|
|
# Ultralytics YOLO 🚀, GPL-3.0 license |
|
|
|
|
|
import contextlib |
|
|
from copy import deepcopy |
|
|
|
|
|
import thop |
|
|
import torch |
|
|
import torch.nn as nn |
|
|
|
|
|
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify, |
|
|
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus, |
|
|
GhostBottleneck, GhostConv, Segment) |
|
|
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, yaml_load |
|
|
from ultralytics.yolo.utils.checks import check_requirements, check_yaml |
|
|
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_weights, intersect_dicts, make_divisible, |
|
|
model_info, scale_img, time_sync) |
|
|
|
|
|
|
|
|
class BaseModel(nn.Module): |
|
|
""" |
|
|
The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family. |
|
|
""" |
|
|
|
|
|
def forward(self, x, profile=False, visualize=False): |
|
|
""" |
|
|
Forward pass of the model on a single scale. |
|
|
Wrapper for `_forward_once` method. |
|
|
|
|
|
Args: |
|
|
x (torch.Tensor): The input image tensor |
|
|
profile (bool): Whether to profile the model, defaults to False |
|
|
visualize (bool): Whether to return the intermediate feature maps, defaults to False |
|
|
|
|
|
Returns: |
|
|
(torch.Tensor): The output of the network. |
|
|
""" |
|
|
return self._forward_once(x, profile, visualize) |
|
|
|
|
|
def _forward_once(self, x, profile=False, visualize=False): |
|
|
""" |
|
|
Perform a forward pass through the network. |
|
|
|
|
|
Args: |
|
|
x (torch.Tensor): The input tensor to the model |
|
|
profile (bool): Print the computation time of each layer if True, defaults to False. |
|
|
visualize (bool): Save the feature maps of the model if True, defaults to False |
|
|
|
|
|
Returns: |
|
|
(torch.Tensor): The last output of the model. |
|
|
""" |
|
|
y, dt = [], [] # outputs |
|
|
for m in self.model: |
|
|
if m.f != -1: # if not from previous layer |
|
|
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers |
|
|
if profile: |
|
|
self._profile_one_layer(m, x, dt) |
|
|
x = m(x) # run |
|
|
y.append(x if m.i in self.save else None) # save output |
|
|
if visualize: |
|
|
LOGGER.info('visualize feature not yet supported') |
|
|
# TODO: feature_visualization(x, m.type, m.i, save_dir=visualize) |
|
|
return x |
|
|
|
|
|
def _profile_one_layer(self, m, x, dt): |
|
|
""" |
|
|
Profile the computation time and FLOPs of a single layer of the model on a given input. |
|
|
Appends the results to the provided list. |
|
|
|
|
|
Args: |
|
|
m (nn.Module): The layer to be profiled. |
|
|
x (torch.Tensor): The input data to the layer. |
|
|
dt (list): A list to store the computation time of the layer. |
|
|
|
|
|
Returns: |
|
|
None |
|
|
""" |
|
|
c = m == self.model[-1] # is final layer, copy input as inplace fix |
|
|
o = thop.profile(m, inputs=(x.clone() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs |
|
|
t = time_sync() |
|
|
for _ in range(10): |
|
|
m(x.clone() if c else x) |
|
|
dt.append((time_sync() - t) * 100) |
|
|
if m == self.model[0]: |
|
|
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") |
|
|
LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') |
|
|
if c: |
|
|
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") |
|
|
|
|
|
def fuse(self): |
|
|
""" |
|
|
Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the |
|
|
computation efficiency. |
|
|
|
|
|
Returns: |
|
|
(nn.Module): The fused model is returned. |
|
|
""" |
|
|
if not self.is_fused(): |
|
|
for m in self.model.modules(): |
|
|
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'): |
|
|
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv |
|
|
delattr(m, 'bn') # remove batchnorm |
|
|
m.forward = m.forward_fuse # update forward |
|
|
self.info() |
|
|
|
|
|
return self |
|
|
|
|
|
def is_fused(self, thresh=10): |
|
|
""" |
|
|
Check if the model has less than a certain threshold of BatchNorm layers. |
|
|
|
|
|
Args: |
|
|
thresh (int, optional): The threshold number of BatchNorm layers. Default is 10. |
|
|
|
|
|
Returns: |
|
|
(bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise. |
|
|
""" |
|
|
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() |
|
|
return sum(isinstance(v, bn) for v in self.modules()) < thresh # True if < 'thresh' BatchNorm layers in model |
|
|
|
|
|
def info(self, verbose=False, imgsz=640): |
|
|
""" |
|
|
Prints model information |
|
|
|
|
|
Args: |
|
|
