385 lines
17 KiB
Python
Executable File
385 lines
17 KiB
Python
Executable File
import torch.nn.functional as F
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from utils.parse_config import *
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from utils.utils import *
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ONNX_EXPORT = False
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def create_modules(module_defs, img_size):
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"""
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Constructs module list of layer blocks from module configuration in module_defs
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"""
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hyperparams = module_defs.pop(0)
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output_filters = [int(hyperparams['channels'])]
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module_list = nn.ModuleList()
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routs = [] # list of layers which rout to deeper layes
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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 = int(mdef['batch_normalize'])
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filters = int(mdef['filters'])
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kernel_size = int(mdef['size'])
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pad = (kernel_size - 1) // 2 if int(mdef['pad']) else 0
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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=kernel_size,
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stride=int(mdef['stride']),
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padding=pad,
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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=0.1))
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if mdef['activation'] == 'leaky': # TODO: 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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# modules.add_module('activation', Swish())
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elif mdef['type'] == 'maxpool':
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kernel_size = int(mdef['size'])
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stride = int(mdef['stride'])
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maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
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if kernel_size == 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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modules = nn.Upsample(scale_factor=int(mdef['stride']), mode='nearest')
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elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer
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layers = [int(x) for x in mdef['layers'].split(',')]
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filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
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routs.extend([l if l > 0 else l + i for l in layers])
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# if mdef[i+1]['type'] == 'reorg3d':
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# modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest') # reorg3d
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elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
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filters = output_filters[int(mdef['from'])]
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layer = int(mdef['from'])
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routs.extend([i + layer if layer < 0 else layer])
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elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
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# torch.Size([16, 128, 104, 104])
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# torch.Size([16, 64, 208, 208]) <-- # stride 2 interpolate dimensions 2 and 3 to cat with prior layer
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pass
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elif mdef['type'] == 'yolo':
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yolo_index += 1
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mask = [int(x) for x in mdef['mask'].split(',')] # anchor mask
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modules = YOLOLayer(anchors=mdef['anchors'][mask], # anchor list
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nc=int(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 or 2
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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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return module_list, routs
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class Swish(nn.Module):
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def __init__(self):
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super(Swish, self).__init__()
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def forward(self, x):
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return x * torch.sigmoid(x)
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class YOLOLayer(nn.Module):
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def __init__(self, anchors, nc, img_size, yolo_index):
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super(YOLOLayer, self).__init__()
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self.anchors = torch.Tensor(anchors)
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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.nx = 0 # initialize number of x gridpoints
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self.ny = 0 # initialize number of y gridpoints
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if ONNX_EXPORT: # grids must be computed in __init__
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stride = [32, 16, 8][yolo_index] # stride of this layer
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nx = int(img_size[1] / stride) # number x grid points
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ny = int(img_size[0] / stride) # number y grid points
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create_grids(self, img_size, (nx, ny))
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def forward(self, p, img_size, var=None):
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if ONNX_EXPORT:
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bs = 1 # batch size
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else:
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bs, ny, nx = p.shape[0], p.shape[-2], p.shape[-1]
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if (self.nx, self.ny) != (nx, ny):
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create_grids(self, img_size, (nx, ny), p.device, p.dtype)
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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.nc + 5, 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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# Constants CAN NOT BE BROADCAST, ensure correct shape!
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ngu = self.ng.repeat((1, self.na * self.nx * self.ny, 1))
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grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view((1, -1, 2))
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anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view((1, -1, 2)) / ngu
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p = p.view(-1, 5 + self.nc)
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xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y
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wh = torch.exp(p[..., 2:4]) * anchor_wh[0] # width, height
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p_conf = torch.sigmoid(p[:, 4:5]) # Conf
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p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like conf
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return torch.cat((xy / ngu[0], wh, p_conf, p_cls), 1).t()
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# p = p.view(1, -1, 5 + self.nc)
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# xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y
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# wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height
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# p_conf = torch.sigmoid(p[..., 4:5]) # Conf
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# p_cls = p[..., 5:5 + self.nc]
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# # Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py
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# # p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf
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# p_cls = torch.exp(p_cls).permute((2, 1, 0))
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# p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent
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# p_cls = p_cls.permute(2, 1, 0)
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# return torch.cat((xy / ngu, wh, p_conf, p_cls), 2).squeeze().t()
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else: # inference
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# s = 1.5 # scale_xy (pxy = pxy * s - (s - 1) / 2)
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io = p.clone() # inference output
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io[..., 0:2] = torch.sigmoid(io[..., 0:2]) + self.grid_xy # xy
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io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
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# io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method
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io[..., :4] *= self.stride
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arc = 'normal' # (normal, uCE, uBCE) architecture types
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if arc == 'normal':
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io[..., 4:] = torch.sigmoid(io[..., 4:])
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elif arc == 'uCE':
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io[..., 4:] = F.softmax(io[..., 4:], dim=4) # unified detection CE
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io[..., 4] = 1
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elif arc == 'uBCE':
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io[..., 4] = io[..., 5:].max(4)[0] # unified detection BCE
