import os from collections import defaultdict,OrderedDict import torch.nn as nn from utils.parse_config import * from utils.utils import * from utils.syncbn import SyncBN import time import math batch_norm=SyncBN #nn.BatchNorm2d def create_modules(module_defs): """ Constructs module list of layer blocks from module configuration in module_defs """ hyperparams = module_defs.pop(0) output_filters = [int(hyperparams['channels'])] module_list = nn.ModuleList() yolo_layer_count = 0 for i, module_def in enumerate(module_defs): modules = nn.Sequential() if module_def['type'] == 'convolutional': bn = int(module_def['batch_normalize']) filters = int(module_def['filters']) kernel_size = int(module_def['size']) pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0 modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1], out_channels=filters, kernel_size=kernel_size, stride=int(module_def['stride']), padding=pad, bias=not bn)) if bn: modules.add_module('batch_norm_%d' % i, batch_norm(filters)) if module_def['activation'] == 'leaky': modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1)) elif module_def['type'] == 'maxpool': kernel_size = int(module_def['size']) stride = int(module_def['stride']) if kernel_size == 2 and stride == 1: modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1))) maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2)) modules.add_module('maxpool_%d' % i, maxpool) elif module_def['type'] == 'upsample': # upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest') # WARNING: deprecated upsample = Upsample(scale_factor=int(module_def['stride'])) modules.add_module('upsample_%d' % i, upsample) elif module_def['type'] == 'route': layers = [int(x) for x in module_def['layers'].split(',')] filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers]) modules.add_module('route_%d' % i, EmptyLayer()) elif module_def['type'] == 'shortcut': filters = output_filters[int(module_def['from'])] modules.add_module('shortcut_%d' % i, EmptyLayer()) elif module_def['type'] == 'yolo': anchor_idxs = [int(x) for x in module_def['mask'].split(',')] # Extract anchors anchors = [float(x) for x in module_def['anchors'].split(',')] anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)] anchors = [anchors[i] for i in anchor_idxs] nC = int(module_def['classes']) # number of classes img_size = (int(hyperparams['width']),int(hyperparams['height'])) # Define detection layer yolo_layer = YOLOLayer(anchors, nC, hyperparams['nID'], img_size, yolo_layer_count, cfg=hyperparams['cfg']) modules.add_module('yolo_%d' % i, yolo_layer) yolo_layer_count += 1 # Register module list and number of output filters module_list.append(modules) output_filters.append(filters) return hyperparams, module_list class EmptyLayer(nn.Module): """Placeholder for 'route' and 'shortcut' layers""" def __init__(self): super(EmptyLayer, self).__init__() def forward(self, x): return x class Upsample(nn.Module): # Custom Upsample layer (nn.Upsample gives deprecated warning message) def __init__(self, scale_factor=1, mode='nearest'): super(Upsample, self).__init__() self.scale_factor = scale_factor self.mode = mode def forward(self, x): return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode) class YOLOLayer(nn.Module): def __init__(self, anchors, nC, nID, img_size, yolo_layer, cfg): super(YOLOLayer, self).__init__() self.layer = yolo_layer nA = len(anchors) self.anchors = torch.FloatTensor(anchors) self.nA = nA # number of anchors (3) self.nC = nC # number of classes (80) self.nID = nID # number of identities self.img_size = 0 self.emb_dim = 512 self.SmoothL1Loss = nn.SmoothL1Loss() self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1) self.CrossEntropyLoss = nn.CrossEntropyLoss() self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1) self.s_c = nn.Parameter(1*torch.ones(1)) # -4.15 self.s_r = nn.Parameter(1*torch.ones(1)) # -4.85 self.s_id = nn.Parameter(1*torch.ones(1)) # -2.3 self.emb_scale = math.sqrt(2) * math.log(self.nID-1) def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False): p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...] nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1] if self.img_size != img_size: create_grids(self, img_size, nGh, nGw) if p.is_cuda: self.grid_xy = self.grid_xy.cuda() self.anchor_wh = self.anchor_wh.cuda() # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh) p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction p_emb = p_emb.permute(0,2,3,1).contiguous() p_box = p[..., :4] p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf # Training if targets is not None: if test_emb: tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw) else: tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw) tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda() mask = tconf > 0 # Compute losses nT = sum([len(x) for x in targets]) # number of targets nM = mask.sum().float() # number of anchors (assigned to targets) nP = torch.ones_like(mask).sum().float() if nM > 0: lbox = self.SmoothL1Loss(p_box[mask], tbox[mask]) else: FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor lbox, lconf = FT([0]), FT([0]) lconf = self.SoftmaxLoss(p_conf, tconf) lid = torch.Tensor(1).fill_(0).squeeze().cuda() emb_mask,_ = mask.max(1) # For convenience we use max(1) to decide the id, TODO: more reseanable strategy tids,_ = tids.max(1) tids = tids[emb_mask] embedding = p_emb[emb_mask].contiguous() embedding = self.emb_scale * F.normalize(embedding) nI = emb_mask.sum().float() if test_emb: if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0: return torch.zeros(0, self. emb_dim+1).cuda() emb_and_gt = torch.cat([embedding, tids.float()], dim=1) return emb_and_gt if len(embedding) > 1: logits = classifier(embedding).contiguous() lid = self.IDLoss(logits, tids.squeeze()) # Sum loss components #loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \ # (self.s_r + self.s_c + self.s_id) #loss *= 0.5 loss = 0*lbox + 0*lconf + 1*lid return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT else: p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1) p_emb = p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous() p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1) p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p)) p[..., :4] *= self.stride # reshape from [nB, nA, nGh, nGw, 5 + nD] to [nB, -1, 5+nD] return p.view(nB, -1, p.shape[-1]) class Darknet(nn.Module): """YOLOv3 object detection model""" def __init__(self, cfg_path, img_size=(1088, 608), nID=1591, test_emb=False): super(Darknet, self).