470 lines
21 KiB
Python
Executable File
470 lines
21 KiB
Python
Executable File
from utils.google_utils import *
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from utils.layers import *
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from utils.parse_config import *
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ONNX_EXPORT = False
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def create_modules(module_defs, img_size):
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# Constructs module list of layer blocks from module configuration in module_defs
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img_size = [img_size] * 2 if isinstance(img_size, int) else img_size # expand if necessary
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_ = module_defs.pop(0) # cfg training hyperparams (unused)
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output_filters = [3] # input channels
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module_list = nn.ModuleList()
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routs = [] # list of layers which rout to deeper layers
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yolo_index = -1
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for i, mdef in enumerate(module_defs):
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modules = nn.Sequential()
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if mdef['type'] == 'convolutional':
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bn = mdef['batch_normalize']
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filters = mdef['filters']
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k = mdef['size'] # kernel size
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stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
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if isinstance(k, int): # single-size conv
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modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],
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out_channels=filters,
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kernel_size=k,
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stride=stride,
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padding=k // 2 if mdef['pad'] else 0,
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groups=mdef['groups'] if 'groups' in mdef else 1,
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bias=not bn))
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else: # multiple-size conv
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modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],
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out_ch=filters,
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k=k,
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stride=stride,
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bias=not bn))
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if bn:
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modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))
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else:
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routs.append(i) # detection output (goes into yolo layer)
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if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441
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modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
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# modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10))
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elif mdef['activation'] == 'swish':
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modules.add_module('activation', Swish())
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elif mdef['type'] == 'BatchNorm2d':
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filters = output_filters[-1]
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modules = nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4)
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if i == 0 and filters == 3: # normalize RGB image
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# imagenet mean and var https://pytorch.org/docs/stable/torchvision/models.html#classification
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modules.running_mean = torch.tensor([0.485, 0.456, 0.406])
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modules.running_var = torch.tensor([0.0524, 0.0502, 0.0506])
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elif mdef['type'] == 'maxpool':
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k = mdef['size'] # kernel size
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stride = mdef['stride']
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maxpool = nn.MaxPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)
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if k == 2 and stride == 1: # yolov3-tiny
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modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
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modules.add_module('MaxPool2d', maxpool)
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else:
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modules = maxpool
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elif mdef['type'] == 'upsample':
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if ONNX_EXPORT: # explicitly state size, avoid scale_factor
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g = (yolo_index + 1) * 2 / 32 # gain
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modules = nn.Upsample(size=tuple(int(x * g) for x in img_size)) # img_size = (320, 192)
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else:
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modules = nn.Upsample(scale_factor=mdef['stride'])
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elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer
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layers = mdef['layers']
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filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
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routs.extend([i + l if l < 0 else l for l in layers])
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modules = FeatureConcat(layers=layers)
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elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
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layers = mdef['from']
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filters = output_filters[-1]
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routs.extend([i + l if l < 0 else l for l in layers])
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modules = WeightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
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elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
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pass
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elif mdef['type'] == 'yolo':
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yolo_index += 1
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stride = [32, 16, 8, 4, 2][yolo_index] # P3-P7 stride
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layers = mdef['from'] if 'from' in mdef else []
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modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']], # anchor list
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nc=mdef['classes'], # number of classes
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img_size=img_size, # (416, 416)
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yolo_index=yolo_index, # 0, 1, 2...
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layers=layers, # output layers
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stride=stride)
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# Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
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try:
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j = layers[yolo_index] if 'from' in mdef else -1
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bias_ = module_list[j][0].bias # shape(255,)
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bias = bias_[:modules.no * modules.na].view(modules.na, -1) # shape(3,85)
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bias[:, 4] += -4.5 # obj
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bias[:, 5:] += math.log(0.6 / (modules.nc - 0.99)) # cls (sigmoid(p) = 1/nc)
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module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)
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except:
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print('WARNING: smart bias initialization failure.')