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if self.nc == 1:
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io[..., 5] = 1 # single-class model https://github.com/ultralytics/yolov3/issues/235
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# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
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return io.view(bs, -1, 5 + self.nc), p
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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)):
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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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# 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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def forward(self, x, var=None):
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img_size = x.shape[-2:]
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layer_outputs = []
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output = []
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for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)):
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mtype = mdef['type']
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if mtype in ['convolutional', 'upsample', 'maxpool']:
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x = module(x)
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elif mtype == 'route':
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layers = [int(x) for x in mdef['layers'].split(',')]
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if len(layers) == 1:
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x = layer_outputs[layers[0]]
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else:
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try:
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x = torch.cat([layer_outputs[i] for i in layers], 1)
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except: # apply stride 2 for darknet reorg layer
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layer_outputs[layers[1]] = F.interpolate(layer_outputs[layers[1]], scale_factor=[0.5, 0.5])
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x = torch.cat([layer_outputs[i] for i in layers], 1)
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# print(''), [print(layer_outputs[i].shape) for i in layers], print(x.shape)
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elif mtype == 'shortcut':
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x = x + layer_outputs[int(mdef['from'])]
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elif mtype == 'yolo':
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x = module(x, img_size)
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output.append(x)
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layer_outputs.append(x if i in self.routs else [])
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if self.training:
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return output
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elif ONNX_EXPORT:
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output = torch.cat(output, 1) # cat 3 layers 85 x (507, 2028, 8112) to 85 x 10647
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nc = self.module_list[self.yolo_layers[0]].nc # number of classes
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return output[5:5 + nc].t(), output[:4].t() # ONNX scores, boxes
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else:
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io, p = list(zip(*output)) # inference output, training output
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return torch.cat(io, 1), p
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def fuse(self):
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# Fuse Conv2d + BatchNorm2d layers throughout model
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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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# model_info(self) # yolov3-spp reduced from 225 to 152 layers
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def get_yolo_layers(model):
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return [i for i, x in enumerate(model.module_defs) if x['type'] == 'yolo'] # [82, 94, 106] for yolov3
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def create_grids(self, img_size=416, ng=(13, 13), device='cpu', type=torch.float32):
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nx, ny = ng # x and y grid size
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self.img_size = max(img_size)
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self.stride = self.img_size / max(ng)
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# build xy offsets
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yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
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self.grid_xy = torch.stack((xv, yv), 2).to(device).type(type).view((1, 1, ny, nx, 2))
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# build wh gains
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self.anchor_vec = self.anchors.to(device) / self.stride
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self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2).to(device).type(type)
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self.ng = torch.Tensor(ng).to(device)
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self.nx = nx
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self.ny = ny
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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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# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
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file = Path(weights).name
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# Try to download weights if not available locally
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msg = weights + ' missing, download from https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI'
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if not os.path.isfile(weights):
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try:
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url = 'https://pjreddie.com/media/files/' + file
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print('Downloading ' + url)
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os.system('curl -f ' + url + ' -o ' + weights)
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except IOError:
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print(msg)
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assert os.path.exists(weights), msg # download missing weights from Google Drive
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# Establish cutoffs
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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_layer = 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_layer = module[1]
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num_b = bn_layer.bias.numel() # Number of biases
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# Bias
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bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
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bn_layer.bias.data.copy_(bn_b)
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ptr += num_b
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# Weight
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bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
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bn_layer.weight.data.copy_(bn_w)
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ptr += num_b
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# Running Mean
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bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
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bn_layer.running_mean.data.copy_(bn_rm)
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ptr += num_b
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# Running Var
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bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
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bn_layer.running_var.data.copy_(bn_rv)
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ptr += num_b
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else:
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# Load conv. bias
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num_b = conv_layer.bias.numel()
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conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
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conv_layer.bias.data.copy_(conv_b)
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ptr += num_b
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# Load conv. weights
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num_w = conv_layer.weight.numel()
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conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
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conv_layer.weight.data.copy_(conv_w)
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ptr += num_w
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return cutoff
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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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# Initialize model
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model = Darknet(cfg)
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# Load weights and save
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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
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_ = load_darknet_weights(model, weights)
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chkpt = {'epoch': -1,
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'best_fitness': None,
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'training_results': None,
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'model': model.state_dict(),
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'optimizer': None}
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torch.save(chkpt, 'converted.pt')
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print("Success: converted '%s' to 'converted.pt'" % weights)
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else:
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print('Error: extension not supported.')
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