433 lines
20 KiB
Python
Executable File
433 lines
20 KiB
Python
Executable File
from utils.google_utils import *
|
||
from utils.layers import *
|
||
from utils.parse_config import *
|
||
|
||
ONNX_EXPORT = False
|
||
|
||
|
||
def create_modules(module_defs, img_size):
|
||
# Constructs module list of layer blocks from module configuration in module_defs
|
||
|
||
img_size = [img_size] * 2 if isinstance(img_size, int) else img_size # expand if necessary
|
||
_ = module_defs.pop(0) # cfg training hyperparams (unused)
|
||
output_filters = [3] # input channels
|
||
module_list = nn.ModuleList()
|
||
routs = [] # list of layers which rout to deeper layers
|
||
yolo_index = -1
|
||
|
||
for i, mdef in enumerate(module_defs):
|
||
modules = nn.Sequential()
|
||
|
||
if mdef['type'] == 'convolutional':
|
||
bn = mdef['batch_normalize']
|
||
filters = mdef['filters']
|
||
size = mdef['size']
|
||
stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
|
||
if isinstance(size, int): # single-size conv
|
||
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))
|
||
else: # multiple-size conv
|
||
modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],
|
||
out_ch=filters,
|
||
k=size,
|
||
stride=stride,
|
||
bias=not bn))
|
||
|
||
if bn:
|
||
modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))
|
||
else:
|
||
routs.append(i) # detection output (goes into yolo layer)
|
||
|
||
if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441
|
||
modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
|
||
# modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10))
|
||
elif mdef['activation'] == 'swish':
|
||
modules.add_module('activation', Swish())
|
||
|
||
elif mdef['type'] == 'BatchNorm2d':
|
||
filters = output_filters[-1]
|
||
modules = nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4)
|
||
|
||
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[l + 1 if l > 0 else l] for l in layers])
|
||
routs.extend([i + l if l < 0 else l for l in layers])
|
||
|
||
elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
|
||
layers = mdef['from']
|
||
filters = output_filters[-1]
|
||
routs.extend([i + l if l < 0 else l for l in layers])
|
||
modules = WeightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
|
||
|
||
elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
|
||
pass
|
||
|
||
elif mdef['type'] == 'yolo':
|
||
yolo_index += 1
|
||
stride = [32, 16, 8, 4, 2][yolo_index] # P3-P7 stride
|
||
layers = mdef['from'] if 'from' in mdef else []
|
||
modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']], # anchor list
|
||
nc=mdef['classes'], # number of classes
|
||
img_size=img_size, # (416, 416)
|
||
yolo_index=yolo_index, # 0, 1, 2...
|
||
layers=layers, # output layers
|
||
stride=stride)
|
||
|
||
# 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 = layers[yolo_index] if 'from' in mdef else -1
|
||
bias_ = module_list[j][0].bias # shape(255,)
|
||
bias = bias_[:modules.no * modules.na].view(modules.na, -1) # shape(3,85)
|
||
bias[:, 4] += 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)
|
||
|
||
routs_binary = [False] * (i + 1)
|
||
for i in routs:
|
||
routs_binary[i] = True
|
||
return module_list, routs_binary
|
||
|
||
|
||
class YOLOLayer(nn.Module):
|
||
def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):
|
||
super(YOLOLayer, self).__init__()
|
||
self.anchors = torch.Tensor(anchors)
|
||
self.index = yolo_index # index of this layer in layers
|
||
self.layers = layers # model output layer indices
|
||
self.stride = stride # layer stride
|
||
self.nl = len(layers) # number of output layers (3)
|
||
self.na = len(anchors) # number of anchors (3)
|
||
self.nc = nc # number of classes (80)
|
||
self.no = nc + 5 # number of outputs (85)
|
||
self.nx, self.ny = 0, 0 # initialize number of x, y gridpoints
|
||
self.anchor_vec = self.anchors / self.stride
|
||
self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)
|
||
|
||
if ONNX_EXPORT:
|
||
self.create_grids((img_size[1] // stride, img_size[0] // stride)) # number x, y grid points
|
||
|
||
def create_grids(self, ng=(13, 13), device='cpu'):
|
||
self.nx, self.ny = ng # x and y grid size
|
||
self.ng = torch.Tensor(ng).to(device)
|
||
|
||
# build xy offsets
|
||
if not self.training:
|
||
yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])
|
||
self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()
|
||
|
||
if self.anchor_vec.device != device:
|
||
self.anchor_vec = self.anchor_vec.to(device)
|
||
self.anchor_wh = self.anchor_wh.to(device)
|
||
|
||
def forward(self, p, 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):
|
||
self.create_grids((nx, ny), p.device)
|
||
|
||
# outputs and weights
|
||
# w = F.softmax(p[:, -n:], 1) # normalized weights
|
||
w = torch.sigmoid(p[:, -n:]) * (2 / n) # sigmoid weights (faster)
|
||
# w = w / w.sum(1).unsqueeze(1) # normalize across layer dimension
|
||
|
||
# weighted ASFF sum
|
||
p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]
|
||
for j in range(n):
|
||
if j != i:
|
||
p += w[:, j:j + 1] * \
|
||
F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)
|
||
|
||
elif ONNX_EXPORT:
|
||
bs = 1 # batch size
|
||
else:
|
||
bs, _, ny, nx = p.shape # bs, 255, 13, 13
|
||
if (self.nx, self.ny) != (nx, ny):
|
||
self.create_grids((nx, ny), p.device)
|
||
|
||
# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh)
|
||
p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
||
|
||
if self.training:
|
||
return p
|
||
|
||
elif ONNX_EXPORT:
|
||
# Avoid broadcasting for ANE operations
|
||
m = self.na * self.nx * self.ny
|
||
ng = 1 / self.ng.repeat((m, 1))
|
||
grid = self.grid.repeat((1, self.na, 1, 1, 1)).view(m, 2)
|
||
anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view(m, 2) * ng
|
||
|
||
p = p.view(m, self.no)
|
||
xy = torch.sigmoid(p[:, 0:2]) + grid # x, y
|
||
wh = torch.exp(p[:, 2:4]) * anchor_wh # width, height
|
||
p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \
|
||
torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5]) # conf
|
||
return p_cls, xy * ng, wh
|
||
|
||
else: # inference
|
||
io = p.clone() # inference output
|
||
io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid # xy
|
||
io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
|
||
io[..., :4] *= self.stride
|
||
torch.sigmoid_(io[..., 4:])
|
||
return io.view(bs, -1, self.no), p # view [1, 3, 13, 13, 85] as [1, 507, 85]
|
||
|
||
|
||
class Darknet(nn.Module):
|
||
# YOLOv3 object detection model
|
||
|
||
def __init__(self, cfg, img_size=(416, 416)):
|
||
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 self.routs[i] 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 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 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',
|
||
'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)
|