489 lines
21 KiB
Python
Executable File
489 lines
21 KiB
Python
Executable File
import torch.nn.functional as F
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from utils.google_utils import *
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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, arc):
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# Constructs module list of layer blocks from module configuration in module_defs
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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 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 i == 0:
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# modules.add_module('BatchNorm2d_0', nn.BatchNorm2d(output_filters[-1], momentum=0.1))
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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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size = mdef['size']
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stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
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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=size,
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stride=stride,
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padding=(size - 1) // 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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if bn:
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modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.003, 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'] == 'maxpool':
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size = mdef['size']
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stride = mdef['stride']
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maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=(size - 1) // 2)
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if 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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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[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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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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# 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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l = 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=l) # output layers
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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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bo = -4.5 # obj bias
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bc = math.log(1 / (modules.nc - 0.99)) # cls bias: class probability is sigmoid(p) = 1/nc
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j = l[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] += bo - bias[:, 4].mean() # obj
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bias[:, 5:] += bc - bias[:, 5:].mean() # cls, view with utils.print_model_biases(model)
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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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return module_list, routs
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class weightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
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def __init__(self, layers, weight=False):
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super(weightedFeatureFusion, self).__init__()
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self.layers = layers # layer indices
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self.weight = weight # apply weights boolean
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self.n = len(layers) + 1 # number of layers
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if weight:
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self.w = torch.nn.Parameter(torch.zeros(self.n)) # layer weights
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def forward(self, x, outputs):
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# Weights
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if self.weight:
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w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1)
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x = x * w[0]
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# Fusion
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nc = x.shape[1] # input channels
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for i in range(self.n - 1):
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a = outputs[self.layers[i]] * w[i + 1] if self.weight else outputs[self.layers[i]] # feature to add
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ac = a.shape[1] # feature channels
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dc = nc - ac # delta channels
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# Adjust channels
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if dc > 0: # slice input
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x[:, :ac] = x[:, :ac] + a # or a = nn.ZeroPad2d((0, 0, 0, 0, 0, dc))(a); x = x + a
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elif dc < 0: # slice feature
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x = x + a[:, :nc]
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else: # same shape
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x = x + a
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return x
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class SwishImplementation(torch.autograd.Function):
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@staticmethod
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def forward(ctx, i):
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ctx.save_for_backward(i)
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return i * torch.sigmoid(i)
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@staticmethod
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def backward(ctx, grad_output):
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sigmoid_i = torch.sigmoid(ctx.saved_variables[0])
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return grad_output * (sigmoid_i * (1 + ctx.saved_variables[0] * (1 - sigmoid_i)))
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class MemoryEfficientSwish(nn.Module):
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def forward(self, x):
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return SwishImplementation.apply(x)
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class Swish(nn.Module):
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def forward(self, x):
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return x.mul_(torch.sigmoid(x))
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class Mish(nn.Module): # https://github.com/digantamisra98/Mish
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def forward(self, x):
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return x.mul_(F.softplus(x).tanh())
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class YOLOLayer(nn.Module):
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def __init__(self, anchors, nc, img_size, yolo_index, layers):
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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.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 = 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:
