import torch.nn.functional as F from utils.google_utils import * from utils.parse_config import * from utils.utils import * ONNX_EXPORT = False def create_modules(module_defs, img_size, arc): # 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() routs = [] # list of layers which rout to deeper layes yolo_index = -1 for i, mdef in enumerate(module_defs): modules = nn.Sequential() if mdef['type'] == 'convolutional': bn = int(mdef['batch_normalize']) filters = int(mdef['filters']) kernel_size = int(mdef['size']) stride = int(mdef['stride']) if 'stride' in mdef else (int(mdef['stride_y']), int(mdef['stride_x'])) pad = (kernel_size - 1) // 2 if int(mdef['pad']) else 0 modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1], out_channels=filters, kernel_size=kernel_size, stride=stride, padding=pad, bias=not bn)) if bn: modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.1)) if mdef['activation'] == 'leaky': # TODO: activation study https://github.com/ultralytics/yolov3/issues/441 modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True)) # modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10)) # modules.add_module('activation', Swish()) elif mdef['type'] == 'maxpool': kernel_size = int(mdef['size']) stride = int(mdef['stride']) maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2)) if kernel_size == 2 and stride == 1: # yolov3-tiny modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1))) modules.add_module('MaxPool2d', maxpool) else: modules = maxpool elif mdef['type'] == 'upsample': modules = nn.Upsample(scale_factor=int(mdef['stride']), mode='nearest') elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer layers = [int(x) for x in mdef['layers'].split(',')] filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers]) routs.extend([l if l > 0 else l + i for l in layers]) # if mdef[i+1]['type'] == 'reorg3d': # modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest') # reorg3d elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer filters = output_filters[int(mdef['from'])] layer = int(mdef['from']) routs.extend([i + layer if layer < 0 else layer]) elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale # torch.Size([16, 128, 104, 104]) # torch.Size([16, 64, 208, 208]) <-- # stride 2 interpolate dimensions 2 and 3 to cat with prior layer pass elif mdef['type'] == 'yolo': yolo_index += 1 mask = [int(x) for x in mdef['mask'].split(',')] # anchor mask modules = YOLOLayer(anchors=mdef['anchors'][mask], # anchor list nc=int(mdef['classes']), # number of classes img_size=img_size, # (416, 416) yolo_index=yolo_index, # 0, 1 or 2 arc=arc) # yolo architecture # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3) try: if arc == 'defaultpw' or arc == 'Fdefaultpw': # default with positive weights b = [-4, -3.6] # obj, cls elif arc == 'default': # default no pw (40 cls, 80 obj) b = [-5.5, -4.0] elif arc == 'uBCE': # unified BCE (80 classes) b = [0, -8.5] elif arc == 'uCE': # unified CE (1 background + 80 classes) b = [10, -0.1] elif arc == 'Fdefault': # Focal default no pw (28 cls, 21 obj, no pw) b = [-2.1, -1.8] elif arc == 'uFBCE' or arc == 'uFBCEpw': # unified FocalBCE (5120 obj, 80 classes) b = [0, -6.5] elif arc == 'uFCE': # unified FocalCE (64 cls, 1 background + 80 classes) b = [7.7, -1.1] bias = module_list[-1][0].bias.view(len(mask), -1) # 255 to 3x85 bias[:, 4] += b[0] - bias[:, 4].mean() # obj bias[:, 5:] += b[1] - bias[:, 5:].mean() # cls # bias = torch.load('weights/yolov3-spp.bias.pt')[yolo_index] # list of tensors [3x85, 3x85, 3x85] module_list[-1][0].bias = torch.nn.Parameter(bias.view(-1)) # utils.print_model_biases(model) except: print('WARNING: smart bias initialization failure.') else: print('Warning: Unrecognized Layer Type: ' + mdef['type']) # Register module list and number of output filters module_list.append(modules) output_filters.append(filters) return module_list, routs class Swish(nn.Module): def __init__(self): super().__init__() def forward(self, x): return x.mul_(torch.sigmoid(x)) class Mish(nn.Module): # https://github.com/digantamisra98/Mish def __init__(self): super().__init__() def forward(self, x): return x.mul_(F.softplus(x).tanh()) class YOLOLayer(nn.Module): def __init__(self, anchors, nc, img_size, yolo_index, arc): super(YOLOLayer, self).