3
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[ -1, 3, C3, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[ -1, 9, C3, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[ -1, 9, C3, [ 512 ] ],
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
[ -1, 3, C3, [ 768 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
[ -1, 3, C3, [ 1024 ] ],
[ -1, 1, Conv, [ 1280, 3, 2 ] ], # 11-P7/128
[ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ],
[ -1, 3, C3, [ 1280, False ] ], # 13
]
# YOLOv5 head
head:
[ [ -1, 1, Conv, [ 1024, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat backbone P6
[ -1, 3, C3, [ 1024, False ] ], # 17
[ -1, 1, Conv, [ 768, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
[ -1, 3, C3, [ 768, False ] ], # 21
[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 3, C3, [ 512, False ] ], # 25
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 3, C3, [ 256, False ] ], # 29 (P3/8-small)
[ -1, 1, Conv, [ 256, 3, 2 ] ],
[ [ -1, 26 ], 1, Concat, [ 1 ] ], # cat head P4
[ -1, 3, C3, [ 512, False ] ], # 32 (P4/16-medium)
[ -1, 1, Conv, [ 512, 3, 2 ] ],
[ [ -1, 22 ], 1, Concat, [ 1 ] ], # cat head P5
[ -1, 3, C3, [ 768, False ] ], # 35 (P5/32-large)
[ -1, 1, Conv, [ 768, 3, 2 ] ],
[ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P6
[ -1, 3, C3, [ 1024, False ] ], # 38 (P6/64-xlarge)
[ -1, 1, Conv, [ 1024, 3, 2 ] ],
[ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P7
[ -1, 3, C3, [ 1280, False ] ], # 41 (P7/128-xxlarge)
[ [ 29, 32, 35, 38, 41 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6, P7)
]

18
models/yolo.py
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@ -1,17 +1,13 @@
import argparse import argparse
import logging import logging
import math
import sys import sys
from copy import deepcopy from copy import deepcopy
from pathlib import Path from pathlib import Path
import torch
import torch.nn as nn
sys.path.append('./') # to run '$ python *.py' files in subdirectories sys.path.append('./') # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, C3, Concat, NMS, autoShape from models.common import *
from models.experimental import MixConv2d, CrossConv from models.experimental import MixConv2d, CrossConv
from utils.autoanchor import check_anchor_order from utils.autoanchor import check_anchor_order
from utils.general import make_divisible, check_file, set_logging from utils.general import make_divisible, check_file, set_logging
@ -89,7 +85,7 @@ class Model(nn.Module):
# Build strides, anchors # Build strides, anchors
m = self.model[-1] # Detect() m = self.model[-1] # Detect()
if isinstance(m, Detect): if isinstance(m, Detect):
s = 128 # 2x min stride s = 256 # 2x min stride
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
m.anchors /= m.stride.view(-1, 1, 1) m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m) check_anchor_order(m)
@ -109,7 +105,7 @@ class Model(nn.Module):
f = [None, 3, None] # flips (2-ud, 3-lr) f = [None, 3, None] # flips (2-ud, 3-lr)
y = [] # outputs y = [] # outputs
for si, fi in zip(s, f): for si, fi in zip(s, f):
xi = scale_img(x.flip(fi) if fi else x, si) xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
yi = self.forward_once(xi)[0] # forward yi = self.forward_once(xi)[0] # forward
# cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
yi[..., :4] /= si # de-scale yi[..., :4] /= si # de-scale
@ -242,13 +238,17 @@ def parse_model(d, ch): # model_dict, input_channels(3)
elif m is nn.BatchNorm2d: elif m is nn.BatchNorm2d:
args = [ch[f]] args = [ch[f]]
elif m is Concat: elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) c2 = sum([ch[x if x < 0 else x + 1] for x in f])
elif m is Detect: elif m is Detect:
args.append([ch[x + 1] for x in f]) args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f) args[1] = [list(range(args[1] * 2))] * len(f)
elif m is Contract:
c2 = ch[f if f < 0 else f + 1] * args[0] ** 2
elif m is Expand:
c2 = ch[f if f < 0 else f + 1] // args[0] ** 2
else: else:
c2 = ch[f] c2 = ch[f if f < 0 else f + 1]
m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type t = str(m)[8:-2].replace('__main__.', '') # module type

16
models/yolov5l.yaml
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@ -14,14 +14,14 @@ backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9 [-1, 3, C3, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
@ -29,20 +29,20 @@ head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

