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): # 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 layers yolo_index = -1 for i, mdef in enumerate(module_defs): modules = nn.Sequential() # if i == 0: # modules.add_module('BatchNorm2d_0', nn.BatchNorm2d(output_filters[-1], momentum=0.1)) if mdef['type'] == 'convolutional': bn = mdef['batch_normalize'] filters = mdef['filters'] size = mdef['size'] stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x']) modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1], out_channels=filters, kernel_size=size, stride=stride, padding=(size - 1) // 2 if mdef['pad'] else 0, groups=mdef['groups'] if 'groups' in mdef else 1, bias=not bn)) if bn: modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.003, 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)) # modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10)) elif mdef['activation'] == 'swish': modules.add_module('activation', Swish()) elif mdef['type'] == 'maxpool': size = mdef['size'] stride = mdef['stride'] maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=(size - 1) // 2) if 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': 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]) 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 l = 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=l) # output layers # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3) try: bo = -4.5 #  obj bias bc = math.log(1 / (modules.nc - 0.99)) # cls bias: class probability is sigmoid(p) = 1/nc j = l[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) bias[:, 4] += bo - bias[:, 4].mean() # obj bias[:, 5:] += bc - bias[:, 5:].mean() # cls, view with utils.print_model_biases(model) 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 weightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 def __init__(self, layers, weight=False): super(weightedFeatureFusion, self).__init__() self.layers = layers # layer indices self.weight = weight # apply weights boolean self.n = len(layers) + 1 # number of layers if weight: self.w = torch.nn.Parameter(torch.zeros(self.n)) # layer weights def forward(self, x, outputs): # Weights if self.weight: w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1) x = x * w[0] # Fusion nc = x.shape[1] # input channels for i in range(self.n - 1): a = outputs[self.layers[i]] * w[i + 1] if self.weight else outputs[self.layers[i]] # feature to add ac = a.shape[1] # feature channels dc = nc - ac # delta channels # Adjust channels if dc > 0: # slice input x[:, :ac] = x[:, :ac] + a # or a = nn.ZeroPad2d((0, 0, 0, 0, 0, dc))(a); x = x + a elif dc < 0: # slice feature x = x + a[:, :nc] else: # same shape x = x + a return x class SwishImplementation(torch.autograd.Function): @staticmethod def forward(ctx, i): ctx.save_for_backward(i) return i * torch.sigmoid(i) @staticmethod def backward(ctx, grad_output): sigmoid_i = torch.sigmoid(ctx.saved_variables[0]) return grad_output * (sigmoid_i * (1 + ctx.saved_variables[0] * (1 - sigmoid_i))) class MemoryEfficientSwish(nn.Module): def forward(self, x): return SwishImplementation.apply(x) class Swish(nn.Module): def forward(self, x): return x.mul_(torch.sigmoid(x)) class Mish(nn.Module): # https://github.com/digantamisra98/Mish def forward(self, x): return x.mul_(F.softplus(x).tanh()) class YOLOLayer(nn.Module): def __init__(self, anchors, nc, img_size, yolo_index, layers): 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.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 = 0 # initialize number of x gridpoints self.ny = 0 # initialize number of y gridpoints if ONNX_EXPORT: stride = [32, 16, 8][yolo_index] # stride of this layer nx = img_size[1] // stride # number x grid points ny = img_size[0] // stride # number y grid points create_grids(self, img_size, (nx, ny)) def forward(self, p, img_size, 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): create_grids(self, img_size, (nx, ny), p.device, p.dtype) # 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): 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.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_xy = self.grid_xy.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_xy # 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 # 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)): super(Darknet, self).__init__() self.module_defs = parse_model_cfg(cfg) self.module_list, self.routs = create_modules(self.module_defs, img_size) 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 self.info() # print model description def forward(self, x, verbose=False): img_size = x.shape[-2:] yolo_out, out = [], [] if verbose: str = '' print('0', x.shape) 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 == 'shortcut': # sum if verbose: l = [i - 1] + module.layers # layers s = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)]) x = module(x, out) # weightedFeatureFusion() elif mtype == 'route': # concat layers = mdef['layers'] if verbose: l = [i - 1] + layers # layers s = [list(x.shape)] + [list(out[i].shape) for i in layers] # shapes str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)]) if len(layers) == 1: x = out[layers[0]] else: try: x = torch.cat([out[i] for i in layers], 1) except: # apply stride 2 for darknet reorg layer out[layers[1]] = F.interpolate(out[layers[1]], scale_factor=[0.5, 0.5]) x = torch.cat([out[i] for i in layers], 1) # print(''), [print(out[i].shape) for i in layers], print(x.shape) elif mtype == 'yolo': yolo_out.append(module(x, img_size, out)) out.append(x if self.routs[i] else []) if verbose: print('%g/%g %s -' % (i, len(self.module_list), mtype), 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: # test io, p = zip(*yolo_out) # inference output, training output return torch.cat(io, 1), p def fuse(self): # Fuse Conv2d + BatchNorm2d layers throughout model print('Fusing Conv2d() and BatchNorm2d() 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() # 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, 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).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 = 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']) 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, try downloading from https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0' if weights 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', 'ultralytics49.pt': '158g62Vs14E3aj7oPVPuEnNZMKFNgGyNq', 'ultralytics68.pt': '1Jm8kqnMdMGUUxGo8zMFZMJ0eaPwLkxSG', '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)