__init__() self.anchors = torch.Tensor(anchors) self.na = len(anchors) # number of anchors (3) self.nc = nc # number of classes (80) self.nx = 0 # initialize number of x gridpoints self.ny = 0 # initialize number of y gridpoints self.arc = arc if ONNX_EXPORT: # grids must be computed in __init__ stride = [32, 16, 8][yolo_index] # stride of this layer nx = int(img_size[1] / stride) # number x grid points ny = int(img_size[0] / stride) # number y grid points create_grids(self, img_size, (nx, ny)) def forward(self, p, img_size, var=None): if ONNX_EXPORT: bs = 1 # batch size else: bs, ny, nx = p.shape[0], p.shape[-2], p.shape[-1] if (self.nx, self.ny) != (nx, ny): create_grids(self, img_size, (nx, ny), p.device, p.dtype) # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh) p = p.view(bs, self.na, self.nc + 5, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction if self.training: return p elif ONNX_EXPORT: # Constants CAN NOT BE BROADCAST, ensure correct shape! ngu = self.ng.repeat((1, self.na * self.nx * self.ny, 1)) grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view((1, -1, 2)) anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view((1, -1, 2)) / ngu p = p.view(-1, 5 + self.nc) xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y wh = torch.exp(p[..., 2:4]) * anchor_wh[0] # width, height p_conf = torch.sigmoid(p[:, 4:5]) # Conf p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like conf return torch.cat((xy / ngu[0], wh, p_conf, p_cls), 1).t() # p = p.view(1, -1, 5 + self.nc) # xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y # wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height # p_conf = torch.sigmoid(p[..., 4:5]) # Conf # p_cls = p[..., 5:5 + self.nc] # # Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py # # p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf # p_cls = torch.exp(p_cls).permute((2, 1, 0)) # p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent # p_cls = p_cls.permute(2, 1, 0) # return torch.cat((xy / ngu, wh, p_conf, p_cls), 2).squeeze().t() else: # inference # s = 1.5 # scale_xy (pxy = pxy * s - (s - 1) / 2) io = p.clone() # inference output io[..., 0:2] = torch.sigmoid(io[..., 0:2]) + self.grid_xy # xy io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method # io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method io[..., :4] *= self.stride if 'default' in self.arc: # seperate obj and cls torch.sigmoid_(io[..., 4:]) elif 'BCE' in self.arc: # unified BCE (80 classes) torch.sigmoid_(io[..., 5:]) io[..., 4] = 1 elif 'CE' in self.arc: # unified CE (1 background + 80 classes) io[..., 4:] = F.softmax(io[..., 4:], dim=4) io[..., 4] = 1 if self.nc == 1: io[..., 5] = 1 # single-class model https://github.com/ultralytics/yolov3/issues/235 # reshape from [1, 3, 13, 13, 85] to [1, 507, 85] return io.view(bs, -1, 5 + self.nc), p class Darknet(nn.Module): # YOLOv3 object detection model def __init__(self, cfg, img_size=(416, 416), arc='default'): super(Darknet, self).__init__() self.module_defs = parse_model_cfg(cfg) self.module_list, self.routs = create_modules(self.module_defs, img_size, arc) self.yolo_layers = get_yolo_layers(self) # Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346 self.version = np.array([0, 2, 5], dtype=np.int32) # (int32) version info: major, minor, revision self.seen = np.array([0], dtype=np.int64) # (int64) number of images seen during training def forward(self, x, var=None): img_size = x.shape[-2:] layer_outputs = [] output = [] for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)): mtype = mdef['type'] if mtype in ['convolutional', 'upsample', 'maxpool']: x = module(x) elif mtype == 'route': layers = [int(x) for x in mdef['layers'].split(',')] if len(layers) == 1: x = layer_outputs[layers[0]] else: try: x = torch.cat([layer_outputs[i] for i in layers], 1) except: # apply stride 2 for darknet reorg layer layer_outputs[layers[1]] = F.interpolate(layer_outputs[layers[1]], scale_factor=[0.5, 0.5]) x = torch.cat([layer_outputs[i] for i in layers], 1) # print(''), [print(layer_outputs[i].shape) for i in layers], print(x.shape) elif mtype == 'shortcut': x = x + layer_outputs[int(mdef['from'])] elif mtype == 'yolo': x = module(x, img_size) output.append(x) layer_outputs.append(x if i in self.routs else []) if self.training: return output elif ONNX_EXPORT: output = torch.cat(output, 1) # cat 3 layers 85 x (507, 2028, 8112) to 85 x 10647 nc = self.module_list[self.yolo_layers[0]].nc # number of classes return output[5:5 + nc].t(), output[:4].t() # ONNX scores, boxes else: io, p = list(zip(*output)) # inference output, training output return torch.cat(io, 1), p def fuse(self): # Fuse Conv2d + BatchNorm2d layers throughout model fused_list = nn.ModuleList() for a in list(self.children())[0]: if isinstance(a, nn.Sequential): for i, b in enumerate(a): if isinstance(b, nn.modules.batchnorm.BatchNorm2d): # fuse this bn layer with the previous conv2d layer conv = a[i - 1] fused = torch_utils.fuse_conv_and_bn(conv, b) a = nn.Sequential(fused, *list(a.children())[i + 1:]) break fused_list.append(a) self.module_list = fused_list # model_info(self) # yolov3-spp reduced from 225 to 152 layers def get_yolo_layers(model): return [i