from .yolo_utils.google_utils import * from .yolo_utils.layers import * from .yolo_utils.parse_config import * ONNX_EXPORT = False def create_modules(module_defs, img_size, cfg): # Constructs module list of layer blocks from module configuration in module_defs img_size = [img_size] * 2 if isinstance(img_size, int) else img_size # expand if necessary _ = module_defs.pop(0) # cfg training hyperparams (unused) output_filters = [3] # input channels module_list = nn.ModuleList() routs = [] # list of layers which rout to deeper layers yolo_index = -1 for i, mdef in enumerate(module_defs): modules = nn.Sequential() if mdef['type'] == 'convolutional': bn = mdef['batch_normalize'] filters = mdef['filters'] k = mdef['size'] # kernel size stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x']) if isinstance(k, int): # single-size conv modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1], out_channels=filters, kernel_size=k, stride=stride, padding=k // 2 if mdef['pad'] else 0, groups=mdef['groups'] if 'groups' in mdef else 1, bias=not bn)) else: # multiple-size conv modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1], out_ch=filters, k=k, stride=stride, bias=not bn)) if bn: modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4)) else: routs.append(i) # detection output (goes into yolo layer) if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441 modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True)) elif mdef['activation'] == 'swish': modules.add_module('activation', Swish()) elif mdef['activation'] == 'mish': modules.add_module('activation', Mish()) elif mdef['type'] == 'BatchNorm2d': filters = output_filters[-1] modules = nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4) if i == 0 and filters == 3: # normalize RGB image # imagenet mean and var https://pytorch.org/docs/stable/torchvision/models.html#classification modules.running_mean = torch.tensor([0.485, 0.456, 0.406]) modules.running_var = torch.tensor([0.0524, 0.0502, 0.0506]) elif mdef['type'] == 'maxpool': k = mdef['size'] # kernel size stride = mdef['stride'] maxpool = nn.MaxPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2) if k == 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': if ONNX_EXPORT: # explicitly state size, avoid scale_factor g = (yolo_index + 1) * 2 / 32 # gain modules = nn.Upsample(size=tuple(int(x * g) for x in img_size)) # img_size = (320, 192) else: modules = nn.Upsample(scale_factor=mdef['stride']) elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer layers = mdef['layers'] filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers]) routs.extend([i + l if l < 0 else l for l in layers]) modules = FeatureConcat(layers=layers) elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer layers = mdef['from'] filters = output_filters[-1] routs.extend([i + l if l < 0 else l for l in layers]) modules = WeightedFeatureFusion(layers=layers, weight='weights_type' in mdef) elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale pass elif mdef['type'] == 'yolo': yolo_index += 1 stride = [32, 16, 8] # P5, P4, P3 strides if any(x in cfg for x in ['panet', 'yolov4', 'cd53']): # stride order reversed stride = list(reversed(stride)) layers = mdef['from'] if 'from' in mdef else [] modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']], # anchor list nc=mdef['classes'], # number of classes img_size=img_size, # (416, 416) yolo_index=yolo_index, # 0, 1, 2... layers=layers, # output layers stride=stride[yolo_index]) try: j = layers[yolo_index] if 'from' in mdef else -1 bias_ = module_list[j][0].bias # shape(255,) bias = bias_[:modules.no * modules.na].view(modules.na, -1) # shape(3,85) with torch.no_grad(): # avoids "requires grad is being used in an in-place operation" bias.data[:, 4] += -4.5 # obj bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99)) # cls (sigmoid(p) = 1/nc) module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad) 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) routs_binary = [False] * (i + 1) for i in routs: routs_binary[i] = True return module_list, routs_binary class YOLOLayer(nn.Module): def __init__(self, anchors, nc, img_size, yolo_index, layers, stride): super(YOLOLayer, self).