import torch.nn.functional as F
from utils.google_utils import *
from utils.parse_config import *
from utils.utils import *
ONNX_EXPORT = False
def create_modules(module_defs, img_size):
# Constructs module list of layer blocks from module configuration in module_defs
hyperparams = module_defs.pop(0)
output_filters = [int(hyperparams['channels'])]
module_list = nn.ModuleList()
routs = [] # list of layers which rout to deeper layers
yolo_index = -1
for i, mdef in enumerate(module_defs):
modules = nn.Sequential()
# if i == 0:
# modules.add_module('BatchNorm2d_0', nn.BatchNorm2d(output_filters[-1], momentum=0.1))
if mdef['type'] == 'convolutional':
bn = mdef['batch_normalize']
filters = mdef['filters']
size = mdef['size']
stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters,
kernel_size=size,
stride=stride,
padding=(size - 1) // 2 if mdef['pad'] else 0,
groups=mdef['groups'] if 'groups' in mdef else 1,
bias=not bn))
if bn:
modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.003, eps=1E-4))
else:
routs.append(i) # detection output (goes into yolo layer)
if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441
modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
# modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10))
elif mdef['activation'] == 'swish':
modules.add_module('activation', Swish())
elif mdef['type'] == 'maxpool':
size = mdef['size']
stride = mdef['stride']
maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=(size - 1) // 2)
if size == 2 and stride == 1: # yolov3-tiny
modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
modules.add_module('MaxPool2d', maxpool)
else:
modules = maxpool
elif mdef['type'] == 'upsample':
if ONNX_EXPORT: # explicitly state size, avoid scale_factor
g = (yolo_index + 1) * 2 / 32 # gain
modules = nn.Upsample(size=tuple(int(x * g) for x in img_size)) # img_size = (320, 192)
else:
modules = nn.Upsample(scale_factor=mdef['stride'])
elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer
layers = mdef['layers']
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
routs.extend([l if l > 0 else l + i for l in layers])
# if mdef[i+1]['type'] == 'reorg3d':
# modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest') # reorg3d
elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
layers = mdef['from']
filters = output_filters[-1]
routs.extend([i + l if l < 0 else l for l in layers])
modules = weightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
# torch.Size([16, 128, 104, 104])
# torch.Size([16, 64, 208, 208]) <-- # stride 2 interpolate dimensions 2 and 3 to cat with prior layer
pass
elif mdef['type'] == 'yolo':
yolo_index += 1
l = mdef['from'] if 'from' in mdef else []
modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']], # anchor list
nc=mdef['classes'], # number of classes
img_size=img_size, # (416, 416)
yolo_index=yolo_index, # 0, 1, 2...
layers=l) # output layers
# Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
try:
bo = -4.5 # obj bias
bc = math.log(1 / (modules.nc - 0.99)) # cls bias: class probability is sigmoid(p) = 1/nc
j = l[yolo_index] if 'from' in mdef else -1
bias_ = module_list[j][0].bias # shape(255,)
bias = bias_[:modules.no * modules.na].view(modules.na, -1) # shape(3,85)
bias[:, 4] += bo - bias[:, 4].mean() # obj
bias[:, 5:] += bc - bias[:, 5:].mean() # cls, view with utils.print_model_biases(model)
module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)
except:
print('WARNING: smart bias initialization failure.')
