PyTorch Hub updates

README.md

@@ -87,7 +87,7 @@ To access an up-to-date working environment (with all dependencies including CUD
 
 - **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) 
 - **Google Colab Notebook** with 12 hours of free GPU time. <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart)
+- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)
 
 
 ## Citation

hubconf.py

@@ -0,0 +1,90 @@
+"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
+
+Usage:
+    import torch
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
+"""
+
+dependencies = ['torch', 'pyyaml']
+import torch
+
+from models.yolo import Model
+from utils import google_utils
+
+
+def create(name, pretrained, channels, classes):
+    """Creates a specified YOLOv5 model
+
+    Arguments:
+        name (str): name of model, i.e. 'yolov5s'
+        pretrained (bool): load pretrained weights into the model
+        channels (int): number of input channels
+        classes (int): number of model classes
+
+    Returns:
+        pytorch model
+    """
+    model = Model('models/%s.yaml' % name, channels, classes)
+    if pretrained:
+        ckpt = '%s.pt' % name  # checkpoint filename
+        google_utils.attempt_download(ckpt)  # download if not found locally
+        state_dict = torch.load(ckpt)['model'].state_dict()
+        state_dict = {k: v for k, v in state_dict if model.state_dict()[k].numel() == v.numel()}  # filter
+        model.load_state_dict(state_dict, strict=False)  # load
+    return model
+
+
+def yolov5s(pretrained=False, channels=3, classes=80):
+    """YOLOv5-small model from https://github.com/ultralytics/yolov5
+
+    Arguments:
+        pretrained (bool): load pretrained weights into the model, default=False
+        channels (int): number of input channels, default=3
+        classes (int): number of model classes, default=80
+
+    Returns:
+        pytorch model
+    """
+    return create('yolov5s', pretrained, channels, classes)
+
+
+def yolov5m(pretrained=False, channels=3, classes=80):
+    """YOLOv5-medium model from https://github.com/ultralytics/yolov5
+
+    Arguments:
+        pretrained (bool): load pretrained weights into the model, default=False
+        channels (int): number of input channels, default=3
+        classes (int): number of model classes, default=80
+
+    Returns:
+        pytorch model
+    """
+    return create('yolov5m', pretrained, channels, classes)
+
+
+def yolov5l(pretrained=False, channels=3, classes=80):
+    """YOLOv5-large model from https://github.com/ultralytics/yolov5
+
+    Arguments:
+        pretrained (bool): load pretrained weights into the model, default=False
+        channels (int): number of input channels, default=3
+        classes (int): number of model classes, default=80
+
+    Returns:
+        pytorch model
+    """
+    return create('yolov5l', pretrained, channels, classes)
+
+
+def yolov5x(pretrained=False, channels=3, classes=80):
+    """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5
+
+    Arguments:
+        pretrained (bool): load pretrained weights into the model, default=False
+        channels (int): number of input channels, default=3
+        classes (int): number of model classes, default=80
+
+    Returns:
+        pytorch model
+    """
+    return create('yolov5x', pretrained, channels, classes)

models/common.py

@@ -1,8 +1,6 @@
 # This file contains modules common to various models
 
 
-import torch.nn.functional as F
-
 from utils.utils import *
 
 
@@ -58,17 +56,6 @@ class BottleneckCSP(nn.Module):
         return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
 
 
-class ConvPlus(nn.Module):
-    # Plus-shaped convolution
-    def __init__(self, c1, c2, k=3, s=1, g=1, bias=True):  # ch_in, ch_out, kernel, stride, groups
-        super(ConvPlus, self).__init__()
-        self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias=bias)
-        self.cv2 = nn.Conv2d(c1, c2, (1, k), s, (0, k // 2), groups=g, bias=bias)
-
-    def forward(self, x):
-        return self.cv1(x) + self.cv2(x)
-
-
 class SPP(nn.Module):
     # Spatial pyramid pooling layer used in YOLOv3-SPP
     def __init__(self, c1, c2, k=(5, 9, 13)):
@@ -107,27 +94,3 @@ class Concat(nn.Module):
 
     def forward(self, x):
         return torch.cat(x, self.d)
-
-
-class MixConv2d(nn.Module):
-    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
-        super(MixConv2d, self).__init__()
-        groups = len(k)
-        if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
-        else:  # equal weight.numel() per group
-            b = [c2] + [0] * groups
-            a = np.eye(groups + 1, groups, k=-1)
-            a -= np.roll(a, 1, axis=1)
-            a *= np.array(k) ** 2
-            a[0] = 1
-            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
-
-        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-
-    def forward(self, x):
-        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))

models/experimental.py

@@ -2,7 +2,7 @@ from models.common import *
 
 
 class Sum(nn.Module):
-    # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
     def __init__(self, n, weight=False):  # n: number of inputs
         super(Sum, self).__init__()
         self.weight = weight  # apply weights boolean
@@ -23,6 +23,7 @@ class Sum(nn.Module):
 