16
models/yolov5m.yaml
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@ -14,14 +14,14 @@ backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9 [-1, 3, C3, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
@ -29,20 +29,20 @@ head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

16
models/yolov5s.yaml
View File

@ -14,14 +14,14 @@ backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9 [-1, 3, C3, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
@ -29,20 +29,20 @@ head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

16
models/yolov5x.yaml
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@ -14,14 +14,14 @@ backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9 [-1, 3, C3, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
@ -29,20 +29,20 @@ head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

14
train.py
View File

@ -104,6 +104,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
nbs = 64 # nominal batch size nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules(): for k, v in model.named_modules():
@ -164,7 +165,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
del ckpt, state_dict del ckpt, state_dict
# Image sizes # Image sizes
gs = int(max(model.stride)) # grid size (max stride) gs = int(model.stride.max()) # grid size (max stride)
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode # DP mode
@ -187,7 +189,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers, world_size=opt.world_size, workers=opt.workers,
image_weights=opt.image_weights) image_weights=opt.image_weights, quad=opt.quad)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
@ -214,7 +216,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Model parameters # Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset hyp['cls'] *= nc / 80. # scale hyp['cls'] to class count
hyp['obj'] *= imgsz ** 2 / 640. ** 2 * 3. / nl # scale hyp['obj'] to image size and output layers
model.nc = nc # attach number of classes to model model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
@ -290,6 +293,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
if rank != -1: if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode loss *= opt.world_size # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward # Backward
scaler.scale(loss).backward() scaler.scale(loss).backward()
@ -458,10 +463,10 @@ if __name__ == '__main__':
parser.add_argument('--project', default='runs/train', help='save to project/name') parser.add_argument('--project', default='runs/train', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--quad', action='store_true', help='quad dataloader')
opt = parser.parse_args() opt = parser.parse_args()
# Set DDP variables # Set DDP variables
opt.total_batch_size = opt.batch_size
opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
set_logging(opt.global_rank) set_logging(opt.global_rank)
@ -486,6 +491,7 @@ if __name__ == '__main__':
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run
# DDP mode # DDP mode
opt.total_batch_size = opt.batch_size
device = select_device(opt.device, batch_size=opt.batch_size) device = select_device(opt.device, batch_size=opt.batch_size)
if opt.local_rank != -1: if opt.local_rank != -1:
assert torch.cuda.device_count() > opt.local_rank assert torch.cuda.device_count() > opt.local_rank

1
utils/autoanchor.py
View File

@ -110,6 +110,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
print('WARNING: Extremely small objects found. ' print('WARNING: Extremely small objects found. '
'%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0))) '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
# wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
# Kmeans calculation # Kmeans calculation
print('Running kmeans for %g anchors on %g points...' % (n, len(wh))) print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))