else:
print('Warning: Unrecognized Layer Type: ' + mdef['type'])
# Register module list and number of output filters
module_list.append(modules)
output_filters.append(filters)
return module_list, routs
class weightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
def __init__(self, layers, weight=False):
super(weightedFeatureFusion, self).__init__()
self.layers = layers # layer indices
self.weight = weight # apply weights boolean
self.n = len(layers) + 1 # number of layers
if weight:
self.w = torch.nn.Parameter(torch.zeros(self.n)) # layer weights
def forward(self, x, outputs):
# Weights
if self.weight:
w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1)
x = x * w[0]
# Fusion
nc = x.shape[1] # input channels
for i in range(self.n - 1):
a = outputs[self.layers[i]] * w[i + 1] if self.weight else outputs[self.layers[i]] # feature to add
ac = a.shape[1] # feature channels
dc = nc - ac # delta channels
# Adjust channels
if dc > 0: # slice input
x[:, :ac] = x[:, :ac] + a # or a = nn.ZeroPad2d((0, 0, 0, 0, 0, dc))(a); x = x + a
elif dc < 0: # slice feature
x = x + a[:, :nc]
else: # same shape
x = x + a
return x
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(i)
return i * torch.sigmoid(i)
@staticmethod
def backward(ctx, grad_output):
sigmoid_i = torch.sigmoid(ctx.saved_variables[0])
return grad_output * (sigmoid_i * (1 + ctx.saved_variables[0] * (1 - sigmoid_i)))
class MemoryEfficientSwish(nn.Module):
def forward(self, x):
return SwishImplementation.apply(x)
class Swish(nn.Module):
def forward(self, x):
return x.mul_(torch.sigmoid(x))
class Mish(nn.Module): # https://github.com/digantamisra98/Mish
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
class YOLOLayer(nn.Module):
def __init__(self, anchors, nc, img_size, yolo_index, layers):
super(YOLOLayer, self).__init__()
self.anchors = torch.Tensor(anchors)
self.index = yolo_index # index of this layer in layers
self.layers = layers # model output layer indices
self.nl = len(layers) # number of output layers (3)
self.na = len(anchors) # number of anchors (3)
self.nc = nc # number of classes (80)
self.no = nc + 5 # number of outputs (85)
self.nx = 0 # initialize number of x gridpoints
self.ny = 0 # initialize number of y gridpoints
if ONNX_EXPORT:
stride = [32, 16, 8][yolo_index] # stride of this layer
nx = img_size[1] // stride # number x grid points
ny = img_size[0] // stride # number y grid points
create_grids(self, img_size, (nx, ny))
def forward(self, p, img_size, out):
ASFF = False # https://arxiv.org/abs/1911.09516
if ASFF:
i, n = self.index, self.nl # index in layers, number of layers
p = out[self.layers[i]]
bs, _, ny, nx = p.shape # bs, 255, 13, 13
if (self.nx, self.ny) != (nx, ny):
create_grids(self, img_size, (nx, ny), p.device, p.dtype)
# outputs and weights
# w = F.softmax(p[:, -n:], 1) # normalized weights
w = torch.sigmoid(p[:, -n:]) * (2 / n) # sigmoid weights (faster)
# w = w / w.sum(1).unsqueeze(1) # normalize across layer dimension
# weighted ASFF sum
p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]
for j in range(n):
if j != i:
p += w[:, j:j + 1] * \
F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)
elif ONNX_EXPORT:
bs = 1 # batch size
else:
bs, _, ny, nx = p.shape # bs, 255, 13, 13
if (self.nx, self.ny) != (nx, ny):
create_grids(self, img_size, (nx, ny), p.device, p.dtype)
# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction
if self.training:
return p
elif ONNX_EXPORT:
# Avoid broadcasting for ANE operations
m = self.na * self.nx * self.ny
ng = 1 / self.ng.repeat((m, 1))
grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view(m, 2)
anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view(m, 2) * ng
p = p.view(m, self.no)
xy = torch.sigmoid(p[:, 0:2]) + grid_xy # x, y
wh = torch.exp(p[:, 2:4]) * anchor_wh # width, height
p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \
torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5]) # conf
return p_cls, xy * ng, wh
else: # inference
io = p.clone() # inference output
io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid_xy # xy
io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
io[..., :4] *= self.stride
torch.sigmoid_(io[..., 4:])
return io.view(bs, -1, self.no), p # view [1, 3, 13, 13, 85] as [1, 507, 85]
class Darknet(nn.Module):
# YOLOv3 object detection model
def __init__(self, cfg, img_size=(416, 416)):
super(Darknet, self).__init__()
self.module_defs = parse_model_cfg(cfg)
self.module_list, self.routs = create_modules(self.module_defs, img_size)
self.yolo_layers = get_yolo_layers(self)
# Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
self.version = np.array([0, 2, 5], dtype=np.int32) # (int32) version info: major, minor, revision
self.seen = np.array([0], dtype=np.int64) # (int64) number of images seen during training
self.info() # print model description
def forward(self, x, verbose=False):
img_size = x.shape[-2:]
yolo_out, out = [], []
if verbose:
str = ''
print('0', x.shape)
for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)):
mtype = mdef['type']
if mtype in ['convolutional', 'upsample', 'maxpool']:
x = module(x)
elif mtype == 'shortcut': # sum
if verbose:
l = [i - 1] + module.layers # layers
s = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes
str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
x = module(x, out) # weightedFeatureFusion()
elif mtype == 'route': # concat
layers = mdef['layers']
if verbose:
l = [i - 1] + layers # layers
s = [list(x.shape)] + [list(out[i].shape) for i in layers] # shapes
str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
if len(layers) == 1:
x = out[layers[0]]
else:
try:
x = torch.cat([out[i] for i in layers], 1)
except: # apply stride 2 for darknet reorg layer
out[layers[1]] = F.interpolate(out[layers[1]], scale_factor=[0.5, 0.5])
x = torch.cat([out[i] for i in layers], 1)
# print(''), [print(out[i].shape) for i in layers], print(x.shape)
elif mtype == 'yolo':
yolo_out.append(module(x, img_size, out))
out.append(x if i in self.routs else [])
if verbose:
print('%g/%g %s -' % (i, len(self.module_list), mtype), list(x.shape), str)
str = ''
if self.training: # train
return yolo_out
elif ONNX_EXPORT: # export
x = [torch.cat(x, 0) for x in zip(*yolo_out)]
return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4
else: # test
io, p = zip(*yolo_out) # inference output, training output
return torch.cat(io, 1), p
def fuse(self):
# Fuse Conv2d + BatchNorm2d layers throughout model
print('Fusing Conv2d() and BatchNorm2d() layers...')