 
 class GhostConv(nn.Module):
+    # Ghost Convolution https://github.com/huawei-noah/ghostnet
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
         super(GhostConv, self).__init__()
         c_ = c2 // 2  # hidden channels
@@ -35,6 +36,7 @@ class GhostConv(nn.Module):
 
 
 class GhostBottleneck(nn.Module):
+    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
     def __init__(self, c1, c2, k, s):
         super(GhostBottleneck, self).__init__()
         c_ = c2 // 2
@@ -46,3 +48,38 @@ class GhostBottleneck(nn.Module):
 
     def forward(self, x):
         return self.conv(x) + self.shortcut(x)
+
+
+class ConvPlus(nn.Module):
+    # Plus-shaped convolution
+    def __init__(self, c1, c2, k=3, s=1, g=1, bias=True):  # ch_in, ch_out, kernel, stride, groups
+        super(ConvPlus, self).__init__()
+        self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias=bias)
+        self.cv2 = nn.Conv2d(c1, c2, (1, k), s, (0, k // 2), groups=g, bias=bias)
+
+    def forward(self, x):
+        return self.cv1(x) + self.cv2(x)
+
+
+class MixConv2d(nn.Module):
+    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+        super(MixConv2d, self).__init__()
+        groups = len(k)
+        if equal_ch:  # equal c_ per group
+            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
+        else:  # equal weight.numel() per group
+            b = [c2] + [0] * groups
+            a = np.eye(groups + 1, groups, k=-1)
+            a -= np.roll(a, 1, axis=1)
+            a *= np.array(k) ** 2
+            a[0] = 1
+            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
+
+        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.LeakyReLU(0.1, inplace=True)
+
+    def forward(self, x):
+        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))

models/yolo.py

@@ -2,7 +2,7 @@ import argparse
 
 import yaml
 
-from models.common import *
+from models.experimental import *
 
 
 class Detect(nn.Module):
@@ -56,12 +56,12 @@ class Model(nn.Module):
         # Define model
         if nc:
             self.md['nc'] = nc  # override yaml value
-        self.model, self.save, ch = parse_model(self.md, ch=[ch])  # model, savelist, ch_out
-        # print([x.shape for x in self.forward(torch.zeros(1, 3, 64, 64))])
+        self.model, self.save = parse_model(self.md, ch=[ch])  # model, savelist, ch_out
+        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
 
         # Build strides, anchors
         m = self.model[-1]  # Detect()
-        m.stride = torch.tensor([64 / x.shape[-2] for x in self.forward(torch.zeros(1, 3, 64, 64))])  # forward
+        m.stride = torch.tensor([64 / x.shape[-2] for x in self.forward(torch.zeros(1, ch, 64, 64))])  # forward
         m.anchors /= m.stride.view(-1, 1, 1)
         self.stride = m.stride
 
@@ -200,7 +200,7 @@ def parse_model(md, ch):  # model_dict, input_channels(3)
         save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
         layers.append(m_)
         ch.append(c2)
-    return nn.Sequential(*layers), sorted(save), ch
+    return nn.Sequential(*layers), sorted(save)
 
 
 if __name__ == '__main__':

models/yolov3-spp_csp.yaml

@@ -1,55 +0,0 @@
-# parameters
-nc: 80  # number of classes
-depth_multiple: 1.0  # expand model depth
-width_multiple: 1.0  # expand layer channels
-
-# anchors
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# darknet53 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [32, 3, 1]],  # 0
-   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
-   [-1, 1, BottleneckCSP, [64]],
-   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
-   [-1, 2, BottleneckCSP, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
-   [-1, 8, BottleneckCSP, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
-   [-1, 8, BottleneckCSP, [512]],
-   [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
-   [-1, 4, BottleneckCSP, [1024]],  # 10
-  ]
-
-# yolov3-spp head
-# na = len(anchors[0])
-head:
-  [[-1, 1, Bottleneck, [1024, False]],  # 11
-   [-1, 1, SPP, [512, [5, 9, 13]]],
-   [-1, 1, Conv, [1024, 3, 1]],
-   [-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, Conv, [1024, 3, 1]],
-   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 16 (P5/32-large)
-
-   [-3, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
-   [-1, 1, Bottleneck, [512, False]],
-   [-1, 1, Bottleneck, [512, False]],
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, Conv, [512, 3, 1]],
-   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 24 (P4/16-medium)
-
-   [-3, 1, Conv, [128, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
-   [-1, 1, Bottleneck, [256, False]],
-   [-1, 2, Bottleneck, [256, False]],
-   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 30 (P3/8-small)
-
-   [[], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
-  ]