113
utils/datasets.py
View File

@ -15,6 +15,7 @@ from threading import Thread
import cv2 import cv2
import numpy as np import numpy as np
import torch import torch
import torch.nn.functional as F
from PIL import Image, ExifTags from PIL import Image, ExifTags
from torch.utils.data import Dataset from torch.utils.data import Dataset
from tqdm import tqdm from tqdm import tqdm
@ -55,7 +56,7 @@ def exif_size(img):
def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
rank=-1, world_size=1, workers=8, image_weights=False): rank=-1, world_size=1, workers=8, image_weights=False, quad=False):
# Make sure only the first process in DDP process the dataset first, and the following others can use the cache # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
with torch_distributed_zero_first(rank): with torch_distributed_zero_first(rank):
dataset = LoadImagesAndLabels(path, imgsz, batch_size, dataset = LoadImagesAndLabels(path, imgsz, batch_size,
@ -79,7 +80,7 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
num_workers=nw, num_workers=nw,
sampler=sampler, sampler=sampler,
pin_memory=True, pin_memory=True,
collate_fn=LoadImagesAndLabels.collate_fn) collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
return dataloader, dataset return dataloader, dataset
@ -578,6 +579,32 @@ class LoadImagesAndLabels(Dataset): # for training/testing
l[:, 0] = i # add target image index for build_targets() l[:, 0] = i # add target image index for build_targets()
return torch.stack(img, 0), torch.cat(label, 0), path, shapes return torch.stack(img, 0), torch.cat(label, 0), path, shapes
@staticmethod
def collate_fn4(batch):
img, label, path, shapes = zip(*batch) # transposed
n = len(shapes) // 4
img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale
for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW
i *= 4
if random.random() < 0.5:
im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
0].type(img[i].type())
l = label[i]
else:
im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
img4.append(im)
label4.append(l)
for i, l in enumerate(label4):
l[:, 0] = i # add target image index for build_targets()
return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
# Ancillary functions -------------------------------------------------------------------------------------------------- # Ancillary functions --------------------------------------------------------------------------------------------------
def load_image(self, index): def load_image(self, index):
@ -617,7 +644,7 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
def load_mosaic(self, index): def load_mosaic(self, index):
# loads images in a mosaic # loads images in a 4-mosaic
labels4 = [] labels4 = []
s = self.img_size s = self.img_size
@ -674,6 +701,80 @@ def load_mosaic(self, index):
return img4, labels4 return img4, labels4
def load_mosaic9(self, index):
# loads images in a 9-mosaic
labels9 = []
s = self.img_size
indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices
for i, index in enumerate(indices):
# Load image
img, _, (h, w) = load_image(self, index)
# place img in img9
if i == 0: # center
img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
h0, w0 = h, w
c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
elif i == 1: # top
c = s, s - h, s + w, s
elif i == 2: # top right
c = s + wp, s - h, s + wp + w, s
elif i == 3: # right
c = s + w0, s, s + w0 + w, s + h
elif i == 4: # bottom right
c = s + w0, s + hp, s + w0 + w, s + hp + h
elif i == 5: # bottom
c = s + w0 - w, s + h0, s + w0, s + h0 + h
elif i == 6: # bottom left
c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
elif i == 7: # left
c = s - w, s + h0 - h, s, s + h0
elif i == 8: # top left
c = s - w, s + h0 - hp - h, s, s + h0 - hp
padx, pady = c[:2]
x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords
# Labels
x = self.labels[index]
labels = x.copy()
if x.size > 0: # Normalized xywh to pixel xyxy format
labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padx
labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + pady
labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padx
labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + pady
labels9.append(labels)
# Image
img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
hp, wp = h, w # height, width previous
# Offset
yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y
img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
# Concat/clip labels
if len(labels9):
labels9 = np.concatenate(labels9, 0)
labels9[:, [1, 3]] -= xc
labels9[:, [2, 4]] -= yc
np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective
# img9, labels9 = replicate(img9, labels9) # replicate
# Augment
img9, labels9 = random_perspective(img9, labels9,
degrees=self.hyp['degrees'],
translate=self.hyp['translate'],
scale=self.hyp['scale'],
shear=self.hyp['shear'],
perspective=self.hyp['perspective'],
border=self.mosaic_border) # border to remove
return img9, labels9
def replicate(img, labels): def replicate(img, labels):
# Replicate labels # Replicate labels
h, w = img.shape[:2] h, w = img.shape[:2]
@ -811,12 +912,12 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea
return img, targets return img, targets
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n) def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
w1, h1 = box1[2] - box1[0], box1[3] - box1[1] w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
w2, h2 = box2[2] - box2[0], box2[3] - box2[1] w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
def cutout(image, labels): def cutout(image, labels):

11
utils/general.py
View File

@ -281,6 +281,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
# Settings # Settings
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
max_det = 300 # maximum number of detections per image max_det = 300 # maximum number of detections per image
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 10.0 # seconds to quit after time_limit = 10.0 # seconds to quit after
redundant = True # require redundant detections redundant = True # require redundant detections
multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
@ -328,13 +329,12 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
# if not torch.isfinite(x).all(): # if not torch.isfinite(x).all():
# x = x[torch.isfinite(x).all(1)] # x = x[torch.isfinite(x).all(1)]
# If none remain process next image # Check shape
n = x.shape[0] # number of boxes n = x.shape[0] # number of boxes
if not n: if not n: # no boxes
continue continue
elif n > max_nms: # excess boxes
# Sort by confidence x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
# x = x[x[:, 4].argsort(descending=True)]
# Batched NMS # Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
@ -352,6 +352,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
output[xi] = x[i] output[xi] = x[i]
if (time.time() - t) > time_limit: if (time.time() - t) > time_limit:
print(f'WARNING: NMS time limit {time_limit}s exceeded')
break # time limit exceeded break # time limit exceeded
return output return output