fused_list = nn.ModuleList()
for a in list(self.children())[0]:
if isinstance(a, nn.Sequential):
for i, b in enumerate(a):
if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
# fuse this bn layer with the previous conv2d layer
conv = a[i - 1]
fused = torch_utils.fuse_conv_and_bn(conv, b)
a = nn.Sequential(fused, *list(a.children())[i + 1:])
break
fused_list.append(a)
self.module_list = fused_list
self.info() # yolov3-spp reduced from 225 to 152 layers
def info(self, verbose=False):
torch_utils.model_info(self, verbose)
def get_yolo_layers(model):
return [i for i, x in enumerate(model.module_defs) if x['type'] == 'yolo'] # [82, 94, 106] for yolov3
def create_grids(self, img_size=416, ng=(13, 13), device='cpu', type=torch.float32):
nx, ny = ng # x and y grid size
self.img_size = max(img_size)
self.stride = self.img_size / max(ng)
# build xy offsets
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
self.grid_xy = torch.stack((xv, yv), 2).to(device).type(type).view((1, 1, ny, nx, 2))
# build wh gains
self.anchor_vec = self.anchors.to(device) / self.stride
self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2).to(device).type(type)
self.ng = torch.Tensor(ng).to(device)
self.nx = nx
self.ny = ny
def load_darknet_weights(self, weights, cutoff=-1):
# Parses and loads the weights stored in 'weights'
# Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)
file = Path(weights).name
if file == 'darknet53.conv.74':
cutoff = 75
elif file == 'yolov3-tiny.conv.15':
cutoff = 15
# Read weights file
with open(weights, 'rb') as f:
# Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
self.version = np.fromfile(f, dtype=np.int32, count=3) # (int32) version info: major, minor, revision
self.seen = np.fromfile(f, dtype=np.int64, count=1) # (int64) number of images seen during training
weights = np.fromfile(f, dtype=np.float32) # the rest are weights
ptr = 0
for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
if mdef['type'] == 'convolutional':
conv = module[0]
if mdef['batch_normalize']:
# Load BN bias, weights, running mean and running variance
bn = module[1]
nb = bn.bias.numel() # number of biases
# Bias
bn.bias.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.bias))
ptr += nb
# Weight
bn.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.weight))
ptr += nb
# Running Mean
bn.running_mean.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_mean))
ptr += nb
# Running Var
bn.running_var.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_var))
ptr += nb
else:
# Load conv. bias
nb = conv.bias.numel()
conv_b = torch.from_numpy(weights[ptr:ptr + nb]).view_as(conv.bias)
conv.bias.data.copy_(conv_b)
ptr += nb
# Load conv. weights
nw = conv.weight.numel() # number of weights
conv.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nw]).view_as(conv.weight))
ptr += nw
def save_weights(self, path='model.weights', cutoff=-1):
# Converts a PyTorch model to Darket format (*.pt to *.weights)
# Note: Does not work if model.fuse() is applied
with open(path, 'wb') as f:
# Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
self.version.tofile(f) # (int32) version info: major, minor, revision
self.seen.tofile(f) # (int64) number of images seen during training
# Iterate through layers
for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
if mdef['type'] == 'convolutional':
conv_layer = module[0]
# If batch norm, load bn first
if mdef['batch_normalize']:
bn_layer = module[1]
bn_layer.bias.data.cpu().numpy().tofile(f)
bn_layer.weight.data.cpu().numpy().tofile(f)
bn_layer.running_mean.data.cpu().numpy().tofile(f)
bn_layer.running_var.data.cpu().numpy().tofile(f)
# Load conv bias
else:
conv_layer.bias.data.cpu().numpy().tofile(f)
# Load conv weights
conv_layer.weight.data.cpu().numpy().tofile(f)
def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
# 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)