import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from layers import * from data import voc300, voc512, coco300, coco512 import os import warnings import math import numpy as np import cv2 class SSD_CON(nn.Module): """Single Shot Multibox Architecture The network is composed of a base VGG network followed by the added multibox conv layers. Each multibox layer branches into 1) conv2d for class conf scores 2) conv2d for localization predictions 3) associated priorbox layer to produce default bounding boxes specific to the layer's feature map size. See: https://arxiv.org/pdf/1512.02325.pdf for more details. Args: phase: (string) Can be "test" or "train" size: input image size base: VGG16 layers for input, size of either 300 or 500 extras: extra layers that feed to multibox loc and conf layers head: "multibox head" consists of loc and conf conv layers """ def __init__(self, phase, size, base, extras, head, num_classes): super(SSD_CON, self).__init__() self.phase = phase self.num_classes = num_classes if(size==300): self.cfg = (coco300, voc300)[num_classes == 21] else: self.cfg = (coco512, voc512)[num_classes == 21] self.priorbox = PriorBox(self.cfg) self.priors = Variable(self.priorbox.forward(), volatile=True) self.size = size # SSD network self.vgg = nn.ModuleList(base) # Layer learns to scale the l2 normalized features from conv4_3 self.L2Norm = L2Norm(512, 20) self.extras = nn.ModuleList(extras) self.loc = nn.ModuleList(head[0]) self.conf = nn.ModuleList(head[1]) self.softmax = nn.Softmax(dim=-1) if phase == 'test': # self.softmax = nn.Softmax(dim=-1) # self.detect = Detect(num_classes, 0, 200, 0.01, 0.45) self.detect = Detect() def forward(self, x): """Applies network layers and ops on input image(s) x. Args: x: input image or batch of images. Shape: [batch,3,300,300]. Return: Depending on phase: test: Variable(tensor) of output class label predictions, confidence score, and corresponding location predictions for each object detected. Shape: [batch,topk,7] train: list of concat outputs from: 1: confidence layers, Shape: [batch*num_priors,num_classes] 2: localization layers, Shape: [batch,num_priors*4] 3: priorbox layers, Shape: [2,num_priors*4] """ x_flip = x.clone() x_flip = flip(x_flip,3) sources = list() loc = list() conf = list() # apply vgg up to conv4_3 relu for k in range(23): x = self.vgg[k](x) s = self.L2Norm(x) sources.append(s) # apply vgg up to fc7 for k in range(23, len(self.vgg)): x = self.vgg[k](x) sources.append(x) # apply extra layers and cache source layer outputs for k, v in enumerate(self.extras): x = F.relu(v(x), inplace=True) if k % 2 == 1: sources.append(x) # apply multibox head to source layers for (x, l, c) in zip(sources, self.loc, self.conf): loc.append(l(x).permute(0, 2, 3, 1).contiguous()) conf.append(c(x).permute(0, 2, 3, 1).contiguous()) loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1) conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1) # zero_mask = torch.cat([o.view(o.size(0), -1) for o in zero_mask], 1) if self.phase == "test": # output = self.detect( output = self.detect.apply(self.num_classes, 0, 200, 0.01, 0.45, loc.view(loc.size(0), -1, 4), # loc preds self.softmax(conf.view(conf.size(0), -1, self.num_classes)), # conf preds self.priors.type(type(x.data)) # default boxes ) else: output = ( loc.view(loc.size(0), -1, 4), conf.view(conf.size(0), -1, self.num_classes), self.priors ) loc = loc.view(loc.size(0), -1, 4) conf = self.softmax(conf.view(conf.size(0), -1, self.num_classes)) # basic sources_flip = list() loc_flip = list() conf_flip = list() # apply vgg up to conv4_3 relu for k in range(23): x_flip = self.vgg[k](x_flip) s_flip = self.L2Norm(x_flip) sources_flip.append(s_flip) # apply vgg up to fc7 for k in range(23, len(self.vgg)): x_flip = self.vgg[k](x_flip) sources_flip.append(x_flip) # apply extra layers and cache source layer outputs for k, v in enumerate(self.extras): x_flip = F.relu(v(x_flip), inplace=True) if k % 2 == 1: sources_flip.append(x_flip) # apply multibox head to source layers for (x_flip, l, c) in zip(sources_flip, self.loc, self.conf): append_loc = l(x_flip).permute(0, 2, 3, 1).contiguous() append_conf = c(x_flip).permute(0, 2, 3, 1).contiguous() append_loc = flip(append_loc,2) append_conf = flip(append_conf,2) loc_flip.append(append_loc) conf_flip.append(append_conf) loc_flip = torch.cat([o.view(o.size(0), -1) for o in loc_flip], 1) conf_flip = torch.cat([o.view(o.size(0), -1) for o in conf_flip], 1) loc_flip = loc_flip.view(loc_flip.size(0), -1, 4) conf_flip = self.softmax(conf_flip.view(conf.size(0), -1, self.num_classes)) if self.phase == "test": return output else: return output, conf, conf_flip, loc, loc_flip def load_weights(self, base_file): other, ext = os.path.splitext(base_file) if ext == '.pkl' or '.pth': print('Loading weights into state dict...') self.load_state_dict(torch.load(base_file, map_location=lambda storage, loc: storage)) print('Finished!') else: print('Sorry only .pth and .pkl files supported.') # This function is derived from torchvision VGG make_layers() # https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py def vgg(cfg, i, batch_norm=False): layers = [] in_channels = i for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] elif v == 'C': layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6) conv7 = nn.Conv2d(1024, 1024, kernel_size=1) layers += [pool5, conv6, nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)] return layers def add_extras(cfg, i, batch_norm=False): # Extra layers added to VGG for feature scaling layers = [] in_channels = i flag = False for k, v in enumerate(cfg): if in_channels != 'S': if v == 'S': layers += [nn.Conv2d(in_channels, cfg[k + 1], kernel_size=(1, 3)[flag], stride=2, padding=1)] elif v=='K': layers += [nn.Conv2d(in_channels, 256, kernel_size=4, stride=1, padding=1)] else: layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])] flag = not flag in_channels = v return layers def multibox(vgg, extra_layers, cfg, num_classes): loc_layers = [] conf_layers = [] vgg_source = [21, -2] for k, v in enumerate(vgg_source): loc_layers += [nn.Conv2d(vgg[v].out_channels, cfg[k] * 4, kernel_size=3, padding=1)] conf_layers += [nn.Conv2d(vgg[v].out_channels, cfg[k] * num_classes, kernel_size=3, padding=1)] for k, v in enumerate(extra_layers[1::2], 2): loc_layers += [nn.Conv2d(v.out_channels, cfg[k] * 4, kernel_size=3, padding=1)] conf_layers += [nn.Conv2d(v.out_channels, cfg[k] * num_classes, kernel_size=3, padding=1)] return vgg, extra_layers, (loc_layers, conf_layers) base = { '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M', 512, 512, 512], '512': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512], } extras = { '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256], '512': [256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128, 'K'], } mbox = { '300': [4, 6, 6, 6, 4, 4], # number of boxes per feature map location '512': [4, 6, 6, 6, 6, 4, 4], } def flip(x, dim): dim = x.dim() + dim if dim < 0 else dim return x[tuple(slice(None, None) if i != dim else torch.arange(x.size(i)-1, -1, -1).long() for i in range(x.dim()))] class GaussianNoise(nn.Module): def __init__(self, batch_size, input_size=(3, 300, 300), mean=0, std=0.15): super(GaussianNoise, self).__init__() self.shape = (batch_size, ) + input_size self.noise = Variable(torch.zeros(self.shape).cuda()) self.mean = mean self.std = std def forward(self, x): self.noise.data.normal_(self.mean, std=self.std) if x.size(0) == self.noise.size(0): return x + self.noise else: #print('---- Noise Size ') return x + self.noise[:x.size(0)] def build_ssd_con(phase, size=300, num_classes=21): if phase != "test" and phase != "train": print("ERROR: Phase: " + phase + " not recognized") return # if size != 300: # print("ERROR: You specified size " + repr(size) + ". However, " + # "currently only SSD300 (size=300) is supported!") # return base_, extras_, head_ = multibox(vgg(base[str(size)], 3), add_extras(extras[str(size)], 1024), mbox[str(size)], num_classes) return SSD_CON(phase, size, base_, extras_, head_, num_classes)