__init__() self.anchors = torch.Tensor(anchors) self.index = yolo_index # index of this layer in layers self.layers = layers # model output layer indices self.stride = stride # layer stride self.nl = len(layers) # number of output layers (3) self.na = len(anchors) # number of anchors (3) self.nc = nc # number of classes (80) self.no = nc + 5 # number of outputs (85) self.nx, self.ny, self.ng = 0, 0, 0 # initialize number of x, y gridpoints self.anchor_vec = self.anchors / self.stride self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2) if ONNX_EXPORT: self.training = False self.create_grids((img_size[1] // stride, img_size[0] // stride)) # number x, y grid points def create_grids(self, ng=(13, 13), device='cpu'): self.nx, self.ny = ng # x and y grid size self.ng = torch.tensor(ng, dtype=torch.float) # build xy offsets if not self.training: yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)]) self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float() if self.anchor_vec.device != device: self.anchor_vec = self.anchor_vec.to(device) self.anchor_wh = self.anchor_wh.to(device) def forward(self, p, out): ASFF = False # https://arxiv.org/abs/1911.09516 if ASFF: i, n = self.index, self.nl # index in layers, number of layers p = out[self.layers[i]] bs, _, ny, nx = p.shape # bs, 255, 13, 13 if (self.nx, self.ny) != (nx, ny): self.create_grids((nx, ny), p.device) # outputs and weights # w = F.softmax(p[:, -n:], 1) # normalized weights w = torch.sigmoid(p[:, -n:]) * (2 / n) # sigmoid weights (faster) # w = w / w.sum(1).unsqueeze(1) # normalize across layer dimension # weighted ASFF sum p = out[self.layers[i]][:, :-n] * w[:, i:i + 1] for j in range(n): if j != i: p += w[:, j:j + 1] * \ F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False) elif ONNX_EXPORT: bs = 1 # batch size else: bs, _, ny, nx = p.shape # bs, 255, 13, 13 if (self.nx, self.ny) != (nx, ny): self.create_grids((nx, ny), p.device) # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh) p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction if self.training: return p elif ONNX_EXPORT: # Avoid broadcasting for ANE operations m = self.na * self.nx * self.ny ng = 1. / self.ng.repeat(m, 1) grid = self.grid.repeat(1, self.na, 1, 1, 1).view(m, 2) anchor_wh = self.anchor_wh.repeat(1, 1, self.nx, self.ny, 1).view(m, 2) * ng p = p.view(m, self.no) xy = torch.sigmoid(p[:, 0:2]) + grid # x, y wh = torch.exp(p[:, 2:4]) * anchor_wh # width, height p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \ torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5]) # conf return p_cls, xy * ng, wh else: # inference io = p.clone() # inference output io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid # xy io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method io[..., :4] *= self.stride torch.sigmoid_(io[..., 4:]) return io.view(bs, -1, self.no), p # view [1, 3, 13, 13, 85] as [1, 507, 85] class Darknet(nn.Module): # YOLOv3 object detection model def __init__(self, cfg, img_size=(416, 416), verbose=False): super(Darknet, self).__init__() self.module_defs = parse_model_cfg(cfg) self.module_list, self.routs = create_modules(self.module_defs, img_size, cfg) self.yolo_layers = get_yolo_layers(self) # torch_utils.initialize_weights(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 # self.info(verbose) if not ONNX_EXPORT else None # print model description def forward(self, x, augment=False, verbose=False): if not augment: return self.forward_once(x) else: # Augment images (inference and test only) https://github.com/ultralytics/yolov3/issues/931 img_size = x.shape[-2:] # height, width s = [0.83, 0.67] # scales y = [] for i, xi in enumerate((x, torch_utils.scale_img(x.flip(3), s[0], same_shape=False), # flip-lr and scale torch_utils.scale_img(x, s[1], same_shape=False), # scale )): # cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) y.append(self.forward_once(xi)[0]) y[1][..., :4] /= s[0] # scale y[1][..., 0] = img_size[1] - y[1][..., 0] # flip lr y[2][..., :4] /= s[1] # scale # for i, yi in enumerate(y): # coco small, medium, large = < 32**2 < 96**2 < # area = yi[..., 2:4].prod(2)[:, :, None] # if i == 1: # yi *= (area < 96. ** 2).float() # elif i == 2: # yi *= (area > 32. ** 2).float() # y[i] = yi y = torch.cat(y, 1) return y, None def forward_once(self, x, augment=False, verbose=False): img_size = x.shape[-2:] # height, width yolo_out, out = [], [] if verbose: print('0', x.shape) str = '' # Augment images (inference and test only) if augment: # https://github.com/ultralytics/yolov3/issues/931 nb = x.shape[0] # batch size s = [0.83, 0.67] # scales x = torch.cat((x, torch_utils.scale_img(x.flip(3), s[0]), # flip-lr and scale torch_utils.scale_img(x, s[1]), # scale ), 0) for i, module in enumerate(self.module_list): name = module.