model_with_confidence.py

import torch.nn as nn
import torch
import math
import time
import torch.utils.model_zoo as model_zoo
from utils import BasicBlock, Bottleneck, BBoxTransform, ClipBoxes
from anchors import Anchors
import losses_with_confidence as losses
# from lib.nms.pth_nms import pth_nms
from lib_new.nms.gpu_nms import gpu_nms


def nms(dets, thresh):
	"Dispatch to either CPU or GPU NMS implementations.\
	Accept dets as tensor"""
	dets = dets.cpu().detach().numpy()
	return gpu_nms(dets, thresh)


model_urls = {
	'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
	'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
	'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
	'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
	'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


class PyramidFeatures(nn.Module):
	def __init__(self, C3_size, C4_size, C5_size, feature_size=256):
		super(PyramidFeatures, self).__init__()

		# upsample C5 to get P5 from the FPN paper
		self.P5_1 = nn.Conv2d(C5_size, feature_size, kernel_size=1, stride=1, padding=0)
		self.P5_upsampled = nn.Upsample(scale_factor=2, mode='nearest')
		self.P5_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=1, padding=1)

		# add P5 elementwise to C4
		self.P4_1 = nn.Conv2d(C4_size, feature_size, kernel_size=1, stride=1, padding=0)
		self.P4_upsampled = nn.Upsample(scale_factor=2, mode='nearest')
		self.P4_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=1, padding=1)

		# add P4 elementwise to C3
		self.P3_1 = nn.Conv2d(C3_size, feature_size, kernel_size=1, stride=1, padding=0)
		self.P3_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=1, padding=1)

		# "P6 is obtained via a 3x3 stride-2 conv on C5"
		self.P6 = nn.Conv2d(C5_size, feature_size, kernel_size=3, stride=2, padding=1)

		# "P7 is computed by applying ReLU followed by a 3x3 stride-2 conv on P6"
		self.P7_1 = nn.ReLU()
		self.P7_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=2, padding=1)

	def forward(self, inputs):
		C3, C4, C5 = inputs

		P5_x = self.P5_1(C5)
		P5_upsampled_x = self.P5_upsampled(P5_x)
		P5_x = self.P5_2(P5_x)

		P4_x = self.P4_1(C4)
		P4_x = P5_upsampled_x + P4_x
		P4_upsampled_x = self.P4_upsampled(P4_x)
		P4_x = self.P4_2(P4_x)

		P3_x = self.P3_1(C3)
		P3_x = P3_x + P4_upsampled_x
		P3_x = self.P3_2(P3_x)

		P6_x = self.P6(C5)

		P7_x = self.P7_1(P6_x)
		P7_x = self.P7_2(P7_x)

		return [P3_x, P4_x, P5_x, P6_x, P7_x]


class RegressionModel(nn.Module):
	def __init__(self, num_features_in, num_anchors=9, feature_size=256):
		super(RegressionModel, self).__init__()

		self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1)
		self.act1 = nn.ReLU()

		self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
		self.act2 = nn.ReLU()

		self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
		self.act3 = nn.ReLU()

		self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
		self.act4 = nn.ReLU()

		self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size=3, padding=1)

	def forward(self, x):
		out = self.conv1(x)
		out = self.act1(out)

		out = self.conv2(out)
		out = self.act2(out)

		out = self.conv3(out)
		out = self.act3(out)

		out = self.conv4(out)
		out = self.act4(out)

		out = self.output(out)

		# out is B x C x W x H, with C = 4*num_anchors
		out = out.permute(0, 2, 3, 1)

		return out.contiguous().view(out.shape[0], -1, 4)


class ClassificationModel(nn.Module):
	def __init__(self, num_features_in, num_anchors=9, num_classes=80, prior=0.01, feature_size=256):
		super(ClassificationModel, self).__init__()

		self.num_classes = num_classes
		self.num_anchors = num_anchors

		self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1)
		self.act1 = nn.ReLU()

		self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
		self.act2 = nn.ReLU()

		self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
		self.act3 = nn.ReLU()

		self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
		self.act4 = nn.ReLU()

		self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1)
		self.output_act = nn.Sigmoid()

	def forward(self, x):
		out = self.conv1(x)
		out = self.act1(out)

		out = self.conv2(out)
		out = self.act2(out)

		out = self.conv3(out)
		out = self.act3(out)

		out = self.conv4(out)
		out = self.act4(out)

		out = self.output(out)
		out = self.output_act(out)

		# out is B x C x W x H, with C = n_classes + n_anchors
		out1 = out.permute(0, 2, 3, 1)

		batch_size, width, height, channels = out1.shape

		out2 = out1.view(batch_size, width, height, self.num_anchors, self.num_classes)

		return out2.contiguous().view(x.shape[0], -1, self.num_classes)


class ResNet(nn.Module):

	def __init__(self, num_classes, block, layers):
		self.inplanes = 64
		super(ResNet, self).__init__()
		self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
		self.bn1 = nn.BatchNorm2d(64)
		self.relu = nn.ReLU(inplace=True)
		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
		self.layer1 = self._make_layer(block, 64, layers[0])
		self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
		self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
		self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

		if block == BasicBlock:
			fpn_sizes = [self.layer2[layers[1] - 1].conv2.out_channels, self.layer3[layers[2] - 1].conv2.out_channels,
						 self.layer4[layers[3] - 1].conv2.out_channels]
		elif block == Bottleneck:
			fpn_sizes = [self.layer2[layers[1] - 1].conv3.out_channels, self.layer3[layers[2] - 1].conv3.out_channels,
						 self.layer4[layers[3] - 1].conv3.out_channels]

		self.fpn = PyramidFeatures(fpn_sizes[0], fpn_sizes[1], fpn_sizes[2])

		self.regressionModel = RegressionModel(256)
		self.classificationModel = ClassificationModel(256, num_classes=num_classes)

		self.anchors = Anchors()

		self.regressBoxes = BBoxTransform()

		self.clipBoxes = ClipBoxes()

		self.focalLoss = losses.FocalLoss()

