import numpy as np
import random
import torch
import torch.nn as nn
from models.common import Conv, DWConv
from utils.google_utils import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
super(CrossConv, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, (1, k), (1, s))
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class Sum(nn.Module):
# 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
self.iter = range(n - 1) # iter object
if weight:
self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
def forward(self, x):
y = x[0] # no weight
if self.weight:
w = torch.sigmoid(self.w) * 2
for i in self.iter:
y = y + x[i + 1] * w[i]
else:
for i in self.iter:
y = y + x[i + 1]
return y
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)))
class Ensemble(nn.ModuleList):
# Ensemble of models
def __init__(self):
super(Ensemble, self).__init__()
def forward(self, x, augment=False):
y = []
for module in self:
y.append(module(x, augment)[0])
# y = torch.stack(y).max(0)[0] # max ensemble
# y = torch.stack(y).mean(0) # mean ensemble
y = torch.cat(y, 1) # nms ensemble
return y, None # inference, train output
class ORT_NMS(torch.autograd.Function):
'''ONNX-Runtime NMS operation'''
@staticmethod
def forward(ctx,
boxes,
scores,
max_output_boxes_per_class=torch.tensor([100]),
iou_threshold=torch.tensor([0.45]),
score_threshold=torch.tensor([0.25])):
device = boxes.device
batch = scores.shape[0]
num_det = random.randint(0, 100)
batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device)
idxs = torch.arange(100, 100 + num_det).to(device)
zeros = torch.zeros((num_det,), dtype=torch.int64).to(device)
selected_indices = torch.cat([batches[None], zeros[None], idxs[None]], 0).T.contiguous()
selected_indices = selected_indices.to(torch.int64)
return selected_indices
@staticmethod
def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold):
return g.op("NonMaxSuppression", boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
class TRT_NMS(torch.autograd.Function):
'''TensorRT NMS operation'''
@staticmethod
def forward(
ctx,
boxes,
scores,
background_class=-1,
box_coding=1,
iou_threshold=0.45,
max_output_boxes=100,
plugin_version="1",
score_activation=0,
score_threshold=0.25,
):
batch_size, num_boxes, num_classes = scores.shape
num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32)
det_boxes = torch.randn(batch_size, max_output_boxes, 4)
det_scores = torch.randn(batch_size, max_output_boxes)
det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32)
return num_det, det_boxes, det_scores, det_classes
@staticmethod
def symbolic(g,
boxes,
scores,
background_class=-1,
box_coding=1,
iou_threshold=0.45,
max_output_boxes=100,
plugin_version="1",
score_activation=0,
score_threshold=0.25):
out = g.op("TRT::EfficientNMS_TRT",
boxes,
scores,
background_class_i=background_class,
box_coding_i=box_coding,
iou_threshold_f=iou_threshold,
max_output_boxes_i=max_output_boxes,
plugin_version_s=plugin_version,
score_activation_i=score_activation,
score_threshold_f=score_threshold,
outputs=4)
nums, boxes, scores, classes = out
return nums, boxes, scores, classes
class ONNX_ORT(nn.Module):
'''onnx module with ONNX-Runtime NMS operation.'''
def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None):
super().__init__()
self.device = device if device else torch.device("cpu")
self.max_obj = torch.tensor([max_obj]).to(device)
self.iou_threshold = torch.tensor([iou_thres]).to(device)
self.score_threshold = torch.tensor([score_thres]).to(device)
self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic
self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
dtype=torch.float32,
device=self.device)
def forward(self, x):
boxes = x[:, :, :4]
conf = x[:, :, 4:5]
scores = x[:, :, 5:]
scores *= conf
boxes @= self.convert_matrix
max_score, category_id = scores.max(2, keepdim=True)
dis = category_id.float() * self.max_wh
nmsbox = boxes + dis
max_score_tp = max_score.transpose(1, 2).contiguous()
selected_indices = ORT_NMS.apply(nmsbox, max_score_tp, self.max_obj, self.iou_threshold, self.score_threshold)
X, Y = selected_indices[:, 0], selected_indices[:, 2]
selected_boxes = boxes[X, Y, :]
selected_categories = category_id[X, Y, :].float()
selected_scores = max_score[X, Y, :]
X = X.unsqueeze(1).float()
return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1)
class ONNX_TRT(nn.Module):
'''onnx module with TensorRT NMS operation.'''
def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None):
super().__init__()
assert max_wh is None
self.device = device if device else torch.device('cpu')
self.background_class = -1,
self.box_coding = 1,
self.iou_threshold = iou_thres
self.max_obj = max_obj
self.plugin_version = '1'
self.score_activation = 0
self.score_threshold = score_thres
def forward(self, x):
boxes = x[:, :, :4]
conf = x[:, :, 4:5]
scores = x[:, :, 5:]
scores *= conf
num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(boxes, scores, self.background_class, self.box_coding,
self.iou_threshold, self.max_obj,
self.plugin_version, self.score_activation,
self.score_threshold)
return num_det, det_boxes, det_scores, det_classes
class End2End(nn.Module):
'''export onnx or tensorrt model with NMS operation.'''
def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None):
super().__init__()
device = device if device else torch.device('cpu')
assert isinstance(max_wh,(int)) or max_wh is None
self.model = model.to(device)
self.model.model[-1].end2end = True
self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT
self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device)
self.end2end.eval()
def forward(self, x):
x = self.model(x)
x = self.end2end(x)
return x
def attempt_load(weights, map_location=None):
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
attempt_download(w)
ckpt = torch.load(w, map_location=map_location) # load
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model
# Compatibility updates
for m in model.modules():
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
m.inplace = True # pytorch 1.7.0 compatibility
elif type(m) is nn.Upsample:
m.recompute_scale_factor = None # torch 1.11.0 compatibility
elif type(m) is Conv:
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if len(model) == 1:
return model[-1] # return model
else:
print('Ensemble created with %s\n' % weights)
for k in ['names', 'stride']:
setattr(model, k, getattr(model[-1], k))
return model # return ensemble