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else:
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print('Warning: Unrecognized Layer Type: ' + mdef['type'])
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# Register module list and number of output filters
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module_list.append(modules)
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output_filters.append(filters)
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routs_binary = [False] * (i + 1)
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for i in routs:
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routs_binary[i] = True
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return module_list, routs_binary
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class YOLOLayer(nn.Module):
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def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):
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super(YOLOLayer, self).__init__()
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self.anchors = torch.Tensor(anchors)
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self.index = yolo_index # index of this layer in layers
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self.layers = layers # model output layer indices
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self.stride = stride # layer stride
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self.nl = len(layers) # number of output layers (3)
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self.na = len(anchors) # number of anchors (3)
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self.nc = nc # number of classes (80)
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self.no = nc + 5 # number of outputs (85)
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self.nx, self.ny, self.ng = 0, 0, 0 # initialize number of x, y gridpoints
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self.anchor_vec = self.anchors / self.stride
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self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)
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if ONNX_EXPORT:
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self.training = False
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self.create_grids((img_size[1] // stride, img_size[0] // stride)) # number x, y grid points
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def create_grids(self, ng=(13, 13), device='cpu'):
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self.nx, self.ny = ng # x and y grid size
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self.ng = torch.tensor(ng)
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# build xy offsets
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if not self.training:
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yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])
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self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()
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if self.anchor_vec.device != device:
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self.anchor_vec = self.anchor_vec.to(device)
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self.anchor_wh = self.anchor_wh.to(device)
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def forward(self, p, out):
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ASFF = False # https://arxiv.org/abs/1911.09516
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if ASFF:
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i, n = self.index, self.nl # index in layers, number of layers
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p = out[self.layers[i]]
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bs, _, ny, nx = p.shape # bs, 255, 13, 13
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if (self.nx, self.ny) != (nx, ny):
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self.create_grids((nx, ny), p.device)
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# outputs and weights
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# w = F.softmax(p[:, -n:], 1) # normalized weights
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w = torch.sigmoid(p[:, -n:]) * (2 / n) # sigmoid weights (faster)
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# w = w / w.sum(1).unsqueeze(1) # normalize across layer dimension
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# weighted ASFF sum
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p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]
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for j in range(n):
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if j != i:
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p += w[:, j:j + 1] * \
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F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)
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elif ONNX_EXPORT:
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bs = 1 # batch size
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else:
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bs, _, ny, nx = p.shape # bs, 255, 13, 13
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if (self.nx, self.ny) != (nx, ny):
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self.create_grids((nx, ny), p.device)
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# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh)
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p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction
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if self.training:
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return p
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elif ONNX_EXPORT:
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# Avoid broadcasting for ANE operations
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m = self.na * self.nx * self.ny
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ng = 1 / self.ng.repeat((m, 1))
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grid = self.grid.repeat((1, self.na, 1, 1, 1)).view(m, 2)
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anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view(m, 2) * ng
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p = p.view(m, self.no)
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xy = torch.sigmoid(p[:, 0:2]) + grid # x, y
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wh = torch.exp(p[:, 2:4]) * anchor_wh # width, height
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p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \
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torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5]) # conf
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return p_cls, xy * ng, wh
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else: # inference
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io = p.clone() # inference output
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io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid # xy
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io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
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io[..., :4] *= self.stride
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torch.sigmoid_(io[..., 4:])
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return io.view(bs, -1, self.no), p # view [1, 3, 13, 13, 85] as [1, 507, 85]
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class Darknet(nn.Module):
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# YOLOv3 object detection model
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def __init__(self, cfg, img_size=(416, 416), verbose=False):
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super(Darknet, self).__init__()
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self.module_defs = parse_model_cfg(cfg)
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self.module_list, self.routs = create_modules(self.module_defs, img_size)
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self.yolo_layers = get_yolo_layers(self)
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# torch_utils.initialize_weights(self)
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# Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
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self.version = np.array([0, 2, 5], dtype=np.int32) # (int32) version info: major, minor, revision
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self.seen = np.array([0], dtype=np.int64) # (int64) number of images seen during training
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self.info(verbose) if not ONNX_EXPORT else None # print model description
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def forward(self, x, augment=False, verbose=False):
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if not augment:
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return self.forward_once(x)
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else: # Augment images (inference and test only) https://github.com/ultralytics/yolov3/issues/931
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img_size = x.shape[-2:] # height, width
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s = [0.83, 0.67] # scales
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y = []
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for i, xi in enumerate((x,
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torch_utils.scale_img(x.flip(3), s[0], same_shape=False), # flip-lr and scale
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torch_utils.scale_img(x, s[1], same_shape=False), # scale
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)):
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# cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])
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y.append(self.forward_once(xi)[0])
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y[1][..., :4] /= s[0] # scale
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y[1][..., 0] = img_size[1] - y[1][..., 0] # flip lr
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y[2][..., :4] /= s[1] # scale
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# for i, yi in enumerate(y): # coco small, medium, large = < 32**2 < 96**2 <
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# area = yi[..., 2:4].prod(2)[:, :, None]
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# if i == 1:
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# yi *= (area < 96. ** 2).float()
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# elif i == 2:
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# yi *= (area > 32. ** 2).float()
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# y[i] = yi
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y = torch.cat(y, 1)
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return y, None
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def forward_once(self, x, augment=False, verbose=False):
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img_size = x.shape[-2:] # height, width
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yolo_out, out = [], []
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if verbose:
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print('0', x.shape)
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str = ''
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# Augment images (inference and test only)
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if augment: # https://github.com/ultralytics/yolov3/issues/931
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nb = x.shape[0] # batch size
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s = [0.83, 0.67] # scales
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x = torch.cat((x,
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torch_utils.scale_img(x.flip(3), s[0]), # flip-lr and scale
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torch_utils.scale_img(x, s[1]), # scale
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), 0)
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for i, module in enumerate(self.module_list):
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name = module.__class__.__name__
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if name in ['WeightedFeatureFusion', 'FeatureConcat']: # sum, concat
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if verbose:
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l = [i - 1] + module.layers # layers
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sh = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes
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str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, sh)])
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x = module(x, out) # WeightedFeatureFusion(), FeatureConcat()
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elif name == 'YOLOLayer':
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yolo_out.append(module(x, out))
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else: # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc.
