Simplified inference (#1153)

models/common.py

@@ -1,9 +1,12 @@
 # This file contains modules common to various models
-import math
 
+import math
+import numpy as np
 import torch
 import torch.nn as nn
-from utils.general import non_max_suppression
+
+from utils.datasets import letterbox
+from utils.general import non_max_suppression, make_divisible, scale_coords
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -101,17 +104,68 @@ class Concat(nn.Module):
 
 class NMS(nn.Module):
     # Non-Maximum Suppression (NMS) module
-    conf = 0.3  # confidence threshold
-    iou = 0.6  # IoU threshold
+    conf = 0.25  # confidence threshold
+    iou = 0.45  # IoU threshold
     classes = None  # (optional list) filter by class
 
-    def __init__(self, dimension=1):
+    def __init__(self):
         super(NMS, self).__init__()
 
     def forward(self, x):
         return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
 
 
+class autoShape(nn.Module):
+    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    img_size = 640  # inference size (pixels)
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    classes = None  # (optional list) filter by class
+
+    def __init__(self, model):
+        super(autoShape, self).__init__()
+        self.model = model
+
+    def forward(self, x, size=640, augment=False, profile=False):
+        # supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
+        #   opencv:     x = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
+        #   PIL:        x = Image.open('image.jpg')  # HWC x(720,1280,3)
+        #   numpy:      x = np.zeros((720,1280,3))  # HWC
+        #   torch:      x = torch.zeros(16,3,720,1280)  # BCHW
+        #   multiple:   x = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        p = next(self.model.parameters())  # for device and type
+        if isinstance(x, torch.Tensor):  # torch
+            return self.model(x.to(p.device).type_as(p), augment, profile)  # inference
+
+        # Pre-process
+        if not isinstance(x, list):
+            x = [x]
+        shape0, shape1 = [], []  # image and inference shapes
+        batch = range(len(x))  # batch size
+        for i in batch:
+            x[i] = np.array(x[i])[:, :, :3]  # up to 3 channels if png
+            s = x[i].shape[:2]  # HWC
+            shape0.append(s)  # image shape
+            g = (size / max(s))  # gain
+            shape1.append([y * g for y in s])
+        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
+        x = [letterbox(x[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 = 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
+
+        # Inference
+        x = self.model(x, augment, profile)  # forward
+        x = non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
+
+        # Post-process
+        for i in batch:
+            if x[i] is not None:
+                x[i][:, :4] = scale_coords(shape1, x[i][:, :4], shape0[i])
+        return x
+
+
 class Flatten(nn.Module):
     # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
     @staticmethod