25
utils/google_utils.py
View File

@ -6,6 +6,7 @@ import subprocess
import time import time
from pathlib import Path from pathlib import Path
import requests
import torch import torch
@ -21,21 +22,14 @@ def attempt_download(weights):
file = Path(weights).name.lower() file = Path(weights).name.lower()
msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/' msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/'
models = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt'] # available models response = requests.get('https://api.github.com/repos/ultralytics/yolov5/releases/latest').json() # github api
redundant = False # offer second download option assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...]
redundant = False # second download option
if file in models and not os.path.isfile(weights):
# Google Drive
# d = {'yolov5s.pt': '1R5T6rIyy3lLwgFXNms8whc-387H0tMQO',
# 'yolov5m.pt': '1vobuEExpWQVpXExsJ2w-Mbf3HJjWkQJr',
# 'yolov5l.pt': '1hrlqD1Wdei7UT4OgT785BEk1JwnSvNEV',
# 'yolov5x.pt': '1mM8aZJlWTxOg7BZJvNUMrTnA2AbeCVzS'}
# r = gdrive_download(id=d[file], name=weights) if file in d else 1
# if r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6: # check
# return
if file in assets and not os.path.isfile(weights):
try: # GitHub try: # GitHub
url = 'https://github.com/ultralytics/yolov5/releases/download/v3.1/' + file tag = response['tag_name'] # i.e. 'v1.0'
url = f'https://github.com/ultralytics/yolov5/releases/download/{tag}/{file}'
print('Downloading %s to %s...' % (url, weights)) print('Downloading %s to %s...' % (url, weights))
torch.hub.download_url_to_file(url, weights) torch.hub.download_url_to_file(url, weights)
assert os.path.exists(weights) and os.path.getsize(weights) > 1E6 # check assert os.path.exists(weights) and os.path.getsize(weights) > 1E6 # check
@ -53,10 +47,9 @@ def attempt_download(weights):
return return
def gdrive_download(id='1uH2BylpFxHKEGXKL6wJJlsgMU2YEjxuc', name='tmp.zip'): def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', name='tmp.zip'):
# Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download() # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download()
t = time.time() t = time.time()
print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='') print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
os.remove(name) if os.path.exists(name) else None # remove existing os.remove(name) if os.path.exists(name) else None # remove existing
os.remove('cookie') if os.path.exists('cookie') else None os.remove('cookie') if os.path.exists('cookie') else None

4
utils/loss.py
View File

@ -106,7 +106,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
# Losses # Losses
nt = 0 # number of targets nt = 0 # number of targets
no = len(p) # number of outputs no = len(p) # number of outputs
balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6 balance = [4.0, 1.0, 0.3, 0.1, 0.03] # P3-P7
for i, pi in enumerate(p): # layer index, layer predictions for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi[..., 0], device=device) # target obj tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
@ -140,7 +140,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
s = 3 / no # output count scaling s = 3 / no # output count scaling
lbox *= h['box'] * s lbox *= h['box'] * s
lobj *= h['obj'] * s * (1.4 if no == 4 else 1.) lobj *= h['obj']
lcls *= h['cls'] * s lcls *= h['cls'] * s
bs = tobj.shape[0] # batch size bs = tobj.shape[0] # batch size

4
utils/plots.py
View File

@ -223,7 +223,7 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
plt.savefig('targets.jpg', dpi=200) plt.savefig('targets.jpg', dpi=200)
def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() def plot_study_txt(path='study/', x=None): # from utils.plots import *; plot_study_txt()
# Plot study.txt generated by test.py # Plot study.txt generated by test.py
fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
ax = ax.ravel() ax = ax.ravel()
@ -246,7 +246,7 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx
ax2.grid() ax2.grid()
ax2.set_xlim(0, 30) ax2.set_xlim(0, 30)
ax2.set_ylim(28, 50) ax2.set_ylim(29, 51)
ax2.set_yticks(np.arange(30, 55, 5)) ax2.set_yticks(np.arange(30, 55, 5))
ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_xlabel('GPU Speed (ms/img)')
ax2.set_ylabel('COCO AP val') ax2.set_ylabel('COCO AP val')

5
utils/torch_utils.py
View File

@ -225,8 +225,8 @@ def load_classifier(name='resnet101', n=2):
return model return model
def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
# scales img(bs,3,y,x) by ratio # scales img(bs,3,y,x) by ratio constrained to gs-multiple
if ratio == 1.0: if ratio == 1.0:
return img return img
else: else:
@ -234,7 +234,6 @@ def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio
s = (int(h * ratio), int(w * ratio)) # new size s = (int(h * ratio), int(w * ratio)) # new size
img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
if not same_shape: # pad/crop img if not same_shape: # pad/crop img
gs = 32 # (pixels) grid size
h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] 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 return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean