initial commit

hubconf.py

@@ -36,9 +36,7 @@ def create(name, pretrained, channels, classes):
             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
-
-            model.add_nms()  # add NMS module
-            model.eval()
+            # model = model.autoshape()  # cv2/PIL/np inference:  predictions = model(cv2.imread('img.jpg'))
         return model
 
     except Exception as e:

models/common.py

@@ -1,9 +1,12 @@
 # This file contains modules common to various models
 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 +104,37 @@ def forward(self, x):
 
 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):
+    # auto-reshape image size model wrapper
+    img_size = 640  # inference size (pixels)
+
+    def __init__(self, model):
+        super(autoShape, self).__init__()
+        self.model = model
+
+    def forward(self, x, shape=640, augment=False, profile=False):  # x = cv2.imread('img.jpg')
+        x0shape = x.shape[:2]
+        p = next(self.model.parameters())
+        x, ratio, (dw, dh) = letterbox(x, new_shape=make_divisible(shape or max(x0shape), int(self.stride.max())))
+        x1shape = x.shape[:2]
+        x = np.ascontiguousarray(x[:, :, ::-1].transpose(2, 0, 1))  # BGR to RGB, to 3x640x640
+        x = torch.from_numpy(x).to(p.device).type_as(p).unsqueeze(0) / 255.  # uint8 to fp16/32
+        x = self.model(x, augment, profile)  # forward
+        x[0][:, :4] = scale_coords(x1shape, x[0][:, :4], x0shape)
+        return x
+
+
 class Flatten(nn.Module):
     # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
     @staticmethod

models/yolo.py

@@ -11,11 +11,11 @@
 import torch
 import torch.nn as nn
 
-from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS
+from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
 from models.experimental import MixConv2d, CrossConv, C3
 from utils.general import check_anchor_order, make_divisible, check_file, set_logging
-from utils.torch_utils import (
-    time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device)
+from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
+    select_device, copy_attr
 
 
 class Detect(nn.Module):
@@ -140,6 +140,7 @@ def forward_once(self, x, profile=False):
         return x
 
     def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
+        # https://arxiv.org/abs/1708.02002 section 3.3
         # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
         m = self.model[-1]  # Detect() module
         for mi, s in zip(m.m, m.stride):  # from
@@ -170,15 +171,27 @@ def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
         self.info()
         return self
 
-    def add_nms(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        if type(self.model[-1]) is not NMS:  # if missing NMS
-            print('Adding NMS module... ')
+    def nms(self, mode=True):  # add or remove NMS module
+        present = type(self.model[-1]) is NMS  # last layer is NMS
+        if mode and not present:
+            print('Adding NMS... ')
             m = NMS()  # module
             m.f = -1  # from
             m.i = self.model[-1].i + 1  # index
             self.model.add_module(name='%s' % m.i, module=m)  # add
+            self.eval()
+        elif not mode and present:
+            print('Removing NMS... ')
+            self.model = self.model[:-1]  # remove
         return self
 
+    def autoshape(self):  # add autoShape module
+        print('Adding autoShape... ')
+        self.nms()  # add NMS
+        m = autoShape(self)  # wrap model
+        copy_attr(m, self, include=('names', 'stride', 'nc', 'autoshape'), exclude=())  # copy attributes
+        return m
+
     def info(self, verbose=False):  # print model information
         model_info(self, verbose)
 
@@ -263,10 +276,6 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
     # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
     # y = model(img, profile=True)
 
-    # ONNX export
-    # model.model[-1].export = True
-    # torch.onnx.export(model, img, opt.cfg.replace('.yaml', '.onnx'), verbose=True, opset_version=11)
-
     # Tensorboard
     # from torch.utils.tensorboard import SummaryWriter
     # tb_writer = SummaryWriter()