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gccopen / models /experimental.py

Javier Flores

de28dc1 over 1 year ago

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	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