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x = module(x)
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out.append(x if self.routs[i] else [])
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if verbose:
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print('%g/%g %s -' % (i, len(self.module_list), name), list(x.shape), str)
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str = ''
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if self.training: # train
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return yolo_out
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elif ONNX_EXPORT: # export
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x = [torch.cat(x, 0) for x in zip(*yolo_out)]
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return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4
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else: # inference or test
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x, p = zip(*yolo_out) # inference output, training output
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x = torch.cat(x, 1) # cat yolo outputs
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if augment: # de-augment results
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x = torch.split(x, nb, dim=0)
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x[1][..., :4] /= s[0] # scale
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x[1][..., 0] = img_size[1] - x[1][..., 0] # flip lr
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x[2][..., :4] /= s[1] # scale
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x = torch.cat(x, 1)
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return x, p
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def fuse(self):
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# Fuse Conv2d + BatchNorm2d layers throughout model
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print('Fusing layers...')
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fused_list = nn.ModuleList()
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for a in list(self.children())[0]:
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if isinstance(a, nn.Sequential):
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for i, b in enumerate(a):
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if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
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# fuse this bn layer with the previous conv2d layer
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conv = a[i - 1]
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fused = torch_utils.fuse_conv_and_bn(conv, b)
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a = nn.Sequential(fused, *list(a.children())[i + 1:])
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break
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fused_list.append(a)
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self.module_list = fused_list
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self.info() if not ONNX_EXPORT else None # yolov3-spp reduced from 225 to 152 layers
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def info(self, verbose=False):
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torch_utils.model_info(self, verbose)
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def get_yolo_layers(model):
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return [i for i, m in enumerate(model.module_list) if m.__class__.__name__ == 'YOLOLayer'] # [89, 101, 113]
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def load_darknet_weights(self, weights, cutoff=-1):
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# Parses and loads the weights stored in 'weights'
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# Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)
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file = Path(weights).name
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if file == 'darknet53.conv.74':
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cutoff = 75
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elif file == 'yolov3-tiny.conv.15':
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cutoff = 15
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# Read weights file
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with open(weights, 'rb') as f:
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# Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
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self.version = np.fromfile(f, dtype=np.int32, count=3) # (int32) version info: major, minor, revision
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self.seen = np.fromfile(f, dtype=np.int64, count=1) # (int64) number of images seen during training
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weights = np.fromfile(f, dtype=np.float32) # the rest are weights
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ptr = 0
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for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
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if mdef['type'] == 'convolutional':
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conv = module[0]
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if mdef['batch_normalize']:
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# Load BN bias, weights, running mean and running variance
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bn = module[1]
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nb = bn.bias.numel() # number of biases
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# Bias
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bn.bias.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.bias))
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ptr += nb
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# Weight
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bn.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.weight))
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ptr += nb
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# Running Mean
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bn.running_mean.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_mean))
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ptr += nb
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# Running Var
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bn.running_var.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_var))
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ptr += nb
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else:
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# Load conv. bias
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nb = conv.bias.numel()
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conv_b = torch.from_numpy(weights[ptr:ptr + nb]).view_as(conv.bias)
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conv.bias.data.copy_(conv_b)
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ptr += nb
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# Load conv. weights
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nw = conv.weight.numel() # number of weights
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conv.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nw]).view_as(conv.weight))
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ptr += nw
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def save_weights(self, path='model.weights', cutoff=-1):
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# Converts a PyTorch model to Darket format (*.pt to *.weights)
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# Note: Does not work if model.fuse() is applied
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with open(path, 'wb') as f:
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# Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
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self.version.tofile(f) # (int32) version info: major, minor, revision
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self.seen.tofile(f) # (int64) number of images seen during training
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# Iterate through layers
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for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
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if mdef['type'] == 'convolutional':
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conv_layer = module[0]
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# If batch norm, load bn first
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if mdef['batch_normalize']:
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bn_layer = module[1]
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bn_layer.bias.data.cpu().numpy().tofile(f)
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bn_layer.weight.data.cpu().numpy().tofile(f)
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bn_layer.running_mean.data.cpu().numpy().tofile(f)
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bn_layer.running_var.data.cpu().numpy().tofile(f)
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# Load conv bias
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else:
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conv_layer.bias.data.cpu().numpy().tofile(f)
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# Load conv weights
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conv_layer.weight.data.cpu().numpy().tofile(f)
|
|
|
|
|
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def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
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# Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa)
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# from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')
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|
|
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# Initialize model
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model = Darknet(cfg)
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|
|
|
# Load weights and save
|
|
if weights.endswith('.pt'): # if PyTorch format
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|
model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
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save_weights(model, path='converted.weights', cutoff=-1)
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print("Success: converted '%s' to 'converted.weights'" % weights)
|
|
|
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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
|
|
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)
|