|
- from utils.google_utils import *
- from utils.layers import *
- from utils.parse_config import *
- from utils import torch_utils
-
- 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'] == 'deformableconvolutional':
- 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('DeformConv2d', DeformConv2d(output_filters[-1],
- filters,
- kernel_size=k,
- padding=k // 2 if mdef['pad'] else 0,
- stride=stride,
- bias=not bn,
- modulation=True))
- 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'] == 'route2': # 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 = FeatureConcat2(layers=layers)
-
- elif mdef['type'] == 'route3': # 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 = FeatureConcat3(layers=layers)
-
- elif mdef['type'] == 'route_lhalf': # nn.Sequential() placeholder for 'route' layer
- layers = mdef['layers']
- filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])//2
- routs.extend([i + l if l < 0 else l for l in layers])
- modules = FeatureConcat_l(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 = [8, 16, 32, 64, 128] # P3, P4, P5, P6, P7 strides
- if any(x in cfg for x in ['yolov4-tiny', 'fpn', 'yolov3']): # P5, P4, P3 strides
- stride = [32, 16, 8]
- 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])
-
- # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
- 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)
- #bias[:, 4] += -4.5 # obj
- bias[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2) # obj (8 objects per 640 image)
- bias[:, 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.sigmoid()
- io[..., :2] = (io[..., :2] * 2. - 0.5 + self.grid)
- io[..., 2:4] = (io[..., 2:4] * 2) ** 2 * self.anchor_wh
- io[..., :4] *= self.stride
- #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', 'FeatureConcat2', 'FeatureConcat3', 'FeatureConcat_l']: # 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', saveto='converted.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)
- ckpt = torch.load(weights) # load checkpoint
- try:
- ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
- model.load_state_dict(ckpt['model'], strict=False)
- save_weights(model, path=saveto, cutoff=-1)
- except KeyError as e:
- print(e)
-
- 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 = {''}
-
- 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)
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