verbose (bool): if True, prints out the model information. Defaults to False |
|
|
imgsz (int): the size of the image that the model will be trained on. Defaults to 640 |
|
|
""" |
|
|
model_info(self, verbose, imgsz) |
|
|
|
|
|
def _apply(self, fn): |
|
|
""" |
|
|
`_apply()` is a function that applies a function to all the tensors in the model that are not |
|
|
parameters or registered buffers |
|
|
|
|
|
Args: |
|
|
fn: the function to apply to the model |
|
|
|
|
|
Returns: |
|
|
A model that is a Detect() object. |
|
|
""" |
|
|
self = super()._apply(fn) |
|
|
m = self.model[-1] # Detect() |
|
|
if isinstance(m, (Detect, Segment)): |
|
|
m.stride = fn(m.stride) |
|
|
m.anchors = fn(m.anchors) |
|
|
m.strides = fn(m.strides) |
|
|
return self |
|
|
|
|
|
def load(self, weights): |
|
|
""" |
|
|
This function loads the weights of the model from a file |
|
|
|
|
|
Args: |
|
|
weights (str): The weights to load into the model. |
|
|
""" |
|
|
# Force all tasks to implement this function |
|
|
raise NotImplementedError("This function needs to be implemented by derived classes!") |
|
|
|
|
|
|
|
|
class DetectionModel(BaseModel): |
|
|
# YOLOv8 detection model |
|
|
def __init__(self, cfg='yolov8n.yaml', ch=3, nc=None, verbose=True): # model, input channels, number of classes |
|
|
super().__init__() |
|
|
self.yaml = cfg if isinstance(cfg, dict) else yaml_load(check_yaml(cfg), append_filename=True) # cfg dict |
|
|
|
|
|
# Define model |
|
|
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels |
|
|
if nc and nc != self.yaml['nc']: |
|
|
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") |
|
|
self.yaml['nc'] = nc # override yaml value |
|
|
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], verbose=verbose) # model, savelist |
|
|
self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict |
|
|
self.inplace = self.yaml.get('inplace', True) |
|
|
|
|
|
# Build strides |
|
|
m = self.model[-1] # Detect() |
|
|
if isinstance(m, (Detect, Segment)): |
|
|
s = 256 # 2x min stride |
|
|
m.inplace = self.inplace |
|
|
forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) |
|
|
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward |
|
|
self.stride = m.stride |
|
|
m.bias_init() # only run once |
|
|
|
|
|
# Init weights, biases |
|
|
initialize_weights(self) |
|
|
if verbose: |
|
|
self.info() |
|
|
LOGGER.info('') |
|
|
|
|
|
def forward(self, x, augment=False, profile=False, visualize=False): |
|
|
if augment: |
|
|
return self._forward_augment(x) # augmented inference, None |
|
|
return self._forward_once(x, profile, visualize) # single-scale inference, train |
|
|
|
|
|
def _forward_augment(self, x): |
|
|
img_size = x.shape[-2:] # height, width |
|
|
s = [1, 0.83, 0.67] # scales |
|
|
f = [None, 3, None] # flips (2-ud, 3-lr) |
|
|
y = [] # outputs |
|
|
for si, fi in zip(s, f): |
|
|
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) |
|
|
yi = self._forward_once(xi)[0] # forward |
|
|
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save |
|
|
yi = self._descale_pred(yi, fi, si, img_size) |
|
|
y.append(yi) |
|
|
y = self._clip_augmented(y) # clip augmented tails |
|
|
return torch.cat(y, -1), None # augmented inference, train |
|
|
|
|
|
@staticmethod |
|
|
def _descale_pred(p, flips, scale, img_size, dim=1): |
|
|
# de-scale predictions following augmented inference (inverse operation) |
|
|
p[:, :4] /= scale # de-scale |
|
|
x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim) |
|
|
if flips == 2: |
|
|
y = img_size[0] - y # de-flip ud |
|
|
elif flips == 3: |
|
|
x = img_size[1] - x # de-flip lr |
|
|
return torch.cat((x, y, wh, cls), dim) |
|
|
|
|
|
def _clip_augmented(self, y): |
|
|
# Clip YOLOv5 augmented inference tails |
|
|
nl = self.model[-1].nl # number of detection layers (P3-P5) |
|
|
g = sum(4 ** x for x in range(nl)) # grid points |
|
|
e = 1 # exclude layer count |
|
|
i = (y[0].shape[-1] // g) * sum(4 ** x for x in range(e)) # indices |
|
|
y[0] = y[0][..., :-i] # large |
|
|
i = (y[-1].shape[-1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices |
|
|
y[-1] = y[-1][..., i:] # small |
|
|
return y |
|
|
|
|
|
def load(self, weights, verbose=True): |
|
|
csd = weights.float().state_dict() # checkpoint state_dict as FP32 |
|
|
csd = intersect_dicts(csd, self.state_dict()) # intersect |
|
|
self.load_state_dict(csd, strict=False) # load |
|
|
if verbose: |
|
|