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stride = [32, 16, 8][yolo_index] # stride of this layer
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nx = img_size[1] // stride # number x grid points
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ny = 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, 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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create_grids(self, img_size, (nx, ny), p.device, p.dtype)
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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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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.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_xy = self.grid_xy.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_xy # 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 # 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), arc='default'):
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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, arc)
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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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self.info() # print model description
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def forward(self, x, verbose=False):
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img_size = x.shape[-2:]
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yolo_out, out = [], []
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if verbose:
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str = ''
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print('0', x.shape)
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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 == 'shortcut': # sum
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if verbose:
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l = [i - 1] + module.layers # layers
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s = [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, s)])
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x = module(x, out) # weightedFeatureFusion()
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elif mtype == 'route': # concat
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layers = mdef['layers']
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if verbose:
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l = [i - 1] + layers # layers
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s = [list(x.shape)] + [list(out[i].shape) for i in layers] # shapes
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str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
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if len(layers) == 1:
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x = out[layers[0]]
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else:
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try:
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x = torch.cat([out[i] for i in layers], 1)
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except: # apply stride 2 for darknet reorg layer
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out[layers[1]] = F.interpolate(out[layers[1]], scale_factor=[0.5, 0.5])
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x = torch.cat([out[i] for i in layers], 1)
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# print(''), [print(out[i].shape) for i in layers], print(x.shape)
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elif mtype == 'yolo':
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yolo_out.append(module(x, img_size, out))
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out.append(x if i in self.routs else [])
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if verbose:
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print('%g/%g %s -' % (i, len(self.module_list), mtype), 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: # test
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io, p = zip(*yolo_out) # 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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print('Fusing Conv2d() and BatchNorm2d() 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() # 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, 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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# 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
|
||
for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||
if mdef['type'] == 'convolutional':
|
||
conv_layer = module[0]
|
||
# If batch norm, load bn first
|
||
if mdef['batch_normalize']:
|
||
bn_layer = module[1]
|
||
bn_layer.bias.data.cpu().numpy().tofile(f)
|
||
bn_layer.weight.data.cpu().numpy().tofile(f)
|
||
bn_layer.running_mean.data.cpu().numpy().tofile(f)
|
||
bn_layer.running_var.data.cpu().numpy().tofile(f)
|
||
# Load conv bias
|
||
else:
|
||
conv_layer.bias.data.cpu().numpy().tofile(f)
|
||
# Load conv weights
|
||
conv_layer.weight.data.cpu().numpy().tofile(f)
|
||
|
||
|
||
def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
|
||
# Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa)
|
||
# from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')
|
||
|
||
# Initialize model
|
||
model = Darknet(cfg)
|
||
|
||
# Load weights and save
|
||
if weights.endswith('.pt'): # if PyTorch format
|
||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
|
||
save_weights(model, path='converted.weights', cutoff=-1)
|
||
print("Success: converted '%s' to 'converted.weights'" % weights)
|
||
|
||
elif weights.endswith('.weights'): # darknet format
|
||
_ = load_darknet_weights(model, weights)
|
||
|
||
chkpt = {'epoch': -1,
|
||
'best_fitness': None,
|
||
'training_results': None,
|
||
'model': model.state_dict(),
|
||
'optimizer': None}
|
||
|
||
torch.save(chkpt, 'converted.pt')
|
||
print("Success: converted '%s' to 'converted.pt'" % weights)
|
||
|
||
else:
|
||
print('Error: extension not supported.')
|
||
|
||
|
||
def attempt_download(weights):
|
||
# Attempt to download pretrained weights if not found locally
|
||
msg = weights + ' missing, try downloading from https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0'
|
||
|
||
if weights and not os.path.isfile(weights):
|
||
d = {'yolov3-spp.weights': '16lYS4bcIdM2HdmyJBVDOvt3Trx6N3W2R',
|
||
'yolov3.weights': '1uTlyDWlnaqXcsKOktP5aH_zRDbfcDp-y',
|
||
'yolov3-tiny.weights': '1CCF-iNIIkYesIDzaPvdwlcf7H9zSsKZQ',
|
||
'yolov3-spp.pt': '1f6Ovy3BSq2wYq4UfvFUpxJFNDFfrIDcR',
|
||
'yolov3.pt': '1SHNFyoe5Ni8DajDNEqgB2oVKBb_NoEad',
|
||
'yolov3-tiny.pt': '10m_3MlpQwRtZetQxtksm9jqHrPTHZ6vo',
|
||
'darknet53.conv.74': '1WUVBid-XuoUBmvzBVUCBl_ELrzqwA8dJ',
|
||
'yolov3-tiny.conv.15': '1Bw0kCpplxUqyRYAJr9RY9SGnOJbo9nEj',
|
||
'ultralytics49.pt': '158g62Vs14E3aj7oPVPuEnNZMKFNgGyNq',
|
||
'ultralytics68.pt': '1Jm8kqnMdMGUUxGo8zMFZMJ0eaPwLkxSG',
|
||
'yolov3-spp-ultralytics.pt': '1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4'}
|
||
|
||
file = Path(weights).name
|
||
if file in d:
|
||
r = gdrive_download(id=d[file], name=weights)
|
||
else: # download from pjreddie.com
|
||
url = 'https://pjreddie.com/media/files/' + file
|
||
print('Downloading ' + url)
|
||
r = os.system('curl -f ' + url + ' -o ' + weights)
|
||
|
||
# Error check
|
||
if not (r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6): # weights exist and > 1MB
|
||
os.system('rm ' + weights) # remove partial downloads
|
||
raise Exception(msg)
|