for i, x in enumerate(model.module_defs) if x['type'] == 'yolo'] # [82, 94, 106] for yolov3 def create_grids(self, img_size=416, ng=(13, 13), device='cpu', type=torch.float32): nx, ny = ng # x and y grid size self.img_size = max(img_size) self.stride = self.img_size / max(ng) # build xy offsets yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) self.grid_xy = torch.stack((xv, yv), 2).to(device).type(type).view((1, 1, ny, nx, 2)) # build wh gains self.anchor_vec = self.anchors.to(device) / self.stride self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2).to(device).type(type) self.ng = torch.Tensor(ng).to(device) self.nx = nx self.ny = ny def load_darknet_weights(self, weights, cutoff=-1): # Parses and loads the weights stored in 'weights' # Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded) file = Path(weights).name if file == 'darknet53.conv.74': cutoff = 75 elif file == 'yolov3-tiny.conv.15': cutoff = 15 # Read weights file with open(weights, 'rb') as f: # Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346 self.version = np.fromfile(f, dtype=np.int32, count=3) # (int32) version info: major, minor, revision self.seen = np.fromfile(f, dtype=np.int64, count=1) # (int64) number of images seen during training weights = np.fromfile(f, dtype=np.float32) # the rest are weights ptr = 0 for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])): if mdef['type'] == 'convolutional': conv_layer = module[0] if mdef['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 return cutoff def save_weights(self, path='model.weights', cutoff=-1): # Converts a PyTorch model to Darket format (*.pt to *.weights) # Note: Does not work if model.fuse() is applied with open(path, 'wb') as f: # Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346 self.version.tofile(f) # (int32) version info: major, minor, revision self.seen.tofile(f) # (int64) number of images seen during training # Iterate through layers for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])): if mdef['type'] == 'convolutional': conv_layer = module[0] # If batch norm, load bn first if mdef['batch_normalize']: bn_layer = module[1] bn_layer.bias.data.cpu().numpy().tofile(f) bn_layer.weight.data.cpu().numpy().tofile(f) bn_layer.running_mean.data.cpu().numpy().tofile(f) bn_layer.running_var.data.cpu().numpy().tofile(f) # Load conv bias else: conv_layer.bias.data.cpu().numpy().tofile(f) # Load conv weights conv_layer.weight.data.cpu().numpy().tofile(f) def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'): # Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa) # from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights') # Initialize model model = Darknet(cfg) # Load weights and save if weights.endswith('.pt'): # if PyTorch format model.load_state_dict(torch.load(weights, map_location='cpu')['model']) save_weights(model, path='converted.weights', cutoff=-1) print("Success: converted '%s' to 'converted.weights'" % weights) elif weights.endswith('.weights'): # darknet format _ = load_darknet_weights(model, weights) chkpt = {'epoch': -1, 'best_fitness': None, 'training_results': None, 'model': model.state_dict(), 'optimizer': None} torch.save(chkpt, 'converted.pt') print("Success: converted '%s' to 'converted.pt'" % weights) else: print('Error: extension not supported.') def attempt_download(weights): # Attempt to download pretrained weights if not found locally msg = weights + ' missing, download from https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI' if weights and not os.path.isfile(weights): file = Path(weights).name if file == 'yolov3-spp.weights': gdrive_download(id='1oPCHKsM2JpM-zgyepQciGli9X0MTsJCO', name=weights) elif file == 'yolov3-spp.pt': gdrive_download(id='1vFlbJ_dXPvtwaLLOu-twnjK4exdFiQ73', name=weights) elif file == 'yolov3.pt': gdrive_download(id='11uy0ybbOXA2hc-NJkJbbbkDwNX1QZDlz', name=weights) elif file == 'yolov3-tiny.pt': gdrive_download(id='1qKSgejNeNczgNNiCn9ZF_o55GFk1DjY_', name=weights) elif file == 'darknet53.conv.74': gdrive_download(id='18xqvs_uwAqfTXp-LJCYLYNHBOcrwbrp0', name=weights) elif file == 'yolov3-tiny.conv.15': gdrive_download(id='140PnSedCsGGgu3rOD6Ez4oI6cdDzerLC', name=weights) elif file == 'ultralytics49.pt': gdrive_download(id='1GKy8hr0h41VlVX2QqURO9re7yKXhaPK7', name=weights) else: try: # download from pjreddie.com url = 'https://pjreddie.com/media/files/' + file print('Downloading ' + url) os.system('curl -f ' + url + ' -o ' + weights) except IOError: print(msg) os.system('rm ' + weights) # remove partial downloads assert os.path.exists(weights), msg # download missing weights from Google Drive if os.path.getsize(weights) < 5E6: # weights < 5MB (too small), download failed os.remove(weights) # delete corrupted weightsfile print(msg)