__class__.__name__ if name in ['WeightedFeatureFusion', 'FeatureConcat']: # sum, concat if verbose: l = [i - 1] + module.layers # layers sh = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, sh)]) x = module(x, out) # WeightedFeatureFusion(), FeatureConcat() elif name == 'YOLOLayer': yolo_out.append(module(x, out)) else: # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc. x = module(x) out.append(x if self.routs[i] else []) if verbose: print('%g/%g %s -' % (i, len(self.module_list), name), list(x.shape), str) str = '' if self.training: # train return yolo_out elif ONNX_EXPORT: # export x = [torch.cat(x, 0) for x in zip(*yolo_out)] return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4 else: # inference or test x, p = zip(*yolo_out) # inference output, training output x = torch.cat(x, 1) # cat yolo outputs if augment: # de-augment results x = torch.split(x, nb, dim=0) x[1][..., :4] /= s[0] # scale x[1][..., 0] = img_size[1] - x[1][..., 0] # flip lr x[2][..., :4] /= s[1] # scale x = torch.cat(x, 1) return x, p def fuse(self): # Fuse Conv2d + BatchNorm2d layers throughout model print('Fusing layers...') 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 self.info() if not ONNX_EXPORT else None # yolov3-spp reduced from 225 to 152 layers def info(self, verbose=False): torch_utils.model_info(self, verbose) def get_yolo_layers(model): return [i for i, m in enumerate(model.module_list) if m.__class__.__name__ == 'YOLOLayer'] # [89, 101, 113] 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 = module[0] if mdef['batch_normalize']: # Load BN bias, weights, running mean and running variance bn = module[1] nb = bn.bias.numel() # number of biases # Bias bn.bias.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.bias)) ptr += nb # Weight bn.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.weight)) ptr += nb # Running Mean bn.running_mean.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_mean)) ptr += nb # Running Var bn.running_var.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_var)) ptr += nb else: # Load conv. bias nb = conv.bias.numel() conv_b = torch.from_numpy(weights[ptr:ptr + nb]).view_as(conv.bias) conv.bias.data.copy_(conv_b) ptr += nb # Load conv. weights nw = conv.weight.numel() # number of weights conv.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nw]).view_as(conv.weight)) ptr += nw 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']) target = weights.rsplit('.', 1)[0] + '.weights' save_weights(model, path=target, cutoff=-1) print("Success: converted '%s' to '%s'" % (weights, target)) 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} target = weights.rsplit('.', 1)[0] + '.pt' torch.save(chkpt, target) print("Success: converted '%s' to '%s'" % (weights, target)) else: print('Error: extension not supported.') def attempt_download(weights): # Attempt to download pretrained weights if not found locally weights = weights.strip() msg = weights + ' missing, try downloading from https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0' if len(weights) > 0 and not os.path.isfile(weights): d = {'yolov3-spp.weights': '16lYS4bcIdM2HdmyJBVDOvt3Trx6N3W2R', 'yolov3.weights': '1uTlyDWlnaqXcsKOktP5aH_zRDbfcDp-y', 'yolov3-tiny.weights': '1CCF-iNIIkYesIDzaPvdwlcf7H9zSsKZQ', 'yolov3-spp.pt': '1f6Ovy3BSq2wYq4UfvFUpxJFNDFfrIDcR', 'yolov3.pt': '1SHNFyoe5Ni8DajDNEqgB2oVKBb_NoEad', 'yolov3-tiny.pt': '10m_3MlpQwRtZetQxtksm9jqHrPTHZ6vo', 'darknet53.conv.74': '1WUVBid-XuoUBmvzBVUCBl_ELrzqwA8dJ', 'yolov3-tiny.conv.15': '1Bw0kCpplxUqyRYAJr9RY9SGnOJbo9nEj', 'yolov3-spp-ultralytics.pt': '1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4'} file = Path(weights).name if file in d: r = gdrive_download(id=d[file], name=weights) else: # download from pjreddie.com url = 'https://pjreddie.com/media/files/' + file print('Downloading ' + url) r = os.system('curl -f ' + url + ' -o ' + weights) # Error check if not (r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6): # weights exist and > 1MB os.system('rm ' + weights) # remove partial downloads raise Exception(msg)