import os import torch.nn.functional as F from utils.parse_config import * from utils.utils import * ONNX_EXPORT = False 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, nn.BatchNorm2d(filters)) if module_def['activation'] == 'leaky': modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1, inplace=True)) 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 = hyperparams['height'] # Define detection layer yolo_layer = YOLOLayer(anchors, nc, 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, img_size, yolo_layer, cfg): 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 if ONNX_EXPORT: # grids must be computed in __init__ stride = [32, 16, 8][yolo_layer] # 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, max(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.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 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:] = torch.sigmoid(io[..., 4:]) # p_conf, p_cls # io[..., 5:] = F.softmax(io[..., 5:], dim=4) # p_cls io[..., :4] *= self.stride 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)): super(Darknet, self).__init__() self.module_defs = parse_model_cfg(cfg) self.module_defs[0]['cfg'] = cfg self.module_defs[0]['height'] = img_size self.hyperparams, self.module_list = create_modules(self.module_defs) 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 = max(x.shape[-2:]) 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': x = module[0](x, img_size) output.append(x) layer_outputs.append(x) 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]][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]: 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): a = [module_def['type'] == 'yolo' for module_def in model.module_defs] return [i for i, x in enumerate(a) if x] # [82, 94, 106] for yolov3 def create_grids(self, img_size=416, ng=(13, 13), device='cpu'): nx, ny = ng # x and y grid size self.img_size = img_size self.stride = 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).float().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) 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' # 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.\nTry https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI') # Establish cutoffs if weights_file == 'darknet53.conv.74': cutoff = 75 elif weights_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, (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 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, (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(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']) print("Success: converted '%s' to 'converted.weights'" % weights) elif weights.endswith('.weights'): # darknet format _ = load_darknet_weights(model, weights) chkpt = {'epoch': -1, 'best_loss': 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.') 文章来源: https://blog.csdn.net/jacke121/article/details/92011159