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights') |
|
|
|
|
|
|
|
|
class SegmentationModel(DetectionModel): |
|
|
# YOLOv8 segmentation model |
|
|
def __init__(self, cfg='yolov8n-seg.yaml', ch=3, nc=None, verbose=True): |
|
|
super().__init__(cfg, ch, nc, verbose) |
|
|
|
|
|
|
|
|
class ClassificationModel(BaseModel): |
|
|
# YOLOv8 classification model |
|
|
def __init__(self, |
|
|
cfg=None, |
|
|
model=None, |
|
|
ch=3, |
|
|
nc=1000, |
|
|
cutoff=10, |
|
|
verbose=True): # yaml, model, number of classes, cutoff index |
|
|
super().__init__() |
|
|
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg, ch, nc, verbose) |
|
|
|
|
|
def _from_detection_model(self, model, nc=1000, cutoff=10): |
|
|
# Create a YOLOv5 classification model from a YOLOv5 detection model |
|
|
from ultralytics.nn.autobackend import AutoBackend |
|
|
if isinstance(model, AutoBackend): |
|
|
model = model.model # unwrap DetectMultiBackend |
|
|
model.model = model.model[:cutoff] # backbone |
|
|
m = model.model[-1] # last layer |
|
|
ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module |
|
|
c = Classify(ch, nc) # Classify() |
|
|
c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type |
|
|
model.model[-1] = c # replace |
|
|
self.model = model.model |
|
|
self.stride = model.stride |
|
|
self.save = [] |
|
|
self.nc = nc |
|
|
|
|
|
def _from_yaml(self, cfg, ch, nc, verbose): |
|
|
self.yaml = cfg if isinstance(cfg, dict) else yaml_load(check_yaml(cfg), append_filename=True) # cfg dict |
|
|
# Define model |
|
|
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels |
|
|
if nc and nc != self.yaml['nc']: |
|
|
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") |
|
|
self.yaml['nc'] = nc # override yaml value |
|
|
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], verbose=verbose) # model, savelist |
|
|
self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict |
|
|
self.info() |
|
|
|
|
|
def load(self, weights): |
|
|
model = weights["model"] if isinstance(weights, dict) else weights # torchvision models are not dicts |
|
|
csd = model.float().state_dict() |
|
|
csd = intersect_dicts(csd, self.state_dict()) # intersect |
|
|
self.load_state_dict(csd, strict=False) # load |
|
|
|
|
|
@staticmethod |
|
|
def reshape_outputs(model, nc): |
|
|
# Update a TorchVision classification model to class count 'n' if required |
|
|
name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module |
|
|
if isinstance(m, Classify): # YOLO Classify() head |
|
|
if m.linear.out_features != nc: |
|
|
m.linear = nn.Linear(m.linear.in_features, nc) |
|
|
elif isinstance(m, nn.Linear): # ResNet, EfficientNet |
|
|
if m.out_features != nc: |
|
|
setattr(model, name, nn.Linear(m.in_features, nc)) |
|
|
elif isinstance(m, nn.Sequential): |
|
|
types = [type(x) for x in m] |
|
|
if nn.Linear in types: |
|
|
i = types.index(nn.Linear) # nn.Linear index |
|
|
if m[i].out_features != nc: |
|
|
m[i] = nn.Linear(m[i].in_features, nc) |
|
|
elif nn.Conv2d in types: |
|
|
i = types.index(nn.Conv2d) # nn.Conv2d index |
|
|
if m[i].out_channels != nc: |
|
|
m[i] = nn.Conv2d(m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) |
|
|
|
|
|
|
|
|
# Functions ------------------------------------------------------------------------------------------------------------ |
|
|
|
|
|
|
|
|
def attempt_load_weights(weights, device=None, inplace=True, fuse=False): |
|
|
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a |
|
|
from ultralytics.yolo.utils.downloads import attempt_download |
|
|
|
|
|
model = Ensemble() |
|
|
for w in weights if isinstance(weights, list) else [weights]: |
|
|
ckpt = torch.load(attempt_download(w), map_location='cpu') # load |
|
|
args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args |
|
|
ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model |
|
|
|
|
|
# Model compatibility updates |
|
|
ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model |
|
|
ckpt.pt_path = weights # attach *.pt file path to model |
|
|
if not hasattr(ckpt, 'stride'): |
|
|
ckpt.stride = torch.tensor([32.]) |
|
|
|
|
|
# Append |
|
|
model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode |
|
|
|
|
|
# Module compatibility updates |
|
|
for m in model.modules(): |
|
|
t = type(m) |
|
|
if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Segment): |
|
|
m.inplace = inplace # torch 1.7.0 compatibility |
|
|
elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): |
|
|
m.recompute_scale_factor = None # torch 1.11.0 compatibility |
|
|
|
|
|
# Return model |
|
|
if len(model) == 1: |
|
|
return model[-1] |
|
|
|
|
|
# Return ensemble |
|
|
print(f'Ensemble created with {weights}\n') |
|
|
for k in 'names', 'nc', 'yaml': |
|
|
setattr(model, k, getattr(model[0], k)) |
|
|
model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride |
|
|
assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' |
|
|
return model |
|
|
|
|
|
|
|
|
def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False): |
|
|
# Loads a single model weights |
|
|
from ultralytics.yolo.utils.downloads import attempt_download |
|
|
|
|
|
weight = attempt_download(weight) |
|
|
try: |
|
|
ckpt = torch.load(weight, map_location='cpu') # load |
|
|
except ModuleNotFoundError: |
|
|
LOGGER.warning(f"WARNING ⚠️ {weight} is deprecated as it requires omegaconf, which is now removed from " |
|
|
"ultralytics requirements.\nAutoInstall will occur now but this feature will be removed for " |
|
|
"omegaconf models in the future.\nPlease train a new model or download updated models " |
|
|
"from https://github.com/ultralytics/assets/releases/tag/v0.0.0") |
|
|
check_requirements('omegaconf') |
|
|
ckpt = torch.load(weight, map_location='cpu') # load |
|
|
args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args |
|
|
model = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model |
|
|
|
|
|
# Model compatibility updates |
|
|
model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model |
|
|
model.pt_path = weight # attach *.pt file path to model |
|
|
if not hasattr(model, 'stride'): |
|
|
model.stride = torch.tensor([32.]) |
|
|
|
|
|
model = model.fuse().eval() if fuse and hasattr(model, 'fuse') else model.eval() # model in eval mode |
|
|
|
|
|
# Module compatibility updates |
|
|
for m in model.modules(): |
|
|
t = type(m) |
|
|
if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Segment): |
|
|
m.inplace = inplace # torch 1.7.0 compatibility |
|
|
elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): |
|
|
m.recompute_scale_factor = None # torch 1.11.0 compatibility |
|
|
|
|
|
# Return model and ckpt |
|
|
return model, ckpt |
|
|
|
|
|
|
|
|
def parse_model(d, ch, verbose=True): # model_dict, input_channels(3) |
|
|
# Parse a YOLO model.yaml dictionary |
|
|
if verbose: |
|
|
LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<45}{'arguments':<30}") |
|
|
nc, gd, gw, act = d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') |
|
|
if act: |
|
|
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() |
|
|
if verbose: |
|
|
LOGGER.info(f"{colorstr('activation:')} {act}") # print |
|
|
|
|
|
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out |
|
|
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args |
|
|
m = eval(m) if isinstance(m, str) else m # eval strings |
|
|
for j, a in enumerate(args): |
|
|
with contextlib.suppress(NameError): |
|
|
args[j] = eval(a) if isinstance(a, str) else a # eval strings |
|
|
|
|
|
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain |
|
|
if m in { |
|
|
Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus, |
|
|
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: |
|
|
c1, c2 = ch[f], args[0] |
|
|
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output) |
|
|
c2 = make_divisible(c2 * gw, 8) |
|
|
|
|
|
args = [c1, c2, *args[1:]] |
|
|
if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}: |
|
|
args.insert(2, n) # number of repeats |
|
|
n = 1 |
|
|
elif m is nn.BatchNorm2d: |
|
|
args = [ch[f]] |
|
|
elif m is Concat: |
|
|
c2 = sum(ch[x] for x in f) |
|
|
elif m in {Detect, Segment}: |
|
|
args.append([ch[x] for x in f]) |
|
|
if m is Segment: |
|
|
args[2] = make_divisible(args[2] * gw, 8) |
|
|
else: |
|
|
c2 = ch[f] |
|
|
|
|
|
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module |
|
|
t = str(m)[8:-2].replace('__main__.', '') # module type |
|
|
m.np = sum(x.numel() for x in m_.parameters()) # number params |
|
|
m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type |
|
|
if verbose: |
|
|
LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<45}{str(args):<30}') # print |
|
|
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist |
|
|
layers.append(m_) |
|
|
if i == 0: |
|
|
ch = [] |
|
|
ch.append(c2) |
|
|
return nn.Sequential(*layers), sorted(save)
|
|
|
|
