autoShape() default for PyTorch Hub models (#1692)

* Add autoshape parameter

* Remove autoshape call in ReadMe

* Update hubconf.py

* file/URI inputs and autoshape check passthrough

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

README.md

@@ -106,7 +106,7 @@ import torch
 from PIL import Image
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).autoshape()  # for PIL/cv2/np inputs and NMS
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)  # for PIL/cv2/np inputs and NMS
 
 # Images
 img1 = Image.open('zidane.jpg')

hubconf.py

@@ -17,7 +17,7 @@ dependencies = ['torch', 'yaml']
 set_logging()
 
 
-def create(name, pretrained, channels, classes):
+def create(name, pretrained, channels, classes, autoshape):
     """Creates a specified YOLOv5 model
 
     Arguments:
@@ -41,7 +41,8 @@ def create(name, pretrained, channels, classes):
             model.load_state_dict(state_dict, strict=False)  # load
             if len(ckpt['model'].names) == classes:
                 model.names = ckpt['model'].names  # set class names attribute
-            # model = model.autoshape()  # for PIL/cv2/np inputs and NMS
+            if autoshape:
+                model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
         return model
 
     except Exception as e:
@@ -50,7 +51,7 @@ def create(name, pretrained, channels, classes):
         raise Exception(s) from e
 
 
-def yolov5s(pretrained=False, channels=3, classes=80):
+def yolov5s(pretrained=False, channels=3, classes=80, autoshape=True):
     """YOLOv5-small model from https://github.com/ultralytics/yolov5
 
     Arguments:
@@ -61,10 +62,10 @@ def yolov5s(pretrained=False, channels=3, classes=80):
     Returns:
         pytorch model
     """
-    return create('yolov5s', pretrained, channels, classes)
+    return create('yolov5s', pretrained, channels, classes, autoshape)
 
 
-def yolov5m(pretrained=False, channels=3, classes=80):
+def yolov5m(pretrained=False, channels=3, classes=80, autoshape=True):
     """YOLOv5-medium model from https://github.com/ultralytics/yolov5
 
     Arguments:
@@ -75,10 +76,10 @@ def yolov5m(pretrained=False, channels=3, classes=80):
     Returns:
         pytorch model
     """
-    return create('yolov5m', pretrained, channels, classes)
+    return create('yolov5m', pretrained, channels, classes, autoshape)
 
 
-def yolov5l(pretrained=False, channels=3, classes=80):
+def yolov5l(pretrained=False, channels=3, classes=80, autoshape=True):
     """YOLOv5-large model from https://github.com/ultralytics/yolov5
 
     Arguments:
@@ -89,10 +90,10 @@ def yolov5l(pretrained=False, channels=3, classes=80):
     Returns:
         pytorch model
     """
-    return create('yolov5l', pretrained, channels, classes)
+    return create('yolov5l', pretrained, channels, classes, autoshape)
 
 
-def yolov5x(pretrained=False, channels=3, classes=80):
+def yolov5x(pretrained=False, channels=3, classes=80, autoshape=True):
     """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5
 
     Arguments:
@@ -103,10 +104,10 @@ def yolov5x(pretrained=False, channels=3, classes=80):
     Returns:
         pytorch model
     """
-    return create('yolov5x', pretrained, channels, classes)
+    return create('yolov5x', pretrained, channels, classes, autoshape)
 
 
-def custom(path_or_model='path/to/model.pt'):
+def custom(path_or_model='path/to/model.pt', autoshape=True):
     """YOLOv5-custom model from https://github.com/ultralytics/yolov5
 
     Arguments (3 options):
@@ -124,13 +125,12 @@ def custom(path_or_model='path/to/model.pt'):
     hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
     hub_model.load_state_dict(model.float().state_dict())  # load state_dict
     hub_model.names = model.names  # class names
-    return hub_model
+    return hub_model.autoshape() if autoshape else hub_model
 
 
 if __name__ == '__main__':
-    model = create(name='yolov5s', pretrained=True, channels=3, classes=80)  # pretrained example
+    model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True)  # pretrained example
     # model = custom(path_or_model='path/to/model.pt')  # custom example
-    model = model.autoshape()  # for PIL/cv2/np inputs and NMS
 
     # Verify inference
     from PIL import Image

models/common.py

@@ -2,6 +2,7 @@
 
 import math
 import numpy as np
+import requests
 import torch
 import torch.nn as nn
 from PIL import Image, ImageDraw
@@ -143,35 +144,42 @@ class autoShape(nn.Module):
         super(autoShape, self).__init__()
         self.model = model.eval()
 
+    def autoshape(self):
+        print('autoShape already enabled, skipping... ')  # model already converted to model.autoshape()
+        return self
+
     def forward(self, imgs, size=640, augment=False, profile=False):
-        # supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
-        #   opencv:     imgs = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
-        #   PIL:        imgs = Image.open('image.jpg')  # HWC x(720,1280,3)
-        #   numpy:      imgs = np.zeros((720,1280,3))  # HWC
-        #   torch:      imgs = torch.zeros(16,3,720,1280)  # BCHW
-        #   multiple:   imgs = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+        # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
+        #   filename:   imgs = 'data/samples/zidane.jpg'
+        #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
+        #   PIL:             = Image.open('image.jpg')  # HWC x(720,1280,3)
+        #   numpy:           = np.zeros((720,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,720,1280)  # BCHW
+        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
 
         p = next(self.model.parameters())  # for device and type
         if isinstance(imgs, torch.Tensor):  # torch
             return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
 
         # Pre-process
-        if not isinstance(imgs, list):
-            imgs = [imgs]
+        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
         shape0, shape1 = [], []  # image and inference shapes
-        batch = range(len(imgs))  # batch size
-        for i in batch:
-            imgs[i] = np.array(imgs[i])  # to numpy
-            if imgs[i].shape[0] < 5:  # image in CHW
-                imgs[i] = imgs[i].transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-            imgs[i] = imgs[i][:, :, :3] if imgs[i].ndim == 3 else np.tile(imgs[i][:, :, None], 3)  # enforce 3ch input
-            s = imgs[i].shape[:2]  # HWC
+        for i, im in enumerate(imgs):
+            if isinstance(im, str):  # filename or uri
+                im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)  # open
+            im = np.array(im)  # to numpy
+            if im.shape[0] < 5:  # image in CHW
+                im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
+            im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3)  # enforce 3ch input
+            s = im.shape[:2]  # HWC
             shape0.append(s)  # image shape
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
+            imgs[i] = im  # update
         shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(imgs[i], new_shape=shape1, auto=False)[0] for i in batch]  # pad
-        x = np.stack(x, 0) if batch[-1] else x[0][None]  # stack
+        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
+        x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
         x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
         x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
 
@@ -181,7 +189,7 @@ class autoShape(nn.Module):
         y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
 
         # Post-process
-        for i in batch:
+        for i in range(n):
             scale_coords(shape1, y[i][:, :4], shape0[i])
 
         return Detections(imgs, y, self.names)