		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
				m.weight.data.normal_(0, math.sqrt(2. / n))
			elif isinstance(m, nn.BatchNorm2d):
				m.weight.data.fill_(1)
				m.bias.data.zero_()

		prior = 0.01

		self.classificationModel.output.weight.data.fill_(0)
		self.classificationModel.output.bias.data.fill_(-math.log((1.0 - prior) / prior))

		self.regressionModel.output.weight.data.fill_(0)
		self.regressionModel.output.bias.data.fill_(0)

		self.freeze_bn()

	def _make_layer(self, block, planes, blocks, stride=1):
		downsample = None
		if stride != 1 or self.inplanes != planes * block.expansion:
			downsample = nn.Sequential(
				nn.Conv2d(self.inplanes, planes * block.expansion,
						  kernel_size=1, stride=stride, bias=False),
				nn.BatchNorm2d(planes * block.expansion),
			)

		layers = []
		layers.append(block(self.inplanes, planes, stride, downsample))
		self.inplanes = planes * block.expansion
		for i in range(1, blocks):
			layers.append(block(self.inplanes, planes))

		return nn.Sequential(*layers)

	def freeze_bn(self):
		'''Freeze BatchNorm layers.'''
		for layer in self.modules():
			if isinstance(layer, nn.BatchNorm2d):
				layer.eval()

	def forward(self, inputs, test=False, score_threshold=0.05):

		if self.training or test:
			img_batch, annotations = inputs
		else:
			img_batch = inputs

		x = self.conv1(img_batch)
		x = self.bn1(x)
		x = self.relu(x)
		x = self.maxpool(x)

		x1 = self.layer1(x)
		x2 = self.layer2(x1)
		x3 = self.layer3(x2)
		x4 = self.layer4(x3)

		features = self.fpn([x2, x3, x4])
		regression = torch.cat([self.regressionModel(feature) for feature in features], dim=1)

		classification = torch.cat([self.classificationModel(feature) for feature in features], dim=1)

		anchors = self.anchors(img_batch)

		if self.training or test:
			return self.focalLoss(classification, regression, anchors, annotations)
		else:
			transformed_anchors = self.regressBoxes(anchors, regression)
			transformed_anchors = self.clipBoxes(transformed_anchors, img_batch)

			scores = torch.max(classification, dim=2, keepdim=True)[0]

			scores_over_thresh = (scores > score_threshold)[0, :, 0]

			if scores_over_thresh.sum() == 0:
				# no boxes to NMS, just return
				return [torch.zeros(0), torch.zeros(0), torch.zeros(0, 4)]

			classification = classification[:, scores_over_thresh, :]
			transformed_anchors = transformed_anchors[:, scores_over_thresh, :]
			scores = scores[:, scores_over_thresh, :]

			anchors_nms_idx = nms(torch.cat([transformed_anchors, scores], dim=2)[0, :, :], 0.3)

			nms_scores, nms_class = classification[0, anchors_nms_idx, :].max(dim=1)

			return [nms_scores, nms_class, transformed_anchors[0, anchors_nms_idx, :]]


def resnet18(num_classes, pretrained=False, **kwargs):
	"""Constructs a ResNet-18 model.
	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(num_classes, BasicBlock, [2, 2, 2, 2], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet18'], model_dir='.'), strict=False)
	return model


def resnet34(num_classes, pretrained=False, **kwargs):
	"""Constructs a ResNet-34 model.
	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(num_classes, BasicBlock, [3, 4, 6, 3], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet34'], model_dir='.'), strict=False)
	return model


def resnet50(num_classes, pretrained=False, **kwargs):
	"""Constructs a ResNet-50 model.
	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(num_classes, Bottleneck, [3, 4, 6, 3], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet50'], model_dir='.'), strict=False)
	return model


def resnet101(num_classes, pretrained=False, **kwargs):
	"""Constructs a ResNet-101 model.
	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(num_classes, Bottleneck, [3, 4, 23, 3], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet101'], model_dir='.'), strict=False)
	return model


def resnet152(num_classes, pretrained=False, **kwargs):
	"""Constructs a ResNet-152 model.
	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(num_classes, Bottleneck, [3, 8, 36, 3], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet152'], model_dir='.'), strict=False)
	return model


if __name__ == '__main__':
	import os

	os.environ['CUDA_VISIBLE_DEVICES'] = '5'
	model = resnet18(num_classes=2, pretrained=False).cuda()
	x = torch.rand(1, 3, 512, 512).cuda()
	label = [[10, 20, 30, 40, 50, 60, 0]]
	label = torch.Tensor(label).unsqueeze(0).cuda()
	# model.eval()
	y = model([x, label])
	print(y)