__init__() self.module_defs = parse_model_cfg(cfg_path) self.module_defs[0]['cfg'] = cfg_path self.module_defs[0]['nID'] = nID self.hyperparams, self.module_list = create_modules(self.module_defs) self.img_size = img_size self.loss_names = ['loss', 'box', 'conf', 'id', 'nT'] self.losses = OrderedDict() for ln in self.loss_names: self.losses[ln] = 0 self.emb_dim = 512 self.classifier = nn.Linear(self.emb_dim, nID) self.test_emb=test_emb def forward(self, x, targets=None, targets_len=None): self.losses = OrderedDict() for ln in self.loss_names: self.losses[ln] = 0 is_training = (targets is not None) and (not self.test_emb) #img_size = x.shape[-1] layer_outputs = [] output = [] for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)): mtype = module_def['type'] if mtype in ['convolutional', 'upsample', 'maxpool']: x = module(x) elif mtype == 'route': layer_i = [int(x) for x in module_def['layers'].split(',')] if len(layer_i) == 1: x = layer_outputs[layer_i[0]] else: x = torch.cat([layer_outputs[i] for i in layer_i], 1) elif mtype == 'shortcut': layer_i = int(module_def['from']) x = layer_outputs[-1] + layer_outputs[layer_i] elif mtype == 'yolo': if is_training: # get loss targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)] x, *losses = module[0](x, self.img_size, targets, self.classifier) for name, loss in zip(self.loss_names, losses): self.losses[name] += loss elif self.test_emb: targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)] x = module[0](x, self.img_size, targets, self.classifier, self.test_emb) else: # get detections x = module[0](x, self.img_size) output.append(x) layer_outputs.append(x) if is_training: self.losses['nT'] /= 3 output = [o.squeeze() for o in output] return sum(output), torch.Tensor(list(self.losses.values())).cuda() elif self.test_emb: return torch.cat(output, 0) return torch.cat(output, 1) def create_grids(self, img_size, nGh, nGw): self.stride = img_size[0]/nGw assert self.stride == img_size[1] / nGh # build xy offsets grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float() grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float() #grid_y = grid_x.permute(0, 1, 3, 2) self.grid_xy = torch.stack((grid_x, grid_y), 4) # build wh gains self.anchor_vec = self.anchors / self.stride self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2) def load_darknet_weights(self, weights, cutoff=-1): # Parses and loads the weights stored in 'weights' # cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved) weights_file = weights.split(os.sep)[-1] # Try to download weights if not available locally if not os.path.isfile(weights): try: os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights) except IOError: print(weights + ' not found') # Establish cutoffs if weights_file == 'darknet53.conv.74': cutoff = 75 elif weights_file == 'yolov3-tiny.conv.15': cutoff = 15 # Open the weights file fp = open(weights, 'rb') header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values # Needed to write header when saving weights self.header_info = header self.seen = header[3] # number of images seen during training weights = np.fromfile(fp, dtype=np.float32) # The rest are weights fp.close() ptr = 0 for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])): if module_def['type'] == 'convolutional': conv_layer = module[0] if module_def['batch_normalize']: # Load BN bias, weights, running mean and running variance bn_layer = module[1] num_b = bn_layer.bias.numel() # Number of biases # Bias bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias) bn_layer.bias.data.copy_(bn_b) ptr += num_b # Weight bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight) bn_layer.weight.data.copy_(bn_w) ptr += num_b # Running Mean bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean) bn_layer.running_mean.data.copy_(bn_rm) ptr += num_b # Running Var bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var) bn_layer.running_var.data.copy_(bn_rv) ptr += num_b else: # Load conv. bias num_b = conv_layer.bias.numel() conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias) conv_layer.bias.data.copy_(conv_b) ptr += num_b # Load conv. weights num_w = conv_layer.weight.numel() conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight) conv_layer.weight.data.copy_(conv_w) ptr += num_w """ @:param path - path of the new weights file @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved) """ def save_weights(self, path, cutoff=-1): fp = open(path, 'wb') self.header_info[3] = self.seen # number of images seen during training self.header_info.tofile(fp) # Iterate through layers for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])): if module_def['type'] == 'convolutional': conv_layer = module[0] # If batch norm, load bn first if module_def['batch_normalize']: bn_layer = module[1] bn_layer.bias.data.cpu().numpy().tofile(fp) bn_layer.weight.data.cpu().numpy().tofile(fp) bn_layer.running_mean.data.cpu().numpy().tofile(fp) bn_layer.running_var.data.cpu().numpy().tofile(fp) # Load conv bias else: conv_layer.bias.data.cpu().numpy().tofile(fp) # Load conv weights conv_layer.weight.data.cpu().numpy().tofile(fp) fp.close()